Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Experience of Translating pH-Weighted MRI to Image Human Subjects at 3 Tesla

Background and Purpose— In acute stroke, mismatch between lesions seen on diffusion- (DWI) and perfusion-weighted (PWI) MRI has been used to identify ischemic tissue before irreversible damage. Nevertheless, the concept of PWI/DWI mismatch is oversimplified and the ischemic tissue metabolic status and outcome are often heterogeneous. Tissue pH, a well-regulated physiological index that alters on disrupted tissue metabolism, may provide a surrogate metabolic imaging marker that augments the DWI and PWI for penumbra imaging. Methods— pH-weighted MRI was obtained by probing the pH-dependent amide proton transfer between endogenous mobile proteins/peptides and tissue water. The technique was validated using animal stroke models, optimized for human use, and preliminarily tested for imaging healthy volunteers. Results— pH-weighted MRI is sensitive and specific to ischemic tissue acidosis. pH MRI can be optimized for clinical use, and a pilot human study showed it is feasible using a standard 3 Tesla MRI scanner. Conclusions— Ischemic acidosis can be imaged via an endogenous pH-weighted MRI technique, which complements conventional PWI and DWI for penumbra imaging. pH-weighted MRI has been optimized and appears feasible and practical in imaging human subjects. Additional study is necessary to elucidate the diagnostic use of pH MRI in stroke patients.
Distributed rotation recovers spatial diversity

In relay networks, a conventional way to exploit spatial diversity is to introduce distributed space-time processing at the relays. In our work, we show that even simple time-varying distributed rotation can recover spatial diversity. The main idea is to convert the inherent spatial diversity to time diversity by creating an artificial fast fading channel. It turns out that the proposed framework is both tractable from the theoretical point of view and simple from the practical point of view. Furthermore, the framework is quite general and can be applied to a wide range of linear/nonlinear relaying strategies. As applications, we first propose a linear relaying scheme called rotate-and-forward for multiple-antenna two-hop layered networks. It is shown that, in some non-trivial setting, this scheme outperforms existing schemes and achieves the optimal diversity-multiplexing tradeoff. The second application is a decode-and-forward scheme based on the same idea in the single-antenna multiple-relay channel. It is shown to achieve the maximum diversity with low signaling complexity.
Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point

One motivation of grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient grid fault tolerance system is an essential part of the grid. Rather than covering the whole grid fault tolerance area, this survey provides a review of the subject mainly from the perspective of check point. In this review the challenges for fault tolerance are identified. In grid environments, execution failures can occur for various reasons such as network failure, overloaded resource conditions, or non-availability of required software components. Thus, fault-tolerant systems should be able to identify and handle failures and support reliable execution in the presence of concurrency and failures. In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. In this paper we propose a task level fault tolerance. Task-level techniques mask the effects of the execution failure of tasks. Four task level techniques are retry, alternate resource, check point and replication. Check point technique strategy achieves optimal load balance across different grid sites. These fault tolerance task level techniques can upgrade grid performance significantly at only a moderate in extra resources or scheduling delays in a risky grid computing environment.
Unmasking unmasked: neural dynamics following stroke.

Recovery from disease involves the interaction of the biological substrate with behavior. Nowhere is this more clearly seen than in the setting of a neurological disease such as stroke, where mind and behavior are directly, though sometimes subtly, affected. During recovery from stroke, a large web of causal chains lead to an improved condition or to continued disability. Recovery starts with the mental and physical process of rehabilitation therapy, which alters the physical state of the brain as it teaches new strategies and enhances muscle tone. Subsequently, the altered brain patterns in part determine what further rehabilitative actions the patient is capable of, leading to a vicious or virtuous cycle. Although many aspects of these healing cycles are beyond our control and will likely remain so, there are points at which we can intervene. In order to achieve the greatest rehabilitative effect, we can choose the timing and type of rehabilitation strategy and pharmacotherapeutic measures that will produce desirable brain changes. The difficulty in connecting brain and behavior is a prominent part of the more general problem of causality in neuroscience. Assigning cause is difficult because of the enormous complexity of this organ. Like the better understood liver or lung, the brain is studied using a variety of techniques
Probabilistic models and machine learning in structural bioinformatics

