Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the road network has high nonlinearity and complexity, and also, the traffic flow is dynamic due to the continuous changing of the traffic environment. Thus, it is very important to model the spatial relation and temporal dependence simultaneously to simulate the true traffic conditions. Most of the existing destination prediction methods have limited ability to model large-scale spatial data that changes dynamically with time, so they cannot obtain satisfactory prediction results. This paper proposes a human-in-loop Spatial-Temporal Attention Mechanism with Graph Convolutional Network (STAGCN) model to explore the spatial-temporal dependencies for destination prediction. The main contributions of this study are as follows. First, the traffic network is represented as a graph network by grid region dividing, then the spatial-temporal correlations of the traffic network can be learned by convolution operations in time on the graph network. Second, the attention mechanism is exploited for the analysis of features with loop periodicity and enhancing the features of key nodes in the grid. Finally, the spatial and temporal features are combined as the input of the Long-Short Term Memory network (LSTM) to further capture the spatial-temporal dependences of the traffic data to reach more accurate results. Extensive experiments conducted on the large scale urban real dataset show that the proposed STAGCN model has achieved better performance in urban car-hailing destination prediction compared with the traditional baseline models. According to the statistics of the China Online car-hailing Regulatory Information Exchange Platform, as of 31 July 2021, the platform received 7,76,564 million orders in July, up 10.7% from the previous month. With the increasing demand for online car-hailing services, online car-hailing has become an important choice for residents' daily travel. A large amount of online car-hailing data is generated during daily travel, these data include valuable information such as the user's travel time, departure place, and destination  and other extended information , travel destination prediction has always been a research hotspot. Most of the early destination prediction methods are based on probability theory, and the model is based on the Markov chain   t et al.  use the t et al.  use a Gat et al.  use a clt et al.  perform Classical machine learning methods. In recent years, researchers have tried to solve this problem using machine learning methods. Zong et al. ((2) g et al.  used a Hg et al.  proposedg et al.  modeled g et al.  proposedDeep learning methods. The key to solving the problem of destination prediction is to make full use of the deep features of traffic spatial-temporal data. Many studies have begun to improve the graph convolution network model to transform complex traffic data into Euclidean spatial graph and capture spatial features between vertices of grid graph with spatial information. Yu et al. ((3) u et al.  proposedu et al.  proposedThe research shows that the destination of online car-hailing has certain periodic regularity , where V is the set of nodes, ∣V∣ = N means there are N nodes in the graph; use the edges of a graph to represent the connectivity of nodes in the graph, E is the set of edges, A ∈ ℝN × N is the spatial adjacency matrix representing the distance dependence between nodes. Each node on the spatial grid G will detect P time series data with the same sampling frequency, i.e., each node will generate a feature vector of length P at each time stamp .Definition 4 Destination volume matrix. The purpose of our study is to predict the volume of destination orders in the future based on the history data. Thus, we establish the destination volume matrix for the historical data. Given the destination grid indexed  and the time slot indexed t, we define the entry The framework of the proposed Spatial-Temporal Attention and Graph Convolutional Network Model (STAGCN) is shown in The use of grid matrices to represent graph convolutional neural networks have been applied in many previous studies  was counted, and then the correlation between every two grids was calculated as the weight of the destination relationship matrix .Graph fusion: The destination volume graph, region adjacency graph, and destination relation graph are fused into a fusion graph with multi-graph characteristics. We perform fusion by weighting the adjacency matrix of the above graph. Since the adjacency matrix values of different graphs may differ greatly, we first normalize the adjacency matrix A of each graph above.F is the graph fusion result that will be used in the graph convolution network module.I is the identity matrix, A ∈ Rn × n) is a nonlinear activation function.Graph convolutional networks have achieved great success in dealing with spatial relations in non-Euclidean Spaces. In this article, the graph convolution network theory proposed by Kipf and Welling  is used t. n hidden layer. STAGCN model adopts two hidden layer GCN models, 20 convolution kernels are used for spatial dimension graph convolution, and 5 convolution kernels of the same size are used for temporal dimension graph convolution. The result dimension of spatial-temporal graph convolution is consistent with the target dimension by full connection operation. A fully connected module maps all the hidden node features to a 1 × 10 vector of the second hidden layer of GCN.According to the principle of GCN, the convolution of the graph can be expressed as follows by the above formula:Long-Short Term Memory is a type of temporal recurrent neural network that determines the storage and discarding of information by adding gating  to RNN. LSTM solves the problem of gradient dispersion of RNN models and can better portray data with spatial-temporal correlation. The LSTM prediction model based on the structure of the spatial-temporal attention encoder-decoder includes three parts: LSTM encoding network, LSTM decoding network, and spatial-temporal attention layer, as shown in ft, it, and ot respectively forgotten gate, update gate, and output gate. ct and ht are cell memory state vectors and hidden state vectors, respectively. In these equations, σ is the sigmoid function, ⊙ is element wise product, and xt is the input vector. W and b are the weight and bias in the training process. We simplify the LSTM representations in Equation (18) as the in Equation (19) shows.ht and ct are the hidden state of the t-th time step and the encoder latent vector, respectively through (Equation 19).The LSTM model has proved to be very effective in processing time-series data with long time dependent features. The LSTM encoder in this model learns its periodic time-dependence and spatial-temporal correlation of learning the long-term spatial-temporal sequence of the input, then the generated vector is combined with the spatial-temporal attention vector to construct the deep spatial-temporal feature representation of historical destination sequence data. The LSTM decoder decodes based on the encoded spatial-temporal attention vector and takes the reconstructed destination sequence as the target predictive value as shown in ct and ht passed from the encoder. First, the decoder receives y0 and uses initial vectors The decoder parses the state vectors and feeds the prediction results back to the network recursively to slow down the rapid accumulation of errors with the prediction compensation and achieve a more accurate multi-step prediction. The decoder process decodes the hidden state vectors and cell state vectors generated by the encoder process to obtain the output sequence. The decoder accepts We use the spatial-temporal attention mechanism to capture the dynamic spatial-temporal correlation on the transportation network . The temr-th spatial-temporal block. Cr−1 is the number of channels of the input data in the r-th layer. Vs,bs ∈ℝℕ × ℕ, S is dynamically computed according to the current input of this layer. The value of an element Si,j in S semantically represents the correlation strength between node i and node j.In terms of spatial dimension, the location of passengers arriving at their destination influences each other, which is highly dynamic. Therefore, we use attentional mechanisms to adaptively capture dynamic associations between nodes in spatial dimensions  are companies, schools, and hospitals. In the evening peak (16:00–19:00), there are more residential clusters. The temporal attention mechanism learns the long time dependent characteristics of historical data and assigns higher weights to more relevant destinations over a particular time period. Therefore, we can grasp the underlying movement patterns by analyzing destination weights and making the attention model easier to interpret.Ve,beEi,j in E semantically represents the correlation strength between the time i and time j.The temporal attention mechanism analyses the temporal behavior features vector by capturing the encoder's temporal hiding state during future temporal vector generation  is selected as the loss function of model training, and the segmentation strategy of the training set and test set for destination prediction modeling is as follows: the first 70% of the data set is taken as the model training set, and the remaining 30% is taken as the test set. In addition, the min-max function was used to standardize the relevant sequence data to the interval . The basic parameter configuration of the benchmark deep learning model is the same as that of the STAGCN model. The specific configuration is as follows: the batch size is 100, the neuron discards rate of each layer is 0.3, and the learning rate parameter is 0.01. The default neural network layer number of the benchmark deep learning model is set at 2, and the number of neurons in each hidden layer is 100. Adam function is used as a model training optimizer for all deep learning models. The size of the filters parameter for graph convolution is set to 64, and the number of neurons in each layer of the LSTM network is set to 100. The activation function of the output layer of the model adopts a Linear function for the final prediction. Finally, RMSE and MAE are used as model error analysis indexes to evaluate the prediction performance of each model. The error indexes are calculated as follows:As shown in Based on the real data set for experimental analysis and evaluation, the overall prediction performance of the STAGCN model and other comparative models are compared during the experiment. The experimental comparison results are shown in The STAGCN reduces the RMSE and MAE metrics by 17.53% and 17.6% at 15 min, compared with the traditional HA method. The above experiments show that the traditional method is not suitable for spatial-temporal prediction tasks.The STAGCN compared with the MLP method, the RMSE and MAE metrics at 15 min are reduced by 23.54 and 13.71%; Compared with the LSTM model, STAGCN reduces the two metrics at 15 min by 6.06 and 9.83%, respectively; compared with the GCGRU model, the two metrics are reduced by 4.38 and 4.35%, respectively; the machine learning-based LSTM and GRU models have better prediction accuracy than the traditional statistical models, but only the time dependence is considered.The STAGCN compared with the STGCN model, the RMSE and MAE metrics at 15 min are reduced by 3.55 and 2.08%. Although the STGCN model models spatial-temporal correlation, it does not consider the spatial-temporal characteristics of key locations.Compared with the traditional model, the STAGCN model can be applied to handle spatial-temporal data based on graph structure representation, and the spatial-temporal attention mechanism can better reveal and illustrate