Miracle-dz/UniFAR-FTUs huggingface dataset:
VarifocalReader — In-Depth Visual Analysis of Large Text Documents

Interactive visualization provides valuable support for exploring, analyzing, and understanding textual documents. Certain tasks, however, require that insights derived from visual abstractions are verified by a human expert perusing the source text. So far, this problem is typically solved by offering overview-detail techniques, which present different views with different levels of abstractions. This often leads to problems with visual continuity. Focus-context techniques, on the other hand, succeed in accentuating interesting subsections of large text documents but are normally not suited for integrating visual abstractions. With VarifocalReader we present a technique that helps to solve some of these approaches' problems by combining characteristics from both. In particular, our method simplifies working with large and potentially complex text documents by simultaneously offering abstract representations of varying detail, based on the inherent structure of the document, and access to the text itself. In addition, VarifocalReader supports intra-document exploration through advanced navigation concepts and facilitates visual analysis tasks. The approach enables users to apply machine learning techniques and search mechanisms as well as to assess and adapt these techniques. This helps to extract entities, concepts and other artifacts from texts. In combination with the automatic generation of intermediate text levels through topic segmentation for thematic orientation, users can test hypotheses or develop interesting new research questions. To illustrate the advantages of our approach, we provide usage examples from literature studies.
Diaqua{6,6′-dimethoxy-2,2′-[ethane-1,2-diylbis(nitrilomethylidyne)]diphenolato-κ2 O,N,N′,O′}manganese(III) perchlorate 18-crown-6 hemisolvate monohydrate

In the cation of the title compound, [Mn(C18H18N2O4)(H2O)2]ClO4·0.5C12H24O6·H2O, the MnIII ion is coordinated by two water O atoms, and two O atoms and two N atoms from the tetradentate 6,6′-dimethoxy-2,2′-[ethane-1,2-diylbis(nitrilomethylidyne)]diphenolate ligand, completing a distorted octahedral geometry. One O atom of the 18-crown-6-ether is disordered over two positions with occupancies of 0.70 (2) and 0.30 (2).
AtexRace: across thread and execution sampling for in-house race detection

Data race is a major source of concurrency bugs. Dynamic data race detection tools (e.g., FastTrack) monitor the execu-tions of a program to report data races occurring in runtime. However, such tools incur significant overhead that slows down and perturbs executions. To address the issue, the state-of-the-art dynamic data race detection tools (e.g., LiteRace) ap-ply sampling techniques to selectively monitor memory access-es. Although they reduce overhead, they also miss many data races as confirmed by existing studies. Thus, practitioners face a dilemma on whether to use FastTrack, which detects more data races but is much slower, or LiteRace, which is faster but detects less data races. In this paper, we propose a new sam-pling approach to address the major limitations of current sampling techniques, which ignore the facts that a data race involves two threads and a program under testing is repeatedly executed. We develop a tool called AtexRace to sample memory accesses across both threads and executions. By selectively monitoring the pairs of memory accesses that have not been frequently observed in current and previous executions, AtexRace detects as many data races as FastTrack at a cost as low as LiteRace. We have compared AtexRace against FastTrack and LiteRace on both Parsec benchmark suite and a large-scale real-world MySQL Server with 223 test cases. The experiments confirm that AtexRace can be a replacement of FastTrack and LiteRace.
Optimal walking trajectory generation for a biped robot using genetic algorithm

Information of suitable additive via-points, which determine the shapes of trajectories, such as velocities and accelerations at the via-points, are needed for smooth biped walking. Also, incorrect information can cause indifferentiability on the trajectory and result in discontinuous walking motion. In the paper optimal via-points data are found using a genetic algorithm which minimizes the sum of deviation of velocities and accelerations as well as a jerk. As a result, we obtain the continuity on the whole trajectory interval, the energy distribution through optimum velocities and accelerations. Smooth and stable walking with the leg trajectory optimized by genetic algorithm is shown by walking experiments.
TransAhead: A Computer-Assisted Translation and Writing Tool

