nils-herrmann/clean_scirepeval huggingface dataset:
Kernel Distillation for Gaussian Processes

Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, kernel distillation, for kernel matrix approximation. The idea adopts from knowledge distillation in deep learning community, where we approximate a fully trained teacher kernel matrix of size n × n with a student kernel matrix. We combine inducing points method with sparse low-rank approximation in the distillation procedure. The distilled student kernel matrix only cost O(m) storage where m is the number of inducing points and m n. We also show that one application of kernel distillation is for fast GP prediction, where we demonstrate empirically that our approximation provide better balance between the prediction time and the predictive performance compared to the alternatives.
A Desire to Be Taught : Instructional Consequences of Intrinsic Motivation I

This paper summarizes the results from a series of studies designed to test the hypothesis that making learning more fun will produce corresponding increases both in learning and retention and in subsequent interest in the subject matter itself. Each study examined the effects o f two or more versions o f an educational activity, each designed to involve identical instructional content, but to differ in motivational appeal The data from the studies presented provide good general support for the hypothesized cognitive and motivational benefits of appropriately designed motivational embellishments of educational activ#ies. Exceptions to this rule, however, and a more general theoretical analysis of the conditions under which such positive effects are (and are not) expected to occur, are also discussed.
Smudge Attacks on Smartphone Touch Screens

Touch screens are an increasingly common feature on personal computing devices, especially smartphones, where size and user interface advantages accrue from consolidating multiple hardware components (keyboard, number pad, etc.) into a single software definable user interface. Oily residues, or smudges, on the touch screen surface, are one side effect of touches from which frequently used patterns such as a graphical password might be inferred. In this paper we examine the feasibility of such smudge attacks on touch screens for smartphones, and focus our analysis on the Android password pattern. We first investigate the conditions (e.g., lighting and camera orientation) under which smudges are easily extracted. In the vast majority of settings, partial or complete patterns are easily retrieved. We also emulate usage situations that interfere with pattern identification, and show that pattern smudges continue to be recognizable. Finally, we provide a preliminary analysis of applying the information learned in a smudge attack to guessing an Android password pattern.
Motivational Leadership: Tips From the Business World.

It is an important task for leadership to identify the motivating factors for employees and motivate them to fulfill their individual and organizational goals. Although there are several motivational factors (extrinsic and intrinsic), intrinsic motivational factors such as autonomy, mastery, and purpose are more important for deeper lasting job satisfaction and higher performance. In this article, the authors discuss how an understanding of these factors that influence motivation has the potential to transform an organization.
A Probabilistic Analysis of Link Duration in Vehicular Ad Hoc Networks

The past decade has witnessed a phenomenal market penetration of wireless communications and a steady increase in the number of mobile users. Unlike wired networks, where communication links are inherently stable, in wireless networks, the lifetime of a link is a random variable whose probability distribution depends on mobility, transmission range, and various impairments of radio communications. Because of the very dynamic nature of Vehicular Ad hoc NETworks (VANETs) and the short transmission range mandated by the Federal Communications Commission (FCC), individual communication links come into existence and vanish unpredictably, making the task of establishing and maintaining routing paths between fast-moving vehicles very challenging. The main contribution of this work is to investigate the probability distribution of the lifetime of individual links in a VANET under the combined assumptions of a realistic radio transmission model and a realistic probability distribution model of intervehicle headway distance. Our analytical results were validated and confirmed by extensive simulation.
Search mode and purchase intention in online shopping behavior

This study focuses on the effect of website visitors' goal-oriented search mode on purchase intention in online environments. In a study of 874 respondents recruited from 13 online shops representing a diversity of product categories and customer segments, the effect of visitors' goal-oriented search mode on purchase intention is found to be moderated by product involvement and product risk. Furthermore, product involvement, product risk, product knowledge and Internet experience are found to have positive effects on the degree of goaloriented search mode of the visitors. Also, product knowledge and Internet experience are reported to have direct positive effects on purchase intention. The results point to the importance of understanding the characteristics of website visitors, and to customize the support and search services offered on the website to the characteristics and preferences of the individual visitor. Customization of this kind may partly be based on immediate visitor history (referrer) and may be used to increase purchase intention, and eventually online sales.
Crime analysis and prediction using data mining