the potential destination and improve the performance of destination prediction.Remove the graph convolution module and use LSTM for destination prediction.Remove the spatial-temporal attention module to remove the focus spatial and temporal weights.We conduct further STAGCN ablation experiments. The experiments are performed by reducing the relevant modules for comparative analysis of STAGCN to measure the performance gain of different modules in STAGCN. For this purpose, two comparative versions of STAGCN were constructed:As shown in In general, as the prediction interval becomes longer, the corresponding difficulty of prediction becomes greater, so the prediction error also increases. As can be seen from In summary, through the analysis of experimental results on real data sets, the proposed spatial-temporal attention mechanism with graph convolutional network model (STAGCN) has better prediction performance than the benchmark model and the latest proposed model. Under different time gap prediction conditions, the destination prediction value of the STAGCN model can maintain a good match with the true value. At the same time, the prediction error of the model is lower than that of the benchmark model under various time gap prediction conditions. This verifies the performance of the STAGCN destination prediction model, which can effectively learn nonlinear correlation features and spatial-temporal dependence features in spatial-temporal series data.Destination prediction can predict the user's destination to infer the user's intention, which in turn can make targeted recommendations. For urban transportation, it can also improve cab utilization and assist in urban transportation planning. By learning the advantages and disadvantages of existing destination prediction frameworks and methods, we address the following shortcomings: (1) The traditional statistical approach to destination prediction has a large uncertainty. It has high volatility for different data sets, high sampling process and professionalism requirements for historical data, and poor data representativeness. Traditional methods and feature construction schemes are not suitable for massive and rapidly changing historical data; (2) Historical data of destinations hide rich information about user behavior preferences, user interests, etc., and existing methods do not make full use of the spatial-temporal correlation characteristics of users; (3) Research related to destination prediction is specific to the problem of large differences between different scenarios, and many deep learning scheme models are directly applied to destination prediction. The problem of poor generality in the field of prediction makes destination prediction in the field of deep learning still have a lot of room for innovation.This article proposes STAGCN which uses graph convolutional networks to capture spatial features, uses spatial-temporal attention mechanisms to effectively obtain dynamic spatial-temporal correlations in data and uses LSTM to generate prediction results. The STAGCN model is evaluated on a real data set. Experiments show that the proposed model achieves better performance than the five baseline methods in terms of two evaluation indicators. Compared with the traditional model, the STAGCN model is suitable for processing spatial-temporal data represented by a graph structure. The spatial-temporal attention mechanism can better reveal and explain the potential destination rules and improve the performance of destination prediction.Destination prediction is a hot topic of current research and provides practical value for various types of location-based services. However, the field has been suffering from data sparsity and long-term dependence on historical data. In future study, to reduce the data sparsity problem and make the model applicable to predict unvisited destinations, multiple data sources, such as point of interest (POI), event datasets, and weather datasets, can be integrated to extend the model to semantic level destination prediction  can be found in the article/supplementary material.CL contributed to the conception of the study and wrote the manuscript. HZ contributed significantly to the analysis and manuscript preparation. YW performed the experiment. ZW collated the experimental data and visualized the results of the experimental data. All the authors contributed to the article and approved the submitted version.This research was supported by the National Natural Science Foundation of China (Grant No. 61772386), Natural Science Foundation of Hubei Province (Grant No. 2020CFB795), and Zhejiang Provincial Natural Science Foundation of China General Project (Grant No. LY18F020021).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Since 2007, black soldier fly meal has become the main substitute suggested in studies to replace fish meal in fish feeds. The quantitative results of these studies have been analyzed in this paper in order to assess the relevance of such substitution. The analysis focused on the impact of this insect on the growth and nutritional quality of fish. The results showed variable conclusions between studies. These variations are due to the fish species or to the protein substitution rate of the fish meal. Although no definite conclusions have been reached, it is possible to consider high levels of substitution.The search for quality alternatives to fishmeal and fish oil in the fish feed industry has occupied many researchers worldwide. The use of black soldier fly meal (BSFM) as a substitute has increased. This study evaluated the effect of this substitution on fish growth and nutritional quality through a meta-analysis of the literature. A list of studies was selected after an exhaustive literature search followed by the extraction of growth and nutritional parameters. Two random-effects models were used to estimate the differences between the experimental parameters and the controls. The results showed significant heterogeneity between studies for all parameters. The sources of heterogeneity between studies were mainly fish species and protein substitution rate. High substitutions can be considered without necessarily worrying about an adverse effect. Financial profitability studies of the fish production chain from BSFM should be carried out to validate or invalidate the economic viability of this substitution. Aquaculture production has been growing worldwide rapidly for several decades to contribute to food security . Its expHermetia illucens (L.1758), commonly named “Black Soldier Fly” (BSF) [The search for adequate substitutes to fishmeal led to the belief that insects may become one of the future protein sources for animal production, principally thanks to their attributes both biological  and nutritional  ,5. The my” (BSF) . Severalucens L.158, commoy” (BSF) ,10. HoweThe meta-analysis of data resulting from a nearly exhaustive list of studies is an aggregative method of knowledge synthesis allowing an inferential approach . It is pTherefore, the currently presented research aims to evaluate the effect of the substitution of fishmeal with BSFM meal on fish growth and nutritional value through a meta-analysis.Hermetia illucens”) AND Fish AND Substitution OR Replacement.To build the experimental meta-database, systematic bibliographic research was conducted in June 2022 in the search engine of the University of Liege (ULiège library). This library includes several other search engines: Google Scholar, Isidore, Lens, Microsoft Academic, PubMed, Scopus, Scribe and Unicat. A query formulated from keywords related to the research topic has been introduced. The syntax of the query is as follows: (“Black soldier fly”) OR  of the search engine were used to reduce, step by step, the number of proposed answers to limit the noise. Scientific articles were retained after eliminating conference proceedings, patents and press articles. Articles dealing with the incorporation of BSF in poultry feed and ornamental fish such as zebrafish were excluded. The next level of sorting was to keep articles related to the use of BSF in fish feed based on the titles and abstracts. The final selection was made based on the simultaneous presence in the data of studies describing the growth, nutritional composition of BSFM, fish diets and fish reared with BSFM. The water temperature of the rearing environments was also to be provided.A data extraction form from each article was designed and used by two meta-analysts independently. Information collected from each manuscript was: author, year of publication, country, fish species, FM protein and lipid content (%), BSFM protein and lipid content (%), final  and initial  body weight, temperature , experiment duration , feed conversion ratio (FCR), protein  and lipid  levels in fish whole-body.The substitution levels of FM by BSFM were adjusted to the percentage of protein and lipid of FM replaced by BSFM, based on the protein content of these ingredients extracted from studies. The food habits of the fish (carnivorous or omnivorous) were assigned to each species by referring to the FishBase catalog. The TGC of each study was computed following the formula:The above parameters’ measures of variability (the standard deviation or standard error of the mean) were also extracted for analysis. Standard deviations not provided in the studies were generated by the multiple imputation method of missing variances . For thiThe analytical method was adapted from Moula and Detilleux . For eacThe meta-analysis was performed using two random-effects models.i is the estimated measure  for the ith trial , N being the number of trials included in the meta-analysis, µ being the overall mean of all trials.yi and ei each represent the expression of the random effects of the model. They are assumed to be independent with zero means and respectively inter-study (vti) and intra-study (vei) variances.t2 values greater than 50% for the same parameter suggest heterogeneity between studies. The higher the value is, the larger the differences between studies become. Effect size estimates from each study as a function of sample size were plotted on a funnel plot. This allows detection of possible publication bias related to the studies in the meta-analysis. In case of absence of publication bias, the effects obtained will be homogeneously distributed around the true effect size. Conversely, this distribution is not homogeneous when there is a publication bias. The statistical test related to this heterogeneity is the Egger Test [The I2 index assessed the extent of heterogeneity between studies. It measures the percentage of total inter-study variation that cannot be explained by only chance but by the number of studies analyzed . For exager Test . This moger Test .Model 2ijkl is the measure for the ith trial , jth  fish species, kth fish feeding habits , lth average temperature in each study, mth diet protein and nth diet lipid;yij, sik, cil, zim and qin are the fixed effects for the jth fish species, kth fish feeding habits, lth average temperature, mth diet protein and nth diet lipid, respectively. The covariate effect pijklmn is the protein fishmeal substitution rate for BSFM and covariate effect aijklmn is the lipid fishmeal substitution rate for BSFM. All these effects represent potential sources of heterogeneity between measures yijklmn;h1 and b2 are the regression coefficients linking the protein substitution rate and lipid substitution rate to the measure yijklmn.bThe different models were run in the R-4.02 software. The package “bayesmeta” was used to construct the funnel plots and obtain the models’ effects estimates.After screening the literature search through the different selection filters, 28 studies were