We introduce a method for learning to predict text completion given a source text and partial translation. In our approach, predictions are offered aimed at alleviating users' burden on lexical and grammar choices, and improving productivity. The method involves learning syntax-based phraseology and translation equivalents. At run-time, the source and its translation prefix are sliced into ngrams to generate and rank completion candidates, which are then displayed to users. We present a prototype writing assistant, TransAhead, that applies the method to computer-assisted translation and language learning. The preliminary results show that the method has great potentials in CAT and CALL with significant improvement in translation quality across users.
Stereoscopic Depth Perception Using a Model Based on the Primary Visual Cortex

This work describes an approach inspired by the primary visual cortex using the stimulus response of the receptive field profiles of binocular cells for disparity computation. Using the energy model based on the mechanism of log-Gabor filters for disparity encodings, we propose a suitable model to consistently represent the complex cells by computing the wide bandwidths of the cortical cells. This way, the model ensures the general neurophysiological findings in the visual cortex (V1), emphasizing the physical disparities and providing a simple selection method for the complex cell response. The results suggest that our proposed approach can achieve better results than a hybrid model with phase-shift and position-shift using position disparity alone.
Time series prediction competition : The CATS benchmark 1

Time series forecasting is a challenge in many fields. In finance, one forecasts stock exchange courses or stock market indices; data processing specialists forecast the flow of information on their networks; producers of electricity forecast the load of the following day. The common point to their problems is the following: how can one analyze and use the past to predict the future? Many techniques exist: linear methods such as ARX, ARMA, etc. [1,7], and nonlinear ones such as artificial neural networks [2–5,9,11]. In general, these methods try to build a model of the process that is to be predicted. The model is then used on the last values of the series to predict future ones. The common difficulty to all methods is the determination of sufficient and necessary information for a good prediction. If the information is insufficient, the forecasting will be poor. On the contrary, if information is useless or redundant, modeling will be difficult or even skewed. In parallel with this determination, a prediction model has to be selected. In order to compare different prediction methods several competitions have been organized, for example: The Santa Fe Competition [11]; The K.U. Leuven Competition: Advanced Black-Box Techniques for Nonlinear Modeling: Theory and Applications [6]; The EUNITE competition [9]. After the competitions, their results have been published and the time series have become widely used benchmarks. The goal of these competitions is the prediction of the following values of a given time series (30–100 values to predict). Unfortunately, the long-term prediction of time series is a very difficult task, more difficult than the short-term prediction. Furthermore, after the publication of results, the real values that had to be predicted are also published. Thereafter it becomes more difficult to trust in new results that are published: knowing the results of a challenge may lead, even unconsciously, to bias the selection of model; some speak about ‘‘data snooping’’. It becomes therefore more difficult to assess newly developed methods, and new competitions have to be organized. In the present CATS competition, the goal was the prediction of 100 missing values of the time series; they are grouped in 5 sets of 20 successive values. The prediction methods have then to be applied several times, allowing a better comparison of the performances. Twenty-four
Intelligence and Adolescent Theft

Recently, Hirschi and Hindelang (1977) presented compelling arguments against the contemporary textbook position rejecting intelligence as an important cause of criminal behavior. They proposed a causal model in which IQ is linked to delinquency by a set of school-related variables. Costner's multiple indicator procedure was used to examine this model and the environmental influence argument against prior findings of a significant relationship between intelligence and delinquency. A model with teacher appeal intervening between intelligence and adolescent theft is supported, while the absence of differential bias discredits the environmental argument.
An Inhibitory Antibody against Dipeptidyl Peptidase IV Improves Glucose Tolerance in Vivo

