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Most EEG recording applications came with a toolset to convert the recording files to TXT or CSV files. Afterward, we could pick the subset of data we planned to use for further analysis; we recommended starting with the absolute value of the EEG signals.

The results of our study show that fNIRS based classification of tasks can be increased by using deep learning methods. This approach can be an essential step to enhance fNIRS based classification systems. The proposed ternary classification system can classify MA, MI, and IS-related brain activation patterns. Some form of preprocessing was required for all the methods used in this paper. However, the feature selection step can be eliminated by using CNN networks.

View Direction: View direction does have a conspicuous influence on retrieval accuracy. In Fig. REF , the accuracies calculated from front-view images are higher than those from side-view images. This phenomenon happens in every method, indicating that front-view images is less challenging than side-view images in VPR, as we expected previously.

Machine learning has found applications in many fields related to Civil Engineering. It is no different in the case of construction safety, the latest deep learning applications touches a variety of its aspects .

On the other hand, by their very nature, volume-based representations do not allow for defects like intersections, holes, gaps, overlaps or inconsistent normal orientations and thus such problems can be solved by appropriately converting a surface model into its volumetric representation. On the downside, the conversion to and from a voxelized model necessitates resampling, which introduces aliasing defects, destroys the structure and connectivity of the input model, introduces numerical approximation errors and is quite memory intensive.

Another interesting observation is that the cased models outperform the uncased models. This is similar in both bert and bert-multilingual models, where the cased models slightly outperform the uncased models. We believe that cased models can perform better in detecting MWEs than uncased models according to this dataset.

This finalpaper paper is structured as follows. Section  finaloverviews related work overviews related work. finalS Section  finalrecalls recalls the finalnecessary necessary background. Section  introduces our model architecture. finalS Section  describes finalour novel training technique by explaining our novel training technique by explaining how we apply local search, finalthe the policy rollout baseline, and curriculum learning finalduring training during training. finalSection  presents the experimental results and Section  concludes. Section  presents the experimental results and Section  concludes.

These limitations make collecting ever increasing amounts of hand labeled data unsustainable. We advocate for a shift away from the standard paradigm towards a world where training data comes from an infinite collection of automatically generated labeled images. Such a dataset generation approach can allow ML practitioners to synthesize datasets in a controlled manner, unlocking new model development paradigms such as controlling the quality of generated labels and mitigating the long-tail problem.

In our implementation, we used a classic design principle in systems design; separation of control plane from the data plane. We optimized the reliable request dissemination of Mandator by delegating the task of client request dissemination to a separate child process that runs in the same replica machine: each replica has one or more child processes that are assigned to it. Child processes are stateless, and are concerned only about reliably broadcasting client request batches. With child processes in place, the typical execution of Mandator is as follows. The client sends a batch of requests to a child process, the child process collects one or more such client request batches, forms a child-batch, and sends it to a majority of child processes in other replica machines. Each child process, upon receiving a new child-batch, will send an acknowledgement to the originator, and will also send the received child batch to the main replica process. The sending child process, upon receiving a majority of acknowledgements, will send a child-batch-confirm message to the replica. The replica uses the confirmed child batch identifiers in the Mandator batches.

In this section, we use experiments to verify the performance of the TSGD algorithm proposed in this paper. From the experiments, it can be seen that TSGD achieves the best results in terms of convergence speed and accuracy. Compared with the plain stochastic gradient descent, the convergence speed, stability, and accuracy of TSGD algorithm have been greatly improved. Compared with the adaptive gradient descent, TSGD does not need to calculate the second moment, reducing the computational complexity. In particular, the convergence speed even exceeds the convergence speed of the adaptive gradient descent. In general, the TSGD algorithm has better performance.

mass gatherings On 13 March, it was announced at an official press conference that a four-week ban on public gatherings of more than 100 persons would be put into effect as of Monday 16 March. Integer Maximum number of people in social gatherings allowed by the government

To obtain results that we can future use for contour detection or facade detection should be a balance between edges detected from the outer boundary of the building and facade details. The edges that do not belong to the buildings should be preferably discarded in this step. As we can see in Figures REF -REF the filters 3x3 obtain better results than the 5x5 ones.

We organize the rest of this paper as follows. In sec:problem, we formulate our problem statement and then discuss induced relations for Answer Selection problem in sec:Induced Relational Views. We then detail the operationalization of these induced relations in Graph Convolution framework in sec:gcn and introduce our gradient boosting based aggregator approach in sec:aggregation. sec:experiments describes experiments. We discuss related work in sec:related and then conclude in sec:conclusion.

