nils-herrmann/clean_scirepeval huggingface dataset:
Learning Transferable Policies for Monocular Reactive MAV Control

The ability to transfer knowledge gained in previous tasks into new contexts is one of the most important mechanisms of human learning. Despite this, adapting autonomous behavior to be reused in partially similar settings is still an open problem in current robotics research. In this paper, we take a small step in this direction and propose a generic framework for learning transferable motion policies. Our goal is to solve a learning problem in a target domain by utilizing the training data in a different but related source domain. We present this in the context of an autonomous MAV flight using monocular reactive control, and demonstrate the efficacy of our proposed approach through extensive real-world flight experiments in outdoor cluttered environments.
PLANT GROWTH AND DEVELOPMENT UNDER SALINITY STRESS

Plant growth and development are adversely affected by salinity – a major environmental stress that limits agricultural production. This chapter provides an overview of the physiological mechanisms by which growth and development of crop plants are affected by salinity. The initial phase of growth reduction is due to an osmotic effect, is similar to the initial response to water stress and shows little genotypic differences. The second, slower effect is the result of salt toxicity in leaves. In the second phase a salt sensitive species or genotype differs from a more salt tolerant one by its inability to prevent salt accumulation in leaves to toxic levels. Most crop plants are salt tolerant at germination but salt sensitive during emergence and vegetative development. Root and shoot growth is inhibited by salinity; however, supplemental Ca partly alleviates the growth inhibition. The Ca effect appears related to the maintenance of plasma membrane selectivity for K over Na. Reproductive development is considered less sensitive to salt stress than vegetative growth, although in wheat salt stress can hasten reproductive growth, inhibit spike development and decrease the yield potential, whereas in the more salt sensitive rice, low yield is primarily associated with reduction in tillers, and by sterile spikelets in some cultivars. Plants with improved salt tolerance must thrive under saline field conditions with numerous additional stresses. Salinity shows interactions with several stresses, among others with boron toxicity, but the mechanisms of salinity-boron interactions are still poorly known. To better understand crop tolerance under saline field conditions, future research should focus on tolerance of crops to a combination of stresses
FENCE: Continuous access control enforcement in dynamic data stream environments

In this paper, we address the problem of continuous access control enforcement in dynamic data stream environments, where both data and query security restrictions may potentially change in real-time. We present FENCE framework that ffectively addresses this problem. The distinguishing characteristics of FENCE include: (1) the stream-centric approach to security, (2) the symmetric model for security settings of both continuous queries and streaming data, and (3) two alternative security-aware query processing approaches that can optimize query execution based on regular and security-related selectivities. In FENCE, both data and query security restrictions are modeled symmetrically in the form of security metadata, called "security punctuations" embedded inside data streams. We distinguish between two types of security punctuations, namely, the data security punctuations (or short, dsps) which represent the access control policies of the streaming data, and the query security punctuations (or short, qsps) which describe the access authorizations of the continuous queries. We also present our encoding method to support XACML(eXtensible Access Control Markup Language) standard. We have implemented FENCE in a prototype DSMS and present our performance evaluation. The results of our experimental study show that FENCE's approach has low overhead and can give great performance benefits compared to the alternative security solutions for streaming environments.
Cambricon: An Instruction Set Architecture for Neural Networks

Neural Networks (NN) are a family of models for a broad range of emerging machine learning and pattern recondition applications. NN techniques are conventionally executed on general-purpose processors (such as CPU and GPGPU), which are usually not energy-efficient since they invest excessive hardware resources to flexibly support various workloads. Consequently, application-specific hardware accelerators for neural networks have been proposed recently to improve the energy-efficiency. However, such accelerators were designed for a small set of NN techniques sharing similar computational patterns, and they adopt complex and informative instructions (control signals) directly corresponding to high-level functional blocks of an NN (such as layers), or even an NN as a whole. Although straightforward and easy-to-implement for a limited set of similar NN techniques, the lack of agility in the instruction set prevents such accelerator designs from supporting a variety of different NN techniques with sufficient flexibility and efficiency.
 In this paper, we propose a novel domain-specific Instruction Set Architecture (ISA) for NN accelerators, called Cambricon, which is a load-store architecture that integrates scalar, vector, matrix, logical, data transfer, and control instructions, based on a comprehensive analysis of existing NN techniques. Our evaluation over a total of ten representative yet distinct NN techniques have demonstrated that Cambricon exhibits strong descriptive capacity over a broad range of NN techniques, and provides higher code density than general-purpose ISAs such as ×86, MIPS, and GPGPU. Compared to the latest state-of-the-art NN accelerator design DaDianNao  (which can only accommodate 3 types of NN techniques), our Cambricon-based accelerator prototype implemented in TSMC 65nm technology incurs only negligible latency/power/area overheads, with a versatile coverage of 10 different NN benchmarks.
A Visual Recognition of Static Hand Gestures in Indian Sign Language based on Kohonen Self-Organizing Map Algorithm

