nils-herrmann/clean_scirepeval huggingface dataset:
Scalable Inference for Nested Chinese Restaurant Process Topic Models

Nested Chinese Restaurant Process (nCRP) topic models are powerful nonparametric Bayesian methods to extract a topic hierarchy from a given text corpus, where the hierarchical structure is automatically determined by the data. Hierarchical Latent Dirichlet Allocation (hLDA) is a popular instance of nCRP topic models. However, hLDA has only been evaluated at small scale, because the existing collapsed Gibbs sampling and instantiated weight variational inference algorithms either are not scalable or sacrice inference quality with mean-eld assumptions. Moreover, an efcient distributed implementation of the data structures, such as dynamically growing count matrices and trees, is challenging. In this paper, we propose a novel partially collapsed Gibbs sampling (PCGS) algorithm, which combines the advantages of collapsed and instantiated weight algorithms to achieve good scalability as well as high model quality. An initialization strategy is presented to further improve the model quality. Finally, we propose an ecient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy. Empirical studies show that our algorithm is 111 times more ecient than the previous open-source implementation for hLDA, with comparable or even beer model quality. Our distributed implementation can extract 1,722 topics from a 131-million-document corpus with 28 billion tokens, which is 4-5 orders of magnitude larger than the previous largest corpus, with 50 machines in 7 hours.
Applying Docker Swarm Cluster into Software Defined Internet of Things

Internet of Things (IoT), cloud computing and integrated deployment are becoming central topics in Internet development. In this paper, a cloud containerization solution, Docker, has been introduced to the original "Bluetooth Based Software Defined Function" (BT-SDF), a framework designed to simplify the IoT function redefining process. With the assistance of Docker and its clustering, Docker Swarm, the BT-SDF will be transformed to a scalable, extensible and flexible IoT function redefining framework named "Cloud and Bluetooth based software defined function".
A General Multi-agent Epistemic Planner Based on Higher-order Belief Change

In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we propose a general representation language for multi-agent epistemic planning where the initial KB and the goal, the preconditions and effects of actions can be arbitrary multi-agent epistemic formulas, and the solution is an action tree branching on sensing results. To support efficient reasoning in the multi-agent KD45 logic, we make use of a normal form called alternating cover disjunctive formulas (ACDFs). We propose basic revision and update algorithms for ACDFs. We also handle static propositional common knowledge, which we call constraints. Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.
SoK: Systematic Classification of Side-Channel Attacks on Mobile Devices

Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices. In this paper, we propose a new categorization system for side-channel attacks on mobile devices, which is necessary since side-channel attacks have evolved significantly since their introduction during the smartcard era. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive overview of existing attacks and attack strategies provides valuable insights on the evolving field of side-channel attacks on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.
Miniature implantable and wearable on-body antennas: towards the new era of wireless body-centric systems [antenna applications corner]

Wireless body-centric sensing systems have an important role in the fields of biomedicine, personal healthcare, safety, and security. Body-centric radio-frequency identification (RFID) technology provides a wireless and maintenance-free communication link between the human body and the surroundings through wearable and implanted antennas. This enables real-time monitoring of human vital signs everywhere. Seamlessly integrated wearable and implanted miniaturized antennas thus have the potential to revolutionize the everyday life of people, and to contribute to independent living. Low-cost and low-power system solutions will make widespread use of such technology become reality. The primary target applications for this research are body-centric sensing systems and the relatively new interdisciplinary field of wireless brain-machine interface (BMI) systems. Providing a direct wireless pathway between the brain and an external device, a wireless brain-machine interface holds an enormous potential for helping people suffering from severely disabling neurological conditions to communicate and manage their everyday life more independently. In this paper, we discuss RFID-inspired wireless brain-machine interface systems. We demonstrate that mm-size loop implanted antennas are capable of efficiently coupling to an external transmitting loop antenna through an inductive link. In addition, we focus on wearable antennas based on electrically conductive textiles and threads, and present design guidelines for their use as wearable-antenna conductive elements. Overall, our results constitute an important milestone in the development of wireless brain-machine interface systems, and a new era of wireless body-centric systems.
Niclosamide: Beyond an antihelminthic drug.

