nils-herrmann/clean_scirepeval huggingface dataset:
Defending continuous collision detection against errors

Numerical errors and rounding errors in continuous collision detection (CCD) can easily cause collision detection failures if they are not handled properly. A simple and effective approach is to use error tolerances, as shown in many existing CCD systems. Unfortunately, finding the optimal tolerance values is a difficult problem for users. Larger tolerance values will introduce false positive artifacts, while smaller tolerance values may cause collisions to be undetected. The biggest issue here is that we do not know whether or when CCD will fail, even though failures are extremely rare. In this paper, we demonstrate a set of simple modifications to make a basic CCD implementation failure-proof. Using error analysis, we prove the safety of this method and we formulate suggested tolerance values to reduce false positives. The resulting algorithms are safe, automatic, efficient, and easy to implement.
Hypertool: A Programming Aid for Message-Passing Systems

|As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more di cult and errorprone. This paper discusses programming assistance and automation concepts and their application to a program development tool for message-passing systems called Hypertool. It performs scheduling and handles the communication primitive insertion automatically. Two algorithms, based on the critical-path method, are presented for scheduling processes statically. Hypertool also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs.
Optic nerve head segmentation

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.
Pneumatic Networks for Soft Robotics that Actuate Rapidly

Bobak Mosadegh, Panagiotis Polygerinos, Christoph Keplinger, Sophia Wennstedt, Robert F. Shepherd, Unmukt Gupta, Jongmin Shim, Katia Bertoldi, Conor J. Walsh, and George M. Whitesides 1 Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA. 2 Wyss Institute for Biologically Inspired Engineering, Harvard University, 60 Oxford Street, Cambridge, MA 02138, USA. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
A Parallel PARAFAC Implementation & Scalability Testing for Large-Scale Dense Tensor Decomposition

Parallel Factor Analysis (PARAFAC) is used in many scientific disciplines to decompose multidimensional datasets into principal factors in order to uncover relationships in the data. While quite popular, the common implementations of PARAFAC are single server solutions that do not scale well to very large datasets. To address this limitation, a Parallel PARAFAC algorithm has been designed and implemented in C using MPI. The end-to-end pipeline includes a parallel read of the input data from a file, the execution of the parallel algorithm, and concludes with a parallel write of the results to a file. The implementation has been evaluated using a strong scaling study on an IBM Blue Gene/Q supercomputer. The compute time, as well as the communication, file read, and file write bandwidths were each captured across multiple scenarios to evaluate the overall system performance and scalability. Results indicate the implementation scales well—with a 128x increase in the number of parallel processes, the system executed 200x faster. Further, the communication time at its peak accounted for only 12% of the total processing time, indicating the implementation is currently CPU bound and thus should continue to scale well across more and more nodes.
How does communication heal? Pathways linking clinician-patient communication to health outcomes.

Although prior research indicates that features of clinician-patient communication can predict health outcomes weeks and months after the consultation, the mechanisms accounting for these findings are poorly understood. While talk itself can be therapeutic (e.g., lessening the patient's anxiety, providing comfort), more often clinician-patient communication influences health outcomes via a more indirect route. Proximal outcomes of the interaction include patient understanding, trust, and clinician-patient agreement. These affect intermediate outcomes (e.g., increased adherence, better self-care skills) which, in turn, affect health and well-being. Seven pathways through which communication can lead to better health include increased access to care, greater patient knowledge and shared understanding, higher quality medical decisions, enhanced therapeutic alliances, increased social support, patient agency and empowerment, and better management of emotions.



Future research should hypothesize pathways connecting communication to health outcomes and select measures specific to that pathway.



