nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study of 6T, 8T and 9T decanano SRAM cell

Data retention and leakage current reduction are among the major area of concern in today's CMOS technology. In this paper 6T, 8T and 9T SRAM cell have been compared on the basis of read noise margin (RNM), write noise margin (WNM), read delay, write delay, data retention voltage (DRV), layout and parasitic capacitance. Corner and statistical simulation of the noise margin has been carried out to analyze the effect of intrinsic parameter fluctuations. Both 8T SRAM cell and 9T SRAM cell provides higher read noise margin (around 4 times increase in RNM) as compared to 6T SRAM cell. Although the size of 9T SRAM cell is around 1.35 times higher than that of the 8T SRAM cell but it provides higher write stability. Due to single ended bit line sensing the write stability of 8T SRAM cell is greatly affected. The 8T SRAM cell provides a write “1” noise margin which is approximately 3 times smaller than that of the 9T SRAM cell. The data retention voltage for 8T SRAM cell was found to be 93.64mV while for 9T SRAM cell it was 84.5mV and for 6T SRAM cell it was 252.3mV. Read delay for 9T SRAM cell is 98.85ps while for 6T SRAM cell it is 72.82ps and for 8T SRAM cell it is 77.72ps. The higher read delay for 9T SRAM cell is attributed to the fact that dual threshold voltage technology has been in it in order to reduce the leakage current. Write delay for 9T SRAM cell was found to be 10ps, 45.47ps for 8T SRAM cell and 8.97ps for 6T SRAM cell. The simulation has been carried out on 90nm CMOS technology. .
Learning acoustic frame labeling for speech recognition with recurrent neural networks

We explore alternative acoustic modeling techniques for large vocabulary speech recognition using Long Short-Term Memory recurrent neural networks. For an acoustic frame labeling task, we compare the conventional approach of cross-entropy (CE) training using fixed forced-alignments of frames and labels, with the Connectionist Temporal Classification (CTC) method proposed for labeling unsegmented sequence data. We demonstrate that the latter can be implemented with finite state transducers. We experiment with phones and context dependent HMM states as acoustic modeling units. We also investigate the effect of context in acoustic input by training unidirectional and bidirectional LSTM RNN models. We show that a bidirectional LSTM RNN CTC model using phone units can perform as well as an LSTM RNN model trained with CE using HMM state alignments. Finally, we also show the effect of sequence discriminative training on these models and show the first results for sMBR training of CTC models.
Wireless Power Transfer for Vehicular Applications: Overview and Challenges

More than a century-old gasoline internal combustion engine is a major contributor to greenhouse gases. Electric vehicles (EVs) have the potential to achieve eco-friendly transportation. However, the major limitation in achieving this vision is the battery technology. It suffers from drawbacks such as high cost, rare material, low energy density, and large weight. The problems related to battery technology can be addressed by dynamically charging the EV while on the move. In-motion charging can reduce the battery storage requirement, which could significantly extend the driving range of an EV. This paper reviews recent advances in stationary and dynamic wireless charging of EVs. A comprehensive review of charging pad, power electronics configurations, compensation networks, controls, and standards is presented.
Image-based reconstruction and synthesis of dense foliage

Flora is an element in many computer-generated scenes. But trees, bushes and plants have complex geometry and appearance, and are difficult to model manually. One way to address this is to capture models directly from the real world. Existing techniques have focused on extracting macro structure such as the branching structure of trees, or the structure of broad-leaved plants with a relatively small number of surfaces. This paper presents a finer scale technique to demonstrate for the first time the processing of densely leaved foliage - computation of 3D structure, plus extraction of statistics for leaf shape and the configuration of neighboring leaves. Our method starts with a mesh of a single exemplar leaf of the target foliage. Using a small number of images, point cloud data is obtained from multi-view stereo, and the exemplar leaf mesh is fitted non-rigidly to the point cloud over several iterations. In addition, our method learns a statistical model of leaf shape and appearance during the reconstruction phase, and a model of the transformations between neighboring leaves. This information is useful in two ways - to augment and increase leaf density in reconstructions of captured foliage, and to synthesize new foliage that conforms to a user-specified layout and density. The result of our technique is a dense set of captured leaves with realistic appearance, and a method for leaf synthesis. Our approach excels at reconstructing plants and bushes that are primarily defined by dense leaves and is demonstrated with multiple examples.
Design of a haptic arm exoskeleton for training and rehabilitation

