The most common use of reclaimed asphalt pavement (RAP) is in the lower layers of a pavement structure, where it has been proven as a valid substitute for virgin materials. The use of RAP in surface mixes is more limited, since a major concern is that the high-RAP mixes may not perform as well as traditional mixes. To reduce risks or compromised performance, the use of RAP has commonly been controlled by specifications that limit the allowed amount of recycled material in the mixes. However, the ability to include greater quantities of RAP in the surface mix while maintaining a satisfying field performance would result in potential cost savings for the agencies and environmental savings for the public. The main purpose of this research was to produce highly recycled surface mixes capable of performing well in the field, verify the performance-based design procedure, and analyze the results. To produce the mixes, a balanced mix design (BMD) methodology was used and a comparison with traditional mixes, prepared in accordance with the requirements of the Virginia Department of Transportation’s volumetric mix design, was performed. Through the BMD procedure, which featured the indirect tensile cracking test for evaluating cracking resistance and the Asphalt Pavement Analyzer (APA) for evaluating rutting resistance, it was possible to obtain a highly recycled mix (45% RAP) capable of achieving a better overall laboratory performance than traditional mixes designed using volumetric constraints while resulting in a reduction in production cost. When the Superpave mix design system was introduced in 1993, it featured performance tests that supplemented a series of material specifications and volumetric requirements ; to counIn parallel to concerns about low asphalt content, agencies started using larger amounts of recycled materials . This has been linked to mix deterioration due to cracking ,5. In orWith consistently stagnant or shrinking budgets, agencies are aiming to include growing quantities of recycled and non-conventional materials in their mixes. However, the use of such mixes leaves open questions about the impact on long-term performances. To reduce those risks, agencies use specifications that limit the maximum percentages allowed. One of the tools that would support the extensive use of these mixes is BMD. Compared to the volumetric-only mix design, BMD can obtain an indication of mixture behavior by testing in the laboratory at the design stage. This is expected to provide more confidence about the expected mix performance in the field. For instance, the New Jersey Department of Transportation (NJDOT) established a mix design procedure which features at least one of the following three laboratory performance tests: Asphalt Pavement Analyzer (APA) to test rutting resistance, fatigue to evaluate fatigue life, and Overlay Tester (OT) to test cracking resistance ; the conCurrently, in Virginia, the maximum percentages by weight of the mixture of RAP allowed in asphalt mixtures are 30% for surface and intermediate mixes, 35% for base mixes, and 20% for stone matrix asphalt . As a meThe main purpose of this research was to produce high recycled content surface mixes capable of performing well in the field, verify the performance-based design procedure, and analyze the results. To produce the mixes, a BMD methodology was used and a comparison with traditional mixes was performed. In particular, two mixes, which acted as control mixes, were prepared in accordance with the requirements of the VDOT volumetric mix design, which originated from the Superpave system. Two additional mixes were designed following a BMD procedure, which featured the indirect tensile cracking test for evaluating the cracking resistance  and the Additionally, the goal was to explore the feasibility of exceeding the limitations of the volumetric mix design, moving towards a performance-based mix design. From this perspective, the results of the laboratory tests would become the main indicators of how to increase the RAP content while obtaining satisfying performances.Today, the most established use of RAP is in the lower layers of a pavement structure, where it has been proven as a valid substitute for virgin materials. For instance, base layers can achieve excellent performances while including up to 100% RAP through a cold process in which virgin asphalt is added as a recycling agent ,18. On tCompared to the bottom layers, the surface mix has to better withstand higher stresses and aging. Current specifications typically require that the mix components of surface layers (both aggregates and virgin binder) need to be composed of higher percentages of virgin materials, as they are thought to be of higher quality. Being able to include greater quantities of RAP in the surface mix while maintaining a satisfying field performance would support the achievement of potential cost savings for the agencies  and enviFirst, the research team defined a high-RAP-content control mix which met the current VDOT specifications. The mix, which will be referred to as 30-Superpave, contained 30% RAP by weight of total mixture, which is the current upper limit for surface mixes . An addiBased on the performance information (obtained through the indirect tensile test and APA rut test) of the afore-mentioned mixes, changes to the mix composition were made. Two more mixes were designed with a different aggregate structure, which did not meet the VDOT specification requirements but met the current tentative BMD thresholds . The newThe inclusion of RAP in hot mix asphalt designed using the Superpave procedure was formalized through NCHRP Project 9–12, in which a three-tier system was developed. The system was based on the properties of the hardened RAP binder and blending charts were developed for high RAP contents ,23. ThroTo include higher RAP quantities, recycling agents such as rejuvenators and softening agents have also been introduced. Rejuvenators are chemical agents capable of restoring the physical and chemical properties of the old binder. They differ from softening agents, which lower the viscosity of the aged binder . It is iHuang, et al.  conducteKim, Byron, Sholar and Kim  reportedWidyatmoko  indicateApeagyei, et al.  reportedAl-Qadi, Aurangzeb, Carpenter, Pine and Trepanier  reportedMcDaniel, et al.  found thIzaks, et al.  observedOver time, it has been possible to observe that there is no final agreement between researchers with respect to the impact of RAP on field performances:Many factors contribute to the properties of an asphalt mix and, by consequence, do not allow for generalized conclusions on the RAP effects: the inherent variability of RAP, the difficulty of defining the interaction level of the virgin binder, recycled binder and additives, and the open questions concerning rejuvenators have led to the definition of new mix design methods . The BMDLaboratory performance tests must be associated with appropriate management of the RAP. It is fundamental that the material used during the design stage maintains consistent properties throughout the production process. To support the construction of highly recycled mixes, RAP needs to be appropriately processed, crushed, and screened. RAP stockpiles need to be regularly sampled for quality control to ensure that consistent gradation and binder content are maintained .All the evaluated mixes had a nominal maximum aggregate size (NMAS) equal to 9.5 mm, and the selected binder performance grade (PG) was 64-22. The volumetric parameters of the control mixes are reported in To analyze mix performance at the mix design stage, appropriate aging needs to be applied to the specimens produced in the laboratory. The American Association of State Highway Transportation Officials (AASHTO) recommends 4 h of aging at 135 °C . VDOT moBased on the comparison with specimens taken directly from the plant, the 2-h aging produced more similar results, while the 4-h aging significantly reduced the laboratory performances of the mixes. For this reason, the aging applied to specimens during the study was 2 h at compaction temperature.The performance evaluation was first conducted on the control mixes, with respect to both cracking and rutting resistance, evaluated through the indirect tensile test and APA, respectively. In accordance with the test methods ,16, all Both mixes met the volumetric specifications; however, To improve the mixes’ cracking resistance and obtain better overall performance while increasing the RAP content, a different aggregate structure was studied and tested at different asphalt contents. The definition of optimum binder content was based on balancing both performance requirements. As expected, the impact of increasing the asphalt content was noticeable with regard to both cracking and rutting. As the mixture binder content increased, the mix became less stiff, allowing it to have higher cracking resistance. The mix containing 30% RAP showed the potential of reaching very high values of cracking resistance, while the 45% RAP mix appeared to be more limited. This limitation can be explained by the partial contribution of the RAP binder: when comparing the two mixes with the same binder content design value, the partial contribution of the RAP binder is going to appear, with a greater impact on the 45% RAP mix. The 45-BMD mix showed an increase in cracking resistance with respect to increasing binder content, while the rutting susceptibility is not consistent with increasing binder content. For 30-BMD, higher binder contents corresponded to a higher cracking resistance, with a measured maximum CT Index value at 7% AC more than ten times larger the maximum measured at 6% AC. The rutting resistance for 30-BMD showed a slight deteriorating trend, however, the rutting resistance was well below the 8-mm limit in all cases.The test results indicated that to achieve satisfying performances, the minimum design AC for 30-BMD was approximately 6.2%, while for 45-BMD it was 6.5%. The design asphalt contents were selected based on compliance with the minimum CT Index criteria plus a safety factor. Both cases represented a meaningful improvement in terms of performance when compared to the control mixes.To better visualize the different performances with respect to cracking resistance, To further examine the feasibility of implementing the BMD mix design process rather than the volumetric requirements, a cost analysis was conducted to evaluate the impact on the mixes’ production cost. For both mixes, 30-BMD and 45-BMD, the design AC was selected so that it was possible for the BMD mixes to outperform the control mixes and achieve acceptable levels of cracking and rutting resistance with respect to the VDOT-proposed requirements. A summary of the mixes’ composition is shown in The material costs and final estimates are summarized in In general, as shown by the comparison between the two control mixes in To further examine the economic feasibility of the BMD process, a simplified life-cycle cost analysis (LCCA) was conducted. The LCCA examined the impact of production and operating (maintenance) costs over a period of 30 years. The control mix 30-Superpave was used as a reference, with a service life varying between 8 and 12 years. At the end of the service life, mill and overlay operations were planned for all mixes. The scheduled operations over the course of the 30-year analysis period were discounted at a rate of 4%. Different scenarios were evaluated for both the