Indian Sign Language (ISL) or Indo-Pakistani Sign Language is possibly the prevalent sign language variety in South Asia used by at least several hundred deaf signers. It is different in the phonetics, grammar and syntax from other country’s sign languages. Since ISL got standardized only recently, there is very little research work that has happened in ISL recognition. Considering the challenges in ISL gesture recognition, a novel method for recognition of static signs of Indian sign language alphabets and numerals for Human Computer Interaction (HCI) has been proposed in this thesis work. The developed algorithm for the hand gesture recognition system in ISL formulates a vision-based approach, using the Two-Dimensional Discrete Cosine Transform (2D-DCT) for image compression and the Self-Organizing Map (SOM) or Kohonen Self Organizing Feature Map (SOFM) Neural Network for pattern recognition purpose, simulated in MATLAB. To design an efficient and user friendly hand gesture recognition system, a GUI model has been implemented. The main advantage of this algorithm is its high-speed processing capability and low computational requirements, in terms of both speed and memory utilization. KeywordsArtificial Neural Network, Hand Gesture Recognition, Human Computer Interaction (HCI), Indian Sign Language (ISL), Kohonen Self Organizing Feature Map (SOFM), Two-Dimensional Discrete Cosine Transform (2D-
C . News versus Sentiment : Predicting Stock Returns from News Stories

This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a longdelayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement. Steven L. Heston and Nitish Ranjan Sinha.  We thank Tim Loughran, Paul Tetlock, seminar participants at University of Maryland (finance brownbag), Acadian Asset Management and the Office of Financial Research. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors.
Quasi-one-dimensional metal oxide materials — Synthesis , properties and applications

Recent advances in the field of nanotechnology have led to the synthesis and characterization of an assortment of quasi-one-dimensional (Q1D) structures, such as nanowires, nanoneedles, nanobelts and nanotubes. These fascinating materials exhibit novel physical properties owing to their unique geometry with high aspect ratio. They are the potential building blocks for a wide range of nanoscale electronics, optoelectronics, magnetoelectronics, and sensing devices. Many techniques have been developed to grow these nanostructures with various compositions. Parallel to the success with group IV and groups III–V compounds semiconductor nanostructures, semiconducting metal oxide materials with typically wide band gaps are attracting increasing attention. This article provides a comprehensive review of the state-of-the-art research activities that focus on the Q1D metal oxide systems and their physical property characterizations. It begins with the synthetic mechanisms and methods that have been exploited to form these structures. A range of remarkable characteristics are then presented, organized into sections covering a number of metal oxides, such as ZnO, In2O3, SnO2, Ga2O3, and TiO2, etc., describing their electrical, optical, magnetic, mechanical and chemical sensing properties. These studies constitute the basis for developing versatile applications based on metal oxide Q1D systems, and the current progress in device development will be highlighted.
Computation offloading and resource allocation for low-power IoT edge devices

With the proliferation of portable and mobile IoT devices and their increasing processing capability, we witness that the edge of network is moving to the IoT gateways and smart devices. To avoid Big Data issues (e.g. high latency of cloud based IoT), the processing of the captured data is starting from the IoT edge node. However, the available processing capabilities and energy resources are still limited and do not allow to fully process the data on-board. It calls for offloading some portions of computation to the gateway or servers. Due to the limited bandwidth of the IoT gateways, choosing the offloading levels of connected devices and allocating bandwidth to them is a challenging problem. This paper proposes a technique for managing computation offloading in a local IoT network under bandwidth constraints. The existing bandwidth allocation and computation offloading management techniques underutilize the gateway's resources (e.g. bandwidth) due to the fragmentation issue. This issue stems from the discrete coarse-grained choices (i.e. offloading levels) on the IoT end nodes. Our proposed technique addresses this issue, and utilizes the available resources of the gateway effectively. The experimental results show on average 1 hour (up to 1.5 hour) improvement in battery life of edge devices. The utilization of gateway's bandwidth increased by 40%.
What Makes You Click ? – Mate Preferences in Online Dating ∗

