nils-herrmann/clean_scirepeval huggingface dataset:
Predicting occupancy trends in Barcelona's bicycle service stations using open data

In 2008, the CEO of the company that manages and maintains the public bicycle service in Barcelona1 recognized that one may not expect to always find a place to leave the rented bike nearby their destination, similarly to the case when, driving a car, people may not find a parking lot2. In this work, we make predictions about the statuses of the stations of the public bicycle service in Barcelona. We show that it is feasible to correctly predict nearly half of the times when the stations are either completely full of bikes or completely empty, up to 2 days before they actually happen. That is, users might avoid stations at times when they could not return a bicycle that they have rented before, or when they would not find a bike to rent. To achieve that, we apply the Random Forest algorithm to classify the status of the stations and improve the lifetime of the models using publicly available data, such as information about the weather forecast. Finally, we expect that the results of the predictions can be used to improve the quality of the service and make it more reliable for the users.
A dynamic bandwidth allocation algorithm in mobile networks with big data of users and networks

Data collection has become easy due to the rapid development of both mobile devices and wireless networks. In each second, numerous data are generated by user devices and collected through wireless networks. These data, carrying user and network related information, are invaluable for network management. However, they were seldom employed to improve network performance in existing research work. In this article we propose a bandwidth allocation algorithm to increase the throughput of cellular network users by exploring user and network data collected from user devices. With the aid of these data, users can be categorized into clusters and share bandwidth to improve the resource utilization of the network. Simulation results indicate that the proposed scheme is able to rationally form clusters among mobile users and thus significantly increase the throughput and bandwidth efficiency of the network.
Energy Efficient Neural Codes

In 1969 Barlow introduced the phrase economy of impulses to express the tendency for successive neural systems to use lower and lower levels of cell firings to produce equivalent encodings. From this viewpoint, the ultimate economy of impulses is a neural code of minimal redundancy. The hypothesis motivating our research is that energy expenditures, e.g., the metabolic cost of recovering from an action potential relative to the cost of inactivity, should also be factored into the economy of impulses. In fact, coding schemes with the largest representational capacity are not, in general, optimal when energy expenditures are taken into account. We show that for both binary and analog neurons, increased energy expenditure per neuron implies a decrease in average firing rate if energy efficient information transmission is to be maintained.
Learning to Compute Word Embeddings On the Fly

Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the “long tail” of this distribution requires enormous amounts of data. Representations of rare words trained directly on end-tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained against the end task. We show that this improves results against baselines where embeddings are trained on the end task in a reading comprehension task, a recognizing textual entailment task, and in language modelling.
Multilevel Converters: An Enabling Technology for High-Power Applications

Multilevel converters are considered today as the state-of-the-art power-conversion systems for high-power and power-quality demanding applications. This paper presents a tutorial on this technology, covering the operating principle and the different power circuit topologies, modulation methods, technical issues and industry applications. Special attention is given to established technology already found in industry with more in-depth and self-contained information, while recent advances and state-of-the-art contributions are addressed with useful references. This paper serves as an introduction to the subject for the not-familiarized reader, as well as an update or reference for academics and practicing engineers working in the field of industrial and power electronics.
An Empirical Study of Operating System Errors

We present a study of operating system errors found by automatic, static, compiler analysis applied to the Linux and OpenBSD kernels. Our approach differs from previous studies that consider errors found by manual inspection of logs, testing, and surveys because static analysis is applied uniformly to the entire kernel source, though our approach necessarily considers a less comprehensive variety of errors than previous studies. In addition, automation allows us to track errors over multiple versions of the kernel source to estimate how long errors remain in the system before they are fixed.We found that device drivers have error rates up to three to seven times higher than the rest of the kernel. We found that the largest quartile of functions have error rates two to six times higher than the smallest quartile. We found that the newest quartile of files have error rates up to twice that of the oldest quartile, which provides evidence that code "hardens" over time. Finally, we found that bugs remain in the Linux kernel an average of 1.8 years before being fixed.
*droid: Assessment and Evaluation of Android Application Analysis Tools