optimized mixes, which supposedly would achieve extended service lives, thus requiring fewer maintenance operations. Four different scenarios of service life variation were evaluated, in which the cost change of each optimized mix was analyzed from a decrease of 1 year to an increase of 2 years. The results are shown in It is possible to observe how, for 30-BMD, a life extension of 1 year is enough to present a more convenient solution, with the exception of the scenario in which the reference mix, 30-Superpave, provides a service life equal to or greater than 12-years. Instead, 45-BMD can achieve cost savings even if the service life is 1 year shorter than the control mix .The use of gradation and volumetric requirements did not guarantee a satisfactory performance (in terms of laboratory cracking and rutting resistance) for the 30-Superpave and 45-Superpave mixes, which were designed in accordance with Superpave requirements;The mixtures designed using the Superpave gradation and volumetric requirements were outperformed in the laboratory by the selected BMD mixes;As expected, for both the control and optimized mixes, the inclusion of higher RAP contents corresponded to lower cracking resistance and higher rutting resistance in the laboratory;Even if high RAP contents may require higher asphalt contents to achieve satisfactory cracking resistance, the impact on rutting performance was very limited for the mixtures evaluated in this study;Compared to the control mixes, the optimized mixes showed potential economic savings. The 45-BMD mix resulted in lower production costs and presented a better laboratory performance than the control mixes. A simplified LCCA showed how a 1-year service life extension would be enough to justify the higher production cost of 30-BMD. The LCCA also showed that, when compared to the control mix 30-Superpave, 45-BMD would allow for achieving significant savings, even if it would not extend the service life.In this study, it was possible to investigate how the implementation of a BMD system could represent a significant upgrade of the current design practice. Through this performance-based procedure, it was possible to obtain a high recycled content mix, such as 45-BMD, which would provide a better overall performance while providing a reduction in production cost when compared to traditional mixes. In addition to the economic savings, the higher RAP content improves the environmental impact of the mix as it uses less virgin materials. Overall, the following conclusions were drawn:The study used two simple performance tests to measure the cracking and rutting resistance of the mixes. However, the selection of appropriate laboratory tests is fundamental for the effectiveness of a BMD approach. In particular, field performance is needed to verify the conclusions that are based on laboratory testing. Techniques like pavement recycling are generally promoted because of their economic savings and the environmental benefits they entail. However, only if the field performances are adequate would it be possible to fully take advantage of the recycling process. If the RAP inclusion results in a shorter pavement lifespans or inappropriate performance, the initial benefits would become irrelevant due to the necessity of additional maintenance;The RAP binder content affects the mixture’s final asphalt content, which may result in the mix being higher or lower than the designed AC. If the asphalt content of the RAP changes, the mixes could become under-asphalted or over-asphalted, resulting in a poor pavement performance. Therefore, because of its inherent variability, the properties  of the RAP taken from the plant stockpiles need to be checked throughout the process. The need to track the sources of the RAP and maintain separate stockpiles should be investigated. Changes in the binder content of the RAP source stockpile and the effects of using multiple RAP stockpiles should be investigated to determine the influence on producing a consistent mixture with high recycled contents;Even though part of the aged RAP binder contributes to the mix properties, the 45% RAP mix required a higher overall AC percentage than the 30% RAP to achieve the same level of performance. This is because, at higher RAP contents, the aged binder contributes to a larger proportion of the mixture properties. For this reason, the use of rejuvenators should be investigated to further optimize the necessary quantity of virgin binder, especially at high RAP contents.Based on the findings of this research, the following recommendations are made:


Introduction: Cancerous Tissue Recognition (CTR) methodologies are continuously integrating advancements at the forefront of machine learning and computer vision, providing a variety of inference schemes for histopathological data. Histopathological data, in most cases, come in the form of high-resolution images, and thus methodologies operating at the patch level are more computationally attractive. Such methodologies capitalize on pixel level annotations (tissue delineations) from expert pathologists, which are then used to derive labels at the patch level. In this work, we envision a digital connected health system that augments the capabilities of the clinicians by providing powerful feature descriptors that may describe malignant regions.Material and Methods: We start with a patch level descriptor, termed Covariance-Kernel Descriptor (CKD), capable of compactly describing tissue architectures associated with carcinomas. To leverage the recognition capability of the CKDs to larger slide regions, we resort to a multiple instance learning framework. In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. The WAID is computed on bags of patches corresponding to larger image regions for which binary labels  are provided, thus obviating the necessity for tissue delineations.Results: The CKD was seen to outperform all the considered descriptors, reaching classification accuracy (ACC) of 92.83%. and area under the curve (AUC) of 0.98. The CKD captures higher order correlations between features and was shown to achieve superior performance against a large collection of computer vision features on a private breast cancer dataset. The WAID outperform all other descriptors on the Breast Cancer Histopathological database (BreakHis) where correctly classified malignant (CCM) instances reached 91.27 and 92.00% at the patient and image level, respectively, without resorting to a deep learning scheme achieves state-of-the-art performance.Discussion: Our proposed derivation of the CKD and WAID can help medical experts accomplish their work accurately and faster than the current state-of-the-art. About one in eight U.S. women about 12%) will develop invasive breast cancer over the course of her lifetime2% will dCancerous tissue recognition (CTR) from histopathological data is a particularly challenging task since it requires a close examination of tissue slides from suspected regions under a microscope which can be time-consuming hence constraining the number of cases pathologists can handle daily.An automated identification of the regions that are highly likely to be cancerous can assist experts in finding them among the surrounding tissues efficiently, resulting in faster diagnosis. This is a part of a larger vision in digital connected health that will enable clinicians not matter where they are located to provide more informed assessments and decision-making than the current state-of-the-art.In order to be trained effectively, most available cancerous tissue recognition (CTR) schemes require pixel level annotations, collected in the form of tissue delineations from expert pathologists  and, ] which is equivalent to projecting the CKDs to the tangent to the cone plane which was shown to have a positive effect on similarity computations for SPD matrices (Given a positive bag matrices  as descrΨ (·) which linearly approximate the Jensen-Shannon's homogenous kernel based on the work by Vedaldi and Zisserman . The classifier is, in that way, computed in a kernel Hilbert space H for which the inner product is defined as Toward allowing for non-linear classification boundaries in the SVM model, we compute explicit feature maps isserman . This alWith the above notation, we propose our multiple-instance max-margin WAID learning as:via cross validation or from experience.where ed ∞-SVM , that hak to handle the non-separability of the samples as defined below.To accommodate constraints that are difficult to cater to, we incorporate slack variables denoted by ξvia standard optimization machinery, the η-constraint makes it combinatorial. An important observation for solving this problem is the effect of the regularization parameter C on the objective; larger values of C penalize more steeply misclassified instances. Toward satisfying the η-constraint, computed on the ratio C. In particular, starting with a small value for the parameter C we retrieve a solution and check if the η-constraint is satisfied based on that. In the case that the condition is not satisfied, the parameter C is rescaled to a larger value making the formulation less tolerant to misclassifications and thus steering it toward making more positive predictions. In the case that the condition is met, the SVM objective is solved for that value of C and the parameters of the classifier (WI and bI) are extracted and used to form the WAID. More formally, the WAID . The Weakly Annotated Image Descriptor for an image I is defined as:Once the WAID is computed for every sample in a given set of images, standard machine learning techniques are implemented toward learning based on the patterns uncovered by the descriptor.The overall pipeline for processing the aforementioned benchmark is illustrated in In this section, we present our experiments on the two databases described in the methods. First, we present a thorough evaluation of the CKD on the FABCD against a very large collection of image descriptors computed at the patch level. Following that, we evaluate the WAID on the BreakHis dataset against Multiple Instance Learning (MIL) alternatives as well as schemes that have been previously proposed for providing inference on the dataset.We present comparisons using SVMs, while for all the learned models we evaluate the classification performance using two different metrics, namely (i) classification accuracy (ACC), and (ii) the Area Under the Curve (AUC) computed from Receiver Operating Characteristic (ROC) curves in a 10-fold cross-validation. For RCDs and CKDs, we use Radial Basis Function (RBF) Mercer kernels based on the LERM and the JBLD measures stated in the Even though CNN based representations would require patch level annotations for their crafting, we believe that presenting comparisons against popular CNN topologies is very important. It should be noted though, that in the general weakly supervised setup patch level annotations are not necessarily available. Since we have data limited to a few thousand samples, we fine-tuned two popular CNN topologies with weights learned on the 1M image database of the ILSVRC challenge. For this study, we established a comparison against the Alexnet  and VGG1The results of this section are delivered in the form of ACC and AUC in a 10-fold validation setup in Our experimentation indicated that working with tissue slides collected at 40x magnification level and patches of size 50 × 50 yielded the highest training accuracy as also illustrated in Among the