A high-quality haptic interface is typically characterized by low apparent inertia and damping, high structural stiffness, minimal backlash, and absence of mechanical singularities in the workspace. In addition to these specifications, exoskeleton haptic interface design involves consideration of space and weight limitations, workspace requirements, and the kinematic constraints placed on the device by the human arm. These constraints impose conflicting design requirements on the engineer attempting to design an arm exoskeleton. In this paper, the authors present a detailed review of the requirements and constraints that are involved in the design of a high-quality haptic arm exoskeleton. In this context, the design of a five-degree-of-freedom haptic arm exoskeleton for training and rehabilitation in virtual environments is presented. The device is capable of providing kinesthetic feedback to the joints of the lower arm and wrist of the operator, and will be used in future work for robot-assisted rehabilitation and training. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuries. As a training tool, the device provides a means to implement flexible, repeatable, and safe training methodologies.
The Earth Is Round ( p < . 05 )

After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred .05 criterion—still persists. This article reviews the problems with this practice, including its near-universal misinterpretation ofp as the probability that Ho is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects Ho one thereby affirms the theory that led to the test. Exploratory data analysis and the use of graphic methods, a steady improvement in and a movement toward standardization in measurement, an emphasis on estimating effect sizes using confidence intervals, and the informed use of available statistical methods is suggested. For generalization, psychologists must finally rely, as has been done in all the older sciences, on replication.
Towards Music Structural Segmentation across Genres: Features, Structural Hypotheses, and Annotation Principles

This article faces the problem of how different audio features and segmentation methods work with different music genres. A new annotated corpus of Chinese traditional Jingju music is presented. We incorporate this dataset with two existing music datasets from the literature in an integrated retrieval system to evaluate existing features, structural hypotheses, and segmentation algorithms outside a Western bias. A harmonic-percussive source separation technique is introduced to the feature extraction process and brings significant improvement to the segmentation. Results show that different features capture the structural patterns of different music genres in different ways. Novelty- or homogeneity-based segmentation algorithms and timbre features can surpass the investigated alternatives for the structure analysis of Jingju due to their lack of harmonic repetition patterns. Findings indicate that the design of audio features and segmentation algorithms as well as the consideration of contextual information related to the music corpora should be accounted dependently in an effective segmentation system.
Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM

Any motor action results from a dynamic interplay of various brain regions involved in different aspects of movement preparation and execution. Establishing a reliable model of how these areas interact is crucial for a better understanding of the mechanisms underlying motor function in both healthy subjects and patients. We used fMRI and dynamic causal modeling to reveal the specific excitatory and inhibitory influences within the human motor system for the generation of voluntary hand movements. We found an intrinsic balance of excitatory and inhibitory couplings among core motor regions within and across hemispheres. Neural coupling within this network was specifically modulated upon uni- and bimanual movements. During unimanual movements, connectivity towards the contralateral primary motor cortex was enhanced while neural coupling towards ipsilateral motor areas was reduced by both transcallosal inhibition and top-down modulation. Bimanual hand movements were associated with a symmetric facilitation of neural activity mediated by both increased intrahemispheric connectivity and enhanced transcallosal coupling of SMA and M1. The data suggest that especially the supplementary motor area represents a key structure promoting or suppressing activity in the cortical motor network driving uni- and bilateral hand movements. Our data demonstrate that fMRI in combination with DCM allows insights into intrinsic properties of the human motor system and task-dependent modulations thereof.
Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT

Latest results indicate that features learned via convolutional neural networks outperform previous descriptors on classification tasks by a large margin. It has been shown that these networks still work well when they are applied to datasets or recognition tasks different from those they were trained on. However, descriptors like SIFT are not only used in recognition but also for many correspondence problems that rely on descriptor matching. In this paper we compare features from various layers of convolutional neural nets to standard SIFT descriptors. We consider a network that was trained on ImageNet and another one that was trained without supervision. Surprisingly, convolutional neural networks clearly outperform SIFT on descriptor matching.
Representation and Mimesis in Generative Art : Creating Fifty Sisters

Fifty Sisters is a generative artwork commissioned for the Ars Electronica Museum in Linz. The work consists of fifty 1m ∞ 1m images of computer-synthesized plant-forms, algorithmically ‘grown’ from computer code using artificial evolution and generative grammars. Each plant-like form is derived from the primitive graphic elements of oil company logos. The title of the work refers to the original ‘Seven Sisters’ — a cartel of seven oil companies that dominated the global petrochemical industry and Middle East oil production from the mid–1940s until the oil crisis of the 1970s. In this paper I discuss the issue of representation in generative art and how dialogues in mimesis inform the production of a generative artwork, using Fifty Sisters as an example. I also provide information on how these concepts translate into the technical and how issues of representation necessarily pervade all computer-based generative art. C o m p u t i n g C o m m u n i c a t i o n A e s t h e t i c s a n d X . B e r g a m o , I t a l y . x c o a x . o r g
LIMSI$@$WMT'16: Machine Translation of News

