Miracle-dz/UniFAR-FTUs huggingface dataset:
Grip Force Adjustments Reflect Prediction of Dynamic Consequences in Varying Gravitoinertial Fields

One remarkable capacity when we grasp and manipulate tools relies on the ability to predict the grip force required to handle them in relation to their mechanical properties and the surrounding environment. However, rapid changes in the dynamical context may constitute a substantial challenge. Here, we test how participants can switch between different and never experienced dynamical environments induced by centrifugation of the body. Seven subjects lifted an object four times in a row successively in 1, 1.5, 2, 2.5, 2, 1.5 and 1g. We continuously measured grip force, load force and the gravitoinertial acceleration that was aligned with body axis (perceived gravity). Participants adopted stereotyped grasping movements immediately upon entry in a new environment and needed only one trial to adapt grip forces to a stable performance in each new gravity environment. While participants predictively applied larger grip forces when they expected increasing gravity steps, they did not decrease grip force proportionally when they expected decreasing gravity steps, indicating imperfect anticipation in that condition. The subjects’ performance could rather be explained by a combination of successful scaling of grip force according to gravity changes and a separate safety factor. The data suggest that in highly unfamiliar dynamic environments, grip force regulation is characterized by a combination of a successful anticipation of the experienced environmental condition, a safety factor reflecting strategic response to uncertainties about the environment and rapid feedback mechanisms to optimize performance under constant conditions.
Distributable Consistent Multi-Graph Matching

In this paper we propose an optimization-based framework to multiple graph matching. The framework takes as input maps computed between pairs of graphs, and outputs maps that 1) are consistent among all pairs of graphs, and 2) preserve edge connectivity between pairs of graphs. The central idea of our approach is to divide the input graph into overlapping sub-graphs and enforce consistency among sub-graphs. This leads to a distributed formulation, which is scalable to large-scale datasets. We also present an equivalence condition between this decoupled scheme and the original scheme. Experiments on both synthetic and real-world datasets show that our framework is competent against state-of-the-art global optimization-based techniques.
GPU acceleration of Eff2 descriptors using CUDA

Video analysis using local descriptors requires a high-throughput descriptor creation process. This speed can be obtained from modern GPUs. In this paper, we adapt the computation of the Eff2 descriptors, a SIFT variant, to the GPU. We compare our GPU-Eff descriptors to SiftGPU and show that while both variants yield similar results, the GPU-Eff descriptors require significantly less processing time.
Joint FEC/CRC coding scheme for energy constrained IOT devices

Energy efficiency is of utmost importance in internet of things (IOT) as it tends to rely on battery-powered devices constrained by their limited lifetime, which entails the need of energy-aware protocols. As transmission energy is the most energy consuming, and reliability is of paramount in IOT applications, we advocate the use of error control coding as means to provide reliability and transmission energy saving. Forward error correction (FEC) aims at saving transmission energy and correcting channel errors at the receiver by using channel coding and decoding, yet the complexity of the decoding process affects both the energy and reliability performance of wearable devices. In this work, we propose a joint FEC/CRC scheme to alleviate the decoding energy depletion and provide communication reliability. We thoroughly evaluate the performance of the proposed coding scheme in terms of energy efficiency and reliability in a multi-hop network.
The Effects of Next-Day Class Characteristics on Alcohol Demand in College Students

Behavioral economic principles have been useful for addressing strategies to reduce alcohol consumption among college students. For example, academic variables (such as class schedule or academic rigor) have been found to affect alcohol demand assessed with a hypothetical alcohol purchase task (APT). The present studies used the APT to address the effects of 2 academic variables: next-day course level (no class, introductory level or upper level) and class size (no class, 30-student or 12-student). In each of 2 experiments, undergraduate participants read a description of a drinking context (either a no-class control version or 1 of the academic constraint conditions) and were asked to indicate how many drinks they would purchase at a variety of prices. Hursh and Silberberg’s (2008) exponential demand equation was used to determine intensity and elasticity of demand, and Hursh and Roma’s (2015) essential value (EV) parameter was calculated to assess essential value. In both experiments, a next-day class reduced alcohol demand, and alcohol consumption decreased as drink price increased. The presence of a smaller next-day class reduced alcohol demand compared with a larger next-day class; however, course level did not differentially affect alcohol demand. These results suggest that smaller next-day classes may reduce alcohol demand among college students and also provide initial evidence for the reliability of EV across studies.
Clusters of midlife women by physical activity and their racial/ethnic differences