In this work, motivated by the limitation of image modality for font generation tasks, we proposed a new graph representation for font glyphs. To better capture both global content and local details, we proposed a cross-modality auto-encoder framework to leverage the graphs as an intermediate representation for conversion between different font modalities. We build the first graph representation dataset for font glyphs by transforming SVG curves and point sets to hierarchical graphs. Our method achieves significant performance improvements on font completion in both visual quality and quantitative metrics, compared with the previous image-to-image translation methods. Our graph representation also exhibits high scalability and convenience for manual manipulation, which was demonstrated in font manipulation and interpolation tasks. In summary, our graph representation and cross-modality framework provide the font community with a new learning strategy as well as a new benchmark.

For all the experiments we report results averaged over 10 seeds where the shaded area represents the standard deviation and the results are smoothed using an average window of length 100. All the hyper-parameters used for each algorithm are reported in the appendix.

This paper presented a novel streaming discovery technique capable of extracting declarative models, expressed using the DCR language, from event streams. Additionally, a model-to-model metric is reported which allows understanding if and to what extent two DCR models are the same. A thorough experimental evaluation, comprising both synthetic and real data, validated the two contributions separately as well as their combination in a qualitative fashion, which included interviews with the process owner.

To what degree is the model capable of performing OTE extraction for unseen languages? Is there a benefit in training on more than one source language? What improvements can be expected when a small amount of samples for the target language are available? How big is the impact of the used alignment method on the OTE extraction performance?

In this paper, we propose REDER, the reversible duplex Transformer for reversible sequence-to-sequence problem and apply it to machine translation that for the first time shows the feasibility of a reversible machine translation system. REDER is a fully reversible model that can transform one sequence to the other one forth and back, by reading and generating through its two ends. We verify our our motivation and the effectiveness of REDER on several widely-used NMT benchmarks, where REDER shows appealing performance over strong baselines.

We presented a novel approach to online depth map fusion with real-time capability. The key idea is to perform the fusion operation in a learned latent space that allows to encode additional information about undesired outliers and super-resolution complex shape information. The separation of scene representations for fusion and final output allows for an end-to-end trainable post-filtering as a translator network, which takes the latent scene encoding and decodes it into a standard TSDF representation. Our experiments on various synthetic and real-world datasets demonstrate superior reconstruction results, especially in the presence of large amounts of noise and outliers.

Communities can have different capacities for obtaining resources. These different capacities can happen due to the Gross Domestic Country differences, commercial relations, or having a strong local production of those products. The capacities of having stocked resources are translated into our model as the maximum stock. By using different maximum stock levels, we simulate how stock prevalence impacts those asymmetrical communities.

Implications: These findings show potential for this type of interaction as a means of alleviating stress. Future research needs to be conducted to solidify this possibility. Diverse communities, targeted interaction experiments, and a larger number of participants can be used to conduct further research in different settings.

Three simulation experiments were performed. Firstly, the optimal route was computed for three different fields to visually inspect the effects of the objective function. Secondly, the coverage path was computed for 38 convex fields with every possible combination of the algorithms provided by the library. The combination of algorithms for creating a coverage path were compared using the path length as the objective function. Thirdly, the time for computing coverage paths was recorded using several objective functions of the Swath Generator module. The relationship between the area of the field and the computation time was found.

Other stigmergic algorithms have addressed more control and coordination based tasks. To mimic ant behavior when crossing large gaps, Malley et. al. designed soft, adhesive robots armed with a stigmergic rule to cooperatively traverse a ravine. Similar algorithms have been designed to collectively sort and group large objects or assemble into formations autonomously .

In this section, we first specify the journey reweighting and causal conversion prediction respectively. After that, the calculation of attribution credits is detailed. In the end, we provide the theoretical analysis of CausalMTA.

Table REF presents the result on real volume data. Similar to the performance on synthetic data, the proposed method achieve high F1-score on A4C retrieval compared with baselines. For more difficult tasks on TVV and TAS retrieval, since SIFT feature cannot fully capture the characteristics in TAS, the fusion of STIP and SIFT does not gain significant improvement where CCA even suffers great loss from SIFT. However, the proposed method is able to obtain close or better performance on recognizing all three views.

To demonstrate the effectiveness of VLKD, we evaluate it on generative multimodal tasks for both zero-shot and finetuning. Specifically, we test the image captioning task, and also the VQA task under the open-ended scenario. Furthermore, we also run the model on NLU and NLG tasks to investigate the influence of VLKD on the text processing ability of the original pre-trained BART.

In this section, we investigate the performance of our proposed method and compare it to other state-of-the-art algorithms. We evaluate DELAD on a wide range of images including traditional computer vision quantitative datasets as well as simulated and real microscopy images.

The results of our formative study show the diversity of users' expressions when they are not given guidance on their NL input. A structured representation is required to execute users' intents. Further, we need to formulate them into intent units that can be combined so that users' utterances can be represented with flexibility.