kept for the meta-analysis . The majOncorhynchus mykiss . Species Dicentrarchus labrax  and Oreochromis niloticus , each represent 10% of the publications  and 5 omnivorous (29.41%) were tested for nutrition with BSFM. The species with the highest occurrence (27%) is ications .The protein substitution rates of fishmeal by BSFM varied from 0 to 100% . Severalx-axis. The standard error is shown on the y axis. Most studies cluster on the top of the funnel; this results in high precision within studies for each estimated parameter. Moreover, the studies included for these parameters mostly used large sample sizes.The dispersion of effect estimates across individual studies relative to the standard error is shown for each parameter  in p < 0.05). This could imply that the effect of experimental diets on fish growth and nutritional parameters would not be significantly different from the control.For each funnel plot, the null hypothesis is rejected outside the white and dark grey areas  performed on the biological parameters showed the presence of publication bias for DIFF_TGC . In contThe potential sources of heterogeneity (Effects) between the results of the studies in the meta-analysis are presented in p < 0.05). Fish species also had a significant effect on DIFF_FCR but was not noticeable on DIFF_PROT and DIFF_LIP. The other effects considered had no discernible impacts on fish growth. Across these studies, an increase of 1% BSFM protein has the moderate effect of increasing the overall DIFF_FCR of fish by 0.02%. In contrast, a 1% increase in lipid substitution in the feed seems to induce a decrease in the DIFF_FCR and in the level of lipid in the fish. The meta-analysis does not show an effect of the protein and lipid composition of the diets on DIFF_FCR and DIFF_PROT but shows an influence on the lipid level in the fish.Only three species seem to be the source of the heterogeneity observed for DIFF_TGC ”. The drop in the number of studies observed in 2022 is temporary because the meta-analysis carried out only occurs in the middle of the year. Studies on the subject continue to be published. The data structure of this meta-analysis shows a dominance of studies conducted in Europe. This result can be explained by establishing a correlation with the species of fish studied. The most frequent species in the data is The effect of FM substitution with BSFM on different fish species was analyzed in this study. Understanding the biological behavior of fish in response to a dietary change requires the integration of several growth and nutritional parameters. Four parameters were studied in this meta-analysis: TGC, FCR, protein and lipid levels in the whole body of fish. Negative asymmetries were found in the meta-analysis except for FCR, suggesting publication bias. This kind of bias generally occurs because research publications depend on the statistical significance of the results or on the direction some researchers give them ,20. AnalThe main difficulty of a meta-analysis on fishes lies in the comparison of parameters affected by the intrinsic traits of each species. Thus, the diversity of fish makes this analysis arduous. The use of TGC as a measure of growth could limit interpretation bias. The use of the protein substitution level instead of the BSFM inclusion level could enhance the conclusions of this study. Overall, the results of this analysis suggest that high substitution levels had no effect on fish growth. This trend is in contrast to the findings of Hua’s  study, wOther factors may also be important in the response of the fish. The fatty acid compositions of the BSF could substantially impact fish growth and fish quality. BSF are naturally rich in saturated fatty acids which limit their inclusion in the fish feed . In mostAll the trials in this study focused on the biological aspects of the use of BSF in fish feed. However, the search for substitutes for fish meal and fish oil has become urgent, particularly in view of the cost of fish production influenced by the cost of fish meal ,8,27. ThThis study reviewed the literature on substituting fish meal with black soldier fly meal in fish diets. The quantitative method used allows for more accurate preliminary conclusions than previous studies. The issue of total substitution of FM by BSFM is not completely resolved; however, it is conceivable that high levels of substitution could be achieved without necessarily risking a negative impact on fish. In addition, economic considerations would allow future research to be directed towards ways to optimize the profitability of fish fed with BSF.


Several proteins are secreted outside the cell, and in many cases, they may be identified by a characteristic signal peptide. However, more and more studies point to the evidence for an “unconventional” secretion, where proteins without a hitherto unknown signal are secreted, possibly in conditions of starvation. In this work, we analyse a set of 202 RNA binding mammalian proteins, whose unconventional secretion has recently been established. Analysis of these proteins secreted by LC3 mediation, the largest unconventionally secreted dataset to our knowledge, identifies the role of KKX motif as well as triacidic amino acid motif in unconventional secretion, the latter being an extension of the recent implicated diacidic amino acid motif. Further data analysis evolves a hypothesis on the sequence or structural proximity of the triacidic or KKX motifs to the LC3 interacting region, and a phosphorylatable amino acid such as serine as a statistically significant feature among these unconventionally secreted proteins. This hypothesis, although needs to be validated in experiments that challenge the specific details of each of these aspects, appears to be one of the early steps in defining what may be a plausible signal for unconventional protein secretion. The pioreceptor .de novo vesicles, that have cargo specificity. One such selective form of autophagy that participates in UCPS is known as secretory autophagy  bypasses the conventional endoplasmic reticulum (ER)-Golgi route. Studies suggest four principal types of UCPS that can be further distinguished into non-vesicular and vesicular pathways . The nonutophagy  wherein Unlike the classical secretory proteins that follow the canonical route of secretion , the unconventionally secreted protein cargoes follow a plethora of divergent secretory mechanisms. There are no concrete studies on the motif analysis of UCPS. Even the signals that may trigger this UCPS are not clear. One of the early indications for what may be a possible signal in this fascinating unconventional secretion process, has only recently been discovered. The discovery of the diacidic motif, DE as the signal for UCPS of SOD1  along wiWith the DE motif as a potential UCPS export signal, the LIR containing proteins possess specific membrane associated receptors and the cells might use this in combination for the type III secretion. This can be resonated with the hypothesis that the UCPS cargo containing DE binds to a specific binding partner .Predicting whether a protein undergoes a conventional secretion is a relatively well understood phenomenon. Several predictors, such as SecretomeP  identifyIncreasing evidence implicates the role of autophagy proteins (ATGs) in the process of secretion. Indeed, genetic loss-of-function studies have revealed ATGs are required for the efficient secretion of inflammatory cytokines , extracehttp://119.3.41.228:8080/SPRomeDB/download_enabled.php). For convenience, these data sets are also provided in our Supplementary Data .The 202 unconventionally secreted proteins used in the analysis are obtained from the set of proteins proved to be secreted by LC3-mediated mechanism in the analysis of Leidal et al. . The sethttps://github.com/ehsanasgari/dimotif/blob/master/notebook/DiMotif_step_by_step_example.ipynb.Discriminatory motif (DiMotif) analysis  of a setIn addition to the DiMotif analysis, we performed a motif search using our script to analyse the differential occurrence of the motifs. The analyses presented in this work are based on 3 amino acid motifs, and which can result in 4200 combinations or 8000 combinations respectively with and without considering the mirror symmetry of the motifs. The proteins from the conventionally secreted and the LC3-mediated groups were scanned for these motif combinations, and the presence or absence of the motif was noted. Similarly, scripts were also used for analysing 4 amino acid motifs as well as the LIR motif (WXXL) .The presence of a signature signal sequence is common in protein sorting. To identify the presence of a signal sequence in the 202 RNA binding proteins, we performed two different analyses:To capture the unique signature in the 202 LC3 interacting proteins (UCPS-ATG dataset), they were compared against 20,117 proteins from the Swiss-Prot database using the DiMotif server . This diIn addition to the above-mentioned analysis from DiMotif server, we also performed a custom motif search comparing the LC3 interacting proteins with the database of conventionally secreted proteins used for training the SecretomeP . In thisThe transport of the specific set of proteins analysed in this work is mediated through the LC3 domains. We identified all the LC3 Interacting Regions (LIR) in each protein by performing a search for WXXL motif , and stuSince the unconventional secretion is usually activated under conditions of stress, we explored the possibility that a post-translational modification may be required for its activation. We searched for the presence of serine, threonine, or tyrosine within 3 amino acid positions from the differentiating motifs. For almost all the reference motifs we analysed, the S/T/Y amino acid in the proximity of the motifs occurred preferentially among the proteins from the positive set .p < 0.0002 in a Z-test).The discriminatory motifs identified relative to the Swiss-Prot database and the conventional protein secretion dataset were re-grouped to identify the common patterns among them. Two major patterns emerge among these three amino acid motifs: triacidic motifs (EEE/DDD/DEE/etc) occurring in 160 of the 202 from the positive set, and 625 of the 1576 in the negative set, and basic motifs (KKX) occurring in 187 of the 202 from the positive set and 796 of the 1576 in the negative set. Considering either of these triacidic or KKX motifs as a signal, the difference in the proportion between the positive and the negative data sets is statistically significant .Unlike conventional secretion, the UCPS is activated under conditions of stress, suggesting the possibility that post-translational modifications may play a role in activating the signal. In continuation of the hypothesis that the triacidic or KKX motifs may be the “signal”, we explored the possibility that the amino acids S/T/Y in the proximity are responsible for activating this signal. S/T/Y amino acids in the proximity of triacidic motifs appeared in 133 of the 202 proteins from the positive-set, and in 422 of the 1576 proteins from the negative set. Similarly, S/T/Y amino acids in the proximity of KKX appeared in 170 of the 202 proteins from the positive set, and in 603 of the 1576 proteins from the negative set. The statistical significance of the difference between the two sets remains high (The positive set being analysed here, is about the set of proteins where LC3 conjugation machinery is involved in their secretion . HoweverGiven the importance of unconventional protein secretion, it is pivotal to identify the signals that trigger it, if such signals exist. The key to building hypotheses is to work with highly reliable data, preferably from fewer sources to avoid any biases in the experimental