nils-herrmann/clean_scirepeval huggingface dataset:
Deep neural networks are robust to weight binarization and other non-linear distortions

Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we show that this is just the tip of the iceberg: these same networks, during testing, also exhibit a remarkable robustness to distortions beyond quantization, including additive and multiplicative noise, and a class of non-linear projections where binarization is just a special case. To quantify this robustness, we show that one such network achieves 11% test error on CIFAR-10 even with 0.68 effective bits per weight. Furthermore, we find that a common training heuristic— namely, projecting quantized weights during backpropagation—can be altered (or even removed) and networks still achieve a base level of robustness during testing. Specifically, training with weight projections other than quantization also works, as does simply clipping the weights, both of which have never been reported before. We confirm our results for CIFAR-10 and ImageNet datasets. Finally, drawing from these ideas, we propose a stochastic projection rule that leads to a new state of the art network with 7.64% test error on CIFAR-10 using no data augmentation.
ASF: AMD64 Extension for Lock-Free Data Structures and Transactional Memory

Advanced Synchronization Facility (ASF) is an AMD64 hardware extension for lock-free data structures and transactional memory. It provides a speculative region that atomically executes speculative accesses in the region. Five new instructions are added to demarcate the region, use speculative accesses selectively, and control the speculative hardware context. Programmers can use speculative regions to build flexible multi-word atomic primitives with no additional software support by relying on the minimum guarantee of available ASF hardware resources for lock-free programming. Transactional programs with high-level TM language constructs can either be compiled directly to the ASF code or be linked to software TM systems that use ASF to accelerate transactional execution. In this paper we develop an out-of-order hardware design to implement ASF on a future AMD processor and evaluate it with an in-house simulator. The experimental results show that the combined use of the L1 cache and the LS unit is very helpful for the performance robustness of ASF-based lock free data structures, and that the selective use of speculative accesses enables transactional programs to scale with limited ASF hardware resources.
FluxPaper: Reinventing Paper with Dynamic Actuation Powered by Magnetic Flux

FluxPaper is a new paper-based medium that enables physical movement and dynamic interaction between a high-power magnetized paper and a programmable magnetic field. FluxPaper has a very thin patterned magnetic layer (0.1 mm) pasted behind the paper. A thin but strong neodymium-based magnet realizes fast, powerful, and precise physical actions while retaining the original characteristics of the paper that is widely used in our daily lives. Owing to an effective magnetic pattern and a computer-controlled magnetic field, FluxPaper can add new interaction modality to ordinary paper. We describe the functions of magnetized paper; challenges through realization; and the interaction scenarios in several applications, such as self-alignment, self-construction, floating on the board, and quickly picking out a target card from a stack.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

P̃ Ñ learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate ρ1 for positive examples and ρ0 for negative examples. We propose Rank Pruning (RP) to solve P̃ Ñ learning and the open problem of estimating the noise rates, i.e. the fraction of wrong positive and negative labels. Unlike prior solutions, RP is time-efficient and general, requiring O(T ) for any unrestricted choice of probabilistic classifier with T fitting time. We prove RP has consistent noise estimation and equivalent expected risk as learning with uncorrupted labels in ideal conditions, and derive closed-form solutions when conditions are non-ideal. RP achieves state-of-the-art noise estimation and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the amount of noise and performs similarly impressively when a large portion of training examples are noise drawn from a third distribution. To highlight, RP with a CNN classifier can predict if an MNIST digit is a one or not with only 0.25% error, and 0.46% error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.
Women outperform men in remembering to remember.

The study of gender differences in prospective memory (i.e., remembering to remember) has received modest attention in the literature. The few reported studies investigating either subjective or objective evaluations of prospective memory have shown inconsistent data. In this study, we aimed to verify the presence of gender differences during the performance of an objective prospective memory test by considering the weight of specific variables such as length of delay, type of response, and type of cue. We submitted a sample of 100 healthy Italian participants (50 men and 50 women) to a test expressly developed to assess prospective memory: The Memory for Intentions Screening Test. Women performed better than men in remembering to do an event-based task (i.e., prompted by an external event) and when the task required a physical response modality. We discuss the behavioural differences that emerged by considering the possible role of sociological, biological, neuroanatomical, and methodological variables.
Evidence of Different Thermoregulatory Mechanisms between Two Sympatric Scarabaeus Species Using Infrared Thermography and Micro-Computer Tomography

