nils-herrmann/clean_scirepeval huggingface dataset:
The detection and biotransformation of guanabenz in horses: a preliminary report.

Guanabenz (2,6-dichlorobenzylidene-amino-guanidine) is a centrally acting antihypertensive drug whose mechanism of action is via alpha2 adrenoceptors or, more likely, imidazoline receptors. Guanabenz is marketed as an antihypertensive agent in human medicine (Wytensin tablets, Wyeth Pharmaceuticals). Guanabenz has reportedly been administered to racing horses and is classified by the Association of Racing Commissioners International as a class 3 foreign substance. As such, its identification in a postrace sample may result in significant sanctions against the trainer of the horse. The present study examined liquid chromatographic/tandem quadrupole mass spectrometric (LC-MS/MS) detection of guanabenz in serum samples from horses treated with guanabenz by rapid i.v. injection at 0.04 and 0.2 mg/kg. Using a method adapted from previous work with clenbuterol, the parent compound was detected in serum with an apparent limit of detection of approximately 0.03 ng/ml and the limit of quantitation was 0.2 ng/ml. Serum concentrations of guanabenz peaked at approximately 100 ng/ml after the 0.2 mg/kg dose, and the parent compound was detected for up to 8 hours after the 0.04 mg/kg dose. Urine samples tested after administration of guanabenz at these dosages yielded evidence of at least one glucuronide metabolite, with the glucuronide ring apparently linked to a ring hydroxyl group or a guanidinium hydroxylamine. The LC-MS/MS results presented here form the basis of a confirmatory test for guanabenz in racing horses.
Stability of Routine Biochemical Analytes in Whole Blood and Plasma From Lithium Heparin Gel Tubes During 6-hr Storage.

The stability of biochemical analytes has already been investigated, but results strongly differ depending on parameters, methodologies, and sample storage times. We investigated the stability for many biochemical parameters after different storage times of both whole blood and plasma, in order to define acceptable pre- and postcentrifugation delays in hospital laboratories.



Twenty-four analytes were measured (Modular® Roche analyzer) in plasma obtained from blood collected into lithium heparin gel tubes, after 2-6 hr of storage at room temperature either before (n = 28: stability in whole blood) or after (n = 21: stability in plasma) centrifugation. Variations in concentrations were expressed as mean bias from baseline, using the analytical change limit (ACL%) or the reference change value (RCV%) as acceptance limit.



In tubes stored before centrifugation, mean plasma concentrations significantly decreased after 3 hr for phosphorus (-6.1% [95% CI: -7.4 to -4.7%]; ACL 4.62%) and lactate dehydrogenase (LDH; -5.7% [95% CI: -7.4 to -4.1%]; ACL 5.17%), and slightly decreased after 6 hr for potassium (-2.9% [95% CI: -5.3 to -0.5%]; ACL 4.13%). In plasma stored after centrifugation, mean concentrations decreased after 6 hr for bicarbonates (-19.7% [95% CI: -22.9 to -16.5%]; ACL 15.4%), and moderately increased after 4 hr for LDH (+6.0% [95% CI: +4.3 to +7.6%]; ACL 5.17%). Based on RCV, all the analytes can be considered stable up to 6 hr, whether before or after centrifugation.



This study proposes acceptable delays for most biochemical tests on lithium heparin gel tubes arriving at the laboratory or needing to be reanalyzed.
Biochemical and Physiological Mechanisms Related to Vernalization , Atonik and Benzyl Adenine in Pisum sativum

Vernalization at 5°C for 5 days, either singly or in combination with a foliar application of atonik at 250, 500 and 1000 mg/l or 6-benzyl adenine (BA) at 25, 50 and 100 mg/l was studied on the growth parameters and flowering response, photosynthetic pigments, different carbohydrate and nitrogen fractions, ion contents and endogenous level of different phytohormones of Pisum sativum (cv. ‘Master Bean’). All determined growth parameters (root and shoot length, fresh weight and dry weight; number of nodes/plant; number of leaves/plant; total leaf area/plant; relative water content; number of flowers/plant) decreased in response to vernalization treatment. In contrast, vernalization in combination with 1000 mg/l atonik or 50 mg/l BA led to a significant increase in these parameters. Vernalization, alone or in combination with atonik or BA, significantly increased all photosynthetic pigments and generally led to a significant increase in different carbohydrate and nitrogen fractions and ion content. On the other hand, vernalization led to a significant decrease in total auxins, gibberellic acid (GA3) and different CK fractions (zeatin, kinetin and BA) in pea plant shoots; ABA increased significantly. In contrast, vernalization combined with atonik or BA at any concentration led to a progressive increase in total auxins, GA3 and different CK fractions while ABA decreased significantly compared with control values. _____________________________________________________________________________________________________________
Elevator Acceleration Sensing: Design and Estimation Recognition Algorithm Using Crowdsourcing

