nils-herrmann/clean_scirepeval huggingface dataset:
Inflammatory processes in muscle injury and repair.

Modified muscle use or injury can produce a stereotypic inflammatory response in which neutrophils rapidly invade, followed by macrophages. This inflammatory response coincides with muscle repair, regeneration, and growth, which involve activation and proliferation of satellite cells, followed by their terminal differentiation. Recent investigations have begun to explore the relationship between inflammatory cell functions and skeletal muscle injury and repair by using genetically modified animal models, antibody depletions of specific inflammatory cell populations, or expression profiling of inflamed muscle after injury. These studies have contributed to a complex picture in which inflammatory cells promote both injury and repair, through the combined actions of free radicals, growth factors, and chemokines. In this review, recent discoveries concerning the interactions between skeletal muscle and inflammatory cells are presented. New findings clearly show a role for neutrophils in promoting muscle damage soon after muscle injury or modified use. No direct evidence is yet available to show that neutrophils play a beneficial role in muscle repair or regeneration. Macrophages have also been shown capable of promoting muscle damage in vivo and in vitro through the release of free radicals, although other findings indicate that they may also play a role in muscle repair and regeneration through growth factors and cytokine-mediated signaling. However, this role for macrophages in muscle regeneration is still not definitive; other cells present in muscle can also produce the potentially regenerative factors, and it remains to be proven whether macrophage-derived factors are essential for muscle repair or regeneration in vivo. New evidence also shows that muscle cells can release positive and negative regulators of inflammatory cell invasion, and thereby play an active role in modulating the inflammatory process. In particular, muscle-derived nitric oxide can inhibit inflammatory cell invasion of healthy muscle and protect muscle from lysis by inflammatory cells in vivo and in vitro. On the other hand, muscle-derived cytokines can signal for inflammatory cell invasion, at least in vitro. The immediate challenge for advancing our current understanding of the relationships between muscle and inflammatory cells during muscle injury and repair is to place what has been learned in vitro into the complex and dynamic in vivo environment.
On the Feasibility of Real-Time 3D Hand Tracking using Edge GPGPU Acceleration

This paper presents the case study of a non-intrusive porting of a monolithic C++ library for real-time 3D hand tracking, to the domain of edge-based computation. Towards a proof of concept, the case study considers a pair of workstations, a computationally powerful and a computationallyweak one. Bywrapping the C++ library in Java container and by capitalizing on a Java-based offloading infrastructure that supports both CPU and GPGPU computations, we are able to establish automatically the required serverclient workflow that best addresses the resource allocation problem in the effort to execute from the weak workstation. As a result, the weak workstation can perform well at the task, despite lacking the sufficient hardware to do the required computations locally. This is achieved by offloading computations which rely on GPGPU, to the powerful workstation, across the network that connects them. We show the edge-based computation challenges associated with the information flow of the ported algorithm, demonstrate how we cope with them, and identify what needs to be improved for achieving even better performance.
Understanding Convolutional Neural Networks for Text Classification

We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text. CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery. We aim to understand the method by which the networks process and classify text. We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors. We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest. Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions).
Rise of Augmented Reality : Current and Future Application Areas

The massive technological advancements around the world have created significant challenging competition among companies where each of the companies tries to attract the customers using different techniques. One of the recent techniques is Augmented Reality (AR). The AR is a new technology which is capable of presenting possibilities that are difficult for other technologies to offer and meet. Nowadays, numerous augmented reality applications have been used in the industry of different kinds and disseminated all over the world. AR will really alter the way individuals view the world. The AR is yet in its initial phases of research and development at different colleges and high-tech institutes. Throughout the last years, AR apps became transportable and generally available on various devices. Besides, AR begins to occupy its place in our audio-visual media and to be used in various fields in our life in tangible and exciting ways such as news, sports and is used in many domains in our life such as electronic commerce, promotion, design, and business. In addition, AR is used to facilitate the learning whereas it enables students to access location-specific information provided through various sources. Such growth and spread of AR applications pushes organizations to compete one another, and every one of them exerts its best to gain the customers. This paper provides a comprehensive study of AR including its history, architecture, applications, current challenges and future trends.
Integrating blockchain for data sharing and collaboration in mobile healthcare applications

