nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal cue integration through Hypotheses Verification for RGB-D object recognition and 6DOF pose estimation

This paper proposes an effective algorithm for recognizing objects and accurately estimating their 6DOF pose in scenes acquired by a RGB-D sensor. The proposed method is based on a combination of different recognition pipelines, each exploiting the data in a diverse manner and generating object hypotheses that are ultimately fused together in an Hypothesis Verification stage that globally enforces geometrical consistency between model hypotheses and the scene. Such a scheme boosts the overall recognition performance as it enhances the strength of the different recognition pipelines while diminishing the impact of their specific weaknesses. The proposed method outperforms the state-of-the-art on two challenging benchmark datasets for object recognition comprising 35 object models and, respectively, 176 and 353 scenes.
Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks

Recently, Graph Neural Networks (GNNs) are trending in the machine learning community as a family of architectures that specializes in capturing the features of graph-related datasets, such as those pertaining to social networks and chemical structures. Unlike for other families of the networks, the representation power of GNNs has much room for improvement, and many graph networks to date suffer from the problem of underfitting. In this paper we will introduce a Graph Warp Module, a supernode-based auxiliary network module that can be attached to a wide variety of existing GNNs in order to improve the representation power of the original networks. Through extensive experiments on molecular graph datasets, we will show that our GWM indeed alleviates the underfitting problem for various existing networks, and that it can even help create a network with the state-of-the-art generalization performance.
Optimal disturbance rejection control design for Electric Power Steering systems

Nowadays many automobile manufacturers are switching to Electric Power Steering (EPS) for its advantages on performance and cost. In this paper, a mathematical model of a column type EPS system is established, and its state-space expression is constructed. Then three different control methods are implemented and performance, robustness and disturbance rejection properties of the EPS control systems are investigated. The controllers are tested via simulation and results show a modified Linear Quadratic Gaussian (LQG) controller can track the characteristic curve well and effectively attenuate external disturbances.
Antibiotic Resistance of Pseudomonas aeruginosa in Pneumonia at a Single University Hospital Center in Germany over a 10-Year Period

Pseudomonas aeruginosa is a common cause of community-acquired and nosocomial-acquired pneumonia. The development of resistance of P. aeruginosa to antibiotics is increasing globally due to the overuse of antibiotics. This article examines, retrospectively, the antibiotic resistance in patients with community-acquired versus nosocomial-acquired pneumonia caused by P. aeruginosa or multidrug-resistant (MDR) P. aeruginosa.



Data from patients with community-acquired and nosocomial-acquired pneumonia caused by P. aeruginosa and MDR P. aeruginosa were collected from the hospital charts at the HELIOS Clinic, Witten/Herdecke University, Wuppertal, Germany, between January 2004 and August 2014. An antibiogram was created from all study patients with community-acquired and nosocomial-acquired pneumonia caused by P. aeruginosa or MDR P. aeruginosa.



A total of 168 patients with mean age 68.1 ± 12.8 (113 [67.3% males and 55 [32.7%] females) were identified; 91 (54.2%) had community-acquired and 77 (45.8%) had nosocomial-acquired pneumonia caused by P. aeruginosa. Patients with community-acquired versus nosocomial-acquired pneumonia had a mean age of 66.4 ± 13.8 vs. 70.1 ± 11.4 years [59 vs. 54 (64.8% vs. 70.1%) males and 32 vs. 23 (35.2% vs. 29.9%) females]. They included 41 (24.4%) patients with pneumonia due to MDR P. aeruginosa: 27 (65.9%) community-acquired and 14 (34.1%) nosocomial-acquired cases. P. aeruginosa and MDR P. aeruginosa showed a very high resistance to fosfomycin (community-acquired vs. nosocomial-acquired) (81.0% vs. 84.2%; 0 vs. 85.7%). A similar resistance pattern was seen with ciprofloxacin (35.2% vs. 24.0%; 70.4% vs. 61.5%), levofloxacin (34.6% vs. 24.5%; 66.7% vs. 64.3%), ceftazidime (15.9% vs. 30.9; 33.3% vs. 61.5%), piperacillin (24.2% vs. 29.9%; 44.4% vs. 57.1%), imipenem (28.6% vs. 27.3%; 55.6% vs. 50.0%), piperacillin and tazobactam (23.1% vs. 28.6%; 44.4% vs. 50.0%), tobramycin (28.0% vs. 17.2%; 52.0% vs. 27.3%), gentamicin (26.4% vs. 18.2%; 44.4% vs. 21.4%), and meropenem (20.2% vs. 20.3%; 42.3% vs. 50.0%). An elevated resistance of P. aeruginosa and MDR P. aeruginosa was found for cefepime (11.1% vs. 23.3%; 25.9% vs. 50.0%), and amikacin (10.2% vs. 9.1%; 27.3% vs. 9.1%). Neither pathogen was resistant to colistin (P = 0.574).



