nils-herrmann/clean_scirepeval huggingface dataset:
The sulfur-containing amino acids: an overview.

Methionine, cysteine, homocysteine, and taurine are the 4 common sulfur-containing amino acids, but only the first 2 are incorporated into proteins. Sulfur belongs to the same group in the periodic table as oxygen but is much less electronegative. This difference accounts for some of the distinctive properties of the sulfur-containing amino acids. Methionine is the initiating amino acid in the synthesis of virtually all eukaryotic proteins; N-formylmethionine serves the same function in prokaryotes. Within proteins, many of the methionine residues are buried in the hydrophobic core, but some, which are exposed, are susceptible to oxidative damage. Cysteine, by virtue of its ability to form disulfide bonds, plays a crucial role in protein structure and in protein-folding pathways. Methionine metabolism begins with its activation to S-adenosylmethionine. This is a cofactor of extraordinary versatility, playing roles in methyl group transfer, 5'-deoxyadenosyl group transfer, polyamine synthesis, ethylene synthesis in plants, and many others. In animals, the great bulk of S-adenosylmethionine is used in methylation reactions. S-Adenosylhomocysteine, which is a product of these methyltransferases, gives rise to homocysteine. Homocysteine may be remethylated to methionine or converted to cysteine by the transsulfuration pathway. Methionine may also be metabolized by a transamination pathway. This pathway, which is significant only at high methionine concentrations, produces a number of toxic endproducts. Cysteine may be converted to such important products as glutathione and taurine. Taurine is present in many tissues at higher concentrations than any of the other amino acids. It is an essential nutrient for cats.
Collaborative and Adversarial Network for Unsupervised Domain Adaptation

In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain-adversarial training of neural networks. We add several domain classifiers on multiple CNN feature extraction blocks1, in which each domain classifier is connected to the hidden representations from one block and one loss function is defined based on the hidden presentation and the domain labels (e.g., source and target). We design a new loss function by integrating the losses from all blocks in order to learn domain informative representations from lower blocks through collaborative learning and learn domain uninformative representations from higher blocks through adversarial learning. We further extend our CAN method as Incremental CAN (iCAN), in which we iteratively select a set of pseudo-labelled target samples based on the image classifier and the last domain classifier from the previous training epoch and re-train our CAN model by using the enlarged training set. Comprehensive experiments on two benchmark datasets Office and ImageCLEF-DA clearly demonstrate the effectiveness of our newly proposed approaches CAN and iCAN for unsupervised domain adaptation.
Cooperative Denoising for Distantly Supervised Relation Extraction

Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts. However, it suffers from noisy labeling problem, which can degrade its performance. Meanwhile, the useful information expressed in knowledge graph is still underutilized in the state-of-the-art methods for distantly supervised relation extraction. In the light of these challenges, we propose CORD, a novel COopeRative Denoising framework, which consists two base networks leveraging text corpus and knowledge graph respectively, and a cooperative module involving their mutual learning by the adaptive bi-directional knowledge distillation and dynamic ensemble with noisy-varying instances. Experimental results on a real-world dataset demonstrate that the proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods.
Culture Matters: Factors Affecting the Adoption of Telemedicine

The present research investigates the influence of culture on telemedicine adoption and patient information privacy, security, and policy. The results, based on the SEM analysis of the data collected in the United States, demonstrate that culture plays a significant role in telemedicine adoption. The results further show that culture also indirectly influences telemedicine adoption through information security, information privacy, and information policy. Our empirical results further indicate that information security, privacy, and policy impact telemedicine adoption.
Electronic document management: A paperless university model

Currently Computer based systems are used by most of the universities around the world however, these systems are still paper based which involve physical paper existence. At present most of the universities are suffering obstacles in document management due to using paper based or semi automated systems. The objective of this paper is to present a paperless model for the university management system. A survey is conducted that enlisted some fundamental characteristics required to implement successful paperless environment. It was noted that simply converting paper-based activities to digital ones will not achieve a system without paper. Instead it is required to address complete model and its influencing factors at once. At the last we present a case study that reveals that tools and technologies are available for implementing paperless system but only there interweaving is required in a systematic manner.
Factorised Spatial Representation Learning: Application in Semi-supervised Myocardial Segmentation

