nils-herrmann/clean_scirepeval huggingface dataset:
A PLL control for self-tuning of parallel wireless power transfer receivers utilizing switch-mode gyrator emulated inductors

In multiple receivers wireless power transfer (WPT) systems, it is preferable to retune the resonant frequency of every receiver to the transmitter operating frequency in front of frequency mismatches. This paper discusses a proposal for electronic tuning for WPT receivers by means of a variable active switch-mode inductance. The proposed method benefits from the gyrator concept to emulate a variable inductance. Instead of the conventional approach of linear amplifier based implementation of a gyrator, a switch-mode gyrator circuit is exploited for more efficient operation. Additionally, a PLL-like control is presented to enable self-tuning for the receiver resonant tank. Furthermore, a design-space characterization for the system dynamic behavior has been discussed to show the control robustness and the instabilities (including slow-scale and fast-scale chaotic instabilities) it may undergo.
Comparison and Analysis of Single-Phase Transformerless Grid-Connected PV Inverters

Leakage current minimization is one of the most important considerations in transformerless photovoltaic (PV) inverters. In the past, various transformerless PV inverter topologies have been introduced, with leakage current minimized by the means of galvanic isolation and common-mode voltage (CMV) clamping. The galvanic isolation can be achieved via dc-decoupling or ac-decoupling, for isolation on the dc- or ac-side of the inverter, respectively. It has been shown that the latter provides lower losses due to the reduced switch count in conduction path. Nevertheless, leakage current cannot be simply eliminated by galvanic isolation and modulation techniques, due to the presence of switches' junction capacitances and resonant circuit effects. Hence, CMV clamping is used in some topologies to completely eliminate the leakage current. In this paper, several recently proposed transformerless PV inverters with different galvanic isolation methods and CMV clamping technique are analyzed and compared. A simple modified H-bridge zero-voltage state rectifier is also proposed, to combine the benefits of the low-loss ac-decoupling method and the complete leakage current elimination of the CMV clamping method. The performances of different topologies, in terms of CMV, leakage current, total harmonic distortion, losses and efficiencies are compared. The analyses are done theoretically and via simulation studies, and further validated with experimental results. This paper is helpful for the researchers to choose the appropriate topology for transformerless PV applications and to provide the design principles in terms of common-mode behavior and efficiency.
Disentangling by Factorising

We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon β-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.
Greedy Layerwise Learning Can Scale to ImageNet

Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-10 dataset. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems lead to a CNN that exceeds AlexNet performance on ImageNet. Extending this training methodology to construct individual layers by solving 2-and-3-hidden layer auxiliary problems, we obtain an 11-layer network that exceeds several members of the VGG model family on ImageNet, and can train a VGG-11 model to the same accuracy as end-to-end learning. To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We illustrate several interesting properties of these models theoretically and conduct a range of experiments to study the properties this training induces on the intermediate representations.
Spectrally efficient time-frequency training OFDM for MIMO systems

The large number of pilots commonly used in OFDM MIMO systems reduces the spectral efficiency in practice. This paper proposes the time-frequency training OFDM (TFT-OFDM) transmission scheme for MIMO systems to solve this problem. The transmission frame is composed of one preamble and the following TFT-OFDM symbols, where each TFT-OFDM symbol without cyclic prefix adopts the time-domain training sequence (TS) and the frequency-domain orthogonal grouped pilots as the time-frequency training information. At the receiver, the time-frequency joint channel estimation directly exploits the “contaminated” time-domain TS to estimate the path delays only, while the path gains are acquired by the frequency-domain grouped pilots. The Cramer-Rao lower bound (CRLB) of the proposed estimator is also derived. Compared with standard OFDM MIMO systems in typical applications, the proposed scheme has about 17% higher spectral efficiency, and has better performance over doubly selective fading channels as indicated by the simulation results.
Impact of Warehouse Management System in a Supply Chain

