Miracle-dz/UniFAR-FTUs huggingface dataset:
Significance of Plaque Ulceration in Symptomatic Patients With High‐Grade Carotid Stenosis

Background and Purpose The importance of carotid plaque ulceration as a cause of cerebral ischemic symptoms remains uncertain. Moreover, its prominence in symptomatic patients with severe carotid stenosis is unknown. Methods The association between angiographically denned plaque ulceration and risk of subsequent stroke was assessed using Cox proportional hazards regression in 659 patients with severe (70% to 99%) carotid stenosis from the North American Symptomatic Carotid Endarterectomy Trial. Results Treatment assignment (medical versus surgical) and degree of ipsilateral stenosis were identified as having a significant influence on the results. The risk of ipsilateral stroke at 24 months for medically treated patients with ulcerated plaques increased incrementally from 26.3% to 73.2% as the degree of stenosis increased from 75% to 95%. For patients with no ulcer, the risk of stroke remained constant at 21.3% for all degrees of stenosis. The net result yielded relative risks of stroke (ulcer versus no ulcer) ranging from 1.24 (95% confidence interval, 0.61 to 2.52) to 3.43 (95% confidence interval, 1.49 to 7.88). Conversely, for surgically treated patients with antecedent presence of an ulcerated plaque, the risk of stroke increased slightly at the highest degrees of stenosis. Overall, carotid endarterectomy reduced the risk of ipsilateral stroke at 24 months by at least 50%. Similar results were obtained for risk of major ipsilateral stroke and risk of all strokes and death. Conclusions The presence of angiographically defined ulceration for medically treated symptomatic patients is associated with an increased risk of stroke. The risk of stroke more than doubles at higher degrees of stenosis. Carotid endarterectomy is beneficial in substantially reducing the risk of stroke, regardless of plaque ulceration and degree of severe carotid stenosis. (Stroke. 199425:304-308.)
Removal of residual crosstalk components in blind source separation using LMS filters

The performance of blind source separation (BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. The degradation is mainly caused by the residual crosstalk components derived from the reverberation of the jammer signal. This paper describes a post-processing method designed to refine output signals obtained by BSS. We propose a new method which uses LMS filters in the frequency domain to estimate the residual crosstalk components in separated signals. The estimated components are removed by non-stational spectral subtraction. The proposed method removes the residual components precisely, thus it compensates for the weakness of BSS in a reverberant environment. Experimental results using speech signals show that the proposed method improves the signal-to-interference ratio by 3 to 5 dB.
Machine Learning based approach for Human Trait Identification from Blog Data

form a major part of a person"s personality. Emotional intelligence (EI) is the ability to identify, assess, and control the emotions of oneself, of others, and of groups. The written expressions reflect author"s personality. Various personality traits can be determined by the analysis of the contents written by a person. This paper proposes a novel technique for human trait identification from the analysis of author"s written expressions. The proposed technique is based on the concept of supervised machine learning and uses Support Vector Machine for classifying the personality of a writer. We classify the personality of a writer into five categories namely, highly extrovert, highly introvert, low introvert, low extrovert and ambivert. Experiments have been carried out on the real world blog data and results demonstrate that the proposed technique can determine the personality traits of a writer with accuracy and speed. We have also implemented a PHP based online system, which reads the contents of a blog and can automatically predict the personality of writer of the blog
Rapid and repeatable shifts in life‐history timing of Rhagoletis pomonella (Diptera: Tephritidae) following colonization of novel host plants in the Pacific Northwestern United States

Abstract Host shifts of phytophagous insect specialists to novel plants can result in divergent ecological adaptation, generating reproductive isolation and potentially new species. Rhagoletis pomonella fruit flies in eastern North America underwent a host shift ~160 ya from native downy hawthorn (Crataegus mollis) to introduced, domesticated apple (Malus domestica). Divergent selection on diapause phenology related to the earlier fruiting time of apples versus downy hawthorns resulted in partial allochronic reproductive isolation between the fly races. Here, we test for how rapid and repeatable shifts in life‐history timing are driving ecological divergence of R. pomonella in the Pacific Northwestern USA. The fly was introduced into the region via larval‐infested apples 40–65 ya and now attacks native black hawthorn (Crataegus douglasii) and introduced ornamental hawthorn (Crataegus monogyna), in addition to early‐ and late‐maturing apple varieties in the region. To investigate the life‐history timing hypothesis, we used a field‐based experiment to characterize the host‐associated eclosion and flight activity patterns of adults, and the feeding times of larvae at a field site in Vancouver, Washington. We also assessed the degree to which differences in host‐fruiting time generate allochronic isolation among apple‐, black hawthorn‐, and ornamental hawthorn‐associated fly populations. We conclude that host‐associated fly populations are temporally offset 24.4% to 92.6% in their seasonal distributions. Our results imply that R. pomonella possesses the capacity for rapid and repeatable shifts in diapause life history to match host‐fruiting phenology, which can generate ecologically based reproductive isolation, and potentially biodiversity in the process.
Antenna Selection for MIMO Systems in Correlated Channels with Diversity Technique

