nils-herrmann/clean_scirepeval huggingface dataset:
A 200ns settling time fully integrated low power LDO regulator with comparators as transient enhancement

The design of a low power low drop out voltage regulator with no off-chip capacitor and fast transient responses is presented in this paper. The LDO regulator uses a combination of a low power operational trans-conductance amplifier and comparators to drive the gate of the PMOS pass element. The amplifier ensures stability and accurate setting of the output voltage in addition to power supply rejection. The comparators ensure fast response of the regulator to any load or line transients. A settling time of less than 200ns is achieved in response to a load transient step of 50mA with a rise time of 100ns with an output voltage spike of less than 200mV at an output voltage of 3.25 V. A line transient step of 1V with a rise time of 100ns results also in a settling time of less than 400ns with a voltage spike of less than 100mV when the output voltage is 2.6V. The regulator is fabricated using a standard 0.35μm CMOS process and consumes a quiescent current of only 26 μA.
Bolt: Data Management for Connected Homes

We present Bolt, a data management system for an emerging class of applications—those that manipulate data from connected devices in the home. It abstracts this data as a stream of time-tag-value records, with arbitrary, application-defined tags. For reliable sharing among applications, some of which may be running outside the home, Bolt uses untrusted cloud storage as seamless extension of local storage. It organizes data into chunks that contains multiple records and are individually compressed and encrypted. While chunking enables efficient transfer and storage, it also implies that data is retrieved at the granularity of chunks, instead of records. We show that the resulting overhead, however, is small because applications in this domain frequently query for multiple proximate records. We develop three diverse applications on top of Bolt and find that the performance needs of each are easily met. We also find that compared to OpenTSDB, a popular time-series database system, Bolt is up to 40 times faster than OpenTSDB while requiring 3–5 times less storage space.
Bakar Kiasan: Flexible Contract Checking for Critical Systems Using Symbolic Execution

Spark, a subset of Ada for engineering safety and security-critical systems, is one of the best commercially available frameworks for formal-methodssupported development of critical software. Spark is designed for verification and includes a software contract language for specifying functional properties of procedures. Even though Spark and its static analysis components are beneficial and easy to use, its contract language is almost never used due to the burdens the associated tool support imposes on developers. Symbolic execution (SymExe) techniques have made significant strides in automating reasoning about deep semantic properties of source code. However, most work on SymExe has focused on bugfinding and test case generation as opposed to tasks that are more verificationoriented such as contract checking. In this paper, we present: (a) SymExe techniques for checking software contracts in embedded critical systems, and (b) Bakar Kiasan, a tool that implements these techniques in an integrated development environment for Spark. We describe a methodology for using Bakar Kiasan that provides significant increases in automation, usability, and functionality over existing Spark tools, and we present results from experiments on its application to industrial examples.
PWM/PDM dual mode controlled soft switching multi resonant high-frequency inverter

In this paper, a novel type auxiliary active resonant capacitor snubber assisted zero current soft switching pulse modulation single-ended push pull (SEPP) series load resonant inverter with two auxiliary resonant lossless inductor snubbers is proposed for consumer high-frequency induction heating (IH) appliances. Its operating principle in steady state is described by using each mode equivalent circuits. The new multi resonant high-frequency inverter can regulate its output power under a condition of a constant frequency zero current soft switching (ZCS) commutation principle on the basis of asymmetrical PWM control scheme. The consumer brand-new IH products using proposed ZCS-PWM series load resonant SEPP high-frequency inverter is evaluated and discussed as compared with conventional high-frequency inverter on the basis of experimental results. In order to extend ZCS operation ranges under a low power setting PWM, the pulse density modulation (PDM) strategy is demonstrated for high frequency multi resonant inverter. Its practical effectiveness is substantially proved from an application point of view
Pyramid-Based Multi-scale LBP Features for Face Recognition

To efficiently extract local and global features in face description and recognition, a pyramid-based multi-scale LBP approach is proposed. Firstly, the face image pyramid is constructed through multi-scale analysis. Then the LBP operator is applied to each level of the image pyramid to extract facial features under various scales. Finally, all the extracted features are concatenated into an enhanced feature vector which is used as the face descriptor. Experimental results on ORL and FERET face databases show that the proposed LBP representation is highly efficient with good performance in face recognition and is robust to illumination, facial expression and position variation.
Discovering latent causes in reinforcement learning

Effective reinforcement learning hinges on having an appropriate state representation. But where does this representation come from? We argue that the brain discovers state representations by trying to infer the latent causal structure of the task at hand, and assigning each latent cause to a separate state. In this paper, we review several implications of this latent cause framework, with a focus on Pavlovian conditioning. The framework suggests that conditioning is not the acquisition of associations between cues and outcomes, but rather the acquisition of associations between latent causes and observable stimuli. A latent cause interpretation of conditioning enables us to begin answering questions that have frustrated classical theories: Why do extinguished responses sometimes return? Why do stimuli presented in compound sometimes summate and sometimes do not? Beyond conditioning, the principles of latent causal inference may provide a general theory of structure learning across cognitive domains.
Using Gravity to Estimate Accelerometer Orientation

Several wearable computing or ubiquitous computing research projects have detected and distinguished user motion activities by attaching accelerometers in known positions and orientations on the user’s body. This paper observes that the orientation constraint can probably be relaxed. An estimate of the constant gravity vector can be obtained by averaging accelerometer samples. This gravity vector estimate in turn enables estimation of the vertical component and the magnitude of the horizontal component of the user’s motion, independently of how the three-axis accelerometer system is oriented.
Power Management of Inverter Interfaced Autonomous Microgrid Based on Virtual Frequency-Voltage Frame

This paper presents the power management scheme for a power electronics based low voltage microgrid in islanding operation. The proposed real and reactive power control is based on the virtual frequency and voltage frame, which can effectively decouple the real and reactive power flows and improve the system transient and stability performance. Detailed analysis of the virtual frame operation range is presented, and a control strategy to guarantee that the microgrid can be operated within the predetermined voltage and frequency variation limits is also proposed. Moreover, a reactive power control with adaptive voltage droop method is proposed, which automatically updates the maximum reactive power limit of a DG unit based on its current rating and actual real power output and features enlarged power output range and further improved system stability. Both simulation and experimental results are provided in this paper.
AdaBoost-based artificial neural network learning

A boosting-based method of learning a feed-forward artificial neural network (ANN) with a single layer of hidden neurons and a single output neuron is presented. Initially, an algorithm called Boostron is described that learns a single-layer perceptron using AdaBoost and decision stumps. It is then extended to learn weights of a neural network with a single hidden layer of linear neurons. Finally, a novel method is introduced to incorporate non-linear activation functions in artificial neural network learning. The proposed method uses series representation to approximate non-linearity of activation functions, learns the coefficients of nonlinear terms by AdaBoost. It adapts the network parameters by a layer-wise iterative traversal of neurons and an appropriate reduction of the problem. A detailed performances comparison of various neural network models learned the proposed methods and those learned using the Least Mean Squared learning (LMS) and the resilient back-propagation (RPROP) is provided in this paper. Several favorable results are reported for 17 synthetic and real-world datasets with different degrees of difficulties for both binary and multi-class problems. Email addresses: mubasher.baig@nu.edu.pk, awais@lums.edu.pk (Mirza M. Baig, Mian. M. Awais), alfy@kfupm.edu.sa (El-Sayed M. El-Alfy)  2017
Antimicrobial Activity of Onion and Ginger against two Food Borne Pathogens Escherichia Coli and Staphylococcus Aureus

The purpose of this project was to investigate the antimicrobial effect of phytochemicals extracted from onion and ginger (fresh and boiled) at different concentrations, against Escherichia coli and Staphylococcus aureus using different methods (MIC, MBC, Disk and well diffusion). This project was chosen because onion and ginger are very common spices and have been claimed to contain several antimicrobial agents.
Inverse Composition Discriminative Optimization for Point Cloud Registration

Rigid Point Cloud Registration (PCReg) refers to the problem of finding the rigid transformation between two sets of point clouds. This problem is particularly important due to the advances in new 3D sensing hardware, and it is challenging because neither the correspondence nor the transformation parameters are known. Traditional local PCReg methods (e.g., ICP) rely on local optimization algorithms, which can get trapped in bad local minima in the presence of noise, outliers, bad initializations, etc. To alleviate these issues, this paper proposes Inverse Composition Discriminative Optimization (ICDO), an extension of Discriminative Optimization (DO), which learns a sequence of update steps from synthetic training data that search the parameter space for an improved solution. Unlike DO, ICDO is object-independent and generalizes even to unseen shapes. We evaluated ICDO on both synthetic and real data, and show that ICDO can match the speed and outperform the accuracy of state-of-the-art PCReg algorithms.
Structural basis of haem-iron acquisition by fungal pathogens

Pathogenic microorganisms must cope with extremely low free-iron concentrations in the host's tissues. Some fungal pathogens rely on secreted haemophores that belong to the Common in Fungal Extracellular Membrane (CFEM) protein family, to extract haem from haemoglobin and to transfer it to the cell's interior, where it can serve as a source of iron. Here we report the first three-dimensional structure of a CFEM protein, the haemophore Csa2 secreted by Candida albicans. The CFEM domain adopts a novel helical-basket fold that consists of six α-helices, and is uniquely stabilized by four disulfide bonds formed by its eight signature cysteines. The planar haem molecule is bound between a flat hydrophobic platform located on top of the helical basket and a peripheral N-terminal ‘handle’ extension. Exceptionally, an aspartic residue serves as the CFEM axial ligand, and so confers coordination of Fe3+ haem, but not of Fe2+ haem. Histidine substitution mutants of this conserved Asp acquired Fe2+ haem binding and retained the capacity to extract haem from haemoglobin. However, His-substituted CFEM proteins were not functional in vivo and showed disturbed haem exchange in vitro, which suggests a role for the oxidation-state-specific Asp coordination in haem acquisition by CFEM proteins.
Scale-up graph processing: a storage-centric view

The determinant of performance in scale-up graph processing on a single system is the speed at which the graph can be fetched from storage: either from disk into memory or from memory into CPU-cache. Algorithms that follow edges perform random accesses to the storage medium for the graph and this can often be the determinant of performance, regardless of the algorithmic complexity or runtime efficiency of the actual algorithm in use. A storage-centric viewpoint would suggest that the solution to this problem lies in recognizing that graphs represent a unique workload and therefore should be treated as such by adopting novel ways to access graph structured data. We approach this problem from two different aspects and this paper details two different efforts in this direction. One approach is specific to graphs stored on SSDs and accelerates random access using a novel prefetcher called RASP. The second approach takes a fresh look at how graphs are accessed and suggests that trading off the low cost of random access for the approach of sequentially streaming a large set of (potentially unrelated) edges can be a winning proposition under certain circumstances: leading to a system for graphs stored on any medium (main-memory, SSD or magnetic disk) called X-stream. RASP and X-stream therefore take - diametrically opposite - storage centric viewpoints of the graph processing problem. After contrasting the approaches and demonstrating the benefit of each, this paper ends with a description of planned future development of an online algorithm that selects between the two approaches, possibly providing the best of both worlds.
Video Description: A Survey of Methods, Datasets and Evaluation Metrics

Video description is the automatic generation of natural language sentences that describe the contents of a given video. It has applications in human-robot interaction, helping the visually impaired and video subtitling. The past few years have seen a surge of research in this area due to the unprecedented success of deep learning in computer vision and natural language processing. Numerous methods, datasets and evaluation metrics have been proposed in the literature, calling the need for a comprehensive survey to focus research efforts in this flourishing new direction. This paper fills the gap by surveying the state of the art approaches with a focus on deep learning models; comparing benchmark datasets in terms of their domains, number of classes, and repository size; and identifying the pros and cons of various evaluation metrics like SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD. Classical video description approaches combined subject, object and verb detection with template based language models to generate sentences. However, the release of large datasets revealed that these methods can not cope with the diversity in unconstrained open domain videos. Classical approaches were followed by a very short era of statistical methods which were soon replaced with deep learning, the current state of the art in video description. Our survey shows that despite the fast-paced developments, video description research is still in its infancy due to the following reasons. Analysis of video description models is challenging because it is difficult to ascertain the contributions, towards accuracy or errors, of the visual features and the adopted language model in the final description. Existing datasets neither contain adequate visual diversity nor complexity of linguistic structures. Finally, current evaluation metrics fall short of measuring the agreement between machine generated descriptions with that of humans. We conclude our survey by listing promising future research directions.
Sub-10 nm monoclinic Gd2O3:Eu3+ nanoparticles as dual-modal nanoprobes for magnetic resonance and fluorescence imaging.

Monoclinic Gd2O3:Eu(3+) nanoparticles (NPs) possess favorable magnetic and optical properties for biomedical application. However, how to obtain small enough NPs still remains a challenge. Here we combined the standard solid-state reaction with the laser ablation in liquids (LAL) technique to fabricate sub-10 nm monoclinic Gd2O3:Eu(3+) NPs and explained their formation mechanism. The obtained Gd2O3:Eu(3+) NPs exhibit bright red fluorescence emission and can be successfully used as fluorescence probe for cells imaging. In vitro and in vivo magnetic resonance imaging (MRI) studies show that the product can also serve as MRI good contrast agent. Then, we systematically investigated the nanotoxicity including cell viability, apoptosis in vitro, as well as the immunotoxicity and pharmacokinetics assays in vivo. This investigation provides a platform for the fabrication of ultrafine monoclinic Gd2O3:Eu(3+) NPs and evaluation of their efficiency and safety in preclinical application.
A Novel Approach for Researching Crossing Behavior and Risk Acceptance: The Pedestrian Simulator

To integrate unpredictable human behavior in the assessment of active and passive pedestrian safety systems, we introduce a virtual reality (VR)-based pedestrian simulation system. The device uses the Xsens Motion Capture platform and can be used without additional infrastructure. To show the systems applicability for pedestrian behavior studies, we conducted a pilot study evaluating the degree of realism such a system can achieve in a typical unregulated pedestrian crossing scenario. Six participants had to estimate vehicle speeds and distances in four scenarios with varying gaps between vehicles. First results indicate an acceptable level of realism so that the device can be used for further user studies addressing pedestrian behavior, pedestrian interaction with (automated) vehicles, risk assessment and investigation of the pre-crash phase without the risk of injuries.
Structure Learning of Bayesian Belief Networks Using Simulated Annealing Algorithm

Basically, Bayesian Belief Networks (BBNs) as probabilistic tools provide suitable facilities for modelling process under uncertainty. A BBN applies a Directed Acyclic Graph (DAG) for encoding relations between all variables in state of problem. Finding the beststructure (structure learning) ofthe DAG is a classic NP-Hard problem in BBNs. In recent years, several algorithms are proposed for this task such as Hill Climbing, Greedy Thick Thinning and K2 search. In this paper, we introduced Simulated Annealing algorithm with complete details as new method for BBNs structure learning. Finally, proposed algorithm compared with other structure learning algorithms based on classification accuracy and construction time on valuable databases. Experimental results of research show that the simulated annealing algorithmis the bestalgorithmfrom the point ofconstructiontime but needs to more attention for classification process.
Digital forensic analysis of Telegram Messenger on Android devices

We provide a thorough description of all the artifacts that are generated by the messenger application Telegram on Android OS. We also provide interpretation of messages that are generated and how they relate to one another. Based on the results of digital forensics investigation and analysis in this paper, an analyst/investigator will be able to read, reconstruct and provide chronological explanations of messages which are generated by the user. Using three different smartphone device vendors and Android OS versions as the objects of our experiments, we conducted tests in a forensically sound manner.
The Biology of CRISPR-Cas: Backward and Forward

In bacteria and archaea, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system against phages and other foreign genetic elements. Here, we review the biology of the diverse CRISPR-Cas systems and the major progress achieved in recent years in understanding the underlying mechanisms of the three stages of CRISPR-Cas immunity: adaptation, crRNA biogenesis, and interference. The ecology and regulation of CRISPR-Cas in the context of phage infection, the roles of these systems beyond immunity, and the open questions that propel the field forward are also discussed.
MIMO systems with antenna selection

Multiple-input-multiple-output (MIMO) wireless systems are those that have multiple antenna elements at both the transmitter and receiver. They were first investigated by computer simulations in the 1980s. Since that time, interest in MIMO systems has exploded. They are now being used for third-generation cellular systems (W-CDMA) and are discussed for future high-performance modes of the highly successful IEEE 802.11 standard for wireless local area networks. MIMO-related topics also occupy a considerable part of today's academic communications research. The multiple antennas in MIMO systems can be exploited in two different ways. One is the creation of a highly effective antenna diversity system; the other is the use of the multiple antennas for the transmission of several parallel data streams to increase the capacity of the system. This article presented an overview of MIMO systems with antenna selection. The transmitter, the receiver, or both use only the signals from a subset of the available antennas. This allows considerable reductions in the hardware expense.
Cultivating mindfulness in health care professionals: a review of empirical studies of mindfulness-based stress reduction (MBSR).

Demands faced by health care professionals include heavy caseloads, limited control over the work environment, long hours, as well as organizational structures and systems in transition. Such conditions have been directly linked to increased stress and symptoms of burnout, which in turn, have adverse consequences for clinicians and the quality of care that is provided to patients. Consequently, there exists an impetus for the development of curriculum aimed at fostering wellness and the necessary self-care skills for clinicians. This review will examine the potential benefits of mindfulness-based stress reduction (MBSR) programs aimed at enhancing well-being and coping with stress in this population. Empirical evidence indicates that participation in MBSR yields benefits for clinicians in the domains of physical and mental health. Conceptual and methodological limitations of the existing studies and suggestions for future research are discussed.
Metrological characterization of high-performance delta-sigma ADCs: A case study of CERN DS-22

Metrological characterization of high-performance ΔΣ Analog-to-Digital Converters (ADCs) poses severe challenges to reference instrumentation and standard methods. In this paper, most important tests related to noise and effective resolution, nonlinearity, environmental uncertainty, and stability are proved and validated in the specific case of a high-performance ΔΣ ADC. In particular, tests setups are proposed and discussed and the definitions used to assess the performance are clearly stated in order to identify procedures and guidelines for high-resolution ADCs characterization. An experimental case study of the high-performance ΔΣ ADC DS-22 developed at CERN is reported and discussed by presenting effective alternative test setups. Experimental results show that common characterization methods by the IEEE standards 1241  and 1057  cannot be used and alternative strategies turn out to be effective.