The rest of the paper is organized as follows. Sec.  describes the general problem tackled in the paper and the coflow ordering models, whereas Sec.  describes the proposed algorithms. Numerical results are then provided in Sec. . In Sec. , we describe the literature on deadline-aware coflow scheduling. Concluding remarks and future research directions are given in Sec. .

Here, we see that the baseline T5 examples are already rather strong and outperform earlier unsupervised systems. In particular, LongT5 is pre-trained with a summarization-relevant objective: the gap sentence prediction task. That is a probably cause for its high performance on this task. Even with this high baseline, we find that our simple self-training still leads to further significant improvements.

The problem of subgraph-freeness, and in particular cycle detection, has been extensively studied in the Congested Clique and Congest models. While there are only a few papers which study girth computation, related problems such as diameter computation or shortest paths were also extensively studied in these models.

Before we present existing methods, we give a formal definition of PCM cell population segmentation problem. Given a PCM image that contains a group of cells, we aim to segment each individual cell in it. There are three main challenges in PCM cell population segmentation as below:

These numerous studies for image quality assessment and their divergent or even contradictory conclusions point to the lack of universally accepted definitions and measurements of image quality. All studies we mentioned are motivated by the emergence of more and more effective TMOs, applied to the richer content of an HDR image. This combination has introduced new degrees of freedom in image rendering. The new local TMOs are indeed incredibly flexible, and can at will enhance locally the image and manage its colors. Given these new degrees of freedom, the reviewed studies primarily aim at establishing quantitative aesthetic criteria to orient TMO's and fix their parameters. The variety of image quality criteria indicates that they are subjective, culture-dependent. This is why they must be calibrated by subjects.

The trained neural network is susceptible to noise in the input BSPs as seen in Fig REF . Note that there is a large drop in predicted HSP quality when going from BSP inputs with 20dB to 10dB signal-to-noise ratio in subplot a of Fig REF . Furthermore, subplot b of Fig REF suggests that, as expected, noisier BSPs will produce noisier HSPs as neural network outputs. However, this is expected as network is trained purely on G3D basis function data without noise. Methods to improve robustness of predictions at signal-to-noise ratios smaller than 20dB will be explored in the future. In subplot a of Fig REF , it shows that the neural network prediction results become increasingly erroneous when predicting testing set HSPs from hearts that are increasingly distant from the spatial location of the heart used to generate the training set. Encouragingly, the predicted time series in subplot b of Fig REF show that the shape of the predicted HSP signal is largely unchanged between 0mm and 40mm heart location shifts. Furthermore, in both Fig REF and Fig REF , there is a non-random change in predicted signal amplitude, peak time, and width as the heart shifts and rotates. Therefore, in the future, there is a possibility of training a generic neural network for solving the cardiac inverse problem for different heart and body geometries by incorporating the geometry information as an input.

Summary:  We observe that a pre-trained encoder makes bagging and KNN more accurate under no attacks and more secure against data poisoning and backdoor attacks. Compared with bagging, KNN is less accurate under no attacks but achieves a stronger certified security guarantee. Bagging with linear probing and bagging with fine-tuning achieve similar testing accuracy under no attacks and certified security guarantees against attacks, but bagging with linear probing is orders of magnitude more space and time efficient.

We first provide experimental settings in Sec. REF . Then we report the internal relationship between RDIM and TIM with the opposite results of different combinations of attack methods in Sec. REF . Finally, we compare the results of our methods with the baseline methods in Sec. REF and Sec. REF .

Enriched Spatial-channel Feature Representation: Most existing hybrid volumetric medical image segmentation approaches typically capture the spatial features through attention computation and ignore the channel information in the form of encoding the inter-dependencies between different channel feature maps. Effectively combining the interactions in the spatial dimensions and the inter-dependencies between the channel features is expected to provide enriched contextual spatial-channel feature representations, leading to improved mask predictions.

We have described three unique MCMC initializations for EBM using different sampling trajectories: shortrun for synthesis, midrun for defense, and long-run for density estimation. Furthermore, we have elaborated on different MCMC initialization strategies used to stabilize these models for different sampling lengths. We have demonstrated the flexibility of these mechanisms by using similar architectures, data, and training platforms to create different EBMs for different applications. We hope that future research incorporates these new training initialization schemes to improve their generative models for a wide variety of tasks.

The new surrogate gradient term describes the timing dependency brought by the reset mechanism: When the membrane potential near the threshold, small perturbation towards threshold has potential to change the membrane potential of the next time-step dramatically.