In recent years, due to the increasing demand of the data transmission rate, a lot of research based on Multiple- Input Multiple-Output (MIMO) system is established. MIMO systems can increase the system capacity and improve transmission reliability. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power and hardware. Antenna selection techniques have been applied in MIMO system design to reduce the system complexity and cost. In this paper, we consider two schemes of antenna selection in correlated Rayleigh channels i.e. the maximal ratio transmission and Orthogonal Space-Time Block Code technique. The simulation results illustrate that; the new antenna selection scheme can obtain performance close to the optimum selection with low computational complexity.
Cooperative spectrum sharing economy for heterogeneous wireless networks

To facilitate ubiquitous access for mobile users/machines in spectrum efficient way, effective integration of multiple wireless networks possibly belong to different service providers (SPs) is greatly required for future wireless spectrum sharing networks, such as Machine-to-Machine (M2M) communications. With the aid of cognitive radio or dynamic spectrum access, cooperative spectrum sharing should ensure sufficient incentives to licensed users (LUs) and additional incentives for SPs such that LUs may leverage other SPs' services accordingly as vertical handoff or roaming, which creates another intellectual challenge. We formulate this fundamental problem as a market model by introducing a roaming rate as the incentive for each SP to gain extra revenue when its LUs temporarily leverage other SPs' service. We obtain the equilibrium at which all SPs and all LUs satisfy the amount of the allocated bandwidth and the price simultaneously by using the concept of demand and supply from economics. Two iterative algorithms are proposed to reach the equilibrium for decentralized implementations and their stable conditions are analyzed. Furthermore, the optimal roaming rate obtained by backward induction with the equilibrium maximizes the overall social welfare (i.e., sum of profit of all SPs and payoff of all LUs) by gradient ascent method, such that all SPs as well as all LUs have satisfactory incentives in this cooperative spectrum sharing scheme.
Video Anomaly Identification [ a Statistical Approach ]

T his article describes a family of unsupervised approaches to video anomaly detection based on statistical activity analysis. Approaches based on activity analysis provide intriguing possibilities for region-of-interest (ROI) processing since relevant activities and their locations are detected prior to higher-level processing such as object tracking, tagging , and classification. This strategy is essential for scalability of video analysis to cluttered environments with a multitude of objects and activities. Activity analysis approaches typically do not involve object tracking, and yet they inherently account for spatiotem-poral dependencies. They are robust to clutter arising from multiple activities and contamination arising from poor background subtraction or occlusions. They can sometimes also be employed for fusing activities from multiple cameras. We illustrate successful application of activity analysis to anomaly detection in various scenarios, including the detection of abandoned objects, crowds of people, and illegal U-turns. INTRODUCTION Video camera networks are ubiquitous. Over 30 million cameras produce close to 4 billion hours of video footage per week in the United States alone [1]. This proliferation is taking place since, unlike other sensing modalities, visible-light cameras provide excellent space-time resolution, long capture range, wide field of view, and low latency. Consequently, large-scale camera networks can provide pervasive, wide-area monitoring to protect national infrastructures. Such a protection necessitates careful video analysis in suitable data space (such as pho-tometric, behavior, and attitude) and with appropriate objectives, from counting objects and object localization to identifying suspicious movement of people or assets. Our focus in this article is on identifying anomalous activity in space and time. Currently, this type of video analysis
Budgeted Coverage of a Maximum Part of a Polygonal Area

