Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI

Title of Thesis: A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI Wenshuai Hou, 2015 Dissertation directed by: Professor Joseph JaJa Department of ECE Mild Traumatic Brain Injury (MTBI) is one of the most common neurological disorders. A subset of patients develop persistent cognitive deficits. A number of the brain studies have been conducted to discover the abnormalities and disruptions in the brain functional networks using similar methods to those employed in more severe brain disorders such as Alzheimer’s or Schizophrenia. Static functional network analysis using resting state brain fMRI images has shown some promising results in identifying characteristics of MTBI. However, recent development in the dynamics of functional networks have been able to reveal insightful information about anomalies in brain activities that have not been observed when using traditional static analysis. Our work focuses on both static and dynamic functional analysis of the brain. Our overall analysis pipeline is data-driven using a dataset of 47 MTBI subjects and a demographically matching healthy control group size of 30. The data-driven approach proactively removes noise, focuses on the entire brain functional networks and performs advanced independent component analysis, followed by statistical tests to characterize the functional networks of MTBI patients. A key distinction of our research is the finer labeling of MTBI subject according to their long term 6 months recovery status. Our results suggest that those MTBI subject who suffer prolonged recovery exhibit disturbed functional networks, and slowed dynamism in functional connectivity than those of the healthy control or those MTBI participants who recovered quickly. A number of useful network measurements have been found to capture the states and changes of the brain functional networks for healthy and different types of MTBI subjects in their resting state. We believe that our findings can shed more light into the impact of MTBI on the effectiveness of several functional networks and can contribute to helping clinicians make more informed decisions to aid in the recovery of MTBI patients. A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI
Understanding virtual world usage: A multipurpose model and empirical testing

This study reports an attempt to enhance our understanding of the reasons behind virtual world usage. By providing a mixture of utilitarian and hedonic value, virtual worlds represent an emerging class of multipurpose information systems (MPIS). Previous research seems to fall short in explaining MPIS adoption, especially since key extrinsic and intrinsic motivators are not considered. Drawing upon IT adoption research, motivation theory and the consumer behavior literature, this research extends available works and provides insight into the influence and roles of extrinsic and intrinsic motivation. Hypotheses are postulated and tested using a sample of 1627 users of the virtual world Second Life. The results confirm the majority of the hypotheses and support the comprehensive model. The findings indicate instantaneous effects of extrinsic and intrinsic motivation, and highlight reinforcing effects of intrinsic motivation. Implications for research and practice are discussed.
Breaking the Waves: Modelling the Potential Impact of Public Health Measures to Defer the Epidemic Peak of Novel Influenza A/H1N1

Background On June 11, 2009, the World Health Organization declared phase 6 of the novel influenza A/H1N1 pandemic. Although by the end of September 2009, the novel virus had been reported from all continents, the impact in most countries of the northern hemisphere has been limited. The return of the virus in a second wave would encounter populations that are still nonimmune and not vaccinated yet. We modelled the effect of control strategies to reduce the spread with the goal to defer the epidemic wave in a country where it is detected in a very early stage. Methodology/Principal Findings We constructed a deterministic SEIR model using the age distribution and size of the population of Germany based on the observed number of imported cases and the early findings for the epidemiologic characteristics described by Fraser (Science, 2009). We propose a two-step control strategy with an initial effort to trace, quarantine, and selectively give prophylactic treatment to contacts of the first 100 to 500 cases. In the second step, the same measures are focused on the households of the next 5,000 to 10,000 cases. As a result, the peak of the epidemic could be delayed up to 7.6 weeks if up to 30% of cases are detected. However, the cumulative attack rates would not change. Necessary doses of antivirals would be less than the number of treatment courses for 0.1% of the population. In a sensitivity analysis, both case detection rate and the variation of R0 have major effects on the resulting delay. Conclusions/Significance Control strategies that reduce the spread of the disease during the early phase of a pandemic wave may lead to a substantial delay of the epidemic. Since prophylactic treatment is only offered to the contacts of the first 10,000 cases, the amount of antivirals needed is still very limited.
Distributed load balancing using alternating direction method of multipliers

