nils-herrmann/clean_scirepeval huggingface dataset:
Toward Multimodal Image-to-Image Translation

Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting. The ambiguity of the mapping is distilled in a low-dimensional latent vector, which can be randomly sampled at test time. A generator learns to map the given input, combined with this latent code, to the output. We explicitly encourage the connection between output and the latent code to be invertible. This helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse, and produces more diverse results. We explore several variants of this approach by employing different training objectives, network architectures, and methods of injecting the latent code. Our proposed method encourages bijective consistency between the latent encoding and output modes. We present a systematic comparison of our method and other variants on both perceptual realism and diversity.
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation in order to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this “reality gap” requires at best a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power, rendering certain reinforcement learning (RL) methods unable to learn the task of interest. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zeroshot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, outperforming a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%.
Wideband bandpass filter design with three-line microstrip structures

Systematic procedure is described for designing bandpass filters with wide bandwidths based on parallel coupled three-line microstrip structures. It is found that the tight gap sizes between the resonators of end stages and feed lines, required for wideband filters based on traditional coupled line design, can be greatly released. The relation between the circuit parameters of a three-line coupling section and an admittance inverter circuit is derived. A design graph for substrate with /spl epsiv//sub r/=10.2 is provided. Two filters of orders 3 and 5 with fractional bandwidths 40% and 50%, respectively, are fabricated and measured. Good agreement between prediction and measurement is obtained.
Scalable mental health analysis in the clinical whitespace via natural language processing

Our increasingly digital life provides a wealth of data about our behavior, beliefs, mood, and well-being. This data provides some insight into the lives of patients outside the healthcare setting, and in aggregate can be insightful for the person's mental health and emotional crisis. Here, we introduce this community to some of the recent advancement in using natural language processing and machine learning to provide insight into mental health of both individuals and populations. We advocate using these linguistic signals as a supplement to those that are collected in the health care system, filling in some of the so-called “whitespace” between visits.
A patient-based assessment of implant-stabilized and conventional complete dentures.

Outcomes of oral implant therapy have been described primarily in terms of implant survival rates and the durability of implant superstructures. Reports of patient-based outcomes of implant therapy have been sparse, and none of these studies have used oral-specific health status measures.



This study assessed the impact of implant-stabilized prostheses on the health status of complete denture wearers using patient-based, oral-specific health status measures. It also assessed the influence of preoperative expectations on outcome.



Three experimental groups requesting replacement of their conventional complete dentures completed an Oral Health Impact Profile (OHIP) and a validated denture satisfaction scale before treatment. One group received an implant-stabilized prosthesis (IG), and 2 groups received new conventional complete dentures (CDG1 and CDG2). After treatment, all subjects completed the health status measures again; preoperative data were compared with postoperative data.



Before treatment, satisfaction with complete dentures was low in all 3 groups. Subjects requesting implants (IG and CDG1) had high expectations for implant-stabilized prostheses. Improvement in denture satisfaction and OHIP scores was reported by all 3 groups after treatment. Subjects who received their preferred treatment (IG and CDG2 subjects) reported a much greater improvement than CDG1 subjects. Preoperative expectation levels did not appear to influence satisfaction with the outcomes of implant therapy in IG subjects.



Subjects who received implants (IG) that replaced conventional complete dentures reported significant improvement after treatment, as did subjects who requested conventional replacement dentures (CDG2). The OHIP appears useful in identifying patients likely to benefit from implant-stabilized prostheses.
SMM Revolutions

The System Management Mode (SMM) is a highly privileged processor operating mode in x86 platforms. The goal of the SMM is to perform system management functions, such as hardware control and power management. Because of this, SMM has powerful resources. Moreover, its executive software executes unnoticed by any other component in the system, including operating systems and hypervisors. For that reason, SMM has been exploited in the past to facilitate attacks, misuse, or alternatively, building security tools capitalising on its resources. In this paper, we discuss how the use of the SMM has been contributing to the arms race between system's attackers and defenders. We analyse the main work published on attacks, misuse and implementing security tools in the SMM and how the SMM has been modified to respond to those issues. Finally, we discuss how Intel Software Guard Extensions (SGX) technology, a sort of "hypervisor in processor", presents a possible answer to the issue of using SMM for security purposes.
Evaluation of nurse engagement in evidence-based practice.

