Miracle-dz/UniFAR-FTUs huggingface dataset:
An eﬃcient space partitioning technique based on linear kd-trees for simulation of short-range interactions in particle methods

We present an eﬃcient pruning algorithm for selecting candidate short-range interaction pairs in particle-based discretization methods such as the discrete element method or smoothed particle hydrodynamics. Geometrically, our technique is based on a recursive bisection of space, forming a particular type of kd-tree. But instead of relying on a tree data structure, our algorithm traverses an implicit tree stored as an associative array. We study its time and memory complexity, and compare the performance of our implementation with that of other algorithms (namely Sweep and Prune and Delaunay triangulation). Preliminary tests show that the linear kd-tree performs more than ﬁfty times faster than the other methods on conventional computers, for highly dynamic simulations with a few thousand elements or more. We also conclude that simulations of several million elements are feasible on a single processor in less than ten seconds per time step.
Study on prediction of operational data of turbine in TRT system based on artificial neural network

Blast furnace top gas recovery turbine unit, TRT for short, is an energy saving system. Turbine is the key equipment in TRT system. The normal operation of turbine is directly related to the capacity of TRT system and the benefit of enterprises. In order to achieve the prediction of operation data of turbine, this paper firstly processed the turbine operation data collected from production scene with the soft and hard threshold compromise algorithm in decomposing the wavelet coefficient, and filtered the noise. Then, this paper established the prediction model of BP neural network, adjusted the structure of the neural network and trained it for the prediction results. The result shows that on the premise of filtering the noise with wavelet transform, the BP neural network can achieve the prediction of turbine operation data in TRT system effectively.
IGRhCellID: integrated genomic resources of human cell lines for identification

Cell line identification is emerging as an essential method for every cell line user in research community to avoid using misidentified cell lines for experiments and publications. IGRhCellID (http://igrcid.ibms.sinica.edu.tw) is designed to integrate eight cell identification methods including seven methods (STR profile, gender, immunotypes, karyotype, isoenzyme profile, TP53 mutation and mutations of cancer genes) available in various public databases and our method of profiling genome alterations of human cell lines. With data validation of 11 small deleted genes in human cancer cell lines, profiles of genomic alterations further allow users to search for human cell lines with deleted gene to serve as indigenous knock-out cell model (such as SMAD4 in gene view), with amplified gene to be the cell models for testing therapeutic efficacy (such as ERBB2 in gene view) and with overlapped aberrant chromosomal loci for revealing common cancer genes (such as 9p21.3 homozygous deletion with co-deleted CDKN2A, CDKN2B and MTAP in chromosome view). IGRhCellID provides not only available methods for cell identification to help eradicating concerns of using misidentified cells but also designated genetic features of human cell lines for experiments.
A New Algorithm to Find a Point in Every CellDe ned by a Family of

We consider s polynomials P 1 ; : : :; P s in k < s variables with coef-cients in an ordered domain A contained in a real closed eld R, each of degree at most d. We present a new algorithm which computes a point in each connected component of each non-empty sign condition over P 1 ; : : :; P s. The output is the set of points together with the sign condition at each point. The algorithm uses s(s=k) k d O(k) arithmetic operations in A. The algorithm is nearly optimal in the sense that the size of the output can be as large as s(O(sd=k)) k. Previous algorithms of Canny and Renegar used (sd) O(k) operations 5, 7, 8, 15]. We use either these algorithms in the case s = 1 as a subroutine in our algorithm. As a bonus, our algorithm yields an independent proof of the bound on the number of connected components in all non-empty sign conditions ((14]) and also yields an independent proof of a theorem of Warren 1 (see 17]) which is used in 14] as well as in many other problems of a combinatorial nature (see Alon 1] for example).
A new hybrid model for time-series prediction