The security research community has invested significant effort in improving the security of Android applications over the past half decade. This effort has addressed a wide range of problems and resulted in the creation of many tools for application analysis. In this article, we perform the first systematization of Android security research that analyzes applications, characterizing the work published in more than 17 top venues since 2010. We categorize each paper by the types of problems they solve, highlight areas that have received the most attention, and note whether tools were ever publicly released for each effort. Of the released tools, we then evaluate a representative sample to determine how well application developers can apply the results of our community’s efforts to improve their products. We find not only that significant work remains to be done in terms of research coverage but also that the tools suffer from significant issues ranging from lack of maintenance to the inability to produce functional output for applications with known vulnerabilities. We close by offering suggestions on how the community can more successfully move forward.
Fear Conditioning in Humans The Influence of Awareness and Autonomic Arousal on Functional Neuroanatomy

The degree to which perceptual awareness of threat stimuli and bodily states of arousal modulates neural activity associated with fear conditioning is unknown. We used functional magnetic neuroimaging (fMRI) to study healthy subjects and patients with peripheral autonomic denervation to examine how the expression of conditioning-related activity is modulated by stimulus awareness and autonomic arousal. In controls, enhanced amygdala activity was evident during conditioning to both "seen" (unmasked) and "unseen" (backward masked) stimuli, whereas insula activity was modulated by perceptual awareness of a threat stimulus. Absent peripheral autonomic arousal, in patients with autonomic denervation, was associated with decreased conditioning-related activity in insula and amygdala. The findings indicate that the expression of conditioning-related neural activity is modulated by both awareness and representations of bodily states of autonomic arousal.
Design considerations and development of gate driver for 15 kV SiC IGBT

The 15 kV SiC N-IGBT is the state-of-the-art high voltage power semiconductor device developed by Cree. The SiC IGBT is exposed to a peak stress of 10-11 kV in power converter systems, with punch-through turn-on dv/dt over 100 kV/μs and turn-off dv/dt about 35 kV/μs. Such high dv/dt requires ultralow coupling capacitance in the dc-dc isolation stage of the gate driver for maintaining fidelity of the signals on the control-supply ground side. Accelerated aging of the insulation in the isolation stage is another serious concern. In this paper, a simple transformer based isolation with a toroid core is investigated for the above requirements of the 15 kV IGBT. The gate driver prototype has been developed with over 100 kV dc insulation capability, and its inter-winding coupling capacitance has been found to be 3.4 pF and 13 pF at 50 MHz and 100 MHz respectively. The performance of the gate driver prototype has been evaluated up to the above mentioned specification using double-pulse tests on high-side IGBT in a half-bridge configuration. The continuous testing at 5 kHz has been performed till 8 kV, and turn-on dv/dt of 85 kV/μs on a buck-boost converter. The corresponding experimental results are presented. Also, the test methodology of evaluating the gate driver at such high voltage, without a high voltage power supply is discussed. Finally, experimental results validating fidelity of the signals on the control-ground side are provided to show the influence of increased inter-winding coupling capacitance on the performance of the gate driver.
A New Resonant Gate-Drive Circuit With Efficient Energy Recovery and Low Conduction Loss

In this paper, a new resonant gate-drive circuit is proposed to recover a portion of the power-MOSFET-gate energy that is typically dissipated in high-frequency converters. The proposed circuit consists of four control switches and a small resonant inductance. The current through the resonant inductance is discontinuous in order to minimize circulating-current conduction loss that is present in other methods. The proposed circuit also achieves quick turn-on and turn-off transition times to reduce switching and conduction losses in power MOSFETs. An analysis, a design procedure, and experimental results are presented for the proposed circuit. Experimental results demonstrate that the proposed driver can recover 51% of the gate energy at 5-V gate-drive voltage.
Handheld devices for applications using dynamic multimedia data

Growing demand for ubiquitous and pervasive computing has triggered a sharp rise in handheld device usage. At the same time, dynamic multimedia data has become accepted as core material which many important applications depend on, despite intensive costs in computation and resources. This paper investigates the suitability and constraints of using handheld devices for such applications. We firstly analyse the capabilities and limitations of current models of handheld devices and advanced features offered by next generation models. We then categorise these applications and discuss the typical requirements of each class. Important issues to be considered include data organisation and management, communication, and input and user interfaces. Finally, we briefly discuss future outlook and identify remaining areas for research.
Why do we punish? Deterrence and just deserts as motives for punishment.