While P. aeruginosa and MDR P. aeruginosa were resistant to a variety of commonly used antibiotics, they were not resistant to colistin in the few isolates recovered from patients with pneumonia.
Rebamipide Alters the Esophageal Microbiome and Reduces the Incidence of Barrett’s Esophagus in a Rat Model

Barrett’s esophagus (BE) is characterized by a distinct Th2-predominant cytokine profile. However, antigens that shift the immune response toward the Th2 profile are unknown. We examined the effects of rebamipide on the esophageal microbiome and BE development in a rat model. BE was induced by esophagojejunostomy in 8-week-old male Wistar rats. Rats were divided into control and rebamipide-treated group receiving either a normal or a 0.225 % rebamipide-containing diet, respectively, and killed 8, 16, 24, and 32 weeks after the operation. PCR-amplified 16S rDNAs extracted from esophageal samples were examined by terminal-restriction fragment length polymorphism (T-RFLP) analysis to assess microbiome composition. The dynamics of four bacterial genera (Lactobacillus, Clostridium, Streptococcus, and Enterococcus) were analyzed by real-time PCR. The incidences of BE in the control and rebamipide group at 24 and 32 weeks were 80 and 100, and 20 and 33 %, respectively. T-RFLP analysis of normal esophagus revealed that the proportion of Clostridium was 8.3 %, while that of Lactobacillales was 71.8 %. The proportions of Clostridium increased and that of Lactobacillales decreased at 8 weeks in both groups. Such changes were consistently observed in the control but not in the rebamipide group. Clostridium and Lactobacillus expression was lower and higher, respectively, in the rebamipide group than in the control group. Rebamipide reduced BE development and altered the esophageal microbiome composition, which might play a role in BE development.
A robust scene-change detection method for video segmentation

This paper proposes a new method that combines the intensity and motion information to detect scene changes such as abrupt scene changes and gradual scene changes. Two major features are chosen as the basic dissimilarity measures, and selfand cross-validation mechanisms are employed via a static scene test. We also develop a novel intensity statistics model for detecting gradualscenechanges.Experimental resultsshowthat theproposed algorithms are effective and outperform the previous approaches.
Review of anatomy, evaluation, and treatment of musculoskeletal pelvic floor pain in women.

The purpose of this review is 2-fold. The first is to provide a review for physiatrists already providing care for women with musculoskeletal pelvic floor pain and a resource for physiatrists who are interested in expanding their practice to include this patient population. The second is to describe how musculoskeletal dysfunctions involving the pelvic floor can be approached by the physiatrist using the same principles used to evaluate and treat others dysfunctions in the musculoskeletal system. This discussion clarifies that evaluation and treatment of pelvic floor pain of musculoskeletal origin is within the scope of practice for physiatrists. The authors review the anatomy of the pelvic floor, including the bony pelvis and joints, muscle and fascia, and the peripheral and autonomic nervous systems. Pertinent history and physical examination findings are described. The review concludes with a discussion of differential diagnosis and treatment of musculoskeletal pelvic floor pain in women. Improved recognition of pelvic floor dysfunction by healthcare providers will reduce impairment and disability for women with pelvic floor pain. A physiatrist is in the unique position to treat the musculoskeletal causes of this condition because it requires an expert grasp of anatomy, function, and the linked relationship between the spine and pelvis. Further research regarding musculoskeletal causes and treatment of pelvic floor pain will help validate these concepts and improve awareness and care for women limited by this condition.
Privacy, accuracy, and consistency too: a holistic solution to contingency table release

