nils-herrmann/clean_scirepeval huggingface dataset:
A High-Power CMOS Switch Using A Novel Adaptive Voltage Swing Distribution Method in Multistack FETs

A high-power CMOS switch using a novel adaptive voltage swing distribution method in a multistack field-effect transistor (FET) scheme is proposed. The proposed adaptive voltage swing distribution method in multistack FETs is very effective in preventing unwanted channel formation with low control voltage supply in OFF-state FETs. This, in turn, increases power-handling capability when a large-signal voltage swing is applied. In the proposed CMOS switch, the behavior of the voltage swing in OFF-state multistack FETs shows a difference with respect to the level of input voltage swing. The characteristics of voltage swing distribution and leakage channel formation in the CMOS switch is fully analyzed with incorporation of the novel adaptive voltage swing distribution method into a three-stacked nMOS Rx switch in a standard 0.18-mum triple-well CMOS process. In addition, linearity of the proposed technique is verified through the measurement data of the single-pole double-throw switches that employ the proposed technique in the Rx switch. Two different types of configurations are implemented and characterized at the Rx switches, which consist of four-stacked nMOS devices, to demonstrate the method of minimizing voltage stress issues on one of the multistacked FETs. Layout consideration was also taken to prevent interference between leakage signals at the substrate. The measured performance of the proposed design shows an input 0.3-dB compression point of 33.5 dBm at 1.9 GHz. To the best of our knowledge, this is the highest power-handling capability of a CMOS switch in a standard CMOS process ever reported. The insertion losses of the Tx and Rx switches are 1.6 and 1.9 dB, respectively, at 1.9 GHz. The isolation of the Tx and Rx switches is around 20 and 30 dB, respectively, at 1.9 GHz.
Verifiable differential privacy

Working with sensitive data is often a balancing act between privacy and integrity concerns. Consider, for instance, a medical researcher who has analyzed a patient database to judge the effectiveness of a new treatment and would now like to publish her findings. On the one hand, the patients may be concerned that the researcher's results contain too much information and accidentally leak some private fact about themselves; on the other hand, the readers of the published study may be concerned that the results contain too little information, limiting their ability to detect errors in the calculations or flaws in the methodology.
 This paper presents VerDP, a system for private data analysis that provides both strong integrity and strong differential privacy guarantees. VerDP accepts queries that are written in a special query language, and it processes them only if a) it can certify them as differentially private, and if b) it can prove the integrity of the result in zero knowledge. Our experimental evaluation shows that VerDP can successfully process several different queries from the differential privacy literature, and that the cost of generating and verifying the proofs is practical: for example, a histogram query over a 63,488-entry data set resulted in a 20 kB proof that took 32 EC2 instances less than two hours to generate, and that could be verified on a single machine in about one second.
Atari games and Intel processors

The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage ActorCritic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions. In this work we present our results on learning strategies in Atari games using a Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0 machine learning framework. We also analyze effects of asynchronous computations on the convergence of reinforcement learning algorithms.
Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan.

The purposes of this study were to identify age-related changes in objectively recorded sleep patterns across the human life span in healthy individuals and to clarify whether sleep latency and percentages of stage 1, stage 2, and rapid eye movement (REM) sleep significantly change with age.



Review of literature of articles published between 1960 and 2003 in peer-reviewed journals and meta-analysis.



65 studies representing 3,577 subjects aged 5 years to 102 years.



The research reports included in this meta-analysis met the following criteria: (1) included nonclinical participants aged 5 years or older; (2) included measures of sleep characteristics by "all night" polysomnography or actigraphy on sleep latency, sleep efficiency, total sleep time, stage 1 sleep, stage 2 sleep, slow-wave sleep, REM sleep, REM latency, or minutes awake after sleep onset; (3) included numeric presentation of the data; and (4) were published between 1960 and 2003 in peer-reviewed journals.