In endotherms insects, the thermoregulatory mechanisms modulate heat transfer from the thorax to the abdomen to avoid overheating or cooling in order to obtain a prolonged flight performance. Scarabaeus sacer and S. cicatricosus, two sympatric species with the same habitat and food preferences, showed daily temporal segregation with S. cicatricosus being more active during warmer hours of the day in opposition to S. sacer who avoid it. In the case of S. sacer, their endothermy pattern suggested an adaptive capacity for thorax heat retention. In S. cicatricosus, an active 'heat exchanger' mechanism was suggested. However, no empirical evidence had been documented until now. Thermographic sequences recorded during flight performance showed evidence of the existence of both thermoregulatory mechanisms. In S. sacer, infrared sequences showed a possible heat insulator (passive thermal window), which prevents heat transfer from meso- and metathorax to the abdomen during flight. In S. cicatricosus, infrared sequences revealed clear and effective heat flow between the thorax and abdomen (abdominal heat transfer) that should be considered the main mechanism of thermoregulation. This was related to a subsequent increase in abdominal pumping (as a cooling mechanism) during flight. Computer microtomography scanning, anatomical dissections and internal air volume measurements showed two possible heat retention mechanisms for S. sacer; the abdominal air sacs and the development of the internal abdominal sternites that could explain the thermoregulation between thorax and abdomen. Our results suggest that interspecific interactions between sympatric species are regulated by very different mechanisms. These mechanisms create unique thermal niches for the different species, thereby preventing competition and modulating spatio-temporal distribution and the composition of dung beetle assemblages.
Mutual Alignment Transfer Learning

Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics. We present an approach – supplemental to fine tuning on the real robot – to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot. The developed approach harnesses auxiliary rewards to guide the exploration for the real world agent based on the proficiency of the agent in simulation and vice versa. In this context, we demonstrate empirically that the reciprocal alignment for both agents provides further benefit as the agent in simulation can optimize its behaviour for states commonly visited by the real-world agent.
Stabilization of inertia wheel inverted pendulum by model reference adaptive IDA-PBC: From simulation to real-time experiments

In this paper an adaptive control technique combined with the so-called IDA-PBC (Interconnexion Damping Assignment, Passivity Based Control) controller is proposed for the stabilization of a class of underactuated mechanical systems, namely, the Inertia Wheel Inverted Pendulum (IWIP). It has two degrees of freedom with one actuator. The IDA-PBC stabilizes for all initial conditions (except a set of zeros measure) the upward position of the IWIP. The efficiency of this controller depends on the tuning of several gains. Motivated by this issue we propose to automatically adapt some of these gains in order to regain performance rapidly. The effectiveness of the proposed adaptive scheme is demonstrated through numerical simulations and experimental results.
Human activity classification using a single accelerometer

Human activity recognition is an area of growing interest facilitated by the current revolution in body-worn sensors. Activity recognition allows applications to construct activity profiles for each subject which could be used effectively for healthcare and safety applications. Automated human activity recognition systems face several challenges such as number of sensors, sensor precision, gait style differences, and others. This work proposes a machine learning system to automatically recognise human activities based on a single body-worn accelerometer. The in-house collected dataset contains 3D acceleration of 50 subjects performing 10 different activities. The dataset was produced to ensure robustness and prevent subject-biased results. The feature vector is derived from simple statistical features. The proposed method benefits from RGB-to-YIQ colour space transform as kernel to transform the feature vector into more discriminable features. The classification technique is based on an adaptive boosting ensemble classifier. The proposed system shows consistent classification performance up to 95% accuracy among the 50 subjects.
Sources of Misinformation in Online Social Networks : Who to suspect ?

An increasing number of military systems are being developed using Service Oriented Architecture (SOA). Some of the features that make SOA appealing, like loose coupling, dynamism and composition-oriented system construction, make securing service-based systems more complicated. We have been developing Advanced Protected Services (APS) technologies for improving the resilience and survival of SOA services under cyber attack. These technologies introduce a layer to absorb, contain, and adapt to cyber attacks prior to the attacks reaching critical services. This paper describes an evaluation of these advanced protection technologies using a set of cooperative red team exercises. In these exercises, an independent red team launched attacks on a protected enclave in order to evaluate the efficacy and efficiency of the prototype protection technologies. The red team was provided full knowledge of the system under test and its protections, was given escalating levels of access to the system, and operated within agreed upon rules of engagement designed to scope the testing on useful evaluation results. We also describe the evaluation results and the use of cooperative red teaming as an effective means of evaluating cyber security.
Evaluating regular expression matching engines on network and general purpose processors

