Miracle-dz/UniFAR-FTUs huggingface dataset:
AVACS -- Automatic Verification and Analysis of Complex Systems

The recently presented constraint solver HySAT tackles the in general undecidable problem of solving mixed Boolean and non-linear arithmetic constraint formulae over the reals involving transcendental functions. The algorithmic core of HySAT is the iSAT algorithm, a tight integration of the Davis-Putnam-Logemann-Loveland algorithm with interval constraint propagation enriched by enhancements like conflict-driven clause learning and non-chronological backtracking. However, it was an open question whether HySAT’s conflict analysis scheme, an adapted first unique implication point technique as applied in most modern SAT solvers, performs favorably compared to other schemes. In this paper, we compare several conflict analysis heuristics to answer this question. Furthermore, we consider the integration of restarts into the iSAT algorithm and investigate their impact. For our empirical results we use benchmarks from the hybrid systems domain.
AVACS – Automatic Verification and Analysis of Complex Systems REPORTS of SFB / TR 14 AVACS Editors : Board of SFB / TR 14 AVACS Probabilistic Reachability for Parametric Markov Models by

Given a parametric Markov model, we consider the problem of computing the rational function expressing the probability of reaching a given set of states. To attack this principal problem, Daws has suggested to first convert the Markov chain into a finite automaton, from which a regular expression is computed. Afterwards, this expression is evaluated to a closed form function representing the reachability probability. This paper investigates how this idea can be turned into an effective procedure. It turns out that the bottleneck lies in the growth of the regular expression relative to the number of states (n ). We therefore proceed differently, by tightly intertwining the regular expression computation with its evaluation. This allows us to arrive at an effective method that avoids this blow up in most practical cases. We give a detailed account of the approach, also extending to parametric models with rewards and with non-determinism. Experimental evidence is provided, illustrating that our implementation provides meaningful insights on non-trivial models.
Cross-Lingual Topic Alignment in Time Series Japanese / Chinese News

Among various types of recent information explosion, that in news stream is also a kind of serious problems. This paper studies issues regarding topic modeling of information flow in multilingual news streams. If someone wants to find differences in the topics of Japanese news and Chinese news, it is usually necessary for him/her to carefully watch every article in Japanese and Chinese news streams at every moment. In such a situation, topic models such as LDA (Latent Dirichlet Allocation) and DTM (dynamic topic model) are quite effective in estimating distribution of topics over a document collection such as articles in a news stream. Especially, as a topic model, this paper employs DTM, but not LDA,since it canconsidercorrespondence between topics of consecutive dates. Based on the results of estimating distribution of topics in Japanese / Chinese news streams, this paper proposes how to analyze cross-lingual alignment of topics in time series Japanese / Chinese news streams.
Inconsistencies between observed, reconstructed, and simulated precipitation indices for England since the year 1650 CE

Abstract. The scarcity of long instrumental records, uncertainty in reconstructions, and insufficient skill in model simulations hamper assessing how regional precipitation changed over past centuries. Here, we use standardized precipitation data to compare a regional climate simulation, reconstructions, and long observational records of seasonal (March to July) mean precipitation in England and Wales over the past 350 years. The Standardized Precipitation Index is a valuable tool for assessing agreement between the different sources of information, as it allows for a comparison of the temporal evolution of percentiles of the precipitation distributions. These evolutions are not consistent among reconstructions, a regional simulation, and instrumental observations for severe and extreme dry and wet conditions. The lack of consistency between the different data sets may be due to the dominance of internal climate variability over the impact of natural exogenous forcing conditions on multi-decadal timescales. The disagreement between sources of information reduces our confidence in inferences about the origins of hydroclimate variability for small regions. However, it is encouraging that there is still some agreement between a regional simulation and observations. Our results emphasize the complexity of hydroclimate changes during the recent centuries and stress the necessity of a thorough understanding of the processes affecting forced and unforced precipitation variability.
AISeL ) All Sprouts Content Sprouts 12-6-2010 Antecedents and Outcomes of Information Privacy Concerns in Online Social Networking : A Theoretical Perspective

This article attempts to contribute to the information privacy literature by providing a comprehensive theory on antecedents and outcomes of Online Social Network (OSN) usersâ information privacy concerns. Based on a review of existing literature on information privacy and considering the unique characteristics of OSN setting, this paper develops a conceptual model with 14 propositions. The goal of this theory is twofold: (1) to explicate OSN provider organizationâs information practices that lead emergence of usersâ information privacy concerns and discuss the specific conditions under which these practices are perceived privacy issues, (2) to identify the behavioural and affective outcomes of usersâ perceived information privacy concerns.
A Telescopic Independent Component Analysis on Functional Magnetic Resonance Imaging Data Set