In human activity sensing areas, we focus on elevator recognition using accelerometer. In this paper, our main challenge is to create an elevator acceleration recognition algorithm. In order to put this algorithm into real world use, This algorithm should be robust enough that can ignore the differences of the elevators, devices and subjects. To realize this aim, we build a crowdsourcing system which consists of an android application and a server. Android users can use this application to calculate elevator's moving distance. They can also upload elevators' accelerometer signals to the server, so we can get elevator acceleration data from all over the world. after publishing the system, we use the data we collected to build an elevator acceleration database. By using this database, we build a robust elevator recognition algorithm. we also use the features of elevators to distinguish them.
Consensus in Asynchronous Distributed Systems: A Concise Guided Tour

It is now recognized that the Consensus problem is a fundamental problem when one has to design and implement reliable asynchronous distributed systems. This chapter is on the Consensus problem. It studies Consensus in two failure models, namely, the Crash/no Recovery model and the Crash/Recovery model. The assumptions related to the detection of failures that are required to solve Consensus in a given model are particularly emphasized.
Fuzzy Prototypes: From a Cognitive View to a Machine Learning Principle

Cognitive psychology works have shown that the cognitive representation of categories is based on a typicality notion: all objects of a category do not have the same representativeness, some are more characteristic or more typical than others, and better exemplify their category. Categories are then defined in terms of prototypes, i.e. in terms of their most typical elements. Furthermore, these works showed that an object is all the more typical of its category as it shares many features with the other members of the category and few features with the members of other categories. In this paper, we propose to profit from these principles in a machine learning framework: a formalization of the previous cognitive notions is presented, leading to a prototype building method that makes it possible to characterize data sets taking into account both common and discriminative features. Algorithms exploiting these prototypes to perform tasks such as classification or clustering are then presented. The formalization is based on the computation of typicality degrees that measure the representativeness of each data point. These typicality degrees are then exploited to define fuzzy prototypes: in adequacy with human-like description of categories, we consider a prototype as an intrinsically imprecise notion. The fuzzy logic framework makes it possible to model sets with unsharp boundaries or vague and approximate concepts, and appears most appropriate to model prototypes. We then exploit the computed typicality degrees and the built fuzzy prototypes to perform machine learning tasks such as classification and clustering. We present several algorithms, justifying in each case the chosen parameters. We illustrate the results obtained on several data sets corresponding both to crisp and fuzzy data.
Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube

Traditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages of the proposed method.
MCAT Math Retrieval System for NTCIR-12 MathIR Task

This paper describes the participation of our MCAT search system in the NTCIR-12 MathIR Task. We introduce three granularity levels of textual information, new approach for generating dependency graph of math expressions, score normalization, cold-start weights, and unification. We find that these modules, except the cold-start weights, have a very good impact on the search performance of our system. The use of dependency graph significantly improves precision of our system, i.e., up to 24.52% and 104.20% relative improvements in the Main and Simto subtasks of the arXiv task, respectively. In addition, the implementation of unification delivers up to 2.90% and 57.14% precision improvements in the Main and Simto subtasks, respectively. Overall, our best submission achieves P@5 of 0.5448 in the Main subtask and 0.5500 in the Simto subtask. In the Wikipedia task, our system also performs well at the MathWikiFormula subtask. At the MathWiki subtask, however, due to a problem with handling queries formed as questions that contain many stop words, our system finishes second.
A semiotic analysis of the genetic information system *