Background: Suppression of DPP-IV activity improves type 2 diabetic symptoms. Results: Inhibitory antibodies suppress DPP-IV activity, promote glucose tolerance, and increase plasma GLP-1 levels in hyperglycemic Zucker fatty rats. Conclusion: Inhibitory antibody against DPP-IV offers pro-incretin effects in vivo. Significance: This study validates a large molecule approach for targeting DPP-IV activity. Dipeptidyl peptidase IV (DPP-IV) degrades the incretin hormone glucagon-like peptide 1 (GLP-1). Small molecule DPP-IV inhibitors have been used as treatments for type 2 diabetes to improve glucose tolerance. However, each of the marketed small molecule drugs has its own limitation in terms of efficacy and side effects. To search for an alternative strategy of inhibiting DPP-IV activity, we generated a panel of tight binding inhibitory mouse monoclonal antibodies (mAbs) against rat DPP-IV. When tested in vitro, these mAbs partially inhibited the GLP-1 cleavage activity of purified enzyme and rat plasma. To understand the partial inhibition, we solved the co-crystal structure of one of the mAb Fabs (Ab1) in complex with rat DPP-IV. Although Ab1 does not bind at the active site, it partially blocks the side opening, which prevents the large substrates such as GLP-1 from accessing the active site, but not small molecules such as sitagliptin. When Ab1 was tested in vivo, it reduced plasma glucose and increased plasma GLP-1 concentration during an oral glucose tolerance test in rats. Together, we demonstrated the feasibility of using mAbs to inhibit DPP-IV activity and to improve glucose tolerance in a diabetic rat model.
Towards an Intelligent Bed Sensor: Non-intrusive Monitoring of Sleep Irregularities with Computer Vision Techniques

This paper proposes a novel approach for monitoring sleep using pressure data. The goal of sleep monitoring is to detect and log events of normal breathing, sleep apnea and body motion. The proposed approach is based on translating the signal data to the image domain by computing a sequence of inter-frame similarity matrices from pressure maps acquired with a mattress of pressure sensors. Periodicity analysis was performed on similarity matrices via a new algorithm based on segmentation of elementary patterns using the watershed transform, followed by aggregation of quasi-rectangular patterns into breathing cycles. Once breathing events are detected, all remaining elementary patterns aligned on the main diagonal are considered as belonging to either apnea or motion events. The discrimination between these two events is based on detecting movement times from a statistical analysis of pressure data. Experimental results confirm the validity of our approach.
Work motivation as a determinant of organizational and professional commitment in case of temporary organizations : theoretical perspectives

The purpose of this paper is to present a theoretical framework to investigate the relationship between work motivation, organizational commitment, and professional commitment in temporary organizations. Through a review of theory, we contend that work motivation has two major patterns- internal motivation (that includes intrinsic, need-based, and self-deterministic theories), and external motivation (that includes cognitive or process-based theories of motivation) through which it has been investigated. We also subsume the nature of employee commitment to be of three types- affective, continuance, and normative. This commitment may either be towards organization or profession. A literature review reveals that the characteristics of the temporary organization - specifically tenure, and task - regulate the relationship between work motivation, and organizational commitment, and professional commitment. Relevant propositions are presented.
Detecting trunk motion changes due to pregnancy using pattern recognition techniques

There are anatomical changes during pregnancy due to the increased and altered mass distribution in the trunk that could lead to changes in gait. There is little research, however, regarding adaptations in trunk motion with pregnancy. In this paper, we investigated the application of two pattern recognition techniques: support vector machine (SVM) and linear discriminant analysis (LDA) to detect differences in trunk kinematics, when walking, between women in late pregnancy and nulliparous (control) women. Test results indicate that the SVM can identify the trunk motion of pregnant women from their counterparts with a better accuracy compared to the LDA (71.43% vs 28.57% respectively). Furthermore, with a feature selection technique applied, the accuracy improved to 95.24% % using only 2 features namely the pelvic sagittal plane displacement and thoracic lateral tilt displacement at heel contact. The results suggest that for better detection of trunk motion changes in pregnant women, non-linear analysis may be required. The SVM was able to effectively differentiate pregnancy related trunk motion changes during a walking task which may indicate altered musculoskeletal loads with potential for injury or pain.
Congenic Strains for Genetic Analysis of Virulence Traits in Cryptococcus gattii

