nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Sample Using Stein Discrepancy

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient [1] that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. By allowing to “learn to draw samples”, our method opens a host of applications. We present two examples in this paper: 1) we propose an amortized MLE method for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realistic-looking images competitive with the state-of-the-art results. 2) by treating stochastic gradient Langevin dynamics as a black-box sampler, we train it to automatically adjust its learning rate to maximize its convergence speed, and get better performances than the hand-designed learning rate schemes.
SUPPRESSION OF GATE INDUCED DRAIN LEAKAGE CURRENT ( GIDL ) BY GATE WORKFUNCTION ENGINEERING : ANALYSIS AND MODEL

Leakage current reduction is of primary importance as the technology scaling trends continue towards deep sub-micrometer regime. One of the leakage mechanisms which contribute significantly to power dissipation is the Gate Induced Drain Leakage (GIDL). GIDL sets an upper limit on the VLSI MOSFET scaling and may even lead to device breakdown. Thus, in order to improve performance, static power consumption becomes a major concern in such miniaturized devices. With the CMOS technology in the nanometer regime, metal gates emerge as a powerful booster over the poly-Si gates, keeping the leakage mechanisms in control. This work presents a systematic study of the Gate Induced Drain Leakage current reduction by changing the gate workfunction. In this work, an attempt has been made to model the metal gates in the field equations in the gate-drain overlap region. The modeling has been accomplished by taking the physical property of the metals into account i.e. the workfunction. The analytical model has been used to study the impact of gate workfunction engineering on GIDL. The model includes the calculation of band bending, the resultant electric field in the gate-drain overlap region and the GIDL current. It has been observed that the electric field and the GIDL current decreases as the gate workfunction is increased continuously from 4.0~5.2 eV. Further, the results are more interesting at low applied voltages where the decrease in the current is of about 6 orders of magnitude as compared to that at higher voltages.
A Multi-Threading Architecture to Support Interactive Visual Exploration

During continuous user interaction, it is hard to provide rich visual feedback at interactive rates for datasets containing millions of entries. The contribution of this paper is a generic architecture that ensures responsiveness of the application even when dealing with large data and that is applicable to most types of information visualizations. Our architecture builds on the separation of the main application thread and the visualization thread, which can be cancelled early due to user interaction. In combination with a layer mechanism, our architecture facilitates generating previews incrementally to provide rich visual feedback quickly. To help avoiding common pitfalls of multi-threading, we discuss synchronization and communication in detail. We explicitly denote design choices to control trade-offs. A quantitative evaluation based on the system VI S P L ORE shows fast visual feedback during continuous interaction even for millions of entries. We describe instantiations of our architecture in additional tools.
Text detection, recognition in images and video frames

This paper presents a new method for detecting and recognizing text in complex images and video frames. Text detection is performed in a two-step approach that combines the speed of a text localization step, enabling text size normalization, with the strength of a machine learning text veri3cation step applied on background independent features. Text recognition, applied on the detected text lines, is addressed by a text segmentation step followed by an traditional OCR algorithm within a multi-hypotheses framework relying on multiple segments, language modeling and OCR statistics. Experiments conducted on large databases of real broadcast documents demonstrate the validity of our approach. ? 2003 Pattern Recognition Society.
A Differential Covariance Matrix Adaptation Evolutionary Algorithm for real parameter optimization