protocols. In this work, we chose to work with a very specific data set from the LC3 machinery driven protein secretion with 202 proteins, and to build a few hypotheses on what may be the signal for the unconventional secretion. The presence of three amino acid motifs, triacidic or KKX, appears recurrently in the set of 202 UCPS proteins, significantly more than it occurred either in the conventionally secreted proteins or in the Swiss-Prot database. Although the 202 proteins are believed to be secreted by the LC3 dependent pathway, 5 of these proteins do not have an LIR motif that can interact with the LC3 region. Interestingly even in these proteins, triacidic motif in the proximity of a phosphorylatable amino acid is a common occurrence. Among the proteins that had the LIR motif, it was found mostly in the sequence or a structural proximity from the triacidic or the KKX motifs. Thus, it appears that triacidic or KKX amino acid motifs in the proximity of LIR and/or phosphorylatable amino acids may play a significant role in triggering the unconventional secretion. This result was also validated in the independently curated dataset of unconventionally secreted proteins from other mammalian cells , where aIn conclusion, we explored the plausible signals for a very fundamental cellular process - unconventional protein secretion. The field is still in its nascent stages compared to conventional protein secretion where the signals as well as the mechanisms are clearly identified. Exploiting the recent experimental findings of a large set of unconventionally secreted proteins, we could perform bioinformatic analyses as well build hypotheses on the potential role of triacidic amino acids or KKX motif in the proximity of LIR region and phosphorylatable amino acids. As the next steps, we will be exploring collaboration with the relevant experimental groups to validate these hypotheses as well as explore the possibility of deciphering the patterns using interpretable deep-learning methods on the same datasets.


Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power. Increasing Physical Activity (PA) is a significant part of daily life, which can have beneficial health effects . PhysicaManaging the expected demand in social and health services will need to harness opportunities from new technologies. Ambient Assistive Living (AAL) technology has the potential to relieve some pressure from both families and social services, and provide more care in the home and community, which will reduce national health and care spending and has the potential to improve wellbeing . MonitorIn the past, a common way of assessing PA was the use of questionnaires, such as the Physical Activity Scale for the Elderly . Such quThis has led to a great deal of interest in the potential of wearable technologies for monitoring PA. Currently, the accelerometer and gyroscope are the most widely used wearable sensors for PA monitoring. They have been used in a variety of applications, including heath monitoring, fall detection , human mActivity recognition is also employed in industrial applications. According to the Industry 4.0, monitoring and recognising human activities in manufacturing space is the most important component to integrated into a cyber-physical system . In manuAccording to the literature, accelerometers have been placed on many positions on the body, such as the waist (belt), wrist, upper arm, chest, pelvis, thigh, and ankle . SeveralMost activities in real life situations consist of several complex motions, which means that simple wearable sensors may not be enough to recognise these activities. However, they are very effective for recognising simple activities . In this paper, a comprehensive list of features is extracted from 3-axial signals, which includes a set of features that never been used before for human activity recognition. Filter feature selection method is used to investigate the significance of each feature for the activity recognition. The comparison involves comparing the effects of sampling rate, sensor placement, and complexity of activities on the significance of the features, and on the classification performance. Typical ADL recognition using an accelerometer is usually achieved by a task that consists of several steps: signal pre-processing, feature extraction, dimensionality reduction and classification . The claIt is well known that classification accuracy can be enhanced through reducing dimensionality . In ADL The two main techniques used for dimensionality reduction are feature extraction and feature selection. Feature extraction involves the creation of a new feature space as a combination of the original features. The most common methods are Principal Component Analysis (PCA) , Linear Researchers usually collect their own data to train and validate their proposed system. This makes it very difficult to compare the performance of the different proposed techniques in an objective way .Accelerometers have been used in many ADL recognition approaches in the literature. However, each study focuses on different activities and extracts diverse features, which makes it difficult to compare performance between studies. Many machine learning techniques have been used for ADL recognition, such as Random Forest , SupportFeature selection has also been employed for activity recognition applications. Preece et al. (2009)  comparedRelief-F and Sequential Forward Floating Search (SFFS) wrapping methods were applied to a range of features from the literature . The autCapela et al. (2015)  used thrAtallah et al. (2010)  used sixChernbumroong et al. (2011)  used an Suto et al. (2017)  used ninRosati et al. (2018) , dividedOrlov et al. (2019)  employedAhmed et al. (2020)  employedFinally, Thakur et al. (2022)  employedMost of the previous studies extracted limited sets of features. Moreover, they used a limited set of features, and most of them employed just one specific dataset, which was collected as part of the study. The missing of the collected datasets and other key details of those studies does not allow us to evaluate the proposed approach against some of those studies.In contrast, this paper extracts a comprehensive set of features from accelerometer raw data, some of which has never been used for this application before. Then, a framework is employed to measure the significance of the features from four publicly available datasets. These datasets have different sampling frequencies and different sensor placement locations; we have used them to comprehensively investigate the significance of the features for the classification. The investigation was performed at the study level; the significance of the features which were extracted from all the participants’ data have been analysed together. This framework consists of several steps and employs a state-of-the-art feature selection method: The Joint Mutual Information Maximisation (JMIM) method . The papThe rest of the paper is organised as follows. To define the significant features for the activity recognition task using accelerometers, an analysis was performed using four different datasets with various characteristics. Two datasets are publicly available from the UCI machine learning repository , and theThis dataset was collected in a controlled laboratory environment using an accelerometer sensor on a smartphone placed at the waist. The data was collected from 29 participants at a sampling frequency of 20 Hz. The data consisted of six activities . No demographics about the participants were provided. The HARDS dataset  has been collected using a variety of smartphones and smart watches from nine participants (age range 25–30 years) in a controlled setting using specific routes outside the laboratory. All of the participants were asked to perform five minutes of each activity following a given script. Several sampling frequencies were used to collect the data based on the device used. In this paper, only the part of the data collected using Samsung and LG smart watches were used. The watches used sampling rates of 100 and 200 Hz, respectively, to collect the data. Six activities were monitored . A group of thirty participants (age range 19–48 years) were recruited to collect this dataset in a controlled environment. They were asked to perform six activities . A smartphone placed on the waist was used to capture the activities at a sampling frequency of 50 Hz using the built-in tri-axial accelerometers and 3-axial gyroscopes. In this study, we are focusing only on the data from accelerometers. This dataset contains 12 different activities. Nine participants  were recruited to collect this data using three Inertial Measurement Units and heart rate monitoring in a controlled environment outside the laboratory. One inertial measurement unit (IMU) was placed on the wrist on the dominant arm, another on the chest, and the last one on the dominant side’s ankle. The IMU is a device that consists of triaxial accelerometers, triaxial gyroscope, and triaxial magnetometer. The gyroscope is known for it is tendency to drift over time even when the sensor is not rotating . In thisThe evaluation framework used in this study is shown in It has been reported in the literature that the best classification accuracy can be achieved using a sliding window between 2.5–3.5 s . TherefoRecurrence Quantification Analysis (RQA) : RecurreA periodic system is characterized by long and non-interrupted diagonals in RPs. The vertical distance between these diagonal lines reflects the period of oscillation. While in the chaotic system, diagonals are formed in RP, but shorter than periodic systems. Uncorrelated stochastic signals generate RP with many single points. Therefore, the shorter the diagonals in RP, the less predictable the system. Therefore, each activity is expected to have its diagonal characteristics.Recurrence Rate (RR): The percentage of recurrence points in a recurrence plot. It is equivalent to the probability that a specific state will recur to its Determinism (DET): The percentage of recurrence points in a recurrence plot that form the diagonal lines of minimal length Entropy (ENTR): The Shannon information theory entropy of the probability distribution of the diagonal line length.L): The average length of the diagonal lines  : PE is aLyapunov Exponent (LE) : LE quannt (LLE) . The algIn this study, a small data quantity method is employed to calculate the largest Lyapunov exponent (LLE) . The algThe Lyapunov Exponent is calculated with the assumption that the acceleration signal of each feature has a different level of chaos. Total Harmonic Distortion (THD) : THD quaRaw accelerometer data cannot be used directly by most ML algorithms. Features that measure the characteristics of the signal windows are extracted to capture the patterns among the data. Since this study investigates accelerometer signal features, the decision was made to extract most of the features that were reported in the literature. Time domain and frequency domain are commonly used measures as features for accelerometer data. In addition to the common features, features across the domain boundaries were included, which are not commonly used for activity recognition, such as:In addition to the features discussed earlier, common features include mean, standard deviation, root mean square value, autocorrelation of each signal, Fast Fourier Transform (FFT), Wavelet Packet Decomposition (WPD), power peak features, and autocorrelation. All the features were calculated after the filtration except the mean and standard deviation, which were calculated using the raw data. Many FFT coefficients capture the high frequency part of the signal, these coefficients are likely to have low amplitude which are unlikely to carry