Load balancing is a major issue in networking, where the traffic load is effectively distributed across multiple paths of a network graph. Practical networks have recently grown larger in size and complexity and demand, which necessitates the notion of decentralized load balancing. This paper provides an algorithm for balancing the traffic load in a general network setting. Unlike most of state-of-the-art algorithms in load balancing context, the proposed method is fully distributed. The effective distribution of loads is realized through solving a convex optimization problem where the p-norm of network load is minimized subject to network physical constraints. We employ the Alternating Direction Method of Multipliers (ADMM) to solve the p-norm problem in a distributed fashion, where each node only exchanges information with its neighboring nodes. Numerical results show that the proposed algorithm requires very few communication steps to balance the traffic load on bottleneck for random network graphs.
Information structures in optimal decentralized control

This tutorial paper provides a comprehensive characterization of information structures in team decision problems and their impact on the tractability of team optimization. Solution methods for team decision problems are presented in various settings where the discussion is structured in two foci: The first is centered on solution methods for stochastic teams admitting state-space formulations. The second focus is on norm-optimal control for linear plants under information constraints.
Doing age and ageing: language, discourse and social interaction

In this chapter, we provide an overview of socio-pragmatic, sociolinguistic and discourse-analytic studies of age and ageing by documenting the shift from earlier normative accounts that treated age as a static and homogeneous social variable towards recent, practice-based studies, which locate language choices in specific sites and activities that social actors are engaged in. Unusually, we bring together in our discussion two foci of inquiry which have developed in considerable isolation from one another in the literature, namely research on youth language, discourse and social interaction on the one hand and research on ageing on the other. In this way, not only do we put forth the key-concepts that have informed research in each case but we also build a case for an inclusive approach to the socio-pragmatic study of age.
PSEUDOMONAS RUBESCENS, A NEW SPECIES FROM SOLUBLE OIL EMULSIONS

The bacteriology of soluble oil emulsions has received only sporadic attention, most of it during the past three decades. Some of the investigations were stimulated by reports of dermatitis among people in machine shops where oil emulsions are used as coolants and lubricants; others resulted from studies of the microbial spoilage of the emulsions. At present, few workers believe that the indigenous microflora of the emulsions contributes to dermatitis, but most investigators are of the opinion that it does cause spoilage of the emulsions. The present studies were initiated in order to learn more about the microorganisms which are concerned with decomposition of soluble oil emulsions. The predominant members of the microflora in soluble oil emulsions are pseudomonads. Lee and Chandler (1941) described and named Pseudoonas oleovorans, an organism which occurred in almost pure culture in the emulsions which they investigated. Duffett, Gold, and Weirich (1943) have produced the most comprehensive study to date. They isolated Pseudomonas aeruginosa, P. oleovorans, Aerobacter aerogenes, Escherichia coli, Bacillus alvei, as well as two species of unidentified pink, gram negative rods, six new species of the genus Pseudomonas, and various yeasts and molds. Unfortunately, only an abstract of their work was published, and additional information was not available (Weirich, 1951, personal communication). Page and Bushnell (1921) found Aerobacter aerogenes, Escherichia coli communis, and an organism which resembled Proteus vulgaris in emulsions which they investigated. Pivnick (1952) studied 90 cultures isolated from used emulsions obtained from several machine shops. Most of these cultures belonged to the genus Pseudomonas but could not be identified with any known species. A few cultures belonging to the genera Escherichia, Micrococcus, Sarcina,
High resolution scan mode SAR using compressive sensing

Conventional scan-mode synthetic aperture radar (SAR) covers a large area at the expense of reduced azimuth resolution, compared to other operation modes. In this paper, we develop a high resolution scan-mode SAR using random steering and compressive sensing (CS) based techniques. Our approach randomizes the beam steering pattern of conventional scan-mode SAR in both azimuth and range directions such that each pulse beam illuminates a different, randomly chosen, part of the area of interest. We model the image to be reconstructed as a combination of a sparse and a dense component, and combine sparse recovery with least squares methods to estimate them. Our experimental results demonstrate that the proposed randomly steered scan-mode SAR can improve imaging resolution without compromising the covered area size.
Multiple access channel with states known noncausally at one encoder and only strictly causally at the other encoder