Translation between structural and diffusion images has been shown using CycleGAN. The synthetic FA and MD images are remarkably similar to their ground truth. Quantitative evaluation using MSSIM of 65 test subjects shows that the trained CycleGAN works well for all test subjects, and that training using a larger number of slices improves the results.

Emerging technologies are impacting our lives in two different ways. First, these technologies are improving our standard of living. For example, Artificial and Machine Learning are the technologies behind personalized health care, intelligent transport services, free and open education to all. Second, they are also improving the quality of service we expect from service providers. Technologies such as the internet and mobile communication are providing the quality of services which was unimaginable a few years back. For example, these technologies enable 24 X 7 banking services, global-market for selling local products, and opportunities to monetize excess personal resources through aggregated services like Airbnb.

In this work, we investigate the efficiency of the Insertion Transformer model and propose a Fractional Positional Encoding scheme to mitigate the incompatibility between the conventional absolute positional representations in Transformer and the insertion-based generation strategy. With experiments on various tasks and datasets, we show the effectiveness of this simple scheme, which eliminate the need of re-encoding and keep the model's efficiency for both single-instance and batched decoding modes.

We introduce the WebCariA dataset with the annotation of fifty intrinsic face attributes on caricatures. We propose a novel unsupervised attention adaptation framework for the recognition of attributes on unlabeled caricatures, which outperforms the state-of-the-art methods. We propose the attention-consistency learning which transfers the most task-discriminate features to achieve a more efficient adaptation.

In this paper, we propose a deep learning method for unsupervised image segmentation, which formulates the image segmentation as a graph partitioning problem and integrates the deep representation. To learn the deep embedding, we design a SuperAE, which also smooths the original image and conductive to the superpixels generation. For segmentation, we propose a novel clustering method DSC which measures the deep similarity between superpixels and partitions them into perceptual regions by soft association. Experiment results on BSDS500 demonstrate the efficacy of our proposed, and our DSC outperforms most of the unsupervised segmentation methods.

Which qualities a measure is expected to cover? Is it truly automated or require human-written references? Does the measure depend on a domain, data or models trained specifically for the task? How much does the measure depend on its parameters? Was correlation with human scores a decisive factor in selecting the parameters? How interpretable is the measure itself and its judgements? Rank correlations with human scores; arguably Kendall Tau-c best suits the purpose.

Since in archaeology labeled data is scarce, we propose a training process that utilizes both labeled and unlabeled data, which exists in much larger numbers. We show that the mere existence of image-drawing pairs, even when unlabeled, helps. In particular, Section  shows that by training in this semi-supervised manner, we achieve better results than by using just the labeled data.

Text Block Filling. For the text block filling downstream task, our proposed LAMPreT model outperforms the baselines in both the precision and F-1 score metrics. It is worth noting that for both LAMPreT and the single-level LayoutLM, the unimodal text-only version performs slightly better than the multimodal version. We hypothesize that such results can be attributed to the sub-optimal multimodal representation fusing, that it can be potentially alleviated with more sophisticated and finer-grained multimodal grounding paradigms. Among the models, CNN-Grid baselines performs the worst, of which the attention mechanism in transformers is hypothesized to capture the block-level interactions better.

The main goal of this study is to solve the reaching task in the NRP with RL while leveraging curriculum learning. Although an extensive survey of other approaches for RL in robotic control is beyond the scope of this study, we will review a few recent efforts.

We conduct a test study for assessment purposes because our primary target customers are instructors. We performed a voluntary study with 29 educators from university level, inviting them to utilize DIY Graphics Tab to record their lectures. We divide the teachers into groups depending on their technological abilities. The following are the categories.

Organization. In Section , we define all the notions and definitions related to fairness and efficiency used in this paper. In Section , we present results related to preserving fairness under transformation along with algorithmic analysis. In Section , we study efficiency preservation upon transformation and its corresponding algorithmic analysis.

Nevertheless, this example demonstrates that once the right conditions are met, quantum computing and especially quantum arithmetic can truly supercharge humanity's information processing volume, rendering this field into a fascinating area of research.

Regarding the relationship between this semantic gap and the quality of customer-to-customer communication in social commerce, our results do not provide direct evidence of its existence. However, it is plausible to infer that customer satisfaction is also related to better communication due to less ambiguous language.

In this section, we will introduce the datasets and the setting of experimental hyperparameters, then compare with some current state-of-the-art methods, and finally discuss some factors that affect the recognition accuracy and visualize the results.

If we consider an average sized university with 4 in-take batches and each batch having 6 courses, then the total number of courses become 24. If each batch has two separable sections and each section has two lab-groups, then the total number of deliverable lectures becomes 48. A system that can successfully generate schedule for such a dataset can be regarded as a successful scheduler. In our experiments, our system could successfully generate result for much larger datasets as well. For a dataset with 400 teachers, 400 in-take batches, and 600 courses in-total, our system took around 27 hours to generate the routine on a Ryzen 2600 CPU with 8GB RAM.

