nils-herrmann/clean_scirepeval huggingface dataset:
Metabolic signals and innate immune activation in obesity and exercise.

The combination of a sedentary lifestyle and excess energy intake has led to an increased prevalence of obesity which constitutes a major risk factor for several co-morbidities including type 2 diabetes and cardiovascular diseases. Intensive research during the last two decades has revealed that a characteristic feature of obesity linking it to insulin resistance is the presence of chronic low-grade inflammation being indicative of activation of the innate immune system. Recent evidence suggests that activation of the innate immune system in the course of obesity is mediated by metabolic signals, such as free fatty acids (FFAs), being elevated in many obese subjects, through activation of pattern recognition receptors thereby leading to stimulation of critical inflammatory signaling cascades, like IκBα kinase/nuclear factor-κB (IKK/NF- κB), endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) and NOD-like receptor P3 (NLRP3) inflammasome pathway, that interfere with insulin signaling. Exercise is one of the main prescribed interventions in obesity management improving insulin sensitivity and reducing obesity- induced chronic inflammation. This review summarizes current knowledge of the cellular recognition mechanisms for FFAs, the inflammatory signaling pathways triggered by excess FFAs in obesity and the counteractive effects of both acute and chronic exercise on obesity-induced activation of inflammatory signaling pathways. A deeper understanding of the effects of exercise on inflammatory signaling pathways in obesity is useful to optimize preventive and therapeutic strategies to combat the increasing incidence of obesity and its comorbidities.
Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.
Genetic variability of DNA repair mechanisms influences chemotherapy outcome of gastric cancer.

Genetic variability of DNA repair mechanisms influences chemotherapy treatment outcome of gastric cancer. We conducted a cohort study to investigate the role of ERCC1-ERCC2 gene polymorphisms in the chemotherapy response and clinic outcome of gastric cancer. Between March 2011 and March 2013, 228 gastric patients who were newly diagnosed with histopathology were enrolled in our study. Genotypes of ERCC1 rs11615, rs3212986, rs2298881 and ERCC2 rs3212986 were conducted by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) assay. We found that individuals carrying TT genotype of ERCC1 rs11615 and CC genotype of ERCC1 rs2298881 were associated with better response to chemotherapy and longer survival time of gastric cancer. Moreover, individuals with AA genotype of ERCC2 rs1799793 were correlated with shorter survival of gastric cancer. In conclusion, ERCC1 rs11615, rs2298881 and ERCC2 rs1799793 polymorphism play an important role in the treatment outcome of gastric cancer.
Can a Machine Think ? The World of Mathematics Universal Turing Machine

The refinement of checksums is an essential grand challenge. Given the current status of lossless information, theorists clearly desire the refinement of the locationidentity split, which embodies the essential principles of operating systems. Our focus in this paper is not on whether IPv4 can be made relational, constant-time, and decentralized, but rather on proposing new linear-time symmetries (YEW).
Lymphangiogenesis and cancer metastasis

Metastasis is a characteristic trait of most tumour types and the cause for the majority of cancer deaths. Many tumour types, including melanoma and breast and prostate cancers, first metastasize via lymphatic vessels to their regional lymph nodes. Although the connection between lymph node metastases and shorter survival times of patients was made decades ago, the active involvement of the lymphatic system in cancer, metastasis has been unravelled only recently, after molecular markers of lymphatic vessels were identified. A growing body of evidence indicates that tumour-induced lymphangiogenesis is a predictive indicator of metastasis to lymph nodes and might also be a target for prevention of metastasis. This article reviews the current understanding of lymphangiogenesis in cancer anti-lymphangiogenic strategies for prevention and therapy of metastatic disease, quantification of lymphangiogenesis for the prognosis and diagnosis of metastasis and in vivo imaging technologies for the assessment of lymphatic vessels, drainage and lymph nodes.
A robust model for on-line handwritten japanese text recognition