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds

We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sum problems have witnessed a huge surge of interest in recent years, all existing work is limited to vector space problems. We introduce Riemannian SVRG, a new variance reduced Riemannian optimization method. We analyze this method for both geodesically smooth convex and nonconvex functions. Our analysis reveals that Riemannian SVRG comes with advantages of the usual SVRG method, but with factors depending on manifold curvature that influence its convergence. To the best of our knowledge, ours is the first fast stochastic Riemannian method. Moreover, our work offers the first non-asymptotic complexity analysis for nonconvex Riemannian optimization (even for the batch setting). Our results have several implications; for instance, they offer a Riemannian perspective on variance reduced PCA, which promises a short, transparent convergence analysis.
Low-Cost Wideband and High-Gain Slotted Cavity Antenna Using High-Order Modes for Millimeter-Wave Application

A novel single-fed low-cost wideband and high-gain slotted cavity antenna based on substrate integrated waveguide (SIW) technology using high-order cavity modes is demonstrated in this paper. High-order resonant modes (TE130, TE310, TE330) inside the cavity are simply excited by a coaxial probe which is located at the center of the antenna. Energy is coupled out of the cavity by a 3 × 3 slot array etched on the top surface of the cavity. Two antennas with different polarizations are designed and tested. Measured results show that the linearly polarized prototype achieves an impedance bandwidth (|S11| <; -10 dB) of > 26% (28 to 36.6 GHz), and a 1-dB gain bandwidth of 14.1% (30.3 to 34.9 GHz). In addition, a measured maximum gain of 13.8 dBi and radiation efficiency of 92% are obtained. To generate circularly polarized radiation, a rotated dipole array is placed in front of the proposed linearly polarized antenna. Measured results show that the circularly polarized antenna exhibits a common bandwidth (10-dB return loss bandwidth, 3-dB axial ratio bandwidth, and 1-dB gain bandwidth) of 11%.
A Compliant, Underactuated Hand for Robust Manipulation

This paper introduces the i-HY Hand, an underactuated hand driven by 5 actuators that is capable of performing a wide range of grasping and in-hand manipulation tasks. This hand was designed to address the need for a durable, inexpensive, moderately dexterous hand suitable for use on mobile robots. The primary focus of this paper will be on the novel minimalistic design of i-HY, which was developed by choosing a set of target tasks around which the design of the hand was optimized. Particular emphasis is placed on the development of underactuated fingers that are capable of both firm power grasps and lowstiffness fingertip grasps using only the passive mechanics of the finger mechanism. Experimental results demonstrate successful grasping of a wide range of target objects, the stability of fingertip grasping, as well as the ability to adjust the force exerted on grasped objects using the passive finger mechanics.
The Feasibility of Using Augmented Auditory Feedback From a Pressure Detecting Insole to Reduce the Knee Adduction Moment: A Proof of Concept Study.

The objective of this work was to conduct a proof of concept study utilizing auditory feedback from a pressure-detecting shoe insole to shift plantar pressure medially in order to reduce the knee adduction moment (KAM). When compared with normal walking, 32 healthy subjects significantly reduced their peak KAM using feedback (p < 0.001). When compared with medial thrust gait, an established gait modification, walking with pressure-based feedback was equally effective at reducing the peak KAM, yet it successfully mitigated other potentially detrimental gait measures such as the peak knee flexion moment (KFM), knee internal rotation moment (KIrM), and a reduction in speed.
Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.
Low- and high-anxious hypermobile Ehlers–Danlos syndrome patients: comparison of psychosocial and health variables