This paper proposed a new hybrid model in order to increase time series prediction accuracy. This hybrid model considers the routine time prediction technique like AR, ANN or any others as atomic building block. A linear hybrid technique is used to combine their forecast result into the final result. The hybrid algorithm was tested against three different kinds of time series data. Experiments results showed the effectiveness of the proposed hybrid model.
A Lazy way to Chart-Parse with Categorial Grammars

There has recently been a revival of interest in Categorial Grammars (CG) among computational linguists. The various versions noted below which extend pure CG by including operations such as functional composition have been claimed to offer simple and uniform accounts of a wide range of natural language (NL) constructions involving bounded and unbounded "movement" and coordination "reduction" in a number of languages. Such grammars have obvious advantages for computational applications, provided that they can be parsed efficiently. However, many of the proposed extensions engender proliferating semantically equivalent surface syntactic analyses. These "spurious analyses" have been claimed to compromise their efficient parseability.The present paper describes a simple parsing algorithm for our own "combinatory" extension of CG. This algorithm offers a uniform treatment for "spurious" syntactic ambiguities and the "genuine" structural ambiguities which any processor must cope with, by exploiting the associativity of functional composition and the procedural neutrality of the combinatory rules of grammar in a bottom-up, left-to-right parser which delivers all semantically distinct analyses via a novel unification-based extension of chart-parsing.
Corpus Annotation as a Test of a Linguistic Theory

In the present contribution we claim that corpus annotation serves, among other things, as an invaluable test for linguistic theories standing behind the annotation schemes, and as such represents an irreplaceable resource of linguistic information for the build-up of grammars. To support this claim we present four linguistic phenomena for the study and relevant description of which in grammar a deep layer of corpus annotation as introduced in the Prague Dependency Treebank has brought important observations, namely the information structure of the sentence, condition of projectivity and word order, types of dependency relations and textual coreference.
SEGMENTING CORPORA OF TEXTS ( Segmentação de Corpora de Textos )

The aim of the research presented here is to report on a corpus-based method for discourse analysis that is based on the notion of segmentation, or the division of texts into cohesive portions. For the purposes of this investigation, a segment is defined as a contiguous portion of written text consisting of at least two sentences. The segmentation procedure developed for the study is called LSM (link set median), which is based on the identification of lexical repetition in text. The data analysed in this investigation were three corpora of 100 texts each. Each corpus was composed of texts of one particular genre: research articles, annual business reports, and encyclopaedia entries. The total number of words in the three corpora was 1,262,710 words. The segments inserted in the texts by the LSM procedure were compared to the internal section divisions in the texts. Afterwards, the results obtained through the LSM procedure were then compared to segmentation carried out at random. The results indicated that the LSM procedure worked better than random, suggesting that lexical repetition accounts in part for the way texts are segmented into sections. KEY-WORDS: Corpus linguistics; Discourse analysis; Segmentation; Lexical cohesion; Repetition. RESUMO: O objetivo da pesquisa apresentada é relatar um método baseado em corpus para análise de discurso que se baseia na noção de segmentação, isto é, a divisão de textos em porções coesas. Para os propósitos desse estudo, um segmento é definido como uma porção contígua de texto que consiste em pelo menos sentenças. O procedimento de segmentação desenvolvido para a pesquisa chama-se LSM (‘link set median’) e se baseia na identificação da repetição lexical nos textos. Os dados analisados foram três corpora de 100 textos cada. Cada corpus representava um gênero específico: artigos de pesquisa, relatórios anuais de negócio e artigos de enciclopédia. O tamanho total do corpus é 1.262. 710 palavras. A segmentação por LSM foi comparada à divisão interna em seções de cada texto. A seguir, os resultados do procedimento LSM foram compa274 D.E. L.T. A. , 18:2 rados a uma segmentação feita aleatoriamente. Os resultados indicaram que o procedimento LSM funcionou melhor do que o método aleatório, o que sugere que a repetição lexical responde em parte pela maneira pela qual os textos segmentam-se em seções. PALAVRAS-CHAVE: Lingüística de Corpus; Análise de discurso; Segmentação; Coesão lexical; Repetição.
Preservice Teacher Perspectives on Prereferral Intervention and Student Support Teams.