Brain function can be modeled as the dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed “Telescopic independent component analysis (ICA),” designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive ICA strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to study the default mode network (DMN) and evaluate the difference between healthy people and individuals with schizophrenia. We show that the telescopic ICA approach can detect the spatial hierarchy of DMN and their associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.
Hepatocyte differentiation requires anisotropic expansion of bile canaliculi

During liver development, bipotential progenitor cells called hepatoblasts differentiate into hepatocytes or cholangiocytes. Hepatocyte differentiation is uniquely associated with multi-axial polarity, enabling the anisotropic expansion of apical lumina between adjacent cells and formation of a three-dimensional network of bile canaliculi (BC). Cholangiocytes, the cells forming the bile ducts, exhibit the vectorial polarity common to epithelial cells. Whether and how cell polarization feeds back on the gene regulatory pathways governing hepatoblast differentiation is unknown. Here, we used primary hepatoblasts to investigate the contribution of anisotropic apical expansion to hepatocyte differentiation. Silencing of the small GTPase Rab35 caused isotropic lumen expansion and formation of multicellular cysts with the vectorial polarity of cholangiocytes. Gene expression profiling revealed that these cells express reduced levels of hepatocyte markers and upregulate genes associated with cholangiocyte identity. Time-course RNA sequencing demonstrated that loss of lumen anisotropy precedes these transcriptional changes. Independent alterations in apical lumen morphology induced either by modulation of the subapical actomyosin cortex or increased intraluminal pressure caused similar transcriptional changes. These findings suggest that cell polarity and lumen morphogenesis feedback to hepatoblast-to-hepatocyte differentiation. Summary statement Differentiation of liver progenitors to functional hepatocytes requires anisotropic elongation of their nascent apical surfaces into tubular bile canaliculi.
VYSOKÉ UČENÍ TECHNICKÉ V BRNĚ Fakulta elektrotechniky a komunikačních technologií Ústav fyziky

This PhD thesis describes, and covers the preparation of field-emission cathodes that represent a high-quality, inexpensive source of electrons for devices that work with a focused electron beam. In this work, an improved method of electrochemical fabrication is used for the preparation of a composite field-emission cathode. The composite structure improves current stability in comparison to tungesten-based pure field-emission cathodes. Improvements to the technology used were implemented on the basis of findings obtained using spectrum noise diagnostics, which are based on measuring and evaluating the emission current in the time and in frequency domains. Additionally, noise spectroscopy provides important information about charge transport, electron mobility, and lifespan, which is essential for future research. Results obtained using the experimental method have been further supplemented with theoretical data provided by a computer simulation in order to establish characterization procedures for improved field-emission cathodes. ABSTRAKT Téma doktorské práce se zabývá přípravou a popisem katod na bázi studené polní emise, jenž představují kvalitní a levný elektronový zdroj pro zařízení pracující se zaostřeným elektronovým svazkem. Pro přípravu kompozitní autoemisní katody byla využita elektrochemická metoda výroby. Kompozitní struktura katody zlepšuje proudovou stabilitu ve srovnání s čistě autoemisními katodami na bázi wolframu. Na základě charakterizace katody, jenž byla nově provedena metodou šumové spektroskopie, byla implementována technologická zlepšení stávající výroby. Metoda šumové spektroskopie je založena na analýze emisního proudu v časové a kmitočtové rovině, ale především poskytuje informace o nosiči náboje, o jeho pohyblivosti a dále o životnosti katody. Výsledky experimentální části byly rozšířeny teoretickými simulacemi, vedoucími k návrhu metodiky charakterizace vylepšené autoemisní katody. KNÁPEK, Alexandr Methods of Preparation and Characterisation of Experimental FieldEmission Cathodes: doctoral thesis. Brno: Brno University of Technology, Faculty of Electrical Engineering and Communication, Department of Physics, 2013. 31 p. Supervised by Prof. Ing. Lubomír Grmela, CSc.
SHORTENED TITLE : Action planning needs perception Choosing between alternative wrist postures : action planning needs perception

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ART EDUCATION FOR LEARNERS WITH SPECIAL EDUCATIONAL NEEDS: CASE STUDY OF VISUAL ARTEFACTS PRODUCED BY HEARING-IMPAIRED PUPILS OF TETTEH OCLOO STATE SCHOOL FOR THE DEAF

