nils-herrmann/clean_scirepeval huggingface dataset:
Faecal microbiota transplantation: applications and limitations in treating gastrointestinal disorders

The process of stool transfer from healthy donors to the sick, known as faecal microbiota transplantation (FMT), has an ancient history. However, only recently researchers started investigating its applications in an evidence-based manner. Current knowledge of the microbiome, the concept of dysbiosis and results of preliminary research suggest that there is an association between gastrointestinal bacterial disruption and certain disorders. Researchers have studied the effects of FMT on various gastrointestinal and non-gastrointestinal diseases, but have been unable to precisely pinpoint specific bacterial strains responsible for the observed clinical improvement or futility of the process. The strongest available data support the efficacy of FMT in the treatment of recurrent Clostridium difficile infection with cure rates reported as high as 90% in clinical trials. The use of FMT in other conditions including inflammatory bowel disease, functional gastrointestinal disorders, obesity and metabolic syndrome is still controversial. Results from clinical studies are conflicting, which reflects the gap in our knowledge of the microbiome composition and function, and highlights the need for a more defined and personalised microbial isolation and transfer.
Patient-centered care: the key to cultural competence.

Much of the early literature on 'cultural competence' focuses on the 'categorical' or 'multicultural' approach, in which providers learn relevant attitudes, values, beliefs, and behaviors of certain cultural groups. In essence, this involves learning key 'dos and don'ts' for each group. Literature and educational materials of this kind focus on broad ethnic, racial, religious, or national groups, such as 'African American', 'Hispanic', or 'Asian'. The problem with this categorical or 'list of traits' approach to clinical cultural competence is that culture is multidimensional and dynamic. Culture comprises multiple variables, affecting all aspects of experience. Cultural processes frequently differ within the same ethnic or social group because of differences in age cohort, gender, political association, class, religion, ethnicity, and even personality. Culture is therefore a very elusive and nebulous concept, like art. The multicultural approach to cultural competence results in stereotypical thinking rather than clinical competence. A newer, cross cultural approach to culturally competent clinical practice focuses on foundational communication skills, awareness of cross-cutting cultural and social issues, and health beliefs that are present in all cultures. We can think of these as universal human beliefs, needs, and traits. This patient centered approach relies on identifying and negotiating different styles of communication, decision-making preferences, roles of family, sexual and gender issues, and issues of mistrust, prejudice, and racism, among other factors. In the current paper, we describe 'cultural' challenges that arise in the care of four patients from disparate cultures, each of whom has advanced colon cancer that is no longer responding to chemotherapy. We then illustrate how to apply principles of patient centered care to these challenges.
A Single-Phase Single-Stage High Step-Up AC–DC Matrix Converter Based on Cockcroft–Walton Voltage Multiplier With PFC

This paper proposes a high-performance transformerless single-stage high step-up ac-dc matrix converter based on Cockcroft-Walton (CW) voltage multiplier. Deploying a four-bidirectional-switch matrix converter between the ac source and CW circuit, the proposed converter provides high quality of line conditions, adjustable output voltage, and low output ripple. The matrix converter is operated with two independent frequencies. One of which is associated with power factor correction (PFC) control, and the other is used to set the output frequency of the matrix converter. Moreover, the relationship among the latter frequency, line frequency, and output ripple will be discussed. This paper adopts one-cycle control method to achieve PFC, and a commercial control IC associating with a preprogrammed complex programmable logic device is built as the system controller. The operation principle, control strategy, and design considerations of the proposed converter are all detailed in this paper. A 1.2-kV/500-W laboratory prototype of the proposed converter is built for test, measurement, and evaluation. At full-load condition, the measured power factor, the system efficiency, and the output ripple factor are 99.9%, 90.3%, and 0.3%, respectively. The experimental results demonstrate the high performance of the proposed converter and the validity for high step-up ac-dc applications.
Use of CAD/CAM technologies in pediatric dentistry

The first computer system helping with restorations – CEREC (initially Siemens, now Sirona) was implemented about 30 years ago. Many systems are already available to use both in the dental office and the technician’s laboratory. Now every type of ceramic material can be used in a restoration for almost all indications of aesthetic dentistry. The functional and aesthetic restorations for severely damaged primary and permanent children’s teeth require materials which must be biocompatible, mechanically durable during mastication and with unchanging colour. In the literature data there are evidences about Computer Aided Design/Computer Aided Manufacturing (CAD/CAM) technology usage in pediatric dentistry for dental restorations of extensive carious lesions, eroded and abraded teeth, primary teeth with absence of a permanent successor, dental dysplasia or dental trauma of hard tooth tissues.
GRAPH/Z: A Key-Value Store Based Scalable Graph Processing System