Objective: The purpose of this study was to identify clusters of midlife women by physical activity and to determine racial/ethnic differences in physical activities in each cluster. Methods: This was a secondary analysis of the data from 542 women (157 non-Hispanic [NH] Whites, 127 Hispanics, 135 NH African Americans, and 123 NH Asian) in a larger Internet study on midlife women's attitudes toward physical activity. The instruments included the Barriers to Health Activities Scale, the Physical Activity Assessment Inventory, the Questions on Attitudes toward Physical Activity, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention, and the Kaiser Physical Activity Survey. The data were analyzed using hierarchical cluster analyses, analysis of variance, and multinominal logistic analyses. Results: A three-cluster solution was adopted: cluster 1 (high active living and sports/exercise activity group; 48%), cluster 2 (high household/caregiving and occupational activity group; 27%), and cluster 3 (low active living and sports/exercise activity group; 26%). There were significant racial/ethnic differences in occupational activities of clusters 1 and 3 (all P < 0.01). Compared with cluster 1, cluster 2 tended to have lower family income, less access to health care, higher unemployment, higher perceived barriers scores, and lower social influences scores (all P < 0.01). Compared with cluster 1, cluster 3 tended to have greater obesity, less access to health care, higher perceived barriers scores, more negative attitudes toward physical activity, and lower self-efficacy scores (all P < 0.01). Conclusions: Midlife women's unique patterns of physical activity and their associated factors need to be considered in future intervention development.
Optimal metrics for identifying long term patterns of depression in older HIV-infected and HIV-uninfected men who have sex with men

ABSTRACT Objectives: Center of Epidemiologic Studies–Depression Scale (CES-D) provides a snapshot of symptom severity at a single point in time. However, the best way of using CES-D to classify long-term depression is unclear. Method: To identify long-term depression among HIV-infected and HIV–uninfected 50+ year-old men who have sex with men (MSM) with at least 5 years of follow-up, we compared sensitivities and specificities of CES-D–based metrics (baseline CES-D; four consecutive CES-Ds; group-based trajectory models) thresholded at 16 and 20 to a clinician's evaluation of depression phenotype based on all available data including CES-D history, depression treatment history, drug use history, HIV disease factors, and demographic characteristics. Results: A positive depressive phenotype prevalence was common among HIV-infected (prevalence = 33.1%) and HIV-uninfected MSM (prevalence = 23.2%). Compared to the depressive phenotype, trajectory models of CES-D≥20 provided highest specificities among HIV-infected (specificity = 99.9%, 95% Confidence Interval [CI]:99.4%–100.0%) and HIV-uninfected MSM (specificity = 99.0%, 95% CI:97.4%–99.7%). Highest sensitivities resulted from classifying baseline CES-D ≥ 16 among HIV-infected MSM (sensitivity = 75.0%, 95% CI:67.3%–81.7%) and four consecutive CES-Ds ≥ 16 among HIV-uninfected MSM (sensitivity = 81.0%, 95% CI:73.7%–87.0%). Conclusion: Choice of method should vary, depending on importance of false positive or negative rate for long-term depression in HIV-infected and HIV-uninfected MSM.
Empirical Analysis and Multiple Level Views in Massive Social Networks

With the emergence of massive social media, massive social networks have led to a huge interest in data analysis. In this paper, we propose an empirical study on several massive social networks including 4 mobile call graphs, a fixed-line call graph, two co-authorship networks and two Email networks. We find that call graphs tend to be more locality than the co-authorship networks and Email networks. To our surprise, we even find that there is no significant relations between community sizes and their quality scores for most extracted communities. We also find that some very huge community with high mean quality values, and we can not find the universal ``V'' shape in their mean quality values.
Spatial dynamics and mixing of bluefin tuna in the Atlantic Ocean and Mediterranean Sea revealed using next‐generation sequencing

The Atlantic bluefin tuna is a highly migratory species emblematic of the challenges associated with shared fisheries management. In an effort to resolve the species’ stock dynamics, a genomewide search for spatially informative single nucleotide polymorphisms (SNPs) was undertaken, by way of sequencing reduced representation libraries. An allele frequency approach to SNP discovery was used, combining the data of 555 larvae and young‐of‐the‐year (LYOY) into pools representing major geographical areas and mapping against a newly assembled genomic reference. From a set of 184,895 candidate loci, 384 were selected for validation using 167 LYOY. A highly discriminatory genotyping panel of 95 SNPs was ultimately developed by selecting loci with the most pronounced differences between western Atlantic and Mediterranean Sea LYOY. The panel was evaluated by genotyping a different set of LYOY (n = 326), and from these, 77.8% and 82.1% were correctly assigned to western Atlantic and Mediterranean Sea origins, respectively. The panel revealed temporally persistent differentiation among LYOY from the western Atlantic and Mediterranean Sea (FST = 0.008, p = .034). The composition of six mixed feeding aggregations in the Atlantic Ocean and Mediterranean Sea was characterized using genotypes from medium (n = 184) and large (n = 48) adults, applying population assignment and mixture analyses. The results provide evidence of persistent population structuring across broad geographic areas and extensive mixing in the Atlantic Ocean, particularly in the mid‐Atlantic Bight and Gulf of St. Lawrence. The genomic reference and genotyping tools presented here constitute novel resources useful for future research and conservation efforts.
Fully connected CRF with data-driven prior for multi-class brain tumor segmentation