The purpose of this project was to explore nurses' willingness to question and change practice. Nurses were invited to report practice improvement opportunities, and participants were supported through the process of a practice change. The project leader engaged to the extent desired by the participant. Meetings proceeded until the participant no longer wished to continue, progress was blocked, or practice was changed. Evaluation of the evidence-based practice change process occurred. Fifteen nurses reported 23 practice improvement opportunities. The majority (12 of 15) preferred to have the project leader review the evidence. Fourteen projects changed practice; 4 were presented at conferences. Multiple barriers were identified throughout the process and included loss of momentum, the proposed change involved other disciplines, and low level or controversial evidence. Practice issues were linked to quality metrics, cost of care, patient satisfaction, regulatory compliance, and patient safety. Active engagement by nurse leaders was needed for a practice change to occur. Participants identified important problems previously unknown to hospital administrators. The majority of nurses preferred involvement in practice change based on clinical problem solving when supported by others to provide literature review and manage the process through committees. Recommendations include supporting a culture that encourages employees to report practice improvement opportunities and provide resources to assist in navigating the identified practice change.
CHINA’S BELT AND ROAD INITIATIVE: CHALLENGES FOR ARBITRATION IN ASIA

The focus of international arbitration has begun to shift to Asia in the wake of that continent’s increasingly prominent role in cross-border trade and investment. China’s ambitious Belt and Road Initiative (“BRI”)2 promises to exacerbate this trend. The BRI will require a broad range of complex, inter-related commercial transactions with significant political and economic ramifications. These transactions can be expected to generate both commercial and investor-state disputes, principally in Asia, and many of these disputes will be submitted to arbitration. This article considers the significant challenges that these developments will present for international arbitration. I. Arbitration’s Shift Toward Asia The number of international commercial arbitrations involving Asia has grown rapidly in recent decades, in pace with Asia’s greatly expanded role in international manufacturing, trade, and investment.3 Arbitration has become a favored option for dispute *1 The author is an independent arbitrator in the New York City area and a Member of Arbitration Chambers of Hong Kong and London. He was formerly the managing partner of a major law firm’s Beijing office. Prior to that, Mr. Norton served as the Assistant Legal Adviser for East Asian and Pacific Affairs at the U.S. Department of State and Counsel to the Senate Select Intelligence Committee. 2 The BRI initiative is also sometimes referred to as the “One Belt One Road Initiative” (“OBOR”) based on a direct translation of its Chinese name, Yi Dai Yi Lu (一带一路)。 3 See, Dr. Nicolas Wiegand & Dr. Tom C. Pröstler, Arbitration Is Becoming Increasingly Asian, BUS. L. MAG.
A network defense system for detecting and preventing potential hacking attempts

Many computing systems have recently suffered significantly from hacking, and preventing hacking is important in protecting business, sensitive information and every day network communications. Many efforts have been made to provide security assurance by proposing security solutions. However, the gap between hacking incidents and current security solutions is significant. Fortunately, hacking attempts can be addressed if the pre-hacking step, called scanning, is properly investigated and good counter measures are in place. The importance of scanning appears is in providing sophisticated hackers with the necessary information about nominated victims' systems which eventually forms their hacking strategies. Therefore, this article proposes a security solution that aims to make scanning difficult by addressing its properties, which makes developing appropriate hacking strategies against protected computer networks unpractical. The proposed security solution protects computer networks by dynamically generating a unique protocol to replace the expected standard protocols and changing network paths periodically in order to confuse scanning attempts, as well as prevent unauthorized scanning and hacking traffic.
The FimH Gene in Uropathogenic Escherichia coli Strains Isolated From Patients With Urinary Tract Infection

Urinary tract infections (UTIs) are one of main health problems caused by many microorganisms, including uropathogenic Escherichia coli (UPEC). UPEC strains are the most frequent pathogens responsible for 85% and 50% of community and hospital acquired UTIs, respectively. UPEC strains have special virulence factors, including type 1 fimbriae, which can result in worsening of UTIs.



This study was performed to detect type 1 fimbriae (the FimH gene) among UPEC strains by molecular method.



A total of 140 isolated E. coli strains from patients with UTI were identified using biochemical tests and then evaluated for the FimH gene by polymerase chain reaction (PCR) analysis.



