nils-herrmann/clean_scirepeval huggingface dataset:
GenAx: A Genome Sequencing Accelerator

Genomics can transform health-care through precision medicine. Plummeting sequencing costs would soon make genome testing affordable to the masses. Compute efficiency, however, has to improve by orders of magnitude to sequence and analyze the raw genome data. Sequencing software used today can take several hundreds to thousands of CPU hours to align reads to a reference sequence. This paper presents GenAx, an accelerator for read alignment, a time-consuming step in genome sequencing. It consists of a seeding and seed-extension accelerator. The latter is based on an innovative automata design that was designed from the ground-up to enable hardware acceleration. Unlike conventional Levenshtein automata, it is string independent and scales quadratically with edit distance, instead of string length. It supports critical features commonly used in sequencing such as affine gap scoring and traceback. GenAx provides a throughput of 4,058K reads/s for Illumina 101 bp reads. GenAx achieves 31.7× speedup over the standard BWA-MEM sequence aligner running on a 56-thread dualsocket 14-core Xeon E5 server processor, while reducing power consumption by 12× and area by 5.6×.
Git Loss for Deep Face Recognition

Convolutional Neural Networks (CNNs) have been widely used in computer vision tasks, such as face recognition, and have achieved state-of-the-art results due to their ability to learn discriminative deep features. Conventionally, CNNs have been trained with Softmax as supervision signal to penalize the classification loss. In order to further enhance discriminative capability of deep features, we introduced a joint supervision signal, Git loss, which leverages on Softmax and Center loss functions. The aim of our loss function is to minimizes the intra-class variances as well as maximizes the interclass distances. Such minimization and maximization of deep features are considered ideal for face recognition task. Results obtained on two popular face recognition benchmarks datasets show that our proposed loss function achieves maximum separability between deep face features of different identities and achieves state-of-the-art accuracy on two major face recognition benchmark datasets: Labeled Faces in the Wild (LFW) and YouTube Faces (YTF).
Dense Pruning Sparsity Constraint Sparse Increase Model Capacity Re-Dense Dense

Modern deep neural networks have a large number of parameters, making them very hard to train. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance. In the first D (Dense) step, we train a dense network to learn connection weights and importance. In the S (Sparse) step, we regularize the network by pruning the unimportant connections with small weights and retraining the network given the sparsity constraint. In the final D (re-Dense) step, we increase the model capacity by removing the sparsity constraint, re-initialize the pruned parameters from zero and retrain the whole dense network. Experiments show that DSD training can improve the performance for a wide range of CNNs, RNNs and LSTMs on the tasks of image classification, caption generation and speech recognition. On ImageNet, DSD improved the Top1 accuracy of GoogLeNet by 1.1%, VGG-16 by 4.3%, ResNet-18 by 1.2% and ResNet-50 by 1.1%, respectively. On the WSJ’93 dataset, DSD improved DeepSpeech and DeepSpeech2 WER by 2.0% and 1.1%. On the Flickr-8K dataset, DSD improved the NeuralTalk BLEU score by over 1.7. DSD is easy to use in practice: at training time, DSD incurs only one extra hyper-parameter: the sparsity ratio in the S step. At testing time, DSD doesn’t change the network architecture or incur any inference overhead. The consistent and significant performance gain of DSD experiments shows the inadequacy of the current training methods for finding the best local optimum, while DSD effectively achieves superior optimization performance for finding a better solution. DSD models are available to download at https://songhan.github.io/DSD.
Electrophysiologic Insights Into Site of Atrioventricular Block: Lessons From Permanent His Bundle Pacing.

This study sought to report the feasibility of permanent His bundle pacing (HBP) in patients with advanced atrioventricular block (AVB) and electrophysiological observations into site of block in patients with infranodal AVB.



HBP is a physiological alternative to right ventricular pacing. Historic studies have reported a low incidence of intra-His AVB. Recent studies of permanent HBP reported limited success in patients with infranodal AVB.



Consecutive patients with advanced AVB underwent permanent HBP using Medtronic 3830 lead (Minneapolis, Minnesota) and a fixed-shaped catheter (C315 His). The HB was mapped using unipolar recording from the lead tip or by pace mapping. Success of HBP, type of AVB, and pacing outcomes were documented. Patients were followed at 2 weeks, 2 months, and then yearly.



A total of 100 patients with advanced AVB (age 75 ± 12 years; male 62%; AV nodal 46%; infranodal 54%) underwent permanent HBP. HBP was successful in 84 patients (84%; AV nodal 93%, infranodal 76%). Mean procedure time was 71 ± 21 min, mean fluoroscopy time was 11 ± 6 min. Baseline QRS duration was 122 ± 27 ms; paced QRSd was 124 ± 22 ms. The HB pacing threshold at implant, 2 weeks, 2 months, and last follow-up (19 ± 12 months; range: 6 to 46 months) was 1.3 ± 0.9 V, 1.6 ± 1.0 V, 1.6 ± 1.1 V, and 1.7 ± 1.0 V at 0.5 ms, respectively. Five patients required lead revision.



