nils-herrmann/clean_scirepeval huggingface dataset:
Symmetry-aware Sparse Voxel DAGs (SSVDAGs) for compression-domain tracing of high-resolution geometric scenes

Voxelized representations of complex 3D scenes are widely used to accelerate visibility queries in many GPU rendering techniques. Since GPU memory is limited, it is important that these data structures can be kept within a strict memory budget. Recently, directed acyclic graphs (DAGs) have been successfully introduced to compress sparse voxel octrees (SVOs), but they are limited to sharing identical regions of space. In this paper, we show that a more efficient lossless compression of geometry can be achieved while keeping the same visibility-query performance. This is accomplished by merging subtrees that are identical through a similarity transform and by exploiting the skewed distribution of references to shared nodes to store child pointers using a variabile bit-rate encoding. We also describe how, by selecting plane reflections along the main grid directions as symmetry transforms, we can construct highly compressed GPU-friendly structures using a fully out-of-core method. Our results demonstrate that state-of-the-art compression and real-time tracing performance can be achieved on highresolution voxelized representations of real-world scenes of very different characteristics,
Combination of Texture and Geometric Features for Age Estimation in Face Images

Automatic age estimation from facial images has recently received an increasing interest due to a variety of applications, such as surveillance, human-computer interaction, forensics, and recommendation systems. Despite such advances, age estimation remains an open problem due to several challenges associated with the aging process. In this work, we develop and analyze an automatic age estimation method from face images based on a combination of textural and geometric features. Experiments are conducted on the Adience dataset (Adience Benchmark, 2017; Eidinger et al., 2014), a large known benchmark used to evaluate both age and gender classification approaches.
Value creation in web services: An integrative model

Web services are redefining the way organizations exchange business-critical information internally and externally with customers, suppliers, and other business associates. In this paper, we develop an integrative model for understanding value creation in web services from a provider’s perspective. The model integrates the static representation of conventional business values with the fluidity of the emergent IT services domain. It captures the complexity and contradictions facing Web services providers in their drive towards market leadership, strategic differentiation and revenue generation from web services. The model comprises twelve propositions to guide our understanding and future research and practice in this increasingly important segment in the IT field.
Net2Vec: Deep Learning for the Network

We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network. Net2Vec is able to capture data from the network at more than 60Gbps, transform it into meaningful tuples and apply predictions over the tuples in real time. This platform can be used for different purposes ranging from traffic classification to network performance analysis.
 Finally, we showcase the use of Net2Vec by implementing and testing a solution able to profile network users at line rate using traces coming from a real network. We show that the use of deep learning for this case outperforms the baseline method both in terms of accuracy and performance.
Training Student Networks for Acceleration with Conditional Adversarial Networks

There is an increasing interest on accelerating neural networks for real-time applications. We study the student-teacher strategy, in which a small and fast student network is trained with the auxiliary information learned from a large and accurate teacher network. We propose to use conditional adversarial networks to learn the loss function to transfer knowledge from teacher to student. The proposed method is particularly effective for relatively small student networks. Moreover, experimental results show the effect of network size when the modern networks are used as student. We empirically study the trade-off between inference time and classification accuracy, and provide suggestions on choosing a proper student network.
Band-Notched Small Square-Ring Antenna With a Pair of T-Shaped Strips Protruded Inside the Square Ring for UWB Applications

A novel printed monopole antenna with constant gain over a wide bandwidth for ultra wideband applications with desired notch-band characteristic is presented. The proposed antenna consists of a square-ring radiating patch with a pair of T-shaped strips protruded inside the square ring and a coupled T-shaped strip and a ground plane with a protruded strip, which provides a wide us able fractional bandwidth of more than 130% (3.07-14.6 GHz). By using the square-ring radiating patch with a pair of T-shaped strips protruded inside it, the frequency bandstop performance is generated, and we can control its characteristics such as band-notch frequency and its bandwidth by electromagnetically adjusting coupling between a pair of T-shaped strips protruded inside the square ring. The designed antenna has a small size of 12 × 18 mm2, or about 0.15λ × 0.25λ at 4.2 GHz, while showing the band-rejection performance in the frequency band of 5.05-5.95 GHz.
A BIOMECHANICAL ANALYSIS OF FRONT VERSUS BACK SQUAT: INJURY IMPLICATIONS

