nils-herrmann/clean_scirepeval huggingface dataset:
HIGH DIMENSIONAL SPACES

In this paper, we analyze deep learning from a mathematical point of view and derive several novel results. The results are based on intriguing mathematical properties of high dimensional spaces. We first look at perturbation based adversarial examples and show how they can be understood using topological arguments in high dimensions. We point out fallacy in an argument presented in a published paper in 2015 by Goodfellow et al., see reference , and we present a more rigorous, general and correct mathematical result to explain adversarial examples in terms of topology of image manifolds. Second, we look at optimization landscapes of deep neural networks and examine the number of saddle points relative to that of local minima. Third, we show how multiresolution nature of images explains perturbation based adversarial examples in form of a stronger result. Our results state that expectation of L2-norm of adversarial perturbations shrinks to 0 as image resolution becomes arbitrarily large. Finally, by incorporating the parts-whole manifold learning hypothesis for natural images, we investigate the working of deep neural networks and root causes of adversarial examples and discuss how future improvements can be made and how adversarial examples can be eliminated.
Fears, phobias, and preparedness: toward an evolved module of fear and fear learning.

An evolved module for fear elicitation and fear learning with 4 characteristics is proposed. (a) The fear module is preferentially activated in aversive contexts by stimuli that are fear relevant in an evolutionary perspective. (b) Its activation to such stimuli is automatic. (c) It is relatively impenetrable to cognitive control. (d) It originates in a dedicated neural circuitry, centered on the amygdala. Evidence supporting these propositions is reviewed from conditioning studies, both in humans and in monkeys; illusory correlation studies; studies using unreportable stimuli; and studies from animal neuroscience. The fear module is assumed to mediate an emotional level of fear learning that is relatively independent and dissociable from cognitive learning of stimulus relationships.
Combining multiple matchers for a high security fingerprint verification system

Integration of various ®ngerprint matching algorithms is a viable method to improve the performance of a ®ngerprint veri®cation system. Dierent ®ngerprint matching algorithms are often based on dierent representations of the input ®ngerprints and hence complement each other. We use the logistic transform to integrate the output scores from three dierent ®ngerprint matching algorithms. Experiments conducted on a large ®ngerprint database con®rm the eectiveness of the proposed integration scheme. Ó 1999
Push and rotate: cooperative multi-agent path planning

In cooperative multi-agent path planning, agents must move between start and destination locations and avoid collisions with each other. Many recent algorithms require some sort of restriction in order to be complete, except for the Push and Swap algorithm , which claims only to require two unoccupied locations in a connected graph. Our analysis shows, however, that for certain types of instances Push and Swap may fail to find a solution. We present the Push and Rotate algorithm, an adaptation of the Push and Swap algorithm, and prove that by fixing the latter’s shortcomings, we obtain an algorithm that is complete for the class of instances with two unoccupied locations in a connected graph. In addition, we provide experimental results that show our algorithm to perform competitively on a set of benchmark problems from the video game industry.
Controllability and Posture Control of a Wheeled Pendulum Moving on an Inclined Plane

The paper considers a specific class of wheeled mobile robots, namely, mobile wheeled pendulums (MWPs). Robots pertaining to this class are composed of two wheels rotating about a central body. The main feature of MWP pertains to the central body, which can rotate about the wheel axis. As such motion is undesirable, the problem of the stabilization of the central body in an MWP is crucial. The novelty of the work reported here resides in the construction of: 1) the system controllability Lie algebra for the purpose of a rigorous controllability analysis and the computation of the largest feedback-linearizable subsystem; 2) a controller by input-output linearization of the system for achieving the desired steering rate of the robot while stabilizing the central body; 3) a controller based on the internal properties of the system to achieve the desired heading velocity of the robot; and 4) a controller based on the sliding-mode approach for controlling both the position and the orientation of the robot. The entire control structure that permits full control of the robot posture comprises three imbricated loops. Simulations showing good performance of the controlled system are provided. Preliminary tests performed on an experimental platform confirm the validity of the controller.
Extending drift-diffusion paradigm into the era of FinFETs and nanowires

This paper presents a feasibility study that the drift-diffusion model can capture the ballistic transport of FinFETs and nanowires with a simple model extension. For FinFETs, Monte Carlo simulation is performed and the ballistic mobility is calibrated to linear & saturation currents. It is validated that the calibrated model works over a wide range of channel length and channel stress. The ballistic mobility model is then applied to a nanowire with 5nm design rules. Finally, the technology scaling trend of the ballistic ratio is explored.
Supporting Complex Search Tasks