three different types of background bags , we found that working with random noise images yielded the highest accuracy. Low dimensional embeddings by der Maaten and Hinton  of CKDs via a trial and error process in an effort to control the amount of over-fitting of the model on the training sets. Additionally, for the MCIL-Boost  was reported. The results are shown in terms of accuracy and the area under the curve averaged across the 5-folds provided with the benchmark. First, we considered the MIL-SVM Andrews et al.  scheme, IL-Boost  scheme wIL-Boost . FurtherIL-Boost .Summarizing the contents of In this section, we establish comparisons against visual learning schemes that have been previously deployed for providing inference on the selected magnification x40) of the BreakHis dataset. We compare against the work by Spanhol et al.  for whic0 of the In this work, we presented a framework for the analysis of histopathological breast cancer data in the presence of weak supervision. The proposed Covariance-Kernel descriptor (CKD) manages to capture higher order correlations between edges and color information (as the result of the staining process) that are very important for the recognition of malignant areas while enclosing them in a compact representation. Although the CKD successfully characterizes tissue architectures at the patch level, its performance deteriorates as the targeted slide regions increase in size. This can be attributed to the fusion of different tissue types in larger slide regions . To address this shortcoming, while leveraging the recognition capability of the CKD to larger regions of the slide , we derive an image descriptor in a Multiple Instance Learning (MIL)  frameworAs personalized medicine becomes prevalent, medical experts are faced with high demands to create automation of their most recurrent tasks and for a more complex set of analyses to be done . The aveSome limitations of this study are: (1) the dataset is not large enough, (2) the descriptors may not work for other cancers since they may need different weights, and (3) future studies are needed to validate the model.In this work, we presented a framework for the analysis of histopathological breast cancer data in the presence of weak supervision. This work was concerned with the derivation of a scheme demanding less annotation effort by medical experts. We initiated our analysis with the derivation of an intermediate image representation (patch level), termed CKD, which outperformed a very large collection of popular computer vision descriptors on a private, fully supervised H&E Breast cancer dataset (FABCD). Following that, we proposed an image descriptor, termed WAID, which was derived in a MIL setup for characterizing larger image regions. WAID achieved state-of-the-art performance on the considered magnification level of the BreakHis both against MIL-based schemes as well as prior methods on the database.Delays in diagnosing cancer is either from providers simply not consider cancer in their differential diagnosisAll datasets generated for this study are included in the article/The studies involving human participants were reviewed and approved by UMN IRB. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.PS has contributed 30% of the paper's content while the other authors have equal contributions. All authors contributed to the article and approved the submitted version.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.


Posturography is utilized to assess the influence of aging on postural control. Although this measurement is advantageous for finding group-level differences between the young and the elderly, it is unclear whether it has the potential to differentiate elderly individuals who are affected by various impacts of aging. The purpose of this study was to determine the utility of posturography to discriminate elderly individuals from young adults. We investigated the performances of the random forest classifiers constructed from center of pressure (COP) indices for discriminating standing postures between healthy elderly and young people. Postural sways in 19 young and 31 community-dwelling elderly participants were measured using force plates in 4 standing conditions: bipedal standing, standing on a narrow base, standing on foam rubber, and standing with eyes closed. We further verified the informative predictors that contributed to the prediction model. As the results, the classifier based on the COP indices for standing on foam rubber showed the best performance , followed by the classifier for standing with eyes closed. The informative predictors varied depending on the postural conditions. Our findings demonstrated the potential of posturography for identifying elderly postures. The evaluation of sensory re-weighting using the appropriate COP indices would be a useful clinical tool for detecting the progress of aging on postural control. These effects of aging can be attributed to a reorganization of the neurological system, which includes higher corticospinal excitability6 and a lack of reflex modulation7, as well as to muscle weakness. The alterations of the neural bases for postural control make it difficult for the elderly to tune their postures for context-dependent requirements.Balance impairment is a critical cause of falling in elderly people. Falling is one of the major geriatric syndromes that leads to bone fractures, head trauma, and other health problems. It has been demonstrated that the influence of aging on standing posture is characterized by increased co-contraction in the lower-limb muscles11. Posturography of quiet standing is one of the common tools for quantifying postural sway in clinical practice. The center of pressure (COP) is generally evaluated in temporal, topographic, and frequency domains, and not all COP indices in each domain are affected by aging. Several studies have investigated which COP indices can be used detect the impact of aging on standing balance14. A set of multiple indices was thought to be more useful than a single index for assessing postural steadiness16. The selection of more sensitive indices from multivariate candidates could make this assessment more accurate.Age-related changes in standing balance are typically assessed with several clinical tests, such as the Sensory Organization test, the Functional Reach test, the Timed Up and Go test, and the motor perturbation test17. This result was potentially explained by the finding that elderly people have insufficient sensory re-weighting which is used to adjust their postures according to the sensory context19. Elderly postures are also highly susceptible to restriction of the base-of-support area on which more elaborate joint configurations are required21. A moderate challenging posture would amplify the impacts of aging even before postural steadiness decreases.The question of which postural condition is evaluated is also important. Measurements of the COP with sensory and/or mechanical constraints highlight the age-related changes in posture. For example, when visual information was occluded, postural sway in the elderly became faster and larger in the mediolateral (ML) direction, while young people were not affected25. However, there is little evidence regarding the sensitivity of the COP for the extent of aging in elderly individuals. Given that the impacts of aging on posture vary widely among elderly people26, it may be reasonable that some elderly individuals do not show clear differences from young individuals28. Although a more sensitive assessment for such individuals would be valuable in terms of the prevention of balance impairment, it is unclear whether the COP could be used to differentiate elderly individuals who do not have obvious postural changes due to aging. A classifier, based on a machine learning method, using informative features selected from the multiple COP indices could be useful for this differentiation. When a particular individual is tested, such a classifier would indicate whether or not the individual’s standing posture is categorized as being in an elderly group, which provides definitive information; a comparison with normative benchmarks in each COP index would also be informative. The purpose of this study was to clarify whether COP could be used to discriminate elderly postures from young postures regardless of the extent of aging, and, if the COP can do so, we attempted to identify which indices are the most informative. In this process, the utility of COP as an indicator of the progress of aging on postural control would be supported.Combining those approaches, numerous studies revealed group-level differences in the COP indices between elderly and young peopleIn contrast to previous studies that compared group-level differences, we investigated how accurately elderly individuals were identified from among unlabeled participants by using multiple COP indices in different postural conditions. For this purpose, we used a random forest (RF), which is a tree-based ensemble approach based on machine-learning algorithms. First, each participant’s COP was measured in four different postures with sensory and mechanical constraints to amplify the differences between young and elderly postures. From the time-series of the COP excursions, 69 COP indices were calculated in each condition, and subsequently, the RF classifiers were constructed using those indices in the four conditions. We further searched for informative predictors that can be used as sensitive COP indices for age-related changes based on the permutation importance measure. Finally, we compared the performances of these classifiers by using the confusion matrix in each condition.Nineteen healthy young adults and 33 healthy elderly adults participated in this study. The data of three participants  were excluded because of a recording error for one young person and neurological signs in two elderly people. Thus, the remaining data of 49 participants  were analyzed. All 49 participants had no known major orthopedic or neuromuscular diseases and no physical disabilities and were able to stand unaided. They provided written informed consent in accordance with the Declaration of Helsinki. This study was approved by the ethics board of Chiba Prefectural University of Health and Sciences.Ground reaction forces were collected from a force plate system which digitized the data sequentially to obtain COP coordinates in anteroposterior (AP) and ML directions with a sampling frequency of 100 Hz . The force data were filtered by a second-order, zero-phase-lag Butterworth low-pass filter with a cut-off frequency of 10 Hz. The participants were instructed to keep standing on one- or two-force plates according to the experimental conditions. The participants gazed at a visual target that was displayed 1.5 m in front of their standing position at eye level. They were instructed not to move intentionally during the measurement. We also recorded 29 reflective-marker positions which were attached to the participant’s whole body as a Helen Hayes-marker set using eight infrared cameras . In this study, however, only the force plate data were analyzed.2, a density of 0.06 g/cm3, an elongation stretch percentage of 110%, and a thickness of 3.5 cm.Measurements were performed in 10 trials for each participant. Participants were instructed to stand in 5 different postures with eyes opened and eyes closed, respectively: bipedal standing, standing on a narrow base, standing on foam rubber, standing