PRESERVICE TEACHER PERSPECTIVES ON PREREFERRAL INTERVENTION AND STUDENT SUPPORT TEAMS by Kathryn R. Grogg This qualitative inquiry evaluated the Student Support Team Project and its effects on preservice teachers‟ knowledge and perceptions of prereferral intervention and student support teams. This investigation is important because prereferral intervention and student support teams have been used increasingly to provide assistance to teachers and to students who need assistance with academic, social and emotional problems. This has created a need to provide preservice education that helps to prepare teachers to use these resources to help their students. This investigation demonstrated a specific approach to such instruction for preservice teachers (i.e., the Student Support Team Project), including an evaluation to determine changes in perceptions and knowledge that resulted during and after participation in this project. Participants were preservice teachers enrolled in an alternative teacher certification program. The research design was qualitative. Data collection included semi-structured interviews, written reflections from the preservice teachers, input from key informants, field notes and research team reflective journals. The collection and analysis of data were done recursively and used constant comparative methods. Analysis of the data revealed three main categories: Knowledge, Knowledge Needed, and Project Feedback. Knowledge reflected the participants‟ understanding of student support teams and their implementation and included the following themes: Knowledge about Data Collection, Knowledge about Intervention, Knowledge about Student Support Teams, and Generalization of Knowledge. Knowledge Needed reflected the knowledge participants needed to understand and work effectively with these teams, including the following themes: Knowledge Needed about Data Collection, Knowledge Needed about Intervention, Knowledge Needed about Student Support Teams, and Other Knowledge Needed. Project Feedback included perceived strengths and weaknesses of the Student Support Team Project, including the following themes: Project Helpful, Project Struggles, and Project Suggestions. One key finding was how preservice teachers‟ understanding of student support teams evolved from vague ideas about teams, to increasingly specific case-focused ideas, and finally, to generalized understandings. Findings are discussed in relationship to the literature on prereferral intervention and teacher development. Implications for preservice teacher education are discussed. Future research is also
Survey on Mobile Ad Hoc Network Routing Protocols and Cross-Layer Design

A "mobile ad hoc network" (MANET) is an autonomous system of mobile routers connected by wireless links. The routers are free to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change rapidly and unpredictably. Such a network may operate in a standalone fashion, or may be connected to the Internet. Multi hop, mobility, large network size combined with device heterogeneity, bandwidth and battery power constrain make the design of adequate routing protocols a major challenge. In recent years, many routing protocols have been proposed for MANET. Basically these protocols can be fit in one of two major categories: on-demand such as AODV [1] and DSR [2], and proactive such as DSDV [3] and OLSR [4]. The review and performance comparison of these protocols are in [5][6][7]. A more comprehensive survey can be found in [8]. In this survey, we will not focus on individual routing protocols; instead we will discuss some new ideas proposed recently mainly to improve MANET throughput and scalability in different ways with some new routing metrics, new technologies such as multi-rate, multi-channel and hierarchical structure, by using cross-layer design.
Comparing Chord, CAN, and Pastry overlay networks for resistance to DoS attacks

Overlay networks enable applications to communicate with users without disclosing their IP addresses; hence overlay networks are used to protect applications against DoS attacks by hiding an applicationpsilas location. This paper analyzes three popular overlay networks (Chord, CAN, and Pastry) by simulation to answer to this question: which topology is more favorable for location-hiding and resisting against DoS attacks? Simulation results show that a CAN overlay network has a better topology structure for location-hiding and resistance against DoS attacks than Chord and Pastry. Simulation results also show the topologies with low average vertex degree are favorable for location-hiding techniques.
Examining the Relationships among Authentic Leadership, Interprofessional Collaboration, and Nurse Assessed Adverse Events: A Mediation Model