The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method’s utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI. Code will be made available at https://github.com/agis85/spatial_factorisation.
A Survey on Reliability Protocols in Wireless Sensor Networks

Wireless Sensor Network (WSN) applications have become more and more attractive with the miniaturization of circuits and the large variety of sensors. The different application domains, especially critical fields of WSN use, make the reliability of data acquisition and communication a hot research field that must be tackled efficiently. Indeed, the quality of largely used, cheap-cost wireless sensors and their scarce energy supply support these reliability challenges that lead to data loss or corruption. For solving this problem, the conception of a reliability mechanism that detects these shortcomings and recovers to them becomes necessary. In this article, we present a survey on existing reliability protocols conceived especially for WSNs due to their special features. The deep classification and discussion in this study allow for understanding the pros and cons of state-of-the-art works in order to enhance the existing schemes and fill the gaps. We have classified the works according to the required level of reliability, the manner to identify the origins of the lack of reliability, and the control to recover this lack of reliability. Across the discussion along this study, we deduce that the cross-layer design between MAC, routing, and transport layers presents a good concept to efficiently overcome the different reliability holes.
Automatic recognition of serial numbers in bank notes

This paper presents a new topic of automatic recognition of bank note serial numbers, which will not only facilitate the prevention of forgery crimes, but also have a positive impact on the economy. Among all the different currencies, we focus on the study of RMB (renminbi bank note, the paper currency used in China) serial numbers. For evaluation, a new database NUST-RMB2013 has been collected from scanned RMB images, which contains the serial numbers of 35 categories with 17,262 training samples and 7000 testing samples in total. We comprehensively implement and compare two classic and one newly merged feature extraction methods (namely gradient direction feature, Gabor feature, and CNN trainable feature), four different types of well-known classifiers (SVM, LDF, MQDF, and CNN), and five multiple classifier combination strategies (including a specially designed novel cascade method). To further improve the recognition accuracy, the enhancements of three different kinds of distortions have been tested. Since high reliability is more important than accuracy in financial applications, we introduce three rejection schemes of first rank measurement (FRM), first two ranks measurement (FTRM) and linear discriminant analysis based measurement (LDAM). All the classifiers and classifier combination schemes are combined with different rejection criteria. A novel cascade rejection measurement achieves 100% reliability with less rejection rate compared with the existing methods. Experimental results show that MQDF reaches the accuracy of 99.59% using the gradient direction feature trained with gray level normalized data; the cascade classifier combination achieves the best performance of 99.67%. The distortions have been proved to be very helpful because the performances of CNNs boost at least 0.5% by training with transformed samples. With the cascade rejection method, 100% reliability has been obtained by rejecting 1.01% test samples. & 2014 Elsevier Ltd. All rights reserved.
Secure and trusted telemedicine in Internet of Things IoT

Internet of Things (IoT) is a revolutionary technology for the modern society. IoT can connect every surrounding objects for various applications like security, medical fields, monitoring and other industrial applications. This paper considers the application of IoT in the field of medicine. IoT in E-medicine can take the advantage of emerging technologies to provide immediate treatment to the patient as well as monitors and keeps track of health record for healthy person. IoT then performs complex computations on these collected data and can provide health related advice. Though IoT can provide a cost effective medical services to any people of all age groups, there are several key issues that need to be addressed. System security, IoT interoperability, dynamic storage facility and unified access mechanisms are some of the many fundamental issues associated with IoT. This paper proposes a system level design solution for security and flexibility aspect of IoT. In this paper, the functional components are bound in security function group which ensures the management of privacy and secure operation of the system. The security function group comprises of components which offers secure communication using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Since CP-ABE are delegated to unconstrained devices with the assumption that these devices are trusted, the producer encrypts data using AES and the ABE scheme is protected through symmetric key solutions.
Paxos Made Switch-y