One popular justification for punishment is the just deserts rationale: A person deserves punishment proportionate to the moral wrong committed. A competing justification is the deterrence rationale: Punishing an offender reduces the frequency and likelihood of future offenses. The authors examined the motivation underlying laypeople's use of punishment for prototypical wrongs. Study 1 (N = 336) revealed high sensitivity to factors uniquely associated with the just deserts perspective (e.g., offense seriousness, moral trespass) and insensitivity to factors associated with deterrence (e.g., likelihood of detection, offense frequency). Study 2 (N = 329) confirmed the proposed model through structural equation modeling (SEM). Study 3 (N = 351) revealed that despite strongly stated preferences for deterrence theory, individual sentencing decisions seemed driven exclusively by just deserts concerns.
Compact Multiband Antenna for Handsets With a Conducting Edge

This communication proposes a compact multiband antenna fed by microstrip coupling for handsets with conducting edge applications. The antenna is composed of a U-shaped loop, an inserted open-end T-shaped slot, and a feed line with a compact antenna area. Three types of resonant modes are excited, namely loop mode, slot mode, and monopole mode. Parametric studies have been performed. After the related parameters are controlled, the bandwidth of this antenna has the potential to cover the mobile bands of GSM (824-960 MHz), DCS (1710-1880 MHz), PCS (1850-1990 MHz), UMTS (1920-2170 MHz), LTE bands (FDD-LTE bands 1-10, 15, 16, 18-20, 22, 23, 25-27, and 30 and TDD-LTE bands 33-43). Good radiation characteristics, such as gain and radiation efficiency, are obtained with these operating bands. In addition, the structure of the inserted slot antenna reduced the user's hand effects on the radiation efficiency.
Trajectory Clustering with Mixtures of Regression Models

In this paper we address the problem of clustering trajectories, namely sets of short sequences of data measured as a function of a dependent variable such as time. Examples include storm path trajectories, longitudinal data such as drug therapy response, functional expression data in computational biology, and movements of objects or individuals in video sequences. Our clustering algorithm is based on a principled method for probabilistic modelling of a set of trajectories as individual sequences of points generated from a nite mixture model consisting of regression model components. Unsupervised learning is carried out using maximum likelihood principles. Speci cally, the EM algorithm is used to cope with the hidden data problem (i.e., the cluster memberships). We also develop generalizations of the method to handle non-parametric (kernel) regression components as well as multi-dimensional outputs. Simulation results comparing our method with other clustering methods such as K-means and Gaussian mixtures are presented as well as experimental results on real data sets. 0 5 10 15 20 25 30 35 200 220 240 260 280 300 320 340 Frame Number (in video sequence) V er tic al p ix el c oo rd in at e of e st im at ed c en tr oi d of h an d Figure 1: Trajectories of the estimated vertical position of a moving hand as a function of time, estimated from 6 di erent video sequences.
A 5.4/2.7/1.62-Gb/s Receiver for DisplayPort Version 1.2 With Multi-Rate Operation Scheme

A 5.4/2.7/1.62-Gb/s multi-rate receiver is designed for Display Port version 1.2. A dual-mode binary phase detector supports half-rate and quarter-rate phase detections to enable the multi-rate operation of the receiver without a wide-tuning VCO. In addition, a low voltage-drop active inductor with a voltage booster is implemented in the dual-mode binary phase detector to extend the bandwidth and reduce the power consumption. The voltage booster generates 1.904 V from 1.2-V supply with fast voltage generation time and small area consumption. A bandwidth controllable equalizer is proposed to optimize channel loss compensation even if the Nyquist frequency of input data changes. The BER for all input data rates is less than 10-12 for 27-1 PRBS and the measured jitter characteristics indicate that the proposed receiver exceeds the DisplayPort jitter tolerance specification. The recovered 1.35-GHz clock shows the peak-to-peak jitter of 29.9 ps and the rms jitter of 3.215 ps for 5.4-Gb/s input. The energy efficiency of the CDR circuit in the receiver is 19.3 pJ/bit at 5.4 Gb/s. The receiver occupies 0.672 mm2 including decoupling capacitors and the CDR core area is 0.44 mm2.
Better call Saul: Flexible Programming for Learning and Inference in NLP

We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP). We enrich Saul with components that are necessary for a broad range of learning based Natural Language Processing tasks at various levels of granularity. We illustrate these advances using three different, well-known NLP problems, and show how these generic learning and inference modules can directly exploit Saul’s graph-based data representation. These properties allow the programmer to easily switch between different model formulations and configurations, and consider various kinds of dependencies and correlations among variables of interest with minimal programming effort. We argue that Saul provides an extremely useful paradigm both for the design of advanced NLP systems and for supporting advanced research in NLP.
The increasing burden of depression

