nils-herrmann/clean_scirepeval huggingface dataset:
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields

Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution. We introduce Coulomb GANs, which pose the GAN learning problem as a potential field, where generated samples are attracted to training set samples but repel each other. The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field. Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes. We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution. We show the efficacy of Coulomb GANs on LSUN bedrooms, celebA faces and CIFAR-10. For LSUN and celebA, Coulomb GANs set a new state of the art and produce a previously unseen variety of different samples.
Scalpel: Customizing DNN pruning to the underlying hardware parallelism

As the size of Deep Neural Networks (DNNs) continues to grow to increase accuracy and solve more complex problems, their energy footprint also scales. Weight pruning reduces DNN model size and the computation by removing redundant weights. However, we implemented weight pruning for several popular networks on a variety of hardware platforms and observed surprising results. For many networks, the network sparsity caused by weight pruning will actually hurt the overall performance despite large reductions in the model size and required multiply-accumulate operations. Also, encoding the sparse format of pruned networks incurs additional storage space overhead. To overcome these challenges, we propose Scalpel that customizes DNN pruning to the underlying hardware by matching the pruned network structure to the data-parallel hardware organization. Scalpel consists of two techniques: SIMD-aware weight pruning and node pruning. For low-parallelism hardware (e.g., microcontroller), SIMD-aware weight pruning maintains weights in aligned fixed-size groups to fully utilize the SIMD units. For high-parallelism hardware (e.g., GPU), node pruning removes redundant nodes, not redundant weights, thereby reducing computation without sacrificing the dense matrix format. For hardware with moderate parallelism (e.g., desktop CPU), SIMD-aware weight pruning and node pruning are synergistically applied together. Across the microcontroller, CPU and GPU, Scalpel achieves mean speedups of 3.54x, 2.61x, and 1.25x while reducing the model sizes by 88%, 82%, and 53%. In comparison, traditional weight pruning achieves mean speedups of 1.90x, 1.06x, 0.41x across the three platforms.
Discovering essential code elements in informal documentation

To access the knowledge contained in developer communication, such as forum posts, it is useful to determine automatically the code elements referred to in the discussions. We propose a novel traceability recovery approach to extract the code elements contained in various documents. As opposed to previous work, our approach does not require an index of code elements to find links, which makes it particularly well-suited for the analysis of informal documentation. When evaluated on 188 StackOverflow answer posts containing 993 code elements, the technique performs with average 0.92 precision and 0.90 recall. As a major refinement on traditional traceability approaches, we also propose to detect which of the code elements in a document are salient, or germane, to the topic of the post. To this end we developed a three-feature decision tree classifier that performs with a precision of 0.65-0.74 and recall of 0.30-0.65, depending on the subject of the document.
Polyphonic Audio Matching for Score Following and Intelligent Audio Editors

Getting computers to understand and process audio recordings in terms of their musical content is a difficult challenge. We describe a method in which general, polyphonic audio recordings of music can be aligned to symbolic score information in standard MIDI files. Because of the difficulties of polyphonic transcription, we perform matching directly on acoustic features that we extract from MIDI and audio. Polyphonic audio matching can be used for polyphonic score following, building intelligent editors that understand the content of recorded audio, and the analysis of expressive performance.
Stair ascent assistance for cerebral palsy with robot suit HAL

Cerebral palsy (CP) patients require a wheelchair for moving when their lower limbs are severely paralyzed. Wheelchair users have issues moving around due to the lack of optimal infrastructure. We achieved a voluntary walking of a CP patient who uses wheelchair by using our developed hybrid assistive limb (HAL). In addition to voluntary walking, if HAL realizes voluntary stair climbing, it is expected that patients will acquire a wider range of activities than using a wheelchair. This study focuses on stair ascent. The purpose of this study is to propose an automatic control method for stair ascent assistance, and to verify the effectiveness of the proposed method. In this method, a phase of stair ascent is determined by proposed phase determination based on a height of landing area, CoGRF in sagittal plane and lateral plane. Proposed automatic control generates torque for assisting each phase movement during stair ascent. As an experiment, healthy male subject went through a course including flat area and stairs. As a result, we verified that the phase determination could determine the phase during stair ascent and switch between the walking assistance method and stair ascent assistance method. The proposed automatic control generated torque for assisting movement of each phase. We found this proposed method is applicable to for walking and stair ascent assistance.
Recursive teaching dimension, VC-dimension and sample compression

