Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Inference from Power Law Distributed Web-Based Social Interactions

Power law distributions describe naturally-occurring events such as the magnitude of earthquakes, the diameter of moon craters or the complexity of the nervous system (Albert Lazslo Barabasi & Albert, 1999; Newman, 2005). Many man-made phenomena also tend to exhibit power law distributions, for example, the distribution of book titles sold, the sizes of cities or intensity of wars (Newman, 2005). Social networks, such as co-authorship networks of scientific collaborations or Hollywood actor networks and web-based social spaces, all follow power law distributions (A. L. Barabasi et al., 2002; Ravid & Rafaeli, 2004). This article focuses on the power law distributions found in web-based social spaces and proposes a method to perform statistical inference on data from such distributions in order to explain behavior and social phenomena.
An interactive and adaptive building layer: strategies for allowing people to become advanced building-users

This paper reports outcomes of a pilot study to develop a conceptual framework to allow people to retrofit a building-layer to gain better control of their own built-environments. The study was initiated by the realisation that discussions surrounding the improvement of building performances tend to be about top-down technological solutions rather than to help and encourage bottom-up involvement of building-users. While users are the ultimate beneficiaries and their feedback is always appreciated, their direct involvements in managing buildings would often be regarded as obstruction or distraction. This is largely because casual interventions by uninformed building-users tend to disrupt the system. Some earlier researches showed however that direct and active participation of users could improve the building performance if appropriate training and/or systems were introduced. We also speculate this in long run would also make the built environment more sustainable. With this in mind, we looked for opportunities to retrofit our own office with an interactive layer to study how we could introduce ad-hoc systems for building-users. The aim of this paper is to describe our vision and initial attempts followed by discussion.
Sparse LMS for system identification

We propose a new approach to adaptive system identification when the system model is sparse. The approach applies ℓ1 relaxation, common in compressive sensing, to improve the performance of LMS-type adaptive methods. This results in two new algorithms, the zero-attracting LMS (ZA-LMS) and the reweighted zero-attracting LMS (RZA-LMS). The ZA-LMS is derived via combining a ℓ1 norm penalty on the coefficients into the quadratic LMS cost function, which generates a zero attractor in the LMS iteration. The zero attractor promotes sparsity in taps during the filtering process, and therefore accelerates convergence when identifying sparse systems. We prove that the ZA-LMS can achieve lower mean square error than the standard LMS. To further improve the filtering performance, the RZA-LMS is developed using a reweighted zero attractor. The performance of the RZA-LMS is superior to that of the ZA-LMS numerically. Experiments demonstrate the advantages of the proposed filters in both convergence rate and steady-state behavior under sparsity assumptions on the true coefficient vector. The RZA-LMS is also shown to be robust when the number of non-zero taps increases.
Memory Access Characterization of Scientific Applications on GPU and Its Implication on Low Power Optimization

Following current IC design technology trend, modern GPUs integrate more and more processing cores, and the speed gap between processor and memory system becomes even larger. As the number of cores continually increases, the available bandwidth per core decreases correspondingly. Therefore, memory access performance has been one of the most critical bottlenecks for better performance. This paper analyzes the impact of memory system on performance and scalability for GPU with several scientific applications using a cycle-accurate simulator. Two observations we make are (1) that memory bandwidth has relatively greater impact on performance than memory latency, because the latter factor could be well hidden with tremendous concurrent executing threads supported in modern GPU architecture, and (2) that through examining the performance scalability of variable active cores, using the maximum hardware-supported cores may not bring in better performance, especially for the memory-intensive applications. In the end we suggest a better power-efficient exploitation of GPU is to make judicious concurrency-throttling based on the memory usage in application.
Sport summer camp for children and youth with visual impairment: Descriptive case study of Camp Abilities

The purpose of this study was to describe the operational characteristics of Camp Abilities, an educational sports summer camp for children and youth with visual impairment. In this descriptive multiple case study, the data collected comprises interviews with seven adapted physical education faculty members, questionnaire, and document analysis. Camps were often run at university campuses, where undergraduate and graduate physical education students perform the role of instructing campers and eventually helping in the camp organizing process. These camps include a minimum of 30 to more than 120 participants (i.e. directors, campers, coaches, and specialist) each session, lasting from 4 to 7 days. In this scenario, although each camp has particular features, camps’ purposes, participants, activities, and schedules are very similar, showing that this is a feasible and reproducible approach, which can provide enriching opportunities for diverse groups.
A parallel repetition theorem