in tandem position, and single-leg standing. We analyzed 4 of the 10 trials as the experimental conditions in this study: standing with eyes opened , narrow-base standing with eyes opened (Narrow), standing on a foam rubber with eyes opened (Foam), and standing with eyes closed (EC). The data for tandem and single-leg standing were excluded because not all elderly people could keep standing for the full recording time in both conditions. In the Normal, Foam, and EC conditions, two force plates were used for measurements. Participants stood with their right and left heels separated by 15.0 cm and aligning with parallel lines in the AP direction on each plate. In the Narrow condition, participants stood with both feet closed on one force plate. In the Foam condition, participants stood on foam rubber which was composed of natural rubber with a tensile strength of 2.1 kgf/cmEvery trial was recorded for 60 s, and the first 10 s were excluded in the follow-up analysis. All participants were able to keep standing independently during recording in all conditions. Measurements were performed after practice so that participants understood the test position. The order of the 10 trials  was randomized for each participant.31, 69 indices in planar and AP/ML directions were computed from the COP time-series  errors reached a plateau was determined in each condition  in the different conditions are shown in Fig. As a result of the parameter tuning, the number of trees was set to 5000. Table Regarding the high performance of classifiers in the Foam and the EC conditions, the informative predictors were determined based on the variable importance measure Fig. A,C. To vThe similarities among indices were confirmed by cluster analysis using the correlation coefficient of 69 indices Fig. . The corWe further compared the average values of all indices in each condition to test the group-level differences. Similar to the previous studies, significant group differences between the young and elderly participants were found in a number of indices (22 in the Normal condition, 31 in the Narrow condition, 41 in the Foam condition, and 35 in the EC condition; Supplementary Table 14. These findings suggest that the COP has the potential to be used to evaluate the impact of aging on postural control at both the individual and group levels, and the manipulation of proprioceptive and visual inputs during measurement could highlight it.The present study demonstrated that the standing posture of elderly individuals could be discriminated by the COP indices, especially when they were measured using foam rubber, from the standing posture of young people with high accuracy. The velocity-related indices, including the COP velocity, the SD of the COM acceleration, and the SD of the COP velocity in AP direction, were extracted as informative predictors for detecting aging in the Foam condition. The COP-based classifier from the standing with eyes closed condition also performed this discrimination well, along with the different informative predictors from the standing on foam rubber condition. The statistical analysis revealed significant differences in a number of indices between the young and elderly groups in all conditions, regardless of the performance of the RF classifier, a finding that supported the results of previous studies38. For example, Fujimoto et al. compared the diagnostic accuracy of the COP parameters for peripheral vestibular patients between normal standing and standing on foam rubber36, and that population of patients was identified with high accuracy by using Romberg’s ratio of the COP velocity measured on a foam rubber mat, regardless of the patients’ age. They suggested that sensory manipulation in the COP measurement was advantageous for the preliminary assessment of vestibulopathy.The central nervous system organizes the visual, vestibular, and somatosensory inputs for stable standing. It has been proposed that an assessment of the re-weighting be conducted for patients with neurological disease or for the forecasting of future falling37. In that study, the principal components of the 17 COP indices were distinguishable among non-fallers, rare fallers, and frequent fallers. In support of that previous research, our present results demonstrated that the evaluation of sensory re-weighting is valuable for discriminating the healthy elderly from young people. The results imply that the elderly cannot compensate for the distortion of the proprioceptive or visual input using other sensory systems. This finding may remain controversial because healthy elderly people may be capable of sensory re-weighting at the same level as the young when only one sensory input is removed39. We postulate that a selection of the COP indices was also important for detecting age-related changes that do not cause a fall directly. In the present analysis, since the informative predictors were automatically selected from many candidates, the elderly individuals could be separated from the young with high accuracy.With regard to the risk of falling, the use of posturographic data recorded on a compliant surface was able to differentiate the individuals who had had a fall experience38. Since the velocity-related indices are averaged on time-series data, they are far more stationary than the spatial indices. In addition, the COP velocity reflects the body’s acceleration, which is the controlled variable for static standing40, when approximated with the inverted pendulum model. These indices can therefore be appropriate predictors of the elderly posture.In general, the postural sway in the elderly is larger, faster, and more random than that in young people. We observed that the velocity-related indices were the most informative predictors in the RF model in the Foam condition. This finding is in line with the abundant literature showing that the COP velocity is significantly increased in the elderly compared to the youngNotably, the informative predictors were not consistent between the Foam and EC conditions. The Mean-cross AP and MFREQ AP, which are calculated based on the mean COP position, meant that the COP excursion of the elderly tended to move away from the mean position with high-frequency postural sway when their vision was occluded. The inconsistency of informative predictors was also confirmed by the dendrogram in which those predictors were assigned in different clusters. The difference in informative predictors may reflect distinctive responses of sensory re-weighting against proprioceptive and visual disturbance.42, while all of the informative predictors in the present study were categorized in the AP direction. As supported by several studies44, it is possible that postural sway in elderly people can be altered in the AP direction. We concluded that alteration in the AP direction emerges even in elderly persons who do not indicate an obvious postural change due to aging.On the other hand, the above-described informative indices were not consistent with the markers of future falls. It is well established that changes in velocity and frequency in the ML direction are relevant to the risk of fallingThe preservation of the ML stability in the elderly participants was also confirmed by the results in the Narrow condition, in which the postural sway in the elderly was similar to that in the young people, regardless of any restriction of stance width in the ML. For the purpose of discriminating between the postures of the young and the healthy elderly, the COP indices in the AP direction were more appropriate rather than the ML, even though significant group differences were observed in both directions.45. It is thought to be related to alterations of the sensitivity of the muscle spindle47, the excitability of the spinal reflex, and structural changes in the cortical and subcortical regions49. Those changes cause a decline of sensory re-weighting, which is linked to increased co-contraction and decreased joint coordination. The manipulation of the proprioceptive and visual input would highlight those deficits and result in the amplification of the differences between young and elderly postures. Future studies are expected to investigate the causal relation between the results of the discrimination using the COP indices and physiological changes.Such differences in the COP indices due to aging can be attributed to changes in the sensory receptors and central processing. Standing posture in the AP direction is mainly controlled by both the ankle and hip joints. The joint coordination between the hip and ankle joints varies due to aging, which induces a larger COM acceleration in elderly people33, although we illustrated that the young and elderly postures were partitionable based on this measure. In future studies, novel methods should be applied to determine the most important COP indices for assessing the progress of aging on standing posture.There is a limitation regarding the implications of the variable importance measure. We set the cut-off value for the permutation importance to visualize their distribution in the low-dimensional space. This could not conclude whether or not other variables are important. It is possible that the correlated predictors bias the value of variable importance measureOur study demonstrated the utility of posturography for identifying the COPs of the healthy elderly. The RF classifier showed high accuracy in 2 conditions, namely standing on foam rubber and standing with eyes closed. The COP indices that were informative for that discrimination depended on the postural condition: the velocity-related indices in the Foam condition and the mean frequency and amplitude of postural sway relative to the mean-COP position in the EC condition. These results suggest that the age-related changes of standing posture can be captured with the appropriate COP indices measured under sensory-manipulated conditions. Combined with the conventional analysis comparing with a normative benchmark, the classification method described herein can be used to detect postural changes in elderly individuals. Further studies are necessary to establish objective indices that can identify individuals who are at high risk of falling, toward the indices’ use as a clinical tool.Supplementary Information