This paper is concerned with various combinatorial parameters of classes that can be learned from a small set of examples. We show that the recursive teaching dimension, recently introduced by Zilles et al. (2008), is strongly connected to known complexity notions in machine learning, e.g., the self-directed learning complexity and the VC-dimension. To the best of our knowledge these are the first results unveiling such relations between teaching and query learning as well as between teaching and the VC-dimension. It will turn out that for many natural classes the RTD is upper-bounded by the VCD, e.g., classes of VCdimension 1, intersection-closed classes and finite maximum classes. However, we will also show that there are certain (but rare) classes for which the recursive teaching dimension exceeds the VC-dimension. Moreover, for maximum classes, the combinatorial structure induced by the RTD, called teaching plan, is highly similar to the structure of sample compression schemes. Indeed one can transform any repetition-free teaching plan for a maximum class C into an unlabeled sample compression scheme for C and vice versa, while the latter is produced by (i) the corner-peeling algorithm of Rubinstein and Rubinstein (2012) and (ii) the tail matching algorithm of Kuzmin and Warmuth (2007).
Gaussian Process Change Point Models

•Combine Bayesian change point detection with Gaussian Processes to define a nonstationary time series model. •Central aim is to react to underlying regime changes in an online manner. •Able to integrate out all latent variables and optimize hyperparameters sequentially. •Explore three alternative ways of augmenting GP models to handle nonstationarity (GPTS, ARGPCP and NSGP – see below). •A Bayesian approach (BOCPD) for online change point detection was introduced in . •BOCPD introduces a latent variable representing the run length at time t and adapts predictions via integrating out the run length. •BOCPD has two key ingredients: –Any model which can construct a predictive density for future observations, in particular, p(xt|x(t−τ ):(t−1), θm), i.e., the “underlying predictive model” (UPM). –A hazard function H(r|θh) which encodes our prior belief in a change point occuring after observing a run length r.
Semi-supervised Zero-Shot Learning by a Clustering-based Approach

In some of object recognition problems, labeled data may not be available for all categories. Zero-shot learning utilizes auxiliary information (also called signatures) d escribing each category in order to find a classifier that can recognize samples from categories with no labeled instance . In this paper, we propose a novel semi-supervised zero-shot learning method that works on an embedding space corresponding to abstract deep visual features. We seek a linear transformation on signatures to map them onto the visual features, such that the mapped signatures of the seen classe s are close to labeled samples of the corresponding classes and unlabeled data are also close to the mapped signatures of one of the unseen classes. We use the idea that the rich deep visual features provide a representation space in whic h samples of each class are usually condensed in a cluster. The effectiveness of the proposed method is demonstrated through extensive experiments on four public benchmarks improving the state-of-the-art prediction accuracy on thr ee of them.
Photometric stereo with non-parametric and spatially-varying reflectance

We present a method for simultaneously recovering shape and spatially varying reflectance of a surface from photometric stereo images. The distinguishing feature of our approach is its generality; it does not rely on a specific parametric reflectance model and is therefore purely ldquodata-drivenrdquo. This is achieved by employing novel bi-variate approximations of isotropic reflectance functions. By combining this new approximation with recent developments in photometric stereo, we are able to simultaneously estimate an independent surface normal at each point, a global set of non-parametric ldquobasis materialrdquo BRDFs, and per-point material weights. Our experimental results validate the approach and demonstrate the utility of bi-variate reflectance functions for general non-parametric appearance capture.
Integrating Automated Product Derivation and Individual User Interface Design

Software Product Lines, in conjunction with modeldriven product derivation, are successful examples for extensive automation and reuse in software development. However, often each single product requires an individual, tailored user interface of its own to achieve the desired usability. Moreover, in some cases (e.g., online shops, games) it is even mandatory that each product has an individual, unique user interface of its own. Usually, this results in manual user interface design independent from the model-driven product derivation. Consequently, each product configuration has to be mapped manually to a corresponding user interface which can become a tedious and error-prone task for large and complex product lines. This paper addresses this problem by integrating concepts from SPL product derivation and Model-based User Interface Development. This facilitates both (1) a systematic and semi-automated creation of user interfaces during product derivation while (2) still supporting for individual, creative design.
An efficient retraction-based RRT planner

