Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Homomorphisms and Phase Transitions Bell Laboratories 2c-379 Lucent Technologies 700 Mountain Ave

We model physical systems with \hard constraints" by the space Hom(G;H) of homomorphisms from a locally nite graph G to a xed nite constraint graph H. For any assignment of positive real activities to the nodes of H, there is at least one Gibbs measure on Hom(G;H); when G is in nite, there may be more than one. When G is a regular tree, the simple, invariant Gibbs measures on Hom(G;H) correspond to node-weighted branching random walks on H. We show that such walks exist for every H and , and characterize those H which, by admitting more than one such construction, exhibit phase transition behavior.
A performance analysis of VANETs routing protocols using different mobility models

In order to evaluate a vehicular network performance it is important to develop and use mobility models that accurately capture the realistic mobility characteristics of moving vehicles. In this paper, we present performance evaluation of various routing protocols including SIFT, GPSR and GOSR using different mobility models such as Random Waypoint Model (RWM), Fluid Traffic Model (FTM) and Intelligent Driver Model with Intersection Management (IDM-IM). We present simulation results that illustrate the importance of choosing a mobility model in the simulation of a vehicular network protocol. We illustrate how the performance results considerably change as a result of changing the simulated mobility model. We show how unreliable results can be obtained by using some models such as RWM and FTM. In addition, we demonstrate how more realistic mobility models such as IDM-IM often lead to more accurate results.
Gap Junctions Linking the Dendritic Network of GABAergic Interneurons in the Hippocampus

The network of GABAergic interneurons connected by chemical synapses is a candidate for the generator of synchronized oscillations in the hippocampus. We present evidence that parvalbumin (PV)-containing GABAergic neurons in the rat hippocampal CA1 region, known to form a network by mutual synaptic contacts, also form another network connected by dendrodendritic gap junctions. Distal dendrites of PV neurons run parallel to the alveus (hippocampal white matter) and establish multiple contacts with one another at the border between the stratum oriens and the alveus. In electron microscopic serial section analysis, gap junctions could be identified clearly at 24% of these contact sites. A dendrodendritic chemical synapse and a mixed synapse also were found between PV-immunoreactive dendrites. Three-dimensional reconstruction of the dendritic arborization revealed that both PV neurons of the well known vertical type (presumptive basket cells and axoaxonic cells) and those of another horizontal type constitute the dendritic network at the light microscopic level. The extent of dendritic fields of single PV neurons in the lateral direction was 538 ± 201 μm (n = 5) in the vertical type and 838 ± 159 μm (n = 6) in the horizontal type. Our previous and present observations indicate that PV-containing GABAergic neurons in the hippocampus form the dual networks connected by chemical and electrical synapses located at axosomatic and dendrodendritic contact sites, respectively. Gap junctions linking the dendritic network may mediate coherent synaptic inputs to distant interneurons and thereby facilitate the synchronization of oscillatory activities generated in the interneuron network.
Managing change: Experiences from a new e-Health initiative for patients with diabetes and cardiovascular disease

New Information and Communication Technology (ICT) solutions for e-Health are increasingly being implemented and deployed. Many studies associated with this venture usually include market research, usability studies, and testing. However one of the more important aspects which is often overlooked but directly affects the sustainability of the new technology is the management of the change process. This paper presents some guidelines grounded in the Social Sciences that could foster the success of implementing new ICT initiatives in e-Health. A case study is presented on the introduction of a mobile health care management system called myDR (my Daily Record). The objective of the research is to highlight the importance of the change management process and the role it plays in the deployment of new health care initiatives. The case study indicates that people are more inclined to use the new system if the proper change management mechanisms are in place.
A delay time analysis for multi-hop V2V communications over a linear VANET

Due to their highly dynamic nature, vehicular ad hoc networks (VANETs) are occasionally found difficult for their parameter optimization. Some attempts have been made to challenge the difficulty by modeling VANETs mathematically to predict parameter values successfully such as average cluster size and the lifetime of a path. Nevertheless, there have been no systematic approaches to analyze both node mobility and communication performances over a VANET by investigating its microspopic conditions.
Watershed: A High Performance Distributed Stream Processing System

The task of extracting information from datasets that become larger at a daily basis, such as those collected from the web, is an increasing challenge, but also provides more interesting insights and analysis. Current analyses went beyond content and now focus on tracking and understanding users' relationships and interactions. Such computation is intensive both in terms of the processing demand imposed by the algorithms and also the sheer amount of data that has to handled. In this paper we introduce Watershed, a distributed computing framework designed to support the analysis of very large data streams online and in real-time. Data are obtained from streams by the system's processing components, transformed, and directed to other streams, creating large flows of information. The processing components are decoupled from each other and their connections are strictly data-driven. They can be dynamically inserted and removed, providing an environment in which it is feasible that different applications share intermediate results or cooperate to a global purpose. Our experiments demonstrate the flexibility in creating a set of data analysis algorithms and their composition into a powerful stream analysis environment.
A standard cell characterization flow for non-standard voltage supplies