We present methods to automatically identify and recommend sub-tasks to help people explore and accomplish complex search tasks. Although Web searchers often exhibit directed search behaviors such as navigating to a particular Website or locating a particular item of information, many search scenarios involve more complex tasks such as learning about a new topic or planning a vacation. These tasks often involve multiple search queries and can span multiple sessions. Current search systems do not provide adequate support for tackling these tasks. Instead, they place most of the burden on the searcher for discovering which aspects of the task they should explore. Particularly challenging is the case when a searcher lacks the task knowledge necessary to decide which step to tackle next. In this paper, we propose methods to automatically mine search logs for tasks and build an association graph connecting multiple tasks together. We then leverage the task graph to assist new searchers in exploring new search topics or tackling multi-step search tasks. We demonstrate through experiments with human participants that we can discover related and interesting tasks to assist with complex search scenarios.
Effective Dynamic Voltage Scaling Through CPU-Boundedness Detection

Dynamic voltage scaling (DVS) allows a program to execute at a non-peak CPU frequency in order to reduce CPU power, and hence, energy consumption; however, it is done at the cost of performance degradation. For a program whose execution time is bounded by peripherals’ performance rather than the CPU speed, applying DVS to the program will result in negligible performance penalty. Unfortunately, existing DVS-based power-management algorithms are conservative in the sense that they overly exaggerate the impact that the CPU speed has on the execution time, e.g., they assume that the execution time will double if the CPU speed is halved. Based on a new single-coefficient performance model, we propose a DVS algorithm that detects the CPU-boundedness of a program on the fly (via a regression method on the past MIPS rate) and then adjusts the CPU frequency accordingly. To illustrate its effectiveness, we compare our algorithm with other DVS algorithms on real systems via physical measurements.
A survey of trust in internet applications

Trust is an important aspect of decision making for Internet applications and particularly influences the specification of security policy, i.e., who is authorized to perform actions as well as the techniques needed to manage and implement security to and for the applications. This survey examines the various definitions of trust in the literature and provides a working definition of trust for Internet applications. The properties of trust relationships are explained and classes of different types of trust identified in the literature are discussed with examples. Some influential examples of trust management systems are described.
Bee Colony Optimization with local search for traveling salesman problem

Many real world industrial applications involve finding a Hamiltonian path with minimum cost. Some instances that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a bee colony optimization (BCO) algorithm for traveling salesman problem (TSP). The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The model is integrated with 2-opt heuristic to further improve prior solutions generated by the BCO model. Experimental results comparing the proposed BCO model with existing approaches on a set of benchmark problems are presented.
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets

A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.
It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.
A 5GHz Digital Fractional- $N$ PLL Using a 1-bit Delta–Sigma Frequency-to-Digital Converter in 65 nm CMOS

A highly digital two-stage fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> phase-locked loop (PLL) architecture utilizing a first-order 1-bit <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> frequency-to-digital converter (FDC) is proposed and implemented in a 65nm CMOS process. Performance of the first-order 1-bit <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC is improved by using a phase interpolator-based fractional divider that reduces phase quantizer input span and by using a multiplying delay-locked loop that increases its oversampling ratio. We also describe an analogy between a time-to-digital converter (TDC) and a <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC followed by an accumulator that allows us to leverage the TDC-based PLL analysis techniques to study the impact of <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC characteristics on <inline-formula> <tex-math notation="LaTeX">$ \Delta \!\Sigma $ </tex-math></inline-formula> FDC-based fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> PLL (FDCPLL) performance. Utilizing proposed techniques, a prototype PLL achieves 1 MHz bandwidth, −101.6 dBc/Hz in-band phase noise, and <inline-formula> <tex-math notation="LaTeX">$ {\textrm {1.22 ps}_{\textrm {rms}}}$ </tex-math></inline-formula> (1 kHz–40 MHz) jitter while generating 5.031GHz output from 31.25MHz reference clock input. For the same output frequency, the stand-alone second-stage fractional-<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> FDCPLL achieves 1MHz bandwidth, −106.1dBc/Hz in-band phase noise, and <inline-formula> <tex-math notation="LaTeX">$ {\textrm {403 fs}_{\textrm {rms}}}$ </tex-math></inline-formula> jitter with a 500MHz reference clock input. The two-stage PLL consumes 10.1mW power from a 1V supply, out of which 7.1 mW is consumed by the second-stage FDCPLL.
CRIME PLACES IN CRIME THEORY by