Hybridization in context to Evolutionary Computation (EC) aims at combining the operators and methodologies from different EC paradigms to form a single algorithm that may enjoy a statistically superior performance on a wide variety of optimization problems. In this article we propose an efficient hybrid evolutionary algorithm that embeds the difference vector-based mutation scheme, the crossover and the selection strategy of Differential Evolution (DE) into another recently developed global optimization algorithm known as Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). CMA-ES is a stochastic method for real parameter (continuous domain) optimization of non-linear, non-convex functions. The algorithm includes adaptation of covariance matrix which is basically an alternative method of traditional Quasi-Newton method for optimization based on gradient method. The hybrid algorithm, referred by us as Differential Covariance Matrix Adaptation Evolutionary Algorithm (DCMA-EA), turns out to possess a better blending of the explorative and exploitative behaviors as compared to the original DE and original CMAES, through empirical simulations. Though CMA-ES has emerged itself as a very efficient global optimizer, its performance deteriorates when it comes to dealing with complicated fitness landscapes, especially landscapes associated with noisy, hybrid composition functions and many real world optimization problems. In order to improve the overall performance of CMA-ES, the mutation, crossover and selection operators of DE have been incorporated into CMA-ES to synthesize the hybrid algorithm DCMA-EA. We compare DCMA-EA with original DE and CMA-EA, two best known DE-variants: SaDE and JADE, and two state-of-the-art real optimizers: IPOP-CMA-ES (Restart Covariance Matrix Adaptation Evolution Strategy with increasing population size) and DMS-PSO (Dynamic Multi Swarm Particle Swarm Optimization) over a test-suite of 20 shifted, rotated, and compositional benchmark functions and also two engineering optimization problems. Our comparative study indicates that although the hybridization scheme does not impose any serious burden on DCMA-EA in terms of number of Function Evaluations (FEs), DCMA-EA still enjoys a statistically superior performance over most of the tested benchmarks and especially over the multi-modal, rotated, and compositional ones in comparison to the other algorithms considered here.
Design Considerations for Switched Reluctance Machines With a Higher Number of Rotor Poles

Switched-reluctance-machine (SRM) technology is a potential candidate for the propulsion systems of hybrid and plug-in hybrid electric vehicles. They are robust in harsh operational conditions and have a wide constant power speed range. Conventional SRM configurations have a higher number of stator poles than rotor poles. This paper presents the advantages of a novel SRM configuration with the number of rotor poles greater than the number of stator poles. It also investigates different design challenges toward the traction applications. The proposed SRM configuration is based on a pole-design formula. Geometrical design equations and related challenges are introduced, and their applicability has been verified on a three-phase 6/10 SRM using finite-element-analysis simulations.
Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization.
Learning End-to-End Goal-Oriented Dialog

End-to-end dialog systems, in which all components are learnt simultaneously, have recently obtained encouraging successes. However these were mostly on conversations related to chit-chat with no clear objective and for which evaluation is difficult. This paper proposes a set of tasks to test the capabilities of such systems on goal-oriented dialogs, where goal completion ensures a well-defined measure of performance. Built in the context of restaurant reservation, our tasks require to manipulate sentences and symbols, in order to properly conduct conversations, issue API calls and use the outputs of such calls. We show that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations. We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (Henderson et al., 2014a).
Folding the Hyperbolic Crane

T he purest form of origami is widely considered to be folding only, from an uncut square. This purity is, of course, a modern innovation, as historical origami included both cuts and odd-sized sheets of paper, and the 20th-century blossoming of origami in Japan and the west used multiple sheets for both composite and modular folding. That diversity of starting material continues today. Composite origami (in which multiple sheets are folded into different parts of a subject and then fitted together) fell out of favor in the 1970s and 1980s, but then came roaring back with the publication of Issei Yoshino’s Super Complex Origami [Yoshino 96] and continues to make regular appearances at origami exhibitions in the form of plants and flowers. Modular origami (in which multiple sheets are folded into one or a few identical units that are then assembled) never really diminished at all; the kusudamas of the past evolved into the extensive collections of modulars described in books by Kasahara [Kasahara 88], Fuse [Fuse 90], Mukerji [Mukerji 07], and more. Even if we stick to uncut single-sheet folding, however, there are still ways we can vary the starting sheet: by shape, for example. There are numerous origami forms from shapes other than square, including rectangles that are golden ð1 : ð ffiffiffi
Brain–machine interfaces: past, present and future

Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.
The privacy paradox - Investigating discrepancies between expressed privacy concerns and actual online behavior - A systematic literature review

http://dx.doi.org/10.1016/j.tele.2017.04.013 0736-5853/ 2017  Corresponding author at: University of Twente, Faculty of Electrical Engineering, Mathematics and Computer Science, Services, Cybersecurity an Research Group, PO Box 217, 7500 AE Enschede, The Netherlands. E-mail addresses: s.barth@utwente.nl (S. Barth), m.d.t.dejong@utwente.nl (M.D.T. de Jong). Susanne Barth a,b,⇑, Menno D.T. de Jong b
A cable driven upper arm exoskeleton for upper extremity rehabilitation

Conventional robotic rehabilitation devices for upper extremity are bulky, heavy or lack the ability to provide joint level rehabilitation. Some designs address these issues by replacing rigid links of the exoskeletons with light weight cables. However these designs are controlled in position mode instead of force control which is desirable for rehabilitation. In this paper, a 5 degree-of-freedom cable-driven upper arm exoskeleton, with control of force, is proposed. In this design, attachment points of cables on the arm are adjustable. The attachment points are optimized to achieve large workspace to perform activities of daily living. Simulation results of force field control for training and rehabilitation of the arm are presented. Experiments have been performed on a dummy robotic arm in the upper arm exoskeleton.
Relational recurrent neural networks

Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architectures may struggle at tasks that heavily involve an understanding of the ways in which entities are connected – i.e., tasks involving relational reasoning. We then improve upon these deficits by using a new memory module – a Relational Memory Core (RMC) – which employs multi-head dot product attention to allow memories to interact. Finally, we test the RMC on a suite of tasks that may profit from more capable relational reasoning across sequential information, and show large gains in RL domains (e.g. Mini PacMan), program evaluation, and language modeling, achieving state-of-the-art results on the WikiText-103, Project Gutenberg, and GigaWord datasets.
Sense & response service architecture (SARESA): an approach towards a real-time business intelligence solution and its use for a fraud detection application

The dynamic business environment of many organizations require massive monitoring of their processes in real-time in order to proactively respond to exceptional situations and to take advantage of time-sensitive business opportunities. The ability to sense and interpret events about a changing business environment requires an event-driven IT infrastructure for pwerforming fast and well-informed decisions and putting them into action. However, traditional Business Intelligence (BI) and Data Warehousing technologies do not directly address time sensitive monitoring and analytical requirements. We introduce an enhanced BI architecture that covers the complete process to sense, interpret, predict, automate and respond to business environments and thereby aims to decrease the reaction time needed for business decisions. We propose an event-driven IT infrastructure to operate BI applications which enable real-time analytics across corporate business processes, notifies the business of actionable recommendations or automatically triggers business operations, and effectively closing the gap between Business Intelligence systems and business processes. A scenario from the area of mobile phone fraud detection was chosen for building a prototype that illustrates the proposed approach by using current available IT technologies.
Compression-Based Averaging of Selective Naive Bayes Classifiers

The naive Bayes classifier has proved to be very effective on many real data applications. Its performance usually benefits from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a Bayesian regularization technique to select the most probable subset of variables compliant with the naive Bayes assumption. We also study the limits of Bayesian model averaging in the case of the naive Bayes assumption and introduce a new weighting scheme based on the ability of the models to conditionally compress the class labels. The weighting scheme on the models reduces to a weighting scheme on the variables, and finally results in a naive Bayes classifier with “soft variable selection”. Extensive experiments show that the compressionbased averaged classifier outperforms the Bayesian model averaging scheme.
Comparing Business and Household Sector Innovation in Consumer Products : Findings from a Representative Study in the United Kingdom

In a first survey of its type, we measure development and modification of consumer products by product users in a representative sample of 1,173 UK consumers aged 18 and over. We estimate this previously unmeasured type of innovation to be quite large: 6.1% of UK consumers – nearly 2.9 million individuals have engaged in consumer product innovation during the prior three years. In aggregate, consumers’ annual product development expenditures are more than 1.4 times larger than the annual consumer product R&D expenditures of all firms in the UK combined. Consumers engage in many small projects which seem complementary to the innovation efforts of incumbent producers. Consumer innovators very seldom protect their innovations via intellectual property, and 17% diffuse to others. These results imply that official statistics partly miss relevant innovation activity, and that existing companies should reconfigure their product development systems to find and build upon prototypes developed by consumers. 1 MIT Sloan School of Management, Cambridge MA, USA. 2 RSM Erasmus University, Rotterdam, The Netherlands. 3 EIM Business and Policy Research, Zoetermeer, The Netherlands. 4 Centre for Research in Innovation Management, University of Brighton, United Kingdom.  Corresponding author.  +1 617-253-7155
Cloud Computing: The impact on digital forensic investigations

Cloud Computing (CC) as a concept and business opportunity is likely to see many organisations experiencing the 'credit crunch', embrace the relatively low cost option of CC to ensure continued business viability and sustainability. The pay-as-you-go structure of the CC business model is typically suited to SME's who do not have the resources to completely fulfil their IT requirements. Private end users will also look to utilise the colossal pool of resources that CC offers in an attempt to provide mobile freedom of information. However, as with many opportunities that offer legitimate users enormous benefits, unscrupulous and criminal users will also look to use CC to exploit the loopholes that may exist within this new concept, design and business model. This paper will outline the tasks that the authors undertook for the CLOIDIFIN project and highlight where the Impact of CC will diversely effect digital forensic investigations.
The development of macrophages from human CD34+ haematopoietic stem cells in serum-free cultures is optimized by IL-3 and SCF.

The derivation of human macrophages from peripheral blood monocytes remains a convenient method for the study of macrophage biology. However, for macrophage differentiation, a significant proportion of development has occurred prior to the monocyte stage; monocyte subsets also have varying potential for differentiation. Differentiation of macrophages from a less mature precursor, such as CD34+ haematopoietic stem cells, can further inform with regard to the development of macrophage-lineage cells. CD34+ cells were cultured in serum-free medium containing Flt3L, SCF, IL-3, IL-6 and M-CSF. Using differing combinations of growth factors, the effect on cell proliferation and differentiation to adherent macrophage-like cells was determined. The proliferative response of CD34+ cells to M-CSF was determined during the initial phase of cell culture. Thirteen combinations of SCF, IL-3, IL-6 and M-CSF were then compared to determine the optimum combination for proliferation. Adherence was used to isolate mature macrophages, and the macrophage-like phenotype was confirmed by analyses of surface markers, histo-morphology and phagocytosis. This study refines the means by which large numbers of macrophages are obtained under serum-free conditions from haematopoietic precursors.
Efficient Visual-Inertial Navigation using a Rolling-Shutter Camera with Inaccurate Timestamps

In order to develop Vision-aided Inertial Navigation Systems (VINS) on mobile devices, such as cell phones and tablets, one needs to consider two important issues, both due to the commercial-grade underlying hardware: (i) The unknown and varying time offset between the camera and IMU clocks (ii) The rolling-shutter effect caused by CMOS sensors. Without appropriately modelling their effect and compensating for them online, the navigation accuracy will significantly degrade. In this work, we introduce a linear-complexity algorithm for fusing inertial measurements with time-misaligned, rolling-shutter images using a highly efficient and precise linear interpolation model. As a result, our algorithm achieves a better accuracy and improved speed compared to existing methods. Finally, we validate the superiority of the proposed algorithm through simulations and real-time, online experiments on a cell phone.
Detection of ulcerative colitis severity in colonoscopy video frames

Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. The therapeutic goals of UC are to first induce and then maintain disease remission. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms associated with UC, and large variations in their patterns. To address this, we objectively measure and classify the severity of UC presented in optical colonoscopy video frames based on the image textures. To extract distinct textures, we are using a hybrid approach in which a new proposed feature based on the accumulation of pixel value differences is combined with an existing feature such as LBP (Local Binary Pattern). The experimental results show the hybrid method can achieve more than 90% overall accuracy.