Enabled by mobile and wearable technology, personal health data delivers immense and increasing value for healthcare, benefiting both care providers and medical research. The secure and convenient sharing of personal health data is crucial to the improvement of the interaction and collaboration of the healthcare industry. Faced with the potential privacy issues and vulnerabilities existing in current personal health data storage and sharing systems, as well as the concept of self-sovereign data ownership, we propose an innovative user-centric health data sharing solution by utilizing a decentralized and permissioned blockchain to protect privacy using channel formation scheme and enhance the identity management using the membership service supported by the blockchain. A mobile application is deployed to collect health data from personal wearable devices, manual input, and medical devices, and synchronize data to the cloud for data sharing with healthcare providers and health insurance companies. To preserve the integrity of health data, within each record, a proof of integrity and validation is permanently retrievable from cloud database and is anchored to the blockchain network. Moreover, for scalable and performance considerations, we adopt a tree-based data processing and batching method to handle large data sets of personal health data collected and uploaded by the mobile platform.
Combining Word2Vec with Revised Vector Space Model for Better Code Retrieval

API example code search is an important applicationin software engineering. Traditional approaches to API codesearch are based on information retrieval. Recent advance inWord2Vec has been applied to support the retrieval of APIexamples. In this work, we perform a preliminary study thatcombining traditional IR with Word2Vec achieves better retrievalaccuracy. More experiments need to be done to study differenttypes of combination among two lines of approaches.
HoME: a Household Multimodal Environment

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, soundbased navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.
Virtual, Augmented, and Mixed Realities in Education

Virtual reality (VR) typically results in the illusion of presence. The participant in a VR scenario typically has the illusion of being in the virtual place, and under the right conditions the further illusion that events that are occurring there are really occurring. We review how these properties are useful for the application of VR in education. We present a further illusion that can be triggered in VR referred to as body ownership. This can occur when the participant sees a life-sized virtual body substituting her or his own, from first person perspective. This virtual body can be programmed to move synchronously with the participant’s real body movements, thus leading to the perceptual illusion that the virtual body is her or his actual body. We survey various experiments that show that the form of the virtual body can result in implicit changes in attitudes, perception and cognition, and changes in behavior. We compare this with the process of implicit learning and conclude that virtual body ownership and its consequences may be used as a form of implicit learning. We conclude by suggesting how the study of the relationship between body ownership and implicit learning might be taken forward.
Addendum to the AAPM's TG-51 protocol for clinical reference dosimetry of high-energy photon beams.

An addendum to the AAPM's TG-51 protocol for the determination of absorbed dose to water in megavoltage photon beams is presented. This addendum continues the procedure laid out in TG-51 but new kQ data for photon beams, based on Monte Carlo simulations, are presented and recommendations are given to improve the accuracy and consistency of the protocol's implementation. The components of the uncertainty budget in determining absorbed dose to water at the reference point are introduced and the magnitude of each component discussed. Finally, the consistency of experimental determination of ND,w coefficients is discussed. It is expected that the implementation of this addendum will be straightforward, assuming that the user is already familiar with TG-51. The changes introduced by this report are generally minor, although new recommendations could result in procedural changes for individual users. It is expected that the effort on the medical physicist's part to implement this addendum will not be significant and could be done as part of the annual linac calibration.
Exploring the association between cannabis use and depression.

To examine the evidence on the association between cannabis and depression and evaluate competing explanations of the association.



A search of Medline, Psychinfo and EMBASE databases was conducted. All references in which the terms 'cannabis', 'marijuana' or 'cannabinoid', and in which the words 'depression/depressive disorder/depressed', 'mood', 'mood disorder' or 'dysthymia' were collected. Only research studies were reviewed. Case reports are not discussed.



