nils-herrmann/clean_scirepeval huggingface dataset:
Generalized Graph Convolutional Networks for Skeleton-based Action Recognition

With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skeletons based on RNN or CNN, which has limited expressive power for irregular joints. In this paper, we represent skeletons naturally on graphs and propose a generalized graph convolutional neural networks (GGCN) for skeleton-based action recognition, aiming to capture space-time variation via spectral graph theory. In particular, we construct a generalized graph over consecutive frames, where each joint is not only connected to its neighboring joints in the same frame strongly or weakly, but also linked with relevant joints in the previous and subsequent frames. The generalized graphs are then fed into GGCN along with the coordinate matrix of the skeleton sequence for feature learning, where we deploy high-order and fast Chebyshev approximation of spectral graph convolution in the network. Experiments show that we achieve the state-of-the-art performance on the widely used NTU RGB+D, UT-Kinect and SYSU 3D datasets.
Discourse and Information Structure

In addition to deciding what to say, s p e a k ers must decide how t o s a y it. The central premise of studies on the relationship between syntax and discourse function is that a speaker's use of a particular structural option is constrained by s p e-ciic aspects of the context of utterance. Work in discourse has uncovered a variety of speciic discourse functions served by individual syntactic constructions. 1 More recently, in Birner & Ward 1998 we examine generalizations that apply across constructions, identifying ways in which a given functional principle is variously realized in similar but distinct constructions. 1 We use the term`construction' in the conventional sense, to refer to each of the various grammatical conngurations of constituents within a particular language. See Fillmore 1988, Prince 1994, and Goldberg 1995, inter alia, for alternative views of what constitutes a linguistic construction.
A Novel Shadow-Free Feature Extractor for Real-Time Road Detection

Road detection is one of the most important research areas in driver assistance and automated driving field. However, the performance of existing methods is still unsatisfactory, especially in severe shadow conditions. To overcome those difficulties, first we propose a novel shadow-free feature extractor based on the color distribution of road surface pixels. Then we present a road detection framework based on the extractor, whose performance is more accurate and robust than that of existing extractors. Also, the proposed framework has much low-complexity, which is suitable for usage in practical systems.
A Muddle of Models of Motivation for Using Peer-to-Peer Economy Systems

This paper reports on a study of motivations for the use of peer-to-peer or sharing economy services. We interviewed both users and providers of these systems to obtain different perspectives and to determine if providers are matching their system designs to the most important drivers of use. We found that the motivational models implicit in providers' explanations of their systems' designs do not match well with what really seems to motivate users. Providers place great emphasis on idealistic motivations such as creating a better community and increasing sustainability. Users, on the other hand are looking for services that provide what they need whilst increasing value and convenience. We discuss the divergent models of providers and users and offer design implications for peer system providers.
Fractional Spectral Analysis of Randomly Sampled Signals and Applications

Nonuniform sampling can be utilized to achieve certain desirable results. Periodic nonuniform sampling can decrease the required sampling rate for signals. Random sampling can be used as a digital alias-free signal processing method in analog-to-digital conversion. In this paper, we first present the fractional spectrum estimation of signals that are bandlimited in the fractional Fourier domain based on the general periodic random sampling approach. To show the estimation effect, the unbiasedness, the variance, and the optimal estimation condition are analyzed. The reconstruction of the fractional spectrum from the periodic random samples is also proposed. Second, the effects of sampling jitters and observation errors on the performance of the fractional spectrum estimation are analyzed, where the new defined fractional characteristic function is used to compensate the estimation bias from sampling jitters. Furthermore, we investigate the fractional spectral analysis from two widely used random sampling schemes, i.e., simple random sampling and stratified random sampling. Finally, all of the analysis results are applied and verified using a radar signal processing system.
Intervals , Thresholds , and Long Slow Distance : the Role of Intensity and Duration in Endurance Training