This paper describes LIMSI’s submissions to the shared WMT’16 task “Translation of News”. We report results for Romanian-English in both directions, for English to Russian, as well as preliminary experiments on reordering to translate from English into German. Our submissions use mainly NCODE and MOSES along with continuous space models in a post-processing step. The main novelties of this year’s participation are the following: for the translation into Russian and Romanian, we have attempted to extend the output of the decoder with morphological variations and to use a CRF model to rescore this new search space; as for the translation into German, we have been experimenting with source-side pre-ordering based on a dependency structure allowing permutations in order to reproduce the tar-
Hand Gesture Recognition System

The task of gesture recognition is highly challenging due to complex background, presence of nongesture hand motions, and different illumination environments. In this paper, a new technique is proposed which begins by detecting the hand and determining its canter, tracking the hands trajectory and analyzing the variations in the hand locations, and finally recognizing the gesture. The proposed technique overcomes background complexity and distance from camera which is up to 2.5 meters. Experimental results show that the proposed technique can recognize 12 gestures with rate above 94%.  Hand gesture recognition, skin detection, Hand tracking.
Forward-backward retraining of recurrent neural networks

This paper describes the training of a recurrent neural network as the letter posterior probability estimator for a hidden Markov model, off-line handwriting recognition system. The network estimates posterior distributions for each of a series of frames representing sections of a handwritten word. The supervised training algorithm, backpropagation through time, requires target outputs to be provided for each frame. Three methods for deriving these targets are presented. A novel method based upon the forwardbackward algorithm is found to result in the recognizer with the lowest error rate.
Tractable Learning for Complex Probability Queries

Tractable learning aims to learn probabilistic models where inference is guaranteed to be efficient. However, the particular class of queries that is tractable depends on the model and underlying representation. Usually this class is MPE or conditional probabilities Pr(x|y) for joint assignments x,y. We propose a tractable learner that guarantees efficient inference for a broader class of queries. It simultaneously learns a Markov network and its tractable circuit representation, in order to guarantee and measure tractability. Our approach differs from earlier work by using Sentential Decision Diagrams (SDD) as the tractable language instead of Arithmetic Circuits (AC). SDDs have desirable properties, which more general representations such as ACs lack, that enable basic primitives for Boolean circuit compilation. This allows us to support a broader class of complex probability queries, including counting, threshold, and parity, in polytime.
How to Architect a Query Compiler

This paper studies architecting query compilers. The state of the art in query compiler construction is lagging behind that in the compilers field. We attempt to remedy this by exploring the key causes of technical challenges in need of well founded solutions, and by gathering the most relevant ideas and approaches from the PL and compilers communities for easy digestion by database researchers. All query compilers known to us are more or less monolithic template expanders that do the bulk of the compilation task in one large leap. Such systems are hard to build and maintain. We propose to use a stack of multiple DSLs on different levels of abstraction with lowering in multiple steps to make query compilers easier to build and extend, ultimately allowing us to create more convincing and sustainable compiler-based data management systems. We attempt to derive our advice for creating such DSL stacks from widely acceptable principles. We have also re-created a well-known query compiler following these ideas and report on this effort.
Metal–Oxide RRAM

In this paper, recent progress of binary metal-oxide resistive switching random access memory (RRAM) is reviewed. The physical mechanism, material properties, and electrical characteristics of a variety of binary metal-oxide RRAM are discussed, with a focus on the use of RRAM for nonvolatile memory application. A review of recent development of large-scale RRAM arrays is given. Issues such as uniformity, endurance, retention, multibit operation, and scaling trends are discussed.
Handbook of genetic algorithms

In this paper, the effect of Genetic algorithm (GA) on the enhancement of the texture and quality of plants visual L. Davis, The handbook of Genetic Algorithms. Genetic Algorithm is one of many methods that can be used to create a schedule. This method Davis, L.: Handbook on Genetic Algorithms. Van Nostrand. In this research, genetic algorithm approach has been applied for solving exam (18) Davis L., Handbook of Genetic Algorithms, New York, Van Nostrand.
Simulation of ocean waves imaging by an along-track interferometric synthetic aperture radar