Recent epidemiological surveys conducted in general populations have found that the lifetime prevalence of depression is in the range of 10% to 15%. Mood disorders, as defined by the World Mental Health and the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, have a 12-month prevalence which varies from 3% in Japan to over 9% in the US. A recent American survey found the prevalence of current depression to be 9% and the rate of current major depression to be 3.4%. All studies of depressive disorders have stressed the importance of the mortality and morbidity associated with depression. The mortality risk for suicide in depressed patients is more than 20-fold greater than in the general population. Recent studies have also shown the importance of depression as a risk factor for cardiovascular death. The risk of cardiac mortality after an initial myocardial infarction is greater in patients with depression and related to the severity of the depressive episode. Greater severity of depressive symptoms has been found to be associated with significantly higher risk of all-cause mortality including cardiovascular death and stroke. In addition to mortality, functional impairment and disability associated with depression have been consistently reported. Depression increases the risk of decreased workplace productivity and absenteeism resulting in lowered income or unemployment. Absenteeism and presenteeism (being physically present at work but functioning suboptimally) have been estimated to result in a loss of $36.6 billion per year in the US. Worldwide projections by the World Health Organization for the year 2030 identify unipolar major depression as the leading cause of disease burden. This article is a brief overview of how depression affects the quality of life of the subject and is also a huge burden for both the family of the depressed patient and for society at large.
A rapid hierarchical radiosity algorithm

This paper presents a rapid hierarchical radiosity algorithm for illuminating scenes containing large polygonal patches. The algorithm constructs a hierarchical representation of the form factor matrix by adaptively subdividing patches into subpatches according to a user-supplied error bound. The algorithm guarantees that all form factors are calculated to the same precision, removing many common image artifacts due to inaccurate form factors. More importantly, the algorithm decomposes the form factor matrix into at most O(n) blocks (where n is the number of elements). Previous radiosity algorithms represented the element-to-element transport interactions with n2 form factors. Visibility algorithms are given that work well with this approach. Standard techniques for shooting and gathering can be used with the hierarchical representation to solve for equilibrium radiosities, but we also discuss using a brightness-weighted error criteria, in conjunction with multigridding, to even more rapidly progressively refine the image.
Sensorless drive of permanent magnet brushless DC motor with 180 degree commutation

The three-phase wye connected permanent magnet brushless dc motor is conventionally driven by 120 degree commutation. Two phases are conducting current and the other one is always floating without any torque produced in each conduction interval. Rather than the conventional 120 degree drive, all three phases of 180 degree commutation are expected to conduct current in all sectors, which results in more power delivered from inverter side to the motor side for the same power supply voltage. In this paper, a recently proposed sensorless algorithm is highlighted with well performance in low speed operation. Based on dSPACE, comparison of different dynamic conditions between 120 and 180 degree commutation is presented and analyzed comprehensively. Extensive experiment tests show excellent results on dynamic performance of 180 degree commutation, which matches the simulation results from Simulink/Matlab. 180 degree commutation is verified to work properly with the ability to deliver more power when compared with conventional 120 degree commutation.
Unmanned aerial vehicle smart device ground control station cyber security threat model

The Department of the Defense has transitioned smart devices into the battlefield as a portable hand-held unmanned aerial vehicle ground control station without adequate cyber security protections, putting critical mission data at risk to cyber security attacks. Industry has developed software apps for smart phones and tablets that allows soldiers to not only pilot unmanned aerial vehicles (UAVs) but to share and receive intelligence and reconnaissance videos and images remotely from the ground control station (GCS) or directly from the UAV. The Department of Defense has not developed a secure communication network that will support a large quantity of smart devices, nor certification, standards or policies for operation of secure smart devices. Therefore, mission critical information will be shared through unsecured, mobile and wireless networks and through unclassified, unsecure smart technology that are vulnerable to cyber security risks. Lack of security of the mobile and wireless networks and smart devices could result in the unintentional sharing of data as well as loss of control of the UAV to enemies. The Department of Defense has failed to develop a threat model and risk assessment to identify the cyber security threats and ensure the proper security countermeasures are in place. This paper will analyze the cyber security vulnerabilities within the communication links, smart devices hardware, specifically smart phones and tablets, and software applications to develop a risk model of the threat profile of the GCS networking hub. This model will help designers and users of the military and civilian UAV communities to understand the threat profile of the GCS networking hub to develop a secure communication network based upon the vulnerabilities identified for smart phones and tablets.