The aim of this study was to examine the differences in trunk and lower limb kinematics between the front and back squat. 2D kinematic data was collected as participants (n = 12) completed three repetitions of both front and back squat exercises at 50 % of their back squat one repetition maximum. Stance width was standardised at 107(±10) % of biacromial breadth. The Wilcoxon signed ranks test was used to examine significant differences in dependent variables between both techniques. Results showed that the back squat exhibited a significantly greater trunk lean than the front squat throughout (p < 0.05) with no differences occurring in knee joint kinematics. The results of this study in conjunction with other squat related literature (Russell et al., 1989) suggest that the back squat gives rise to an increased risk of lower back injury.
OpenStreetMap for the disaster management in Bangladesh

OpenStreetMap (OSM) is a world-wide campaign for developing open source maps. The prospect of such open source maps is very bright in commercial, educational and political points of views. With the burgeoning growth of mobile phones, the necessity of location based services is increasing day by day. Starting from the 3G mobile applications to cheap SMS-based services, we need maps everywhere. Various applications of digital maps are also seen in different national and international issues. Hence, OSM has been growing at a rapid pace in different parts of the world. However, it is a bit challenging task to develop this sort of voluntary efforts in the developing third world countries, like Bangladesh. In this paper, we discuss the possible fields of disaster responses in Bangladesh where OSM can play a vital role. We also discuss about the problems we face while developing OSM for this region. Finally, we talk about a number of ways which can be instrumental while trying to make free open source maps avoiding all the difficulties.
Integrative Neuromuscular Training in Youth Athletes. Part II

THE SECOND PART OF THIS REVIEW PROVIDES A FLEXIBLE APPROACH TO INTEGRATIVE NEUROMUSCULAR TRAINING (INT) WITH THE GOALS TO IMPROVE INJURY RESILIENCE AND TO ENHANCE SPORT AND MOTOR PERFORMANCE ABILITIES IN YOUTH POPULATIONS. THE PROPOSED MODEL OF INT IN THIS MANUSCRIPT PRESENTS 6 ESSENTIAL COMPONENTS: DYNAMIC STABILITY (LOWER LIMB AND CORE), STRENGTH, PLYOMETRICS, COORDINATION, SPEED AND AGILITY, AND FATIGUE RESISTANCE. THE DEVELOPMENT OF THESE 6 CAPACITIES ARE INTEGRAL IN ESTABLISHING AN IMPORTANT FOUNDATION BY INITIALLY DEVELOPING FUNDAMENTAL MOVEMENT SKILL COMPETENCY BEFORE BUILDING UPON THESE SKILLS TO ENRICH SPORTS-SPECIFIC AND ACTIVITY-SPECIFIC SKILL SETS. FOR A VIDEO ABSTRACT OF THIS ARTICLE, SEE SUPPLEMENTAL DIGITAL CONTENT 1 (SEE VIDEO, http://links.lww.com/SCJ/A190).
A single nutrient feed supports both chemically defined NS0 and CHO fed-batch processes: Improved productivity and lactate metabolism.

A chemically defined nutrient feed (CDF) coupled with basal medium preloading was developed to replace a hydrolysate-containing feed (HCF) for a fed-batch NS0 process. The CDF not only enabled a completely chemically defined process but also increased recombinant monoclonal antibody titer by 115%. Subsequent tests of CDF in a CHO process indicated that it could also replace the hydrolysate-containing nutrient feed in this expression system as well as providing an 80% increase in product titer. In both CDF NS0 and CHO processes, the peak lactate concentrations were lower and, more interestingly, lactate metabolism shifted markedly from net production to net consumption when cells transitioned from exponential to stationary growth phase. Subsequent investigations of the lactate metabolic shift in the CHO CDF process were carried out to identify the cause(s) of the metabolic shift. These investigations revealed several metabolic features of the CHO cell line that we studied. First, glucose consumption and lactate consumption are strictly complementary to each other. The combined cell specific glucose and lactate consumption rate was a constant across exponential and stationary growth phases. Second, Lactate dehydrogenase (LDH) activity fluctuated during the fed-batch process. LDH activity was at the lowest when lactate concentration started to decrease. Third, a steep cross plasma membrane glucose gradient exists. Intracellular glucose concentration was more than two orders of magnitude lower than that in the medium. Fourth, a large quantity of citrate was diverted out of mitochondria to the medium, suggesting a partially truncated tricarboxylic acid (TCA) cycle in CHO cells. Finally, other intermediates in or linked to the glycolytic pathway and the TCA cycle, which include alanine, citrate, isocitrate, and succinate, demonstrated a metabolic shift similar to that of lactate. Interestingly, all these metabolites are either in or linked to the pathway downstream of pyruvate, but upstream of fumarate in glucose metabolism. Although the specific mechanisms for the metabolic shift of lactate and other metabolites remain to be elucidated, the increased understanding of the metabolism of CHO cultures could lead to future improvements in medium and process development.
Automatic Tempo-based Classification of Argentine Tango Music