We first introduce notation and discuss the local and global approaches for constructing permutation-invariant image-document score functions at the core of the learning procedure. We then instantiate the framework for a specific choice of aggregators for contrastive learning.

1. Local group membership prevalence is defined as the ratio of users who live in a county and who are active members of at least one local or very local group, to all active members in the same county. In the following exploratory analyses, we use sub-components of this indicator, broken down by privacy, size, and locality of the groups, but for illustration purposes here we use the composite indicator.

In this section, we have considered some benchmark problems to demonstrate the results obtained by the proposed scheme WENO-UD5. For the numerical comparison purpose, we compare the results with the WENO-LOC and WENO-JS5 schemes. We first show the behavior of nonlinear weights by performing on a test case i.e, we analyze how the nonlinear weights converges to the linear weights and subsequently we test the proposed scheme for the one-dimensional and two-dimensional system of Euler equations with the CFL number 0.5.

Celebrity: In this dataset, we consider the series of frames extracted from youtube videos of multiple celebrities as event sequences where event-time denotes the video-time and the mark is decided upon the coordinates of the frame where the celebrity is located. Here, a query corpus sequence pair is relevant if they are from a video file having a common celebrity.

In this work, we review the past works on human-robot trust based on the research topics and discuss selected trends in this field. Based on these reviews, we finally propose some ideas and areas of potential future research at the end of this work. The overall purpose of this document is to explore different studies concentrated on trust in HRI and review these studies. The selected trends of human-robot trust that are going to be discussed in this work are as follow: first, we review different definitions of trust and talk about the multidimensional nature of trust in HRI, then we will talk about factors affecting trust in HRI and classify those factors, after that we will talk about trust repair and trust calibration and finally, we will talk about modeling trust and trust measurement techniques. Based on the reviews and comparison of the available works on trust in HRI, shortcoming and challenges of studying trust in HRI that has not been answered yet and more avenues of research need to be concentrated on them, are mentioned in the conclusion and future work section.

However, the generated datasets do not allow an evaluation to measure directly how well a model deals with the semantic phenomena present in the original dataset, since some sentences use artificially generated reported speech.

In future works, we will make further use of the continuous time series-based characteristics of the model to meet the requirements of real-time prediction and apply the model to the passenger. At the same time, we will further optimize the time and space efficiency of the model to enable it to run on mobile devices such as mobile phones.

However, these contrastive approaches require generating effective positive and negative pairs which is not feasible in every task such as nuclei segmentation or skin lesion segmentation, where the input samples are almost related and it is relatively hard to generate negative pair of samples. Following this context, a redundancy reduction based strategy is adopted that does not require generation of positive and negative pairs for pre-training. Here, the aim is to obtain invariant and independent feature representations for every neuron of a model by minimizing the true and observed cross-correlation matrices which is the opposite of mutual information between the representations.

In this section, we fit and derive statistically significant, robust power laws that describe how distributed training time scales with the number of GPUs utilized in training using the data collected from our training experiments in Section 3. The workflow is presented diagrammatically in Figure REF ; we note that this framework is easily extendable to experiments with other models beyond those in our paper.

Even though data-driven approaches are potentially able to provide a more accurate representation of human gaze behavior as compared to heuristic models, they are restricted by their dependence on collecting appropriate gaze data. It is also unclear how well they generalize to settings different from that in which the data was recorded. Another problem is that the speakers' intentions are not available in the data, which makes it difficult for data-driven models to account for planning.

To sum up, previous works discussed either general attack vectors on autonomous vehicle systems and defenses rather than examples of their applicability for the systems, or the general guides of security risk management of vehicular system, while none of the studies comprises a comprehensive overview of managing information security risks on a scenario level. Therefore, this paper aims to illustrate the the first stages of information risk management by incorporating a more technical and detailed attack methods discovery, and higher-level risk management approaches.

This paper investigates the online behavior during the Euromaidan revolution in Ukraine, including several months after the end of the protest. The Euromaidan is well described in the literature , , , , . Sufficient to say, it was a large grassroots political movement with hundreds of thousands of Ukrainians protesting in Kyiv and other cities across the country. The protest was caused by the President of Ukraine, Viktor Yanukovych, who did not sign an association agreement with the European Union. Instead, he announced that economic ties between Ukraine and Russia would be a priority. A small group of students and young people organized a protest in the center of Kyiv. The police brutally attacked this protest, which mobilized a wide range of social groups to join the protest. New protesters criticized police brutality, the legitimacy of the regime, and the pro-Russian agenda of Viktor Yanukovych. From the very beginning, this protest was significantly affected by social media. With time, in a series of dramatic events, the protest escalated to violent clashes. Hundreds of protesters died, snipers shot many. Viktor Yanukovich fled the country to Russia. These events escalated relations between Ukraine and Russia, resulting in the annexation of Crimea and the beginning of war in Donbas.