Permanent HBP was successful in 84% of unselected patients with AVB. His-Purkinje conduction could be normalized in 76% of patients with infranodal block, suggesting intra-His block. Incidence of infra-His AVB was low (24%) in this series. Routine HBP in patients with AVB is feasible and safe for at least up to 18 months.
A systematic review of the global prevalence of low back pain.

To perform a systematic review of the global prevalence of low back pain, and to examine the influence that case definition, prevalence period, and other variables have on prevalence.



We conducted a new systematic review of the global prevalence of low back pain that included general population studies published between 1980 and 2009. A total of 165 studies from 54 countries were identified. Of these, 64% had been published since the last comparable review.



Low back pain was shown to be a major problem throughout the world, with the highest prevalence among female individuals and those aged 40-80 years. After adjusting for methodologic variation, the mean ± SEM point prevalence was estimated to be 11.9 ± 2.0%, and the 1-month prevalence was estimated to be 23.2 ± 2.9%.



As the population ages, the global number of individuals with low back pain is likely to increase substantially over the coming decades. Investigators are encouraged to adopt recent recommendations for a standard definition of low back pain and to consult a recently developed tool for assessing the risk of bias of prevalence studies.
Craniopagus parasiticus. Everard Home's Two-Headed Boy of Bengal and some other cases.

Craniopagus parasiticus, or épicome, is a rare teratological type, of which only six cases have been recorded in the medical literature. It differs from craniopagus conjoined twins in that the body and limbs of the parasitic twin are underdeveloped, leaving in some cases only a parasitic head, inserted on the crown of the autositic twin. The first case of this malformation was Everard Home's famous Twin-Headed Boy of Bengal, whose skull is preserved at the Hunterian Museum. In this historical review, Home's case is presented in some detail, and the later cases are used to explain further some of its particulars.
Secure the Cloud: From the Perspective of a Service-Oriented Organization

In response to the revival of virtualized technology by Rosenblum and Garfinkel [2005], NIST defined cloud computing, a new paradigm in service computing infrastructures. In cloud environments, the basic security mechanism is ingrained in virtualization—that is, the execution of instructions at different privilege levels. Despite its obvious benefits, the caveat is that a crashed virtual machine (VM) is much harder to recover than a crashed workstation. When crashed, a VM is nothing but a giant corrupt binary file and quite unrecoverable by standard disk-based forensics. Therefore, VM crashes should be avoided at all costs. Security is one of the major contributors to such VM crashes. This includes compromising the hypervisor, cloud storage, images of VMs used infrequently, and remote cloud client used by the customer as well as threat from malicious insiders. Although using secure infrastructures such as private clouds alleviate several of these security problems, most cloud users end up using cheaper options such as third-party infrastructures (i.e., private clouds), thus a thorough discussion of all known security issues is pertinent. Hence, in this article, we discuss ongoing research in cloud security in order of the attack scenarios exploited most often in the cloud environment. We explore attack scenarios that call for securing the hypervisor, exploiting co-residency of VMs, VM image management, mitigating insider threats, securing storage in clouds, abusing lightweight software-as-a-service clients, and protecting data propagation in clouds. Wearing a practitioner's glasses, we explore the relevance of each attack scenario to a service company like Infosys. At the same time, we draw parallels between cloud security research and implementation of security solutions in the form of enterprise security suites for the cloud. We discuss the state of practice in the form of enterprise security suites that include cryptographic solutions, access control policies in the cloud, new techniques for attack detection, and security quality assurance in clouds.
Wideband Printed MIMO/Diversity Monopole Antenna for WiFi/WiMAX Applications