Adverse events that occur in healthcare settings may have serious negative implications for patients, families, health care providers, and healthcare organizations. There is a need for strong leadership to prevent and mitigate the damaging effects of adverse events. Authentic leaders have been shown to enhance interprofessional collaboration among healthcare providers and reduce the frequency of adverse events. The purpose of this study was to test the relationships among authentic leadership, interprofessional collaboration, and nurse assessed adverse events in a mediation model. A secondary analysis of 269 experienced registered nurses randomly selected from three provinces (Alberta, Nova Scotia, and Ontario) were included. A predictive non-experimental study design was used. Hayes’ PROCESS version 3 SPSS macro for mediation analysis was used to test the hypothesized path model. There was a negative association between authentic leadership and nurse assessed adverse events via the indirect effect of interprofessional collaboration (B= -.034, 95% CI [-.069, -.004]). Furthermore, the mediation model accounted for 8% of the variance seen within nurse assessed adverse events. Study findings highlight the importance of healthcare leaders in advancing the patient safety agenda.
Conditional Knock-Out of Vesicular GABA Transporter Gene from Starburst Amacrine Cells Reveals the Contributions of Multiple Synaptic Mechanisms Underlying Direction Selectivity in the Retina

Direction selectivity of direction-selective ganglion cells (DSGCs) in the retina results from patterned excitatory and inhibitory inputs onto DSGCs during motion stimuli. The inhibitory inputs onto DSGCs are directionally tuned to the antipreferred (null) direction and therefore potently suppress spiking during motion in the null direction. However, whether direction-selective inhibition is indispensable for direction selectivity is unclear. Here, we selectively eliminated the directional tuning of inhibitory inputs onto DSGCs by disrupting GABA release from the presynaptic interneuron starburst amacrine cell in the mouse retina. We found that, even without directionally tuned inhibition, direction selectivity can still be implemented in a subset of On-Off DSGCs by direction-selective excitation and a temporal offset between excitation and isotropic inhibition. Our results therefore demonstrate the concerted action of multiple synaptic mechanisms for robust direction selectivity in the retina. SIGNIFICANCE STATEMENT The direction-selective circuit in the retina has been a classic model to study neural computations by the brain. An important but unresolved question is how direction selectivity is implemented by directionally tuned excitatory and inhibitory mechanisms. Here we specifically removed the direction tuning of inhibition from the circuit. We found that direction tuning of inhibition is important but not indispensable for direction selectivity of DSGCs' spiking activity, and that the residual direction selectivity is implemented by direction-selective excitation and temporal offset between excitation and inhibition. Our results highlight the concerted actions of synaptic excitation and inhibition required for robust direction selectivity in the retina and provide critical insights into how patterned excitation and inhibition collectively implement sensory processing.
Common Criteria for Genre Classification : Annotation and Granularity

In this paper,we present two experiments that use machine learning for automatically classifying web pages by genre. These experiments highlight the influence that genre annotation and genre granularity can have on the accuracy of the classification. From a practical point of view these experiments show that a collection annotated with the criteria of ‘objective sources’ and consistent genre granularity ensures a very good classification accuracy (Experiment 1). Additionally, the classification model built out of such a collection can be exported more profitably for predictive tasks on an unclassified web page collection (Experiment 2). These experiments represent a starting point for a discussion about the need of common criteria for building a genre collection in the absence of an official genre-annotated benchmark.
WEAK SHARP MINIMA IN MATHEMATICAL PROGRAMMING

The notion of a sharp, or strongly unique, minimumis extended to include the possibility of a nonunique solution set. These minima will be called weak sharp minima. Conditions necessary for the solution set of a minimization problem to be a set of weak sharp minima are developed in both the unconstrained and constrained cases. These conditions are also shown to be suucient under the appropriate convexity hypotheses. The existence of weak sharp minima is characterized in the cases of linear and quadratic convex programming and for the linear complementarity problem. In particular, we reproduce a result of Mangasarian and Meyer that shows that the solution set of a linear program is always a set of weak sharp minima whenever it is nonempty. Consequences for the convergence theory of algorithms is also examined, especially conditions yielding nite termination.
Determination of categories for tagging and automated classification of film scenes