Given is a polygonal area and the goal is to cover the polygon’s interior or boundary by placing a number of stations in the interior or on the boundary. There are several parameters for this problem: what part of the polygon (interior, boundary) is to be covered, and where can stations be placed (vertices, edges, interior points); Furthermore there are minimization problems (minimum number of stations) or maximization problems (given the number of stations, maximize the covered portion of the polygon). Most of these problems are NP-hard and some are even APX-hard. We consider here a new more realistic version: the boundary is subdivided into segments with lengths and costs and the meaning of covering is relaxed and budgeted. More specifically we consider maximization problems in polygons with weighted edge segments and placement costs. We introduce a new concept: each candidate place for a station has a cost. We study the following two problems: Given a polygon with or without holes and a budget B, place stations so that the total cost of stations does not exceed B and a) the length of the boundary covered is maximized or b) the total weight of segments watched or overseen is maximized. We present constant ratio approximation algorithms for all the above problems.
Effects of Conventional Heating on the Stability of Major Olive Oil Phenolic Compounds by Tandem Mass Spectrometry and Isotope Dilution Assay

The quality of olive oils is sensorially tested by accurate and well established methods. It enables the classification of the pressed oils into the classes of extra virgin oil, virgin oil and lampant oil. Nonetheless, it would be convenient to have analytical methods for screening oils or supporting sensorial analysis using a reliable independent approach based on exploitation of mass spectrometric methodologies. A number of methods have been proposed to evaluate deficiencies of extra virgin olive oils resulting from inappropriate technological treatments, such as high or low temperature deodoration, and home cooking processes. The quality and nutraceutical value of extra virgin olive oil (EVOO) can be related to the antioxidant property of its phenolic compounds. Olive oil is a source of at least 30 phenolic compounds, such as oleuropein, oleocanthal, hydroxytyrosol, and tyrosol, all acting as strong antioxidants, radical scavengers and NSAI-like drugs. We now report the efficacy of MRM tandem mass spectrometry, assisted by the isotope dilution assay, in the evaluation of the thermal stability of selected active principles of extra virgin olive oil.
Thermal-driven multilevel routing for 3D ICs

3D IC has a great potential for improving circuit performance and degree of integration. It is also an attractive platform for system-on-chip or system-in-package solutions. A critical issue in 3D circuit design is heat dissipation. In this paper we propose an efficient 3D multilevel routing approach that includes a novel through-the-silicon via (TS-via) planning algorithm. The proposed approach features an adaptive lumped resistive thermal model and a two-step multilevel TS-via planning scheme. Experimental results show that with multilevel TS-via planning, the thermal-driven approach can reduce the maximum temperature to the required temperature with reasonable wirelength increase. Compared to a post processing approach for dummy TS-via insertion, to achieve the same required temperature, our approach uses 80% fewer TS-vias. To our knowledge, this proposed approach is the first thermal-driven 3D routing algorithm.
Inhomogeneous cellular neural networks: Possibility of functional device design

The possibility of applying cellular neural networks (CNNs) and their combinations to the design of various functional devices is discussed. Examples of the use of this approach for associative memories, complex oscillation generators, chaotic memory scanning, and pattern recognition are given. The advantage of this approach is in the use of universal CNN chips for different purposes. <<ETX>>
A novel signature for fast video retrieval

Many video signatures have been used in video copy detection and retrieval, but most of them are extracted from frames of video. So the time cost would become intolerable when the video database become large enough. To implement the fast retrieval or copy detection on large scale video database, we propose a novel video signature based on data count fluctuation independent upon video frame content just relevant about frame data count for compress domain MPEG video, which first calculate the data count for each frame on compressed domain as signature, then slide the window in the step of GOP(Group of Picture) length after the alignment of I frame, and then compare the difference of data count fluctuation between the query clip and the target video, at last the similarity result was given. This signature could be used both in video database retrieval and online video matching. The experiments show that the novel signature based method is much faster than current retrieval method, and the recall can achieve a high level.
On naive Bayes in speech recognition

