nils-herrmann/clean_scirepeval huggingface dataset:
On the analysis of low output impedance characteristic of flipped voltage follower (FVF) and FVF LDOs

The flipped voltage follower (FVF), a variant of the common-drain transistor amplifier, comprising local feedback, finds application in circuits such as voltage buffers, current mirrors, class AB amplifiers, frequency compensation circuits and low dropout voltage regulators (LDOs). One of the most important characteristics of the FVF, is its low output impedance. In this tutorial-flavored paper, we perform a theoretical analysis of the transfer function, poles and zeros of the output impedance of the FVF and correlate it with transistor-level simulation results. Utilization of the FVF and its variants has wide application in the analog, mixed-signal and power management circuit design space.
An Overview of Multi-Task Learning in Deep Neural Networks

Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery. This article aims to give a general overview of MTL, particularly in deep neural networks. It introduces the two most common methods for MTL in Deep Learning, gives an overview of the literature, and discusses recent advances. In particular, it seeks to help ML practitioners apply MTL by shedding light on how MTL works and providing guidelines for choosing appropriate auxiliary tasks.
On the complexity of shallow and deep neural network classifiers

Recently, deep networks were proved to be more effective than shallow architectures to face complex real–world applications. However, theoretical results supporting this claim are still few and incomplete. In this paper, we propose a new topological measure to study how the depth of feedforward networks impacts on their ability of implementing high complexity functions. Upper and lower bounds on network complexity are established, based on the number of hidden units and on their activation functions, showing that deep architectures are able, with the same number of resources, to address more difficult classification problems.
Random Erasing Data Augmentation

In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN). In training, Random Erasing randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Albeit simple, Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and yields consistent improvement over strong baselines in image classification, object detection and person reidentification. Code is available at: https://github. com/zhunzhong07/Random-Erasing.
Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple metric learning modular further boosts our method, making it significantly outperform many recent works.
Why People Participate in the Sharing Economy: A Social Exchange Perspective

We empirically analyze the competitive benefits of sharing economy services to understand why people participate in the sharing economy. We employ the social exchange theory to examine the participation intention in sharing over owning. We emphasize on the importance of service platform as a trusted third party and its influence on reducing the perceived risk of sharing economy. The research model includes the key antecedents to trust and relative advantages of sharing economy services. The model will be tested with the Airbnb users’ data. The research results are expected to contribute to researchers and practitioners to understand the sharing economy.
Weather Forecasting using Incremental K-means Clustering

Clustering is a powerful tool which has been used in several forecasting works, such as time series forecasting, real time storm detection, flood forecasting and so on. In this paper, a generic methodology for weather forecasting is proposed by the help of incremental K-means clustering algorithm. Weather forecasting plays an important role in day to day applications.Weather forecasting of this paper is done based on the incremental air pollution database of west Bengal in the years of 2009 and 2010. This paper generally uses typical Kmeans clustering on the main air pollution database and a list of weather category will be developed based on the maximum mean values of the clusters.Now when the new data are coming, the incremental K-means is used to group those data into those clusters whose weather category has been already defined. Thus it builds up a strategy to predict the weather of the upcoming data of the upcoming days. This forecasting database is totally based on the weather of west Bengal and this forecasting methodology is developed to mitigating the impacts of air pollutions and launch focused modeling computations for prediction and forecasts of weather events. Here accuracy of this approach is also measured.
The Impact of Physical Classroom Environment on Student Satisfaction and Student Evaluation of Teaching in the University Environment

Recently, many colleges and universities have made significant investments in upgraded classrooms and learning centers, incorporating such factors as tiered seating, customized lighting packages, upgraded desk and seat quality, and individual computers. To date, few studies have examined the impact of classroom environment at post-secondary institutions. The purpose of this study is to analyze the impact of classroom environment factors on individual student satisfaction measures and on student evaluation of teaching in the university environment. Two-hundred thirty-seven undergraduate business students were surveyed regarding their perceptions of classroom environment factors and their satisfaction with their classroom, instructor, and course. The results of the study indicate that students do perceive significant differences between standard and upgraded classrooms. Additionally, students express a preference for several aspects of upgraded classrooms, including tiered seating, lighting, and classroom noise control. Finally, students rate course enjoyment, classroom learning, and instructor organization higher in upgraded classrooms than in standard classrooms. The results of this study should benefit administrators who make capital and infrastructure decisions regarding college and university classroom improvements, faculty members who develop and rely upon student evaluations of teaching, and researchers who examine the factors impacting student satisfaction and learning.
Transformation Pursuit for Image Classification

A simple approach to learning invariances in image classification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a compact subset is challenging. Indeed, all transformations are not equally informative and adding uninformative transformations increases training time with no gain in accuracy. We propose a principled algorithm -- Image Transformation Pursuit (ITP) -- for the automatic selection of a compact set of transformations. ITP works in a greedy fashion, by selecting at each iteration the one that yields the highest accuracy gain. ITP also allows to efficiently explore complex transformations, that combine basic transformations. We report results on two public benchmarks: the CUB dataset of bird images and the ImageNet 2010 challenge. Using Fisher Vector representations, we achieve an improvement from 28.2% to 45.2% in top-1 accuracy on CUB, and an improvement from 70.1% to 74.9% in top-5 accuracy on ImageNet. We also show significant improvements for deep convnet features: from 47.3% to 55.4% on CUB and from 77.9% to 81.4% on ImageNet.
Smooth PARAFAC Decomposition for Tensor Completion