Formal education becomes most authentic, inclusive, and learner-centred when Art is at its forefront. The Arts provide learners with authentic learning experiences that engage their minds, hearts, hands, and entire bodies. The general relevance of Art education for learners with special educational needs has been variously and solidly argued by previous studies. However, much is not known about the creative artefacts of learners with special educational needs. The focus of this qualitative case study was to artistically appreciate selected visual artefacts produced by hearing-impaired pupils of Tetteh Ocloo State School for the Deaf located at Adjei-Kojo, a suburb of Accra in Ghana. Adopting triangulation of instruments (semi-structured interviews, field observation & photography), the study gathered in-depth data from sixteen (16) purposively sampled respondents with findings analysed using qualitative descriptive tenets. The study ascertained that the Creative Arts subject is dedicatedly taught in Tetteh Ocloo State School for the Deaf by Art specialist teachers. As a result, the study observed that the hearing-impaired primary pupils of Tetteh Ocloo State School for the Deaf were able to produce intriguing Visual artefacts (ranging from drawings/painting, clay work & papercraft). Aesthetic appreciation of some of the artefacts revealed that the works represent the oral accounts of the pupils’ worldview of salient experiences in their immediate social and physical environments. It is recommended that the Art specialist teachers of Tetteh Ocloo State School for the Deaf should continue to teach the Creative Arts subject with dedication as this would nurture and inculcate the needed creative, innovative, and inventive skills onto the pupils for responsible adulthood. Article visualizations:
Music2Dance: DanceNet for Music-driven Dance Generation

Synthesize human motions from music, i.e., music to dance, is appealing and attracts lots of research interests in recent years. It is challenging due to not only the requirement of realistic and complex human motions for dance, but more importantly, the synthesized motions should be consistent with the style, rhythm and melody of the music. In this paper, we propose a novel autoregressive generative model, DanceNet, to take the style, rhythm and melody of music as the control signals to generate 3D dance motions with high realism and diversity. To boost the performance of our proposed model, we capture several synchronized music-dance pairs by professional dancers, and build a high-quality music-dance pair dataset. Experiments have demonstrated that the proposed method can achieve the state-of-the-art results.
A Multistage Deep Belief Networks Application on Arrhythmia Classification

: An electrocardiogram (ECG) is a biomedical signal type that determines the normality and abnormality of heart beats using the electrical activity of the heart and has a great importance for cardiac disorders. The computer-aided analysis of biomedical signals has become a fabulous utilization method over the last years. This study introduces a multistage deep learning classification model for automatic arrhythmia classification. The proposed model includes a multi-stage classification system that uses ECG waveforms and the Second Order Difference Plot (SODP) features using a Deep Belief Network (DBN) classifier which has a greedy layer wise training with Restricted Boltzmann Machines algorithm. The multistage DBN model classified the MIT-BIH Arrhythmia Database heartbeats into 5 main groups defined by ANSI/AAMI standards. All ECG signals are filtered with median filters to remove the baseline wander. ECG waveforms were segmented from long-term ECG signals using a window with a length of 501 data points (R wave centered). The extracted waveforms and elliptical features from the SODP are utilized as the input of the model. The proposed DBN-based multistage arrhythmia classification model has discriminated five types of heartbeats with a high accuracy rate of 96.10%.
On Gabbay's Temporal Xed Point Operator

We discuss the temporal logic`USF', involving Until, Since and the xed point operator ' of Gabbay, with semantics over the natural numbers. We show that any formula not involving Until is equivalent to one without nested xed point operators. We then prove that USF has expressive power matching that of the monadic second-order logic S1S. The proof shows that any USF-formula is equivalent to one with at most two nested xed point operators | i.e., no branch of its formation tree has more than two ''s. We then axiomatise USF and prove that it is decidable, with PSPACE-complete satissability problem. Finally, we discuss an application of these results to the executable temporal logic system`MetateM'.
Analysis of Dynamic Structures for E(cid:14)cient Parallel Execution (cid:3)

Programs written in high-level programming languages and in particular object-oriented languages make heavy use of references and dynamically allocated structures. As a result, precise analysis of such features is critical for producing e(cid:14)cient implementations. The information produced by this analysis is invaluable for compiling programs for both sequential and parallel machines. This paper presents a new structure analysis technique handling references and dynamic structures which enables precise analysis of in(cid:12)nite recursive data structures. The precise analysis depends on an enhancement of Chase et al.'s Storage Shape Graph (SSG) called the Abstract Storage Graph (ASG) which extends SSG's with choice nodes, identity paths, and specialized storage nodes and references. These extensions allow ASG's to precisely describe singly-and multiply-linked lists as well as a number of other pointer structures such as octrees, and to analyze programs which manipulate them. We describe program analysis to produce the ASG, and focus on the key operations: the transfer functions, summarization and deconstruction. Summarization compresses the ASG in such a way as to capture critical interdependencies between references. Deconstruction uses this information, stored by identity paths and re(cid:12)ned references and nodes, to retrieve individual nodes for strong updates.
Poisson-Networks: A Model for Structured Poisson Processes.