Static timing analysis (STA) is a widely used technique to perform timing verification of digital circuits analytically. These models are available in standard cell libraries, and are usually generated based on data acquired from timing characterization performed by foundries. Even though advanced node libraries are characterized for some corners, they currently do not cover voltage levels that will likely drive ultra-low power applications as those present in domains like IoT and wearable devices, a trend for upcoming years. This work contributes to solve this issue, by proposing a characterization flow that can be applied to any standard cell set. The flow is foundry-independent and relies solely on information that can be obtained from any cell library. It employs commercial tools incremented by a set of in-house scripts. As a case study, the article explores the characterization of a commercial standard cell library for a 28nm FDSOI technology. The analysis shows that this library can be characterized for voltages as low as 250mV and still guarantee that its cells, or a subset of these, work as intended by the technology rules. A set of experiments show that the flow obtains characterization results that match those of the associated commercial library within 5% of error, for those voltages where the latter provides information.
Logic of Probabilistic Arguments

1 CHARLES RIVER ANALYTICS, Cambridge, MA 02138, USA Abstract We present a logic for reasoning with probabilistic arguments to help decision making under uncertainty. The syntax of the logic is essentially modal propositional, and arguments of decision makers are expressed as sentences of the logic, with associated supports drawn from a probability dictionary. To aggregate a set of arguments for and against some decision options, we construct a Bayesian belief network based on the argument set without requiring any additional information from the decision-maker. Evidence converted from the underlying knowledge of the decision maker is posted at the relevant nodes of the belief network to compute probability distributions, and hence rankings, among the decision options. Decision-making based on such rankings of decision options is therefore guaranteed to be consistent with probability theory. We develop possible world semantics of the logic, and establish soundness and completeness results. We illustrate the proposed decision-making framework in the context of a concrete example.
The Additive Inflammatory In Vivo and In Vitro Effects of IL-7 and TSLP in Arthritis Underscore the Therapeutic Rationale for Dual Blockade

Introduction The cytokines interleukin (IL)-7 and thymic stromal lymphopoietin (TSLP) signal through the IL-7R subunit and play proinflammatory roles in experimental arthritis and rheumatoid arthritis (RA). We evaluated the effect of inhibition of IL-7R- and TSLPR-signalling as well as simultaneous inhibition of IL-7R- and TSLPR-signalling in murine experimental arthritis. In addition, the effects of IL-7 and TSLP in human RA dendritic cell (DC)/T-cell co-cultures were studied. Methods Arthritis was induced with proteoglycan in wildtype mice (WT) and in mice deficient for the TSLP receptor subunit (TSLPR-/-). Both mice genotypes were treated with anti-IL-7R or phosphate buffered saline. Arthritis severity was assessed and local and circulating cytokines were measured. Autologous CD1c-positive DCs and CD4 T-cells were isolated from peripheral blood of RA patients and were co-cultured in the presence of IL-7, TSLP or both and proliferation and cytokine production were assessed. Results Arthritis severity and immunopathology were decreased in WT mice treated with anti-IL-7R, in TSLPR-/- mice, and the most robustly in TSLPR-/- mice treated with anti-IL-7R. This was associated with strongly decreased levels of IL-17, IL-6 and CD40L. In human DC/T-cell co-cultures, TSLP and IL-7 additively increased T-cell proliferation and production of Th17-associated cytokines, chemokines and tissue destruction factors. Conclusion TSLP and IL-7 have an additive effect on the production of Th17-cytokines in a human in vitro model, and enhance arthritis in mice linked with enhanced inflammation and immunopathology. As both cytokines signal via the IL-7R, these data urge for IL-7R-targeting to prevent the activity of both cytokines in RA.
INVESTIGATING POST-PURCHASE DISSONANCE WITHIN THE GROCERY SHOPPING CONTEXT: A STUDY USING VIRTUAL REALITY SIMULATION