Clinicians and patients should maximize the therapeutic effects of communication by explicitly orienting communication to achieve intermediate outcomes (e.g., trust, mutual understanding, adherence, social support, self-efficacy) associated with improved health.
Compressed Representations for Web and Social Graphs 1

Compressed representations have become effective to store and access large Web and social graphs, in order to support various graph querying and mining tasks. The existing representations exploit various typical patterns in those networks and provide basic navigation support. In this paper we obtain unprecedented results by finding “dense subgraph” patterns and combining them with techniques such as node orderings and compact data structures. On those representations we support out-neighbor and out/in-neighbor queries, as well as mining queries based on dense subgraphs. First, we propose a compression scheme for Web graphs that reduces edges by representing dense subgraphs with “virtual nodes”; over this scheme we apply node orderings and other compression techniques. With this approach we match the best current compression ratios that support out-neighbor queries (i.e., nodes pointed from a given node), using 1.0–1.8 bits per edge (bpe) on large Web graphs, and retrieving each neighbor of a node in 0.6–1.0 microseconds (μsec). When supporting both outand in-neighbor queries, instead, our technique generally offers the best time when using little space. If the reduced graph, instead, is represented with a compact data structure that supports bidirectional navigation, we obtain the most compact Web graph representations (0.9–1.5 bpe) that support out/in-neighbor navigation, yet the time per neighbor extracted raises to around 5–20 μsec. We also propose a compact data structure that represents dense subgraphs without using virtual nodes. It allows us to recover out/in-neighbors and answer other more complex queries on the dense subgraphs identified. This structure is not competitive on Web graphs, but on social networks it achieves 4–13 bpe and 8–12 μsec per out/in-neighbor retrieved, which improves upon all existing representations.
An Image Processing Approach for Detection, Quantification, and Identification of Plant Leaf Diseases -A Review

Disease detection and health monitoring on the plant are very critical issue for sustainable agriculture. This paper aims to analyze different methods that are used in image processing techniques to detect, classify & quantify plant leaf diseases from digital images. In this paper, the authors evaluate mainly in three well regulated manners: first Detection of Leaf Disease, second Classification i.e. recognizes the type of disease and the last one is Quantification of Disease i.e. measures the severity of Disease. The findings of authors and limitations in each methodology with respect to applications are consolidated. Each methodology has its unique way of usefulness apart from its limitations. Most of the times some pre-processing technique as enhancement, feature extraction, and noise removal techniques need to introduce to detect the leaf disease are compared here. An identification issue deals with associating a given input pattern with one of the distinct classes. So, there is various successful machine learning and soft computing techniques to classify the disease. Deciding on the method for identification is often a difficult task because the quality of the results can be varying for different input data. The review also considers disease severity to predict yield loss and monitoring & forecasting epidemics, for assessing crop disease resistance. This review considers leaf disease severity assessment of plant at the scale of individual and defines our current understanding of the sources which need some more improvements. Image processing, Plant leaf disease detection & classification, Quantification of plant leaf disease.
Learning Gradient Descent: Better Generalization and Longer Horizons

Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters. Trying different combinations can be quite labor-intensive and time consuming. Recently, researchers have tried to use deep learning algorithms to exploit the landscape of the loss function of the training problem of interest, and learn how to optimize over it in an automatic way. In this paper, we propose a new learning-to-learn model and some useful and practical tricks. Our optimizer outperforms generic, hand-crafted optimization algorithms and state-of-the-art learning-to-learn optimizers by DeepMind in many tasks. We demonstrate the effectiveness of our algorithms on a number of tasks, including deep MLPs, CNNs, and simple LSTMs.
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only single high-confidence rule. In this study, we propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large database efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted analysis using multiple strong association rules. Our extensive experiments on databases from UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.
The Effect of Vibrotactile Stimulation on the Emotional Response to Horror Films

Over the last decade consumers have witnessed a dramatic increase in the use of low fidelity, discrete vibrotactile feedback to enhance or replace audio stimuli in entertainment systems. However, use of high-resolution continuous vibrotactile displays remains quite uncommon. The Emoti-Chair is a high-resolution continuous vibrotactile display that may be driven by any type of audio signal, and is purported to convey the emotional properties of sound through organized vibrotactile stimulation. In the current study, we examined the ability of the Emoti-Chair to convey the emotional content of the soundtrack in a horror film. Increases in skin conductance levels were observed when vibrotactile stimuli were added to audio/visual film content. These results provide evidence that users can augment the communication of emotion in film through use of a high-resolution continuous vibrotactile display.
Estimating the Customer-Level Demand for Electricity Under Real-Time Market Prices