significant information about the activity. Most of the information is contained below the frequency of 17 Hz. Activities like walking are low frequency activities. Therefore, WPD shown in To simplify the presentation of the results, the features are divided into five groups, denoted by giving each group a different colour in All the features were normalized in order to minimize the effect of any abnormal sensor readings. This can reduce the weight of abnormally high values among the data. Normalization was performed using the following equation: Continuous features in the data were discretised using the Equal Width Discretisation (EWD) method  to avoidFeature selection is a dimensionality reduction technique. The current study employs a subset of this technique for two reasons. First, it helps to reduce dimensionality and improves the performance of the learning algorithm by removing the redundant and irrelevant features that may confuse the learning algorithm. Second, it measures the discriminative power of each feature and selects the most informative that produces the highest classification accuracy. The JMIM method was also employed in this research to select the significant feature subset for the discrimination task. This method was chosen, as it was proved that the method outperformed other state-of-the-art methods in the literature, and because it is computationally inexpensive. F is the original feature set, S is the selected subset feature, JMIM is based on the use of mutual information and the ‘maximum of the minimum’ criterion. The method starts with the most significant feature, then it begins to select features one by one and add them into the selected subset. The feature is selected when it maximises the goal function: Set (Computation of the MI with the output class) For (Choice of the first feature) Find a feature (Greedy selection) Repeat until(Output) Output the set The method selects the features that share the highest information with the class label in the context of the features that have already been selected. The method employs the following forward greedy search algorithm:The greedy algorithms may not find the optimal solution for some of the heuristic problems when each node in the problem has a fixed value. However, that is not the case for mutual information-based feature selections methods. Although the amount of information between each feature and the class label is theoretically fixed, when the feature is within a subset then this amount of information is no longer fixed, as there is factor called information interaction between the features that can reduce or increase this amount of information. Moreover, the feature may share redundant information that is covered by other features. In addition, the used goal function does not include any summation, it employs maximum of the minimum criterion. Therefore, the search for the features is not as simple as the common heuristic problems. The traditional understanding of local minima problem does not apply here because of the information redundancy and information interaction between features which makes everything dynamic with the searched features. The key point of this work is to investigate the significance of the accelerometer signal features. Classification accuracy is used to evaluate the significance of the selected subset of features. For this reason, two classification algorithms have been used; K-nearest neighbour classifier (KNN) (k = 3), and the Support Vector Machine (SVM) classifier with linear kernel; these classifiers have been chosen because they make few (if any) assumptions about the data. Therefore, the performance will be mainly due to the feature selection not the classifier’s ability to handle the data. They were also chosen because they have been used in similar studies .We used the Matlab Statistics Toolbox for SVM and KNN classifiers. Five-fold cross validation was used for the feature’s significance evaluation. Each fold of the dataset (20%) was used for the validation once, while the rest of the data (80%) were used for the classifiers. This process was repeated five times, which means that the whole dataset was used for the validation. The selected subset of features was evaluated after adding each new feature, starting with only the most significant feature in the selected subset. This continued until all 193 features were used together for the training and testing. The classification accuracy produced reflected the discriminative power of the whole subset after adding the new selected feature and was not based on the newly selected feature only. This section describes the results of applying the proposed framework to define the significant feature selection for each dataset. The JMIM method has been used to define the most significant features when classifying the activity within the datasets. This method is classifier independent: it measures the shared information between the class label and the subset of features. Therefore, the features are ranked based on the amount of information that they add about the class, as the selected features add more information when classification accuracy improves. The results of the WISDM dataset show that the Root Mean Square (RMS) features shared the highest information with class. Mean, FFT, autocorrelation and wavelet decomposition were among the most significant as well. These five features make up 29 out of the most significant 30 features. The RMS feature is shown as the most significant feature in the results of SBRHA dataset. The results also showed that Mean, FFT, autocorrelation and wavelet decomposition are also among the most significant features. The 30 most significant features comprise 28 features from these four feature groups. The results of both datasets show that uncommon features, such as RQA, permutation entropy, Lyapunov exponent and total harmonic disorder were not among the most significant features for these two datasets. The results of the LG watch and Samsung watch datasets (HARDS) are slightly different. Features that measure the recurrence, regularity and degree of chaos were shown to be among the ten most significant features. These two datasets use relatively high sampling frequencies, which provide more values and more information about the details of the movement. Therefore, features related to repeatability, regularity and level of chaos appeared in the ten most significant features especially for the LG watch data which has a higher sampling frequency, 18 of the 30 most significant features belong to RQA, Permutation entropy, and Lyapunov exponent. Wavelet features also appeared within this list five times. While for the Samsung watch 11 of the 30 most significant features were wavelet features; repeatability related features appeared 10 times in this list. The most significant features were the standard deviation features for both datasets. Many of the twenty most significant features belong to wavelet decomposition features. Due to the unique characteristics of PAMAP2 dataset in terms of the number of activities and the level of movement complexity of these activities, the order of the ranked features is different. Features related to Recurrence rate in the signal, WPD, permutation entropy, and autorepression make up most of the features that produce the highest classification accuracy. On the other hand, the results were different when the ankle dataset was down sampled to 20 Hz. The results showed the simple time domain features such mean, and frequency domain features such FFT, WPD, and autorepression features are among the most significant features. Features related to recurrence rate in the signal and permutation entropy appeared less significant when the sampling frequency was reduced. Therefore, features related to Recurrence rate in the signal, wavelet decompensation, permutation entropy can be important for discriminating the activities if the sampling frequency is high and more samples are available for each window. The results also showed that there is no major effect of the sensor placement location on the order of the selected features.Two classifiers were used to evaluate the significance of the ranked selected subset of features for the classification accuracy. As mentioned earlier, each classifier was trained and tested after adding every single feature to the subset. Five-folds cross validation was used for every training and testing iteration. Classification accuracy and F-measure are used to measure the performance of the selected subset of features. In the binary classification, classes are named positive and negative. The confusion matrix describes the ways in which the classifier is confused when making predictions. The confusion matrix has four values representing the relation between real and predicted classes: namely: TP (true positives): the number of positive elements classified correctly.FP : the number of negative elements misclassified as positive.FN : the number of positive elements misclassified as negative.TN (true negatives): the number of negative elements misclassified as negative.Extending these classification metrics to multi-class problems can be achieved by considering one of the classes as a positive class and the remaining ones as negative class : ClassifThe results of the SBRHA dataset showed that with only the most significant feature, the SVM classifier was able to classify the activities with the different accuracy; jogging (93.8%), walking (67.89%), sitting (95%), standing (89.46%), while the classifier missed classifying all the walking upstairs and downstairs activities. The results of using the subset of 30 features showed that walking upstairs and downstairs are also the most misclassified, as walking and jogging have an accuracy of (57.55%), while standing is with the highest accuracy (99%), then jogging (97.02%), walking (92.92%) and sitting (95%).Although the dataset is not balanced, As noted earlier, only the data collected by the smart watches from HARDS dataset was used in this study, the rest of HARDS dataset was not included. In terms of classification rate of each activity when the most 120 significant features are used, biking scored the highest classification (98.74%) followed by sitting (98.73%), standing (92.91%), walking (88.18%), and walking up and down (84.06%). This order stays almost the same when the most 30 significant features were used, the only difference is swapping the order between walking and walking up the stairs.The results of the Samsung watch dataset are shown in The results for the PAMAP2 datasets for different sensor placement locations are shown in To evaluate the discriminative power of the uncommon features, which are the features related to RQA, PE, LE, and THE, the classification algorithms were trained without using the uncommon features. The drop of the classification accuracy is used as indication of the discriminative power of these features. Adding the uncommon features to the dataset enhanced the classification accuracy by 2.3–8.6%. The results showed that the enhancement is larger when the sampling frequency is higher, which means the uncommon features are able to extract more patterns when the sampling frequency is high. The significance of accelerometer features was successfully identified using the state-of-the-art JMIM feature selection method. The evaluation of the selected subset was carried out using SVM and KNN classifiers. Four publicly available datasets were used in this study because they have been previously used in activity recognition research and also to study the effect of sampling frequency as well as on body placement location on the feature significance for classification accuracy. The sampling frequency of the datasets