Tempo-based classification is a popular technique to classify music, with applications in media-art projects, systematic study of music etc. The success of this technique using dance music genres has typically been found to be over 70%, yet it was mostly evaluated using standardized music genres, such as ballroom music. Argentine tango is a music genre, originating in Buenos Aires at the end of the 19th century, which has gained a lot of popularity since its creation and is currently being danced in social settings all over the world (see side Photo taken in San Francisco, courtesy of Ref. ). There are three music types which define the Argentine tango played in social settings; although these types have different meters from one another, their tempi feature variations influenced by the trends of the era of composition and the composersʼ personal style. Therefore, Argentine tango seemingly makes a good, yet interesting candidate to explore for tempo-based classification schemes. In this report, we construct and evaluate an automatic, bayes tempo classifier with application to Argentine tango music, using a library with more than 2,000 pieces. We find that the accuracy of our classifier stands at 79%, which is well within the bound set by similar schemes applied to ballroom music, proving our original point that tempo is a promising feature in the classification of Argentine tango. We also come up with interesting insights about how to improve classification schemes for this particular musical genre.
Approximate Computing for Long Short Term Memory (LSTM) Neural Networks

Long Short Term Memory (LSTM) networks are a class of recurrent neural networks that are widely used for machine learning tasks involving sequences, including machine translation, text generation, and speech recognition. Large-scale LSTMs, which are deployed in many real-world applications, are highly compute intensive. To address this challenge, we propose AxLSTM, an application of approximate computing to improve the execution efficiency of LSTMs. An LSTM is composed of cells, each of which contains a cell state along with multiple gating units that control the addition and removal of information from the state. The LSTM execution proceeds in timesteps, with a new symbol of the input sequence processed at each timestep. AxLSTM consists of two techniques—Dynamic Timestep Skipping (DTS) and Dynamic State Reduction (DSR). DTS identifies, at runtime, input symbols that are likely to have little or no impact on the cell state and skips evaluating the corresponding timesteps. In contrast, DSR reduces the size of the cell state in accordance with the complexity of the input sequence, leading to a reduced number of computations per timestep. We describe how AxLSTM can be applied to the most common application of LSTMs, viz., sequence-to-sequence learning. We implement AxLSTM within the TensorFlow deep learning framework and evaluate it on 3 state-of-the-art sequence-to-sequence models. On a 2.7 GHz Intel Xeon server with 128 GB memory and 32 processor cores, AxLSTM achieves  $ {1.08\times -1.31 \times }$  speedups with minimal loss in quality, and  $ {1.12 \times -1.37 \times }$  speedups when moderate reductions in quality are acceptable.
When Is It Too Early for Single Sport Specialization?

Over the past 15 years, there has been an increase in youth sports participation with a concomitant increase in early year-round training in a single sport. Many factors contribute to the desire of parents and coaches to encourage early single sport specialization, including the desire to give the young athlete an edge in competition, pursuit of scholarships, and potential professional status, and the ability to label a young athlete as elite at an early age. Despite these perceived advantages, some data suggest that early sport specialization does not lead to a competitive advantage over athletes who participate in multiple sports. Although the data are limited, there is some evidence that early sport specialization may put the young athlete at risk for overuse injuries. The focus of this review is to highlight the evidence regarding early sport specialization and risk for injury; discuss the risk factors for overuse injury in high-risk sports including ice hockey, swimming, gymnastics, and baseball; and discuss future potential research that would help define the risk of injury for young athletes who participate in early sport specialization.
Social Crowdsourcing to Friends: An Incentive Mechanism for Multi-Resource Sharing

