nils-herrmann/clean_scirepeval huggingface dataset:
Batch Size Matters: A Diffusion Approximation Framework on Nonconvex Stochastic Gradient Descent

In this paper, we study the stochastic gradient descent method in analyzing nonconvex statistical optimization problems from a diffusion approximation point of view. Using the theory of large deviation of random dynamical system, we prove in the small stepsize regime and the presence of omnidirectional noise the following: starting from a local minimizer (resp. saddle point) the SGD iteration escapes in a number of iteration that is exponentially (resp. linearly) dependent on the inverse stepsize. We take the deep neural network as an example to study this phenomenon. Based on a new analysis of the mixing rate of multidimensional Ornstein-Uhlenbeck processes, our theory substantiate a very recent empirical results by Keskar et al. (2016), suggesting that large batch sizes in training deep learning for synchronous optimization leads to poor generalization error.
An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface

Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain–computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system).
Residual Networks are Exponential Ensembles of Relatively Shallow Networks

In this work, we introduce a novel interpretation of residual networks showing they are exponential ensembles. This observation is supported by a large-scale lesion study that demonstrates they behave just like ensembles at test time. Subsequently, we perform an analysis showing these ensembles mostly consist of networks that are each relatively shallow. For example, contrary to our expectations, most of the gradient in a residual network with 110 layers comes from an ensemble of very short networks, i.e., only 10-34 layers deep. This suggests that in addition to describing neural networks in terms of width and depth, there is a third dimension: multiplicity, the size of the implicit ensemble. Ultimately, residual networks do not resolve the vanishing gradient problem by preserving gradient flow throughout the entire depth of the network – rather, they avoid the problem simply by ensembling many short networks together. This insight reveals that depth is still an open research question and invites the exploration of the related notion of multiplicity.
SLL suppressed monopulse microstrip antenna design

It is aimed to design a X-band monopulse microstrip antenna array that can be used almost in all modern tracking radars and having superior properties in angle detection and angular accuracy than the classical ones. In order to create a monopulse antenna array, a rectangular microstrip antenna is designed and 16 of it gathered together using the nonlinear central feeding to suppress the side lobe level (SLL) of the antenna. The monopulse antenna is created by the combining 4 of these 4×4 array antennas with a microstrip comparator designed using four branch line coupler. Good agreement is noted between the simulation and measurement results.
A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a Distributed Wireless Sensor Network

Wireless sensor networks (WSNs) are a rapidly emerging technology with a great potential in many ubiquitous applications. Although these sensors can be inexpensive, they are often relatively unreliable when deployed in harsh environments characterized by a vast amount of noisy and uncertain data, such as urban traffic control, earthquake zones, and battlefields. The data gathered by distributed sensors—which serve as the eyes and ears of the system—are delivered to a decision center or a gateway sensor node that interprets situational information from the data streams. Although many other machine learning techniques have been extensively studied, real-time data mining of high-speed and nonstationary data streams represents one of the most promising WSN solutions. This paper proposes a novel stream mining algorithm with a programmable mechanism for handling missing data. Experimental results from both synthetic and real-life data show that the new model is superior to standard algorithms.
Parallel auto-encoder for efficient outlier detection

Detecting outliers from big data plays an important role in network security. Previous outlier detection algorithms are generally incapable of handling big data. In this paper we present an parallel outlier detection method for big data, based on a new parallel auto-encoder method. Specifically, we build a replicator model of the input data to obtain the representation of sample data. Then, the replicator model is used to measure the replicability of test data, where records having higher reconstruction errors are classified as outliers. Experimental results show the performance of the proposed parallel algorithm.
Robot therapy in a care house - its sociopsychological and physiological effects on the residents

Robot therapy for elderly residents in a care house has been conducted from June, 2005. Two therapeutic seal robots were introduced, and activated for over 9 hours every day to interact with the residents. This paper presents a progress report of this experiment. In order to investigate psychological and social influences of the robots, each subject was interviewed, and their social network was analysed. In addition, their hormones in urine: 17 Ketosteroid sulfate (17-KS-S) and 17-hydroxycorticosteroids (17-OHCS) were obtained and analysed. The results indicate that the density of the social networks was increased through interaction with the seal robots. Furthermore, urinary tests showed that the reactions of the subjects' vital organs to stress were improved after the introduction of the robots
Multi-objective reinforcement learning using sets of pareto dominating policies