This paper describes a robust context integration model for on-line handwritten Japanese text recognition. Based on string class probability approximation, the proposed method evaluates the likelihood of candidate segmentation–recognition paths by combining the scores of character recognition, unary and binary geometric features, as well as linguistic context. The path evaluation criterion can flexibly combine the scores of various contexts and is insensitive to the variability in path length, and so, the optimal segmentation path with its string class can be effectively found by Viterbi search. Moreover, the model parameters are estimated by the genetic algorithm so as to optimize the holistic string recognition performance. In experiments on horizontal text lines extracted from the TUAT Kondate database, the proposed method achieves the segmentation rate of 0.9934 that corresponds to a f-measure and the character recognition rate of 92.80%.
A recommendation engine for travel products based on topic sequential patterns

Travel products recommendation has become one of emerging issues in the realm of recommendation systems. The widely-used collaborative filtering algorithms are usually difficult to be used for recommending travel products due to a number of reasons, including (1) the content of travel products is very complex, (2) the user-item matrix is extremely sparse, and (3) the cold-start users are widely existing. To tackle these issues, we try to exploit Web server logs for generating recommendation, and present a novel recommendation engine (SECT for short) for travel products based on topic sequential patterns. In detail, we first extract topics from semantic description of every Web page. Then, we mine topic frequent sequential patterns and their target products to form click patterns library. At last, we propose a Markov n-gram model for matching the real-time click-stream of users with the click patterns library and thus computing recommendation scores. Experimental results on a real-world travel dataset demonstrate that the SECT prevails over the state-of-art baseline algorithms. In particular, SECT shows merits in improving the both coverage and accuracy for recommending products to cold-start users. Also, SECT is effective to recommend long tail items and outperform baseline algorithms.
Double-gate SOI devices for low-power and high-performance applications

Double-gate (DG) transistors have emerged as promising devices for nano-scale circuits due to their better scalability compared to bulk CMOS. Among the various types of DG devices, quasi-planar SOI FinFETs are easier to manufacture compared to planar double-gate devices. DG devices with independent gates (separate contacts to back and front gates) have recently been developed. DG devices with symmetric and asymmetric gates have also been demonstrated. Such device options have direct implications at the circuit level. Independent control of front and back gate in DG devices can be effectively used to improve performance and reduce power in sub-50nm circuits. Independent gate control can be used to merge parallel transistors in noncritical paths. This results in reduction in the effective switching capacitance and hence power dissipation. We show a variety of circuits in logic and memory that can benefit from independent gate operation of DG devices. As examples, we show the benefit of independent gate operation in circuits such as dynamic logic circuits, Schmitt triggers, sense amplifiers, and SRAM cells. In addition to independent gate option, we also investigate the usefulness of asymmetric devices and the impact of width quantization and process variations on circuit design.
Pharmacologic Glycemic Management of Type 2 Diabetes in Adults.

• Healthy behaviour interventions should be initiated in people newly diagnosed with type 2 diabetes. • In people with type 2 diabetes with A1C <1.5% above the person’s individualized target, antihyperglycemic pharmacotherapy should be added if glycemic targets are not achieved within 3 months of initiating healthy behaviour interventions. • In people with type 2 diabetes with A1C ≥1.5% above target, antihyperglycemic agents should be initiated concomitantly with healthy behaviour interventions, and consideration could be given to initiating combination therapy with 2 agents. • Insulin should be initiated immediately in individuals with metabolic decompensation and/or symptomatic hyperglycemia. • In the absence of metabolic decompensation, metformin should be the initial agent of choice in people with newly diagnosed type 2 diabetes, unless contraindicated. • Dose adjustments and/or additional agents should be instituted to achieve target A1C within 3 to 6 months. Choice of second-line antihyperglycemic agents should be made based on individual patient characteristics, patient preferences, any contraindications to the drug, glucose-lowering efficacy, risk of hypoglycemia, affordability/access, effect on body weight and other factors. • In people with clinical cardiovascular (CV) disease in whom A1C targets are not achieved with existing pharmacotherapy, an antihyperglycemic agent with demonstrated CV outcome benefit should be added to antihyperglycemic therapy to reduce CV risk. • In people without clinical CV disease in whom A1C target is not achieved with current therapy, if affordability and access are not barriers, people with type 2 diabetes and their providers who are concerned about hypoglycemia and weight gain may prefer an incretin agent (DPP-4 inhibitor or GLP-1 receptor agonist) and/or an SGLT2 inhibitor to other agents as they improve glycemic control with a low risk of hypoglycemia and weight gain. • In people receiving an antihyperglycemic regimen containing insulin, in whom glycemic targets are not achieved, the addition of a GLP-1 receptor agonist, DPP-4 inhibitor or SGLT2 inhibitor may be considered before adding or intensifying prandial insulin therapy to improve glycemic control with less weight gain and comparable or lower hypoglycemia risk.
Large Scale Distributed Data Science using Apache Spark

