nils-herrmann/clean_scirepeval huggingface dataset:
A Stochastic Quasi-Newton Method for Large-Scale Optimization

The question of how to incorporate curvature information in stochastic approximation methods is challenging. The direct application of classical quasiNewton updating techniques for deterministic optimization leads to noisy curvature estimates that have harmful effects on the robustness of the iteration. In this paper, we propose a stochastic quasi-Newton method that is efficient, robust and scalable. It employs the classical BFGS update formula in its limited memory form, and is based on the observation that it is beneficial to collect curvature information pointwise, and at regular intervals, through (sub-sampled) Hessian-vector products. This technique differs from the classical approach that would compute differences of gradients at every iteration, and where controlling the quality of the curvature estimates can be difficult. We present numerical results on problems arising in machine learning that suggest that the proposed method shows much promise. ∗Department of Computer Science, University of Colorado, Boulder, CO, USA. This author was supported by National Science Foundation grant DMS-1216554 and Department of Energy grant DE-SC0001774. †Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, USA. This author was supported by National Science Foundation grant DMS-0810213. ‡Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA. This material is based upon work supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research,  This author was also supported by the Office of Naval Research award N000141410313. §Google Research
Treatment of Divided Nevus of the Penis with Circumcision and Free Inner Prepuce Grafting

Divided nevus of the penis is exceedingly rare. Desruelles et al. reported the first divided nevus on the penis in 1998, and, since then, only 17 cases have been reported in the English language literature. This article presents the successful excision and histopathologic evaluation of the nevi. The glans was reconstructed by a full-thickness skin graft using remnant foreskin. Six months after the operation, the patient showed no deformity of the glans and no loss of sensation. The lesion on the glans can be successfully reconstructed using the remnant foreskin with satisfactory aesthetic and functional outcome. This method is desirable with minimal donor-site morbidity and inconspicuous donor-site scars.
Writing to and reading from a nano-scale crossbar memory based on memristors.

We present a design study for a nano-scale crossbar memory system that uses memristors with symmetrical but highly nonlinear current-voltage characteristics as memory elements. The memory is non-volatile since the memristors retain their state when un-powered. In order to address the nano-wires that make up this nano-scale crossbar, we use two coded demultiplexers implemented using mixed-scale crossbars (in which CMOS-wires cross nano-wires and in which the crosspoint junctions have one-time configurable memristors). This memory system does not utilize the kind of devices (diodes or transistors) that are normally used to isolate the memory cell being written to and read from in conventional memories. Instead, special techniques are introduced to perform the writing and the reading operation reliably by taking advantage of the nonlinearity of the type of memristors used. After discussing both writing and reading strategies for our memory system in general, we focus on a 64 x 64 memory array and present simulation results that show the feasibility of these writing and reading procedures. Besides simulating the case where all device parameters assume exactly their nominal value, we also simulate the much more realistic case where the device parameters stray around their nominal value: we observe a degradation in margins, but writing and reading is still feasible. These simulation results are based on a device model for memristors derived from measurements of fabricated devices in nano-scale crossbars using Pt and Ti nano-wires and using oxygen-depleted TiO(2) as the switching material.
Colorization of black-and-white cartoons

We introduce a novel colorization framework for old black-and-white cartoons which has been originally produced by a cel or paper based technology. In this case the dynamic part of the scene is represented by a set of outlined homogeneous regions that superimpose static background. To reduce a large amount of manual intervention we combine unsupervised image segmentation, background reconstruction and structural prediction. Our system in addition allows the user to specify the brightness of applied colors unlike the most of previous approaches which operate only with hue and saturation. We also present a simple but effective color modulation, composition and dust spot removal techniques able produce color images in broadcast quality without additional user intervention.
Efficient, High-Quality, GPU-Based Visualization of Voxelized Surface Data with Fine and Complicated Structures

This paper proposes a GPU-based method that can visualize voxelized surface data with fine and complicated features, has high rendering quality at interactive frame rates, and provides low memory consumption. The surface data is compressed using run-length encoding (RLE) for each level of detail (LOD). Then, the loop for the rendering process is performed on the GPU for the position of the viewpoint at each time instant. The scene is raycasted in planes, where each plane is perpendicular to the horizontal plane in the world coordinate system and passes through the viewpoint. For each plane, one ray is cast to rasterize all RLE elements intersecting this plane, starting from the viewpoint and ranging up to the maximum view distance. This rasterization process projects each RLE element passing the occlusion test onto the screen at a LOD that decreases with the distance of the RLE element from the viewpoint. Finally, the smoothing of voxels in screen space and full screen anti-aliasing is performed. To provide lighting calculations without storing the normal vector inside the RLE data structure, our algorithm recovers the normal vectors from the rendered scene’s depth buffer. After the viewpoint changes, the same process is re-executed for the new viewpoint. Experiments using different scenes have shown that the proposed algorithm is faster than the equivalent CPU implementation and other related methods. Our experiments further prove that this method is memory efficient and achieves high quality results. key words: volume data, voxels, raycasting, splatting, view-transform, run-length encoding
Feature based terrain generation using diffusion equation