Improvements in quality of care have led to a significant reduction in mortality and morbidity in preterm infants, especially very-low-birth-weight (VLBW) infants. Consequently, more efforts are increasingly being made to improve their long-term health.A central factor of long-term health is nutritional care, and human milk is considered essential in reducing the incidence of comorbidities and improving long-term outcomes. Indeed, it is well known that human milk is the optimal food source for newborns, primarily preterm infants, and that donor human milk is the best alternative when the mother’s milk is not available or is insufficient ,2,3.p = 0.055), especially among the group of infants born at a gestational age between 28 and 32 weeks .Several observational studies have shown that human milk is superior to formula milk in reducing the rate of comorbidities. It is difficult to determine the direct role of human milk in modulating the risk of comorbidities, as randomized controlled trials cannot be conducted due to the ethical implications inherent in the randomization of human milk feeding; thus, observational studies are useful in situations in which randomized controlled trials are unethical . DespiteConsidering the ethical issues related to the randomization of mothers’ milk, randomized controlled trials studies can be designed to compare the only effects of donor human milk and those of formula milk. Cristofalo et al. showed that the use of donor human milk to supplement the mother’s milk led to a shorter duration of parenteral nutrition and a lower rate of NEC than did formula feeding .A recent meta-analysis that included randomized controlled trials, including a total of 1879 preterm or low-birth-weight infants, demonstrated that formula feeding leads to a higher risk of NEC  than does donor human milk feeding .There is evidence regarding the beneficial effect of mother milk on neurodevelopment of preterm infants ,17, wherAnother variable important for the evaluation of the possible effect of human milk on morbidity is the intake of human milk and the duration of human milk consumption. Indeed, the available studies showed different designs in terms of the dose and duration of the intervention. For example, Corpeleijn et al.  randomlyMeinzen-Derr et al. conducted a large study and aimed to determine the association between the dose of human milk consumed and the risk of NEC or death among 1272 extremely-low-birth-weight infants. The authors demonstrated that there is a dose-dependent effect of human milk feeding on the risk of NEC or death after the first 2 weeks of life. Specifically, the risk of NEC or death decreased by 13% for every 100 mL/kg of human milk consumed in the first 14 days of life . MoreoveIn 2018, Miller et al. conducted a very comprehensive systematic review and meta-analysis of the studies published after 1990 to summarize the available data regarding the mode and dose of feeding and the incidence of comorbidities  in preterm infants born before a gestational age of 28 weeks. Specifically, they compared the effects of different modes of feeding, such as exclusive human milk versus exclusive preterm formula (EPTF), any human milk versus EPTF, a higher versus a lower dose of human milk, and unpasteurized versus pasteurized human milk, on the rate of preterm morbidities. The authors concluded that there is evidence of a clear protective effect of a high dose of human milk against NEC and a possible reduction in the risk of developing late onset sepsis and severe ROP in exclusively human milk-fed infants. Compared to unpasteurized human milk, pasteurized human milk did not show any negative effects on preterm comorbidities. This extensive systematic analysis focused on how the mode of feeding in premature infants can have a protective effect against NEC in particular, whose pathogenesis is still unknown. The authors suggest that the higher the human milk intake is, the lower the occurrence rate of NEC. However, the authors conclude that feeding even small doses of human milk is better than exclusive formula feeding [In practice, the available data demonstrate that human milk feeding plays a key role in improving the clinical outcomes of preterm infants; nevertheless, the existing studies have some limitations, mainly related to the study design.In conclusion, a multidisciplinary approach is crucial for preventing the occurrence of comorbidities and improving the clinical outcomes of preterm infants. The best strategy for preterm infants requiring a high level of care should include not only a neonatologist, but also a nutritionist that can design a personalized nutritional intervention.


This metric quantifies the Shannon entropy of the distance distribution to a specific node from all other nodes. It allows to characterize the influence exerted on a specific node considering statistics of the overall network structure. The measurement and characterization of this structural information has the potential to greatly improve our understanding of sustained activity and other emergent behaviors in networks. We apply this method to study how the psychedelic infusion Ayahuasca affects the functional connectivity of the human brain in resting state. We show that the geodesic entropy is able to differentiate functional networks of the human brain associated with two different states of consciousness in the awaking resting state: (i) the ordinary state and (ii) a state altered by ingestion of the Ayahuasca. The functional brain networks from subjects in the altered state have, on average, a larger geodesic entropy compared to the ordinary state. Finally, we discuss why the geodesic entropy may bring even further valuable insights into the study of the human brain and other empirical networks.With the aim of further advancing the understanding of the human brain’s functional connectivity, we propose a network metric which we term the  In the last few decades, new scientific fields have taken advantages of complex network approaches. This interest emerged, in part, by virtue of technological advances that generate new datasets in computational, social, biological, and others sciences ,2,3. ExaAttempts to characterize new datasets bring up the challenge of extracting relevant features regarding the network’s structure. One of the main concerns is to identify the role of each node in the network and how the nodes cooperate to give rise to emergent behaviors. The majority of measurements that have been proposed in the last few decades allow the ranking of nodes’ importance by the number of connections, centrality, etc. ,6,7.Instead of ranking a node’s relative importance, we ask how the nodes contribute locally to the global connectivity of the network, with the aim of better understanding the individual role played by each node in the network. We wish to characterize this role by quantifying the structural information of the diversity of influences among nodes.The nodes in a network interact with their neighbors and, indirectly, with the neighbors of neighbors; and also with more distant nodes with even greater “neighborhood radius” .geodesic entropy. Nodes with a great diversity of influences  may play an important role in, for example, to guaranteeing specialization of functional patterns. Besides, nodes with a low diversity of influences may indicate constraints relevant to network robustness. The statistical details of the distribution of distances, quantified by the Shannon entropy, may be a key to understanding how emergent behaviors arise.We aim to quantify the diversity of radii or distance of influences on a given node exercised by all other nodes in the network. For each node, we calculate the Shannon entropy functional  of the pWe illustrate and apply our method to real network data. We use the geodesic entropy to analyze human brain functional networks under the influence of the psychedelic brew Ayahuasca.Ayahuasca is a sacred brew from Amazonian indigenous culture made by decoction of two plants from Amazonian flora—the leaves of the bush Psychotria Viridis, that contains N, N-Dimethyltryptamine (DMT), and the vine Banisteriopsis caapi, that contains monoamine oxidase inhibitors MAOi . The DMTAyahuasca ingestion may cause deep changes in the cognition and perceptions, promoting substantial alterations in the sense of the reality and the self ,17. AccoN nodes, the geodesic entropy of all nodes is an N-dimensional vector. Our second key contribution regarding methodological novelty is the entropy-degree diagram, which is a method for projecting the N-dimensional geodesic distance vectors onto a 2-dimensional plot, with node degree being the second dimension. The entropy-degree diagram is a visual representation of both the connectivity  as well as the entropy for each node: 2 N-dimensional vectors are projected as N points in 2 dimensions.Our most important contribution here, regarding the method, is the geodesic entropy. The mean (or first moment) geodesic distance is a standard and fundamental quantity of interest in the study of complex networks. In addition to the first moment of the geodesic distance, however, there is much insight to be gained from studying other functionals  of its distribution. In this context, one of the most important functionals of a probability distribution is the Shannon entropy. This crucial point motivates our definition of the geodesic entropy. Moreover, we define this entropy for each node. For a network of This paper is organized as follow: In V is a set with N nodes and E is the set of links. It is represented numerically by a i and j are connected, the matrix element is A complex network is a schematic representation of the relations (links) between elements (nodes) of a system with a nontrivial topology of interactions ,18,19. CNodes directly connected are called nearest first neighbors. A node can also influence and be influenced by the neighbors of its neighbors, called second nearest neighbors. Considering a connected network, the influences may be extended to all neighborhood radius. Our goal is to quantify the amount of information involved in the diversity of influence extending over the network. For this purpose, we calculate the Shannon entropy  consideri and j, following a specific trail. The shortest path length surface of constant distance to the node i, where in D dimensions, this surface has dimension The distribution The geodesic entropy is given by:The value of Distinct from the entropy of the degree distribution that quantifies the constraints imposed by the network degree distribution , the geoversus the nodal degree k normalized by the maximum number of connections possible, Next, we introduce the entropy-degree diagram, a visualization tool to depict each node along two axes: (i) geodesic entropy and (ii) node degree. Entropy-degree diagram is built plotting the geodesic entropy  are influenced not only by first neighbors but may also be influenced indirectly by more distant nodes. The geodesic entropy is a statistical quantity that measures, in the frame of reference of a given node, the level of constraints in the (average of the) aggregated influences imposed by the distribution of neighborhood radius.Functional networks are usually defined according to statistical dependencies between brain regions activities. Assumed as a fully connected network, all pairs of brain regions will have some interdependence. This can be measured directly, by means of the correlation between their activities, or inferred indirectly from the network structure. In this precise sense, the increase of the characteristic geodesic entropy in the functional networks after Ayahuasca intake indicates that the overall interdependencies are less constrained than before Ayahuasca ingestion.r of geodesic distance. A recent work from Stella and Domenico proposes to calculate the Shannon entropy of distances to characterize centrality [We briefly compare and relate the geodesic entropy to similar quantities that have been used to study networks. The use of geodesic distances to evaluate Shannon entropy was proposed by Chen and collaborators . Insteadntrality . Their eWe have evaluated the geodesic entropy of functional brain network of subjects in the resting state before and after the ingestion of the psychedelic brew Ayahuasca. We have found that during Ayahuasca experience nodes of the functional network tend to have greater geodesic entropy than in the ordinary condition, resulting in networks with higher characteristic geodesic entropy. Hence, the geodesic distances between nodes become less constrained on average, i.e., their distribution becomes “wider”. In a previous work, we showed that the entropy of