We consider a two-user state-dependent multiaccess channel in which the states of the channel are known non-causally to one of the encoders and only strictly causally to the other encoder. Both encoders transmit a common message and, in addition, the encoder that knows the states non-causally transmits an individual message. We study the capacity region of this communication model. In the discrete memoryless case, we establish inner and outer bounds on the capacity region. Although the encoder that sends both messages knows the states fully, we show that the strictly causal knowledge of these states at the other encoder can be beneficial for this encoder, and in general enlarges the capacity region. Furthermore, we find an explicit characterization of the capacity in the case in which the two encoders transmit only the common message. In the Gaussian case, we characterize the capacity region for the model with individual message as well. Our converse proof in this case shows that, for this model, strictly causal knowledge of the state at one of the encoders does not increase capacity if the other is informed non-causally, a result which sheds more light on the utility of conveying a compressed version of the state to the decoder in recent results by Lapidoth and Steinberg on a multiacess model with only strictly causal state at both encoders and independent messages.
A new combined surface and volume registration

3D registration of brain MRI data is vital for many medical imaging applications. However, purely intensitybased approaches for inter-subject matching of brain structure are generally inaccurate in cortical regions, due to the highly complex network of sulci and gyri, which vary widely across subjects. Here we combine a surfacebased cortical registration with a 3D fluid one for the first time, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms. This greatly improves the matching of anatomy in cortical areas. The cortices are segmented and registered with the software Freesurfer. The deformation field is initially extended to the full 3D brain volume using a 3D harmonic mapping that preserves the matching between cortical surfaces. Finally, these deformation fields are used to initialize a 3D Riemannian fluid registration algorithm, that improves the alignment of subcortical brain regions. We validate this method on an MRI dataset from 92 healthy adult twins. Results are compared to those based on volumetric registration without surface constraints; the resulting mean templates resolve consistent anatomical features both subcortically and at the cortex, suggesting that the approach is well-suited for cross-subject integration of functional and anatomic data.
Support Matrix Machine for Large-Scale Data Set

In the computation process of Support Vector Machine (SVM), one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overcome computational and storage problem for large-scale data set, a new framework, Support Matrix Machine (SMM), is proposed. By initially dividing the large scale data set into small subsets, we could treat the autocorrelation matrix of each subset as the special computational unit. A novel polynomial-matrix kernel function is then adopted to compute the similarity between the data matrices in place of vectors. It is also proved that the polynomial kernel is the extreme case of the polynomial-matrix one. The proposed method can greatly reduce the size of kernel matrix, which makes its computation possible. The effectiveness is demonstrated by the experimental results on the artificial and real data set.
An empirical characterisation of electronic document navigation

To establish an empirical foundation for analysis and redesign of document navigation tools, we implemented a system that logs all user actions within Microsoft Word and Adobe Reader. We then conducted a four month longitudinal study of fourteen users' document navigation activities. The study found that approximately half of all documents manipulated are reopenings of previously used documents and that recent document lists are rarely used to return to a document. The two most used navigation tools (by distance moved) are the mousewheel and scrollbar thumb, accounting for 44% and 29% of Word movement and 17% and 31% of Reader navigation. Participants were grouped into stereotypical navigator categories based on the tools they used the most. Majority of the navigation actions observed were short, both in distance (less than one page) and in time (less than one second). We identified three types of within document hunting, with the scrollbar identified as the greatest contributor.
A flexible approach for considering interdependent security objectives in service composition

Current Quality of Service (QoS) models for service compositions are considering security either as a single QoS attribute (measured in abstract units of security) or as a number of independent attributes such as uptime probability. In the face of numerous interdependent protection goals, either general or domain-dependent, this approach is insufficient to capture most real-world scenarios adequately. To cope with this limitation, we introduce in this paper our approach of modeling protection goals as interdependent utility functions over QoS attributes. Furthermore, we present a prototypical tool which supports domain experts in defining QoS- and domain models of any complexity as well as decision makers in finding near-optimal service compositions based on these models.
Cost-effective interference coordination scheme in high dense small cell heterogeneous network

In this paper, we propose a cost-effective dynamic interference coordination scheme, which jointly optimizes the cooperative small cell base stations (SBSs) association and power allocation over subchannels. Our goal is to maximize the total small cell heterogeneous network utility, which consists of four parts: the rate of the macro cell, the rate of the hotzones, the rate of edge small cells as well as the cost from all SBSs. Coordinated multi-point processing scheme is used to reduce the inter-cell interference while increasing the transmission power. Moreover, User Equipments (UEs) behaviors which affect the state of SBS are also taken into consideration. In order to further improve the utility of the network, we deploy the edge SBSs to serve UEs far from macro base station (MBS) and other SBSs. Simulation results validate the effectiveness of the proposed scheme.
Similarity-based Complex Publication Network Analytics for Recommending Potential Collaborations