The currently dominant speech recognition technology, hidden Markov modeling, has long been criticized for its simplistic assumptions about speech, and especially for the naive Bayes combination rule inherent in it. Many sophisticated alternative models have been suggested over the last decade. These, however, have demonstrated only modest improvements and brought no paradigm shift in technology. The goal of this paper is to examine why HMM performs so well in spite of its incorrect bias due to the naive Bayes assumption. To do this we create an algorithmic framework that allows us to experiment with alternative combination schemes and helps us understand the factors that influence recognition performance. From the findings we argue that the bias peculiar to the naive Bayes rule is not really detrimental to phoneme classification performance. Furthermore, it ensures consistent behavior in outlier modeling, allowing efficient management of insertion and deletion errors.
A Generalization of Simion-Schmidt's Bijection for Restricted Permutations

We consider the two permutation statistics which count the distinct pairs obtained from the final two terms of occurrences of patterns 1 ···m 2m(m 1) and 1 ···m 2(m 1)m in a permutation, respectively. By a simple involution in terms of permutation diagrams we will prove their equidistribution over the symmetric group. As a special case we derive a one-to-one correspondence between permutations which avoid each of the patterns 1 ···m 2m(m 1) 2S m and those which avoid each of the patterns 1 ···m 2(m 1)m 2S m . F orm = 3 this correspondence coincides with the bijection given by Simion and Schmidt in (11).
Mapping substructures in dark matter haloes

We present a detailed study of the real and integrals-of-motion space distributions of a disrupting satellite obtained from a fully self-consistent high-resolution cosmological simulation of a galaxy cluster. The satellite has been re-simulated using various analytical halo potentials, and we find that its debris appears as a coherent structure in integrals-of-motion space in all models (‘live’ and analytical potential), although the distribution is significantly smeared for the live host halo. The primary mechanism for the dispersion is the mass growth of the host. However, when quantitatively comparing the effects of ‘live’ and time-varying host potentials, we conclude that not all of the dispersion can be accounted for by the steady growth of the mass of the host. We ascribe the remaining discrepancies to additional effects in the ‘live’ halo such as non-sphericity of the host and interactions with other satellites which have not been modelled analytically.
Effective Moment Feature Vectors for Protein Domain Structures

Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100–400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity.
Speech Recognition on Code-Switching Among the Chinese Dialects

We propose an integrated approach to do automatic speech recognition on code-switching utterances, where speakers switch back and forth between at least 2 languages. This one-pass framework avoids the degradation of accuracy due to the imperfectly intermediate decisions of language detection and language identification. It is based on a three-layer recognition scheme, which consists of a mixed-language HMM-based acoustic model, a knowledge-based plus data-driven probabilistic pronunciation model, and a tree-structured searching net. The traditional multi-pass recognizer including language boundary detection, language identification and language-dependent speech recognition is also implemented for comparison. Experimental results show that the proposed approach, with a much simpler recognition scheme, could achieve as high accuracy as that could be achieved by using the traditional approach
An Online Model Checking Tool for Safety and Liveness Bugs

Modern software model checkers are usually used to find safety violations. However, checking liveness properties can offer a more natural and effective way to detect errors, particularly in complex concurrent and distributed e-business systems. Specifying global liveness properties which should always eventually be true proves to be more desirable, but it is hard for existing software model checkers to verify liveness in real codes because doing so requires finding an infinite execution. For solving such a challenge, this paper proposes an online checking tool to verify the safety and liveness properties of complex systems. We adopt the linear temporal logic to describe the semantics of the finite model checking, use binary instrumentation to obtain the distribute states and apply a checking engine to dynamically verify the finite trace linear temporal logic properties. At last, we demonstrate the method in a distributed system using distributed protocol Paxos and achieve good results by experiments.
Do Social Norms Affect Support for Earthquake-Strengthening Legislation? Comparing the Effects of Descriptive and Injunctive Norms

Social norms have been successfully applied in health promotion and environmental conservation, but their potential for encouraging natural hazard preparation is relatively untested. This research extends the focus theory of normative conduct to natural hazards and cognitive-behavioral outcomes by examining whether focusing individuals on descriptive and injunctive norms increases their support for earthquake-strengthening legislation in a seismically active city: Wellington, New Zealand. In a large community sample (N = 690), the injunctive norm condition increased support for the legislation compared with the control, whereas the descriptive norm condition did not. In contrast, the descriptive norm condition raised judgments of the feasibility of the strengthening work compared with the control, whereas the injunctive norm condition did not. These findings support previous research demonstrating the differing effects of descriptive and injunctive forms of normative information, and suggest that using both in the same communication is the best strategy for enhancing support for earthquake-strengthening legislation.