Criminologists and crime prevention practitioners are increasingly aware of the importance of places of crime. A place is a very small area, usually a street corner, address, building, or street segment. A focus on crime places contrasts with a focus on neighborhoods. Neighborhood theories usually highlight the development of offenders. while place level explanations emphasize crime events. Three perspectives suggest the importance of places for understanding crime: rational choice; routine activity theory; and crime pattern theory. Though these perspectives are mutually supportive, routine activity theory and crime pattern theory provide different explanations for crime occurring at different places. Five areas of research help us understand the importance of places: crime concentration about particular facilities (e.g., bars); the high concentration ofcrime at some addresses and the absence of crime at others; the preventive effects of various place features; the mobility of offenders; and studies of how offenders select targets. Concern has been expressed that efforts to prevent crime at specific locations will only move it to other, unprotected locations. Recent research suggests that these fears may be exaggerated, and that under some circumstances the opposite effect occurs: instead of crime displacing, the benefits of the prevention efforts diffuse to unprotected locations. This paper concludes with a review of the 14 original articles in this volume. Following a rape at an Orlando motel, the victim sued the motel owners. She claimed that the crime was foreseeable and that the motel had not taken sufficient precautions to prevent such incidents. 1 Address correspondence to: John Eck, Department of Criminology and Criminal Justice, Suite 2220 LeFrak Hall, University of Maryland, College Park, MD 20742.
50-Gb/s Direct Modulation of a 1.3-μm InGaAlAs-Based DFB Laser With a Ridge Waveguide Structure

We demonstrate 50-Gb/s direct modulation by using 1.3-μm distributed-feedback lasers with a ridge waveguide structure. We employed InGaAlAs material for a multiple-quantum well to obtain a low damping factor K, and fabricated a ridge waveguide structure buried in benzocyclobutene to realize a structure with a low parasitic capacitance. In addition, to obtain high maximum frequency relaxation oscillations fr, we designed the cavity length L), and achieved a 3-dB-down frequency bandwidth of 34 GHz. We realized 50-Gb/s clear eye openings with a back-to-back configuration, and achieved a mean output power of over 5.0 dBm, and a dynamic extinction ratio of 4.5 dB. We measured the 50-Gb/s transmission characteristics, and obtained clear eye openings for transmissions over 20-, 40-, and 60-km single-mode fibers (SMF). We also measured the bit-error-rate performance, and obtained an error-free operation and a power penalty of less than 0.5 dB after a 10-km SMF transmission.
PeerFlow: Secure Load Balancing in Tor

We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidthscanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.
OpenML: networked science in machine learning

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.
Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation

This paper investigates problems of image style, aesthetics, and quality estimation, which require fine-grained details from high-resolution images, utilizing deep neural network training approach. Existing deep convolutional neural networks mostly extracted one patch such as a down-sized crop from each image as a training example. However, one patch may not always well represent the entire image, which may cause ambiguity during training. We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image. We achieve this by constructing multiple, shared columns in the neural network and feeding multiple patches to each of the columns. More importantly, we propose two novel network layers (statistics and sorting) to support aggregation of those patches. The proposed deep multi-patch aggregation network integrates shared feature learning and aggregation function learning into a unified framework. We demonstrate the effectiveness of the deep multi-patch aggregation network on the three problems, i.e., image style recognition, aesthetic quality categorization, and image quality estimation. Our models trained using the proposed networks significantly outperformed the state of the art in all three applications.
Examining social adjustment to college in the age of social media: Factors influencing successful transitions and persistence

Social adjustment plays a critical role in student persistence at college. Social media such as Facebook, used widely by this population, have the potential to positively enhance students’ transition to college by encouraging connection and interaction among peers. The present study examines the role Facebook plays in students’ social adjustment during their first year of college using survey data (N = 338) collected from students at a private, liberal arts college in the Midwest. We develop and test a model that includes both traditional and Facebook-specific predictors of social support and social adjustment, as well as explore the role that these factors play in predicting students’ enrollment status the following year. Results indicate positive relationships between two Facebook variables the number of Facebook Friends students have at the college and their engagement in collaborative behaviors with classmates through the site and measures of social support and social adjustment, as well as a positive relationship between social adjustment and persistence at the university. Gray, R., Vitak, J., Easton, E. W., & Ellison, N. B. (2013). Examining social adjustment to college in the age of social media: Factors influencing successful transitions and persistence. Computers & Education, 67, 193-207. doi:10.1016/j.compedu.2013.02.021
Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.