nils-herrmann/clean_scirepeval huggingface dataset:
Energy-efficient execution of data-parallel applications on heterogeneous mobile platforms

State-of-the-art mobile system-on-chips (SoC) include heterogeneity in various forms for accelerated and energy-efficient execution of diverse range of applications. The modern SoCs now include programmable cores such as CPU and GPU with very different functionality. The SoCs also integrate performance heterogeneous cores with different power-performance characteristics but the same instruction-set architecture such as ARM big.LITTLE. In this paper, we first explore and establish the combined benefits of functional heterogeneity and performance heterogeneity in improving power-performance behavior of data parallel applications. Next, given an application specified in OpenCL, we present a static partitioning strategy to execute the application kernel across CPU and GPU cores along with voltage-frequency setting for individual cores so as to obtain the best power-performance tradeoff. We achieve over 19% runtime improvement by exploiting the functional and performance heterogeneities concurrently. In addition, energy saving of 36% is achieved by using appropriate voltage-frequency setting without significantly degrading the runtime improvement from concurrent execution.
Data Burst Statistics and Performance Analysis of Optical Burst Switching Networks with Self-Similar Traffic

The self-similar model can reflect the Internet traffic property more precisely than Poisson model, the theoretical and simulation results of burst length distribution based on the time threshold assembly mechanism are shown in this paper. Moreover, the simulation result of OBS schedule algorithm under self-similar traffic model is reported. Both the analytic and simulation results show that burst length distribution is related to the length of assembly period; secondly, the assembly mechanism based on time period can reduce the self-similarity of traffic effectively; at last, the schedule algorithms in OBS have different performance based on the self-similar traffic.
Distributed Power Control for the Downlink of Multi-Cell NOMA Systems

This paper investigates the power control problem for the downlink of a multi-cell non-orthogonal multiple access system. The problem, called P-OPT, aims to minimize the total transmit power of all the base stations subject to the data rate requirements of the users. The feasibility and optimality properties of P-OPT are characterized through a related optimization problem, called Q-OPT, which is constituted by some relevant power control subproblems. First, we characterize the feasibility of Q-OPT and prove the uniqueness of its optimal solution. Next, we prove that the feasibility of P-OPT can be characterized by the Perron-Frobenius eigenvalues of the matrices arising from the power control subproblems. Subsequently, the relationship between the optimal solutions to P-OPT and that to Q-OPT is presented, which motivates us to obtain the optimal solution to P-OPT through solving the corresponding Q-OPT. Furthermore, a distributed algorithm to solve Q-OPT is designed, and the underlying iteration is shown to be a standard interference function. According to Yates’s power control framework, the algorithm always converges to the optimal solution if exists. Numerical results validate the convergence of the distributed algorithm and quantify the improvement of our proposed method over fractional transmit power control and orthogonal multiple access schemes in terms of power consumption and outage probability.
Young adult outcome of hyperactive children: adaptive functioning in major life activities.

The authors report the adaptive functioning of hyperactive and control children in southeastern Wisconsin (Milwaukee) followed to young adulthood.



Interviews with participants concerning major life activities were collected between 1992 and 1996 and used along with employer ratings and high school records at the young adult follow-up (mean = 20 years, range 19-25) for this large sample of hyperactive (H; n = 149) and community control (CC; n = 72) children initially seen in 1978-1980 and studied for at least 13 years. Age, duration of follow-up, and IQ were statistically controlled as needed.



The H group had significantly lower educational performance and attainment, with 32% failing to complete high school. H group members had been fired from more jobs and manifested greater employer-rated attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms and lower job performance than the CC group. Socially, the H group had fewer close friends, more trouble keeping friends, and more social problems as rated by parents. Far more H than CC group members had become parents (38% versus 4%) and had been treated for sexually transmitted disease (16% versus 4%). Severity of lifetime conduct disorder was predictive of several of the most salient outcomes (failure to graduate, earlier sexual intercourse, early parenthood) whereas attention-deficit/hyperactivity disorder and oppositional defiant disorder at work were predictive of job performance and risk of being fired.