A two-dimensional (2-D) model for describing the imaging of ocean waves by an along-track interferometric synthetic aperture radar (AT-INSAR) is derived. It includes the modulation of the normalized radar cross section by the long waves, velocity bunching, and azimuthal image smear due to orbital acceleration associated with long waves and due to the orbital velocity spread within the AT-INSAR resolution cell (parameterized by the scene coherence time). By applying the Monte-Carlo method, AT-INSAR amplitude and phase image spectra are calculated for different sea states and radar configurations. The Monte-Carlo simulations show that velocity bunching affects the AT-INSAR imaging mechanism of ocean waves, and that a unimodal ocean wave spectrum may be mapped into a bimodal AT-INSAR phase image spectrum due to an interference between the velocity term and the velocity bunching term in the AT-INSAR imaging model. It is shown that the AT-INSAR imaging mechanism of ocean waves depends on the ratio of the scene coherence time and the time separation between the observations by the two antennas. If this ratio is larger than one, the AT-INSAR phase image spectra are distorted. Furthermore, the simulations show that the AT-INSAR phase image spectrum is quite insensitive to the ocean wave-radar modulation transfer function. Comparing AT-INSAR with conventional SAR imaging of ocean waves, we find that the azimuthal cut-off in AT-INSAR phase image spectra is shifted toward higher wavenumbers than in conventional SAR image spectra. This implies that AT-INSAR can resolve shorter azimuthal wavenumbers than conventional SAR. Thus we conclude that AT-INSAR phase images are better suited for measuring ocean waves spectra than conventional SAR images.
PARAFAC algorithms for large-scale problems

Parallel factor analysis (PARAFAC, called also CP model)) i s a tensor (multiway array) factorization method which allows to find hidden factors (component matrices) from a mul tidimensional data. Most of the existing algorithms for the PARAFAC, especially the alternating least squares ( ALS) algorithm need to compute Khatri-Rao products of tall factors and multiplication of large-scale matrices and due to this require high computational cost and large memory and are not suitable for very large-scale problems. H ence, PARAFAC for large-scale data tensors is still a challenging problem. In this paper, we propose a new approac h b sed on a modified ALS algorithm which computes Hadamard products, instead Khatri-Rao products and employ s relatively small matrices. The new algorithms are able to process extremely large-scale tensors with billions of e ntri s. Extensive experiments confirm the validity and high performance of the developed algorithms in comparison with other well-known algorithms.
Design and Development of an automated Robotic Pick & Stow System for an e-Commerce Warehouse

In this paper, we provide details of a robotic system that can automate the task of picking and stowing objects from and to a rack in an e-commerce fulfillment warehouse. The system primarily comprises of four main modules: (1) Perception module responsible for recognizing query objects and localizing them in the 3-dimensional robot workspace; (2) Planning module generates necessary paths that the robot endeffector has to take for reaching the objects in the rack or in the tote; (3) Calibration module that defines the physical workspace for the robot visible through the on-board vision system; and (4) Gripping and suction system for picking and stowing different kinds of objects. The perception module uses a faster region-based Convolutional Neural Network (R-CNN) to recognize objects. We designed a novel two finger gripper that incorporates pneumatic valve based suction effect to enhance its ability to pick different kinds of objects. The system was developed by IITK-TCS team for participation in the Amazon Picking Challenge 2016 event. The team secured a fifth place in the stowing task in the event. The purpose of this article is to share our experiences with students and practicing engineers and enable them to build similar systems. The overall efficacy of the system is demonstrated through several simulation as well as real-world experiments with actual robots.
3D Human Pose Estimation in RGBD Images for Robotic Task Learning

We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.
IoT data management methods and optimisation algorithms for mobile publish/subscribe services in cloud environments

Recent advances in the Internet of Things (IoT) domain for deploying IoT data systems within the cloud have generated Internet-connected silos of sensor technology which make the collection and processing of sensor-generated information more complex. A viable solution to this problem is the use of local sub-servers acting as collector's hubs between mobile sensing devices and the cloud. Publish/subscribe mechanisms have the capacity to provide full control over the data acquisition and filtering process in mobile IoT environments as well as the methods for implementing optimisations related to energy-efficient data harvesting. The paper formulates design principles for IoT data management methods and optimisation algorithms by means of publish/subscribe middleware and linked data which span over mobile networks and cloud infrastructures to produce a coherent IoT ecosystem. Mobile sensing thus becomes a prominent feature of the OpenIoT platform1, the flaghship Open Source IoT middleware for services based on the automated cloud-based formulation of societies of Internet-connected objects.
160-GBd (320-Gb/s) PAM4 Transmission Using 97-GHz Bandwidth Analog Multiplexer

We demonstrate the transmission of a single-polarization four-level pulsed amplitude modulation (PAM4) optical signal over 10-km single-mode fiber at the gross bit rate of 320 Gb/s and the net data rate of 266.7 Gb/s. A digital-preprocessed analog-multiplexed digital-to-analog converter with a 97-GHz bandwidth analog multiplexer module enables us to generate the Nyquist-shaped PAM4 signal at the high baud rate of 160 Gbd.