In a supply chain, warehousing function is very critical as it acts as a node in linking the material flows between the supplier and customer. In today&apos;s competitive market environment companies are continuously forced to improve their warehousing operations. Many companies have also customized their value proposition to increase their customer service levels, which has led to changes in the role of warehouses. This paper highlights the findings of the study carried out to evaluate performance levels and enhance productivity of the manual warehouses by developing a WMS framework and cost benefit analysis.
A maturity model for information governance

Information Governance (IG) as defined by Gartner is the “specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information. Includes the processes, roles, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals”. In this paper, we present how to create an IG maturity model based on existing reference documents. The process is based on existing maturity model development methods. These methods allow for a systematic approach to maturity model development backed up by a well-known and proved scientific research method called Design Science Research. Then, based on the maturity model proposed in this paper, an assessment is conducted and the results are presented, this assessment was conducted as a self-assessment in the context of the EC-funded E-ARK project for the seven pilots of the project. The main conclusion from this initial assessment is that there is much room for improvement with most pilots achieving results between maturity level two and three. As future work, the goal is to analyze other references from different domains, such as, records management. These references will enhance, detail and help develop the maturity model making it even more valuable for all types of organization that deal with information governance.
Healthy #fondue #dinner: analysis and inference of food and drink consumption patterns on instagram

Social media generate large-scale data to study food and drink consumption in everyday life. Using Instagram posts in Switzerland over five years, our goal is two-fold. First, we extract key food & drink consumption patterns, through the lenses of a data-driven dictionary of popular items extracted from hashtags, and of a food categorization system used by the Swiss Federal government for national statistics purposes. Patterns related to spatial and temporal distributions of food & drink consumption, demographics, and eating events are extracted and compared to official statistics. Second, using the insights from this analysis, we define two eating event classification tasks, including a two-class task (healthy vs. unhealthy) and a six-class task (the three main meals break-fast/lunch/dinner/plus brunch/coffee/tea). Both tasks use hash-tags as labels for supervised learning. We study how content (hashtags and food categories), context (time and location), and social features (likes) can discriminate these eating events. A random forest and a combination of content and context features can classify healthy vs. unhealthy eating posts with 85.8% accuracy, and the six daily eating occasions with 61.7% accuracy.
Service Level Agreement in Cloud Computing

Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement in a Service Oriented Architecture (SOA). We use the third party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.
Cascaded H-Bridge Multilevel Converter for Grid Connected Photovoltaic Generators with Independent Maximum Power Point Tracking of each Solar Array 0

This paper introduces a new control method and proportional PWM modulation of the cascaded H-bridge multilevel converter for grid-connected photovoltaic systems. This control makes each H-bridge module supply different power levels, allowing therefore for each module an independent maximum power point tracking of the corresponding photovoltaic array.
Platform IO and system memory test using L3 cache based test (CBT) and parallel execution of CPGC Intel BIST engine

As the memory industry pushes to increase memory density, device variation is creating more defects. Furthermore, new form factors (phone, tablet, mobile and client PC) and low cost board and platform limits physical access to JTAG or TAP. Taking advantage of the x86 architecture's high functional bandwidth to memory, the quickest way to access and test memory is through CPU core, by storing and running parallel CPGC/BIST test content via CPU L3 cache, one CPGC BIST engine per memory channel. We propose a cache based testing framework that speeds up test time 60× to 170× compared to JTAG or TAP based testing using the same test content. We will present the cache based test (CBT) architecture and infrastructure (MRC/NEM setup, CPGC/IBIST), test content, results, and a side by side comparison of test time to JTAG or TAP. Finally we will discuss and compare this approach to generalized cache based tests.
Toward an Ontopedia for Historical Hebrew Manuscripts