This paper presents a diffusion method for generating terrains from a set of parameterized curves that characterize the landform features such as ridge lines, riverbeds or cliffs. Our approach provides the user with an intuitive vector-based feature-oriented control over the terrain. Different types of constraints (such as elevation, slope angle and roughness) can be attached to the curves so as to define the shape of the terrain. The terrain is generated from the curve representation by using an efficient multigrid diffusion algorithm. The algorithm can be efficiently implemented on the GPU, which allows the user to interactively create a vast variety of landscapes.
Providing Haptics to Walls & Heavy Objects in Virtual Reality by Means of Electrical Muscle Stimulation

We explore how to add haptics to walls and other heavy objects in virtual reality. When a user tries to push such an object, our system actuates the user's shoulder, arm, and wrist muscles by means of electrical muscle stimulation, creating a counter force that pulls the user's arm backwards. Our device accomplishes this in a wearable form factor.
 In our first user study, participants wearing a head-mounted display interacted with objects provided with different types of EMS effects. The repulsion design (visualized as an electrical field) and the soft design (visualized as a magnetic field) received high scores on "prevented me from passing through" as well as "realistic".
 In a second study, we demonstrate the effectiveness of our approach by letting participants explore a virtual world in which all objects provide haptic EMS effects, including walls, gates, sliders, boxes, and projectiles.
PLANT LEAF CLASSIFICATION USING PROBABILISTIC INTEGRATION OF SHAPE , TEXTURE AND MARGIN FEATURES

Plant species classification using leaf samples is a challenging and important problem to solve. This paper introduces a new data set of sixteen samples each of one-hundred plant species; and describes a method designed to work in conditions of small training set size and possibly incomplete extraction of features. This motivates a separate processing of three feature types: shape, texture, and margin; combined using a probabilistic framework. The texture and margin features use histogram accumulation, while a normalised description of contour is used for the shape. Two previously published methods are used to generate separate posterior probability vectors for each feature, using data associated with the k-Nearest Neighbour apparatus. The combined posterior estimates produce the final classification (where missing features could be omitted). We show that both density estimators achieved a 96% mean accuracy of classification when combining the three features in this way (training on 15 samples with unseen cross validation). In addition, the framework can provide an upper bound on the Bayes Risk of the classification problem, and thereby assess the accuracy of the density estimators. Lastly, the high performance of the method is demonstrated for small training set sizes: 91% accuracy is observed with only four training samples.
Semi-Supervised Representation Learning for Cross-Lingual Text Classification

Cross-lingual adaptation aims to learn a prediction model in a label-scarce target language by exploiting labeled data from a labelrich source language. An effective crosslingual adaptation system can substantially reduce the manual annotation effort required in many natural language processing tasks. In this paper, we propose a new cross-lingual adaptation approach for document classification based on learning cross-lingual discriminative distributed representations of words. Specifically, we propose to maximize the loglikelihood of the documents from both language domains under a cross-lingual logbilinear document model, while minimizing the prediction log-losses of labeled documents. We conduct extensive experiments on cross-lingual sentiment classification tasks of Amazon product reviews. Our experimental results demonstrate the efficacy of the proposed cross-lingual adaptation approach.
Exploring the role of dynamic capabilities in fi rm performance under the resource-based view framework

a r t i c l e i n f o This study investigates the role of dynamic capabilities in the resource-based view framework, and also explores the relationships among different resources, different dynamic capabilities and firm performance. Employing samples of top 1000 Taiwanese companies, the findings show that dynamic capabilities can mediate the firm's valuable, rare, inimitable and non-substitutable (VRIN) resources to improve performance. On the contrary, non-VRIN resources have an insignificant mediating effect. Among three types of dynamic capabilities, dynamic learning capability most effectively mediates the influence of VRIN resources on performance. Furthermore, the important role of VRIN resources is addressed because of their direct effects on performance based on RBV, as well as their indirect effect via the mediation of dynamic capabilities.
Nericell: rich monitoring of road and traffic conditions using mobile smartphones

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.).
 To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems

For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.
Chaos: scale-out graph processing from secondary storage