The UPEC isolates were identified using biochemical tests and were screened by PCR. The fimH gene was amplified using specific primers and showed a band about 164 bp. The FimH gene was found in 130 isolates (92.8%) of the UPEC strains. Of 130 isolates positive for the FimH gene, 62 (47.7%) and 68 (52.3%) belonged to hospitalized patients and outpatients, respectively.



The results of this study indicated that more than 90% of E. coli isolates harbored the FimH gene. The high binding ability of FimH could result in the increased pathogenicity of E. coli; thus, FimH could be used as a possible diagnostic marker and/or vaccine candidate.
Nail Psoriasis, the unknown burden of disease.

Psoriasis can be found at several different localizations which may be of various impact on patients' quality of life (QoL). One of the easy visible, and difficult to conceal localizations are the nails.



To achieve more insight into the QoL of psoriatic patients with nail psoriasis, and to characterize the patients with nail involvement which are more prone to the impact of the nail alterations caused by psoriasis.



A self-administered questionnaire was distributed to all members (n = 5400) of the Dutch Psoriasis Association. The Dermatology Life Quality Index (DLQI) and the Nail Psoriasis Quality of life 10 (NPQ10) score were included as QoL measures. Severity of cutaneous lesions was determined using the self-administered psoriasis area and severity index (SAPASI).



Patients with nail psoriasis scored significantly higher mean scores on the DLQI (4.9 vs. 3.7, P = <0.001) and showed more severe psoriasis (SAPASI, 6.6 vs. 5.3, P = <0.001). Patients with coexistence of nail bed and nail matrix features showed higher DLQI scores compared with patients with involvement of one of the two localizations exclusively (5.3 vs. 4.2 vs. 4.3, P = 0.003). Patients with only nail bed alterations scored significant higher NPQ10 scores when compared with patients with only nail matrix features. Patients with psoriatic arthritis (PsA) and nail psoriasis experiences more impairments compared with nail psoriasis patients without PsA (DLQI 5.5 vs. 4.3, NPQ10 13.3 vs. 7.0). Females scored higher mean scores on all QoL scores.



Greater attention should be paid to the possible impact nail abnormalities have on patients with nail psoriasis, which can be identified by nail psoriasis specific questionnaires such as the NPQ10. As improving the severity of disease may have a positive influence on QoL, the outcome of QoL measurements should be taken into account when deciding on treatment strategies.
WSDL-based automatic test case generation for Web services testing

Web services promote the specification based cooperation and collaboration among distributed applications in an open environment. To ensure the quality of the services that are published, bound, invoked and integrated at runtime, test cases have to be automatically generated and testing executed, monitored and analyzed at runtime. This paper presents the research to generate Web services test cases automatically based on the Web services specification language WSDL (Web Services Description Language), which carries the basic information of a service including its interface operations and the data transmitted. The WSDL file is first parsed and transformed into the structured DOM tree. Then, test cases are generated from two perspectives: test data generation and test operation generation. Test data are generated by analyzing the message data types according to standard XML schema syntax. Operation flows are generated based on the operation dependency analysis. Three types of dependencies are defined: input dependency, output dependency, and input/output dependency. Finally, the generated test cases are documented in XML based test files called service test specification.
Trust cues fostering initial consumers' trust: usability inspection of nutrition and healthcare websites

The number of nutrition and healthcare websites increasing dramatically has prompted concerns over the quality of information found in these websites. How do nutrition and healthcare sites fare in fostering trust in consumers? Through reviews of trust and design literatures, we created a checklist incorporating a 5-dimension framework of design factors (graphic, structure, content, social, system) affecting trust cues. Next, we conducted a usability inspection on implementation of trust cues on 86 (30 healthcare, 31 nutrition and 25 healthcare & nutrition) websites. Our findings suggest that Graphics-Driven trust cues were implemented most to foster consumers' trust, with the Socially-Driven trust cues being the least implemented. This is on-going work. The paper concluded with implementations for improvement in the design of trust cues to foster initial consumers' trust in nutrition and healthcare websites.
End-to-End Deep Kronecker-Product Matching for Person Re-identification

Person re-identification aims to robustly measure similarities between person images. The significant variation of person poses and viewing angles challenges for accurate person re-identification. The spatial layout and correspondences between query person images are vital information for tackling this problem but are ignored by most state-of-the-art methods. In this paper, we propose a novel Kronecker Product Matching module to match feature maps of different persons in an end-to-end trainable deep neural network. A novel feature soft warping scheme is designed for aligning the feature maps based on matching results, which is shown to be crucial for achieving superior accuracy. The multi-scale features based on hourglass-like networks and self residual attention are also exploited to further boost the re-identification performance. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach.
Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors

Hyperspectral data acquired from field-based platforms present new challenges for their analysis, particularly for complex vertical surfaces exposed to large changes in the geometry and intensity of illumination. The use of hyperspectral data to map rock types on a vertical mine face is demonstrated, with a view to providing real-time information for automated mining applications. The performance of two classification techniques, namely, spectral angle mapper (SAM) and support vector machines (SVMs), is compared rigorously using a spectral library acquired under various conditions of illumination. SAM and SVM are then applied to a mine face, and results are compared with geological boundaries mapped in the field. Effects of changing conditions of illumination, including shadow, were investigated by applying SAM and SVM to imagery acquired at different times of the day. As expected, classification of the spectral libraries showed that, on average, SVM gave superior results for SAM, although SAM performed better where spectra were acquired under conditions of shadow. In contrast, when applied to hypserspectral imagery of a mine face, SVM did not perform as well as SAM. Shadow, through its impact upon spectral curve shape and albedo, had a profound impact on classification using SAM and SVM.
Clinical and Biological Characterization of Skin Pigmentation Diversity and Its Consequences on UV Impact

Skin color diversity is the most variable and noticeable phenotypic trait in humans resulting from constitutive pigmentation variability. This paper will review the characterization of skin pigmentation diversity with a focus on the most recent data on the genetic basis of skin pigmentation, and the various methodologies for skin color assessment. Then, melanocyte activity and amount, type and distribution of melanins, which are the main drivers for skin pigmentation, are described. Paracrine regulators of melanocyte microenvironment are also discussed. Skin response to sun exposure is also highly dependent on color diversity. Thus, sensitivity to solar wavelengths is examined in terms of acute effects such as sunburn/erythema or induced-pigmentation but also long-term consequences such as skin cancers, photoageing and pigmentary disorders. More pronounced sun-sensitivity in lighter or darker skin types depending on the detrimental effects and involved wavelengths is reviewed.
GPU accelerated convex hull computation

We present a hybrid algorithm to compute the convex hull of points in three or higher dimensional spaces. Our formulation uses a GPU-based interior point filter to cull away many of the points that do not lie on the boundary. The convex hull of remaining points is computed on a CPU. The GPU-based filter proceeds in an incremental manner and computes a pseudo-hull that is contained inside the convex hull of the original points. The pseudo-hull computation involves only localized operations and maps well to GPU architectures. Furthermore, the underlying approach extends to high dimensional point sets and deforming points. In practice, our culling filter can reduce the number of candidate points by two orders of magnitude. We have implemented the hybrid algorithm on commodity GPUs, and evaluated its performance on several large point sets. In practice, the GPU-based filtering algorithm can cull up to 85M interior points per second on an NVIDIA GeForce GTX 580 and the hybrid algorithm improves the overall performance of convex hull computation by 10 − 27 times (for static point sets) and 22 − 46 times (for deforming point sets).
User trends modeling for a content-based recommender system

Recommender systems have been developed to overcome the information overload problem by retrieving the most relevant resources. Constructing an appropriate model to estimate the user interests is the major task of recommender systems. The profile matching and latent factors are two main approaches for user modeling. Although a notion of timestamps has already been applied to address the temporary nature of recommender systems, the evolutionary behavior of such systems is less studied. In this paper, we introduce the concept of trend to capture the interests of user in selecting items among different group of similar items. The trend based user model is constructed by incorporating user profile into a new extension of Distance Dependent Chines Restaurant Process (dd-CRP). dd-CRP which is a Bayesian Nonparametric model, provides a framework for constructing an evolutionary user model that captures the dynamics of user interests. We evaluate the proposed method using a real-world data-set that contains news tweets of three news agencies (New York Times, BBC and Associated Press). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach, and its ability to effectively evolve over time. ∗Corresponding author at: Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, P.O. Box 316, Shahrood, Iran. Tel/Fax: +98(23) 32300251.  rc_bagher@yahoo.com (Bagher Rahimpour Cami), h.hassanpour@shahroodut.ac.ir (Hamid Hassanpour ), hmashayekhi@shahroodut.ac.ir (Hoda Mashayekhi) Preprint submitted to Expert Systems with Applications June 13, 2017