the degree distribution of brain functional connectivity networks under the influence of Ayahuasca is greater than in the ordinary state . The entThe hypothesis of an entropy increase in response to psychedelic states has been discussed in the literature ,27,28. TIn conclusion, we have shown how the geodesic entropy quantifies locally the overall influences of a network. Furthermore, we have used entropy-degree diagrams to evaluate features of each node in the network, giving a clearer view of the network topology and global connectivity. The application to fMRI-based functional connectivity networks sheds insights on how the brain changes under the influence of external influences. Although in this study we used Ayahuasca, there is in fact no reason why the method cannot be applied to study a variety of drugs, psychopathological symptoms, meditative states, etc. We hope that these ideas and methods find use in furthering our understanding of brain function networks and complex networks in general.The experimental procedures were performed in accordance with the guidelines and regulations approved by the Ethics and Research Committee of the University of São Paulo at Ribeirão Preto (process number 14672/2006). All volunteers signed a written informed consent. The fMRI data were acquired from 10 healthy adult volunteers  with no history of neurological or psychiatric disorders—evaluated by DSM-IV structured interview . They hahttp://www.ndcn.ox.ac.uk/divisions/fmrib)). The images were spatially normalized according to the Montreal Neurological Institute (MNI152 template) anatomical standard space using a linear transformation. We evaluated the band-pass filter using maximum overlap wavelet transform (MODWT), considering the Daubechies wavelet to split the signal into 4 scales of distinct frequency bands. We choose the scale 3 (frequency band ≈ 0.03–0.07 Hz) to be in agreement with the literature that considers the low frequency . We evaluated only 104 cortical regions because of an acquisition limitations for some subjects. The cortical regions were used to define the nodes of the brain networks and the correlation between their signals to define the links. The signals corresponding to each cortical region were obtained averaging the time series of all voxels (3D regular grid) into them . We calculated the Pearson correlation of temporal series of all possible pairs of cortical regions, yielding a cross-correlation matrix. Thus, we have for each sample (before and after Ayahuasca) a 104 × 104 correlation matrix considered as an estimation of the brain functional connectivity. Since the cortical regions define the nodes, the correlation matrices were used to define the links of the functional brain networks.For each sample, we generated a set of symmetric binary adjacency matrices by thresholding the absolute value of their correlation matrices. Precisely, whether the absolute value of the element matrix is larger than the defined threshold, a link is formed (ferences ,32,33,34ferences . We consferences . We fixeReferences


Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work. With the rapid development of remote sensing imaging technology, high-resolution remote sensing images are easily obtained and the interpretation of remote sensing images has evolved from the pixel- to the scene-level ,2. In thThe existing classification algorithms, based on deep learning, assume that all of the training data were available before the training began. This is not realistic in real world remote sensing image scene classification, as a large number of new remote sensing images are obtained from different satellites every day. Researchers have done a good deal of work on this problem. For example, Haikel  proposedRecently, continual learning  has madeHowever, there is one major issue that seriously limits the development of continual learning in remote sensing image scene classification. There are no proper datasets and benchmarks in remote sensing to evaluate and compare emerging continual learning methods. It is important for continual learning to split the dataset into several sequential training batches according to certain criteria. However, existing scene classification datasets, such as UC-Merced , AID 1919, NWPU-https://github.com/lehaifeng/CLRS), to solve the limitations of existing datasets. The goal of CLRS is to advance the development of the state-of-the-art continual learning algorithms in remote sensing image scene classification. To our knowledge, this is the first time that continual learning has been introduced to remote sensing scene classification. CLRS provides researchers with the label information for training batch partitioning to fairly compare and develop emerging continual learning methods, in accordance with the same criteria. In addition, we also provide a new method for constructing a large-scale remote sensing image database based on the target detection pretrained model, which can automatically detect the location information of the target scene and effectively save the manual annotation costs. Finally, the experimental results on the CLRS show that the performance of the current mainstream continual learning methods, in avoiding catastrophic forgetting, is still unsatisfactory, and thus the remote sensing community should strive to develop new continual learning methods to improve the performance of the current algorithms. In summary, the contributions of this article are summarized as follows.Due to the above problem, in this paper, we proposed a continual learning benchmark for remote sensing image scene classification, called Continual Learning Benchmark for Remote Sensing We analyzed three continual learning scenarios and propose training batch partitioning criteria for these three scenarios.(2)We constructed a large-scale remote sensing image scene classification database, namely, CLRS. This database can provide researchers with better data resources to evaluate and improve the performance of continual learning methods in remote sensing image scene classification.(3)We provided a new method for constructing a large-scale scene classification database based on the target detection pretrained model, which can save on manual annotation costs.(4)We tested and analyzed several mainstream continual learning methods for three continual scenarios, and the results can be used as a baseline for future work.The rest of this paper is organized as follows. We introduce the detailed construction principles and methods for the CLRS dataset in The application of continual learning to remote sensing image scene classification in the real world is very complicated. First, the model needs to learn multiple batches of data sequentially and cannot access data from old batches. Second, the scene classes in subsequent batches may be ones that the model has learned or ones that the model has not seen at all. We consider the classification scenarios of three continual learning scenarios that were proposed in Reference .New Instances scenario (NI): New instances of the same scene category will exist in subsequent batches. Although these instances belong to the same category, they have different textures, backgrounds, resolutions, regions, etc. (as shown in New Classes scenario (NC): New Instances and Classes scenario (NIC): In the NIC scenario Regarding the selection of the CLRS categories, we have referenced various land-use classification standards. The authors in Reference  construc(2)The scene classification model based on deep learning can easily overfit small datasets. Therefore, the CLRS should have a large amount of sample data. Each class has 600 images, and the size of each CLRS image is (3)In practical applications of the scene classification problem, scenes in the same category will have remarkable differences due to many factors, such as illumination, background, geographical location, spatial resolution, scale, etc. Therefore, the intraclass samples of the CLRS should be diverse and representative and be able to reflect the characteristics of the scene category as truly as possible, in order to improve the robustness and generalization ability of the model. CLRS samples can be collected from multiple sensors to increase the variation of the samples within the same scene category.(4)Many similar scenes exist in the actual scene classification. The similarity of these scenes is very high, which brings some difficulties and challenges to the classification model. Therefore, a small difference between the CLRS scene categories increases their similarity to the actual application. Similar scenarios should be considered in the selection of the CLRS categories.(5)For the above three continual learning scenarios, the CLRS should develop a set of criteria for dividing training batches and ensure that the image data between each batch cannot be duplicated. The spatial resolution can be quantitatively divided according to its numerical value. Therefore, the CLRS should record the spatial resolution of each remote sensing image in the collection process to divide the training batches quantitatively.(6)Remote sensing images are more complicated than natural images due to their background and texture. Therefore, the CLRS must consider the smoothness between different batches to reduce this difficulty. In other words, the training batches division should be as balanced as possible, and the differences should not be extremely large.Obtaining the position information of target image blocks from the original remote sensing images is the key to constructing a large-scale classification database for remote sensing images. We can crop the final target image blocks based on the location informationAs shown in (2)If the detected object is on the edge of the remote sensing image Multisource. To meet the requirements of the deep learning model regarding the diversity of the samples in the dataset, the CLRS ensures the diversity and representativeness of the samples in the collection. In the same way as most of the existing datasets images are collected, such as AID++ [as AID++ , RSD46-Was AID++ ,33, etc.