Endurance training involves manipulation of intensity, duration, and frequency of training sessions. The relative impact of short, high-intensity training versus longer, slower distance training has been studied and debated for decades among athletes, coaches, and scientists. Currently, the popularity pendulum has swung towards high-intensity interval training. Many fitness experts, as well as some scientists, now argue that brief, high-intensity interval work is the only form of training necessary for performance optimization. Research on the impact of interval and continuous training with untrained to moderately trained subjects does not support the current interval craze, but the evidence does suggest that short intense training bouts and longer continuous exercise sessions should both be a part of effective endurance training. Elite endurance athletes perform 80 % or more of their training at intensities clearly below their lactate threshold and use high-intensity training surprisingly sparingly. Studies involving intensification of training in already well-trained athletes have shown equivocal results at best. The available evidence suggests that combining large volumes of low-intensity training with careful use of high-intensity interval training throughout the annual training cycle is the best-practice model for development of endurance performance.
Rethinking Spatiotemporal Feature Learning For Video Understanding

In this paper we study 3D convolutional networks for video understanding tasks. Our starting point is the stateof-the-art I3D model of , which “inflates” all the 2D filters of the Inception architecture to 3D. We first consider “deflating” the I3D model at various levels to understand the role of 3D convolutions. Interestingly, we found that 3D convolutions at the top layers of the network contribute more than 3D convolutions at the bottom layers, while also being computationally more efficient. This indicates that I3D is better at capturing high-level temporal patterns than low-level motion signals. We also consider replacing 3D convolutions with spatiotemporal-separable 3D convolutions (i.e., replacing convolution using a kt×k×k filter with 1× k× k followed by kt× 1× 1 filters); we show that such a model, which we call S3D, is 1.5x more computationally efficient (in terms of FLOPS) than I3D, and achieves better accuracy. Finally, we explore spatiotemporal feature gating on top of S3D. The resulting model, which we call S3D-G, outperforms the state-of-the-art I3D model by 3.5% accuracy on Kinetics and reduces the FLOPS by 34%. It also achieves a new state-of-the-art performance when transferred to other action classification (UCF-101 and HMDB51) and detection (UCF-101 and JHMDB) datasets.
Deep reverse tone mapping

Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.
Packet Leashes: A Defense against Wormhole Attacks in Wireless Networks

As mobile ad hoc network applications are deployed, security emerges as a central requirement. In this paper, we introduce the wormhole attack, a severe attack in ad hoc networks that is particularly challenging to defend against. The wormhole attack is possible even if the attacker has not compromised any hosts, and even if all communication provides authenticity and confidentiality. In the wormhole attack, an attacker records packets (or bits) at one location in the network, tunnels them (possibly selectively) to another location, and retransmits them there into the network. The wormhole attack can form a serious threat in wireless networks, especially against many ad hoc network routing protocols and location-based wireless security systems. For example, most existing ad hoc network routing protocols, without some mechanism to defend against the wormhole attack, would be unable to find routes longer than one or two hops, severely disrupting communication. We present a new, general mechanism, called packet leashes, for detecting and thus defending against wormhole attacks, and we present a specific protocol, called TIK, that implements leashes.
Evaluating biochemical methane production from brewer’s spent yeast

Anaerobic digestion treatment of brewer’s spent yeast (SY) is a viable option for bioenergy capture. The biochemical methane potential (BMP) assay was performed with three different samples (SY1, SY2, and SY3) and SY1 dilutions (75, 50, and 25 % on a v/v basis). Gompertz-equation parameters denoted slow degradability of SY1 with methane production rates of 14.59–4.63 mL/day and lag phases of 10.72–19.7 days. Performance and kinetic parameters were obtained with the Gompertz equation and the first-order hydrolysis model with SY2 and SY3 diluted 25 % and SY1 50 %. A SY2 25 % gave a 17 % of TCOD conversion to methane as well as shorter lag phase (<1 day). Average estimated hydrolysis constant for SY was 0.0141 (±0.003) day−1, and SY2 25 % was more appropriate for faster methane production. Methane capture and biogas composition were dependent upon the SY source, and co-digestion (or dilution) can be advantageous.
Dyscalculia : A unifying concept in understanding mathematics learning disabilities