The contingency table is a work horse of official statistics, the format of reported data for the US Census, Bureau of Labor Statistics, and the Internal Revenue Service. In many settings such as these privacy is not only ethically mandated, but frequently legally as well. Consequently there is an extensive and diverse literature dedicated to the problems of statistical disclosure control in contingency table release. However, all current techniques for reporting contingency tables fall short on at leas one of privacy, accuracy, and consistency (among multiple released tables). We propose a solution that provides strong guarantees for all three desiderata simultaneously.
 Our approach can be viewed as a special case of a more general approach for producing synthetic data: Any privacy-preserving mechanism for contingency table release begins with raw data and produces a (possibly inconsistent) privacy-preserving set of marginals. From these tables alone-and hence without weakening privacy--we will find and output the "nearest" consistent set of marginals. Interestingly, this set is no farther than the tables of the raw data, and consequently the additional error introduced by the imposition of consistency is no more than the error introduced by the privacy mechanism itself.
 The privacy mechanism of [20] gives the strongest known privacy guarantees, with very little error. Combined with the techniques of the current paper, we therefore obtain excellent privacy, accuracy, and consistency among the tables. Moreover, our techniques are surprisingly efficient. Our techniques apply equally well to the logical cousin of the contingency table, the OLAP cube.
Traffic sign detection and classification using colour feature and neural network

Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour based segmentation are applied to find out if a traffic sign is present. If present, the sign will be highlighted, normalized in size and then classified. Neural network is used for classification purposes. For evaluation purpose, four type traffic signs such as Stop Sign, No Entry Sign, Give Way Sign, and Speed Limit Sign are used. Altogether 300 sets images, 75 sets for each type are used for training purposes. 200 images are used testing. The experimental results show the detection rate is above 90% and the accuracy of recognition is more than 88%.
Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs

Recently, Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based interactive mobile applications, and implement their backend computation parts on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.
SAS-2017 Stacked Ensemble Models for Improved Prediction Accuracy

Ensemble modeling is now a well-established means for improving prediction accuracy; it enables you to average out noise from diverse models and thereby enhance the generalizable signal. Basic stacked ensemble techniques combine predictions from multiple machine learning algorithms and use these predictions as inputs to second-level learning models. This paper shows how you can generate a diverse set of models by various methods such as forest, gradient boosted decision trees, factorization machines, and logistic regression and then combine them with stacked-ensemble techniques such as hill climbing, gradient boosting, and nonnegative least squares in SAS Visual Data Mining and Machine Learning. The application of these techniques to real-world big data problems demonstrates how using stacked ensembles produces greater prediction accuracy and robustness than do individual models. The approach is powerful and compelling enough to alter your initial data mining mindset from finding the single best model to finding a collection of really good complementary models. It does involve additional cost due both to training a large number of models and the proper use of cross validation to avoid overfitting. This paper shows how to efficiently handle this computational expense in a modern SAS environment and how to manage an ensemble workflow by using parallel computation in a distributed framework.
Learning Large-Scale Automatic Image Colorization

We describe an automated method for image colorization that learns to colorize from examples. Our method exploits a LEARCH framework to train a quadratic objective function in the chromaticity maps, comparable to a Gaussian random field. The coefficients of the objective function are conditioned on image features, using a random forest. The objective function admits correlations on long spatial scales, and can control spatial error in the colorization of the image. Images are then colorized by minimizing this objective function. We demonstrate that our method strongly outperforms a natural baseline on large-scale experiments with images of real scenes using a demanding loss function. We demonstrate that learning a model that is conditioned on scene produces improved results. We show how to incorporate a desired color histogram into the objective function, and that doing so can lead to further improvements in results.
Biomimetic Flow-Sensor Arrays Based on the Filiform Hairs on the Cerci of Crickets

In this paper we report on the latest developments in biomimetic flow-sensors based on the flow sensitive mechano-sensors of crickets. Crickets have one form of acoustic sensing evolved in the form of mechano receptive sensory hairs. These filiform hairs are highly perceptive to low-frequency sound with energy sensitivities close to thermal threshold. Arrays of artificial hair sensors have been fabricated using a surface micromachining technology to form suspended silicon nitride membranes and double-layer SU-8 processing to form 1 mm long hairs. Previously, we have shown that these hairs are sensitive to low-frequency sound, using a laser vibrometer setup to detect the movements of the nitride membranes. We have now realized readout electronics to detect the movements capacitively, using electrodes integrated on the membranes.
Evolving Neural Networks to Focus Minimax Search

Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are promising enough to be explored further. The networks effectively hide problem states from minimax based on the knowledge they have evolved about the limitations of minimax and the evaluation function. Focus networks were encoded in marker{ based chromosomes and were evolved against a full{width minimax opponent that used the same evaluation function. The networks were able to guide the search away from poor information, resulting in stronger play while examining fewer states. When evolved with a highly sophisticated evaluation function of the Bill program, the system was able to match Bill's performance while only searching a subset of the moves.
The fickle P value generates irreproducible results

Lewis G. Halsey is in the Department of Life Sciences, University of Roehampton, London, UK; Douglas Curran-Everett is in the Division of Biostatistics and Bioinformatics, National Jewish Health, and the Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA; Sarah L. Vowler is at Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; and Gordon B. Drummond is at the University of Edinburgh, Edinburgh, UK.  The fickle P value generates irreproducible results
Antenna selection in MIMO systems

Multiple-antenna systems, also known as multiple-input multiple-output radio, can improve the capacity and reliability of radio communication. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power, and hardware. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. This article reviews classic results on selection diversity, followed by a discussion of antenna selection algorithms at the transmit and receive sides. Extensions of classical results to antenna subset selection are presented. Finally, several open problems in this area are pointed out.
A numerical evaluation of sparse direct solvers for the solution of large sparse symmetric linear systems of equations

In recent years a number of solvers for the direct solution of large sparse symmetric linear systems of equations have been developed. These include solvers that are designed for the solution of positive definite systems as well as those that are principally intended for solving indefinite problems. In this study, we use performance profiles as a tool for evaluating and comparing the performance of serial sparse direct solvers on an extensive set of symmetric test problems taken from a range of practical applications.
Design Space Analysis : Bridging from Theory to Practice via Design Rationale

This paper reports work being carried out under the AMODEUS project (BRA 3066). The goal of the project is to develop interdisciplinary approaches to studying human-computer interaction and to move towards applying the results to the practicalities of design. This paper describes one of the approaches the project is taking to represent design-Design Space Analysis. One of its goals is help us bridge from relatively theoretical concerns to the practicalities of design.  Space Analysis is a central component of a framework for representing the design rationale for designed artifacts. Our current work focusses more specifically on the design of user interfaces. A Design Space Analysis is represented using the QOC notation, which consists of Questions identifying key design issues, Options providing possible answers to the Questions, and Criteria for assessing and comparing the Options. In this paper we give an overview of our approach, some examples of the research issues we are currently tackling and an illustration of its role in helping to integrate the work of some of our project partners with design considerations.
Modulating the sense of agency with external cues

We investigate the processes underlying the feeling of control over one's actions ("sense of agency"). Sense of agency may depend on internal motoric signals, and general inferences about external events. We used priming to modulate the sense of agency for voluntary and involuntary movements, by modifying the content of conscious thought prior to moving. Trials began with the presentation of one of two supraliminal primes, which corresponded to the effect of a voluntary action participants subsequently made. The perceived interval between movement and effect was used as an implicit measure of sense of agency. Primes modulated perceived intervals for both voluntary and involuntary movements, but the modulation was greatest for involuntary movements. A second experiment showed that this modulation depended on prime-movement (temporal) contiguity. We propose that sense of agency is based on a combination of internal motoric signals and external sensory evidence about the source of actions and effects.
A visual domain adaptation method based on enhanced subspace distribution matching

One of the challenges in computer vision is how to learn an accurate classifier for a new domain by using labeled images from an old domain under the condition that there is no available labeled images in the new domain. Domain adaptation is an outstanding solution that tackles this challenge by employing available source-labeled datasets, even with significant difference in distribution and properties. However, most prior methods only reduce the difference in subspace marginal or conditional distributions across domains while completely ignoring the source data label dependence information in a subspace. In this paper, we put forward a novel domain adaptation approach, referred to as Enhanced Subspace Distribution Matching. Specifically, it aims to jointly match the marginal and conditional distributions in a kernel principal dimensionality reduction procedure while maximizing the source label dependence in a subspace, thus raising the subspace distribution matching degree. Extensive experiments verify that it can significantly outperform several state-of-the-art methods for cross-domain image classification problems.
A Comparison of Resampling Methods for Clustering Ensembles

Combination of multiple clusterings is an important task in the area of unsupervised learning. Inspired by the success of supervised bagging algorithms, we propose a resampling scheme for integration of multiple independent clusterings. Individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given data set. In this paper, we compare the efficacy of both subsampling (sampling without replacement) and bootstrap (with replacement) techniques in conjunction with several fusion algorithms. The empirical study shows that a meaningful consensus partition for an entire set of data points emerges from multiple clusterings of subsamples of small size. The purpose of this paper is to show that small subsamples generally suffice to represent the structure of the entire data set in the framework of clustering ensembles. Subsamples of small size can reduce computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data.