This paper presents estimates of the customer-level demand for electricity by industrial and commercial customers purchasing electricity according to the half-hourly energy prices from the England and Wales (E&W) electricity market. These customers also face the possibility of a demand charge on their electricity consumption during the three half-hour periods that are coincident with E&W system peaks. Although energy charges are largely known by 4:00 P.M. the day prior to consumption, a fraction of the energy charge and the identity of the half-hour periods when demand charges occur are only known with certainty ex post of consumption. Four years of data from a Regional Electricity Company (REC) in the United Kingdom is used to quantify the half-hourly customer-level demands under this real-time pricing program. The econometric model developed and estimated here quantifies the extent of intertemporal substitution in electricity consumption across pricing periods within the day due to changes in all of the components of the day-ahead E&W electricity prices, the level of the demand charge and the probability that a demand charge will be imposed. The results of this modeling framework can be used by retail companies supplying consumers purchasing electricity according to real-time market prices to construct demand-side bids into an electricity market open to competition. The paper closes with several examples of how this might be done.
Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform

Contrast enhancement of radiographies based on a multiscale decomposition of the images recently has proven to be a far more versatile and efficient method than regular unsharp-masking techniques, while containing these as a subset. In this paper, we compare the performance of two multiscale-methods, namely the Laplacian Pyramid and the fast wavelet transform (FWT). We find that enhancement based on the FWT suffers from one serious drawback-the introduction of visible artifacts when large structures are enhanced strongly. By contrast, the Laplacian Pyramid allows a smooth enhancement of large structures, such that visible artifacts can be avoided. Only for the enhancement of very small details, for denoising applications or compression of images, the FWT may have some advantages over the Laplacian Pyramid.
Spike detection: a review and comparison of algorithms

For algorithm developers, this review details recent approaches to the problem, compares the accuracy of various algorithms, identifies common testing issues and proposes some solutions. For the algorithm user, e.g. electroencephalograph (EEG) technician or neurologist, this review provides an estimate of algorithm accuracy and comparison to that of human experts. Manuscripts dated from 1975 are reviewed. Progress since Frost's 1985 review of the state of the art is discussed. Twenty-five manuscripts are reviewed. Many novel methods have been proposed including neural networks and high-resolution frequency methods. Algorithm accuracy is less than that of experts, but the accuracy of experts is probably less than what is commonly believed. Larger record sets will be required for expert-level detection algorithms.
Quantum Mechanics helps in searching for a needle in a haystack

Quantum mechanics can speed up a range of search applications over unsorted data. For e imagine a phone directory containing N names arranged in completely random order. To find som one’s phone number with a probability of 50%, any classical algorithm (whether deterministic or p bilistic) will need to access the database a minimum of 0.5N times. Quantum mechanical systems ca be in a superposition of states and simultaneously examine multiple names. By properly adjusti phases of various operations, successful computations reinforce each other while others interfe domly. As a result, the desired phone number can be obtained in only Os p N d accesses to the databas [S0031-9007(97)03564-3]
King: estimating latency between arbitrary internet end hosts

The ability to estimate network latencies between arbitrary Internet end hosts would enable new measurement studies and applications, such as investigating routing path inefficiencies on a wide-scale or constructing topologically sensitive overlay networks. In this paper we present King, a tool that accurately and quickly estimates the latency between arbitrary end hosts by using recursive DNS queries in a novel way. Compared to previous approaches, King has several advantages. Unlike IDMaps, King does not require the deployment of additional infrastructure, and unlike GNP, King does not require end hosts to agree upon a set of reference points. Unlike both IDMaps and GNP, King's estimates are based on direct online measurements rather than offline extrapolation. Because King uses existing DNS infrastructure, King scales naturally both in terms of the number of hosts that can be measured and in terms of the number of hosts performing measurements.After describing the techniques used in King, we present an extensive evaluation and analysis of the accuracy and consistency of our tool. Specifically, our evaluation shows that the accuracy of King is significantly better than the accuracy of IDMaps, and that King tends to preserve order among its latency estimates. Finally, we describe a variety of measurement studies and applications that could benefit from our tool, and present results from one such measurement study.
Dynamic Programming and Optimal Control