Individuals display a mathematics disability when their performance on standardized calculation tests or on numerical reasoning tasks is comparatively low, given their age, education and intellectual reasoning ability. Low performance due to cerebral trauma is called acquired dyscalculia. Mathematical learning difficulties with similar features but without evidence of cerebral trauma are referred to as developmental dyscalculia. This review identifies types of developmental dyscalculia, the neuropsychological processes that are linked with them and procedures for identifying dyscalculia. The concept of dyslexia is one with which professionals working in the areas of special education, learning disabilities are reasonably familiar. The concept of dyscalculia, on the other hand, is less well known. This article describes this condition and examines its implications for understanding mathematics learning disabilities. Individuals display a mathematics disability when their performance on standardized calculation tests or on numerical reasoning tasks is significantly depressed, given their age, education and intellectual reasoning ability ( Mental Disorders IV (DSM IV)). When this loss of ability to calculate is due to cerebral trauma, the condition is called acalculia or acquired dyscalculia. Mathematical learning difficulties that share features with acquired dyscalculia but without evidence of cerebral trauma are referred to as developmental dyscalculia (Hughes, Kolstad & Briggs, 1994). The focus of this review is on developmental dyscalculia (DD). Students who show DD have difficulty recalling number facts and completing numerical calculations. They also show chronic difficulties with numerical processing skills such recognizing number symbols, writing numbers or naming written numerals and applying procedures correctly (Gordon, 1992). They may have low self efficacy and selective attentional difficulties (Gross Tsur, Auerbach, Manor & Shalev, 1996). Not all students who display low mathematics achievement have DD. Mathematics underachievement can be due to a range of causes, for example, lack of motivation or interest in learning mathematics, low self efficacy, high anxiety, inappropriate earlier teaching or poor school attendance. It can also be due to generalised poor learning capacity, immature general ability, severe language disorders or sensory processing. Underachievement due to DD has a neuropsychological foundation. The students lack particular cognitive or information processing strategies necessary for acquiring and using arithmetic knowledge. They can learn successfully in most contexts and have relevant general language and sensory processing. They also have access to a curriculum from which their peers learn successfully. It is also necessary to clarify the relationship between DD and reading disabilities. Some aspects of both literacy and arithmetic learning draw on the same cognitive processes. Both, for example, 1 This article was published in Australian Journal of Learning Disabilities, 2003 8, (4).
Tissue engineering: orthopedic applications.

Because of an aging population and increased occurrence of sports-related injuries, musculoskeletal disorders have become one of the major health concerns in the United States. Current treatments, although fairly successful, do not provide the optimum therapy. These treatments typically rely on donor tissues obtained either from the patient or from another source. The former raises the issue of supply, whereas the latter poses the risk of rejection and disease transfer. This has prompted orthopedic surgeons and scientists to look for viable alternatives. In recent years, tissue engineering has gained increasing support as a method to treat orthopedic disorders. Because it uses principles of engineering, biology, and chemistry, tissue engineering may provide a more effective approach to the treatment of musculoskeletal disorders than traditional methods. This chapter presents a review of current methods and new tissue-engineering techniques for the treatment of disorders affecting bone, ligament, and cartilage.
An LSTM-Based Dynamic Customer Model for Fashion Recommendation

Online fashion sales present a challenging use case for personalized recommendation: Stores offer a huge variety of items in multiple sizes. Small stocks, high return rates, seasonality, and changing trends cause continuous turnover of articles for sale on all time scales. Customers tend to shop rarely, but often buy multiple items at once. We report on backtest experiments with sales data of 100k frequent shoppers at Zalando, Europe’s leading online fashion platform. To model changing customer and store environments, our recommendation method employs a pair of neural networks: To overcome the cold start problem, a feedforward network generates article embeddings in “fashion space,” which serve as input to a recurrent neural network that predicts a style vector in this space for each client, based on their past purchase sequence. We compare our results with a static collaborative filtering approach, and a popularity ranking baseline.
A Memristor-Based Optimization Framework for Artificial Intelligence Applications

Memristors have recently received significant attention as device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify biasing voltages at memristor terminals make them promising candidates to efficiently perform matrix-vector multiplications and solve systems of linear equations. In this article, we discuss how networks of memristors arranged in crossbar arrays can be used for efficiently solving optimization and machine learning problems. We introduce a new memristor-based optimization framework that combines the computational merits of memristor crossbars with the advantages of an operator splitting method, the alternating direction method of multipliers (ADMM). Here, ADMM helps in splitting a complex optimization problem into subproblems that involve the solution of systems of linear equations. The strength of this framework is shown by applying it to linear programming, quadratic programming, and sparse optimization. In addition to ADMM, implementation of a customized power iteration method for eigenvalue/eigenvector computation using memristor crossbars is discussed. The memristor-based power iteration method can further be applied to principal component analysis. The use of memristor crossbars yields a significant speed-up in computation, and thus, we believe, has the potential to advance optimization and machine learning research in artificial intelligence.
- 13086 MICROSURGICAL DEVICES BY POP-UP BOOK MEMS