In recent years we have witnessed a proliferation of data structure and algorithm proposals for efficient deep packet inspection on memory based architectures. In parallel, we have observed an increasing interest in network processors as target architectures for high performance networking applications.
 In this paper we explore design alternatives in the implementation of regular expression matching architectures on network processors (NPs) and general purpose processors (GPPs). Specifically, we present a performance evaluation on an Intel IXP2800 NP, on an Intel Xeon GPP and on a multiprocessor system consisting of four AMD Opteron 850 cores. Our study shows how to exploit the Intel IXP2800 architectural features in order to maximize system throughput, identifies and evaluates algorithmic and architectural trade-offs and limitations, and highlights how the presence of caches affects the overall performances. We provide an implementation of our NP designs within the Open Network Laboratory (http://www.onl.wustl.edu).
Understanding Tradeoffs in the Supplier

Numerous studies have demonstrated that firms consider multiple criteria when selecting key suppliers. In this study, we present, based on econometric choice modeling framework, how manufacturing managers/executives tradeoff between cost, delivery, flexibility, and service features in the supplier selection process, given acceptable quality. Empirical data for this study was collected from manufacturing organizations in Europe (Germany, France, Italy, United Kingdom) using a computer-based supplier selection discrete choice survey. Each survey instrument contained 16 supplier selection choice sets, which compared 23 attributes of the current suppliers with " new " potential supplier. The attributes of new suppliers were varied across two to four levels using established factorial experimental design procedures. The resultant multinomial logit models show the relative impact of cost, flexibility, delivery and service features on supplier selection.
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation

Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limited segmentation performance, since large amounts of spatial information of prostate volumes are discarded during the slice-by-slice segmentation process; and 3D methods also have room for improvement, since they use isotropic kernels to perform 3D convolutions whereas most prostate MR volumes have anisotropic spatial resolution. Besides, the fully convolutional structural methods achieve good performance for localization issues but neglect the per-voxel classification for segmentation tasks. In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation. We first design a 3D ResNet encoder to extract 3D features from prostate scans, and then develop the decoder, which is composed of a multi-scale 3D global convolutional block and a 3D boundary refinement block, to address the classification and localization issues simultaneously for volumetric segmentation. Additionally, we combine the encoder-decoder segmentation network with an adversarial network in the training phrase to enforce the contiguity of long-range spatial predictions. Throughout the proposed model, we use anisotropic convolutional processing for better feature learning on prostate MR scans. We evaluated our 3D GCA-Net model on two public prostate MR datasets and achieved state-of-the-art performances.
Static calibration of the DLR medical robot MIRO, a flexible lightweight robot with integrated torque sensors

This paper presents a method to calibrate the model of serial and flexible lightweight robots with joint sided torque sensors in the assembled state. The calibration is done in an iterative three-step process, based on static robot poses. In the first step the kinematics and stiffnesses of the flexible components are calibrated. Second the models of the integrated torque sensors are identified in a linear least square solution. In the third step the masses, the centers of gravity and the torque sensor offsets are estimated using linear regression. The calibration steps are repeated stepwise to account for their dependencies. The calibration procedure is simulated and experimentally performed with the medical lightweight robot MIRO of the German Aerospace Center. Through the iterative procedure the pose accuracy improves from about 5mm translational error and 2.5 ° rotational error to 1mm and 0.3 ° regarding the entire workspace.
A scalable dc microgrid architecture for rural electrification in emerging regions

We present the design and experimental validation of a scalable dc microgrid architecture for rural electrification. The microgrid design has been driven by field data collected from Kenya and India. The salient features of the microgrid are distributed voltage control and distributed storage, which enable developed world grid cost parity. In this paper, we calculate that the levelized cost of electricity (LCOE) for the proposed dc microgrid system will be less than $0.40 per kW-hr. We also present experimental results from a locally installed dc microgrid prototype that demonstrate the steady state behavior, the perturbation response, and the overall efficiency of the system. The experimental results demonstrate the suitability of the presented dc microgrid architecture as a technically advantageous and cost effective method for electrifying emerging regions.
Students Want to Create Apps: Leveraging Computational Thinking to Teach Mobile Software Development