Retail strategies geared towards prolonging the stay of shoppers or influencing them to impulse buy can actually be contradicting to their financial and marketing goals if the utilitarian nature of grocery shoppers is disregarded. This retailer-shopper conflict arises from the need of grocery shoppers to accomplish their task quickly and effortlessly and the goal of the retailer to make them spend more while in the store. With shoppers being influenced to deviate from their shopping goals, they are able to experience cognitive dissonance in the form of regret, an undesirable state which may have a negative impact on their repatronage towards the store. As a key element of this study, how shoppers rationalize their feelings of regret to maintain positive feedback towards the store by engaging in positive affect search is also investigated. This study considered service encounters assortment variety and salesperson responsiveness and their influence on the time shoppers spend in the store. Furthermore, it aims to determine the effect of prolonged shopping time on cognitive dissonance, as well as the response of shoppers towards cognitive dissonance in terms of their positive affect search and repatronage intention. To achieve these objectives, a simulation of grocery shopping was conducted. Using Multiple Linear Regression (MLR), results indicate that prolonged shopping can cause cognitive dissonance. While cognitive dissonance can lower repatronage, shoppers may engage in positive affect search to sustain it. With these findings, retailers can be aware of what drives customer repatronage in the grocery setting, which is rooted from the minimization of post-purchase dissonance. More importantly, despite modifying the store to prolong the stay of shoppers, it must be ensured that a good shopping experience must be instilled for shoppers to come back to the store.
Local Bifurcations in the Symmetric Model of Selection withFertility

The analysis of diierence equations in population dynamics is frequently connned to the calculation of xed points and the determination of their stability. In general it is not possible to deduce much about the global behavior of the system from such information. This paper presents a study of the bifurcations of xed points in the symmetric model of selection with fertility diierences. Equivalent methods can be applied to other continuous and discrete models to study the dynamics in the vicinity of xed points in a systematic way and as a rst step in an analysis of the global dynamics.
A Network-Cliques Based Role Mining Model

Role mining for Role-Based Access Control (RBAC) has emerged as a challenging area of information security. Current role mining models are mainly based on data mining with RBAC semantics and are not the pure mathematical models. Almost every model need a specific algorithm and the expressions of results and process are not vivid and explicit enough. To address these problems, this study proposes a role mining model called network-clique-finding (NCF) model, which is based on cliques finding of network. By this model, the role mining problem can be mapped onto the classic problems of graph theory and can be solved by using many well-known algorithms and tools. The correctness of NCF model is verified both theoretically and experimentally. Furthermore, some experimental issues is discussed and based on the experiment, our study is compared with some other methods. Finally, we conclude our work
Accurate Estimators for Improving Minwise Hashing and b-Bit Minwise Hashing

Minwise hashing is the standard technique in the context of search and databases for efficiently estimating set (e.g., high-dimensional 0/1 vector) similarities. Recently, b-bit minwise hashing was proposed which significantly improves upon the original minwise hashing in practice by storing only the lowest b bits of each hashed value, as opposed to using 64 bits. b-bit hashing is particularly effective in applications which mainly concern sets of high similarities (e.g., the resemblance >0.5). However, there are other important applications in which not just pairs of high similarities matter. For example, many learning algorithms require all pairwise similarities and it is expected that only a small fraction of the pairs are similar. Furthermore, many applications care more about containment (e.g., how much one object is contained by another object) than the resemblance. In this paper, we show that the estimators for minwise hashing and b-bit minwise hashing used in the current practice can be systematically improved and the improvements are most significant for set pairs of low resemblance and high containment.
Teaching Chronobiology and Sleep Habits in School and University.

— Early morning school schedules are in the opposite direction to the sleep–wake cycle in adolescence and early adulthood. This conflict leads to sleep deprivation and irregular patterns whose consequences are scarcely explored. This article discusses the effects of three educational experiences with high school students, parents, teachers, and medical students. The first experience was developed with high school students in Natal, Brazil, to determine whether sleep habits would improve with increased awareness. Positive effects were observed in some aspects of sleep knowledge and practices. In the second experience in Atlanta, GA, sleep education activities were presented to middle and high school teachers, parents, and students to emphasize the importance of sleep. In the third program in Murcia, Spain, undergraduate medical students were introduced to chronobiology of sleep by a practical exercise that pointed out to what extent they shared most of adolescent sleep characteristics. Educational chronobiological experiences about sleep are essential to develop healthy sleep habits in the general population, particularly in students.
DIMSMAD: A Distributed Information Management System for Mobile Ad-Hoc Networks

Mobile ad-hoc networks are dynamic temporary networks that work without any centralized infrastructure. There is a need to provide a middle layer substrate for higher lever applications to query the state of such networks. In this paper, we propose the design of DIMSMAD, a Distributed Information Management System for Mobile Ad-hoc Networks. Specifically, we show how hierarchical aggregation can be achieved using the concept of mobile clusters. We also show how MADPastry, an existing overlay architecture for mobile ad-hoc networks, can be exploited effectively for overlay routing of aggregation values along aggregation trees.
The separation principle: A programming paradigm