In children and adolescents, total sleep time decreased with age only in studies performed on school days. Percentage of slow-wave sleep was significantly negatively correlated with age. Percentages of stage 2 and REM sleep significantly changed with age. In adults, total sleep time, sleep efficiency, percentage of slow-wave sleep, percentage of REM sleep, and REM latency all significantly decreased with age, while sleep latency, percentage of stage 1 sleep, percentage of stage 2 sleep, and wake after sleep onset significantly increased with age. However, only sleep efficiency continued to significantly decrease after 60 years of age. The magnitudes of the effect sizes noted changed depending on whether or not studied participants were screened for mental disorders, organic diseases, use of drug or alcohol, obstructive sleep apnea syndrome, or other sleep disorders.



In adults, it appeared that sleep latency, percentages of stage 1 and stage 2 significantly increased with age while percentage of REM sleep decreased. However, effect sizes for the different sleep parameters were greatly modified by the quality of subject screening, diminishing or even masking age associations with different sleep parameters. The number of studies that examined the evolution of sleep parameters with age are scant among school-aged children, adolescents, and middle-aged adults. There are also very few studies that examined the effect of race on polysomnographic sleep parameters.
Tuning PID Controller for Speed Control of DC Motor Using Soft Computing Techniques-A Review

This paper presented a review study of tuning of Proportional Integral Derivative (PID) Controller for speed control of DC motor using soft computing techniques. DC motor is widely used in industries even if its maintenance cost is higher than the induction motor. Speed control of DC motor is attracted considerable research and several methods are evolved. The PID controller is the very commonly used compensating controller which is used in nonlinear systems. This controller is widely used in many different areas like aerospace, process control, manufacturing, automation etc. The tuning of PID parameter is very difficult. There are various soft computing techniques which are used for tuning of PID controller to control the speed control of DC motor. Tuning of PID parameters is important because these parameters have a great effect on the stability and performance of the control system.
Towards interactive Machine Learning (iML): Applying Ant Colony Algorithms to Solve the Traveling Salesman Problem with the Human-in-the-Loop Approach

Most Machine Learning (ML) researchers focus on automatic Machine Learning (aML) where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from the availability of “big data”. However, sometimes, for example in health informatics, we are confronted not a small number of data sets or rare events, and with complex problems where aML-approaches fail or deliver unsatisfactory results. Here, interactive Machine Learning (iML) may be of help and the “human-in-the-loop” approach may be beneficial in solving computationally hard problems, where human expertise can help to reduce an exponential search space through heuristics. In this paper, experiments are discussed which help to evaluate the effectiveness of the iML-“human-in-the-loop” approach, particularly in opening the “black box”, thereby enabling a human to directly and indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of proteins.
Data Profiling with Metanome

Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies. We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic discovery of complex metadata. Metanome’s goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the, at times, large metadata sets.
Motivating Creativity in Organizations : O N DOING WHAT YOU LOVE AND LOVING WHAT You Do

A rthur Schawlow, winner of the Nobel prize in physics in 1981, was once asked what, in his opinion, made the difference between highly creative and less creative scientists. He replied, "The labor of love aspect is important. The most successful scientists often are not the most talented. But they are the ones who are impelled by curiosity. They've got to know what the answer is."' Schawlow's insights about scientific creativity highlight the importance of intrinsic motivation: the motivation to work on something because it is interesting, involving, exciting, satisfying, or personally challenging. There is abundant evidence that people will be most creative when they are primarily intrinsically motivated, rather than extrinsically motivated by expected evaluation, surveillance, competition with peers, dictates from superiors, or the promise of rewards.^
Latent dirichlet allocation in web spam filtering

Latent Dirichlet allocation (LDA) (Blei, Ng, Jordan 2003) is a fully generative statistical language model on the content and topics of a corpus of documents. In this paper we apply a modification of LDA, the novel multi-corpus LDA technique for web spam classification. We create a bag-of-words document for every Web site and run LDA both on the corpus of sites labeled as spam and as non-spam. In this way collections of spam and non-spam topics are created in the training phase. In the test phase we take the union of these collections, and an unseen site is deemed spam if its total spam topic probability is above a threshold. As far as we know, this is the first web retrieval application of LDA. We test this method on the UK2007-WEBSPAM corpus, and reach a relative improvement of 11% in F-measure by a logistic regression based combination with strong link and content baseline classifiers.
Numerical calibration for 3-axis accelerometers and magnetometers