Grid conditional random fields (CRFs) are widely applied in both natural and medical image segmentation tasks. However, they only consider the label coherence in neighborhood pixels or regions, which limits their ability to model long-range connections within the image and generally results in excessive smoothing of tumor boundaries. In this paper, we present a novel method for brain tumor segmentation in MR images based on fully-connected CRF (FC-CRF) model that establishes pairwise potentials on all pairs of pixels in the images. We employ a hierarchical approach to differentiate different structures of tumor and further formulate a FC-CRF model with learned data-driven prior knowledge of tumor core. The methods were evaluated on the testing and leaderboard set of Brain Tumor Image Segmentation Benchmark (BRATS) 2013 challenge. The precision of segmented tumor boundaries is improved significantly and the results are competitive compared to the start-of-the-arts.
The Authorization Policy Existence Problem

Constraints such as separation-of-duty are widely used to specify requirements that supplement basic authorization policies. However, the existence of constraints (and authorization policies) may mean that a user is unable to fulfill her/his organizational duties because access to resources has been denied. In short, there is a tension between the need to protect resources (using policies and constraints) and the availability of resources. Recent work on workflow satisfiability and resiliency in access control asks whether this tension compromises the ability of an organization to achieve its objectives. In this paper, we develop a new method of specifying constraints which subsumes much related work and allows a wider range of constraints to be specified. The use of such constraints leads naturally to a range of questions related to “policy existence”, where a positive answer means that an organization's objectives can be realized. We analyze the complexity of these policy existence questions and, for particular sub-classes of constraints defined by our language, develop fixed-parameter tractable algorithms to solve them.11. An extended abstract of this paper appeared in the Proceedings of the Seventh ACM Conference on Data and Application Security and Privacy [1]. Research was partially supported by Leverhulme Trust grant RPG-2018-161 and Royal Society Wolfson Research Merit Award.
Persistent multiple hypothesis tracking for wide area motion imagery

Wide area motion imagery (WAMI) acquired by an airborne sensor enables continuous monitoring of large urban areas. Reliable vehicle tracking in this imagery remains challenging due to low frame rate and small object size. Many approaches solely rely on motion detections provided by frame differencing or background subtraction. Recent approaches for persistent tracking, i.e. tracking vehicles even if they become stationary, compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. We propose a novel single tracker framework based on multiple hypothesis tracking (MHT) that enables persistent tracking in WAMI data by recovering missing motion detections with a classifier-based detector, thus avoiding the additional complexity introduced by combining two trackers. We adapt the MHT approach to the specific context of WAMI tracking by integrating an appearance-based similarity measure, vehicle-collision tests, and clutter handling. An evaluation on a region of interest in the WPAFB 2009 dataset shows state-of-the-art performance.
Ranking video segments with LSTM and determinantal point processes

This paper presents a novel approach to ranking unsupervised video segment proposals. This is important in order to obtain a small set of object proposals that cover diverse objects in the video. This paper utilizes a convolutional-recurrent network framework for learning a score function of video segments. A modified Determinantal Point Processes (DPP) approach is used on the results to sample a diverse and meaningful set of objects from the video. Given a pool of video object proposals, our approach can rapidly find a small and diverse set of objects which could then be used for subsequent processing. Results on the challenging VSB-100 dataset demonstrate the performance of our approach.
Region based image retrieval with query-adaptive feature fusion

Recently, image representation based on convolutional neural network (CNN) becomes more popular than SIFT based feature, such as Fisher vector (FV). However, which of the two works better for image retrieval is not entirely clear yet. In this paper, we propose to fuse CNN and FV to incorporate the advantages of both features for image retrieval. We extract CNN feature and FV from multi-scale regions, which makes the representation more robust to image noise. Then a query-adaptive feature fusion method is proposed, which is used jointly with 2-D inverted index under the framework of bag-of-words. Moreover, we make an evaluation of different CNN feature extraction methods for the region based method. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our method with efficiency in both time cost and memory usage.
Fault attacks on Tiaoxin-346