Historical handwritten Hebrew manuscripts are one of the most unique and authentic witnesses of Jewish culture and thought that survived through the centuries. In order to enable a systematic research of the knowledge embedded in the manuscripts, there is a need for a formal conceptual data model with a high level of semantic granularity, an ontology. We propose to build a dynamic web-based framework that will allow scholars to create, enrich, and consult an “ontopedia” (ontology-based encyclopedia) of Hebrew manuscripts. The framework is based on an ontology especially designed and implemented for this domain and goals. We view a manuscript as a “living entity” and propose to design a new ontological data model of the narrative for a manuscript, stages/milestones in its biography (creation, copying, and acquisition). A sequence of events and places constitutes a timeline of history against which manuscripts, people, and their relationships can be placed. A large-scale automated reasoning based on the ontology will also enable us to construct a semantically rich social network of people and manuscripts, and to compare the effect of time and place on the manuscripts’ qualitative characteristics and quantitative distribution.
Partial Occlusion Handling in Pedestrian Detection With a Deep Model

Part-based models have demonstrated their merit in object detection. However, there is a key issue to be solved on how to integrate the inaccurate scores of part detectors when there are occlusions, abnormal deformations, appearances, or illuminations. To handle the imperfection of part detectors, this paper presents a probabilistic pedestrian detection framework. In this framework, a deformable part-based model is used to obtain the scores of part detectors and the visibilities of parts are modeled as hidden variables. Once the occluded parts are identified, their effects are properly removed from the final detection score. Unlike previous occlusion handling approaches that assumed independence among the visibility probabilities of parts or manually defined rules for the visibility relationship, a deep model is proposed in this paper for learning the visibility relationship among overlapping parts at multiple layers. The proposed approach can be viewed as a general postprocessing of part-detection results and can take detection scores of existing part-based models as input. The experimental results on three public datasets (Caltech, ETH, and Daimler) and a new CUHK occlusion dataset (<;uri xlink:type="simple">http://www.ee.cuhk.edu.hk/~xgwang/CUHK_pedestrian.html<;/uri>), which is specially designed for the evaluation of occlusion handling approaches, show the effectiveness of the proposed approach.
Exploiting Open-Endedness to Solve Problems Through the Search for Novelty

This paper establishes a link between the challenge of solving highly ambitious problems in machine learning and the goal of reproducing the dynamics of open-ended evolution in artificial life. A major problem with the objective function in machine learning is that through deception it may actually prevent the objective from being reached. In a similar way, selection in evolution may sometimes act to discourage increasing complexity. This paper proposes a single idea that both overcomes the obstacle of deception and suggests a simple new approach to open-ended evolution: Instead of either explicitly seeking an objective or modeling a domain to capture the open-endedness of natural evolution, the idea is to simply search for novelty. Even in an objective-based problem, such novelty search ignores the objective and searches for behavioral novelty. Yet because many points in the search space collapse to the same point in behavior space, it turns out that the search for novelty is computationally feasible. Furthermore, because there are only so many simple behaviors, the search for novelty leads to increasing complexity. In fact, on the way up the ladder of complexity, the search is likely to encounter at least one solution. In this way, by decoupling the idea of open-ended search from only artificial life worlds, the raw search for novelty can be applied to real world problems. Counterintuitively, in the deceptive maze navigation task in this paper, novelty search significantly outperforms objective-based search, suggesting a surprising new approach to machine learning.
Minimum PCB footprint point-of-load DC-DC converter realized with Switched-Capacitor architecture

This work reports on the design and test of a CMOS-based Switched Capacitor (SC) DC-DC conversion integrated circuit (IC) for the point-of-load (POL) application conventionally addressed with the buck converter. A 12V-to-1.5V converter is fabricated in a 0.18µm technology, with an active die area of 3mm2. There is a significant reduction in printed circuit board (PCB) footprint, passive component height and cost when compared to surveyed buck converters. The converter achieves 93% peak efficiency and an efficiency of 80% over an output current range of 7mA to 1A. This work demonstrates the vast potential of SC converters for wide-range voltage conversion in deep-submicron CMOS technologies, when cost and efficiency are of critical importance.
Neural Sequence Learning Models for Word Sense Disambiguation

Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.
Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN

Raw depth images captured by consumer depth cameras suffer from noisy and missing values. Despite the success of CNN-based image processing on color image restoration, similar approaches for depth enhancement have not been much addressed yet because of the lack of raw-clean pairwise dataset. In this paper, we propose a pairwise depth image dataset generation method using dense 3D surface reconstruction with a filtering method to remove low quality pairs. We also present a multi-scale Laplacian pyramid based neural network and structure preserving loss functions to progressively reduce the noise and holes from coarse to fine scales. Experimental results show that our network trained with our pairwise dataset can enhance the input depth images to become comparable with 3D reconstructions obtained from depth streams, and can accelerate the convergence of dense 3D reconstruction results.
Kinematics and Dynamics of a New 16 DOF Humanoid Biped Robot with Active Toe Joint

Humanoid biped  robots are  typically complex in design, having numerous Degrees‐of‐Freedom (DOF) due to the ambitious goal of mimicking the human gait. The paper proposes a new architecture for a biped robot with  seven  DOF  per  each  leg  and  one  DOF corresponding  to  the  toe  joint.  Furthermore,  this work presents  close  equations  for  the  forward  and  inverse kinematics by dividing the walking gait into the Sagittal and  Frontal  planes.  This  paper  explains  the mathematical model of  the dynamics  equations  for  the legs  into  the  Sagittal  and  Frontal  planes  by  further applying the principle of Lagrangian dynamics. Finally, a control approach using a PD control law with gravity compensation  was  recurred  in  order  to  control  the desired  trajectories and  finding  the  required  torque by the  joints.  The  paper  contains  several  simulations  and numerical  examples  to  prove  the  analytical  results, using  SimMechanics  of  MATLAB  toolbox  and SolidWorks to verify the analytical results.
Parental and Peer Influences on the Risk of Adolescent Drug Use

Using a probability sample of 4,230 adolescents from grades 7–12, we used negative binomial regression to estimate the effects of peer and six family variables on the risk of adolescent drug use. Peer drug use had relatively strong effects of adolescent drug use. Parental drug attitudes, sibling drug use, and adult drug use had significant direct effects net of peer influences. In addition, they had significant indirect effects that were mediated by peer drug use. The influences of parental monitoring, attachment to mother, and attachment to father were statistically significant but relatively small. The findings applied to alcohol, binge drinking, cigarettes, marijuana, and other illicit drugs. Editors' Strategic Implications: The authors interpret their findings as being more consistent with social learning than social control theory. This research, although cross-sectional and limited to adolescents' self-reports, contributes to a growing literature on the direct and indirect influences of parents on their teens' substance use rates. It speaks to the need for school- and community-based prevention efforts to focus on families as well as peers.
Parental involvement in the development of children's reading skill: a five-year longitudinal study.

This article presents the findings of the final phase of a 5-year longitudinal study with 168 middle- and upper middle-class children in which the complex relations among early home literacy experiences, subsequent receptive language and emergent literacy skills, and reading achievement were examined. Results showed that children's exposure to books was related to the development of vocabulary and listening comprehension skills, and that these language skills were directly related to children's reading in grade 3. In contrast, parent involvement in teaching children about reading and writing words was related to the development of early literacy skills. Early literacy skills directly predicted word reading at the end of grade 1 and indirectly predicted reading in grade 3. Word reading at the end of grade 1 predicted reading comprehension in grade 3. Thus, the various pathways that lead to fluent reading have their roots in different aspects of children's early experiences.
DRPose3D: Depth Ranking in 3D Human Pose Estimation

In this paper, we propose a two-stage depth ranking based method (DRPose3D) to tackle the problem of 3D human pose estimation. Instead of accurate 3D positions, the depth ranking can be identified by human intuitively and learned using the deep neural network more easily by solving classification problems. Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D pose regression in two-stage methods from being ill-posed. In our method, firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to extract depth rankings of human joints from images. Secondly, a coarse-to-fine 3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth rankings and 2D human joint locations. Additionally, to improve the generality of our model, we introduce a statistical method to augment depth rankings. Our approach outperforms the state-of-theart methods in the Human3.6M benchmark for all three testing protocols, indicating that depth ranking is an essential geometric feature which can be learned to improve the 3D pose estimation.