Terms loaded with informational connotations are often employed to refer to genes and their dynamics. Indeed, genes are usually perceived by biologists as basically ‘the carriers of hereditary information.’ Nevertheless, a number of researchers consider such talk as inadequate and ‘just metaphorical,’ thus expressing a skepticism about the use of the term ‘information’ and its derivatives in biology as a natural science. First, because the meaning of that term in biology is not as precise as it is, for instance, in the mathematical theory of communication. Second, because it seems to refer to a purported semantic property of genes without theoretically clarifying if any genuinely intrinsic semantics is involved. Biosemiotics, a field that attempts to analyze biological systems as semiotic systems, makes it possible to advance in the understanding of the concept of information in biology. From the perspective of Peircean biosemiotics, we develop here an account of genes as signs, including a detailed analysis of two fundamental processes in the genetic information system (transcription and protein synthesis) that have not been made so far in this field of research. Furthermore, we propose here an account of information based on Peircean semiotics and apply it to our analysis of transcription and protein synthesis.
Looting the LUTs : FPGA Optimization of AES and AES-like Ciphers for Authenticated Encryption

In this paper, we investigate the efficiency of FPGA implementations of AES and AES-like ciphers, specially in the context of authenticated encryption. We consider the encryption/decryption and the authentication/verification structures of OCB-like modes (like OTR or SCT modes). Their main advantage is that they are fully parallelisable. While this feature has already been used to increase the throughput/performance of hardware implementations, it is usually overlooked while comparing different ciphers. We show how to use it with zero area overhead, leading to a very significant efficiency gain. Additionally, we show that using FPGA technology mapping instead of logic optimization, the area of both the linear and non linear parts of the round function of several AES-like primitives can be reduced, without affecting the run-time performance. We provide the implementation results of two multi-stream implementations of both the LED and AES block ciphers. The AES implementation in this paper achieves an efficiency of 38 Mbps/slice, which is the most efficient implementation in literature, to the best of our knowledge. For LED, achieves 2.5 Mbps/slice on Spartan 3 FPGA, which is 2.57x better than the previous implementation. Besides, we use our new techniques to optimize the FPGA implementation of the CAESAR candidate Deoxys-I in both the encryption only and encryption/decryption settings. Finally, we show that the efficiency gains of the proposed techniques extend to other technologies, such as ASIC, as well.
Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages

The increasing importance of search engines to commercial web sites has given rise to a phenomenon we call "web spam", that is, web pages that exist only to mislead search engines into (mis)leading users to certain web sites. Web spam is a nuisance to users as well as search engines: users have a harder time finding the information they need, and search engines have to cope with an inflated corpus, which in turn causes their cost per query to increase. Therefore, search engines have a strong incentive to weed out spam web pages from their index.We propose that some spam web pages can be identified through statistical analysis: Certain classes of spam pages, in particular those that are machine-generated, diverge in some of their properties from the properties of web pages at large. We have examined a variety of such properties, including linkage structure, page content, and page evolution, and have found that outliers in the statistical distribution of these properties are highly likely to be caused by web spam.This paper describes the properties we have examined, gives the statistical distributions we have observed, and shows which kinds of outliers are highly correlated with web spam.
Intrusion detection of distributed denial of service attack in cloud

Security issue in cloud environment is one of the major obstacle in cloud implementation. Network attacks make use of the vulnerability in the network and the protocol to damage the data and application. Cloud follows distributed technology; hence it is vulnerable for intrusions by malicious entities. Intrusion detection systems (IDS) has become a basic component in network protection infrastructure and a necessary method to defend systems from various attacks. Distributed denial of service (DDoS) attacks are a great problem for a user of computers linked to the Internet. Data mining techniques are widely used in IDS to identify attacks using the network traffic. This paper presents and evaluates a Radial basis function neural network (RBF-NN) detector to identify DDoS attacks. Many of the training algorithms for RBF-NNs start with a predetermined structure of the network that is selected either by means of a priori knowledge or depending on prior experience. The resultant network is frequently inadequate or needlessly intricate and a suitable network structure could be configured only by trial and error method. This paper proposes Bat algorithm (BA) to configure RBF-NN automatically. Simulation results demonstrate the effectiveness of the proposed method.
MIST: Multiple Instance Spatial Transformer Network

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant top-K patches, and feeds these patches to a task-specific network – e.g., auto-encoder or classifier – to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of topK selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurrent structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-theart.
Nonlinear Joint Unsupervised Feature Selection