Computational Thinking (CT) is recognised as one of the fundamental skills of all graduates. Nevertheless, some issues can emerge when trying to introduce CT into schools; for example, teachers might not be willing to add topics to their intensive syllabi. Therefore, out-of-school venues such as summer schools can be considered a great opportunity for exposure to CT. Moreover, summer schools allow students to meet first hand researchers and help them pursue their interest far from the regular school climate. High school students in general are very curious for the creation of mobile apps; however, most of them get discouraged because they perceive this activity as a very difficult task. Here we describe the MobileDev summer school, a one-week training and hands-on experience in current topics of software development for mobile devices. The curiosity of the students for developing mobile apps is used to introduce and train CT via programming mobile applications through exercises (also with "pen and paper'") of increasing difficulty. The school was repeated twice at our university and was targeted to a reduced class of students concluding the third and the fourth year of high school. Participants were in total 19 and coming from different types of schools. This paper describes the structure of MobileDev and discusses the results to provide directions for further research.
Sensitivity and specificity of the Percentage of Consonants Correct-Revised in the identification of speech sound disorder.

The purpose of the study was to determine the sensitivity and specificity, and to establish cutoff points for the severity index Percentage of Consonants Correct - Revised (PCC-R) in Brazilian Portuguese-speaking children with and without speech sound disorders.



72 children between 5:00 and 7:11 years old - 36 children without speech and language complaints and 36 children with speech sound disorders. The PCC-R was applied to the figure naming and word imitation tasks that are part of the ABFW Child Language Test. Results were statistically analyzed. The ROC curve was performed and sensitivity and specificity values ​​of the index were verified.



The group of children without speech sound disorders presented greater PCC-R values in both tasks, regardless of the gender of the participants. The cutoff value observed for the picture naming task was 93.4%, with a sensitivity value of 0.89 and specificity of 0.94 (age independent). For the word imitation task, results were age-dependent: for age group ≤6:5 years old, the cutoff value was 91.0% (sensitivity of 0.77 and specificity of 0.94) and for age group >6:5 years-old, the cutoff value was 93.9% (sensitivity of 0.93 and specificity of 0.94).



Given the high sensitivity and specificity of PCC-R, we can conclude that the index was effective in discriminating and identifying children with and without speech sound disorders.
Online Popularity under Promotion: Viral Potential, Forecasting, and the Economics of Time

Modeling the popularity dynamics of an online item is an important open problem in computational social science. This paper presents an in-depth study of popularity dynamics under external promotions, especially in predicting popularity jumps of online videos, and determining effective and efficient schedules to promote online content. The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reactions. Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion, and to quantify the time it takes for such effects to unfold. We make increasingly accurate forecasts of future popularity by including information about the intrinsic properties of the video, promotions it receives, and the non-linear effects of popularity ranking. We illustrate by simulation the interplay between the unfolding of popularity over time, and the time-sensitive value of resources. Lastly, our model lends a novel explanation of the commonly adopted periodic and constant promotion strategy in advertising, as increasing the perceived viral potential. This study provides quantitative guidelines about setting promotion schedules considering content virality, timing, and economics.
Rainfall prediction using hybrid neural network approach

A novel rainfall prediction method has been proposed. In the present work rainfall prediction in Southern part of West Bengal (India) has been conducted. A two-step method has been employed. Greedy forward selection algorithm is used to reduce the feature set and to find the most promising features for rainfall prediction. First, in the training phase the data is clustered by applying k-means algorithm, then for each cluster a separate Neural Network (NN) is trained. The proposed two step prediction model (Hybrid Neural Network or HNN) has been compared with MLP-FFN classifier in terms of several statistical performance measuring metrics. The data for experimental purpose is collected by Dumdum meteorological station (West Bengal, India) over the period from 1989 to 1995. The experimental results have suggested a reasonable improvement over traditional methods in predicting rainfall. The proposed HNN model outperformed the compared models by achieving 84.26% accuracy without feature selection and 89.54% accuracy with feature selection.
Polyglot: An Extensible Compiler Framework for Java

Polyglot is an extensible compiler framework that supports the easy creation of compilers for languages similar to Java, while avoiding code duplication. The Polyglot framework is useful for domain-specific languages, exploration of language design, and for simplified versions of Java for pedagogical use. We have used Polyglot to implement several major and minor modifications to Java; the cost of implementing language extensions scales well with the degree to which the language differs from Java. This paper focuses on the design choices in Polyglot that are important for making the framework usable and highly extensible. Polyglot source code is available.