We present a novel optimization-based retraction algorithm to improve the performance of sample-based planners in narrow passages for 3D rigid robots. The retraction step is formulated as an optimization problem using an appropriate distance metric in the configuration space. Our algorithm computes samples near the boundary of C-obstacle using local contact analysis and uses those samples to improve the performance of RRT planners in narrow passages. We analyze the performance of our planner using Voronoi diagrams and show that the tree can grow closely towards any randomly generated sample. Our algorithm is general and applicable to all polygonal models. In practice, we observe significant speedups over prior RRT planners on challenging scenarios with narrow passages.
Vector Quantization In Text Dependent Automatic Speaker Recognition Using Mel-frequency Cepstrum Coefficient

Automatic speaker recognition is a field of study attributed in identifying a person from a spoken phrase. The technique makes it possible to use the speaker’s voice to verify their identity and control access to the services such as biometric security system, voice dialing, telephone banking, telephone shopping, database access services, information services, voice mail, and security control for confidential information areas and remote access to the computers. This thesis represents a development of a Matlab based text dependent speaker recognition system. Mel Frequency Cepstrum Coefficient (MFCC) Method is used to extract a speaker’s discriminative features from the mathematical representation of the speech signal. After that Vector Quantization with VQ-LBG Algorithm is used to match the feature.  Speaker Recognition, Human Speech Signal Processing, Vector Quantization
Geiger: monitoring the buffer cache in a virtual machine environment

Virtualization is increasingly being used to address server management and administration issues like flexible resource allocation, service isolation and workload migration. In a virtualized environment, the virtual machine monitor (VMM) is the primary resource manager and is an attractive target for implementing system features like scheduling, caching, and monitoring. However, the lackof runtime information within the VMM about guest operating systems, sometimes called the semantic gap, is a significant obstacle to efficiently implementing some kinds of services.In this paper we explore techniques that can be used by a VMM to passively infer useful information about a guest operating system's unified buffer cache and virtual memory system. We have created a prototype implementation of these techniques inside the Xen VMM called Geiger and show that it can accurately infer when pages are inserted into and evicted from a system's buffer cache. We explore several nuances involved in passively implementing eviction detection that have not previously been addressed, such as the importance of tracking disk block liveness, the effect of file system journaling, and the importance of accounting for the unified caches found in modern operating systems.Using case studies we show that the information provided by Geiger enables a VMM to implement useful VMM-level services. We implement a novel working set size estimator which allows the VMM to make more informed memory allocation decisions. We also show that a VMM can be used to drastically improve the hit rate in remote storage caches by using eviction-based cache placement without modifying the application or operating system storage interface. Both case studies hint at a future where inference techniques enable a broad new class of VMM-level functionality.
An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images

There have been increasing demands for automatically monitoring urban areas in very high detail, and the Unmanned Aerial Vehicle (UAV) with auto-navigation (AUNA) system offers such capability. This study proposes an object-based hierarchical method to detect changes from UAV images taken at different times. It consists of several steps. In the first step, an octocopter with AUNA capability is used to acquire images at different dates. These images are registered automatically, based on SIFT (Scale-Invariant Feature Transform) feature points, via the general bundle adjustment framework. Thus, the Digital Surface Models (DSMs) and orthophotos can be generated for raster-based change analysis. In the next step, a multi-primitive segmentation method combining the spectral and geometric information is proposed for object-based analysis. In the final step, a multi-criteria decision analysis is carried out concerning the height, spectral and geometric coherence, and shape regularity for change determination. Experiments based on UAV images with five-centimeter ground resolution demonstrate the effectiveness of the proposed method, leading to the conclusion that this method is practically applicable for frequent monitoring.
Dominant forces in protein folding.