We show that a parallel repetition of any two-prover one-round proof system (MIP(2,1)) decreases the probability of error at an exponential rate. No constructive bound was previously known. The constant in the exponent (in our analysis) depends only on the original probability of error and on the total number of possible answers of the two provers. The dependency on the total number of possible answers is logarithmic, which was recently proved to be almost the best possible [U. Feige and O. Verbitsky, Proc.11th Annual IEEE Conference on Computational Complexity, IEEE Computer Society Press, Los Alamitos, CA, 1996, pp. 70--76].
Cucumber (Cucumis sativus L.) Nitric Oxide Synthase Associated Gene1 (CsNOA1) Plays a Role in Chilling Stress

Nitric oxide (NO) is a gaseous signaling molecule in plants, transducing information as a result of exposure to low temperatures. However, the underlying molecular mechanism linking NO with chilling stress is not well understood. Here, we functionally characterized the cucumber (Cucumis sativus L.) nitric oxide synthase-associated gene, NITRIC OXIDE ASSOCIATED 1 (CsNOA1). Expression analysis of CsNOA1, using quantitative real-time PCR, in situ hybridization, and a promoter::β-glucuronidase (GUS) reporter assay, revealed that it is expressed mainly in the root and shoot apical meristem (SAM), and that expression is up-regulated by low temperatures. A CsNOA1-GFP fusion protein was found to be localized in the mitochondria, and ectopic expression of CsNOA1 in the A. thaliana noa1 mutant partially rescued the normal phenotype. When overexpressing CsNOA1 in the Atnoa1 mutant under normal condition, no obvious phenotypic differences was observed between its wild type and transgenic plants. However, the leaves from mutant plant grown under chilling conditions showed hydrophanous spots and wilting. Physiology tolerance markers, chlorophyll fluorescence parameter (Fv/Fm), and electrolyte leakage, were observed to dramatically change, compared mutant to overexpressing lines. Transgenic cucumber plants revealed that the gene is required by seedlings to tolerate chilling stress: constitutive over-expression of CsNOA1 led to a greater accumulation of soluble sugars, starch, and an up-regulation of Cold-regulatory C-repeat binding factor3 (CBF3) expression as well as a lower chilling damage index (CI). Conversely, suppression of CsNOA1 expression resulted in the opposite phenotype and a reduced NO content compared to wild type plants. Those results suggest that CsNOA1 regulates cucumber seedlings chilling tolerance. Additionally, under normal condition, we took several classic inhibitors to perform, and detect endogenous NO levels in wild type cucumber seedling. The results suggest that generation of endogenous NO in cucumber leaves occurs largely independently in the (CsNOA1) and nitrate reductase (NR) pathway.
Reorganization of Agent Networks with Reinforcement Learning Based on Communication Delay

We propose the team formation method for task allocations in agent networks by reinforcement learning based on communication delay and by reorganization of agent networks. A task in a distributed environment like an Internet application, such as grid computing and service-oriented computing, is usually achieved by doing a number of subtasks. These subtasks are constructed on demand in a bottom-up manner and must be done with appropriate agents that have capabilities and computational resources required in each subtask. Therefore, the efficient and effective allocation of tasks to appropriate agents is a key issue in this kind of system. In our model, this allocation problem is formulated as the team formation of agents in the task-oriented domain. From this perspective, a number of studies were conducted in which learning and reorganization were incorporated. The aim of this paper is to extend the conventional method from two viewpoints. First, our proposed method uses only information available locally for learning, so as to make this method applicable to real systems. Second, we introduce the elimination of links as well as the generation of links in the agent network to improve learning efficiency. We experimentally show that this extension can considerably improve the efficiency of team formation compared with the conventional method. We also show that it can make the agent network adaptive to environmental changes.
Information Retrieval of a Name by using its Aliases using Pattern Extraction Algorithm

A person may have multiple personal name aliases and that same thing might available be on the web. Identifying aliases of a name is useful in information retrieval, investigating about things, knowledge management, sentiment analysis, relation extraction and name disambiguation. Extracting aliases of an entity is important task in various jobs such as identification of relations among entities, web search and entity disambiguation. The objective of detecting aliases from the web is to retrieve all the information pertaining to a personal name whose content is described with different nick names called aliases in different documents of web. The Web contains aliases of popular personalities in various domains like sports, politics, music, cinema etc., and the problem is that it does not contain alias information or information related to its alias names. Recently, there are proven methods of extracting aliases through lexical pattern based retrieval tested using real-world name-alias pairs in Japanese and English as training data related to limited domains. In this paper, a method is proposed in which a lexical-pattern-based approach to extract aliases of a given name that is helpful in information retrieval. A set of names is used and their aliases to extract lexical patterns that describe numerous ways in which information related to aliases of a name is presented on the web the aliases which will be extracted using the proposed method can be successfully utilized in an information retrieval task and will improve in a relation detection task.
Context-Aware Leasing for Mobile Ad hoc Networks