Apache Spark is an open-source cluster computing framework for big data processing. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce's linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala (and shortly R), and its core data abstraction, the distributed data frame, and it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. This tutorial will provide an accessible introduction to Spark and its potential to revolutionize academic and commercial data science practices.
Efficient Generation of Object Hierarchies from 3D Scenes

This paper describes an efficient technique for com' puting a hierarchical representation of the objects contained in a complex 3 0 scene. First, an adjacency graph keeping the costs of grouping the different pairs of objects in the scene is built. Then the minimum spanning tree (MST) of that graph is determined. A binary clustering tree (BCT) is obtained from the MS'I: Finally, a merging stage joins the adjacent nodes in the BCT which have similar costs. The final result is an n-ary tree which defines an intuitive clustering of the objects of the scene at different levels of abstraction. Experimental results with synthetic 3 0 scenes are presented.
Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC

In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem. The optimization problem is solved by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-varying system constraints in a receding horizon fashion. Formulating the system dynamics linearly in combination with a quadratic cost function has two great advantages. First, the system constraints can be set up not only to achieve collision avoidance with both static and dynamic obstacles, but also aspects of human driving behavior can be considered. Second, the optimization problem can be solved very efficiently, such that the algorithm can be run with little computational effort. In addition, due to an elaborate problem formulation, reference curves with discontinuous, high curvatures will be effortlessly smoothed out by the algorithm. This makes the proposed algorithm applicable to different traffic scenarios, such as parking or highway driving. Experimental results are presented for different real-world scenarios to demonstrate the algorithm’s abilities.
Learning an Optimal Naive Bayes Classifier

The naive Bayes classifier is an efficient classification model that is easy to learn and has a high accuracy in many domains. However, it has two main drawbacks: (i) its classification accuracy decreases when the attributes are not independent, and (ii) it can not deal with nonparametric continuous attributes. In this work we propose a method that deals with both problems, and learns an optimal naive Bayes classifier. The method includes two phases, discretization and structural improvement, which are repeated alternately until the classification accuracy can not be improved. Discretization is based on the minimum description length principle. To deal with dependent and irrelevant attributes, we apply a structural improvement method that eliminates and/or joins attributes, based on mutual and conditional information measures. The method has been tested in two different domains with good results
Identification of lysocin E using a silkworm model of bacterial infection.

New antimicrobials with novel mechanisms need to be developed to combat antimicrobial-resistant pathogenic bacteria. The current authors recently reported discovery of a new antibiotic named "Lysocin E". Lysocin E was identified using a silkworm model of bacterial infection. The current review discusses the advantages of using a silkworm model of bacterial infection to identify and develop therapeutically efficacious antimicrobials. This review also discusses the discovery of lysocin E and its novel mechanism of action.
Attestation in Wireless Sensor Networks: A Survey

Attestation is a mechanism used by a trusted entity to validate the software integrity of an untrusted platform. Over the past few years, several attestation techniques have been proposed. While they all use variants of a challenge-response protocol, they make different assumptions about what an attacker can and cannot do. Thus, they propose intrinsically divergent validation approaches. We survey in this article the different approaches to attestation, focusing in particular on those aimed at Wireless Sensor Networks. We discuss the motivations, challenges, assumptions, and attacks of each approach. We then organise them into a taxonomy and discuss the state of the art, carefully analysing the advantages and disadvantages of each proposal. We also point towards the open research problems and give directions on how to address them.
Deep Light Source Estimation for Mixed Reality