(2)The samples within the class are more diverse. During the acquisition process, the image is considered for various factors, such as the illumination, background, time, scale, and angle. The image locations are widely distributed worldwide, including major cities and regions in Asia, Africa, Europe, North America, South America, and Oceania (as shown in (3)The difference between classes is smaller. Given that the scenes in actual applications are often similar, the CLRS also selects some similar scene categories (as shown in (4)The CLRS provides the training batch partitioning standard. The existing datasets lack training batch partitioning standards, and thus they cannot be used to evaluate and compare the performance of different continual learning algorithms in remote sensing image scene classification. The CLRS provides a set of training batch partitioning standards. Each CLRS image comprehensively records the spatial resolution of the image, and the resolution range of each type of image is counted. Based on the resolution, each type of image is divided into three levels. Each level has 200 images. Category_Number_Resolution Level_Resolution Size.tif.The CLRS clearly gives the training batch partitioning and their corresponding labels in different scenarios. Therefore, researchers can test the related algorithms under the same standards. Specifically, the CLRS divides each image category into three levels according to the resolution. Based on these levels, the training batches can be divided into the following scenarios: the NI scenario, the NC scenario, and the NIC scenario. We randomly take 50 images from each of the three levels of each type of scene class as the test set, at a ratio of 3:1. The test set has 3750 images, including all 25 scene classes of the CLRS. The test set is fixed in the three scenarios. As the training batches increase for the model’s sequential learning, the model learns more and more classes or instances. However, the classification problem is unique, due to the fixed test set. After each batch is trained, the performance is evaluated on the whole test set, including the scene classes that have not yet been seen, which allows us to better compare the trends and characteristics of the model learning behavior . SpecifiNI scenario: The remaining images can be divided into batch1, batch2, and batch3 for training. Each training batch has 25 scene classes and 3750 images. In the NI scenario, all the scene classes in the test set have appeared in the training set, that is, all the classes are known. The classifier only needs to classify 25 classes in each batch and does not need to distinguish new classes.NC scenario: Considering the smoothness between training batches, the NC scenario randomly divides the 25 scene classes into five copies, each of which contains all the images of the three levels in the five scene classes to simplify the difficulty. Each training batch contains five scene classes and 2250 images. In the NC scenario, the test set contains scene classes that have not yet appeared in the training set. A classifier needs to learn to distinguish not only all the classes it has seen so far but also those that have never appeared before. Therefore, the NC scenario is more difficult than the NI scenario.NIC scenario: Considering the five training batches of the NC scenario and the three training batches of the NI scenario, the NIC scenario can be divided into 15 batches. Each training batch has five scene classes and 750 images. In this scenario, the training batch sequence is longer (15 batches), and the model will continue to consolidate the previously learned knowledge while continuously learning new scene classes. Among the scenarios, the NIC scenario is the closest to real world remote sensing image scene classification and the most difficult of the three scenarios.We test several mainstream continual learning methods on the CLRS, including Elastic Weights Consolidation (EWC) , LearninWe utilize the classification accuracy to estimate the performance of different methods. In order to eliminate the effect of training batches order on the results, we only randomly disturb the training batch order five times to reduce the computational complexity in the NI scenario and the NC scenario. We only scramble the training batch order three times due to the longer training batch sequence of the NIC scenario. The final result of each method is the average of the total number of runs. As the model automatically expands the new neuron structure to learn new categories, CWR is only suitable for use in the NC and NIC scenarios  . This also shows that the development of emerging continual learning methods with controlled forgetting strategies has important research value for improving the performance of remote sensing image scene classification.In this paper, we first analyzed three application scenarios of continual learning in remote sensing, as well as gave the criteria for training batches partitioning in different scenarios to alleviate the current dilemma, where existing remote sensing datasets severely limit the development of continual learning in remote sensing image scene classification. Under this standard, we built a large-scale remote sensing image scene classification database called the CLRS. The purpose of the CLRS is to provide researchers with better data resources to develop state-of-the-art continual learning algorithms in remote sensing image scene classification. In addition, in order to reduce the costs of manual annotation, we proposed a new method of constructing a large-scale remote sensing image scene classification database based on the target detection pretrained model. Finally, we used the CLRS dataset to test and analyze several mainstream continual learning approaches in three continual learning scenarios, with results that can serve as a baseline for future works.In future work, we will further expand the number of scene classes and images of the CLRS. At the same time, we will test new continual learning methods using the CLRS as the baseline for developing state-of-the-art algorithms in the field of remote sensing image scene classification. We will also extend the continual learning to other geo-spatial field such as graph convolutional networks on traffic predication  and geo


One may notice a relatively wide range of tactile sensations even when touching the same hard, flat surface in similar ways. Little is known about the reasons for this variability, so we decided to investigate how the perceptual intensity of light stickiness relates to the physical interaction between the skin and the surface. We conducted a psychophysical experiment in which nine participants actively pressed their finger on a flat glass plate with a normal force close to 1.5 N and detached it after a few seconds. A custom-designed apparatus recorded the contact force vector and the finger contact area during each interaction as well as pre- and post-trial finger moisture. After detaching their finger, participants judged the stickiness of the glass using a nine-point scale. We explored how sixteen physical variables derived from the recorded data correlate with each other and with the stickiness judgments of each participant. These analyses indicate that stickiness perception mainly depends on the pre-detachment pressing duration, the time taken for the finger to detach, and the impulse in the normal direction after the normal force changes sign; finger-surface adhesion seems to build with pressing time, causing a larger normal impulse during detachment and thus a more intense stickiness sensation. We additionally found a strong between-subjects correlation between maximum real contact area and peak pull-off force, as well as between finger moisture and impulse. Tactile interactions occur so often that humans rarely recognize their importance. Our daily tactile interactions start in the morning when we reach to turn off our alarm clock and continue during the day with physical contacts mediating our every action in the real world. They also give us a window into the digital: on average, a person touches his or her mobile phone 2,617 times per day Winnick, , though The adhesion and detachment dynamics of a finger on a surface are mediated by the physiology of the finger and the physical bonds that are created. Therefore, research in physics and materials science can give us hints about the physical variables that affect the perception of stickiness. The American Society for Testing and Materials defined sticky or tacky materials as those that need additional force to separate from another item immediately after the creation of contact . To understand the connection between perception and mechanics, we conduct a psychophysical experiment in tandem with physical measurements using a custom apparatus designed for active touch. Based on past research showing the factors that mainly affect adhesion between rigid and viscoelastic materials Johnson, , we defiThis paper is organized as follows: in section 2, we describe our experimental apparatus, define sixteen physical variables of interest, and outline the methods for our human participant experiment. Section 3 presents the physical variables, the perceptual stickiness ratings, and how they both vary across trials and participants. Section 4 discusses the results, particularly which physical variables the participants considered when rating stickiness in this experiment, and section 5 concludes the paper.In this section, we first introduce a custom-made apparatus for the measurement of three-degree-of-freedom (3-DoF) force and contact fingerprint images. Then, the statistics of the participants and the experimental procedures are described. Lastly, we explain how we process the raw data measured during each trial to derive the sixteen physical variables that we expect may relate to the perception of stickiness.We designed an experimental apparatus that can measure contact forces and finger contact area over time to test how these quantities are related to human stickiness perception see . We alsof/16) to maximize the depth of focus, providing a larger focused area.A strain-based force sensor  was mounted above a glass surface to measure the contact force vector with a resolution of 1/171 N in all directions. The force data were collected by a data acquisition board  with a sampling rate of 500 Hz. The non-coated soda lime silicate glass plate  had a thickness of 3 mm and a roughness <10 nm Gläser, . An optiThe light intensity contrast between the contact and non-contact fingerprint areas was emphasized by applying prism-based frustrated total internal reflection (FTIR)  with a mean age of 29 years  participated in the experiments. None of them had current or past sensory or sensory-motor disabilities. All participants were right handed. The participants provided informed consent and received no compensation.Each participant took a seat after washing and drying their hands. First, participants familiarized themselves with the target interaction using the index finger of their dominant hand to touch a glass plate taped to the table. These interactions provided an opportunity to investigate the stickiness of the glass using their choice of exploratory procedures. It was the same material as the glass plate in the apparatus so that the participant could experience levels of stickiness similar to those of the experiment.Once the participant was familiar with the stickiness of the glass plate, he or she started the experimental procedure. The participant's dominant index fingerpad was wiped with isopropyl alcohol once at the start of the experiment to clean it, and the glass plate of the apparatus was cleaned before every trial. Then, the moisture level of the fingerpad was measured three times in quick succession . After tThe raw data collected from the force and image sensors were processed to compute the physical variables that we investigate in this study. First, time-stacked fingerprint image data underwent several processing steps for contact area extraction, and the obtained contact area was then synchronized with the recording of the finger-surface contact forces.Real contact area is one of the main variables that might affect stickiness perception. To compute real contact area, we applied the series of image processing steps shown in 2/pixel) by dividing the actual area of the square by the number of pixels it occupied in the transformed image.An image recorded by the camera cannot be directly used to calculate the contact area because it is distorted by both the round shape of the lens and the viewing angle between the camera and the glass plate. The radial image distortion due to the lens was flattened using camera calibration. This process made use of the intrinsic camera parameters computed from several images of a printed checkerboard taken from different