Distributed memory management is substantially complicated in mobile ad hoc networks due to the fact that nodes in the network only have intermittent connectivity and often lack any kind of centralized coordination facility. Leasing provides a robust mechanism to manage reclamation of remote objects in mobile ad hoc networks. However, leasing techniques limits the lifetime of remote objects based on timeouts. In mobile networks, we also observe that devices need to continuously adapt to changes in their context. In this position paper, we argue that changes in context not only require adaptation in the behaviour of the application but also permeate to distributed memory management, leading to the concept of context-aware leasing.
Effective segmentation for move objects in video

A novel scheme for automatic video segmentation is proposed in this paper by fully exploiting both inter-frame temporal information and intra-frame spatial information from the video sequence. The initial motion regions were detected in each frame by using a time-weighted queue. Many approximately homogenous color-texture regions were formed by using improved watershed based on a spatial similarity criterion and object size. Finally, further merging of temporal and spatial segment results by using a majority rule, and refining of them were performed based on spatial coherency and temporal continuity. As a result, the proposed method is robust and gets fine boundaries of objects. Furthermore, it can be adapted to high-speed and low-speed objects in indoor scene or outdoor scene by modifying a few parameters. The proposed spatial segment scheme can decrease the effect of the shadow as well.
Measuring the Returns to R&D: The Depreciation Problem

Measuring the private returns to R&D requires knowledge of its private depreciation or obsolescence rate, which is inherently variable and responds to competitive pressure. Nevertheless, most of the previous literature has used a constant depreciation rate to construct R&D capital stocks and measure the returns to R&D, a rate usually equal to 15 per cent. In this paper I review the implications of this assumption for the measurement of returns using two different methodologies: one based on the production function and another that uses firm market value to infer returns. Under the assumption that firms choose their R&D investment optimally, that is, marginal expected benefit equals marginal cost, I show that both estimates of returns can be inverted to derive an implied depreciation rate for R&D capital. I then test these ideas on a large unbalanced panel of U.S. manufacturing firms for the years 1974 to 2003. The two methods do not agree, in that the production function approach suggests depreciation rates near zero (or even appreciation) whereas the market value approach implies depreciation rates ranging from 20 to 40 per cent, depending on the period. The concluding section discusses the possible reasons for this funding.
Optimization using a host-parasite model with variable-size distributed populations

This paper presents a model of coevolution between two variable-size, spatially distributed populations, evolving in an environment with a flow of resources. The two populations have a host-parasite relationship where the host species is dependent on the uptake of resources from the environment for their reproduction. The parasites seek to "infect" host organisms and parasitize on their resources to produce parasite offspring. We show how the approach can be used for optimization tasks by using instances of the problem task to determine the outcome of each interaction between a host and a parasite organism. As an initial test of the model, we apply it to the problem of designing sorting networks for several problem sizes: 6, 7, 8, and 9-input.
Plasma Inflammatory Factors Are Associated with Anxiety, Depression, and Cognitive Problems in Adults with and without Methamphetamine Dependence: An Exploratory Protein Array Study

Objectives It is hypothesized that immune factors influence addictive behaviors and contribute to relapse. The primary study objectives were to (1) compare neuropsychiatric symptoms across adults with active methamphetamine (MA) dependence, in early remission from MA dependence, and with no history of substance dependence, (2) determine whether active or recent MA dependence affects the expression of immune factors, and (3) evaluate the association between immune factor levels and neuropsychiatric symptoms. Methods A cross-sectional study was conducted using between group comparisons and regression analyses to investigate associations among variables. Eighty-four adults were recruited into control (CTL) (n = 31), MA-active (n = 17), or MA-remission (n = 36) groups. Participants completed self-report measures of anxiety, depression, and memory complaints and objective tests of attention and executive function. Blood samples were collected, and a panel of immune factors was measured using multiplex technology. Results Relative to CTLs, MA-dependent adults evidenced greater anxiety and depression during active use (p < 0.001) and remission (p < 0.007), and more attention, memory, and executive problems during remission (p < 0.01) but not active dependence. Regression analyses identified 10 immune factors (putatively associated with cytokine–cytokine receptor interactions) associated with anxiety, depression, and memory problems. Conclusion While psychiatric symptoms are present during active MA dependence and remission, at least some cognitive difficulties emerge only during remission. Altered expression of a network of immune factors contributes to neuropsychiatric symptom severity.
An Unexpected Connection between Branching Processes and Optimal Stopping