ABSTRACT Cryptococcus gattii is responsible for a large outbreak of potentially fatal disease that started in the late 1990s on Vancouver Island, Canada. How this fungus and the outbreak isolates in particular cause disease in immunocompetent people is unknown, with differing hypotheses. To explore genetic contributions, a pair of congenic a and α mating type strains was generated by a series of 11 backcrosses to introgress the MAT locus from a nonoutbreak strain into the background of strain R265, isolated from a Vancouver Island patient. The congenic pair was used to investigate three traits: mitochondrial inheritance, the effect of the MAT alleles on virulence, and the impact of a predicted virulence factor on pathogenicity. The two congenic strains show the same virulence in different models of cryptococcosis and equivalent levels of competition in coinfection assays. These results rule out a role of the MAT locus and mitochondrial genotype as major virulence factors in the outbreak strains. Disruption of Bwc2, a light-dependent transcription factor, resulted in reduced virulence, consistent with a similar function in the related species Cryptococcus neoformans. The C. gattii congenic strains represent a new resource for exploring the evolution of virulence in the C. neoformans-C. gattii clade.
Sequence of Two Plasmids from Clostridium perfringens Chicken Necrotic Enteritis Isolates and Comparison with C. perfringens Conjugative Plasmids

Twenty-six isolates of Clostridium perfringens of different MLST types from chickens with necrotic enteritis (NE) (15 netB-positive) or from healthy chickens (6 netB-positive, 5 netB-negative) were found to contain 1–4 large plasmids, with most netB-positive isolates containing 3 large and variably sized plasmids which were more numerous and larger than plasmids in netB-negative isolates. NetB and cpb2 were found on different plasmids consistent with previous studies. The pathogenicity locus NELoc1, which includes netB, was largely conserved in these plasmids whereas NeLoc3, present in the cpb2 containing plasmids, was less well conserved. A netB-positive and a cpb2-positive plasmid were likely to be conjugative, and the plasmids were completely sequenced. Both plasmids possessed the intact tcp conjugative region characteristic of C. perfringens conjugative plasmids. Comparative genomic analysis of nine CpCPs, including the two plasmids described here, showed extensive gene rearrangements including pathogenicity locus and accessory gene insertions around rather than within the backbone region. The pattern that emerges from this analysis is that the major toxin-containing regions of the variety of virulence-associated CpCPs are organized as complex pathogenicity loci. How these different but related CpCPs can co-exist in the same host has been an unanswered question. Analysis of the replication-partition region of these plasmids suggests that this region controls plasmid incompatibility, and that CpCPs can be grouped into at least four incompatibility groups.
BBN at TREC7: Using Hidden Markov Models for Information Retrieval

We present a new method for information retrieval using hidden Markov models (HMMs) and relate our experience with this system on the TREC-7 ad hoc task. We develop a general framework for incorporating multiple word generation mechanisms within the same model. We then demonstrate that an extremely simple realization of this model substantially outperforms tf :idf ranking on both the TREC-6 and TREC7 ad hoc retrieval tasks. We go on to present several algorithmic re nements, including a novel method for performing blind feedback in the HMM framework. Together, these methods form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task, and showed extraordinary performance in development experiments on TREC-6.
Low Cost Rapid Acquisition of Bidirectional Texture Functions for Fabrics.