Niclosamide is an oral antihelminthic drug used to treat parasitic infections in millions of people worldwide. However recent studies have indicated that niclosamide may have broad clinical applications for the treatment of diseases other than those caused by parasites. These diseases and symptoms may include cancer, bacterial and viral infection, metabolic diseases such as Type II diabetes, NASH and NAFLD, artery constriction, endometriosis, neuropathic pain, rheumatoid arthritis, sclerodermatous graft-versus-host disease, and systemic sclerosis. Among the underlying mechanisms associated with the drug actions of niclosamide are uncoupling of oxidative phosphorylation, and modulation of Wnt/β-catenin, mTORC1, STAT3, NF-κB and Notch signaling pathways. Here we provide a brief overview of the biological activities of niclosamide, its potential clinical applications, and its challenges for use as a new therapy for systemic diseases.
Role of antioxidants and trace elements in health and immunity of transition dairy cows.

A number of antioxidants and trace minerals have important roles in immune function and may affect health in transition dairy cows. Vitamin E and beta-carotene are important cellular antioxidants. Selenium (Se) is involved in the antioxidant system via its role in the enzyme glutathione peroxidase. Inadequate dietary vitamin E or Se decreases neutrophil function during the perpariturient period. Supplementation of vitamin E and/or Se has reduced the incidence of mastitis and retained placenta, and reduced duration of clinical symptoms of mastitis in some experiments. Research has indicated that beta-carotene supplementation may enhance immunity and reduce the incidence of retained placenta and metritis in dairy cows. Marginal copper deficiency resulted in reduced neutrophil killing and decreased interferon production by mononuclear cells. Copper supplementation of a diet marginal in copper reduced the peak clinical response during experimental Escherichia coli mastitis. Limited research indicated that chromium supplementation during the transition period may increase immunity and reduce the incidence of retained placenta.
Temporal Dithering of Illumination for Fast Active Vision

Active vision techniques use programmable light sources, such as projectors, whose intensities can be controlled over space and time. We present a broad framework for fast active vision using Digital Light Processing (DLP) projectors. The digital micromirror array (DMD) in a DLP projector is capable of switching mirrors “on” and “off” at high speeds (10/s). An off-the-shelf DLP projector, however, effectively operates at much lower rates (30-60Hz) by emitting smaller intensities that are integrated over time by a sensor (eye or camera) to produce the desired brightness value. Our key idea is to exploit this “temporal dithering” of illumination, as observed by a high-speed camera. The dithering encodes each brightness value uniquely and may be used in conjunction with virtually any active vision technique. We apply our approach to five well-known problems: (a) structured light-based range finding, (b) photometric stereo, (c) illumination de-multiplexing, (d) high frequency preserving motion-blur and (e) separation of direct and global scene components, achieving significant speedups in performance. In all our methods, the projector receives a single image as input whereas the camera acquires a sequence of frames.
Augmented trochanteric bone mineral density after modified physical education classes: a randomized school-based exercise intervention study in prepubescent and early pubescent children.

Of the few exercise intervention studies focusing on pediatric populations, none have confined the intervention to the scheduled physical education curriculum.



To examine the effect of an 8-month school-based jumping program on the change in areal bone mineral density (aBMD), in grams per square centimeter, of healthy third- and fourth-grade children.



Ten elementary schools were randomized to exercise (n = 63) and control groups (n = 81). Exercise groups did 10 tuck jumps 3 times weekly and incorporated jumping, hopping, and skipping into twice weekly physical education classes. Control groups did regular physical education classes. At baseline and after 8 months of intervention, we measured aBMD and lean and fat mass by dual-energy x-ray absorptiometry (Hologic QDR-4500). Calcium intake, physical activity, and maturity were estimated by questionnaire.



The exercise group showed significantly greater change in femoral trochanteric aBMD (4.4% vs 3.2%; P <.05). There were no group differences at other sites. Results were similar after controlling for covariates (baseline aBMD change in height, change in lean, calcium, physical activity, sex, and ethnicity) in hierarchical regression.



An easily implemented school-based jumping intervention augments aBMD at the trochanteric region in the prepubertal and early pubertal skeleton.
Exercise treatment for depression: efficacy and dose response.