This is an updated version of Chapter 4 of the author’s Dynamic Programming and Optimal Control, Vol. II, 4th Edition, Athena Scientific, 2012. It includes new material, and it is substantially revised and expanded (it has more than doubled in size). The new material aims to provide a unified treatment of several models, all of which lack the contractive structure that is characteristic of the discounted problems of Chapters 1 and 2: positive and negative cost models, deterministic optimal control (including adaptive DP), stochastic shortest path models, and risk-sensitive models. Here is a summary of the new material:
Loginson: a transform and load system for very large-scale log analysis in large IT infrastructures

Nowadays, most systems and applications produce log records that are useful for security and monitoring purposes such as debugging programming errors, checking system status, and detecting configuration problems or even attacks. To this end, a log repository becomes necessary whereby logs can be accessed and visualized in a timely manner. This paper presents Loginson, a high-performance log centralization system for large-scale log collection and processing in large IT infrastructures. Besides log collection, Loginson provides high-level analytics through a visual interface for the purpose of troubleshooting critical incidents. We note that Loginson outperforms all of the other log centralization solutions by taking full advantage of the vertical scalability, and therefore decreasing Capital Expenditure (CAPEX) and Operating Expense (OPEX) costs for deployment scenarios with a huge volume of log data.
SelCSP: A Framework to Facilitate Selection of Cloud Service Providers

With rapid technological advancements, cloud marketplace witnessed frequent emergence of new service providers with similar offerings. However, service level agreements (SLAs), which document guaranteed quality of service levels, have not been found to be consistent among providers, even though they offer services with similar functionality. In service outsourcing environments, like cloud, the quality of service levels are of prime importance to customers, as they use third-party cloud services to store and process their clients' data. If loss of data occurs due to an outage, the customer's business gets affected. Therefore, the major challenge for a customer is to select an appropriate service provider to ensure guaranteed service quality. To support customers in reliably identifying ideal service provider, this work proposes a framework, SelCSP, which combines trustworthiness and competence to estimate risk of interaction. Trustworthiness is computed from personal experiences gained through direct interactions or from feedbacks related to reputations of vendors. Competence is assessed based on transparency in provider's SLA guarantees. A case study has been presented to demonstrate the application of our approach. Experimental results validate the practicability of the proposed estimating mechanisms.
Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications

This paper summarizes results on a recently introduced family of Datalog-based languages, called Datalog+/-, which is a new framework for tractable ontology querying, and for a variety of other applications. Datalog+/- extends plain Datalog by features such as existentially quantified rule heads and, at the same time, restricts the rule syntax so as to achieve decidability and tractability. In particular, we discuss three paradigms ensuring decidability: chase termination, guardedness, and stickiness.
Point-to-Multipoint Ultrasonic Communication Modem for Ubiquitous Underwater Wireless Sensor Networks

Recently, researches on underwater sensor networks (USN) for ocean development and disaster prevention have been emerged as one of interesting topics. Since low-power, high-speed and inexpensive communication modem is a prerequisite for deployment of USN, we design and implement an underwater modem by utilizing general-purpose waterproof ultrasonic sensors in this paper. Also, we make an experiment in a water tank containing sensor nodes and a sink node, a gateway, and show that point-to-multipoint communication is possible at a data rate of 1 kbps.
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency

Most simple nonlinear thresholding rules for wavelet-based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. In this paper, we will only consider the dependencies between the coefficients and their parents in detail. For this purpose, new non-Gaussian bivariate distributions are proposed, and corresponding nonlinear threshold functions (shrinkage functions) are derived from the models using Bayesian estimation theory. The new shrinkage functions do not assume the independence of wavelet coefficients. We will show three image denoising examples in order to show the performance of these new bivariate shrinkage rules. In the second example, a simple subband-dependent data-driven image denoising system is described and compared with effective data-driven techniques in the literature, namely VisuShrink, SureShrink, BayesShrink, and hidden Markov models. In the third example, the same idea is applied to the dual-tree complex wavelet coefficients.