This paper describes two different fault injection attacks on the authenticated encryption stream cipher Tiaoxin-346, a third round candidate in the CAESAR cryptographic competition. The first type of fault injection uses a bit-flipping fault model to conduct a forgery attack. The number of faulty bits required for this forgery attack is twice the number of bit modifications made in the input message. The second type of fault injection uses a random fault model in a differential fault attack to recover the secret key of the cipher. A successful attack can be performed with 36 random multi-byte faults and a computational complexity of 236. This second attack improves on the previous key recovery attack of Dey et. al., as the random fault model we use is more practical than the bit flipping model used in their attack.Ed Dawson
Frogs model man: In vivo thyroid hormone signaling during development

Thyroid hormone (TH) signaling comprises TH transport across cell membranes, metabolism by deiodinases, and molecular mechanisms of gene regulation. Proper TH signaling is essential for normal perinatal development, most notably for neurogenesis and fetal growth. Knowledge of perinatal TH endocrinology needs improvement to provide better treatments for premature infants and endocrine diseases during gestation and to counteract effects of endocrine disrupting chemicals. Studies in amphibians have provided major insights to understand in vivo mechanisms of TH signaling. The frog model boasts dramatic TH‐dependent changes directly observable in free‐living tadpoles with precise and easy experimental control of the TH response at developmental stages comparable to fetal stages in mammals. The hormones, their receptors, molecular mechanisms, and developmental roles of TH signaling are conserved to a high degree in humans and amphibians, such that with respect to developmental TH signaling “frogs are just little people that hop. ” The frog model is exceptionally illustrative of fundamental molecular mechanisms of in vivo TH action involving TH receptors, transcriptional cofactors, and chromatin remodeling. This review highlights the current need, recent successes, and future prospects using amphibians as a model to elucidate molecular mechanisms and functional roles of TH signaling during post‐embryonic development.
Babao Dan attenuates hepatic fibrosis by inhibiting hepatic stellate cells activation and proliferation via TLR4 signaling pathway

Babao Dan (BBD), a traditional Chinese medicine, has been widely used as a complementary and alternative medicine to treat chronic liver diseases. In this study, we aimed to observe the protective effect of BBD on rat hepatic fibrosis induced by diethylnitrosamine (DEN) and explore it possible mechanism. BBD was administrated while DEN was given. After eight weeks, values of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) indicated that BBD significantly protected liver from damaging by DEN and had no obvious side effect on normal rat livers. Meanwhile, BBD attenuated hepatic inflammation and fibrosis in DEN-induced rat livers through histopathological examination and hepatic hydroxyproline content. Furthermore, we found that BBD inhibited hepatic stellate cells activation and proliferation without altering the concentration of lipopolysaccharide (LPS) in portal vein. In vitro study, serum from BBD treated rats (BBD-serum) could also significantly suppress LPS-induced HSCs activation through TLR4/NF-κB pathway. In addition, BBD-serum also inhibited the proliferation of HSCs by regulating TLR4/ERK pathway. Our study demonstrated that BBD may provide a new therapy strategy of hepatic injury and hepatic fibrosis.
Monodisperse Polymeric Core–Shell Nanocontainers for Organic Self‐Healing Anticorrosion Coatings

5 ] When external energy such as light, electricity or electromagnetic wave is applied, the network can be self-adaptive to reform materials on the cracks or broken areas. Microencapsulated and microvascular systems have been pioneered by the White group for self-healing coatings based on the auto-release of healing materials from microcap-sules broken near the crack surfaces.
Element-key table based complex key generation (E-CKG) for IoT based health care networks

The authentication procedures are being utilized among the various Network & Communication Systems across the globe, which includes the healthcare networks, wireless sensor networks, Wi-Fi, etc. In the case of IoT networks, the small computational powered nodes are utilized as IoT sensors. The lower computational power creates the major hurdle in the implementation of security algorithms in the IoT node framework. A strong need of robust authentication mechanism with low resource requirement needs be developed to ensure the security of IoT networks. In this paper, the new authentication scheme is proposed over 6-column key table. The complex authentication keys are produced from the selective columns by using the combination of trigonometric functions. This model is created to protect data privacy against the Man-in-the-Middle (MITM) attacks over the IoT networks. The proposed model is designed for the enhancement in the level of security, while optimizing the elapsed time for key table generation (element-key table generation) and key preparation (complex key generation from element-keys). Various experiments have been conducted over the proposed model design and results are obtained in the form of the performance metrics of key table generation and key transmission and verification during the exchange of the keys. The proposed model is found efficient and secure for the purpose of authentication over the healthcare networks based upon the internet of things (IoT).