We estimate mate preferences using a novel data set from an online dating service. The data set contains detailed information on user attributes and the decision to contact a potential mate after viewing his or her profile. This decision provides the basis for our preference estimation approach. A potential problem arises if the site users strategically shade their true preferences. We provide a simple test and a bias correction method for strategic behavior. The main findings are (i) There is no evidence for strategic behavior.  Any previously reported results not contained in this paper or in the companion piece Hitsch et al. (2010) did not prove to be robust and were dropped from the final paper versions. We thank Babur De los Santos, Chris Olivola, Tim Miller, and David Wood for their excellent research assistance. We are grateful to Elizabeth Bruch, Jean-Pierre Dubé, Eli Finkel, Emir Kamenica, Derek Neal, Peter Rossi, Betsey Stevenson, and Utku Ünver for comments and suggestions. Seminar participants at the 2006 AEA meetings, Boston College, the Caltech 2008 Matching Conference, the Choice Symposium in Estes Park, the Conference on Marriage and Matching at New York University 2006, the ELSE Laboratory Experiments and the Field (LEaF) Conference, Northwestern University, the 2007 SESP Preconference in Chicago, SITE 2007, the University of Pennsylvania, the 2004 QME Conference, UC Berkeley, UCLA, the University of Chicago, UCL, the University of Naples Federico II, the University of Toronto, Stanford GSB, and Yale University provided valuable comments. This research was supported by the Kilts Center of Marketing (Hitsch), a John M. Olin Junior Faculty Fellowship, and the National Science Foundation, SES-0449625 (Hortaçsu). Please address all correspondence to Hitsch (guenter.hitsch@chicagobooth.edu), Hortaçsu (hortacsu@uchicago.edu), or Ariely (dandan@duke.edu).
Optimized route network graph as map reference for autonomous cars operating on German autobahn

This paper describes several optimization techniques used to create an adequate route network graph for autonomous cars as a map reference for driving on German autobahn or similar highway tracks. We have taken the Route Network Definition File Format (RNDF) specified by DARPA and identified multiple flaws of the RNDF for creating digital maps for autonomous vehicles. Thus, we introduce various enhancements to it to form a digital map graph called RND-FGraph, which is well suited to map almost any urban transportation infrastructure. We will also outline and show results of fast optimizations to reduce the graph size. The RNDFGraph has been used for path-planning and trajectory evaluation by the behavior module of our two autonomous cars “Spirit of Berlin” and “MadeInGermany”. We have especially tuned the graph to map structured high speed environments such as autobahns where we have tested autonomously hundreds of kilometers under real traffic conditions.
Learning to score the figure skating sports videos

This paper aims at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset – FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos.
Biology and principles of periodontal wound healing/regeneration.

The native periodontium includes cementum, a functionally oriented periodontal ligament, alveolar bone and gingiva. Pathologic and/or traumatic events may lead to the loss or damage of this anatomical structure. Since the 1970s, a number of procedures have been investigated in an attempt to restore such lost tissues. Numerous clinical trials have shown positive outcomes for various reconstructive surgical protocols. Reduced probing depths, clinical attachment gain, and radiographic bone fill have been reported extensively for intrabony and furcation defects following scaling and root planing, open flap debridement, autogenous bone grafting, implantation of biomaterials including bone derivatives and bone substitutes, guided-tissue regeneration (GTR) procedures, and implantation of biologic factors, including enamel matrix proteins. Histological studies have shown that various surgical periodontal procedures can lead to different patterns of healing. Healing by formation of a long junctional epithelium (epithelial attachment) is characterized by a thin epithelium extending apically interposed between the root surface and the gingival connective tissue (4, 23). Connective tissue repair (new attachment) is represented by collagen fibers oriented parallel or perpendicular to a root surface previously exposed to periodontal disease or otherwise deprived of its periodontal attachment. In contrast, periodontal regeneration is characterized by de novo formation of cementum, a functionally oriented periodontal ligament, alveolar bone, and gingiva (restitutio ad integrum). Nevertheless, it would be naive to expect these to occur as distinctly separate biologic outcomes following reconstruction of the periodontal attachment. For example, periodontal regeneration should be expected to include elements of a new, as well as an epithelial, attachment. Predictability of outcomes following surgical procedures is of fundamental importance in medicine. As periodontal-regenerative procedures are time consuming and financially demanding, there is increasing interest by clinicians to learn of factors that may influence the clinical outcome following periodontal reconstructive surgery in order to provide the best possible service to patients. This goal can only be achieved if biological aspects of wound healing and regeneration are taken into consideration. The objectives of the present article are to provide an overview of wound healing following periodontal surgical procedures, to discuss the basic principles of periodontal regeneration, and to illustrate the factors that influence this process.
Generative Adversarial Perturbations

