nils-herrmann/clean_scirepeval huggingface dataset:
The CpxA/CpxR Two-Component System Affects Biofilm Formation and Virulence in Actinobacillus pleuropneumoniae

Gram-negative bacteria have evolved numerous two-component systems (TCSs) to cope with external environmental changes. The CpxA/CpxR TCS consisting of the kinase CpxA and the regulator CpxR, is known to be involved in the biofilm formation and virulence of Escherichia coli. However, the role of CpxA/CpxR remained unclear in Actinobacillus pleuropneumoniae, a bacterial pathogen that can cause porcine contagious pleuropneumonia (PCP). In this report, we show that CpxA/CpxR contributes to the biofilm formation ability of A. pleuropneumoniae. Furthermore, we demonstrate that CpxA/CpxR plays an important role in the expression of several biofilm-related genes in A. pleuropneumoniae, such as rpoE and pgaC. Furthermore, The results of electrophoretic mobility shift assays (EMSAs) and DNase I footprinting analysis demonstrate that CpxR-P can regulate the expression of the pgaABCD operon through rpoE. In an experimental infection of mice, the animals infected with a cpxA/cpxR mutant exhibited delayed mortality and lower bacterial loads in the lung than those infected with the wildtype bacteria. In conclusion, these results indicate that the CpxA/CpxR TCS plays a contributing role in the biofilm formation and virulence of A. pleuropneumoniae.
Multilingual Natural Language Generation within Abstractive Summarization

With the tremendous amount of textual data available in the Internet, techniques for abstractive text summarization become increasingly appreciated. In this paper, we present work in progress that tackles the problem of multilingual text summarization using semantic representations. Our system is based on abstract linguistic structures obtained from an analysis pipeline of disambiguation, syntactic and semantic parsing tools. The resulting structures are stored in a semantic repository, from which a text planning component produces content plans that go through a multilingual generation pipeline that produces texts in English, Spanish, French, or German. In this paper we focus on the lingusitic components of the summarizer, both analysis and generation.
Exploring cross-layer power management for PGAS applications on the SCC platform

High-performance parallel computing architectures are increasingly based on multi-core processors. While current commercially available processors are at 8 and 16 cores, technological and power constraints are limiting the performance growth of the cores and are resulting in architectures with much higher core counts, such as the experimental many-core Intel Single-chip Cloud Computer (SCC) platform. These trends are presenting new sets of challenges to HPC applications including programming complexity and the need for extreme energy efficiency.
 In this paper, we first investigate the power behavior of scientific Partitioned Global Address Space (PGAS) application kernels on the SCC platform, and explore opportunities and challenges for power management within the PGAS framework. Results obtained via empirical evaluation of Unified Parallel C (UPC) applications on the SCC platform under different constraints, show that, for specific operations, the potential for energy savings in PGAS is large; and power/performance trade-offs can be effectively managed using a cross-layer approach. We investigate cross-layer power management using PGAS language extensions and runtime mechanisms that manipulate power/performance tradeoffs. Specifically, we present the design, implementation and evaluation of such a middleware for application-aware cross-layer power management of UPC applications on the SCC platform. Finally, based on our observations, we provide a set of insights that can be used to support similar power management for PGAS applications on other many-core platforms.
Dual Swap Disentangling

Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling (DSD), for disentangling using both labeled and unlabeled data. Unlike conventional weakly supervised methods that rely on full annotations on the group of samples, we require only limited annotations on paired samples that indicate their shared attribute like the color. Our model takes the form of a dual autoencoder structure. To achieve disentangling using the labeled pairs, we follow a “encoding-swap-decoding” process, where we first swap the parts of their encodings corresponding to the shared attribute, and then decode the obtained hybrid codes to reconstruct the original input pairs. For unlabeled pairs, we follow the “encoding-swap-decoding” process twice on designated encoding parts and enforce the final outputs to approximate the input pairs. By isolating parts of the encoding and swapping them back and forth, we impose the dimension-wise modularity and portability of the encodings of the unlabeled samples, which implicitly encourages disentangling under the guidance of labeled pairs. This dual swap mechanism, tailored for semi-supervised setting, turns out to be very effective. Experiments on image datasets from a wide domain show that our model yields state-of-the-art disentangling performances.
Reducing common mode EMI noise in two-switch forward converter

This paper addresses the common mode (CM) electromagnetic interference (EMI) noise issues in the twoswitch forward converter. Two-switch forward converter has low CM noise with its symmetric primary side structure. However, the asymmetric parasitic capacitances and secondary side noise source have adverse effects on the symmetry and degrade the CM noise reduction. In order to solve the issues, CM noise model of the converter and transformer is first derived. Symmetry conditions are then summarized as the guideline for achieving CM noise reduction. Methods are proposed to minimize the CM noise by controlling the transformer termination, parasitic capacitances and structure. Experimental results validated the proposed techniques.
Bistability, Causality, and Complexity in Cortical Networks: An In Vitro Perturbational Study.