Despite the frequent co-ocurrence of hypermobile Ehler–Danlos syndrome (hEDS) and pathological anxiety, little is known about the psychosocial and health implications of such comorbidity. Our aim was to explore the association between high levels of anxiety and psychosocial (catastrophizing, kinesiophobia, somatosensory amplification, social support and functioning), health (pain, fatigue, BMI, tobacco/alcohol use, depression, diagnosis delay, general health), and sociodemographic factors in people with hEDS. In this cross-sectional study, 80 hEDS patients were divided into two groups according to self-reported anxiety levels: low and high. Psychosocial, sociodemographic and health variables were compared between the groups. Forty-one participants reported a high level of anxiety (51.2%). No differences were found in the sociodemographic variables between high-anxious and low-anxious patients. The percentage of participants with severe fatigue and high depressive symptomatology was significantly higher in the high-anxious group (80.5 vs 56.4; 26.8 vs 12.8%, respectively). High-anxious hEDS patients also showed significantly higher levels of pain catastrophizing, somatosensory amplification as well as a poorer social functioning and general health. Multivariate analyses showed that somatosensory amplification, pain catastrophizing and poor social functioning are variables that increase the probability of belonging to the high-anxious group. Despite limitations, this first study comparing high-anxious versus low-anxious hEDS patients with respect to health aspects, highlight the importance of considering the psychosocial factors (many susceptible to modification), to improve the adjustment to this chronic condition and provide support to those affected through a biopsychosocial approach.
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning

Bandit structured prediction describes a stochastic optimization framework where learning is performed from partial feedback. This feedback is received in the form of a task loss evaluation to a predicted output structure, without having access to gold standard structures. We advance this framework by lifting linear bandit learning to neural sequence-to-sequence learning problems using attention-based recurrent neural networks. Furthermore, we show how to incorporate control variates into our learning algorithms for variance reduction and improved generalization. We present an evaluation on a neural machine translation task that shows improvements of up to 5.89 BLEU points for domain adaptation from simulated bandit feedback.
Estimating Fingerprint Deformation

Fingerprint matching is affected by the nonlinear distortion introduced in fingerprint impressions during the image acquisition process. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. In this paper we develop an average deformation model for a fingerprint impression (baseline impression) by observing its relative distortion with respect to several other impressions of the same finger. The deformation is computed using a Thin Plate Spline (TPS) model that relies on ridge curve correspondences between image pairs. The estimated average deformation is used to distort the minutiae template of the baseline impression prior to matching. An index of deformation has been proposed to select the average deformation model with the least variability corresponding to a finger. Preliminary results indicate that the average deformation model can improve the matching performance of a fingerprint matcher.
Exponential Moving Average Model in Parallel Speech Recognition Training

As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently. We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model. It is a non-interference strategy that the exponential moving average model is not broadcasted to distributed workers to update their local models after model synchronization in the training process, and it is implemented as the final model of the training system. Fully-connected feed-forward neural networks (DNNs) and deep unidirectional Long short-term memory (LSTM) recurrent neural networks (RNNs) are successfully trained with proposed method for large vocabulary continuous speech recognition on Shenma voice search data in Mandarin. The character error rate (CER) of Mandarin speech recognition further degrades than state-of-the-art approaches of parallel training.
Racial Bias in Motor Vehicle Searches : Theory and Evidence

Police checking for illegal drugs are much more likely to search the vehicles of African-American motorists than those of white motorists. This paper develops a model of police and motorist behavior that suggests an empirical test for distinguishing whether this disparity is due to racial prejudice or to the police’s objective to maximize arrests. When applied to vehicle search data from Maryland, our test results are consistent with the hypothesis of no racial prejudice against African-American motorists. However, if police have utility only for searches yielding large drug finds, then our analysis would suggest bias against white drivers. The model’s prediction regarding nonrace characteristics is also largely supported by the data.
Not All Oil Price Shocks Are Alike : Disentangling Demand and Supply Shocks in the Crude Oil Market

Using a newly developed measure of global real economic activity, a structural decomposition of the real price of crude oil into three components is proposed: crude oil supply shocks; shocks to the global demand for all industrial commodities; and demand shocks that are specific to the crude oil market. The latter shock is designed to capture shifts in the price of oil driven by higher precautionary demand associated with concerns about future oil supply shortfalls. The paper estimates the dynamic effects of these shocks on the real price of oil. A historical decomposition sheds light on the causes of the major oil price shocks since 1975. The implications of higher oil prices for U.S. real GDP and CPI inflation are shown to depend on the cause of the oil price increase. Changes in the composition of shocks help explain why regressions of macroeconomic aggregates on oil prices tend to be unstable. Evidence that the recent increase in crude oil prices was driven primarily by global aggregate demand shocks helps explain why this oil price shock so far has failed to cause a major recession in the U.S.
An fMRI investigation of emotional engagement in moral judgment.