In the era of big data, one is often confronted with the problem of high dimensional data for many machine learning or data mining tasks. Feature selection, as a dimension reduction technique, is useful for alleviating the curse of dimensionality while preserving interpretability. In this paper, we focus on unsupervised feature selection, as class labels are usually expensive to obtain. Unsupervised feature selection is typically more challenging than its supervised counterpart due to the lack of guidance from class labels. Recently, regression-based methods with L2,1 norms have gained much popularity as they are able to evaluate features jointly which, however, consider only linear correlations between features and pseudo-labels. In this paper, we propose a novel nonlinear joint unsupervised feature selection method based on kernel alignment. The aim is to find a succinct set of features that best aligns with the original features in the kernel space. It can evaluate features jointly in a nonlinear manner and provides a good ‘0/1’ approximation for the selection indicator vector. We formulate it as a constrained optimization problem and develop a Spectral Projected Gradient (SPG) method to solve the optimization problem. Experimental results on several real-world datasets demonstrate that our proposed method outperforms the state-of-the-art approaches significantly.
The biological effects of childhood trauma.

Trauma in childhood is a psychosocial, medical, and public policy problem with serious consequences for its victims and for society. Chronic interpersonal violence in children is common worldwide. Developmental traumatology, the systemic investigation of the psychiatric and psychobiological effects of chronic overwhelming stress on the developing child, provides a framework and principles when empirically examining the neurobiological effects of pediatric trauma. This article focuses on peer-reviewed literature on the neurobiological sequelae of childhood trauma in children and in adults with histories of childhood trauma.
Constructing Thai Opinion Mining Resource: A Case Study on Hotel Reviews

Opinion mining and sentiment analysis has recently gained increasing attention among the NLP community. Opinion mining is considered a domaindependent task. Constructing lexicons for different domains is labor intensive. In this paper, we propose a framework for constructing Thai language resource for feature-based opinion mining. The feature-based opinion mining essentially relies on the use of two main lexicons, features and polar words. Our approach for extracting features and polar words from opinionated texts is based on syntactic pattern analysis. The evaluation is performed with a case study on hotel reviews. The proposed method has shown to be very effective in most cases. However, in some cases, the extraction is not quite straightforward. The reasons are due to, firstly, the use of conversational language in written opinionated texts and, secondly, the language semantic. We provide discussion with possible solutions on pattern extraction for some of the challenging cases.
Practice 5. Fsk Demodulator with Pll 5.1. Objectives

To study the operation of a PLL and its application to demodulate a FSK signal. The LM565 is a general purpose phase locked loop (PLL) containing a stable, highly linear voltage controlled oscillator (VCO) and a double balanced phase detector with good carrier suppression. This device can be used in several kinds of applications: data synchronization, Both the VCO free-running operation frequency and the filter bandwidth can be adjusted by using external resistors and capacitors. Next, the main features of the device are summarized:
Edmodo E-portfolios in EFL – A Case Study

Edmodo is a free and safe virtual learning environment, helping students and teachers connect and collaborate outside the face-to-face learning time, which makes it an ideal tool to be explored and adopted by teachers of English as a foreign language (EFL). Our case study will reflect on using the Edmodo Assignment feature as an ePortfolio of EFL student productions and progress. Written productions, speaking and listening contributions, which would be otherwise rather difficult to process and assess in real time, are accommodated by the platform and contribute to a finer assessment process. The outcomes and qualitative results of employing the Edmodo EFL portfolio for a mixed-ability group of undergraduate Geography of Tourism students, for two-semesters, are presented.
From needs to goals and representations: Foundations for a unified theory of motivation, personality, and development.

Drawing on both classic and current approaches, I propose a theory that integrates motivation, personality, and development within one framework, using a common set of principles and mechanisms. The theory begins by specifying basic needs and by suggesting how, as people pursue need-fulfilling goals, they build mental representations of their experiences (beliefs, representations of emotions, and representations of action tendencies). I then show how these needs, goals, and representations can serve as the basis of both motivation and personality, and can help to integrate disparate views of personality. The article builds on this framework to provide a new perspective on development, particularly on the forces that propel development and the roles of nature and nurture. I argue throughout that the focus on representations provides an important entry point for change and growth.