Measuring the spatiotemporal complexity of cortical responses to direct perturbations provides a reliable index of the brain's capacity for consciousness in humans under both physiological and pathological conditions. Upon loss of consciousness, the complex pattern of causal interactions observed during wakefulness collapses into a stereotypical slow wave, suggesting that cortical bistability may play a role. Bistability is mainly expressed in the form of slow oscillations, a default pattern of activity that emerges from cortical networks in conditions of functional or anatomical disconnection. Here, we employ an in vitro model to understand the relationship between bistability and complexity in cortical circuits. We adapted the perturbational complexity index applied in humans to electrically stimulated cortical slices under different neuromodulatory conditions. At this microscale level, we demonstrate that perturbational complexity can be effectively modulated by pharmacological reduction of bistability and, albeit to a lesser extent, by enhancement of excitability, providing mechanistic insights into the macroscale measurements performed in humans.
Word cloud of online hotel reviews in Chiang Mai for customer satisfaction analysis

This study aims to examine the satisfied and unsatisfied of hotel customers by utilizing a word cloud approach to evaluate online reviews. As a pilot test, online commends of 1,752 hotel guests were collected from TripAdvisor.com for 5 selected hotels in Chiang Mai, Thailand. The research results revealed some common features that are identified in both satisfied and dissatisfied of customer reviews; including staff service skills, hotel environment and facilities and a quality of room and bathroom. On the other hand, the findings shown that dissatisfied customers pointed out more frequently on the booking systems of the hotel. Therefore, this article's results suggests some clearer managerial implications pertaining to understanding of customer satisfaction level through the utilization of world cloud technique via review online platforms.
The continuing epidemics of obesity and diabetes in the United States.

Recent reports show that obesity and diabetes have increased in the United States in the past decade.



To estimate the prevalence of obesity, diabetes, and use of weight control strategies among US adults in 2000.



The Behavioral Risk Factor Surveillance System, a random-digit telephone survey conducted in all states in 2000, with 184 450 adults aged 18 years or older.



Body mass index (BMI), calculated from self-reported weight and height; self-reported diabetes; prevalence of weight loss or maintenance attempts; and weight control strategies used.



In 2000, the prevalence of obesity (BMI >/=30 kg/m(2)) was 19.8%, the prevalence of diabetes was 7.3%, and the prevalence of both combined was 2.9%. Mississippi had the highest rates of obesity (24.3%) and of diabetes (8.8%); Colorado had the lowest rate of obesity (13.8%); and Alaska had the lowest rate of diabetes (4.4%). Twenty-seven percent of US adults did not engage in any physical activity, and another 28.2% were not regularly active. Only 24.4% of US adults consumed fruits and vegetables 5 or more times daily. Among obese participants who had had a routine checkup during the past year, 42.8% had been advised by a health care professional to lose weight. Among participants trying to lose or maintain weight, 17.5% were following recommendations to eat fewer calories and increase physical activity to more than 150 min/wk.



The prevalence of obesity and diabetes continues to increase among US adults. Interventions are needed to improve physical activity and diet in communities nationwide.
An Optimal Energy Management System for Electric Vehicles using Firefly Optimization Algorithm based Dynamic EDF Scheduling

Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV) to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB. Keyword Energy management system (EMS), electric Vehicles (EV), Dynamic EDF scheduling, Firefly optimization algorithm.
Word Embeddings for Code-Mixed Language Processing

We compare three existing bilingual word embedding approaches, and a novel approach of training skip-grams on synthetic code-mixed text generated through linguistic models of code-mixing, on two tasks sentiment analysis and POS tagging for code-mixed text. Our results show that while CVM and CCA based embeddings perform as well as the proposed embedding technique on semantic and syntactic tasks respectively, the proposed approach provides the best performance for both tasks overall. Thus, this study demonstrates that existing bilingual embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embedding from the code-mixed text.
Excessive Invariance Causes Adversarial Vulnerability

Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We show deep networks are not only too sensitive to task-irrelevant changes of their input, as is well-known from -adversarial examples, but are also too invariant to a wide range of task-relevant changes, thus making vast regions in input space vulnerable to adversarial attacks. We show such excessive invariance occurs across various tasks and architecture types. On MNIST and ImageNet one can manipulate the class-specific content of almost any image without changing the hidden activations. We identify an insufficiency of the standard cross-entropy loss as a reason for these failures. Further, we extend this objective based on an informationtheoretic analysis so it encourages the model to consider all task-dependent features in its decision. This provides the first approach tailored explicitly to overcome excessive invariance and resulting vulnerabilities.
GRASP with Path Relinking for the Two-Echelon Vehicle Routing Problem Teodor

We propose a meta-heuristic based on GRASP combined with Path Relinking to address the Two-Echelon Vehicle Routing Problem, an extension of the Capacitated Vehicle Routing Problem in which the delivery from a single depot to customers is achieved by routing and consolidating the freight through intermediate depots called satellites. The problem is treated by separating the depot-to-satellite transfer and the satellite-to-customer delivery, and iteratively solving the two resulting routing subproblems, while adjusting the satellite workloads that link them. The meta-heuristic scheme consists of applying a GRASP and a local search procedure in sequence. Then, the resulting solution is linked to an elite solution by means of a Path Relinking procedure. To escape from infeasible solutions, which are quite common in this kind of problem, a feasibility search procedure is applied within Path Relinking. Extensive computational results on instances with up to 50 customers and 5 satellites show that the meta-heuristic is able to improve literature results, both in efficiency and accuracy.
Virilizing Adrenocortical Carcinoma Advancing to Central Precocious Puberty after Surgery

Adrenocortical carcinoma (ACC) in pediatric and adolescent patients is rare, and it is associated with various clinical symptoms. We introduce the case of an 8-year-old boy with ACC who presented with peripheral precocious puberty at his first visit. He displayed penis enlargement with pubic hair and facial acne. His serum adrenal androgen levels were elevated, and abdominal computed tomography revealed a right suprarenal mass. After complete surgical resection, the histological diagnosis was ACC. Two months after surgical removal of the mass, he subsequently developed central precocious puberty. He was treated with a gonadotropin-releasing hormone agonist to delay further pubertal progression. In patients with functioning ACC and surgical removal, clinical follow-up and hormonal marker examination for the secondary effects of excessive hormone secretion may be a useful option at least every 2 or 3 months after surgery.
Convex-Hull-Based Boundary Detection in Unattended Wireless Sensor Networks

Perimeter protection aims at identifying intrusions across the temporary base established by army in critical regions. Convex-hull algorithm is used to determine the boundary nodes among a set of nodes in the network. To study the effectiveness of such algorithm, we opted three variations, such as distributed approach, centralized, and mobile approach, suitable for wireless sensor networks for boundary detection. The convex-hull approaches are simulated with different node density, and the performance is measured in terms of energy consumption, boundary detection time, and accuracy. Results from the simulations highlight that the convex-hull approach is effective under densely deployed nodes in an environment. The different approaches of convex-hull algorithm are found to be suitable under different sensor network application scenarios.
Using representation learning and out-of-domain data for a paralinguistic speech task

In this work, we study the paralinguistic speech task of eating condition classification and present our submitted classification system for the INTERSPEECH 2015 Computational Paralinguistics challenge. We build upon a deep learning language identification system, which we repurpose for general audio sequence classification. The main idea is that we train local convolutional neural network classifiers that automatically learn representations on smaller windows of the full sequence’s spectrum and to aggregate multiple local classifications towards a full sequence classification. A particular challenge of the task is training data scarcity and the resulting overfitting of neural network methods, which we tackle with dropout, synthetic data augmentation and transfer learning with out-of-domain data from a language identification task. Our final submitted system achieved an UAR score of 75.9% for 7-way eating condition classification, which is a relative improvement of 15% over the baseline.
Brain-inspired computing exploiting carbon nanotube FETs and resistive RAM: Hyperdimensional computing case study

We demonstrate an end-to-end brain-inspired hyperdimensional (HD) computing nanosystem, effective for cognitive tasks such as language recognition, using heterogeneous integration of multiple emerging nanotechnologies. It uses monolithic 3D integration of carbon nanotube field-effect transistors (CNFETs, an emerging logic technology with significant energy-delay product (EDP) benefit vs. silicon CMOS ) and Resistive RAM (RRAM, an emerging memory that promises dense non-volatile and analog storage ). Due to their low fabrication temperature (<250°C), CNFETs and RRAM naturally enable monolithic 3D integration with fine-grained and dense vertical connections (exceeding various chip stacking and packaging approaches) between computation and storage layers using back-end-of-line inter-layer vias . We exploit RRAM and CNFETs to create area-and energy-efficient circuits for HD computing: approximate accumulation circuits using gradual RRAM reset operation (in addition to RRAM single-bit storage) and random projection circuits that embrace inherent variations in RRAM and CNFETs. Our results demonstrate: 1. pairwise classification of 21 European languages with measured accuracy of up to 98% on >20,000 sentences (6.4 million characters) per language pair. 2. One-shot learning (i.e., learning from few examples) using one text sample (∼100,000 characters) per language. 3. Resilient operation (98% accuracy) despite 78% hardware errors (circuit outputs stuck at 0 or 1). Our HD nanosystem consists of 1,952 CNFETs integrated with 224 RRAM cells.
Point Cloud Segmentation to Approximately Convex Surfaces for Fruit Recognition