A novel printed diversity monopole antenna is presented for WiFi/WiMAX applications. The antenna comprises two crescent shaped radiators placed symmetrically with respect to a defected ground plane and a neutralization lines is connected between them to achieve good impedance matching and low mutual coupling. Theoretical and experimental characteristics are illustrated for this antenna, which achieves an impedance bandwidth of 54.5% (over 2.4-4.2 GHz), with a reflection coefficient <;-10 dB and mutual coupling <;-17 dB. An acceptable agreement is obtained for the computed and measured gain, radiation patterns, envelope correlation coefficient, and channel capacity loss. These characteristics demonstrate that the proposed antenna is an attractive candidate for multiple-input multiple-output portable or mobile devices.
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. There is much research that focuses on applied RL in RTS games, and novel advances are therefore anticipated in the not too distant future. However, there are to date few environments for testing RTS AIs. Environments in the literature are often either overly simplistic, such as microRTS, or complex and without the possibility for accelerated learning on consumer hardware like StarCraft II. This paper introduces the Deep RTS game environment for testing cutting-edge artificial intelligence algorithms for RTS games. Deep RTS is a high-performance RTS game made specifically for artificial intelligence research. It supports accelerated learning, meaning that it can learn at a magnitude of 50 000 times faster compared to existing RTS games. Deep RTS has a flexible configuration, enabling research in several different RTS scenarios, including partially observable state-spaces and map complexity. We show that Deep RTS lives up to our promises by comparing its performance with microRTS, ELF, and StarCraft II on high-end consumer hardware. Using Deep RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of the time. Deep RTS is publicly available at https://github.com/cair/DeepRTS.
The Object Image-Schema and Other Dependent Schemas

Image-schemas (e.g. CONTAINER, PATH, FORCE) are pervasive skeletal patterns of a preconceptual nature which arise from everyday bodily and social experiences and which enable us to mentally structure perceptions and events (Johnson 1987; Lakoff 1987, 1989). Within Cognitive Linguistics, these recurrent non-propositional models are taken to unify the different sensory and motor experiences in which they manifest themselves in a direct way and, most significantly, they may be metaphorically projected from the realm of the physical to other more abstract domains. In this paper, we intend to provide a cognitively plausible account of the OBJECT image-schema, which has received rather contradictory treatments in the literature. The OBJECT schema is experientially grounded in our everyday interaction with our own bodies and with other discrete entities. In the light of existence-related language (more specifically, linguistic expressions concerning the creation and destruction of both physical and abstract entities), it is argued that the OBJECT image-schema may be characterized as a basic image-schema, i.e. one that functions as a guideline for the activation of additional models, including other dependent image-schematic patterns (LINK, PART-WHOLE, CENTREPERIPHERY, etc.) which highlight various facets of the higher-level schema.
Robust Text Classification under Confounding Shift

As statistical classifiers become integrated into real-world applications, it is important to consider not only their accuracy but also their robustness to changes in the data distribution. Although identifying and controlling for confounding variables Z – correlated with both the input X of a classifier and its output Y – has been assiduously studied in empirical social science, it is often neglected in text classification. This can be understood by the fact that, if we assume that the impact of confounding variables does not change between the time we fit a model and the time we use it, then prediction accuracy should only be slightly affected. We show in this paper that this assumption often does not hold and that when the influence of a confounding variable changes from training time to prediction time (i.e. under confounding shift), the classifier accuracy can degrade rapidly. We use Pearl’s back-door adjustment as a predictive framework to develop a model robust to confounding shift under the condition that Z is observed at training time. Our approach does not make any causal conclusions but by experimenting on 6 datasets, we show that our approach is able to outperform baselines 1) in controlled cases where confounding shift is manually injected between fitting time and prediction time 2) in natural experiments where confounding shift appears either abruptly or gradually 3) in cases where there is one or multiple confounders. Finally, we discuss multiple issues we encountered during this research such as the effect of noise in the observation of Z and the importance of only controlling for confounding variables.
TrustZone-backed bitcoin wallet

With the increasing popularity of virtual currencies, it has become more important to have highly secure devices in which to store private-key information. Furthermore, ARM has made available an extension of processors architectures, designated TrustZone, which allows for the separation of trusted and non-trusted environments, while ensuring the integrity of the OS code. In this paper, we propose the exploitation of this technology to implement a flexible and reliable bitcoin wallet that is more resilient to dictionary and side-channel attacks. Making use of the TrustZone comes with the downside that writing and reading operations become slower, due to the encrypted storage, but we show that cryptographic operations can in fact be executed more efficiently as a result of platform-specific optimizations.
Automatic Measurement and Control System for Vehicle ECU Based on CAN Bus

This paper illustrates the development of the automatic measurement and control system (AMCS) based on control area network (CAN) bus for the engine electronic control unit (ECU). The system composes of the ECU, test ECU (TECU), simulation ECU (SIMU), usb-CAN communication module (CRU ) and AMCS software platform, which is applied for engine control, data collection, transmission, engine signals simulation and the data conversion from CAN to USB and master control simulation and measurement. The AMCS platform software is designed with VC++ 6.0.This system has been applied in the development of engine ECU widely.
Emerging Digital Technologies : Implications for Extended Conceptions of Cognition and Knowledge