The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their The contributions of this paper are as follows: scalability. This work describes the design and implementation of a new graph processing system based on Bulk Synchronous Parallel model. Our system is built on top of ZHT, a scalable distributed key-value store, which benefits the graph processing in terms of scalability, performance and persistency. The experiment results imply excellent scalability.
Angular Embedding: A Robust Quadratic Criterion

Given the size and confidence of pairwise local orderings, angular embedding (AE) finds a global ordering with a near-global optimal eigensolution. As a quadratic criterion in the complex domain, AE is remarkably robust to outliers, unlike its real domain counterpart LS, the least squares embedding. Our comparative study of LS and AE reveals that AE's robustness is due not to the particular choice of the criterion, but to the choice of representation in the complex domain. When the embedding is encoded in the angular space, we not only have a nonconvex error function that delivers robustness, but also have a Hermitian graph Laplacian that completely determines the optimum and delivers efficiency. The high quality of embedding by AE in the presence of outliers can hardly be matched by LS, its corresponding L1 norm formulation, or their bounded versions. These results suggest that the key to overcoming outliers lies not with additionally imposing constraints on the embedding solution, but with adaptively penalizing inconsistency between measurements themselves. AE thus significantly advances statistical ranking methods by removing the impact of outliers directly without explicit inconsistency characterization, and advances spectral clustering methods by covering the entire size-confidence measurement space and providing an ordered cluster organization.
A Combined Wye-Delta Connection to Increase the Performance of Axial-Flux PM Machines With Concentrated Windings

In this paper, a combined wye-delta connection is introduced and compared with a conventional wye-connection of a concentrated winding. Because the combined wye-delta connection has a higher fundamental winding factor, the output torque is higher for the same current density when a sinusoidal current is imposed. As the combined wye-delta connection has only a minor influence on the losses in the machine, the efficiency of the machine is also increased. The combined wye-delta connection is illustrated in detail for an axial-flux permanent-magnet synchronous machine with a rated power of 4 kW at a fixed speed of 2500 r/min, using finite element computation and measurements on a prototype machine.
Molecular and cellular reorganization of neural circuits in the human lineage

To better understand the molecular and cellular differences in brain organization between human and nonhuman primates, we performed transcriptome sequencing of 16 regions of adult human, chimpanzee, and macaque brains. Integration with human single-cell transcriptomic data revealed global, regional, and cell-type–specific species expression differences in genes representing distinct functional categories. We validated and further characterized the human specificity of genes enriched in distinct cell types through histological and functional analyses, including rare subpallial-derived interneurons expressing dopamine biosynthesis genes enriched in the human striatum and absent in the nonhuman African ape neocortex. Our integrated analysis of the generated data revealed diverse molecular and cellular features of the phylogenetic reorganization of the human brain across multiple levels, with relevance for brain function and disease.
BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset

Bangla handwriting recognition is becoming a very important issue nowadays. It is potentially a very important task specially for Bangla speaking population of Bangladesh and West Bengal. By keeping that in our mind we are introducing a comprehensive Bangla handwritten character dataset named BanglaLekha-Isolated. This dataset contains Bangla handwritten numerals, basic characters and compound characters. This dataset was collected from multiple geographical location within Bangladesh and includes sample collected from a variety of aged groups. This dataset can also be used for other classification problems i.e: gender, age, district. This is the largest dataset on Bangla handwritten characters yet.
Jitter and shimmer measurements for speaker recognition

Jitter and shimmer are measures of the cycle-to-cycle variations of fundamental frequency and amplitude, respectively, which have been largely used for the description of pathological voice quality. Since they characterise some aspects concerning particular voices, it is a priori expected to find differences in the values of jitter and shimmer among speakers. In this paper, several types of jitter and shimmer measurements have been analysed. Experiments performed with the Switchboard-I conversational speech database show that jitter and shimmer measurements give excellent results in speaker verification as complementary features of spectral and prosodic parameters.
Molecular mechanisms of epithelial–mesenchymal transition