angles The finger holding duration while the pressing force is kept around 1.5 N (thold is the duration between the instant that the force in the z-direction reaches 1.5 N (t1) and the time that the pressing force starts to decrease (t2). This holding time leads to changes in physical phenomena, such as sweat secretion, which is known to affect the softness of the fingerpad Detaching duration of the finger after force changes sign (t3 from t4.Fast adapting (FA) mechanoreceptors are known to respond strongly when the applied mechanical stimulus changes t2 to t3). To derive this value, we fit a linear function to the force data between the two time points, and we use the absolute value of the derivative of the function as the detachment rate.Researchers previously showed that a glass ball's peak pull-off force from a polyurethane surface depends on its detachment speed Detachment rate at t3), we define another variable regarding the detachment rate. It is calculated in the same way as Ḟ, but we consider the force rendered in the last 0.01 s before t3.Although separation of a fingerpad from the glass plate happens very quickly, the instantaneous detachment rate often changes across this time span. Focused more at the moment of the separation Root mean square of force in the x-direction during the pull-off (t3 to t4). Therefore, if there are n values of Fx between t3 and t4, the variable is defined as As shown in Fy,rms)RMS of force in the y-direction during the pull-off RMS of force in the z-direction during the pull-off RMS of force during the pull-off  with the RMS of the force vector's magnitude (Frms) can tell us whether a particular force direction  is crucial for people to feel stickiness. The value is calculated as F is force vector magnitude.People could perceive stickiness without considering the direction in which the force occurs , particularly in the case when the interaction time is very short, such as finger detachment. Thus, comparing the RMS force in each direction (Ix)Impulse in the x-direction during the pull-off (tpull). We define the pull-off impulse as the integral of force over time to reflect both the measured time and force; such a calculation is common in mechanics, but we have not found any prior use of impulse in stickiness research. Here, we consider only the x-direction, and we take the absolute value of the force because we believe forces in both directions may cause a sensation of stickiness. Therefore, Ix is calculated as The RMS force calculations average over time and therefore disregard the wide range of pull-off times (Iy)Impulse in the y-direction during the pull-off Impulse in the z-direction during the pull-off Frms as one of the physical variables, we also define the impulse of the force vector magnitude to combine all three directions. Mathematically, this is For the same reason that we set Areal)Maximum real contact area Change in moisture after finger detachment . After each trial, the participant rated the stickiness they experienced using a nine-point scale (1–9), with larger values meaning a stickier interaction.We applied Spearman's rank-order correlation to discover which of the sixteen calculated physical variables are perceptually related to stickiness and how these variables are correlated with each other. This method elucidates how closely the ranked values of one physical variable match the rank order of another variable. Given pairs of a measured value and a rating, where the measured values are sorted in ascending order, the correlation of the pairs is high (ρ≈1) if the order of the ratings is also close to ascending order. This coefficient (ρ) is 0 when there is no order similarity at all between the two lists of values, and it is −1 when they are anti-correlated. We visualize the correlations between all possible pairs of a participant's sixteen variables and ratings as a heatmap. An additional heatmap presents the correlations between the median values of the physical variables across participants.The reliability of the correlation analysis presupposes that all physical conditions, such as the finger's temperature and material properties, do not change significantly across trials. Because the isopropyl alcohol used to clean the finger at the start of the experiment might affect the physical status of the finger, and also because fingers quickly adapt to the interaction condition by changing the speed of sweat secretion  was applied to the force sensor in an extremely short time (median tpull = 0.022 s). These oscillations hindered the calculation of the parameters used to derive the physical variables for this subject, leaving a total of nine participants .Finally, we could not include any of the data collected from one female participant. In most of this individual's trials, the measured z-force did not smoothly converge to zero after detachment. Instead the signal persistently oscillated at a frequency that we suspect to be the resonance frequency of the force sensor and contact platform. We believe too much x-force  stickiness rating.Participants also had different rating distributions. As shown in p < 0.05).The correlations between the 16 physical variable values and the stickiness ratings for each participant were calculated. One hundred and thirty six correlation coefficients (ρ) and corresponding probability values (p) were obtained per participant; all nine of these heatmaps can be viewed in the thold, Iz, I, tpull, Fz,rms, The bottom row in the heatmap  indicateI and Iz and the fact that Iz is more strongly correlated with stickiness than I is, we believe that the z-component of impulse may mainly influence the stickiness rating. Therefore, we seek to understand the two variables based on Iz.Considering the calculational similarity between Iz) to the stickiness rating was higher than that of the RMS of z-force  or that of the peak pull-off force  components of the pull-off action therefore resulted in non-negligible forces generated in the x- and y-directions. Such forces primarily occurred in the x-direction, with larger measured forces along this axis compared to the y- and z-directions ; the medFz,rms correlated significantly better with the stickiness ratings than Fx,rms  and Fy,rms , but the difference with Ix did not reach statistical significance .However, we found that the vertical  component of force and impulse evokes the feeling of stickiness more than the horizontal  component. To evaluate the perceptual value of stickiness cues generated in the vertical (z) direction, we tested how the RMS of the z-force and the z-impulse correlated with the perception of stickiness. For the RMS of the force, we found that p = 0.02; see p = 0.55; Ix to stickiness also did not increase in terms of the participant's median Iz . We found that z-impulse's correlation becomes stronger as the median z-impulse increases  → Rating. The contact time (thold) is the variable that builds the adhesion between the fingerpad and the glass plate. This adhesion requires either a high normal force to pull off the glass or a high normal impulse; tpull correlates more strongly with thold [tpull is a component of Iz, the high correlation coefficient implies that thold contributes more to the creation of the z-impulse than the z-forces do. Therefore, we conclude that thold affects tpull by establishing secure contact, which causes an increase in the z-impulse (Iz) and evokes a feeling of stickiness.We found that the perceived stickiness of a glass plate is significantly affected by the values of  heatmap  indicatetpull) is important for participants to feel stickiness because the time-related variables (tpull and Iz) are more strongly correlated with the stickiness ratings than the force-based variables are. According to tpull [Iz than to We believe that the time taken to break contact with the glass  = 0.65, highlighted with red boxes in Areal) and pull-off force  of an elastomer is similar to the full distance experienced between the fixed elastomeric sample and a pulling indenter , so we cannot know whether the significant mechanical and perceptual results we found would also hold for much younger or older populations, nor for a more diverse sample of individuals from the same age group. Given the design of our study, we also were not able to make conclusions about stickiness perception at normal forces that are lower or higher than 1.5 N.Second, the test material was limited to one type of smooth glass. Because subjects knew that they were touching the same piece of glass in every trial, they might tend to report a constant value of stickiness that did not depend on trial-to-trial variations. We tried to minimize this bias by having each subject touch an identical glass plate under diverse pressing conditions before data collection began. In future research, it would be interesting to understand how important physical variables change depending on the test material. This comparison would give us a more profound understanding of the perceptual mechanism of light stickiness.Third, research on this topic would benefit from an objective metric that can evaluate the perception strength against a stimulus. Our study could not elucidate how two independent people perceive the same interaction because the perception ratings were different from participant to participant. One possible reason for these rating variations is that the fingerpad's physiological composition varies greatly across people [e.g., hydrolipid film composition , the time taken for the finger to detach (tpull), and the impulse in the pressing  direction during the finger detachment after the normal force changes sign (Iz) were significantly related to stickiness. We believe that a longer pressing duration caused a larger impulse during the detachment of the finger from the glass surface and thus a more vivid stickiness sensation. Overall, our results imply that perceptual stickiness may be different from mechanical stickiness.In this paper, we investigated the physical variables that affect the perception of light stickiness. A custom-made apparatus recorded 3-DoF forces over time, contact area over time, and the moisture level of the subject's fingerpad before and after each trial. The recorded data yielded sixteen physical variables, including RMS forces in the three directions, impulses in the three directions, and finger detachment rates. Based on the data from a total of 324 trials by nine participants and their corresponding stickiness ratings, we computed Spearman's correlation coefficients both within and across subjects. We found that the finger's pre-detachment pressing time (All datasets generated for this study are included in the article/The Ethics Council of the Max Planck Society reviewed and approved this research study under HI protocol 18-05B. Written informed consent was obtained from all participants. All research data were collected and analyzed according to the approved experimental protocol.SN developed the apparatus, carried out the experiments, collected and analyzed the data, created the figures, wrote the first draft of the manuscript, edited the manuscript, and implemented changes requested by the other authors. YV, DG, and KK helped to design the psychophysical experiment, gave input on data analysis, guided the creation of the figures, wrote short sections of text, and extensively edited the manuscript. Additionally, KK gave input on the apparatus design and supervised the project.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.