Many real-world problems involve the optimization of multiple, possibly conflicting objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard reinforcement learning where the scalar reward signal is extended to multiple feedback signals, in essence, one for each objective. MORL is the process of learning policies that optimize multiple criteria simultaneously. In this paper, we present a novel temporal difference learning algorithm that integrates the Pareto dominance relation into a reinforcement learning approach. This algorithm is a multi-policy algorithm that learns a set of Pareto dominating policies in a single run. We name this algorithm Pareto Q-learning and it is applicable in episodic environments with deterministic as well as stochastic transition functions. A crucial aspect of Pareto Q-learning is the updating mechanism that bootstraps sets of Q-vectors. One of our main contributions in this paper is a mechanism that separates the expected immediate reward vector from the set of expected future discounted reward vectors. This decomposition allows us to update the sets and to exploit the learned policies consistently throughout the state space. To balance exploration and exploitation during learning, we also propose three set evaluation mechanisms. These three mechanisms evaluate the sets of vectors to accommodate for standard action selection strategies, such as -greedy. More precisely, these mechanisms use multi-objective evaluation principles such as the hypervolume measure, the cardinality indicator and the Pareto dominance relation to select the most promising actions. We experimentally validate the algorithm on multiple environments with two and three objectives and we demonstrate that Pareto Q-learning outperforms current state-of-the-art MORL algorithms with respect to the hypervolume of the obtained policies. We note that (1) Pareto Q-learning is able to learn the entire Pareto front under the usual assumption that each state-action pair is sufficiently sampled, while (2) not being biased by the shape of the Pareto front. Furthermore, (3) the set evaluation mechanisms provide indicative measures for local action selection and (4) the learned policies can be retrieved throughout the state and action space.
Malware Detection Using Dynamic Birthmarks

In this paper, we compare the effectiveness of Hidden Markov Models (HMMs) with that of Profile Hidden Markov Models (PHMMs), where both are trained on sequences of API calls. We compare our results to static analysis using HMMs trained on sequences of opcodes, and show that dynamic analysis achieves significantly stronger results in many cases. Furthermore, in comparing our two dynamic analysis approaches, we find that using PHMMs consistently outperforms our technique based on HMMs.
An ultra low power high sensitivity wake-up radio receiver with addressing capability

In power-limited wireless devices such as wireless sensor networks, wearable components, and Internet of Things devices energy efficiency is a critical concern. These devices are usually battery operated and have a radio transceiver that is typically their most power-hungry block. Wake-up radio schemes can be used to achieve a reasonable balance among energy consumption, range, data receiving capabilities and response time. In this paper, a high-sensitivity low power wake-up radio receiver (WUR) for wireless sensor networks is presented. The wake-up radio is comprised of a fully passive differential RF-to-DC converter that rectifies the incident RF signal, a low-power comparator and an ultra low power microcontroller to detects the envelope of the on-off keying (OOK) wake-up data used as address. We designed and implemented a novel low power tunable wake up radio with addressing capability, a minimal power consumption of only 196nW and a maximum sensitivity of -55dBm and minimal wake up time of 130μs without addressing and around 1,6ms with 2byte addressing at 10Kbit/s data rate. The flexibility of the solution makes the wake up radio suitable for both power constrained low range application (such as Body Area Network) or applications with long range needs. The wake up radio can work also at different frequencies and the addressing capability directly on board helps reduce false positives. Experimental on field results demonstrate the low power of the solution, the high sensitivity and the functionality.
Anti-spoofing in face recognition with liveness detection using pupil tracking

In this work, we focused on liveness detection for facial recognition system's spoofing via fake face movement. We have developed a pupil direction observing system for anti-spoofing in face recognition systems using a basic hardware equipment. Firstly, eye area is being extracted from real time camera by using Haar-Cascade Classifier with specially trained classifier for eye region detection. Feature points have extracted and traced for minimizing person's head movements and getting stable eye region by using Kanade-Lucas-Tomasi (KLT) algorithm. Eye area is being cropped from real time camera frame and rotated for a stable eye area. Pupils are extracted from eye area by using a new improved algorithm subsequently. After a few stable number of frames that has pupils, proposed spoofing algorithm selects a random direction and sends a signal to Arduino to activate that selected direction's LED on a square frame that has totally eight LEDs for each direction. After chosen LED has been activated, eye direction is observed whether pupil direction and LED's position matches. If the compliance requirement is satisfied, algorithm returns data that contains liveness information. Complete algorithm for liveness detection using pupil tracking is tested on volunteers and algorithm achieved high success ratio.
Hierarchical Control for Multiple DC-Microgrids Clusters