These notions of correlation are tied to the landscape structure, in contrast to NSF which only applies to the immediate neighbours of points with a given fitness. NSF holds of many well-known neighbourhood operators, and we suggest that it is even a criterion used in designing such operators.

Sequence labeling CWI. goodingkochmar2019complex introduced a technique based on LSTMs for CWI, which obtained better results on their sequence labeling task than previous approaches based only on feature engineering. The contexts detected by the LSTM offered valuable information, useful for identifying complex tokens placed in sequences.

Depending on the capabilities of the underlying communication library and the target device hardware the actual implementation of the memory transfers differs to leverage heterogeneity-aware communication substrates. Implementing the memory transfers when the application explicitly handles device memory and host staging is used includes the following steps:

The system, shown in the Figure REF , is designed to be scalable for continuous gathering, extraction and validation of npi event. It consists of three subsystems: a data processing pipeline for capturing and extracting potential npi event from Wikipedia articles, a tool called tool for human validation of npi event automatically extracted using the aforementioned pipeline and a data browser for visualizing the data. In the next section, we describe the system and its components at a high level, focusing on key design choices that have a bearing on the quality of the dataset, starting with a brief description of the data collection. inplace

Also very recently,  analyse the generalisation benefit of invariance in kernels and random feature models. Our results differ from  in some key aspects. First,  focus kernel ridge regression with an invariant inner product kernel whereas we study symmetrised predictors from more general kernels. Second, they obtain an expression for the generalisation error that is conditional on the training data and in terms of the projection of the predictor onto a space of high degree polynomials, while we are able to integrate against the training data and express the generalisation benefit directly in terms of properties of the kernel and the group.

The negative pairs in contrastive learning were necessary to avoid a representation collapse in which case the network outputs a trivial representation for all the input images. However, the absence of the negative pairs in BYOL stirred a lot of commotion in the community and several works , , , have tried to understand the phenomenon. It has been shown that BYOL avoids representation collapse by using a predictor network at the end of the online network and by updating its target network using slow-moving averages and not by backpropagating the gradients through the target network. We have also conducted several experiments to rule out the importance or necessity of Batch Normalization layers for the representation learning in BYOL, as suggested by , by replacing them with Layer Normalization and Group Normalization layers .

RQ1: Is CubeRec the new state-of-the-art? RQ2: Are the major components proposed in CubeRec effective? RQ3: How does CubeRec perform w.r.t. different group sizes? RQ4: What is the impact of CubeRec's key hyperparameters?

However, since we release all the comparisons, generated images, and code, it is possible for the community to improve on our results. For instance, one can run a genetic algorithm from a different initialization, for a larger number of iterations, or even with more sophisticated optimization methods. This can easily be done by comparing the new candidates with our images and adding these results to the dataset.

Finally, we should emphasize that the proposed method is the first successful end-to-end learning algorithm based on a neural network that attains superior performance in an unsupervised manner for hyperspectral unmixing problem. That's why, we strongly believe that the findings of our study will provide a basis for further studies into neural network techniques in this domain.

The pedestrians' MCS usage in DL is presented in Fig. REF . Figure REF is related to the only macros scenario, while Fig. REF concerns pedestrians connected to an IAB donor in the mIAB scenario and Fig. REF concerns pedestrians connected to an mIAB node also in the mIAB scenario. First, notice that the histograms in Fig. REF and Fig. REF are similar with small differences, meaning that the SINR of a pedestrian connected to a macro gNB in the only macros scenario and the SINR of a pedestrian connected to an IAB donor in the mIAB scenario were similar. Furthermore, considering that the pedestrian signal strength was also similar in both cases, since the macro gNB and the IAB donor had similar characteristics, we conclude that the interference in both cases was also similar.

Second, compared with TRANX, TRANX-R2L and TRANX-RAND, our TRANX-RL exhibits better performance. This result demonstrates the advantage of dynamically determining branch expansion orders on dealing with multi-branch AST nodes.

Due to the comparatively larger section sizes in the public university considered in this study, the solution proposed by Weeden and Cornwell to prevent the spread of contagion through classroom contact may not be as effective. This work proposes a complementary solution, the scalpel approach, that disrupts paths for the spread of contagion by moving courses that contribute to high betweenness centrality to online instruction. Another recommendation is to maintain classroom teaching of highly specialized graduate and some upper-division courses as students in these courses tend to be tightly knit with little to know connection with other students, while moving all other courses online.