A fruit recognition approach based on segmenting the point cloud acquired by a 3D camera into approximately convex surfaces is considered. A segmentation approach which transforms a depth image into a triangular mesh and then segments this mesh into approximately convex segments is applied to depth images of fruits on trees. An analysis of the results obtained by this approach is performed with the intention to determine how successful the studied method is in detecting fruit as separate objects in a point cloud. The reported analysis gives a valuable insight into the potential applicability of the tested methodology in the preprocessing stage of a fruit recognition system as well as its drawbacks. fruit recognition;3D camera; convex sets; segmentation
A Review of Gaucher Disease Pathophysiology, Clinical Presentation and Treatments

Gaucher disease (GD, ORPHA355) is a rare, autosomal recessive genetic disorder. It is caused by a deficiency of the lysosomal enzyme, glucocerebrosidase, which leads to an accumulation of its substrate, glucosylceramide, in macrophages. In the general population, its incidence is approximately 1/40,000 to 1/60,000 births, rising to 1/800 in Ashkenazi Jews. The main cause of the cytopenia, splenomegaly, hepatomegaly, and bone lesions associated with the disease is considered to be the infiltration of the bone marrow, spleen, and liver by Gaucher cells. Type-1 Gaucher disease, which affects the majority of patients (90% in Europe and USA, but less in other regions), is characterized by effects on the viscera, whereas types 2 and 3 are also associated with neurological impairment, either severe in type 2 or variable in type 3. A diagnosis of GD can be confirmed by demonstrating the deficiency of acid glucocerebrosidase activity in leukocytes. Mutations in the GBA1 gene should be identified as they may be of prognostic value in some cases. Patients with type-1 GD-but also carriers of GBA1 mutation-have been found to be predisposed to developing Parkinson's disease, and the risk of neoplasia associated with the disease is still subject to discussion. Disease-specific treatment consists of intravenous enzyme replacement therapy (ERT) using one of the currently available molecules (imiglucerase, velaglucerase, or taliglucerase). Orally administered inhibitors of glucosylceramide biosynthesis can also be used (miglustat or eliglustat).
What guidance is available for researchers conducting overviews of reviews of healthcare interventions? A scoping review and qualitative metasummary

Overviews of reviews (overviews) compile data from multiple systematic reviews to provide a single synthesis of relevant evidence for decision-making. Despite their increasing popularity, there is limited methodological guidance available for researchers wishing to conduct overviews. The objective of this scoping review is to identify and collate all published and unpublished documents containing guidance for conducting overviews examining the efficacy, effectiveness, and/or safety of healthcare interventions. Our aims were to provide a map of existing guidance documents; identify similarities, differences, and gaps in the guidance contained within these documents; and identify common challenges involved in conducting overviews.



We conducted an iterative and extensive search to ensure breadth and comprehensiveness of coverage. The search involved reference tracking, database and web searches (MEDLINE, EMBASE, DARE, Scopus, Cochrane Methods Studies Database, Google Scholar), handsearching of websites and conference proceedings, and contacting overview producers. Relevant guidance statements and challenges encountered were extracted, edited, grouped, abstracted, and presented using a qualitative metasummary approach.



We identified 52 guidance documents produced by 19 research groups. Relatively consistent guidance was available for the first stages of the overview process (deciding when and why to conduct an overview, specifying the scope, and searching for and including systematic reviews). In contrast, there was limited or conflicting guidance for the latter stages of the overview process (quality assessment of systematic reviews and their primary studies, collecting and analyzing data, and assessing quality of evidence), and many of the challenges identified were also related to these stages. An additional, overarching challenge identified was that overviews are limited by the methods, reporting, and coverage of their included systematic reviews.



This compilation of methodological guidance for conducting overviews of healthcare interventions will facilitate the production of future overviews and can help authors address key challenges they are likely to encounter. The results of this project have been used to identify areas where future methodological research is required to generate empirical evidence for overview methods. Additionally, these results have been used to update the chapter on overviews in the next edition of the Cochrane Handbook for Systematic Reviews of Interventions.