In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models. We present trainable deep neural networks for transforming images to adversarial perturbations. Our proposed models can produce image-agnostic and image-dependent perturbations for targeted and non-targeted attacks. We also demonstrate that similar architectures can achieve impressive results in fooling both classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task. Using extensive experiments on challenging high-resolution datasets such as ImageNet and Cityscapes, we show that our perturbations achieve high fooling rates with small perturbation norms. Moreover, our attacks are considerably faster than current iterative methods at inference time.
Predicting Risk from Financial Reports with Regression

We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the text’s meaning. In this work, the text is an SEC-mandated financial report published annually by a publiclytraded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply wellknown regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting the target variable, and a single model that uses both can significantly outperform past volatility. Interestingly, our approach is more accurate for reports after the passage of the Sarbanes-Oxley Act of 2002, giving some evidence for the success of that legislation in making financial reports more informative.
Recent Advances in Open Set Recognition: A Survey

In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers not only to accurately classify the seen classes, but also to effectively deal with the unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, experiment setup and evaluation metrics. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also overview the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.
GENDER DIFFERENCES IN PERCEPTION OF SEXUAL HARASSMENT AMONG NIGERIAN BANKS ’ EMPLOYEES

Banks in Nigeria need to understand the perceptual difference in both male and female employees to better develop adequate policy on sexual harassment. This study investigated the perceptual differences on sexual harassment among male and female bank employees in two commercial cities (Kano and Lagos) of Nigeria.Two hundred and seventy five employees (149 males, 126 females) were conveniently sampled for this study. A survey design with a questionnaire adapted from Sexual Experience Questionnaire (SEQ) comprises of three dimension scalesof sexual harassment was used. The hypotheses were tested with independent samples t-test. The resultsindicated no perceptual differences in labelling sexual harassment clues between male and female bank employees in Nigeria. Thus, the study recommends that bank managers should support and establish the tone for sexual harassment-free workplace. KeywordsGender Harassment, Sexual Coercion, Unwanted Sexual Attention, Workplace.
Leveraging the Error Resilience of Neural Networks for Designing Highly Energy Efficient Accelerators

In recent years, inexact computing has been increasingly regarded as one of the most promising approaches for slashing energy consumption in many applications that can tolerate a certain degree of inaccuracy. Driven by the principle of trading tolerable amounts of application accuracy in return for significant resource savings-the energy consumed, the (critical path) delay, and the (silicon) area-this approach has been limited to application-specified integrated circuits (ASICs) so far. These ASIC realizations have a narrow application scope and are often rigid in their tolerance to inaccuracy, as currently designed; the latter often determining the extent of resource savings we would achieve. In this paper, we propose to improve the application scope, error resilience and the energy savings of inexact computing by combining it with hardware neural networks. These neural networks are fast emerging as popular candidate accelerators for future heterogeneous multicore platforms and have flexible error resilience limits owing to their ability to be trained. Our results in 65-nm technology demonstrate that the proposed inexact neural network accelerator could achieve 1.78-2.67× savings in energy consumption (with corresponding delay and area savings being 1.23 and 1.46×, respectively) when compared to the existing baseline neural network implementation, at the cost of a small accuracy loss (mean squared error increases from 0.14 to 0.20 on average).