Modelling structured multivariate point process data has wide ranging applications like understanding neural activity, developing faster file access systems and learning dependencies among servers in large networks. In this paper, we develop the Poisson network model for representing multivariate structured Poisson processes. In our model each node of the network represents a Poisson process. The novelty of our work is that waiting times of a process are modelled by an exponential distribution with a piecewise constant rate function that depends on the event counts of its parents in the network in a generalised linear way. Our choice of model allows to perform exact sampling from arbitrary structures. We adopt a Bayesian approach for learning the network structure. Further, we discuss fixed point and sampling based approximations for performing inference of rate functions in Poisson networks.
Parametric Prediction from Parametric Agents

Yuan Luo Department of Information Engineering, The Chinese University of Hong Kong, yluo@ie.cuhk. edu.hk, Nihar B. Shah Department of Electrical Engineering and Computer Sciences, UC Berkeley, nihar@eecs.berkeley.edu Jianwei Huang Department of Information Engineering, The Chinese University of Hong Kong, jwhuang@ie.cuhk. edu.hk Jean Walrand Department of Electrical Engineering and Computer Sciences, UC Berkeley, walrand@berkeley.edu
in Computer

— Statistical modeling technique has pivotal role in better understanding of the software development processes. Among them neural network techniques have enhanced predictive capability than most other statistical models. This paper explains the application of principal component analysis to neural network modeling as a way to improve predictability of neural network. The purpose of principal component analysis is to augment the performance of discriminant software quality models. The accurate neural training can be done by transferring the raw data into principal components. In this paper, the significance of principal components analysis is illustrated with the help of a commercial raw dataset and subsequently neural network modeling is described.
Sugarcane Pollen Viability and Seed Setting as Affected by Daylength Decline Rates and Relative Humidity.

INTRODUCTION . 58 MATERIALS AND METHODS . 59 RESULTS AND DISCUSSION . 68 Humidity level experiment . 68 Pollen viability . 68 Daily pollen germination . 68 Collective pollen germination . 69 Seed production . 72 Humidity duration experiment . 75 Pollen viability . 75 Daily pollen germination . 75 Collective pollen germination . 76 Seed production . 79 REFERENCES . 81 SUMMARY . 83 BIBLIOGRAPHY . 88 APPENDICES . 92 Appendix 1. List of male varieties and the number of crosses made with them during three seasons of . the photoperiod experiment . 92
Operational Interpretations of an Extension of F ! with Control Operatorsy Capsule Review

We study the operational semantics of an extension of Girard's System F! with two control operators: an abort operation that abandons the current control context, and a callcc operation that captures the current control context. Two classes of operational semantics are considered, each with a call-by-value and a call-by-name variant, diiering in their treatment of polymorphic abstraction and instantiation. Under the standard semantics polymorphic abstractions are values and polymorphic instantiation is a signiicant computation step; under the ML-like semantics evaluation proceeds beneath polymorphic abstractions and polymorphic instantiation is computationally insigniicant. Compositional, type-preserving continuation-passing style (cps) transformation algorithms are given for the standard semantics, resulting in terms on which all four evaluation strategies coincide. This has as a corollary the soundness and termination of well-typed programs under the standard evaluation strategies. In contrast, such results are obtained for the call-by-value ML-like strategy only for a restricted sub-language in which con-structor abstractions are limited to values. The ML-like call-by-name semantics is indistinguishable from the standard call-by-name semantics when attention is limited to complete programs. y This is a revised and expanded version of \Explicit Polymorphism and CPS Conversion"
Community-Centric Graph Convolutional Network for Unsupervised Community Detection

Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN method to improve accuracy. However, the existing GCN community-finding methods are semi-supervised, even though community finding is essentially an unsupervised learning problem. We developed a new GCN approach for unsupervised community detection under the framework of Autoencoder. We cast MRFasGCN as an encoder and then derived node community membership in the hidden layer of the encoder. We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space. We designed a scheme of local enhancement to accommodate nodes to have more common neighbors and similar attributes with similar community memberships. Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone.