nils-herrmann/clean_scirepeval huggingface dataset:
A FVF LDO Regulator With Dual-Summed Miller Frequency Compensation for Wide Load Capacitance Range Applications

This paper presents a proposed Flipped Voltage Follower (FVF) based output capacitorless low-dropout (OCL-LDO) regulator using Dual-Summed Miller Frequency Compensation (DSMFC) technique. Validated by UMC 65-nm CMOS process, the simulation results have shown that the proposed LDO regulator can be stabilized by a total compensation capacitance (CC) of 8 pF for a load capacitance (CL) ranging from 10 pF to 10 nF. It consumes 23.7 μA quiescent current with a 1.2 V supply voltage. With a dropout voltage of 200 mV, the LDO regulator can support a maximum 50 mA load current. It can settle in less than 1.7 μs with a 1% accuracy for the whole CL range. The proposed LDO regulator is comparable to other reported works in terms of figure-of-merit (FOM). Most significantly, it can drive the widest range of CL and achieve the highest CL(max)/CC ratio with respect to the counterparts.
Integrating Statistical Prior Knowledge into Convolutional Neural Networks

In this work we show how to integrate prior statistical knowledge, obtained through principal components analysis (PCA), into a convolutional neural network in order to obtain robust predictions even when dealing with corrupted or noisy data. Our network architecture is trained end-to-end and includes a specifically designed layer which incorporates the dataset modes of variation discovered via PCA and produces predictions by linearly combining them. We also propose a mechanism to focus the attention of the CNN on specific regions of interest of the image in order to obtain refined predictions. We show that our method is effective in challenging segmentation and landmark localization tasks.
Geometries and materials for subwavelength surface plasmon modes.

Plasmonic waveguides can guide light along metal-dielectric interfaces with propagating wave vectors of greater magnitude than are available in free space and hence with propagating wavelengths shorter than those in vacuum. This is a necessary, rather than sufficient, condition for subwavelength confinement of the optical mode. By use of the reflection pole method, the two-dimensional modal solutions for single planar waveguides as well as adjacent waveguide systems are solved. We demonstrate that, to achieve subwavelength pitches, a metal-insulator-metal geometry is required with higher confinement factors and smaller spatial extent than conventional insulator-metal-insulator structures. The resulting trade-off between propagation and confinement for surface plasmons is discussed, and optimization by materials selection is described.
New training strategies for constructive neural networks with application to regression problems

Regression problem is an important application area for neural networks (NNs). Among a large number of existing NN architectures, the feedforward NN (FNN) paradigm is one of the most widely used structures. Although one-hidden-layer feedforward neural networks (OHL-FNNs) have simple structures, they possess interesting representational and learning capabilities. In this paper, we are interested particularly in incremental constructive training of OHL-FNNs. In the proposed incremental constructive training schemes for an OHL-FNN, input-side training and output-side training may be separated in order to reduce the training time. A new technique is proposed to scale the error signal during the constructive learning process to improve the input-side training efficiency and to obtain better generalization performance. Two pruning methods for removing the input-side redundant connections have also been applied. Numerical simulations demonstrate the potential and advantages of the proposed strategies when compared to other existing techniques in the literature.
Democratic Centralism: A Hybrid Blockchain Architecture and Its Applications in Energy Internet

Blockchain is attracting more and more attentions from many kinds of fields, such as finance, industry, and theory researchers. Rather than viewing blockchain as a novel technology, we should think it as an innovation for managing digital society, which provides fundamental principles to support democratically distributed applications. Based on the design and application of the Energy Internet, this paper analyzes the core architecture of the initial blockchain technology, and combines the security of the public chain with the efficiency of the private chain, solving the poor efficiency problem of the initial blockchain by using the high efficiency of private chain. At the same time, it inherits the security and non-tampering of initial blockchain. This paper designs a new hybrid blockchain storaging mode that is Block Static Storage(BSS, internal structure is N+X hybrid blockchain, following can be referred to as N+X HBC),for purpose that improving the overall efficiency of the Internet running, achieving decentralized supervision, and provoding a credible, safe and efficient performance of the Energy Internet in the storage of its massive data, as well as a huge business system.
3-D Magnetic Equivalent Circuit Framework for Modeling Electromechanical Devices