is between 20 and 200 Hz. Wearables are placed at the waist, wrist, chest, and ankle. The number of activities also vary, with most of the datasets including only simple activities, and one of them including complex activities.This study focused on the type of the features rather than the acceleration axis to which the signal belongs. The features are divided into five groups: (simple time domain), , , (time-frequency which includes WPD), and finally (autorepression features).The results showed that the Root Mean Square (RMS) features produced 74% accuracy on their own for the WISDM and SBRHA datasets. FFT, Root Mean Square (RMS), mean, autocorrelation and wavelet were among the most significant features for human activity recognition for these two datasets. This result agreed with the results in previous literature .On the other hand, when the sampling frequency is raised, features related to signal repeatability, regularity and level of chaos , and WPD were shown among the most significant. The sampling rate was 100 and 200 Hz, which meant that each contained more data points. This high number of data points provided more detailed information about the movement. That is why these features appeared among the most significant in the subset. Wavelet decomposition features were also among the most significant features for this dataset. This has been shown in the results from the PAMAP2 and HARDS datasets. Even though many participants were recruited to perform the activities during the data collection, which meant that the activities were performed at different speeds and styles, the achieved classification accuracy was very high, especially for SBRHA (more than 98%). The results also showed that SVM worked better than KNN.In conclusion, simple time domain features can be very predictive for recognising simple activities, as the sampling frequency becomes high WPD, and repeatability and regularity features can be very predictive, especially when the recognised activities include complex activities. WPD features can have significant discriminative power for recognising both simple and complex activities. It would be interesting in the future to study the influence of window size and overlapping on the selected feature subset, as well as the effect of applying invariant orientation techniques on the feature significance. Further improvements are needed for the selection method to be able to produce scores for significance degrees. Future research could also be conducted using new collected data with more activities, and more participants could be included. In particular, datasets should report full demographic statistics for participants including at least age, gender, ethnicity, and body mass index. The public datasets have poor descriptions of their participant demographics with only one reporting the number of females (1/9), thus it is likely that the datasets are heavily biased towards young white men. Future public datasets should reflect the variation in real world, including additional participants from diverse age groups and with different ranges of movement capabilities in order to study the feature significance for data collected from a variety of populations, and different patients with movement impairments. A future study can be expanded to include all IMU sensors, which will require studying the gyroscope drift problem, and comparing various compensation approaches. The methodology of this study can also be employed to study controlling powered exoskeletons through developing machine learning-based algorithms to recognise the movement.


As the importance of uncore components, such as shared cache slices and memory controllers, increases in processor architecture, the percentage of uncore power consumption in the overall power consumption of multicore processors rises significantly. To maximize the power efficiency of a multicore processor system, we investigate the uncore frequency scaling (UFS) policy and propose a novel imitation learning-based uncore frequency control policy. This policy performs online learning based on the DAgger algorithm and converts the annotation cost of online aggregation data into fine-tuning of the expert model. This design optimizes the online learning efficiency and improves the generality of the UFS policy on unseen loads. On the other hand, we shift our policy optimization target to Performance Per Watt (PPW), i.e., the power efficiency of the processor, to avoid saving a percentage of power while losing a larger percentage of performance. The experimental results show that our proposed policy outperforms the current advanced UFS policy in the benchmark test sequence of SPEC CPU2017. Our policy has a maximum improvement of about 10% relative to the performance-first policies. In the unseen processor load, the tuning decision made by our policy after collecting 50 aggregation data can maintain the processor stably near the optimal power efficiency state. The rapid development of modern communication technology brings faster data transmission speed and smaller transmission delay and also accelerates the construction of the communication network of everything connected. The increasing number of devices accessing the Internet brings huge data traffic, which leads to higher and higher computing power requirements for servers in data centers. The number of processor cores is gradually increasing with the performance requirements, and multicore processors have become a major architectural component of server computing systems. Multicore processors allow multi-threaded load work to run simultaneously on their available compute cores, thereby increasing the overall computational performance of the processor. However, high performance for servers means massive levels of power consumption. While processor architectures are moving toward highly parallel processing, the server industry as a whole is facing the problem of huge power consumption.As the processor design architecture is updated, processors are divided into core and uncore. Simply put, uncore refers to components other than the compute core and the core’s private cache slice, including memory controllers, shared cache slice, and other important components. It has a vital impact on core computing, cache read/write, and memory access actions. Using the McPAT modeling framework on Niagara processors to evaluate the power consumption of core and uncore components, the researchers show that in a 16-core system with 32 MB of shared cache, the power consumption of the memory controller and shared cache accounts for 27% of the total power consumption of the chip . All of Modern processors internally decouple the core and uncore frequency domains and provide a separate frequency domain for each core, allowing them to support single-core Dynamic Voltage Frequency Scaling (DVFS). There are two main trends in DVFS policy research for different architectures with different types of cores. One trend is to evolve from feedback-based single-step policies to machine learning (ML) policies, while the other is to evolve from offline to online learning policies. Feedback-based policies are limited by the magnitude of configuration changes and can only be adjusted in the adjacent configuration state space. In contrast, the offline ML policy completes modeling based on a sufficient amount of data with a known processor load and maps the processor state to the optimal core configuration at runtime. However, this policy of modeling based on known knowledge does not apply to unseen processor loads, so the research on dynamic power management policies for processors based on online learning has become the current focus of the industry. Imitation learning (IL) ,6 has grThe uncore also has a separate adjustable frequency domain, which means that the processor’s power efficiency can be improved to a greater extent by controlling the uncore frequency. Current research on uncore frequency scaling (UFS) policies has been designed for different objectives, mostly focusing on offline learning. Some researchers emphasize the extensibility of feedback-based policies under unseen load and record the results of real-time operation of the policies as online training sets to feed artificial neural network (ANN) models for online learning . Such a Macroscopically, UFS policies for multicore systems have an overall goal, such as power-aware and maximum power savings with a performance penalty. To maintain performance while respecting power overhead, designers of communication and computing systems have begun to focus on power efficiency ,10. ThusTo tackle the above problem, we propose a novel imitation learning-based UFS policy  for multicore systems, which aims to optimize the processor’s power efficiency to the maximum extent at runtime. Specifically, we first construct an offline UFS learner policy. Based on the previously known load applications in the experimental phase, we construct the learner training dataset with the relevant processor events from different underlying register statistics as the input metadata and match the optimal uncore frequency for each input state as the label for supervised learning. Meanwhile, we use the input metadata and uncore frequency as joint inputs to build a predictive model of processor performance and power consumption. The two are combined as an expert model to provide schematic data for learner policy in the online learning phase. Importantly, the learner model and the expert model are fine-tuned according to the DAgger  algorithWe collect offline training datasets and obtain one UFS policy with 99% classification accuracy and three models with 99% prediction accuracy. The latter three constitute the expert model and optimize the learning efficiency by converting the learning cost of requesting experts for new data annotation into fine-tuning of the prediction model during the online learning phase;We implement uncore governors based on the idea of dynamic power management governors already in use and compare them with the UFS_IL policy under the same load conditions. It is found that the processor performs best in terms of power efficiency under the latter control.Online imitation learning improves the generality of the UFS policy for unseen loads. Experiments show that after collecting about 50 aggregation data, the tuning selection of the UFS_IL policy maintains the processor’s power efficiency under unseen load at near-optimal levels.The remainder of this paper is as follows. The frequency scaling policies for processor uncore are usually investigated in two phases, the first phase models the correlation between uncore versus processor performance and power consumption, respectively. The literature  analyzesThe next phase is focused on building the UFS policy. The literature ,15,16,17In the research on DVFS policies for processor cores, solutions for online learning are gradually gaining importance to allow servers to determine the optimal processor configuration based on unpredictable real-time loads ,25,26,27To sum up, both advanced UFS policies have certain drawbacks. The feedback-based policy is limited by the tuning action, which can only increase or decrease the current frequency. The machine learning-based offline policy performs well on training data, but it cannot achieve good generality on unseen workloads. Therefore, in this work, we adopt the imitation learning idea to design the UFS policy. Its advantages of requiring fewer data and time to learn near-optimal policies compared to RL are strongly in line with our requirements for UFS policies. We also shift the learning focus of the UFS policy to optimize power efficiency. In combination with the DAgger algorithm, we use the aggregated data to simultaneously optimize the expert model as well as the UFS learner policy to improve the generality of the UFS policy for unseen processor loads.K bands of uncore frequencies that can be tuned at runtime. We also assume that there are A_train is used to build offline policies, and the other application cluster A_test is used to test the generality of different policies under unseen loads.Consider a multicore processor system with π will map the system’s real-time state T control epochs in total), i.e.,Each control epoch runs a certain target application We then define the power consumption terature .