The small scale of microsurgery poses significant challenges for developing robust and dexterous tool s t grip, cut, and join sub-millimeter structures such as ves sels and nerves. The main limitation is that traditional man ufacturing techniques are not optimized to create smart, artic ulating structures in the 0.1 – 10 mm scale. Pop-up book ME MS is a new fabrication technology that promises to overcom e this challenge and enable the monolithic fabrication of c mplex, articulated structures with an extensive catalog of materials, embedded electrical components, and automated assem bly with feature sizes down to 20 microns. In this paper, we demonstrate a proof-of-concept microsurgical gripper and evalua te its performance at the component and device level to ch aracterize its strength and robustness. 1-DOF Flexible hinge j oints that constrain motion and allow for out-of-plane actuati on were found to resist torsional loads of 22.8±2.15 N ·mm per mm of hinge width. Adhesive lap joints that join individu al layers in the laminate structure demonstrated a shear strengt h of 26.8±0.53 N/mm. The laminate structures were also shown to resist peel loads of 0.72±0.10 N/mm . Various flexible hinge and adhesive lap components were then designed into an 11layered structure which ‘pops up’ to realize an art iculating microsurgical gripper that includes a cable-driven mechanism for gripping actuation and a flexural return spring to passively open the gripper. The gripper prototype, with final weight of 200 mg, overall footprint of 18 mm by 7.5 mm, and features as small as 200 microns, is able to deftly manipulate objects 100 times is own weight at the required scale, thus dem onstrating its potential for use in microsurgery.  Small joint surgery, such as that in the wrist or f ingers, presents a number of significant challenges due to the limited maneuverable workspace and the presence of many del icate structures that must be avoided, including sensitiv e cartilage surfaces and tendons . Current commercial smalljoint surgical instruments are limited to straight, simpl e tools without any distal articulation which would allow for great r access and dexterity inside the joint . In addition, the ro bust electromechanical surgical tools at the sub-mm scal e required for these procedures are either impossible or comme rcially impractical to make with existing manufacturing tec hniques such as surface/bulk micromachining , wire-EDM [ 4], microinjection molding, or micromilling/lathing . It is our goal to apply an emerging micromachining and assembly techn ique that we have developed to enable robust, dexterous, and practical microsurgical instruments for small joint repair. We have developed a novel micro-manufacturing techn ique known as Pop-Up Book MEMS (‘Pop-Ups’) that allows f or the fabrication of complex, multi-functional electromec hanical devices on the 0.1-10 mm scale  . Pop-Up tec hnology enables the ability to create 3-D, multi-material, monolithic meso and micro-structures using purely 2-D planar
Guess Where? Actor-Supervision for Spatiotemporal Action Localization

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fullysupervised alternatives.
State Layer Hidden Layers Action Layer SQL Vectorization Initial state Action

Join order selection plays a significant role in query performance. Many modern database engine query optimizers use join order enumerators, cost models, and cardinality estimators to choose join orderings, each of which is based on painstakingly hand-tuned heuristics and formulae. Additionally, these systems typically employ static algorithms that ignore the end result (they do not “learn from their mistakes”). In this paper, we argue that existing deep reinforcement learning techniques can be applied to query planning. These techniques can automatically tune themselves, alleviating a massive human effort. Further, deep reinforcement learning techniques naturally take advantage of feedback, learning from their successes and failures. Towards this goal, we present ReJOIN, a proof-of-concept join enumerator. We show preliminary results indicating that ReJOIN can match or outperform the Postgres optimizer.
An application of recurrent nets to phone probability estimation

This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed; a role for which the recurrent net appears suitable. An overview of early developments of recurrent nets for phone recognition is given along with the more recent improvements that include their integration with Markov models. Recognition results are presented for the DARPA TIMIT and Resource Management tasks, and it is concluded that recurrent nets are competitive with traditional means for performing phone probability estimation.