Creating photo realistic images from real word complex materials is a great challenge: the reflectance function of materials, especially fabrics, has glossy or specular highlights, reflectance anisotropy and retroreflections. This increases greatly the complexity of rendering. Bidirectional Texture Functions (BTFs), i.e. 2D textures acquired under varying illumination and viewing directions, have been used to render complex materials. However, the acquisition of textures for BTF requires up to now expensive setups and the measurement process is very timeconsuming as the directional dependent parameters (lighting and viewing directions) have to be controlled accurately. This paper will present in detail a new low cost programmable device for the rapid acquisition of BTF datasets. The device allows to acquire BTF databases at a fraction of the cost of available setups, and allows to experiment when a texture resolution and sample density increase in the parameter space is not perceivable by an observer of the renderings. The paper proves that using smaller resolution textures and decreasing the samples in parameter space does not lead to a loss of picture quality.
Preparatory and field work phases in the qualitative empirical research process in management: the case studies approach

Purpose: The aim of this paper is to promote qualitative methodology within the scientific community of management. The specific objective is oriented to propose an empirical research process based on case study method. This is to ensure rigor in the empirical research process, that future research may follow a similar procedure to that is proposed. Design/methodology/approach: Following a qualitative methodological approach, we propose a research process that develops according to four phases, each with several stages. This study analyses the preparatory and field work phases and their stages. Findings: The paper shows the influence that case studies have on qualitative empirical research process in management. Originality/value:. Case study method assumes an important role within qualitative research by allowing for the study and analysis of certain types of phenomena that occur inside organisations, and in respect of which quantitative studies cannot provide an answer.
MPLS : Managing the New Internet

Traffic requirements on the Internet have changed from reliability to timeliness of delivery as different types of applications are being deployed on it. This situation has prompted the industry and IETF (Internet Engineering Task Force) to develop new protocols and techniques to meet the challenges. One of the important protocols developed is MPLS (Multi Protocol Label Switching) that allows network operators to implement traffic engineering, an important aspect of network operation that was neglected earlier. In this paper, we discuss why traffic engineering is a necessary tool in next generation Internet and discuss several examples in which MPLS traffic engineering implementation results in enhanced network performance. We also look at the recent development of GMPLS (Generalized MPLS) that provides the control plane for lambda switching, waveband switching and fiber switching.
TriCheck: Memory Model Verification at the Trisection of Software, Hardware, and ISA

Memory consistency models (MCMs) which govern inter-module interactions in a shared memory system, are a significant, yet often under-appreciated, aspect of system design. MCMs are defined at the various layers of the hardware-software stack, requiring thoroughly verified specifications, compilers, and implementations at the interfaces between layers. Current verification techniques evaluate segments of the system stack in isolation, such as proving compiler mappings from a high-level language (HLL) to an ISA or proving validity of a microarchitectural implementation of an ISA. This paper makes a case for full-stack MCM verification and provides a toolflow, TriCheck, capable of verifying that the HLL, compiler, ISA, and implementation collectively uphold MCM requirements. The work showcases TriCheck's ability to evaluate a proposed ISA MCM in order to ensure that each layer and each mapping is correct and complete. Specifically, we apply TriCheck to the open source RISC-V ISA [55], seeking to verify accurate, efficient, and legal compilations from C11. We uncover under-specifications and potential inefficiencies in the current RISC-V ISA documentation and identify possible solutions for each. As an example, we find that a RISC-V-compliant microarchitecture allows 144 outcomes forbidden by C11 to be observed out of 1,701 litmus tests examined. Overall, this paper demonstrates the necessity of full-stack verification for detecting MCM-related bugs in the hardware-software stack.
A Long Short-Term Memory Framework for Predicting Humor in Dialogues

We propose a ﬁrst-ever attempt to employ a Long Short-Term memory based framework to predict humor in dialogues. We analyze data from a popular TV-sitcom, whose canned laughters give an indication of when the audience would react. We model the setup-punchline relation of conversational humor with a Long Short-Term Memory, with utter-ance encodings obtained from a Convolutional Neural Network. Out neural network framework is able to improve the F-score of 8% over a Conditional Random Field baseline. We show how the LSTM effectively models the setup-punchline relation reducing the number of false positives and increasing the recall. We aim to employ our humor prediction model to build effective empathetic machine able to understand jokes.