T e purpose of this review is to assess the nature and magnitudes of the dominant forces in protein folding. Since proteins are only marginally stable at room temperature,’ no type of molecular interaction is unimportant, and even small interactions can contribute significantly (positively or negatively) to stability (Alber, 1989a,b; Matthews, 1987a,b). However, the present review aims to identify only the largest forces that lead to the structural features of globular proteins: their extraordinary compactness, their core of nonpolar residues, and their considerable amounts of internal architecture. This review explores contributions to the free energy of folding arising from electrostatics (classical charge repulsions and ion pairing), hydrogen-bonding and van der Waals interactions, intrinsic propensities, and hydrophobic interactions. An earlier review by Kauzmann (1959) introduced the importance of hydrophobic interactions. His insights were particularly remarkable considering that he did not have the benefit of known protein structures, model studies, high-resolution calorimetry, mutational methods, or force-field or statistical mechanical results. The present review aims to provide a reassessment of the factors important for folding in light of current knowledge. Also considered here are the opposing forces, conformational entropy and electrostatics. The process of protein folding has been known for about 60 years. In 1902, Emil Fischer and Franz Hofmeister independently concluded that proteins were chains of covalently linked amino acids (Haschemeyer & Haschemeyer, 1973) but deeper understanding of protein structure and conformational change was hindered because of the difficulty in finding conditions for solubilization. Chick and Martin (191 1) were the first to discover the process of denaturation and to distinguish it from the process of aggregation. By 1925, the denaturation process was considered to be either hydrolysis of the peptide bond (Wu & Wu, 1925; Anson & Mirsky, 1925) or dehydration of the protein (Robertson, 1918). The view that protein denaturation was an unfolding process was
User loyalty and online communities: why members of online communities are not faithful

Online communities are getting increasingly important for several different user groups; at the same time, community members seem to lack loyalty, as they often change from one community to another or use their community less over time. To survive and thrive, online communities must meet members' needs. By using qualitative data are from an extensive online survey of online community users and a representative sample of Internet users, 200 responses to an open quesion regarding community-loyalty was analyzed. Results show that there are 9 main reasons why community-users decrease in their participation over time or, in simple terms, stop using their online community: 1) Lack of interesting people/friends attending, 2) Low quality content, 3) Low usability, 4) Harassment and bullying 5) Time-consuming/isolating, 6) Low trust, 7) Over-commercialized, 8) Dissatisfaction with moderators and 9) Unspecified boring. The results, design implications and future research are discussed.
Prerequisite Relation Learning for Concepts in MOOCs

What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically. In particular, what kinds of information can be leveraged to uncover the potential prerequisite relation between knowledge concepts. We first propose a representation learning-based method for learning latent representations of course concepts, and then investigate how different features capture the prerequisite relations between concepts. Our experiments on three datasets form Coursera show that the proposed method achieves significant improvements (+5.9-48.0% by F1-score) comparing with existing methods.
An online learned CRF model for multi-target tracking

We introduce an online learning approach for multitarget tracking. Detection responses are gradually associated into tracklets in multiple levels to produce final tracks. Unlike most previous approaches which only focus on producing discriminative motion and appearance models for all targets, we further consider discriminative features for distinguishing difficult pairs of targets. The tracking problem is formulated using an online learned CRF model, and is transformed into an energy minimization problem. The energy functions include a set of unary functions that are based on motion and appearance models for discriminating all targets, as well as a set of pairwise functions that are based on models for differentiating corresponding pairs of tracklets. The online CRF approach is more powerful at distinguishing spatially close targets with similar appearances, as well as in dealing with camera motions. An efficient algorithm is introduced for finding an association with low energy cost. We evaluate our approach on three public data sets, and show significant improvements compared with several state-of-art methods.
Fuzzy dual-factor time-series for stock index forecasting

There is an old Wall Street adage goes, ‘‘It takes volume to make price move”. The contemporaneous relation between trading volume and stock returns has been studied since stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China’s stock market. Pacific-Basin Finace Journal, 12, 541–564], Hodgson et al. [Hodgson, A., Masih, A. M. M., & Masih, R. (2006). Futures trading volume as a determinant of prices in different momentum phases. International Review of Financial Analysis, 15, 68–85], and Ting [Ting, J. J. L. (2003). Causalities of the Taiwan stock market.  A, 324, 285–295] have found the correlation between stock volume and price in stock markets. To verify this saying, in this paper, we propose a dual-factor modified fuzzy time-series model, which take stock index and trading volume as forecasting factors to predict stock index. In empirical analysis, we employ the TAIEX (Taiwan stock exchange capitalization weighted stock index) and NASDAQ (National Association of Securities Dealers Automated Quotations) as experimental datasets and two multiplefactor models, Chen’s [Chen, S. M. (2000). Temperature prediction using fuzzy time-series.  (2), 263–275] and Huarng and Yu’s [Huarng, K. H., & Yu, H. K. (2005). A type 2 fuzzy time-series model for stock index forecasting.  A, 353, 445–462], as comparison models. The experimental results indicate that the proposed model outperforms the listing models and the employed factors, stock index and the volume technical indicator, VR(t), are effective in stock index forecasting.