A curious connection exists between the theory of optimal stopping for independent random variables, and branching processes. In particular, for the branching process Z n with offspring distribution Y , there exists a random variable X such that the probability P (Z n = 0) of extinction of the nth generation in the branching process equals the value obtained by optimally stopping the sequence X distributed as X. Generalizations to the inhomogeneous and infinite horizon cases are also considered. This correspondence furnishes a simple 'stopping rule' method for computing various characteristics of branching processes, including rates of convergence of the n th gen-eration's extinction probability to the eventual extinction probability, for the supercritical, critical and subcritical Galton-Watson process. Examples, bounds, further generalizations and a connection to classical prophet inequalities are presented. Throughout, the aim is to show how this unexpected connection can be used to translate methods from one area of applied probability to another, rather than to provide the most general results.
Perfectionism and Well-being in Graduate, Undergraduate and Ph.D. Students: Adaptive versus Maladaptive

The prevalence of perfectionism among university students has been reported particularly high, and new theories have been presented to explain its impacts. This study was conducted to evaluate the effect of adaptive and maladaptive perfectionism on psychological well-being in undergraduate and graduate university students. 668 university students (386 undergraduate and 282 graduate students) who studied at Shahid Chamran University of Ahvaz and Shiraz University participated voluntarily in this study. They completed the Revised Almost Perfect Scale (APS-R) questionnaire and Ryff’s Well-Being Scale. The results showed that the mean scores of adaptive perfectionist students in the psychological well-being and its sub-scales, autonomy, purpose in life, positive relations with others, and environmental mastery, were higher than the group of students with maladaptive perfectionist and nonperfectionist. The statistically significant difference was found (P <0.01). As a result, it can be said that adaptive perfectionist is accompanied by better and higher performance and consequently leads to a higher psychological well-being.
Preserving Pairwise Relationships in Subgraphs

Choosing a subgraph that can concisely represent a large real-world graph is useful in many scenarios. The usual strategy employed is to sample nodes so that the induced subgraph matches the original graph’s degree distribution, clustering coefficient, etc., but no attempt is made to preserve fine-grained relationships between nodes, which are vital for applications like clustering, classification, and ranking. In this work, we model such relationships via the notion of Personalized PageRank Value (PPV). We show that the induced subgraphs output by current sampling methods do not preserve PPVs, and we present algorithms for creating PPVpreserving subgraphs on any given subset of graph nodes. Experiments on three large real-world graphs show that the subgraphs created by our method improve upon induced subgraphs in terms of preserving PPVs, clustering accuracy, and maintaining basic graph properties.
Compositional Information-Flow Security for Interactive Systems

To achieve end-to-end security in a system built from parts, it is important to ensure that the composition of secure components is itself secure. This work investigates the compositionality of two popular conditions of possibilistic noninterference. The first condition, progress-insensitive noninterference (PINI), is the security condition enforced by practical tools like JSFlow, Paragon, sequential LIO, Jif, Flow Caml, and SPARK Examiner. We show that this condition is not preserved under fair parallel composition: composing a PINI system fairly with another PINI system can yield an insecure system. We explore constraints that allow recovering compositionality for PINI. Further, we develop a theory of compositional reasoning. In contrast to PINI, we show what PSNI behaves well under composition, with and without fairness assumptions. Our work is performed within a general framework for nondeterministic interactive systems.
Hierarchical Simultaneous Localization and Mapping Based on Discrete Event Systems

In order to make the simultaneous localization and mapping (SLAM) algorithm universal, a novel hierarchical approach, which realizes both macro and micro localization and mapping for a kind of large environments is proposed. The map is a two-layer architecture which the upper layer is an automaton and the lower layer is a set of feature-based maps. The exploring strategy for unknown space was embodied in the system. It’s described by a supervisor which permits some desired actions to occur and prohibits undesired actions. The approach proposed here has the potential ability of localizing the robot in maze like environments.
2.1 Performance 2.2 Reliability

This report presents research activities carried out during the first year of doctorate. It describes the area of Computer Science I have focused on and the key problems I have addressed, namely reliability and performance issues for emerging Web based applications and infrastructures. Original results already achieved are shortly presented and possible directions of future research work are finally traced.