(2)π*=aTo maintain the processor in a highly power-efficient state under dynamic and rapidly changing load environments, any power management policy must have the ability to be analyzed quickly and implement quickly. The fast analysis depends on the frequency of data sampling, which can be accomplished by compressing the amounts of tasks within each control cycle. The latter, in turn, depends on the location of the frequency controller. The possibility of deploying the power control model to the BIOS level has been explored in our previous research , and theOur problem is to dynamically select the uncore frequency that will optimize the power efficiency of the processor based on its load variation. This is a sequential decision problem and can be described as a Markov decision process. Intelligence interacts and changes with the environment, gets feedback and implements the next action, and repeats the process throughout the decision period. In this process, intelligence aims to maximize the cumulative reward and minimize the cumulative overhead of the decision steps ,6. ExploA of actions made by the experts based on the state S. The learner needs to learn a policy network Unlike exploratory learning, IL relies on supervised learning. One of the most direct IL methods is Behavior Cloning. In this algorithm, all human experts’ schematic data are provided to the learner in the form The smaller the loss function, the closer the decision of the policy network is to the behavior of the experts. In the process of training the learner policy, we can use the data The parameter A_train and in the full frequency domain to obtain a sufficient amount of data.IL relies on an adequate training dataset with the required data complexity to construct learner policies. However, due to inconsistent experimental conditions and learning targets, the datasets collected by different methods for UFS policies are not generalizable. And the processor states are countless, current human experience is far from sufficient to provide schematic data for each processor input state. To solve this problem, we simulate the performance of the processor after tuning the uncore frequency to each value to determine the label data in the current state. Specifically, we apply the same amount of load to the processor and collect power consumption and performance-related event statistics. To ensure that we get the optimal decision in the whole frequency space, we pull up the frequency from the minimum to the maximum at the current uncore frequency, compare the power consumption and performance changes brought by different frequencies, and finally select the frequency that brings the optimal power efficiency as the label. The whole process is repeated on the entire training application set p(a|s) using a schematic state with a marginal distribution q(s), the conditional distribution can only achieve a good generalization performance on q(s). There are two mainstream solutions to the problem of poor generalization ability. One solution is to compress the states in the decision space to reduce the dimensionality of the whole space, thus reducing the complexity of the state distribution in the physical world. Another solution is DAgger, which executes the policy in an unseen environment and collects new metadata, then requests experts for annotation. After that, a new round of Behavior Cloning is performed based on the aggregated schematic dataset. DAgger makes good use of the state distribution difference between the new environment and the training data, and it can achieve a far greater generalization capability than the general Behavior Cloning algorithms. Importantly, DAgger comes with strong theoretical guarantees for learning a policy that is very close to Oracle policy The second challenge is the performance limitation problem. IL methods such as Behavior Cloning reduce the learning difficulty by transforming the decision problem into a supervised learning problem. However, the control policy obtained in this way learns an upper limit of knowledge that does not exceed the knowledge provided by the expert. According to the principle of supervised learning, if we train a conditional distribution erations . HoweverA_train application set in the offline phase. In the online phase, we perform runtime evaluation and collect aggregation data for the A_test application set. The offline models are fine-tuned batch by batch to gradually increase their generality for unseen processor loads. We then describe the details for both parts.The process of constructing the UFS_IL policy can be divided into two phases, and the whole process is shown in Throughout the construction process, we run different benchmarks on the processor to simulate different types of real loads, and then run statistical tools to get the uncore-related events as input metadata. The list of selected events is shown in For the construction of the predictors, we adopt a stochastic path-based data acquisition method to optimize the whole process. After collecting the input metadata, we randomly switch the uncore frequency and add the value to the input. Then we get the processor behaviors  after the frequency change and store the data as The expert policy terature ,23 to moThe initial UFS policy needs to analyze the unseen load in the online phase and continuously give the optimal tuning action to maintain the processor’s power efficiency at a high level. This target is achieved by the DAgger algorithm, but as mentioned before, DAgger algorithm requires constant requests to the expert for data annotation, which has an impact on the learning efficiency in the online phase. Also considering the extremely high requirement of accurate labeling of unseen data in the UFS problem, we split the expert model and insert control rules, and the problem of accurate labeling of new data is transformed into a problem of fine-tuning different models. The rules give the optimal supervised signal as long as the prediction models are guaranteed to be accurate. Besides, fine-tuning means that the model learns for the true data distribution on the basis of the current training, extending its already available learning capability to new similar tasks.A_test application set on the processor to simulate the unseen load. The decision actions of the UFS policy and the expert policy are then compared at each decision epoch, and data is collected for the UFS policy if they do not match. We run the tools after the frequency change to get the real behavior data of the processor. If its error with the data predicted by the predictors is more than 5%, it is included as aggregation data for the expert models. Considering the limitations of the expert’s schematic output The whole process of data aggregation is shown in Our experiments are conducted in a multicore system with an Intel Xeon Gold 5118 CPU . The sysA_train for building UFS policy and prediction models, and A_test for online learning to evaluate the generality of UFS policy. We select nine benchmark applications from SPEC CPU2017  and diviThe construction process of the UFS policy is slightly different from that of the processor behavior prediction model, as the former should be classified as a classification model due to the different types of supervision labels, while the latter belongs to a regression model. Taking the power consumption prediction model as an example, we evaluate the performance of different machine learning regression models under the UFS dataset, as shown in We compare different regression algorithms in terms of R-Squared (R2), Mean Squared Error (MSE), Root-Sum-Squares (RSS), and training time with the same amount of data. The decision tree-based random forest regressor is found to achieve optimal prediction results with the shortest training time. Different from offline machine learning models, the model construction and training time are important for a frequency control policy that runs in real-time. It directly affects the length of the decision epoch and the process of online fine-tuning. The shorter the decision epoch, the finer-grained sequence of frequency regulation actions can be obtained at the same time, and the more flexible it can be to cope with the actual load. Therefore, we use a random forest regressor based on five decision trees to fit the relationship between the input metadata and the labels. All offline models yield R2 determination coefficients close to 1 as well as very small MSE.In this section, we will compare the performance of the UFS_IL policy with two advanced UFS policies on fixed load sequences. DUF  and ML_gDUFθ. Faced with different types of processor loads, DUF first ramps up the uncore frequency to the maximum and then gradually adjusts the frequency according to specified rules to save power consumption while meeting performance requirements. The rules are based on Δflops, Δmemory_bandwidth, and Δllc_bandwidth, i.e., the change in the three metrics over the two intervals before and after. This is a typical single-step feedback policy, where the decision action in the next time interval depends entirely on the outcome of the action in the current time step. It can be interpreted as a reward mechanism for immediate actions. The degree of trust in the next decision interval is determined based on the impact of the current action. If trusted, the same action is chosen, and if not, the opposite action is chosen.The idea of the approach is built on a user-specified acceptable performance degradation index In the specific experiments, we use the Likwid tool to obtain the processor characteristics metrics for each decision period and then choose to turn up, lower, or set the uncore frequency to the maximum value depending on its variation between adjacent decision periods.ML_guided PolicyA_Socre and S_Score, both of which are represented by the statistical values of the underlying register sets on the amounts of relevant events triggered. The former indicates the thirst of each core for uncore resources, while the latter complements the impact of the size of private cache slices on the thirst of uncore resources. ML_guided policy builds two models, the first of which learns the mapping relationship between the processor state and the uncore frequencies. The obtained uncore frequencies are then united with the inputs of the previous model, corresponding to the power consumption on a case-by-case basis. A random forest learner is used to build the relational model, and the decision is completed based on the before-and-after performance change ΔP as well as the predicted power consumption.The policy constructs two evaluation metrics, In the specific experiments, we collect input features during each decision period and then read the current uncore frequency of the processor from MSR as the output label of the first model. The uncore frequencies are then incorporated into the input features and the power consumption obtained from the Likwid tool is used as the output label to train the second model.θ denotes the performance degradation index, and performance_max denotes the maximum performance.The DUF policy focuses on lightweighting and the evaluation of the effect of immediate actions, while the ML_guided policy focuses on the generality of the known load space. And for the overall optimization goal, both are consistent, i.e.,A_train application set, repeated five times for each application. And two θ values of 0.1 and 0.05 are provided for the DUF and ML_guided policies with names like DUF_10 and ML_guided_5 to identify the policies with different parameters. The results are shown in We evaluate the above policies on a sequence containing all applications of the The results of all models are compared to the max_uncore_freq policy, which is generally the default UFS policy set by conventional processors. As we can see from the results, our UFS_IL policy achieves optimal PPW performance under all load applications, with an 8% to 22% improvement relative to the max_uncore_freq policy. There is also a certain improvement relative to the performance-first UFS policy. Under the cactuBSSN application, the PPW under the UFS_IL policy control has about a 6% improvement relative to the ML_guided_10 policy. Under the lbm application, the PPW improvement relative to the DUF_10 policy is about 10%.At different decision nodes, the DUF policy moves step by step toward power savings based on the change in processor performance over adjacent time intervals. While the ML_guided policy can jump directly in the direction of the most power savings based on the performance prediction for the next time step, which is the main difference between the two. However, as shown in θ on the ML_guided policy, the DUF_5 policy achieves a better power efficiency compared to the DUF_10 policy due to the reduced frequency range. The impact of the different policies on this application significantly demonstrates the problem of performance-first UFS policies, which is exactly the optimization point highlighted by the performance-power trade-off model. A certain percentage of power consumption is saved while accepting no less than that percentage of performance degradation.Another point worth analyzing is that it seems the PPW of the processor under the control of both the ML_guided policy and the DUF policy is not significantly better than the max_uncore_freq policy under the mcf application, or even worse. Analyzing the specific situation, we find that the processor is more power efficient by keeping the uncore frequency at its maximum value. If the uncore frequency is continuously reduced within the allowed performance drop, the power efficiency of the processor increases and then decreases, as shown in The actual runtime state does not require the processor to maintain the maximum frequency at all times. To save CPU power and reduce heating, the Linux kernel developers defined a framework, the CPUFreq system, which dynamically adjusts the CPU frequency according to the current CPU load state. The governor of this system selects the appropriate frequency based on the detected CPU load, and then passes this frequency value to CPUFreq_driver to complete the action of frequency regulation. However, these governors are only tuned for core frequencies and do not involve the setting of uncore frequencies. We think it is meaningful to compare our designed policy with the DVFS policies that are now maturely used, so we implement a governor for UFS following the idea of existing governors, and the introduction of each governor is shown in We execute four different governors on the same load sequence as in the previous section to control the frequency of the processor uncore, and then compare the results with those achieved by the UFS_IL policy. It is found that the latter achieves better results under all the apps. The performance of the different models under three of the applications is shown in Under mcf applications, the UFS_IL policy maintains the processor at a higher power efficiency than the better-performing power save governor, achieving a 21.27% performance improvement with 15.93% power consumption. Under the bwaves application, the processor power efficiency under UFS_IL policy control is substantially better than various governors. In this core performance-demanding application, governors choose to maintain a high frequency to ensure performance, while the UFS_IL policy chooses to reduce performance by 2.81% to reap 13.39% in power savings compared with the conservation governor. Different governors have different focuses, some for maximum power savings, some for maximum performance, and some choose to prioritize performance, but there is no governor that really focuses on the performance-power trade-off. The results for the strong core performance load of the bwaves type significantly demonstrate the advanced character of our designed UFS_IL policy regarding this point.θ of 0.1. A larger θ implies a larger uncore frequency tuning range.In this section, we compare the performance of the UFS_IL policy with other advanced UFS policies in the unseen load application The ML_guided policy relies on the uncore frequency prediction model and power consumption prediction model. However, they are completely inaccurate for predictions of unseen load applications, since the new data distribution and the corresponding relationships are not in the knowledge base of the policy. The lowest uncore frequency in the wrong performance range is sufficient to meet the performance requirements, so the ML_guided policy tends to choose the lowest uncore frequency on unseen loads. The DUF_10 policy can be targeted according to processor behavior changes, moving toward minimal power consumption within the exact processor performance range. However, its limited pace of advancement leads to a gap in overall processor behavior relative to the UFS_IL policy.The improvement brought by the DAgger algorithm to the UFS_IL policy is evident. After collecting 20 aggregated data, the predictors of the expert model completed the initial fine-tuning and thus the schematic data it gave are more reliable. In -DSTREAM_ARRAY_SIZE parameter, which specifies the size of the test array and is the parameter that has the greatest impact on the results. We need to set this value to more than four times the processor’s highest-level cache size to ensure that the throughput performance of the CPU cache is properly tested. The results are shown in To keep the experimental results from being generalized, we also perform generalization evaluation on other unseen benchmarks. We conduct evaluation tests on IS application, MG application, and stream benchmark, the first two of which are from NAS Parallel Benchmarks (NPB). It is worth noting that the stream benchmark needs to be compiled with the UFS_IL_offline policy gives better results than the UFS_IL_with_20_epoch policy under IS application. This is because both are not quite sufficient for fine-tuning the prediction models, while the UFS_IL_offline policy unexpectedly falls into a better uncore frequency range and thus achieves better results.The test results under the stream benchmark are remarkable. As the amount of aggregation data increases, the UFS_IL policy’s tuning options get better and better, allowing the processor to maintain high power efficiency. The results under IS and MG applications are slightly scattered, due to a range of fluctuations in processor power efficiency for the same uncore configuration. We observe that the results show the average performance variation of different policies throughout the control period is not very large and the fluctuation range of power consumption is small. With this in mind, we label the average power consumption lines of different policies in the subplots. The results show that the UFS_IL policy also scales well under the NPB benchmark apps based on the DAgger algorithm. One thing to note is that the There is one more construction parameter worth discussing. The aforementioned power management governor in the Linux kernel system makes decisions at periodic intervals of 10–100 ms , which mWe conclude the complexity of our proposed method in this section and compare it with current advanced UFS policies. The complexity mainly refers to the construction time overhead as well as the storage overhead of the whole policy in the experimental platform. Importantly, construction time and storage space are paramount in a small system that makes quick decisions in real-time.The DUF policy has minimal time overhead throughout the experiments due to its design advantages. This feedback-based policy does not require training in advance or data storage in the online phase. Its only overhead is the need to compute some statistical type of features and to store the feature data from the previous decision period. Overall, this feedback UFS policy has the lowest complexity, although its effectiveness is limited.In terms of storage footprint, there is little difference between the UFS_IL and ML_guided policies. This is because both machine learning-based UFS policies require offline training to capture the mapping relationship between input features and optimal tuning actions. In our experiments, the storage size of a single processor behavior prediction model is 474–544 KB. With the inclusion of the DAgger algorithm, our system needs to store the aggregation data in the online phase. As mentioned before, the optimization goal can be achieved with 50 pieces of aggregation data, and 50 pieces of data occupy about 20 KB of storage space. On the other hand, the extra time overhead of the UFS_IL policy compared to the ML_guided policy is reflected in the online phase. the UFS_IL policy needs to feed the aggregation data to the expert model to complete the fine-tuning, which is a necessary overhead for its enhanced generality. In our experiments, the fine-tuning of the prediction model using 50 pieces of augmented data took about 22 ms.In summary, our proposed policy achieves further optimal control of processor power efficiency with the addition of a smaller time overhead and storage overhead, ensuring a relatively proportional performance while saving power.The UFS_IL policy is a novel UFS policy designed based on the idea of imitation learning, which improves the generalization of the advanced offline UFS policy to unseen processor loads. And the overall aim is set to optimize the power efficiency of the processor to avoid losing a larger percentage of performance while saving a certain percentage of power, achieving a true performance-power trade-off. Compared to the advanced single-step feedback UFS policy, the UFS_IL policy increases the regulation pace, optimizes the processor’s uncore frequency regulation speed for different loads, and achieves better overall power efficiency. Our experiments show that the processor maintains optimal power efficiency under the UFS_IL policy for known processor load applications, with an 8% to 22% improvement over the max_uncore_freq policy. Besides, this policy has a maximum improvement of about 10% relative to the offline supervised learning type policy and the single-step regulation type policy with a performance degradation index of 0.1. In the unseen load, our proposed policy with twenty aggregation data is comparable to the DUF policy that relies on immediate feedback. After collecting 50 aggregation data, the UFS_IL policy can make uncore frequency regulation decisions close to the real processor’s optimal power efficiency state.Currently, our approach targets uncore frequencies. This set of methods is also applicable to other processor sequential decision problems, such as core frequency, cache optimization, etc. Our subsequent work will cascade uncore with other components, analyze the detailed relationships between these components, and further optimize the power efficiency of multicore systems.