Structural bioinformatics is concerned with the molecular structure of biomacromolecules on a genomic scale, using computational methods. Classic problems in structural bioinformatics include the prediction of protein and RNA structure from sequence, the design of artificial proteins or enzymes, and the automated analysis and comparison of biomacromolecules in atomic detail. The determination of macromolecular structure from experimental data (for example coming from nuclear magnetic resonance, X-ray crystallography or small angle X-ray scattering) has close ties with the field of structural bioinformatics. Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis and experimental determination of macromolecular structure that are based on such methods. These developments include generative models of protein structure, the estimation of the parameters of energy functions that are used in structure prediction, the superposition of macromolecules and structure determination methods that are based on inference. Although this review is not exhaustive, I believe the selected topics give a good impression of the exciting new, probabilistic road the field of structural bioinformatics is taking.
Identifying the Names of Complex Search Tasks with Task-Related Entities

Conventional search engines usually consider a search query corresponding only to a simple task. Nevertheless, due to the explosive growth of web usage in recent years, more and more queries are driven by complex tasks. A complex task may consist of multiple sub-tasks. To accomplish a complex task, users may need to obtain information of various task-related entities corresponding to the sub-tasks. Users usually have to issue a series of queries for each entity during searching a complex search task. For example, the complex task ＂travel to Beijing＂ may involve several task-related entities, such as ＂hotel room,＂ ＂flight tickets,＂ and ＂maps＂. Understanding complex tasks with task-related entities can allow a search engine to suggest integrated search results for each sub-task simultaneously. To understand and improve user behavior when searching a complex task, we propose an entity-driven complex task model (ECTM) based on exploiting microblogs and query logs. Experimental results show that our ECTM is effective in identifying the comprehensive task-related entities for a complex task and generates good quality complex task names based on the identified task-related entities.
Architecture of an Object-based Tracking System Using Colour Segmentation

This paper presents a robust specialised architecture for real time tracking, using colour segmentation. Most of the existing object-tracking algorithms extract the features which constitute the object (usually called tokens), and track them from one frame to another. We propose a newarchitecture to solve the tracking problem at object level. The first step is the extraction of the object from the scene. The proposed architecture uses the discriminatory properties of three colour attributes: Hue, Saturation and Intensity, in order to segment the object. The second step consists of the computation of the centroid of the object by using the enhanced image from the segmentation module. As a last step, tracking of this centroid is achieved in a sequence of images. The processor presented here can provide the sequence of centroids at video rate.
Noise Cancellation of Multichannel Magnetic Resonance Sounding Measurements with Wiener and Adaptive Filters

Magnetic resonance sounding (MRS) is a unique and promising hydrogeophysical technique. Due to its ability of providing a direct estimate of the water content and estimates of the porosity in the subsurface, MRS has the potential of being a great team player in the field of hydrological prospecting. However, the MRS measurements suffer from a low signal to noise ratio due to the signal being inherently weak and the susceptibility to electromagnetic interference. The low signal to noise ratio currently impedes the applicability of the technique, particularly in urban areas. In recent years the development of multichannel MRS has opened up new possibilities for advanced noise cancellation methods. Before multichannel MRS can realize its full potential robust and reliable methods for noise cancellation must be developed. In this work we compare two noise cancellation methods: The multichannel Wiener filter and a multichannel adaptive noise cancellation filter. The comparison is performed on noise records from a multichannel MRS instrument with or without synthetic signal added. Our results show that adaptive noise cancellation performs better than the Wiener filter for both pure noise removal and for recovery of the parameters of the synthetic signal.
Online Political Discourse: Exploring Differences in Effects of Civil and Uncivil Disagreement in News Website Comments

An experiment (N = 272) demonstrated that disagreement—either civil or uncivil—may have a chilling effect on the public discourse vital to a deliberative democracy. Both forms of disagreement—in comments posted on a news story about abortion—caused negative emotion and aggressive intentions. However, only uncivil disagreement led people to respond back uncivilly and indirectly led to greater intention to participate politically, if it aroused aggressive feelings. Findings support extending face and politeness theories to the computer-mediated space of online commenting. Results are discussed in relation to the impact on the public discourse.
Logic, Emotion and Closure: Motivations for Choices of Faith