This study, conducted between 1998 and 2001 and analyzed in 2002 and 2003, was designed to test (1) whether exercise is an efficacious treatment for mild to moderate major depressive disorder (MDD), and (2) the dose-response relation of exercise and reduction in depressive symptoms.



The study was a randomized 2x2 factorial design, plus placebo control.



All exercise was performed in a supervised laboratory setting with adults (n =80) aged 20 to 45 years diagnosed with mild to moderate MDD.



Participants were randomized to one of four aerobic exercise treatment groups that varied total energy expenditure (7.0 kcal/kg/week or 17.5 kcal/kg/week) and frequency (3 days/week or 5 days/week) or to exercise placebo control (3 days/week flexibility exercise). The 17.5-kcal/kg/week dose is consistent with public health recommendations for physical activity and was termed "public health dose" (PHD). The 7.0-kcal/kg/week dose was termed "low dose" (LD).



The primary outcome was the score on the 17-item Hamilton Rating Scale for Depression (HRSD(17)).



The main effect of energy expenditure in reducing HRSD(17) scores at 12 weeks was significant. Adjusted mean HRSD(17) scores at 12 weeks were reduced 47% from baseline for PHD, compared with 30% for LD and 29% for control. There was no main effect of exercise frequency at 12 weeks.



Aerobic exercise at a dose consistent with public health recommendations is an effective treatment for MDD of mild to moderate severity. A lower dose is comparable to placebo effect.
Audio Sample Rate Conversion in FPGAs An efficient implementation of audio algorithms in programmable logic .

Today, even low-cost FPGAs provide far more computing power than DSPs. Current FPGAs have dedicated multipliers and even DSP multiply/accumulate (MAC) blocks that enable signals to be processed with clock speeds in excess of 550 MHz. Until now, however, these capabilities were rarely needed in audio signal processing. A serial implementation of an audio algorithm working in the kilohertz range uses exactly the same resources required for processing signals in the three-digit megahertz range. Consequently, programmable logic components such as PLDs or FPGAs are rarely used for processing low-frequency signals. After all, the parallel processing of mathematical operations in hardware is of no benefit when compared to an implementation based on classical DSPs; the sampling rates are so low that most serial DSP implementations are more than adequate. In fact, audio applications are characterized by such a high number of multiplications that they previously could
Systemic Complications of Complex Regional Pain Syndrome

Complex Regional Pain Syndrome (CRPS) is a neuropathic pain disorder that is characterized by: 1) Severe pain beyond the area of injury; 2) Autonomic dysregulation; 3) Neuropathic edema; 4) A movement disorder, atrophy and dystrophy. It is most often caused by a fracture, soft-tissue injury or surgical procedure and is divided into Type I, in which no nerve lesion is identified (classic reflex sympathetic dystrophy), and Type II where a specific nerve has been damaged (causalgia). In addition to the peripheral manifestations, there are many internal medical complications whose etiology is often not appreciated. This article will examine how CRPS affects the systems of: cognition; constitutional, cardiac, and respiratory complications; systemic autonomic dysregulation; neurogenic edema; musculoskeletal, endocrine and dermatological manifestations; as well as urological and gastrointestinal function.
Associations between white matter microstructure and infants' working memory

Working memory emerges in infancy and plays a privileged role in subsequent adaptive cognitive development. The neural networks important for the development of working memory during infancy remain unknown. We used diffusion tensor imaging (DTI) and deterministic fiber tracking to characterize the microstructure of white matter fiber bundles hypothesized to support working memory in 12-month-old infants (n=73). Here we show robust associations between infants' visuospatial working memory performance and microstructural characteristics of widespread white matter. Significant associations were found for white matter tracts that connect brain regions known to support working memory in older children and adults (genu, anterior and superior thalamic radiations, anterior cingulum, arcuate fasciculus, and the temporal-parietal segment). Better working memory scores were associated with higher FA and lower RD values in these selected white matter tracts. These tract-specific brain-behavior relationships accounted for a significant amount of individual variation above and beyond infants' gestational age and developmental level, as measured with the Mullen Scales of Early Learning. Working memory was not associated with global measures of brain volume, as expected, and few associations were found between working memory and control white matter tracts. To our knowledge, this study is among the first demonstrations of brain-behavior associations in infants using quantitative tractography. The ability to characterize subtle individual differences in infant brain development associated with complex cognitive functions holds promise for improving our understanding of normative development, biomarkers of risk, experience-dependent learning and neuro-cognitive periods of developmental plasticity.
Representation Learning of Temporal Dynamics for Skeleton-Based Action Recognition