There was a modest association between heavy or problematic cannabis use and depression in cohort studies and well-designed cross-sectional studies in the general population. Little evidence was found for an association between depression and infrequent cannabis use. A number of studies found a modest association between early-onset, regular cannabis use and later depression, which persisted after controlling for potential confounding variables. There was little evidence of an increased risk of later cannabis use among people with depression and hence little support for the self-medication hypothesis. There have been a limited number of studies that have controlled for potential confounding variables in the association between heavy cannabis use and depression. These have found that the risk is much reduced by statistical control but a modest relationship remains.



Heavy cannabis use and depression are associated and evidence from longitudinal studies suggests that heavy cannabis use may increase depressive symptoms among some users. It is still too early, however, to rule out the hypothesis that the association is due to common social, family and contextual factors that increase risks of both heavy cannabis use and depression. Longitudinal studies and studies of twins discordant for heavy cannabis use and depression are needed to rule out common causes. If the relationship is causal, then on current patterns of cannabis use in the most developed societies cannabis use makes, at most, a modest contribution to the population prevalence of depression.
Improving model-based RL with Adaptive Rollout using Uncertainty Estimation

Recently, incorporating a learned dynamic model in generating imagined data has been shown to be an effective way to reduce sample-complexity of model-free RL. Such model-free/model-based hybrid approaches usually require rolling out the dynamic model a fixed number of steps into the future. We argue that such fixed rollout is problematic for several reasons. We propose a simple adaptive rollout algorithm to improve the model-based component of these approaches and conduct experiment on CartPole task to evaluate the effects of adaptive rollout.
Relaxed Quantization for Discretized Neural Networks

Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks that can be effectively discretized without loss of performance, we introduce a differentiable quantization procedure. Differentiability can be achieved by transforming continuous distributions over the weights and activations of the network to categorical distributions over the quantization grid. These are subsequently relaxed to continuous surrogates that can allow for efficient gradient-based optimization. We further show that stochastic rounding can be seen as a special case of the proposed approach and that under this formulation the quantization grid itself can also be optimized with gradient descent. We experimentally validate the performance of our method on MNIST, CIFAR 10 and Imagenet classification.
Molecular Characterization of Clinical Isolates of Aeromonas Species from Malaysia

Aeromonas species are common inhabitants of aquatic environments giving rise to infections in both fish and humans. Identification of aeromonads to the species level is problematic and complex due to their phenotypic and genotypic heterogeneity.



Aeromonas hydrophila or Aeromonas sp were genetically re-identified using a combination of previously published methods targeting GCAT, 16S rDNA and rpoD genes. Characterization based on the genus specific GCAT-PCR showed that 94 (96%) of the 98 strains belonged to the genus Aeromonas. Considering the patterns obtained for the 94 isolates with the 16S rDNA-RFLP identification method, 3 clusters were recognised, i.e. A. caviae (61%), A. hydrophila (17%) and an unknown group (22%) with atypical RFLP restriction patterns. However, the phylogenetic tree constructed with the obtained rpoD sequences showed that 47 strains (50%) clustered with the sequence of the type strain of A. aquariorum, 18 (19%) with A. caviae, 16 (17%) with A. hydrophila, 12 (13%) with A. veronii and one strain (1%) with the type strain of A. trota. PCR investigation revealed the presence of 10 virulence genes in the 94 isolates as: lip (91%), exu (87%), ela (86%), alt (79%), ser (77%), fla (74%), aer (72%), act (43%), aexT (24%) and ast (23%).



This study emphasizes the importance of using more than one method for the correct identification of Aeromonas strains. The sequences of the rpoD gene enabled the unambiguous identication of the 94 Aeromonas isolates in accordance with results of other recent studies. Aeromonas aquariorum showed to be the most prevalent species (50%) containing an important subset of virulence genes lip/alt/ser/fla/aer. Different combinations of the virulence genes present in the isolates indicate their probable role in the pathogenesis of Aeromonas infections.
Virtual reality simulation in neurosurgery: technologies and evolution.