Spirituality and religiosity can play key roles in individual lives through influencing health, social relationships, political views, as well as many other facets (Newberg, D'Aquili & Rause, 2001; Milevsky & Levitt, 2004; Hirsh, Walberg & Peterson, 2013). As important as religious and spiritual beliefs are to societies, cultures, and individuals, little is known about which psychological factors determine choices of faith. Although there are likely many determinants of religious, spiritual, atheist or agnostic beliefs, this study explored four possible factors: critical thinking skills, need for cognition, need for emotional comfort/security, and need for closure. Participants included an undergraduate sample and a community sample. It was hypothesized that religious and spiritual individuals will have lower critical thinking skills, lower needs for cognition, higher needs for emotional comfort/security and higher needs for closure than agnostic and atheist individuals. Hypotheses also included potential interactions between these variables in predicting each faith path. Religiosity was measured using the I/E Religious Orientation Scale - Revised (Gorsuch & McPherson, 1989) and Spirituality was measured utilizing the Brief Multidimensional Measure of Religiousness/Spirituality (BMMRS) (Fetzer Institute, 1999). These two faith paths were also self -reported by participants after definitions of each were provided. Atheist and Agnostic beliefs were only measured through self-report. Results indicated that both measures of logic (critical thinking skills and need for cognition) and emotional comfort/security (Need to Belong and Religious Motivations) predicted various faith paths. Limitations included sample characteristics and small numbers of Atheist and Agnostic individuals. A better understanding of the motivations for choosing either spiritual or non-spiritual paths may assist in further explanation of the multiple roles each faith choice plays in individual lives.
An Open Logistics Interconnection Model for the Physical Internet

Abstract Computer networks have been interconnected through the last decades with huge advantage into a worldwide Digital Internet. Unlike their digital counterparts, logistics networks remain highly fragmented, still mostly dedicated to a company or a specific market. The recently introduced Physical Internet concept proposes to remedy this situation by interconnecting logistics services on a global scale. The implementation and deployment of the Digital Internet has profited extensively from a structured and a standardized approach to interconnect networks. Indeed, the layered structuring of digital services and protocols associated to both the Open System Interconnection (OSI) model and the TCP/IP model has been instrumental in shaping the Digital Internet. This paper proposes, describes and illustrates a seven-layer Open Logistics Interconnection (OLI) model to enable interconnecting logistics services within the Physical Internet. It describes each proposed layer and the way logistic services are organized within and across these layers.
D/A Resolution Impact on a Poly-phase Multipath Transmitter

In recent publications the Poly-phase multipath technique has been shown to produce a clean output spectrum for a power upconverter (PU) architecture. The technique utilizes frequency independent phase shifts before and after a nonlinear element to cancel out the harmonics and sidebands due to the nonlinearity. A major advantage of this technique is that it circumvents the need to use dedicated RF filters which makes it a potential candidate for cognitive radio transmitters. This paper addresses the requirements on the digital and mixed signal part of such a transmitter. An architecture is proposed based on complex multiplication which can be used to generate the digital multiphase signals required by the multipath technique. Due to equal phase difference of all the paths the same digital hardware could be utilized for carrying out all the phase shifts. When the digital signals pass through a D/A converter which doesn’t have a reconstruction filter, the output in this case would be amplitude discrete like that of a zero order hold. The spectrum of this amplitude discrete signal would have distortion components in it. This can be termed as quantization distortion but now in the context of limited D/A resolution. The multipath technique’s effect on harmonic cancellation, in the presence of such a quantization distortion is explored in this paper. It is shown through simulation that when using ideal phase shifts the multipath technique is able to cancel most of the harmonics produced by an amplitude discrete representation of pure sinusoids. When (upconversion) mixers are used for the second set of phase shifts then with multipath the highest quantization spurs go down with roughly 8db/bit for a single tone and around 10db/bit for two tone inputs.
Sequential and parallel synchronous alternating iterative methods

The so-called parallel multisplitting nonstationary iterative Model A was introduced by Bru, Elsner, and Neumann [Linear Algebra and its Applications 103:175-192 (1988)] for solving a nonsingular linear system Ax = b using a weak nonnegative multisplitting of the first type. In this paper new results are introduced when A is a monotone matrix using a weak nonnegative multisplitting of the second type and when A is a symmetric positive definite matrix using a P-regular multisplitting. Also, nonstationary alternating iterative methods are studied. Finally, combining Model A and alternating iterative methods, two new models of parallel multisplitting nonstationary iterations are introduced. When matrix A is monotone and the multisplittings are weak nonnegative of the first or of the second type, both models lead to convergent schemes. Also, when matrix A is symmetric positive definite and the multisplittings are P-regular, the schemes are also convergent.
Stability and Invariance Analysis of Approximate Explicit MPC based on PWA Lyapunov Functions