In this paper, we propose a novel game-based incentive mechanism for multi-resource sharing, where users are motivated to share their idle resources in view of conditional voluntary. Through social networking service platforms, such a crowdsourcing service fully explores the significant influence and computing potential of mobile social networks. Specifically, a combination of task allocation process, profit transfer process, and reputation updating process are involved in this sharing incentive mechanism, satisfying truthfulness, individual rationality, and robustness. To maintain the social fairness-efficiency tradeoff, we further develop a resource sharing algorithm on the basis of dominant resource fairness, revealing that the sacrifice of fairness properties is necessary for the improvement of efficiency. Real-world traces from Facebook are numerically studied, validating social fairness and efficiency of our social crowdsourcing mechanism.
Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes

Given a topic of interest, a contrastive theme is a group of opposing pairs of viewpoints. We address the task of summarizing contrastive themes: given a set of opinionated documents, select meaningful sentences to represent contrastive themes present in those documents. Several factors make this a challenging problem: unknown numbers of topics, unknown relationships among topics, and the extraction of comparative sentences. Our approach has three core ingredients: contrastive theme modeling, diverse theme extraction, and contrastive theme summarization. Specifically, we present a hierarchical non-parametric model to describe hierarchical relations among topics; this model is used to infer threads of topics as themes from the nested Chinese restaurant process. We enhance the diversity of themes by using structured determinantal point processes for selecting a set of diverse themes with high quality. Finally, we pair contrastive themes and employ an iterative optimization algorithm to select sentences, explicitly considering contrast, relevance, and diversity. Experiments on three datasets demonstrate the effectiveness of our method.
Virtual reality exposure therapy of anxiety disorders: a review.

Virtual reality exposure therapy (VRET) is an altered form of behavioral therapy and may be a possible alternative to standard in vivo exposure. Virtual reality integrates real-time computer graphics, body tracking devices, visual displays, and other sensory input devices to immerse patients in a computer-generated virtual environment. Research on this type of treatment for anxiety disorders is discussed in this article, and the mediating and moderating variables that influence VR treatment effectiveness as well. Evidence is found that VRET is effective for participants with fear of heights and of flying. For other phobias, research to date is not conclusive. More randomized clinical trials in which VRET is compared with standard exposure are required. Furthermore, studies are needed in which VRET is not just a component of the treatment package evaluated, but in which VRET should be assessed as a stand-alone treatment.
Deep Learning for Fixed Model Reuse

Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and videos. There are several studies have the sense of model reuse, by trying to reuse pre-trained deep networks architectures or deep model features to train a new deep model. They, however, neglect the fact that there are many other fixed models or features available. In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse). FMR utilizes the learning power of deep models to implicitly grab the useful discriminative information from fixed model/features that have been widely used in general tasks. We firstly arrange the convolution layers of a deep network and the provided fixed model/features in parallel, fully connecting to the output layer nodes. Then, the dependencies between the output layer nodes and the fixed model/features are knockdown such that only the raw feature inputs are needed when the model is being used for testing, though the helpful information in the fixed model/features have already been incorporated into the model. On one hand, by the FMR scheme, the required amount of training data can be significantly reduced because of the reuse of fixed model/features. On the other hand, the fixed model/features are not explicitly used in testing, and thus, the scheme can be quite useful in applications where the fixed model/features are protected by patents or commercial secrets. Experiments on five real-world datasets validate the effectiveness of FMR compared with state-of-the-art deep methods.
Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration

In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver-following algorithms, the first of which utilizes both spatialand frequency-domain features pertaining to human swimming patterns in order to visually track a diver. The second algorithm uses a convolutional neural network-based model for robust tracking-by-detection. In addition, we propose a hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition; subsequently, a finite-state machine performs robust and efficient gesture-to-instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through extensive field experiments in closedand open-water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to existing methods.
DEEP-SEE FACE: A Mobile Face Recognition System Dedicated to Visually Impaired People

In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.
Reconfigurable quarter-mode SIW antenna employing a fluidically switchable via

A new method to fluidically switch through via posts is proposed for the first time. The reconfigurable via post is used toward designing a switchable QMSIW antenna. The switching method is based on filling/emptying a non-plated via hole with/from a liquid metal alloy. A QMSIW antenna is designed to initially work at ~3.2 GHz. Connecting the fluidically switchable via post at the corner of the QMSIW antenna shifts up the operating frequency. This translates into a switching range of 3.2-4.7 GHz. The Polydimethylsiloxane (PDMS) structures including the micro-channels are bonded to the QMSIW circuit board using a unique fabrication technique.