Magnetometers and accelerometers are sensors that are now integrated in objects of everyday life like automotive applications, mobile phones and so on. Some applications need information of acceleration and attitude with a high accuracy. For example, MEMS magnetometers and accelerometers can be integrated in embedded like mobile phones and GPS receivers. The parameters of such sensors must be precisely estimated to avoid drift and biased values. Thus, calibration is an important step to correctly use these sensors and get the expected measurements. This paper presents the theoretical and experimental steps of a method to compute gains, bias and non orthogonality factors of magnetometer and accelerometer sensors. This method of calibration can be used for automatic calibration in embedded systems. The calibration procedure involves arbitrary rotations of the sensors platform and a visual 2D projection of measurements.
Random forest models for identifying motorway Rear-End Crash Risks using disaggregate data

This paper presents an approach to develop motorway Rear-End Crash Risk Identification Models (RECRIM) using disaggregate traffic data, meteorological data and crash database for a study site at a two-lane-per-direction section on Swiss (right-hand driving) motorway A1. Traffic data collected from inductive double loop detectors provide instant vehicle information such as speed, time headway, etc. We define traffic situations (TS) characterized by 22 variables representing traffic status for 5-minute intervals. Our goal is to develop models that can separate TS under non-crash conditions and TS under pre-crash conditions using Random Forest - an ensemble learning method. Non-crash TS were clustered into groups that we call traffic regimes (TR). Precrash TS are classified into TR so that a RECRIM for each TR is developed. Interpreting results of the models suggests that speed variance on the right lane and speed difference between two lanes are the two main causes of the rear-end crashes. The applicability of RECRIM in a real-time framework is also discussed.
Robust Angle Invariant 1D Barcode Detection

Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method.
A genome-wide association study of Cloninger's temperament scales: Implications for the evolutionary genetics of personality

Variation in personality traits is 30-60% attributed to genetic influences. Attempts to unravel these genetic influences at the molecular level have, so far, been inconclusive. We performed the first genome-wide association study of Cloninger's temperament scales in a sample of 5117 individuals, in order to identify common genetic variants underlying variation in personality. Participants' scores on Harm Avoidance, Novelty Seeking, Reward Dependence, and Persistence were tested for association with 1,252,387 genetic markers. We also performed gene-based association tests and biological pathway analyses. No genetic variants that significantly contribute to personality variation were identified, while our sample provides over 90% power to detect variants that explain only 1% of the trait variance. This indicates that individual common genetic variants of this size or greater do not contribute to personality trait variation, which has important implications regarding the genetic architecture of personality and the evolutionary mechanisms by which heritable variation is maintained.
Attempted suicide among transgender persons: The influence of gender-based discrimination and victimization.

To determine the independent predictors of attempted suicide among transgender persons we interviewed 392 male-to-female (MTF) and 123 female-to-male (FTM) individuals. Participants were recruited through targeted sampling, respondent-driven sampling, and agency referrals in San Francisco. The prevalence of attempted suicide was 32% (95% CI = 28% to 36%). In multivariate logistic regression analysis younger age (<25 years), depression, a history of substance abuse treatment, a history of forced sex, gender-based discrimination, and gender-based victimization were independently associated with attempted suicide. Suicide prevention interventions for transgender persons are urgently needed, particularly for young people. Medical, mental health, and social service providers should address depression, substance abuse, and forced sex in an attempt to reduce suicidal behaviors among transgender persons. In addition, increasing societal acceptance of the transgender community and decreasing gender-based prejudice may help prevent suicide in this highly stigmatized population.
Bag-of-colors for improved image search