The Paxos protocol is the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be gained by implementing Paxos logic in network devices. Until recently, the notion of a switch-based implementation of Paxos would be a daydream. However, new flexible hardware is on the horizon that will provide customizable packet processing pipelines needed to implement Paxos. While this new hardware is still not readily available, several vendors and consortia have made the programming languages that target these devices public. This paper describes an implementation of Paxos in one of those languages, P4. Implementing Paxos provides a critical use case for P4, and will help drive the requirements for data plane languages in general. In the long term, we imagine that consensus could someday be offered as a network service, just as point-to-point communication is provided today.
Au th or ' s pe rs on al co py Evidence for publication bias in video game violence effects literature : A meta-analytic review

Violence in video games has come under increasing research attention over the past decade. Researchers in this area have suggested that violent video games may cause aggressive behavior among players. However, the state of the extant literature has not yet been examined for publication bias. The current meta-analysis is designed to correct for this oversight. Results indicated that publication bias does exist for experimental studies of aggressive behavior, as well as for non-experimental studies of aggressive behavior and aggressive thoughts. Research in other areas, including prosocial behavior and experimental studies of aggressive thoughts were less susceptible to publication bias. Moderator effects results also suggested that studies employing less standardized and reliable measures of aggression tended to produce larger effect sizes. Suggestions for future violent video game studies are provided.
The Hidden Geometry of Deformed Grids

or, why space syntax works when it looks as though it shouldn't 0  A common objection to the space syntax analysis of cities is that even in its own terms the technique of using a non-uniform line representation of space and analysing it by measures that are essentially topological, ignores too much geometric and metric detail to be credible. In this paper it is argued that far from ignoring geometric and metric properties the 'line-graph' internalises them into the structure of the graph and in doing so allows the graph analysis to pick up the nonlocal, or extrinsic, properties of spaces that are critical to the movement dynamics through which a city evolves its essential structures. Nonlocal properties are those which are defined by the relation of elements to all others in the system, rather than intrinsic to the element itself. The method also leads to a powerful analysis of urban structures because cities are essentially nonlocal systems. 1 Preliminaries 1.1 The critique of line graphs Space syntax is a family of techniques for representing and analysing spatial layouts of all kinds. A spatial representation is first chosen according to how space is defined for the purposes of the research-rooms, convex spaces, lines, convex isovists, and so on-and then one or more measures of 'configuration' are selected to analyse the patterns formed by that representation. Prior to the researcher setting up the research question, no one representation or measure is privileged over others. Part of the researcher's task is to discover which representation and which measure captures the logic of a particular system, as shown by observation of its functioning. In the study of cities, one representation and one type of measure has proved more consistently fruitful than others: the representation of urban space as a matrix of the 'longest and fewest' lines, the 'axial map', and the analysis of this by translating the line matrix into a graph, and the use of the various versions of the 'topological' (i.e. nonmetric) measure of patterns of line connectivity called 'integration'.  This 'line graph' approach has proved quite unexpectedly successful. It has generated not only models for predicting urban et al 1998), but also strong theoretical results on urban structure, and even a general theory of the dynamics linking the urban grid, movement, land uses and building densities in 'organic' cities …
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

Deep generative models have been successfully applied to many applications. However, existing works experience limitations when generating large images (the literature usually generates small images, e.g. 32× 32 or 128× 128). In this paper, we propose a novel scheme, called deep tensor adversarial generative nets (TGAN), that generates large high-quality images by exploring tensor structures. Essentially, the adversarial process of TGAN takes place in a tensor space. First, we impose tensor structures for concise image representation, which is superior in capturing the pixel proximity information and the spatial patterns of elementary objects in images, over the vectorization preprocess in existing works. Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions. More specifically, we design a tensor super-resolution process that consists of tensor dictionary learning and tensor coefficients learning. Finally, on three datasets, the proposed TGAN generates images with more realistic textures, compared with state-of-the-art adversarial autoencoders. The size of the generated images is increased by over 8.5 times, namely 374× 374 in PASCAL2.
MODELING AND ANALYSIS OF HYSTERESIS IN HARMONIC DRIVE GEARS