The long-standing rationalist tradition in moral psychology emphasizes the role of reason in moral judgment. A more recent trend places increased emphasis on emotion. Although both reason and emotion are likely to play important roles in moral judgment, relatively little is known about their neural correlates, the nature of their interaction, and the factors that modulate their respective behavioral influences in the context of moral judgment. In two functional magnetic resonance imaging (fMRI) studies using moral dilemmas as probes, we apply the methods of cognitive neuroscience to the study of moral judgment. We argue that moral dilemmas vary systematically in the extent to which they engage emotional processing and that these variations in emotional engagement influence moral judgment. These results may shed light on some puzzling patterns in moral judgment observed by contemporary philosophers.
Deadbeat current control of qZS based grid-connected multilevel inverter

This paper proposes a Current Deadbeat Control (DBC) algorithm for a three-phase 9-cells qZS based Cascaded H-Bridge (CHB) inverter to ensure grid current supply with low THD. The studied topology is considered as a single-stage DC/AC topology having the capability of boosting the DC input voltage with high-quality multilevel AC voltage, independent DC-link voltage compensation, and control of the power transfer with high reliability. Moreover, a Repetitive Controller (RC) is connected to the inductor current loop with the aim to reject periodic disturbances (double line-frequency fluctuations). The employed RC has the capability of learning through iterations based on tracking error. During the control, the compensation method uses the error between the DC-link peak voltage reference and the actual value to produce the compensating inductor current reference. Theoretical analysis and simulation results shows that the proposed solution achieves high dynamic performance and reduced cyclical disturbances effects.
StyleBank: An Explicit Representation for Neural Image Style Transfer

We propose StyleBank, which is composed of multiple convolution filter banks and each filter bank explicitly represents one style, for neural image style transfer. To transfer an image to a specific style, the corresponding filter bank is operated on top of the intermediate feature embedding produced by a single auto-encoder. The StyleBank and the auto-encoder are jointly learnt, where the learning is conducted in such a way that the auto-encoder does not encode any style information thanks to the flexibility introduced by the explicit filter bank representation. It also enables us to conduct incremental learning to add a new image style by learning a new filter bank while holding the auto-encoder fixed. The explicit style representation along with the flexible network design enables us to fuse styles at not only the image level, but also the region level. Our method is the first style transfer network that links back to traditional texton mapping methods, and hence provides new understanding on neural style transfer. Our method is easy to train, runs in real-time, and produces results that qualitatively better or at least comparable to existing methods.
11.5 A time-correlated single-photon-counting sensor with 14GS/S histogramming time-to-digital converter

Time-correlated single photon counting (TCSPC) is a photon-efficient technique to record ultra-fast optical waveforms found in numerous applications such as time-of-flight (ToF) range measurement (LIDAR) , ToF 3D imaging [2], scanning optical microscopy , diffuse optical tomography (DOT) and Raman sensing . Typical instrumentation consists of a pulsed laser source, a discrete detector such as an avalanche photodiode (APD) or photomultiplier tube (PMT), time-to-digital converter (TDC) card and a FPGA or PC to assemble and compute histograms of photon time stamps. Cost and size restrict the number of channels of TCSPC hardware. Having few detection and conversion channels, the technique is limited to processing optical waveforms with low intensity, with less than one returned photon per laser pulse, to avoid pile-up distortion . However, many ultra-fast optical waveforms exhibit high dynamic range in the number of photons emitted per laser pulse. Examples are signals observed at close range in ToF with multiple reflections, diffuse reflected photons in DOT or local variations in fluorescent dye concentration in microscopy. This paper provides a single integrated chip that reduces conventional TCSPC pile-up mechanisms by an order of magnitude through ultra-parallel realizations of both photon detection and time-resolving hardware. A TDC architecture is presented which combines the two step iterated TCSPC process of time-code generation, followed by memory lookup, increment and write, into one parallel direct-to-histogram conversion. The sensor achieves 71.4ps resolution, over 18.85ns dynamic range, with 14GS/s throughput. The sensor can process 1.7Gphoton/s and generate 21k histograms/s (with 4.6μs readout time), each capturing a total of 1.7kphotons in a 1μs exposure.