This paper presents a distributed hierarchical control framework to ensure reliable operation of dc microgrid (MG) clusters. In this hierarchy, primary control is used to regulate the common bus voltage inside each MG locally. An adaptive droop method is proposed for this level, which determines droop coefficients according to the state-of-charge (SOC) of batteries automatically. A small-signal model is developed to investigate effects of the system parameters, constant power loads, as well as line impedance between the MGs on stability of these systems. In the secondary level, a distributed consensus-based voltage regulator is introduced to eliminate the average voltage deviation over the MGs. This distributed averaging method allows the power flow control between the MGs to be achieved at the same time, as it can be accomplished only at the cost of having voltage deviation inside the system. Another distributed policy is employed then to regulate the power flow among the MGs according to their local SOCs. The proposed distributed controllers on each MG communicate with only the neighbor MGs through a communication infrastructure. Finally, the developed small-signal model is expanded for MG clusters with all the proposed control loops. The effectiveness of the proposed hierarchical scheme is verified through detailed hardware-in-the-loop simulations.
A Global Perspective of Smart Cities: A Survey

The technological revolution that has taken place in recent decades, driven by advances and developments in Information and Communication Technologies (ICT) has revolutionized the way people communicate, work, travel, live, etc. Cities need to evolve towards intelligent dynamic infrastructures that serve citizens fulfilling the criteria of energy efficiency and sustainability. This article provides an overview of the main smart city applications, and their implementation status in major cities around the world. We also present a study of patents on basic smart city technologies in order to show which countries and companies are making greater efforts to register the intellectual property. The relation between patented technologies and current ongoing smart city applications is also investigated.
The active NPC converter and its loss-balancing control

The three-level neutral-point-clamped voltage-source converter (NPC VSC) is widely used in high-power medium-voltage applications. The unequal loss distribution among the semiconductors is one major disadvantage of this popular topology. This paper studies the loss distribution problem of the NPC VSC and proposes the active NPC VSC to overcome this drawback. The switch states and commutations of the converter are analyzed. A loss-balancing scheme is introduced, enabling a substantially increased output power and an improved performance at zero speed, compared to the conventional NPC VSC.
The SARFID Technique for Discriminating Tagged Items Moving Through a UHF-RFID Gate

The discrimination of tagged items moving along a conveyor belt from other tagged items that are present in the scenario is investigated, when a UHF-RFID gate is installed at a conveyor section. Indeed, tagged items that are static or randomly moving in the scenario (nomad tags) around the reader antenna could be detected even if they are not on the conveyor (false positive readings). The classification procedure here proposed exploits the SARFID phase-based technique used to localize tags on a conveyor belt, which takes advantage of the fact that the tagged items move along a conveyor, whose path and instantaneous speed are both known. The latter can be implemented with only a firmware upgrade, in any conveyor belt scenario already equipped with an RFID system, without any modification of the system infrastructure and additional (reference tags/multiple antennas) or ad hoc hardware. From experimental results in a real scenario, the discrimination between moving tags from static/nomad tags can be obtained with an overall accuracy greater than 99.9%, by employing only one reader antenna.
Breaking the speed and scalability Barriers for Graph exploration on distributed-memory machines

In this paper, we describe the challenges involved in designing a family of highly-efficient Breadth-First Search (BFS) algorithms and in optimizing these algorithms on the latest two generations of Blue Gene machines, Blue Gene/P and Blue Gene/Q. With our recent winning Graph 500 submissions in November 2010, June 2011, and November 2011, we have achieved unprecedented scalability results in both space and size. On Blue Gene/P, we have been able to parallelize a scale 38 problem with 238 vertices and 242 edges on 131,072 processing cores. Using only four racks of an experimental configuration of Blue Gene/Q, we have achieved a processing rate of 254 billion edges per second on 65,536 processing cores. This paper describes the algorithmic design and the main classes of optimizations that we have used to achieve these results.
Weakly Supervised Object Localization with Latent Category Learning