Motion characteristics of human actions can be represented by the position variation of skeleton joints. Traditional approaches generally extract the spatial-temporal representation of the skeleton sequences with well-designed hand-crafted features. In this paper, in order to recognize actions according to the relative motion between the limbs and the trunk, we propose an end-to-end hierarchical RNN for skeleton-based action recognition. We divide human skeleton into five main parts in terms of the human physical structure, and then feed them to five independent subnets for local feature extraction. After the following hierarchical feature fusion and extraction from local to global, dimensions of the final temporal dynamics representations are reduced to the same number of action categories in the corresponding data set through a single-layer perceptron. In addition, the output of the perceptron is temporally accumulated as the input of a softmax layer for classification. Random scale and rotation transformations are employed to improve the robustness during training. We compare with five other deep RNN variants derived from our model in order to verify the effectiveness of the proposed network. In addition, we compare with several other methods on motion capture and Kinect data sets. Furthermore, we evaluate the robustness of our model trained with random scale and rotation transformations for a multiview problem. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.
Neural activations associated with feedback and retrieval success

There is substantial behavioral evidence for a phenomenon commonly called “the testing effect”, i.e. superior memory performance after repeated testing compared to re-study of to-be-learned materials. However, considerably less is known about the underlying neuro-cognitive processes that are involved in the initial testing phase, and thus underlies the actual testing effect. Here, we investigated functional brain activity related to test-enhanced learning with feedback. Subjects learned foreign vocabulary across three consecutive tests with correct-answer feedback. Functional brain-activity responses were analyzed in relation to retrieval and feedback events, respectively. Results revealed up-regulated activity in fronto-striatal regions during the first successful retrieval, followed by a marked reduction in activity as a function of improved learning. Whereas feedback improved behavioral performance across consecutive tests, feedback had a negligable role after the first successful retrieval for functional brain-activity modulations. It is suggested that the beneficial effects of test-enhanced learning is regulated by feedback-induced updating of memory representations, mediated via the striatum, that might underlie the stabilization of memory commonly seen in behavioral studies of the testing effect. Repeated testing with feedback shapes functional brain activity and strengthens new learning. A team led by Carola Wiklund-Hörnqvist at Umeå University, tracked brain activity in humans while being repeatedly tested with or without feedback. Higher hippocampal activity was found for feedback compared to no feedback, and learning from feedback was related to increased activity predominantly in the insula. For retrieval success, up-regulated activity in fronto-striatal regions was evident at the first successful retrieval with a marked decrease across consecutive tests. Those results indicate that while both feedback and retrieval success are key aspects that foster the “testing effect”; they operate at different functional levels in the brain. Future studies are needed to further explore the efficiency of test-enhanced learning in relation to individual variation in cognitive proficiency.
A class of binary burst error-correcting quasi-cyclic codes

A class of binary quasi-cyclic burst error-correcting codes based upon product codes is studied. An expression for the maximum burst error-correcting capability for each code in the class is given. In certain cases the codes reduce to Gilbert codes, which are cyclic. Often codes exist in the class which have the same block length and number of check bits as the Gilbert codes but correct longer bursts of errors than Gilbert codes. By shortening the codes, it is possible to design codes which achieve the Reiger bound.
Empathic concern and the effect of stories in human-robot interaction

People have been shown to project lifelike attributes onto robots and to display behavior indicative of empathy in human-robot interaction. Our work explores the role of empathy by examining how humans respond to a simple robotic object when asked to strike it. We measure the effects of lifelike movement and stories on people's hesitation to strike the robot, and we evaluate the relationship between hesitation and people's trait empathy. Our results show that people with a certain type of high trait empathy (empathic concern) hesitate to strike the robots. We also find that high empathic concern and hesitation are more strongly related for robots with stories. This suggests that high trait empathy increases people's hesitation to strike a robot, and that stories may positively influence their empathic responses.