Abstract For piecewise affine (PWA) systems whose dynamics are only defined in a bounded and possibly non-invariant set X, this paper proposes a numerical approach to analyze the stability of the origin and to find a region of attraction. The approach relies on introducing fake dynamics outside X and on synthesizing a piecewise affine and possibly discontinuous Lyapunov function on a larger bounded set containing X by solving a linear program. The existence of a solution proves that the origin is an asymptotically stable equilibrium of the original PWA system and determines a region of attraction contained in X. The procedure is particularly useful in practical applications for analyzing a posteriori the stability properties of approximate explicit model predictive control laws defined over a bounded set X of states, and to determine whether, for a given set of initial states, the closed-loop system evolves within the domain X where the control law is defined.
INFORMATION RETRIEVAL USING RELEVANCE VECTORS: A SOFT COMPUTING APPROACH

In this paper we propose to design a document retrieval system based on the degree of relevance of a set of terms to the documents. The set of relevance of terms has been divided among a number of expert systems. Each system forms its own set of rules based on CART and subsequent pruning of the tree. The method is observed so that each rule pertains to a number of documents whose average relevance is close to 1. These rules are then fuzzified to take care of boundaries separating the rules. We then propose a possible network to implement the document retrieval and the hybrid learning method to train this net. The rules we obtained are of the Sugeno type and the net is an ANFIS construct. Finally we proposed a method to aggregate the decisions of the different expert systems by using a hierarchical structure involving a gating network.
Noises in Interaction Traces Data and Their Impact on Previous Research Studies

Context: Developers' interaction traces (ITs) are commonly used in software engineering to understand how developers maintain and evolve software systems. Researchers make several assumptions when mining ITs, e.g., edit events are considered to be change activities and the time mined from ITs is considered to be the time spent by the developers performing the maintenance task. Goal: We investigate the extent to which these assumptions are correct. We examine noises in developers'''' ITs data and the impact of these noises on previous results derived from these traces. Approach: We perform an experiment with 15 participants, whom we asked to perform bug-fixing activities and collect Mylyn ITs and VLC video captures. We then investigate noises between the two data sets and propose an approach to correct noises in ITs. Results: We find that Mylyn ITs can miss on average about 6% of the time spent performing a task and contain on average about 28% of false edit-events. We report that these noises may have led researchers to mislabel some participants'''' editing styles in about 34% of the cases and that the numbers of edit-events performed by developers and the times that they spent on tasks are correlated, when they were considered not to be. Conclusion: We show that ITs must be carefully cleaned before being used in research studies.
Detection Proposal Schemes for Spectrum Sensing in Cognitive Radio

The most important components of the cognitive radio concept is its ability to measure, sense and learn. One also should be aware of the parameters related to the radio channel characteristics and the availability of spectrum and power. In cognitive radio technology, primary users can be defined as the users who have the highest priority on the usage of a specific part of the spectrum. Secondary users, have lower priority, and should not cause any interference to the primary users when using the technology. Therefore, the secondary users need to have certain cognitive radio capabilities, such as sensing the spectrum to check whether it is being used by primary user or not, and changing the radio parameters to exploit the unused part of the spectrum. In this paper we proposed a new approach for spectrum sensing, In the first approach the primary signal is known so we use the code value with match filter to detect the primary user, on the other hand, when the primary user signal is unknown we proposed a new strategy for energy detection in both non-cooperation and cooperation schemes. Then we will prove by simulation results that the new approach is better than the conventional energy detection.
Market Conditions and Stock Indices Hedging ; A Markov Regime Switching Approach

This paper utilises a new approach for determining minimum variance hedge ratio in stock index futures markets. More specifically, the performance of time-varying hedge ratios generated from Markov Regime Switching (MRS) models is investigated. The rational behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterised by regime shifts, which in turn suggests that by allowing the hedge ratio to be dependent upon the "state of the market", one may obtain more efficient hedge ratios and hence superior hedging performance, compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models that have been proposed in the literature such as GARCH, Error Correction and OLS models. In and out-of-sample tests indicate that the MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedges only within sample. Overall the results indicate that by using MRS models, market agents may be able to increase the performance of their hedges, measured in terms of variance reduction.
Personalized Recommendation Based on the Shopper ’ s Behavior Patterns from Shopping Habits

In this paper, we propose a scheme to push personalized recommendation and advertisement services for the shopping people based on behavior patterns, inferring by significant shopping stores in the shopping mall from mobile phone data. The store-level localization in the shopping mall is obtained by sparse calibration orientated semi-supervised machine learning method, called Semi-supervised Extreme Learning Machine (SELM). Then, significant shopping stores are discovered by matching the structure layout of shopping mall. In the mining phase, the scheme aims to mine diverse types of meaningful behavior patterns from the shopper’s shopping habits, which can be explored to push personalized recommendation and advertisement services accurately and reasonably.