Localizing objects in cluttered backgrounds is a challenging task in weakly supervised localization. Due to large object variations in cluttered images, objects have large ambiguity with backgrounds. However, backgrounds contain useful latent information, e.g., the sky for aeroplanes. If we can learn this latent information, object-background ambiguity can be reduced to suppress the background. In this paper, we propose the latent category learning (LCL), which is an unsupervised learning problem given only image-level class labels. Firstly, inspired by the latent semantic discovery, we use the typical probabilistic Latent Semantic Analysis (pLSA) to learn the latent categories, which can represent objects, object parts or backgrounds. Secondly, to determine which category contains the target object, we propose a category selection method evaluating each category’s discrimination. We evaluate the method on the PASCAL VOC 2007 database and ILSVRC 2013 detection challenge. On VOC 2007, the proposed method yields the annotation accuracy of 48%, which outperforms previous results by 10%. More importantly, we achieve the detection average precision of 30.9%, which improves previous results by 8% and can be competitive with the supervised deformable part model (DPM) 5.0 baseline 33.7%. On ILSVRC 2013 detection, the method yields the precision of 6.0%, which is also competitive with the DPM 5.0.
Spanner: Google's globally-distributed database

Spanner is Google's scalable, multi-version, globally-distributed, and synchronously-replicated database. It provides strong transactional semantics, consistent replication, and high performance reads and writes for a variety of Google's applications. I'll discuss the design and implementation of Spanner, as well as some of the lessons we have learned along the way. I'll also discuss some open challenges that we still see in building scalable distributed storage systems.
The psychological and neurophysiological concomitants of mindfulness forms of meditation.

To provide a comprehensive review and evaluation of the psychological and neurophysiological literature pertaining to mindfulness meditation.



A search for papers in English was undertaken using PsycINFO (from 1804 onward), MedLine (from 1966 onward) and the Cochrane Library with the following search terms: Vipassana, Mindfulness, Meditation, Zen, Insight, EEG, ERP, fMRI, neuroimaging and intervention. In addition, retrieved papers and reports known to the authors were also reviewed for additional relevant literature.



Mindfulness-based therapeutic interventions appear to be effective in the treatment of depression, anxiety, psychosis, borderline personality disorder and suicidal/self-harm behaviour. Mindfulness meditation per se is effective in reducing substance use and recidivism rates in incarcerated populations but has not been specifically investigated in populations with psychiatric disorders. Electroencephalography research suggests increased alpha, theta and beta activity in frontal and posterior regions, some gamma band effects, with theta activity strongly related to level of experience of meditation; however, these findings have not been consistent. The few neuroimaging studies that have been conducted suggest volumetric and functional change in key brain regions.



Preliminary findings from treatment outcome studies provide support for the application of mindfulness-based interventions in the treatment of affective, anxiety and personality disorders. However, direct evidence for the effectiveness of mindfulness meditation per se in the treatment of psychiatric disorders is needed. Current neurophysiological and imaging research findings have identified neural changes in association with meditation and provide a potentially promising avenue for future research.
Optimal design of a linear Delta robot for the prescribed cuboid dexterous workspace

This paper presents a new idea to determine a set of optimal design parameters of a linear delta robot (LDR) whose workspace is as close as possible of being equal to a prescribed cuboid dexterous workspace (PCDW). The optimal design procedure on the basis of three algorithms is introduced in this paper. The kinematic problem is analyzed in brief to determine the design parameters and their relation. Two algorithms are designed to determine the reachable, rectangular dexterous workspace, and the maximal inscribed rectangle of dexterous workspace in the O-xy plane. Another algorithm is used to solve the optimal problem. As applying example, the results of four cases PCDW to LDR are presented. And the design result is compared with a new concept of the distance between the best state of the LDR and the requirement of the operation task. The method and result of this paper are very useful for the design and comparison of the parallel robot.
Analyzing cloning evolution in the Linux kernel

Identifying code duplication in large multi-platform software system is a challenging problem. This is due to a variety of reasons including the presence of high-level programming languages and structures interleaved with hardware-dependent low-level resources and assembler code, the use of GUI-based configuration scripts generating commands to compile the system, and the extremely high number of possible different configurations. This paper studies the extent and the evolution of code duplications in the Linux kernel. Linux is a large, multi-platform software system; it is based on the Open Source concept, and so there are no obstacles in discussing its implementation. In addition, it is decidedly too large to be examined manually: the current Linux kernel release (2.4.18) is about three million LOCs. Nineteen releases, from 2.4.0 to 2.4.18, were processed and analyzed, identifying code duplication among Linux subsystems by means of a metric-based approach. The obtained results support the hypothesis that the Linux system does not contain a relevant fraction of code duplication. Furthermore, code duplication tends to remain stable across releases, thus suggesting a fairly stable structure, evolving smoothly without any evidence of degradation. q 2002