Chaos scales graph processing from secondary storage to multiple machines in a cluster. Earlier systems that process graphs from secondary storage are restricted to a single machine, and therefore limited by the bandwidth and capacity of the storage system on a single machine. Chaos is limited only by the aggregate bandwidth and capacity of all storage devices in the entire cluster.
 Chaos builds on the streaming partitions introduced by X-Stream in order to achieve sequential access to storage, but parallelizes the execution of streaming partitions. Chaos is novel in three ways. First, Chaos partitions for sequential storage access, rather than for locality and load balance, resulting in much lower pre-processing times. Second, Chaos distributes graph data uniformly randomly across the cluster and does not attempt to achieve locality, based on the observation that in a small cluster network bandwidth far outstrips storage bandwidth. Third, Chaos uses work stealing to allow multiple machines to work on a single partition, thereby achieving load balance at runtime.
 In terms of performance scaling, on 32 machines Chaos takes on average only 1.61 times longer to process a graph 32 times larger than on a single machine. In terms of capacity scaling, Chaos is capable of handling a graph with 1 trillion edges representing 16 TB of input data, a new milestone for graph processing capacity on a small commodity cluster.
A Phrase-Based HMM Approach to Document/Abstract Alignment

We describe a model for creating word-to-word and phrase-to-phrase alignments between documents and their human written abstracts. Such alignments are critical for the development of statistical summarization systems that can be trained on large corpora of document/abstract pairs. Our model, which is based on a novel Phrase-Based HMM, outperforms both the Cut & Paste alignment model (Jing, 2002) and models developed in the context of machine translation (Brown et al., 1993).
Zozzle: Low-overhead Mostly Static JavaScript Malware Detection

JavaScript malware-based attacks account for a large fraction of successful mass-scale exploitation happening today. From the standpoint of the attacker, the attraction is that these drive-by attacks can be mounted against an unsuspecting user visiting a seemingly innocent web page. While several techniques for addressing these types of exploits have been proposed, in-browser adoption has been slow, in part because of the performance overhead these methods tend to incur. In this paper, we propose ZOZZLE, a low-overhead solution for detecting and preventing JavaScript malware that can be deployed in the browser. Our experience also suggests that ZOZZLE may be used as a lightweight filter for a more costly detection technique or for standalone offline
Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Much of human dialogue occurs in semicooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other’s reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly
Local Convergence of SQP Methods for Mathematical Programs with Equilibrium Constraints

Recently, nonlinear programming solvers have been used to solve a range of mathematical programs with equilibrium constraints (MPECs). In particular, sequential quadratic programming (SQP) methods have been very successful. This paper examines the local convergence properties of SQP methods applied to MPECs. SQP is shown to converge superlinearly under reasonable assumptions near a strongly stationary point. A number of examples are presented that show that some of the assumptions are difficult to relax.
Control-flow integrity

Current software attacks often build on exploits that subvert machine-code execution. The enforcement of a basic safety property, Control-Flow Integrity (CFI), can prevent such attacks from arbitrarily controlling program behavior. CFI enforcement is simple, and its guarantees can be established formally even with respect to powerful adversaries. Moreover, CFI enforcement is practical: it is compatible with existing software and can be done efficiently using software rewriting in commodity systems. Finally, CFI provides a useful foundation for enforcing further security policies, as we demonstrate with efficient software implementations of a protected shadow call stack and of access control for memory regions.
Feature Selection for Social Media Data

Feature selection is widely used in preparing high-dimensional data for effective data mining. The explosive popularity of social media produces massive and high-dimensional data at an unprecedented rate, presenting new challenges to feature selection. Social media data consists of (1) traditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that provides social context for posts and describes the relationships between social media users as well as who generates the posts, and so on. The nature of social media also determines that its data is massive, noisy, and incomplete, which exacerbates the already challenging problem of feature selection. In this article, we study a novel feature selection problem of selecting features for social media data with its social context. In detail, we illustrate the differences between attribute-value data and social media data, investigate if linked data can be exploited in a new feature selection framework by taking advantage of social science theories. We design and conduct experiments on datasets from real-world social media Web sites, and the empirical results demonstrate that the proposed framework can significantly improve the performance of feature selection. Further experiments are conducted to evaluate the effects of user--user and user--post relationships manifested in linked data on feature selection, and research issues for future work will be discussed.
Efficient graphlet kernels for large graph comparison

State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of edges. In this article we propose to compare graphs by counting graphlets, i.e., subgraphs with k nodes where k ∈ {3, 4, 5}. Exhaustive enumeration of all graphlets being prohibitively expensive, we introduce two theoretically grounded speedup schemes, one based on sampling and the second one specifically designed for bounded degree graphs. In our experimental evaluation, our novel kernels allow us to efficiently compare large graphs that cannot be tackled by existing graph kernels.