In this article, a mathematical model and its parameter identification scheme are proposed for harmonic drive gears with compliance and hysteresis. The hysteresis phenomenon in harmonic drives is described by a nonlinear differential equation representing the torque–displacement relationship across the flexpline of the harmonic drive. The representation is equivalent to having the combination of nonlinear stiffness and nonlinear viscous damping. Numerical simulations along with experimental data have been used to validate the proposed modeling concept.
CASTLE: fast concurrent internal binary search tree using edge-based locking

We present a new lock-based algorithm for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations. Some of the desirable characteristics of our algorithm are: (i) a search operation uses only read and write instructions, (ii) an insert operation does not acquire any locks, and (iii) a delete operation only needs to lock up to four edges in the absence of contention. Our algorithm is based on an internal representation of a search tree and it operates at edge-level (locks edges) rather than at node-level (locks nodes); this minimizes the contention window of a write operation and improves the system throughput. Our experiments indicate that our lock-based algorithm outperforms existing algorithms for a concurrent binary search tree for medium-sized and larger trees, achieving up to 59% higher throughput than the next best algorithm.
3D detection of automobile scattering centers using UWB radar sensors at 24/77 GHz

Scattering centers of two different vehicles have been evaluated using SAR, and the combination of SAR and DBF. The results show that main scattering centers are located at the wheels, the regions around the license plate, door outer panel, windshield pillar, center pillar, rear pillar, and light units. Furthermore, it has been shown that multipath propagation enables an indirect detection of scattering centers. The analysis shows no significant differences between the scattering center locations at 24 GHz and 77 GHz. However, the increased bandwidth at 77 GHz allows resolving merged scattering centers and improves the contour determination. The comparison between the measurement results of the SAR processing and the mechanical scanning radar confirmed similar main scattering centers. Furthermore, the measurement results show that the contour and orientation of a vehicle can be determined at a distance of 10 m with a bandwidth of 2 GHz and an angular resolution of approximately 2°.
Significant aspects of client-centered therapy.

Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this significant aspects of client centered therapy. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.
Fundamentals of Breast Tomosynthesis Improving the Performance of Mammography

Theory of Tomosynthesis Conventional x-ray mammography is a two-dimensional imaging modality. In conventional mammography, pathologies of interest are sometimes difficult to visualize because of the clutter of signals from objects above and below. This is because the signal detected at a location on the film cassette or digital detector is dependent upon the total attenuation of all the tissues above the location. Tomosynthesis1,2,3,7,8 is a three-dimensional method of imaging that can reduce or eliminate the tissue overlap effect. While stabilizing the breast, images are acquired at a number of different x-ray source angles. Objects at different heights in the breast display differently in the different projections. In Figure 1, two objects (a spiculated lesion and ellipse) superimpose when the x-rays are at 0o, but the off-axis acquisitions shift the objects’ shadows relative to one another in the images. The final step in the tomosynthesis procedure is reconstructing the data to generate images that enhance objects from a given height by appropriate shifting of the projections relative to one another. An example is shown in Figure 2 where we reconstruct a cross sectional slice at one specific height. In this example, the images are summed, shifting one relative to another in a specific way that reinforces the spiculated lesion object and reduces the contrast of the ellipsoidal object by blurring it out. Note that additional acquisitions are not required to enhance the visibility of objects at any given height—one set of acquired data can be reprocessed to generate the entire 3D volume set.
Functional health literacy and the quality of physician-patient communication among diabetes patients.

While patients with poor functional health literacy (FHL) have difficulties reading and comprehending written medical instructions, it is not known whether these patients also experience problems with other modes of communication, such as face-to-face encounters with primary care physicians. We enrolled 408 English- and Spanish-speaking diabetes patients to examine whether patients with inadequate FHL report worse communication than patients with adequate FHL. We assessed patients' experiences of communication using sub-scales from the Interpersonal Processes of Care in Diverse Populations instrument. In multivariate models, patients with inadequate FHL, compared to patients with adequate FHL, were more likely to report worse communication in the domains of general clarity (adjusted odds ratio [AOR] 6.29, P<0.01), explanation of condition (AOR 4.85, P=0.03), and explanation of processes of care (AOR 2.70, p=0.03). Poor FHL appears to be a marker for oral communication problems, particularly in the technical, explanatory domains of clinician-patient dialogue. Research is needed to identify strategies to improve communication for this group of patients.