Mixed reality is the union of virtual and real elements in a single scene. In this composition, of real and virtual elements, perceptual discrepancies in the illumination of objects may occur. We call these discrepancies the illumination mismatch problem. Recovering the lighting information from a real scene is a difficult task. Usually, such task requires prior knowledge of the scene, such as the scene geometry and special measuring equipment. We present a deep learning based technique that estimates point light source position from a single color image. The estimated light source position is used to create a composite image containing both the real and virtual environments. The proposed technique allows the final composite image to have consistent illumination between the real and virtual worlds, effectively reducing the effects of the illumination mismatch in Mixed Reality applications.
Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods

We present an approach to continuous American Sign Language (ASL) recognition, which uses as input three-dimensional data of arm motions. We use computer vision methods for three-dimensional object shape and motion parameter extraction and an Ascension Technologies Flock of Birds interchangeably to obtain accurate three-dimensional movement parameters of ASL sentences, selected from a 53-sign vocabulary and a widely varied sentence structure. These parameters are used as features for Hidden Markov Models (HMMs). To address coarticulation effects and improve our recognition results, we experimented with two different approaches. The first consists of training context-dependent HMMs and is inspired by speech recognition systems. The second consists of modeling transient movements between signs and is inspired by the characteristics of ASL phonology. Our experiments verified that the second approach yields better recognition results.
Enzymatic Amplification of P-Globin Genomic Sequences and Restriction Site Analysis for Diagnosis of Sickle Cell Anemia

Recent advances in recombinant DNA technology have made possible the molecular analysis and prenatal diagnosis of several human genetic diseases. Fetal DNA obtained by aminocentesis or chorionic villus sampling can be analyzed by restriction enzyme digestion, with subsequent electrophoresis, Southern transfer, and specific hybridization to cloned gene or oligonucleotide probes. With This disease results from homozygosity of the sickle-cell allele (rS) at the 3globin gene locus. The S allele differs from the wild-type allele (3A) by substitution of an A in the wild-type to a T at the second position of the sixth codon of the p chain gene, resulting in the replacement of a glutamic acid by a valine in the expressed protein. For the prenatal diagnosis of sickle cell anemia, DNA ob-
Aggravated fragmentation : A case study of SME behaviour in two emerging heritage tourism regions

This paper investigates the behaviour of small and medium sized enterprises (SMEs) within the heritage tourism supply chain (HTSC), in two emerging heritage regions. SMEs are conceptualised as implementers, working within the constraints of government level tourism structures and the heritage tourism supply chain. The research employs a case study approach, focusing on two emerging regions in Northern Ireland. In-depth interviews were carried out with small business owners and community associations operating within the regions. The research identifies SME dissatisfaction with the supply chain and the processes in place for the delivery of the tourism product. To overcome the perceived inadequacies of the heritage tourism supply chain SMEs engage in entrepreneurial behaviour by attempting to deliver specific products and services to meet the need of tourists. The challenge for tourism organisations is how they can integrate the entrepreneurial, innovative activities of SMEs into the heritage tourism system.
Mechanisms of Persistent Activity in Cortical Circuits: Possible Neural Substrates for Working Memory.

A commonly observed neural correlate of working memory is firing that persists after the triggering stimulus disappears. Substantial effort has been devoted to understanding the many potential mechanisms that may underlie memory-associated persistent activity. These rely either on the intrinsic properties of individual neurons or on the connectivity within neural circuits to maintain the persistent activity. Nevertheless, it remains unclear which mechanisms are at play in the many brain areas involved in working memory. Herein, we first summarize the palette of different mechanisms that can generate persistent activity. We then discuss recent work that asks which mechanisms underlie persistent activity in different brain areas. Finally, we discuss future studies that might tackle this question further. Our goal is to bridge between the communities of researchers who study either single-neuron biophysical, or neural circuit, mechanisms that can generate the persistent activity that underlies working memory.
Residential Demand Response Using Reinforcement Learning

We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from $16\%$ to $40\%$ with respect to a price-unaware energy allocation.