In recent years, low-rank based tensor completion, which is a higher order extension of matrix completion, has received considerable attention. However, the low-rank assumption is not sufficient for the recovery of visual data, such as color and 3D images, when the ratio of missing data is extremely high. In this paper, we consider “smoothness” constraints as well as low-rank approximations and propose an efficient algorithm for performing tensor completion that is particularly powerful regarding visual data. The proposed method admits significant advantages, owing to the integration of smooth PARAFAC decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank. Thus, our proposed method is termed as “smooth PARAFAC tensor completion (SPC).” In order to impose the smoothness constraints, we employ two strategies, total variation (SPC-TV) and quadratic variation (SPC-QV), and invoke the corresponding algorithms for model learning. Extensive experimental evaluations on both synthetic and real-world visual data illustrate the significant improvements of our method, in terms of both prediction performance and efficiency, compared with many state-of-the-art tensor completion methods.
Memory Architectures in Recurrent Neural Network Language Models

We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models. Our experiments on the Penn Treebank and Wikitext-2 datasets show that stack-based memory architectures consistently achieve the best performance in terms of held out perplexity. We also propose a generalization to existing continuous stack models (Joulin & Mikolov, 2015; Grefenstette et al., 2015) to allow a variable number of pop operations more naturally that further improves performance. We further evaluate these language models in terms of their ability to capture non-local syntactic dependencies on a subject-verb agreement dataset (Linzen et al., 2016) and establish new state of the art results using memory augmented language models. Our results demonstrate the value of stack-structured memory for explaining the distribution of words in natural language, in line with linguistic theories claiming a context-free backbone for natural language.
Modeling, control and design optimization for a fully-actuated hexarotor aerial vehicle with tilted propellers

Mobility of a hexarotor UAV in its standard configuration is limited, since all the propeller force vectors are parallel and they achieve only 4-DoF actuation, similar, e.g., to quadrotors. As a consequence, the hexarotor pose cannot track an arbitrary trajectory while the center of mass is tracking a position trajectory. In this paper, we consider a different hexarotor architecture where propellers are tilted, without the need of any additional hardware. In this way, the hexarotor gains a 6-DoF actuation which allows to independently reach positions and orientations in free space and to be able to exert forces on the environment to resist any wrench for aerial manipulation tasks. After deriving the dynamical model of the proposed hexarotor, we discuss the controllability and the tilt angle optimization to reduce the control effort for the specific task. An exact feedback linearization and decoupling control law is proposed based on the input-output mapping, considering the Jacobian and task acceleration, for non-linear trajectory tracking. The capabilities of our approach are shown by simulation results.
Social Role Discovery in Human Events

We deal with the problem of recognizing social roles played by people in an event. Social roles are governed by human interactions, and form a fundamental component of human event description. We focus on a weakly supervised setting, where we are provided different videos belonging to an event class, without training role labels. Since social roles are described by the interaction between people in an event, we propose a Conditional Random Field to model the inter-role interactions, along with person specific social descriptors. We develop tractable variational inference to simultaneously infer model weights, as well as role assignment to all people in the videos. We also present a novel YouTube social roles dataset with ground truth role annotations, and introduce annotations on a subset of videos from the TRECVID-MED11  event kits for evaluation purposes. The performance of the model is compared against different baseline methods on these datasets.
Traits: A mechanism for fine-grained reuse

Inheritance is well-known and accepted as a mechanism for reuse in object-oriented languages. Unfortunately, due to the coarse granularity of inheritance, it may be difficult to decompose an application into an optimal class hierarchy that maximizes software reuse. Existing schemes based on single inheritance, multiple inheritance, or mixins, all pose numerous problems for reuse. To overcome these problems we propose traits, pure units of reuse consisting only of methods. We develop a formal model of traits that establishes how traits can be composed, either to form other traits, or to form classes. We also outline an experimental validation in which we apply traits to refactor a nontrivial application into composable units.
Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web

In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web. Its goal is to robustly generate the sequence of atomic actions of seen longer actions in video in order to acquire knowledge for robots. The lower level of the system consists of two convolutional neural network (CNN) based recognition modules, one for classifying the hand grasp type and the other for object recognition. The higher level is a probabilistic manipulation action grammar based parsing module that aims at generating visual sentences for robot manipulation. Experiments conducted on a publicly available unconstrained video dataset show that the system is able to learn manipulation actions by “watching” unconstrained videos with high accuracy.
Significance driven hybrid 8T-6T SRAM for energy-efficient synaptic storage in artificial neural networks