In the development of any technology, there's always a tendency to lose sight of the basic problems that stimulated its introduction in the first place. Technologies in software construction are no exception. One of the essential parts of software construction is the underlying programming paradigm. In the last few decades, different software construction paradigms have evolved - for example, structured programming in the 1970s and object-oriented programming in the 1980s. Although OOP dominates current software construction, it introduces several problems that might raise questions about its understandability and efficiency. W. Cave (1995) was the first to propose and develop the separation principle as a programming paradigm. One major contribution is that it moves most complex issues related to data sharing, parameter passing, and scope into a programming model in which simple graphics can represent access to data. The separation principle can simplify or even eliminate these issues. By expressing access permissions in simple, canonical drawings, the separation principle makes it easy to comprehend the full range of relationships between data and instructions, and thus the code's structure. A simple way to show connectivity, a key property affecting program understandability, is by separating data from instructions. Two of prediction systems' flagship products, the visual software environment (VSE) and the general simulation system (GSS), embody this idea. This paper discusses the separation principle in the context of conventional languages such as C and C++.
A Survey: Network Intrusion Detection System based on Data Mining Techniques

— In spite of growing information system widely, security has remained one hard-hitting area for computers as well as networks. In information protection, Intrusion Detection System (IDS) is used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining is an efficient artifice that can be applied to intrusion detection to ascertain a new outline from the massive network data as well as it use to reduce the strain of the manual compilations of the normal and abnormal behaviour patterns. This work reviews the present state of data mining techniques and compares various data mining techniques used to implement an intrusion detection system such as, Support Vector Machine, Genetic Algorithm, Neural network, Fuzzy Logic, Bayesian Classifier, K-Nearest Neighbour and decision tree Algorithms by highlighting the advantages and disadvantages of each of the techniques
Adaptive Dual-Cache Scheme with Dynamic Prefetching Scheme in Parallel File System

An adaptive Dual-Cache Scheme(DCS) and dynamic prefetching scheme were designed to improve the performance of the Parallel File System for Linux(PFSL). PFSL is a parallel file system for a clustering environment and is implemented using a multi-threaded programming technique with POSIX thread libraries supported by Linux. The proposed adaptive DCS and dynamic prefetching scheme both reflect the current file request. The former reduces the first file request response time, whereas the latter improves the file system performance by adjusting the amount of data for prefetching.
The Resilience of Microbial Community under Drying and Rewetting Cycles of Three Forest Soils

Forest soil ecosystems are associated with large pools and fluxes of carbon (C) and nitrogen (N), which could be strongly affected by variation in rainfall events under current climate change. Understanding how dry and wet cycle events might influence the metabolic state of indigenous soil microbes is crucial for predicting forest soil responses to environmental change. We used 454 pyrosequencing and quantitative PCR to address how present (DNA-based) and potentially active (RNA-based) soil bacterial communities might response to the changes in water availability across three different forest types located in two continents (Africa and Asia) under controlled drying and rewetting cycles. Sequencing of rRNA gene and transcript indicated that Proteobacteria, Actinobacteria, and Acidobacteria were the most responsive phyla to changes in water availability. We defined the ratio of rRNA transcript to rRNA gene abundance as a key indicator of potential microbial activity and we found that this ratio was increased following soil dry-down process whereas it decreased after soil rewetting. Following rewetting Crenarchaeota-like 16S rRNA gene transcript increased in some forest soils and this was linked to increases in soil nitrate levels suggesting greater nitrification rates under higher soil water availability. Changes in the relative abundance of (1) different microbial phyla and classes, and (2) 16S and amoA genes were found to be site- and taxa-specific and might have been driven by different life-strategies. Overall, we found that, after rewetting, the structure of the present and potentially active bacterial community structure as well as the abundance of bacterial (16S), archaeal (16S) and ammonia oxidizers (amoA), all returned to pre-dry-down levels. This suggests that microbial taxa have the ability to recover from desiccation, a critical response, which will contribute to maintaining microbial biodiversity in harsh ecosystems under environmental perturbations, such as significant changes in water availability.
Research on Particle Filter Algorithm Based on Neural Network

Aiming at the weight degeneracy phenomena in particle filter algorithm, the improved particle filtering algorithm based on neural network was presented. BP neural network and generalized regression neural network (GRNN) are adapted to improve resampling algorithm and importance probability density function. This algorithm utilizes the nonlinear mapping function of BP neural network. First of all, to sample from the importance density function of particle weight division, the weighted particle is splitted into two small weight particles. Then, abandon the weight of very small particles, and adjust the particles with smaller weight by using the neural network. This algorithm optimizes the sample from importance density function by generalized regression neural network. Through GRNN, the samples are adjusted. The samples are more nearer to the posterior probability density. Simulation results show that the particle filter algorithm based on neural network can improve the diversity of particle samples, increase the effective particle number, reduce the mean square error, and the filtering performance is improved. It is proved that this particle filter algorithm based on neural network is available and effective.