Magnetic equivalent circuits (MECs) are becoming an accepted alternative to electrical-equivalent lumped-parameter models and finite-element analysis (FEA) for simulating electromechanical devices. Their key advantages are moderate computational effort, reasonable accuracy, and flexibility in model size. MECs are easily extended into three dimensions. But despite the successful use of MEC as a modeling tool, a generalized 3-D formulation useable for a comprehensive computer-aided design tool has not yet emerged (unlike FEA, where general modeling tools are readily available). This paper discusses the framework of a 3-D MEC modeling approach, and presents the implementation of a variable-sized reluctance network distribution based on 3-D elements. Force calculation and modeling of moving objects are considered. Two experimental case studies, a soft-ferrite inductor and an induction machine, show promising results when compared to measurements and simulations of lumped parameter and FEA models.
A Design Methodology for Miniaturized 3-dB Branch-Line Hybrid Couplers Using Distributed Capacitors Printed in the Inner Area

This paper proposes a methodology to design and optimize the footprint of miniaturized 3-dB branch-line hybrid couplers, which consists of high-impedance transmission lines and distributed capacitors. To minimize the physical size of the coupler, the distributed capacitors are placed within the empty space of the hybrid. The proposed design methodology calls for the joint optimization of the length of the reduced high-impedance transmission lines and the area of the distributed capacitors. A prototype at S-band was designed and built to validate the approach. It showed a size reduction by 62% compared with the conventional 3-dB branch-line hybrid coupler while providing similar performance and bandwidth.
Conversational Agents in E-Learning

This paper discusses the use of natural language or „conversational‟ agents in e-learning environments. We describe and contrast the various applications of conversational agent technology represented in the e-learning literature, including tutors, learning companions, language practice and systems to encourage reflection. We offer two more detailed examples of conversational agents, one which provides learning support, and the other support for self-assessment. Issues and challenges for developers of conversational agent systems for e-learning are identified and discussed.
Dynamic Deformation Analysis of Power Transformer Windings in Short-Circuit Fault by FEM

This paper presents the investigations of short-circuit current, electromagnetic force, and transient dynamic response of windings deformation including mechanical stress, strain, and displacements for an oil-immersed-type 220-kV power transformer. The worst-case fault with three-phase short-circuit happening simultaneously is assumed. A considerable leakage magnetic field excited by short-circuit current can produce the dynamical electromagnetic force to act on copper disks in each winding. The two-dimensional finite element method (FEM) is employed to obtain the electromagnetic force and its dynamical characteristics in axial and radial directions. In addition, to calculate the windings deformation accurately, we measured the nonlinear elasticity characteristic of spacer and built three-dimensional FE kinetic model to analyze the axial dynamic deformation. The results of dynamic mechanical stress and strain induced by combining of short-circuit force and prestress are useful for transformer design and fault diagnosis.
Auction-Based Resource Allocation for Sharing Cloudlets in Mobile Cloud Computing

Driven by pervasive mobile devices and ubiquitous wireless communication networks, mobile cloud computing emerges as an appealing paradigm to accommodate demands for running power-hungry or computation-intensive applications over resource-constrained mobile devices. Cloudlets that move available resources closer to the network edge offer a promising architecture to support real-time applications, such as online gaming and speech recognition. To stimulate service provisioning by cloudlets, it is essential to design an incentive mechanism that charges mobile devices and rewards cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. In this paper, we propose an incentive-compatible auction mechanism (ICAM) for the resource trading between the mobile devices as service users (buyers) and cloudlets as service providers (sellers). ICAM can effectively allocate cloudlets to satisfy the service demands of mobile devices and determine the pricing. Both the theoretical analysis and the numerical results show that the ICAM guarantees desired properties with respect to individual rationality, budget balance and truthfulness (incentive compatibility) for both the buyers and the sellers, and computational efficiency.
Learning to Recognize Pedestrian Attribute

Learning to recognize pedestrian attributes at far distance is a novel research topic in video surveillance scenarios where face and body close-shots are hardly available; instead, only far-view video frames of pedestrian are given. In this study, we present an alternative approach that exploits the context of neighboring pedestrian images for improved attribute inference compared to the conventional SVM-based method. In addition, we conduct extensive experiments to evaluate the informativeness of background and foreground features for attribute recognition. Experiments is based on our newly released pedestrian attribute dataset, which is by far the largest and most diverse of its kind.
CNN-based Style Vector for Style Image Retrieval

In this paper, we have examined the effectiveness of "style matrix" which is used in the works on style transfer and texture synthesis by Gatys et al. in the context of image retrieval as image features. A style matrix is presented by Gram matrix of the feature maps in a deep convolutional neural network. We proposed a style vector which are generated from a style matrix with PCA dimension reduction. In the experiments, we evaluate image retrieval performance using artistic images downloaded from Wikiarts.org regarding both artistic styles ans artists. We have obtained 40.64% and 70.40% average precision for style search and artist search, respectively, both of which outperformed the results by common CNN features. In addition, we found PCA-compression boosted the performance.
Fully Integrated, 100-mV Minimum Input Voltage Converter With Gate-Boosted Charge Pump Kick-Started by LC Oscillator for Energy Harvesting

A fully integrated step-up dc–dc converter for energy harvesting applications is presented. A minimum start-up voltage of 100 mV is achieved by the start-up mechanism based on an LC oscillator (LCO). Conventional voltage converters with the LCO-based start-up mechanism provide a significantly low conversion efficiency of around 1%. To overcome this drawback, the following two circuit techniques are proposed in this brief: 1) a gate-boosted charge pump; and 2) an LCO deactivation. The proposed voltage converter is fabricated in the 65-nm silicon-on-thin-buried-oxide process. The measurement results show that when the input voltage is 100 mV, the proposed converter can provide an output of 760 mV at a conversion efficiency of 33%, which is the highest efficiency compared with the conventional fully integrated voltage converters.
Detecting recent positive selection in the human genome from haplotype structure

The ability to detect recent natural selection in the human population would have profound implications for the study of human history and for medicine. Here, we introduce a framework for detecting the genetic imprint of recent positive selection by analysing long-range haplotypes in human populations. We first identify haplotypes at a locus of interest (core haplotypes). We then assess the age of each core haplotype by the decay of its association to alleles at various distances from the locus, as measured by extended haplotype homozygosity (EHH). Core haplotypes that have unusually high EHH and a high population frequency indicate the presence of a mutation that rose to prominence in the human gene pool faster than expected under neutral evolution. We applied this approach to investigate selection at two genes carrying common variants implicated in resistance to malaria: G6PD and CD40 ligand. At both loci, the core haplotypes carrying the proposed protective mutation stand out and show significant evidence of selection. More generally, the method could be used to scan the entire genome for evidence of recent positive selection.
Gamification in Education: A Systematic Mapping Study

While gamification is gaining ground in business, marketing, corporate management, and wellness initiatives, its application in education is still an emerging trend. This article presents a study of the published empirical research on the application of gamification to education. The study is limited to papers that discuss explicitly the effects of using game elements in specific educational contexts. It employs a systematic mapping design. Accordingly, a categorical structure for classifying the research results is proposed based on the extracted topics discussed in the reviewed papers. The categories include gamification design principles, game mechanics, context of applying gamification (type of application, educational level, and academic subject), implementation, and evaluation. By mapping the published works to the classification criteria and analyzing them, the study highlights the directions of the currently conducted empirical research on applying gamification to education. It also indicates some major obstacles and needs, such as the need for proper technological support, for controlled studies demonstrating reliable positive or negative results of using specific game elements in particular educational contexts, etc. Although most of the reviewed papers report promising results, more substantial empirical research is needed to determine whether both extrinsic and intrinsic motivation of the learners can be influenced by gamification.
Adolescent’s and Drugs Abuse in Nigeria

Drug abuse continues to be the major risk behaviour among youth and adolescents, with physical and mental health complications. Despite the known risks associated with the drugs, adolescents continue using these drugs. This paper reveals the prevalence of drug abuse among adolescent’s in Nigeria, problems associated with drugs abuse and reasons why adolescents are vulnerable to drugs abuse. Drug abuse causes a lot of risk among the adolescents; it results to gang formation, armed robbery, mental illness and cultism. Studies revealed that most of the drug addicts started smoking from their young age. As they grow older they seek new thrills and gradually go into hard drugs. There was an indication that 65 percent of high school students used drugs to have good time, 54 percent wanted to experiment to see what it is like, 20–40 percent used it to alter their moods. It concludes by prescribing some ways of curbing the menace arising from drug abuse.
Cherry-picking: Exploiting process variations in dark-silicon homogeneous chip multi-processors

It is projected that increasing on-chip integration with technology scaling will lead to the so-called dark silicon era in which more transistors are available on a chip than can be simultaneously powered on. It is conventionally assumed that the dark silicon will be provisioned with heterogeneous resources, for example dedicated hardware accelerators. In this paper we challenge the conventional assumption and build a case for homogeneous dark silicon CMPs that exploit the inherent variations in process parameters that exist in scaled technologies to offer increased performance. Since process variations result in core-to-core variations in power and frequency, the idea is to cherry pick the best subset of cores for an application so as to maximize performance within the power budget. To this end, we propose a polynomial time algorithm for optimal core selection, thread mapping and frequency assignment for a large class of multi-threaded applications. Our experimental results based on the Sniper multi-core simulator show that up to 22% and 30% performance improvement is observed for homogeneous CMPs with 33% and 50% dark silicon, respectively.
Deep Learning for Music

Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNNRBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone.
Large area compression molding for Fan-out Panel Level Packing

Fan-out Wafer Level Packaging (FOWLP) is one of the latest packaging trends in microelectronics. Mold embedding for this technology is currently done on wafer level up to 12"/300 mm diameter. For higher productivity and therewith lower costs larger mold embedding form factors are forecasted for the near future. Following the wafer level approach then the next step will be a reconfigured wafer size of 450 mm. An alternative option would be leaving the wafer shape and moving to panel sizes leading to Fan-out Panel Level Packaging (FOPLP). Sizes for the panel could range up to 24"×18" or even larger. For reconfigured mold embedding, compression mold processes are used in combination with liquid, granular or sheet compound. As a process alternative also lamination as used e.g. in PCB manufacturing can be taken into account.=Within this paper the evaluation of panel level compression molding with a target form factor of 24”*18” / 610×457 mm2 is described. The large panel size equals a typical PCB manufacturing full format and is selected to achieve process compatibility with cost efficient PCB processes. Here not only conventional compression molding is considered but also the new process compression mold lamination is introduced as a tool-less mold alternative. Panel level molding is compared to 8” and 12” wafer molding as well as to low cost PCB 24”×18” lamination focusing on manufacturing challenges, high volume capability and estimated cost. Technological focus of this study will be the evaluation of liquid, granular and sheet molding compound. This includes thorough material analysis regarding the process relevant material properties as reactivity or viscosity. One key process step for homogeneous large area embedding is material application before compression molding. Where sheet compounds already deliver a uniform material layer the application of liquid and granular compound must be optimized and adapted for a homogeneous distribution without flow marks, knit lines and incomplete fills. Hence, dispense patterns of liquid and granular molding compounds are studied to achieve high yield and reliable mold embedding. In addition applicable thickness ranges, total thickness variations, void risks and warpage will be investigated for the different material types. The overall a process flow will be demonstrated for selected compression mold variants resulting in a 24”×18” / 610×457 mm2 FOPLP using PCB based redistribution layer (RDL) as low cost alternative to thin film technology. For=PCB based RDLs a resin coated copper sheet (RCC) is laminated on the reconfigured wafer or panel, respectively. Micro vias are drilled through the RCC layer to the die pads and electrically connected by Cu plating. Final process step is the etching of Cu lines using laser direct imaging (LDI) techniques for maskless patterning. All process steps are carried out on full format 24”×18” / 610×457 mm2.
Tacotron: Towards End-to-End Speech Synthesis

A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given <text, audio> pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-tosequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it’s substantially faster than sample-level autoregressive methods.
Navigation system for visually impaired people

Navigation assistance for visually impaired (NAVI) refers to systems that are able to assist or guide people with vision loss, ranging from partially sighted to totally blind, by means of sound commands. Many researchers are working to assist visually impaired people in different ways like voice based assistance, ultrasonic based assistance, camera based assistance and in some advance way researchers are trying to give transplantation of real eyes with robotic eyes which can capable enough to plot the real image over patient retina using some biomedical technologies. In other way creating a fusion of sensing technology and voice based guidance system some of the products were developed which could give better result than individual technology. There are some limitation in system like obstacle detection which could not see the object but detection the object and camera based system can't work properly in different light level so the proposed system is a fusion of color sensing sensor and the obstacle sensor along with the voice based assistance system. The main idea of the proposed system to make person aware of path he is walking and also the obstacle in the path.