As communities of researchers continue to become quite large and to grow incessantly, collaboration among researchers can be conducive to greater research pro- ductivity. Nevertheless, it is difficult for a researcher to find suitable collaborators from all researchers around the world. In this paper, we have used bibliographic DBLP data to extract information of a researcher and to discover the relationship between the co-authors and between authors and conferences. We evaluated some of the similarity measures and developed an innovative random walk model to find potential co-authors for a given researcher. These measures were then used to design a best model to rec- ommend co-authors. We have also applied an HITS algorithm and proposed a ranking algorithm to rank researchers and conferences with the intent of recommending authors or conferences.
Decision Making Under Uncertainty—An Example for Seismic Risk Management

Decision‐making techniques are used to select the “best” alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision‐making process. The major thrust of this article is to extend the framework proposed by Yager( 1) for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step‐by‐step formulation of the proposed approach is illustrated using a hypothetical example and a three‐story reinforced concrete building.
Limitations Of Cache Prefetching On A Bus-based Multiprocessor

Compiler-directed cache prefetching has the potential to hide much of the high memory latency seen by current and future high-performance processors. However, prefetching is not without costs, particularly on a multiprocessor. Prefetching can negatively affect bus utilization, overall cache miss rates, memory latencies and data sharing. We simulated the effects of a particular compiler-directed prefetching algorithm, running on a bus-based multiprocesssor. We showed that, despite a high memory latency, this architecture is not very well-suited for prefetching. For several variations on the architecture, speedups for five parallel programs were no greater than 39%, and degradations were as high as 7%, when prefetching was added to the workload. We examined the sources of cache misses, in light of several different prefetching strategies, and pinpointed the causes of the performance changes. Invalidation misses pose a particular problem for current compiler-directed prefetchers. We applied two techniques that reduced their impact: a special prefetching heuristic tailored to write-shared data, and restructuring shared data to reduce false sharing, thus allowing traditional prefetching algorithms to work well.
Constructing a Practical Constituent Parser from a Japanese Treebank with Function Labels

We present an empirical study on constructing a Japanese constituent parser, which can output function labels to deal with more detailed syntactic information. Japanese syntactic parse trees are usually represented as unlabeled dependency structure between bunsetsu chunks, however, such expression is insufficient to uncover the syntactic information about distinction between complements and adjuncts and coordination structure, which is required for practical applications such as syntactic reordering of machine translation. We describe a preliminary effort on constructing a Japanese constituent parser by a Penn Treebank style treebank semi-automatically made from a dependency-based corpus. The evaluations show the parser trained on the treebank has comparable bracketing accuracy as conventional bunsetsu-based parsers, and can output such function labels as the grammatical role of the argument and the type of adnominal phrases.
Optimizing components for handheld two-way speech translation for an English-iraqi Arabic system

This paper described our handheld two-way speech translation system for English and Iraqi. The focus is on developing a field usable handheld device for speech-to-speech translation. The computation and memory limitations on the handheld impose critical constraints on the ASR, SMT, and TTS components. In this paper we discuss our approaches to optimize these components for the handheld device and present performance numbers from the evaluations that were an integral part of the project. Since one major aspect of the TransTac program is to build fieldable systems, we spent significant effort on developing an intuitive interface that minimizesthetrainingtimeforusersbutalsoprovidesusefulinfor
Low-Rank Regularization for Sparse Conjunctive Feature Spaces: An Application to Named Entity Classification

Entity classification, like many other important problems in NLP, involves learning classifiers over sparse highdimensional feature spaces that result from the conjunction of elementary features of the entity mention and its context. In this paper we develop a low-rank regularization framework for training maxentropy models in such sparse conjunctive feature spaces. Our approach handles conjunctive feature spaces using matrices and induces an implicit low-dimensional representation via low-rank constraints. We show that when learning entity classifiers under minimal supervision, using a seed set, our approach is more effective in controlling model capacity than standard techniques for linear classifiers.