Graformer achieves competitive performance on two popular KG-to-text generation benchmarks, showing that our architecture can learn about graph structure without any guidance other than its text generation objective.

Since the GNSS signal is not always reliable due to the multipath effect or signal absorption due to tree canopy , approaches independent to these effects have been proposed. were the first to show that VTR approaches are robust enough to perform large-scale autonomous navigation in GNSS-denied environments. The authors have deployed their system in the Canadian High Arctic. This environment was selected because of its similarity to lunar and martian terrain. Most features consisted of rocks located within the reference trajectory. In this work, they successfully repeated reference paths up to ten hours after they were manually driven. However, sensitivity to illumination change was identified as the main limit of the system. Thus,  later introduced Experience-based navigation to increase the robustness of VTR to scene appearance change, caused by illumination variation or dynamic environment changes. This feature was added in VTR through Multi-experience Localization, with the added ability to use landmarks from previous experiences in the same localization problem . In this work, the authors extend the allowable time between teach and repeat runs from a few hours to multiple days. also added color-constant image transformations to VTR to mitigate the impact of illumination variations. Color-constant image transformations have been used by  to perform autonomous route repeating by using the VTR framework while relying solely on a monocular camera. While vision-based localization was demonstrated to be robust to illumination variation,  have observed that localization frameworks relying only on passive cameras fail to localize in dark conditions.

Godel can be used as an initial model to fine-tune for any open-domain goal-directed dialog tasks with a handful of annotated examples. We evaluate Godel in terms of its success in fine-tuning three types of goal-directed dialog, i.e., knowledge-grounded response generation, task-oriented dialog, and conversational QA:

However, due to the complex neural dynamics and non-differential characteristics of SNNs, it is still a challenge to train SNNs efficiently. Existing SNNs training methods can be roughly divided into three categories: the biologically plausible, conversion, and backpropagation-based strategies.

  The aim of this paper is to present the impact of the boreal forest and winter conditions on autonomous navigation technologies with the goal of enabling true long-term robot autonomy. In this section, we show that while various off-road robotic deployments in winter conditions are documented in the literature, they mostly rely on the GNSS signal for localization . Vision-based localization approaches have enabled autonomous navigation in GNSS-denied environments. However, winter conditions have been shown to severely affect the performance of such approaches . Wintertime autonomous navigation in a boreal forest requires localization capabilities that are resilient to both winter conditions and GNSS-denied environments. Active sensors such as lidars are ideal for solving this problem since they are robust to lighting variation .

In this work we develop classification algorithms to differentiate between nonlinear localized waves and nonlocalized linear waves in numerical simulations of one-dimensional crystal lattice model. Obtained classifiers rely on locally sampled data in opposed to nonlocal time series analysis methods, such as discrete Fourier and wavelet transforms. Such classifiers can be efficiently trained with different given labeled datasets which are not only limited to numerical simulation data. Trained classifiers then can be used to detect localization regions, e.g., in numerical simulations, which is the first step towards fully automated tool for quantitative data-based analysis of complex numerical experiments. Importantly, our analysis and methodology, which follows, extends, in general, to any one-dimensional crystal lattice models which support intrinsic localized mode solutions.

The rest of the paper is structured as follows. Section  describes the KGTK toolkit, and section  presents an introduction to the Kypher query language. Section  introduces five representative use cases to illustrate the benefits of Kypher, and section  reports the times needed to address the use cases using Kypher queries on a laptop; SPARQL queries on a clone of the Wikidata endpoint; and SPARQL queries on on the public Wikidata endpoint. Section  presents conclusions, discussion of the results and directions for future work.

Due to the extreme imbalance of the labels, we evaluate the performance of the model using the F1 score on the minority class. This way, we ensure that the model prioritizes performance on the more difficult task of pain detection. Furthermore, earlier studies carried out on this task used the F1 score metric, making it possible to compare results.

This work is the first to combine privacy preservation with an HFNN designed for heterogeneous big data and a distributed computing environment. Therefore, in this literature review, we briefly cover relevant work in the three critical elements of PP-HFNN: heterogeneous data, HFNNs and distributed machine learning algorithms.

After a total of about 77 epochs of training, in two phases - our model settled on a Levenshtein Mean Distance Score of 2.60753 on the test set. The training error and evaluation word error metric gradually decreased through out the training process. After training phase 1, the model had a training loss of 0.3172, and an evaluation WER of 0.2524 on the validation set.

With regard to the calculation of the FAIR metrics the method proposed in sec:methods leads to reasonable results. Since the images managed in Pangaea and the GFZ Data Services support three out of four quality criteria almost completely, their score is high. The relatively low compliance of images managed in figshare manifests in small scores.