Digital technologies play an increasingly important role in shaping the profile of human thought and action. In the few short decades since its invention, for example, the World Wide Web has transformed the way we shop, date, socialize and undertake scientific endeavours. We are also witnessing an unprecedented rate of technological innovation and change, driven, at least in part, by exponential rates of growth in computing power and performance. The technological landscape is thus a highly dynamic one – new technologies are being introduced all the time, and the rate of change looks set to continue unabated. In view of all this, it is natural to wonder about the effects of new technology on both ourselves and the societies in which we live.
Biennial Review of Pain From migraine genes to mechanisms

Migraine is a common multifactorial episodic brain disorder with strong genetic basis. Monogenic subtypes include rare familial hemiplegic migraine, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, familial advanced sleep-phase syndrome (FASPS), and retinal vasculopathy with cerebral leukodystrophy. Functional studies of diseasecausing mutations in cellular and/or transgenic models revealed enhanced (glutamatergic) neurotransmission and abnormal vascular function as key migraine mechanisms. Common forms of migraine (both with and without an aura), instead, are thought to have a polygenic makeup. Genome-wide association studies have already identified over a dozen genes involved in neuronal and vascular mechanisms. Here, we review the current state of molecular genetic research in migraine, also with respect to functional and pathway analyses.Wewill also discuss how novel experimental approaches for the identification and functional characterization of migraine genes, such as next-generation sequencing, induced pluripotent stem cell, and optogenetic technologies will further our understanding of the molecular pathways involved in migraine pathogenesis.
Andragogy and the Socratic Method : The Adult Learner Perspective

One of the teaching methods available to teachers in the ‘andragogic’ model of teaching is the method of ‘Socratic Seminars’. This is a teacher-directed form of instruction in which questions are used as the sole method of teaching, placing students in the position of having to recognise the limits of their knowledge, and hopefully, motivating them to learn. This paper aims at initiating the discussion on the strengths and drawbacks of this method. Based on empirical research, the paper suggests that the Socratic method seems to be a very effective method for teaching adult learners, but should be used with caution depending on the personality of the learners.
Historical Perspective of Traditional Indigenous Medical Practices: The Current Renaissance and Conservation of Herbal Resources

In recent years, increasing numbers of people have been choosing herbal medicines or products to improve their health conditions, either alone or in combination with others. Herbs are staging a comeback and herbal "renaissance" occurs all over the world. According to the World Health Organization, 75% of the world's populations are using herbs for basic healthcare needs. Since the dawn of mankind, in fact, the use of herbs/plants has offered an effective medicine for the treatment of illnesses. Moreover, many conventional/pharmaceutical drugs are derived directly from both nature and traditional remedies distributed around the world. Up to now, the practice of herbal medicine entails the use of more than 53,000 species, and a number of these are facing the threat of extinction due to overexploitation. This paper aims to provide a review of the history and status quo of Chinese, Indian, and Arabic herbal medicines in terms of their significant contribution to the health promotion in present-day over-populated and aging societies. Attention will be focused on the depletion of plant resources on earth in meeting the increasing demand for herbs.
iSports: A web-oriented expert system for talent identification in soccer

Nowadays soccer is the most practiced sport in the world and moves a multimillionaire market. Therefore, a club that is able to recruit and develop talented players to theirs fullest potential has a lot of advantages and economic benefits. However, in most clubs the players are selected through scouts and coaches recommendation, with predictive success based mostly on intuition than other objective criteria. In addition, it is known that talent development and identification is a multifactorial process involving many characteristics. To this end, this paper proposes the creation of performance indicators based on multivariate statistical analysis. Usual principal components and factor analysis are performed to construct physical, technical and general score and copula modeling is proposed to create the consistency index, which generalizes the Z score method. With these indicators, a web-oriented expert system for analyzing sport data in real time via R software is proposed as a powerful tool for talent identification in soccer. This system, the so called iSports, allows the monitoring and continuous comparison of athletes in a simple and efficient way, taking into account essentials aspects, as well as identifying candidate talented that have above the average performance, that is, who stand out from the studied population of soccer players. In order to promote and popularize the access of information and the statistical science applied in the sports context, the iSports system can be used in any training center of the country, impacting the increase of knowledge of the athletes in training phase at any school, city or region.
Look, Listen and Learn

We consider the question: what can be learnt by looking at and listening to a large number of unlabelled videos? There is a valuable, but so far untapped, source of information contained in the video itself – the correspondence between the visual and the audio streams, and we introduce a novel “Audio-Visual Correspondence” learning task that makes use of this. Training visual and audio networks from scratch, without any additional supervision other than the raw unconstrained videos themselves, is shown to successfully solve this task, and, more interestingly, result in good visual and audio representations. These features set the new state-of-the-art on two sound classification benchmarks, and perform on par with the state-of-the-art selfsupervised approaches on ImageNet classification. We also demonstrate that the network is able to localize objects in both modalities, as well as perform fine-grained recognition tasks.