The transdifferentiation of epithelial cells into motile mesenchymal cells, a process known as epithelial–mesenchymal transition (EMT), is integral in development, wound healing and stem cell behaviour, and contributes pathologically to fibrosis and cancer progression. This switch in cell differentiation and behaviour is mediated by key transcription factors, including SNAIL, zinc-finger E-box-binding (ZEB) and basic helix–loop–helix transcription factors, the functions of which are finely regulated at the transcriptional, translational and post-translational levels. The reprogramming of gene expression during EMT, as well as non-transcriptional changes, are initiated and controlled by signalling pathways that respond to extracellular cues. Among these, transforming growth factor-β (TGFβ) family signalling has a predominant role; however, the convergence of signalling pathways is essential for EMT.
TTMF: A Triple Trustworthiness Measurement Frame for Knowledge Graphs

e Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and conicts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. e model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output condence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. e experimental results showed that compared with other models, our model achieved signicant and consistent improvements.
Determination of pesticides in fruit and fruit juices by chromatographic methods. An overview.

In order to combat a variety of pests, pesticides are widely used in fruits. Several extraction procedures (liquid extraction, single drop microextraction, microwave-assisted extraction, pressurized liquid extraction, supercritical fluid extraction, solid-phase extraction, solid-phase microextraction, matrix solid-phase dispersion, and stir bar sorptive extraction) have been reported to determine pesticide residues in fruits and fruit juices. The significant change in recent years is the introduction of the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) methods in these matrices analysis. A combination of techniques reported the use of new extraction methods and chromatography to provide better quantitative recoveries at low levels. The use of mass spectrometric detectors in combination with liquid and gas chromatography has played a vital role to solve many problems related to food safety. The main attention in this review is on the achievements that have been possible because of the progress in extraction methods and the latest advances and novelties in mass spectrometry, and how these progresses have influenced the best control of food, allowing for an increase in the food safety and quality standards.
Fast Scans on Key-Value Stores

(KVS) are becoming increasingly popular because they scale up and down elastically, sustain high throughputs for get/put workloads and have low latencies. KVS owe these advantages to their simplicity. This simplicity, however, comes at a cost: It is expensive to process complex, analytical queries on top of a KVS because today’s generation of KVS does not support an efficient way to scan the data. The problem is that there are conflicting goals when designing a KVS for analytical queries and for simple get/put workloads: Analytical queries require high locality and a compact representation of data whereas elastic get/put workloads require sparse indexes. This paper shows that it is possible to have it all, with reasonable compromises. We studied the KVS design space and built TellStore, a distributed KVS, that performs almost as well as state-of-the-art KVS for get/put workloads and orders of magnitude better for analytical and mixed workloads. This paper presents the results of comprehensive experiments with an extended version of the YCSB benchmark and a workload from the telecommunication industry.
The cytokine networks of adaptive immunity in fish.

Cytokines, produced at the site of entry of a pathogen, drive inflammatory signals that regulate the capacity of resident and newly arrived phagocytes to destroy the invading pathogen. They also regulate antigen presenting cells (APCs), and their migration to lymph nodes to initiate the adaptive immune response. When naive CD4+ T cells recognize a foreign antigen-derived peptide presented in the context of major histocompatibility complex class II on APCs, they undergo massive proliferation and differentiation into at least four different T-helper (Th) cell subsets (Th1, Th2, Th17, and induced T-regulatory (iTreg) cells in mammals. Each cell subset expresses a unique set of signature cytokines. The profile and magnitude of cytokines produced in response to invasion of a foreign organism or to other danger signals by activated CD4+ T cells themselves, and/or other cell types during the course of differentiation, define to a large extent whether subsequent immune responses will have beneficial or detrimental effects to the host. The major players of the cytokine network of adaptive immunity in fish are described in this review with a focus on the salmonid cytokine network. We highlight the molecular, and increasing cellular, evidence for the existence of T-helper cells in fish. Whether these cells will match exactly to the mammalian paradigm remains to be seen, but the early evidence suggests that there will be many similarities to known subsets. Alternative or additional Th populations may also exist in fish, perhaps influenced by the types of pathogen encountered by a particular species and/or fish group. These Th cells are crucial for eliciting disease resistance post-vaccination, and hopefully will help resolve some of the difficulties in producing efficacious vaccines to certain fish diseases.
Robustness of deep autoencoder in intrusion detection under adversarial contamination