This paper investigates the use of color information when used within a state-of-the-art large scale image search system. We introduce a simple yet effective and efficient color signature generation procedure. It is used either to produce global or local descriptors. As a global descriptor, it outperforms several state-of-the-art color description methods, in particular the bag-of-words method based on color SIFT. As a local descriptor, our signature is used jointly with SIFT descriptors (no color) to provide complementary information. This significantly improves the recognition rate, outperforming the state of the art on two image search benchmarks. We provide an open source package of our signature (http://www.kooaba.com/en/learnmore/labs/).
A Survey of Biometric keystroke Dynamics: Approaches, Security and Challenges

Biometrics technologies are gaining popularity today since they provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working via a keyboard. The authentication process is done by observing the change in the typing pattern of the user. A comprehensive survey of the existing keystroke dynamics methods, metrics, different approaches are given in this study. This paper also discusses about the various security issues and challenges faced by keystroke dynamics. KeywordsBiometris; Keystroke Dynamics; computer Security; Information Security; User Authentication.
Analysis of rail potential and stray current for Taipei Metro

Taipei Metro adopted the diode-grounded scheme for stray current collection in construction of its cross rail network. During operation of the network, a high rail-to-earth potential (V/sub rail/) has been observed at the east end of the Blue Line (i.e., stations BL13-BL16). To find effective countermeasures, a series of field tests in a step-by-step development nature was conducted from 1999-2000, which led to the decision of disconnecting the impedance bond at G11 of the tie line so that the negative return current of the Blue Line cannot flow to the rails of the Red-Green Line, and vice versa (detailed in Sections III-A and V-C). This decision was implemented through contract-out work in 2003. Since then, the V/sub rail/ has been lowered by almost half before disconnection. To gain the insight characteristic before the contract-out, numerical simulations were also conducted by simulating the multi-train and multisection features of the cross transportation network. The simulation results (for V/sub rail/ and stray current, or I/sub stray/) were consistent with the field-test results. This paper presents the design of these field tests and their test results in comparison with the simulation results, based on which the countermeasures for reducing V/sub rail/ and the present status after V/sub rail/ reduction at Taipei Metro are presented.
Stability-Based Stopping Criterion for Active Learning

While active learning has drawn broad attention in recent years, there are relatively few studies on stopping criterion for active learning. We here propose a novel model stability based stopping criterion, which considers the potential of each unlabeled examples to change the model once added to the training set. The underlying motivation is that active learning should terminate when the model does not change much by adding remaining examples. Inspired by the widely used stochastic gradient update rule, we use the gradient of the loss at each candidate example to measure its capability to change the classifier. Under the model change rule, we stop active learning when the changing ability of all remaining unlabeled examples is less than a given threshold. We apply the stability-based stopping criterion to two popular classifiers: logistic regression and support vector machines (SVMs). It can be generalized to a wide spectrum of learning models. Substantial experimental results on various UCI benchmark data sets have demonstrated that the proposed approach outperforms state-of-art methods in most cases.
Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges

Cyber-physical systems (CPSs) integrate the computation with physical processes. Embedded computers and networks monitor and control the physical processes, usually with feedback loops where physical processes affect computations and vice versa. CPS was identified as one of the eight research priority areas in the August 2007 report of the President's Council of Advisors on Science and Technology, as CPS will be the core component of many critical infrastructures and industrial control systems in the near future. However, a variety of random failures and cyber attacks exist in CPS, which greatly restrict their growth. Fortunately, an intrusion detection mechanism could take effect for protecting CPS. When a misbehavior is found by the intrusion detector, the appropriate action can be taken immediately so that any harm to the system will be minimized. As CPSs are yet to be defined universally, the application of the instruction detection mechanism remain open presently. As a result, the effort will be made to discuss how to appropriately apply the intrusion detection mechanism to CPS in this paper. By examining the unique properties of CPS, it intends to define the specific requirements first. Then, the design outline of the intrusion detection mechanism in CPS is introduced in terms of the layers of system and specific detection techniques. Finally, some significant research problems are identified for enlightening the subsequent studies.