These findings corroborate prior research and go further in identifying sexual activity and early parenthood as additional problematic domains of adaptive functioning at adulthood.
Investigating Causal Relations by Econometric Models and Cross-spectral Methods

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A Foray into Conficker's Logic and Rendezvous Points

We present an in depth static analysis of the Conficker worm, primarily through the exploration of the client-side binary logic. In this paper, we summarize various aspects of the inner workings of binary variants A and B,1 which were the first in a chain of recent revisions aimed to keep this epidemic resistant to ongoing eradication attempts. These first two variants have combined to produce a multi-million node population of infected hosts, whose true main purpose has yet to be fully understood. We further validate aspects of our analysis through in-situ network analyses, and discuss some attribution links about its origins.
Optimal Peak Shaving Using Batteries at Datacenters: Charging Risk and Degradation Model

A datacenter’s power consumption is a major contributor to its operational expenditures (op-ex) as determined by peak-demand-over-billing-period based pricing which is often employed by major electric utility providers. There is a growing interest in reducing a datacenters electricity costs by using demand-throttling techniques and/or energy storage devices (batteries which are readily available at most datacenters as a backup energy source). For the latter, we present a Markov Decision Process framework based on power-demand uncertainty and a linearized battery degradation model. This framework also explicitly considers risk of over or under charging the battery resulting in higher cost-savings (up to 2×) with tractable risk. We show the complete characterization of risk-cost trade-off and cost-per-risk as a function of datacenter’s workload characteristics. We also, study a linearized battery degradation model empirically, and show the accuracy of this model for bursty workloads, however there is some discrepancy between the linearized model and reality for workloads with lower variability.
Progress towards the development of the SmartHand transradial prosthesis

This paper presents progress towards the development of an anthropomorphic 16 degree of freedom, 4 degree of mobility prosthetic hand for use by transradial amputees, named SmartHand. The hand is provided with 40 proprioceptive and exteroceptive sensors in the fingers and with a customized control architecture embedded in the palm. The compact size and weight will allow its use to a wide range of amputees in the near future. This paper shows the design and development of the mechanical structure, control architecture, communication protocol developed to deal with the hand, and briefly presents the sensor set embedded in the fingers. Joint sensors plots while a finger was closing at full speed demonstrate hand functionalities in terms of sensory, control and actuation systems. Finally a non-invasive user-prosthesis interface that could be directly connected to the SmartHand for shared control experiments is briefly presented.
A Fast Greedy Algorithm for Outlier Mining

The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.
Temporal Logic Planning and Control of Robotic Swarms by Hierarchical Abstractions

We develop a hierarchical framework for planning and control of arbitrarily large groups (swarms) of fully actuated robots with polyhedral velocity bounds moving in polygonal environments with polygonal obstacles. At the first level of hierarchy, we aggregate the high-dimensional control system of the swarm into a small-dimensional control system capturing its essential features. These features describe the position of the swarm in the world and its size. At the second level, we reduce the problem of controlling the essential features of the swarm to a model-checking problem. In the obtained hierarchical framework, high-level specifications given in natural language, such as linear temporal logic formulas over linear predicates in the essential features, are automatically mapped to provably correct robot control laws. For the particular case of an abstraction based on centroid and variance, we show that swarm cohesion, interrobot collision avoidance, and environment containment can also be specified and automatically guaranteed in our framework. The obtained communication architecture is centralized
CubeNet: Equivariance to 3D Rotation and Translation

3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can’t explain pose-differences risk “diluting” the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape’s global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.
Algorithm 777: HOMPACK90: A Suite of Fortran 90 Codes for Globally Convergent Homotopy Algorithms

HOMPACK90 is a Fortran 90 version of the Fortran 77 package HOMPACK (Algorithm 652), a collection of codes for finding zeros or fixed points of nonlinear systems using globally convergent probability-one homotopy algorithms. Three qualitatively different algorithms— ordinary differential equation based, normal flow, quasi-Newton augmented Jacobian matrix—are provided for tracking homotopy zero curves, as well as separate routines for dense and sparse Jacobian matrices. A high level driver for the special case of polynomial systems is also provided. Changes to HOMPACK include numerous minor improvements, simpler and more elegant interfaces, use of modules, new end games, support for several sparse matrix data structures, and new iterative algorithms for large sparse Jacobian matrices.
SanssouciDB: An In-Memory Database for Processing Enterprise Workloads

In this paper, we present SanssouciDB: a database system designed for serving ERP transactions and analytics out of the same data store. It consists of a columnstore engine for high-speed analytics and transactions on sparse tables, as well as an engine for so-called combined columns, i.e., column groups which are used for materializing result sets, intermediates, and for pocessing transactions on tables touching many attributes at the same time. Our analysis of SAP customer data showed that the vast majority of transactions in an ERP system are of analytical nature. We describe the key concepts of SanssouciDB’s architecture: concurrency control, techniques for compression and parallelization, and logging. To illustrate the potential of combining OLTP and OLAP processing in the same database, we give several examples of new applications which have been built on top of an early version of SanssouciDB and discuss the speedup achieved when running these applications at SAP customer sites.
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks

Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accelerators such as TPU[1]. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as well as the prediction accuracy. Coarse-grained pruning creates regular sparsity patterns, making it more amenable for hardware acceleration but more challenging to maintain the same accuracy. In this paper we quantitatively measure the trade-off between sparsity regularity and prediction accuracy, providing insights in how to maintain accuracy while having more a more structured sparsity pattern. Our experimental results show that coarse-grained pruning can achieve a sparsity ratio similar to unstructured pruning without loss of accuracy. Moreover, due to the index saving effect, coarse-grained pruning is able to obtain a better compression ratio than fine-grained sparsity at the same accuracy threshold. Based on the recent sparse convolutional neural network accelerator (SCNN), our experiments further demonstrate that coarse-grained sparsity saves∼ 2× the memory references compared to fine-grained sparsity. Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.
Repellent and Contact Toxicity of Alpinia officinarum Rhizome Extract against Lasioderma serricorne Adults

The repellent and contact toxicities of Alpinia officinarum rhizome extract on Lasioderma serricorne adults, and its ability to protect stored wheat flour from L. serricorne adults infestation were investigated. The A. officinarum extract exhibited strong repellent and contact toxicities against L. serricorne adults. The toxicities enhanced significantly with the increasing treatment time and treatment dose. The mean percentage repellency value reached 91.3% at class V at the dose of 0.20 μL/cm2 after 48 h of exposure. The corrected mortality reached over 80.0% at the dose of 0.16 μL/cm2 after 48 h of exposure. The A. officinarum extract could significantly reduce L. serricorne infestation level against stored wheat flour. Particularly, the insect infestation was nil in wheat flour packaged with kraft paper bags coated with the A. officinarum extract at the dose of above 0.05 μL/cm2. The naturally occurring A. officinarum extract could be useful for integrated management of L. serricorne.
Analysis of Audio-Visual Features for Unsupervised Speech Recognition

Research on “zero resource” speech processing focuses on learning linguistic information from unannotated, or raw, speech data, in order to bypass the expensive annotations required by current speech recognition systems. While most recent zero-resource work has made use of only speech recordings, here, we investigate the use of visual information as a source of weak supervision, to see whether grounding speech in a visual context can provide additional benefit for language learning. Specifically, we use a dataset of paired images and audio captions to supervise learning of low-level speech features that can be used for further “unsupervised” processing of any speech data. We analyze these features and evaluate their performance on the Zero Resource Challenge 2015 evaluation metrics, as well as standard keyword spotting and speech recognition tasks. We show that features generated with a joint audiovisual model contain more discriminative linguistic information and are less speaker-dependent than traditional speech features. Our results show that visual grounding can improve speech representations for a variety of zero-resource tasks.
An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors

Visual traffic surveillance systems play important roles in intelligent transport systems nowadays. The first step of a visual traffic surveillance system usually needs to correctly detect objects from images or videos and classify them into different categories (e.g., car, truck, and bus). This paper aims to introduce a new vehicle type classification scheme on the images acquired from multi-view visual traffic surveillance sensors. Most image classification algorithms focus on maximizing the percentage of the correct predictions, which have a deficiency that the images from minority categories are prone to be misclassified as the dominant categories. To address this challenge of classifying imbalanced data acquired from visual traffic surveillance sensors, we propose a method, which integrates deep neural networks with balanced sampling in this paper. The proposed method consists of two main stages. In the first stage, data augmentation with balanced sampling is applied to alleviate the unbalanced data set problem. In the second stage, an ensemble of convolutional neural network models with different architectures is constructed with parameters learned on the augmented training data set. Experiments on the MIOvision traffic camera dataset classification challenge data set demonstrate that the proposed method is able to enhance the mean precision of all categories, in the condition of high overall accuracy, compared with the baseline algorithms.
Reduce The Wastage of Data During Movement in Data Warehouse

In this research paper so as to handle Data in warehousing as well as reduce the wastage of data and provide a better results which takes more and more turn into a focal point of the data source business. Data warehousing and on-line analytical processing (OLAP) are vital fundamentals of resolution hold, which has more and more become a focal point of the database manufacturing. Lots of marketable yield and services be at the present accessible, and the entire primary database management organization vendor nowadays have contributions in the area assessment hold up spaces some quite dissimilar necessities on record technology compare to conventional on-line transaction giving out application. This article gives a general idea of data warehousing and OLAP technologies, with the highlighting on top of their latest necessities. So tools which is used for extract, clean-up and load information into back end of a information warehouse; multidimensional data model usual of OLAP; front end client tools for querying and data analysis; server extension for proficient query processing; and tools for data managing and for administration the warehouse. In adding to survey the circumstances of the art, this article also identify a number of capable research issue, a few which are interrelated to
Evaluation methods and decision theory for classification of streaming data with temporal dependence

Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data.
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

We propose a “plan online and learn offline” framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajectory optimization can cope with approximation errors in the value function, and can stabilize and accelerate value function learning. Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions. Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation. This exploration is critical for fast and stable learning of the value function. Combining these components enable solutions to complex control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.