Neurosurgeons are faced with the challenge of learning, planning, and performing increasingly complex surgical procedures in which there is little room for error. With improvements in computational power and advances in visual and haptic display technologies, virtual surgical environments can now offer potential benefits for surgical training, planning, and rehearsal in a safe, simulated setting. This article introduces the various classes of surgical simulators and their respective purposes through a brief survey of representative simulation systems in the context of neurosurgery. Many technical challenges currently limit the application of virtual surgical environments. Although we cannot yet expect a digital patient to be indistinguishable from reality, new developments in computational methods and related technology bring us closer every day. We recognize that the design and implementation of an immersive virtual reality surgical simulator require expert knowledge from many disciplines. This article highlights a selection of recent developments in research areas related to virtual reality simulation, including anatomic modeling, computer graphics and visualization, haptics, and physics simulation, and discusses their implication for the simulation of neurosurgery.
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud

Inference using deep neural networks is often outsourced to the cloud since it is a computationally demanding task. However, this raises a fundamental issue of trust. How can a client be sure that the cloud has performed inference correctly? A lazy cloud provider might use a simpler but less accurate model to reduce its own computational load, or worse, maliciously modify the inference results sent to the client. We propose SafetyNets, a framework that enables an untrusted server (the cloud) to provide a client with a short mathematical proof of the correctness of inference tasks that they perform on behalf of the client. Specifically, SafetyNets develops and implements a specialized interactive proof (IP) protocol for verifiable execution of a class of deep neural networks, i.e., those that can be represented as arithmetic circuits. Our empirical results on threeand four-layer deep neural networks demonstrate the run-time costs of SafetyNets for both the client and server are low. SafetyNets detects any incorrect computations of the neural network by the untrusted server with high probability, while achieving state-of-the-art accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition tasks (75.22%).
Factors that Influence Customers’ Buying Intention on Shopping Online

On-line commerce through Internet is gaining attention from students today. The aim of this research is to study the factors influencing student’s buying intention through internet shopping in an institution of higher learning in Malaysia. Several factors such as usefulness, ease of use, compatibility, privacy, security, normative-beliefs and attitude that influence student’s buying intention were analyzed. Respondents who were selected are studying in a public institution of higher learning in Penang, Malaysia. Based on theory of reasoned action (TRA), the technology acceptance model (TAM) concluded that there are two salient beliefs which are ease of use and usefulness. This theory has been applied on the study to adopt technology user different and has been emerged as a model in investigation to increase predictive power. Such theory was used in this study to explain students’ buying intention on-line. Besides the ease of use and usefulness, others factors such as: compatibility, privacy, security, normative beliefs and self-efficacy are utilized at this TAM. The results support seven hypotheses from nine. Compatibility, usefulness, ease of use and security has been found to be important predictors toward attitude in on-line shopping.
LSTM-CRF for Drug-Named Entity Recognition

Drug-Named Entity Recognition (DNER) for biomedical literature is a fundamental facilitator of Information Extraction. For this reason, the DDIExtraction2011 (DDI2011) and DDIExtraction2013 (DDI2013) challenge introduced one task aiming at recognition of drug names. State-of-the-art DNER approaches heavily rely on hand-engineered features and domain-specific knowledge which are difficult to collect and define. Therefore, we offer an automatic exploring words and characters level features approach: a recurrent neural network using bidirectional long short-term memory (LSTM) with Conditional Random Fields decoding (LSTM-CRF). Two kinds of word representations are used in this work: word embedding, which is trained from a large amount of text, and character-based representation, which can capture orthographic feature of words. Experimental results on the DDI2011 and DDI2013 dataset show the effect of the proposed LSTM-CRF method. Our method outperforms the best system in the DDI2013 challenge.
Generative replay with feedback connections as a general strategy for continual learning

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning problematic. Recently, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more meaningful comparisons, we identified three distinct continual learning scenarios based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as “soft targets”) achieved superior performance in all three scenarios. In addition, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback connections. This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance. We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.