Tags are a useful tool for film search and recommendation because they contain valuable information in a compressed form. However, manual tagging is time-consuming and needs the involvement of many people. We are working on automated cross-modal video classification algorithms based on signal processing. These algorithms assign subcategories to film scenes. The subcategories are useful to cluster scenes in different groups and therefore can also be used as tags. In this paper, we point out the advantages of tags generated by automated video classification and present the determination of five possible categories with their associated subcategories. The determination was obtained with five group studies to ensure that the categories fit the way humans classify. Because they are based on the audiovisual content and do not include additional external information like name of director or year of production, they are suited for automated classification as well. The final categories are called Dynamic, Valence, Interaction, Suspense and Essential Features. They contain between two and nine subcategories.
Enhanced Orai1 and STIM1 expression as well as store operated Ca2+ entry in therapy resistant ovary carcinoma cells

Mechanisms underlying therapy resistance of tumor cells include protein kinase Akt. Putative Akt targets include store-operated Ca2+-entry (SOCE) accomplished by pore forming ion channel unit Orai1 and its regulator STIM1. We explored whether therapy resistant (A2780cis) differ from therapy sensitive (A2780) ovary carcinoma cells in Akt, Orai1, and STIM1 expression, Ca2+-signaling and cell survival following cisplatin (100μM) treatment. Transcript levels were quantified with RT-PCR, protein abundance with Western blotting, cytosolic Ca2+-activity ([Ca2+]i) with Fura-2-fluorescence, SOCE from increase of [Ca2+]i following Ca2+-readdition after Ca2+-store depletion, and apoptosis utilizing flow cytometry. Transcript levels of Orai1 and STIM1, protein expression of Orai1, STIM1, and phosphorylated Akt, as well as SOCE were significantly higher in A2780cis than A2780 cells. SOCE was decreased by Akt inhibitor III (SH-6, 10μM) in A2780cis but not A2780 cells and decreased in both cell lines by Orai1 inhibitor 2-aminoethoxydiphenyl borate (2-ABP, 50μM). Phosphatidylserine exposure and late apoptosis following cisplatin treatment were significantly lower in A2780cis than A2780 cells, a difference virtually abolished by SH-6 or 2-ABP. In conclusion, Orai1/STIM1 expression and function are increased in therapy resistant ovary carcinoma cells, a property at least in part due to enhanced Akt activity and contributing to therapy resistance in those cells.
FPGA Based SCA Resistant AES S-Box Design

A new implementation scheme of AES (Advanced Encryption Standard) is proposed in this paper. The LUT based design of S-box consumes almost 75% of power. Instead of using LUT based S-box, composite field S-box design is used. That can reduce the amount of power consumption. The values of s-box are known to everyone. By masking the each value in the s-box by another masking function increase the system security and reduce the side channel attacks. This masking module can be implemented on any part of AES algorithm and re-masking module is used to remove the mask. It can be used as mask for the entire encrypted message. By using the new implementation of composite field s -box we can save the memory area up to 2.6 Mega Byte that optimizes the chip area and composite field S-box reduces the power consumption and masking module used to increase the system security.
Russian Elections: An Oxymoron of Democracy

Considerable controversy swirls around the extent to which Russia's elections have been falsified. We argue here on the basis of an assessment of aberrant distributions of turnout in official election returns for each of Russia's national elections beginning in 1995, that falsifications in the form of stuffed ballot boxes and artificially augmented election counts, whose significance was first apparent in its ethnic republics, has now spread to and metastasized within both rural and urban oblast districts. That spread, moreover, unashamedly accelerated during the Putin administration - notably the 2004 election - and has sustained itself thru the 2007 Duma parliamentary vote.