The existing state-of-the-art in the field of intrusion detection systems (IDSs) generally involves some use of machine learning algorithms. However, the computer security community is growing increasingly aware that a sophisticated adversary could target the learning module of these IDSs in order to circumvent future detections. Consequently, going forward, robustness of machine-learning based IDSs against adversarial manipulation (i.e., poisoning) will be the key factor for the overall success of these systems in the real world. In our work, we focus on adaptive IDSs that use anomaly-based detection to identify malicious activities in an information system. To be able to evaluate the susceptibility of these IDSs to deliberate adversarial poisoning, we have developed a novel framework for their performance testing under adversarial contamination. We have also studied the viability of using deep autoencoders in the detection of anomalies in adaptive IDSs, as well as their overall robustness against adversarial poisoning. Our experimental results show that our proposed autoencoder-based IDS outperforms a generic PCA-based counterpart by more than 15% in terms of detection accuracy. The obtained results concerning the detection ability of the deep autoencoder IDS under adversarial contamination, compared to that of the PCA-based IDS, are also encouraging, with the deep autoencoder IDS maintaining a more stable detection in parallel to limiting the contamination of its training dataset to just bellow 2%.
The Relational Turn in Psychoanalysis : A Dyadic Systems View from Infant Research

Psychoanalysis is currently seeking an expanded theory of interaction. The “relational turn” in psychoanalysis is at the center of current efforts to articulate this expansion (see for example Aron, 1996; Mitchell, 1993, 1997, 2000; Harris, 1992, 1997; Ghent, 1989, 2002; Benjamin, 1988, 1990, 1995 among others). Within the many streams of thought contributing to this effort, we single out systems views as providing a critical dimension of this expansion. The “dyadic systems view” is our particular contribution to the relational turn in psychoanalysis.
Insights from Deploying See-Through Augmented Reality Signage in the Wild

Typically the key challenges with interactive digital signage are (1) interaction times are short (usually in the order of seconds), (2) interaction needs to be very easy to understand, and (3) interaction needs to provide a benefit that justifies the effort to engage. To tackle these challenges, we propose a see-through augmented reality application for digital signage that enables passersby to observe the area behind the display, augmented with useful data. We report on the development and deployment of our application in two public settings: a public library and a supermarket. Based on observations of 261 (library) and 661 (supermarket) passersby and 14 interviews, we provide early insights and implications for application designers. Our results show a significant increase in attention: the see-through signage was noticed by 46% of the people, compared to 14% with the non-see through version. Furthermore, findings indicate that to best benefit the passersby, the AR displays should clearly communicate their purpose.
Achieving k-Barrier Coverage in Hybrid Directional Sensor Networks

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection) where directional sensors (e.g., cameras) are becoming more popular than omni-directional scalar sensors (e.g., microphones). However, barrier coverage cannot be guaranteed after initial random deployment of sensors, especially for directional sensors with limited sensing angles. In this paper, we study how to efficiently use mobile sensors to achieve \(k\) -barrier coverage. In particular, two problems are studied under two scenarios. First, when only the stationary sensors have been deployed, what is the minimum number of mobile sensors required to form \(k\) -barrier coverage? Second, when both the stationary and mobile sensors have been pre-deployed, what is the maximum number of barriers that could be formed? To solve these problems, we introduce a novel concept of weighted barrier graph (WBG) and prove that determining the minimum number of mobile sensors required to form \(k\) -barrier coverage is related with finding \(k\) vertex-disjoint paths with the minimum total length on the WBG. With this observation, we propose an optimal solution and a greedy solution for each of the two problems. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithms.
On weight initialization in deep neural networks

A proper initialization of the weights in a neural network is critical to its convergence. Current insights into weight initialization come primarily from linear activation functions. In this paper, I develop a theory for weight initializations with non-linear activations. First, I derive a general weight initialization strategy for any neural network using activation functions differentiable at 0. Next, I derive the weight initialization strategy for the Rectified Linear Unit (RELU), and provide theoretical insights into why the Xavier initialization is a poor choice with RELU activations. My analysis provides a clear demonstration of the role of non-linearities in determining the proper weight initializations.