As shown in Figure REF , our method performs well against baselines. We find that human demonstrations are necessary to guide learning because the learned behavior for RL is essential to arbitrarily walk around the grid and interact with items. For simple 1 and 2 step tasks, this is a feasible strategy for the allotted steps for an episode. However, there is little room for error in the most difficult 5-step tasks, as even human demonstrations take on average 40 steps to solve. We also find that for the standard setting, incorporating a high-level network allows the model to achieve good results when comparing our method to SP and SR.

In order to also identify such connections between news sites, we iteratively expand the graph by adding new neighboring nodes for a more comprehensive representation of the audience overlap, which is discussed in detail in section 3.2. The graph is further enhanced by incorporating user engagement statistics as node attributes in order to model the relation between a site and its visitors better. We then use graph neural networks to encode these relations and to obtain node embeddings representing different categories of news sites. We further combine these embeddings with textual representations from articles from each news website.

Once trained, the unseen test subset of the NCT-CRC dataset is processed by each model. The class-wise precision, recall and f1-scores for the MCAE and StaNoSA models can be seen in Tables REF and REF respectively. The f1 scores in bold highlight the best performance in each class across both tables.

As shown in Fig. REF , the sketches generated by MUNIT, DRIT, and NICE-GAN present black inks and geometric deformations. The sketches generated by U-GAT-IT are acceptable, but still contain defects like inks. The images produced by CycleGAN are similar to grayscale photos instead of sketches. AdaIN produced visually comfortable sketches in general. However, the textures produced by AdaIN are over smooth and diverse from real pencil-drawing strokes. In contrast, sketches generated by sRender preserve the content of input photos and present realistic pencil-drawing strokes.

We present a novel real-time underwater system that can achieve acoustic ranging between commodity smartphones. We evaluate our design in various underwater settings and demonstrate its efficacy. We believe that this work explores a new underwater research direction by bringing ranging capabilities to commodity smartphones. Here we discuss design considerations and avenues for future work.

To the best of our knowledge, this is the first work addressing the intersection of representation learning with Federated Learning and resource efficient sampled softmax training. Our contributions are:

We define the problem of optimizer amalgamation, which we hope can inspire better and faster optimizers for researchers and practitioners. In this paper, we provide a procedure for optimizer amalgamation, including differentiable optimizer amalgamation mechanisms and amalgamation stability techniques. Then, we evaluate our problem on different datasets, architectures, and training settings to benchmark the strengths and weaknesses of our amalgamated optimizer. In the future, we hope to bring improve the generalizability of amalgamated optimizers to even more distant problems.

Blockchain is emerged as a disruptive technology in recent times and the blockchain application capabilities are promising to use in the field of cybersecurity. DDoS attacks are well known and still considered as a major threat to disrupt the businesses. We have performed a detailed review of the blockchain based solutions for DDoS attacks detection and mitigation including the consideration of the different network environments such as SDN, IoT, cloud or conventional network. The solutions are categorized based on the solution deployment location such as network based, near attack location, near victim location and hybrid solutions. We determined that most of the existing solutions focused on storing the malicious IP addresses in blockchain transactions implemented using smart contract and distribute the IP addresses across the AS's in the network level. However, limited research is performed to propose near victim location and hybrid solutions. Finally, we described the open challenges based on the existing research contributions and the future directions based on the advancements in blockchain technologies like parallel blockchain, Xroute, Ethereum 2.0 to effectively handle the DDoS attacks.

Our study demonstrates the necessity and effectiveness of exploiting source-side sentential context for NMT, which benefits from fusing useful contextual information across encoder layers. We propose several strategies to better capture useful sentential context for neural machine translation. Experimental results empirically show that the proposed approaches achieve improvement over the strong baseline model Transformer.

RDF data producers face a challenge: the particular structure of their data questions the efficiency of traditional summarisation and visualisation techniques. To address this issue, we presented the concept of path outlines, to produce path-based summaries of RDF data, with an API to analyse them. We interviewed 11 data producers and confirmed their interest. We designed and implemented Path Outlines, a tool to support data producers in browsing path-based summaries of their datasets. We compared Path Outlines with SPARQL-V. Path Outlines was rated as more comfortable and easier. It performed three times faster and lowered the number of dropouts, despite the fact that participants had, on average, 5 years of experience with SPARQL versus 5 minutes with our tool.

The paper is divided in four main sections. In Section we present related open-source solutions supporting the eye-tracking research. In Section we highlight the reasons which motivated the software developments presented in the paper. Section contains a more detailed description of the product, in terms of hardware specifications and software tools available. In Section the open-source suite is presented, focusing more on the functionalities provided than on the implementation details. In conclusion, we discuss current limits and future improvements of the proposed solution.