Multilayered artificial neural networks have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant interest in the development of efficient hardware implementations. In this work, we focus on designing energy-efficient on-chip storage for the synaptic weights, motivated primarily by the observation that the number of synapses is orders of magnitude larger than the number of neurons. Typical digital CMOS implementations of such large-scale networks are power hungry. In order to minimize the power consumption, the digital neurons could be operated reliably at scaled voltages by reducing the clock frequency. On the contrary, the on-chip synaptic storage designed using a conventional 6T SRAM is susceptible to bitcell failures at reduced voltages. However, the intrinsic error resiliency of neural networks to small synaptic weight perturbations enables us to scale the operating voltage of the 6T SRAM. Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200 mV from the nominal operating voltage (950 mV) for practically no loss (less than 0.5%) in accuracy (22 nm predictive technology). Scaling beyond that causes substantial performance degradation owing to increased probability of failures in the MSBs of the synaptic weights. We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due to decoupled read and write paths. In an effort to further minimize the area penalty, we present a synaptic-sensitivity driven hybrid memory architecture consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation framework shows that the proposed synaptic-sensitivity driven architecture provides a 30.91% reduction in the memory access power with a 10.41% area overhead, for less than 1% loss in the classification accuracy.
Building block of a programmable neuromorphic substrate: A digital neurosynaptic core

The grand challenge of neuromorphic computation is to develop a flexible brain-inspired architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of biological neural systems. Toward this end, we fabricated a building block of a modular neuromorphic architecture, a neurosynaptic core. Our implementation consists of 256 integrate-and-fire neurons and a 1,024×256 SRAM crossbar memory for synapses that fits in 4.2mm2 using a 45nm SOI process and consumes just 45pJ per spike. The core is fully configurable in terms of neuron parameters, axon types, and synapse states and its fully digital implementation achieves one-to-one correspondence with software simulation models. One-to-one correspondence allows us to introduce an abstract neural programming model for our chip, a contract guaranteeing that any application developed in software functions identically in hardware. This contract allows us to rapidly test and map applications from control, machine vision, and classification. To demonstrate, we present four test cases (i) a robot driving in a virtual environment, (ii) the classic game of pong, (iii) visual digit recognition and (iv) an autoassociative memory.
Analysis and Design of an Omnidirectional Platform for Operation on Non-Ideal Floors

An omnidirectional platform with an Active Offset Split Caster (ASOC) is described and its ability to operate on non-ideal floors is studied. It is shown that all of its driven wheels of the platform will remain in contact with an uneven floor at all times, a condition necessary to maintain good traction and dead-reckoning capabilities. It is shown that planning algorithms developed for an ideally flat floor perform adequately for a realistic uneven floor. Furthermore, it is shown that the ASOC design consumes less power than other conventional wheel omnidirectional designs and is more suitable to heavier loads. Analytical and experimental results are presented.
Distributional Representations of Words for Short Text Classification

Traditional supervised learning approaches to common NLP tasks depend heavily on manual annotation, which is labor intensive and time consuming, and often suffer from data sparseness. In this paper we show how to mitigate the problems in short text classification (STC) through word embeddings – distributional representations of words learned from large unlabeled data. The word embeddings are trained from the entire English Wikipedia text. We assume that a short text document is a specific sample of one distribution in a Bayesian framework. A Gaussian process approach is used to model the distribution of words. The task of classification becomes a simple problem of selecting the most probable Gaussian distribution. This approach is compared with those based on the classical maximum entropy (MaxEnt) model and the Latent Dirichlet Allocation (LDA) approach. Our approach achieved better performance and also showed advantages in dealing with unseen words.
Modified MPPT with using model predictive control for multilevel boost converter

This paper proposes a modification in the maximum power point tracking (MPPT) by using model predictive control (MPC). The modification scheme of the MPPT control is based on the perturb and observe algorithm (P&O). This modified control is implemented on the dc-dc multilevel boost converter (MLBC) to increase the response of the controller to extract the maximum power from the photovoltaic (PV) module and to boost a small dc voltage of it. The total system consisting of a PV model, a MLBC and the modified MPPT has been analyzed and then simulated with changing the solar radiation and the temperature. The proposed control scheme is implemented under program MATLAB/SIMULINK and the obtained results are validated with real time simulation using dSPACE 1103 ControlDesk. The real time simulation results have been provided for principle validation.
Exploring consumer adoption of new services by analyzing the behavior of 3G subscribers: An empirical case study

As the profit margins of 3G mobile network operators gradually decline, and market competition becomes increasingly intensive, they must develop rich and diverse varieties of brand new application services to attract new subscribers and retain old ones. Understanding the customer’s purchasing behavior is a key issue in this process. The operator must accurately grasp movements in the market based on analysis of the behavior of 3G subscribers. This study proposes a comprehensive customer relationship management strategy framework to furnish a beneficial plan to overcome such challenges. First, we propose a new model to identify who are the high-value customers related to the characteristics of new telecommunication services. After segmenting the customers, we propose a procedure to provide different kinds of usage analysis, including inter-cluster analysis and intra-cluster analysis. The experimental results are determined based on rules extracted from a large number of call detail records generated by the mobile subscribers of leading 3G mobile system operators in Taiwan. The dependency network demonstrates the relationship between voice services, data communications, message services, micropayments and entertainment. Finally, we propose some marketing recommendations for 3G system operators based on these interesting rules.