Miracle-dz/UniFAR-FTUs huggingface dataset:
Collecting Detection Diversity and Complexity Gains in Cooperative Spectrum Sensing

In cognitive radio (CR) networks, multi-CR cooperation is required during spectrum sensing in order to cope with wireless fading effects and the hidden terminal problem. User cooperation offers not only channel diversity gain against fading, but also complexity gain in terms of reduced sampling costs per CR. The latter is particularly useful when the monitored spectrum has very wide bandwidth and yet individual CRs only have limited hardware capability. To jointly collect both diversity gain and complexity gain, this paper develops a novel cooperative spectrum sensing technique based on matrix rank minimization. Subject to sampling-rate limitations, CRs individually collect digital measurements from a segment of the wide spectrum via coordinated selective filtering, with optional compressive sampling to further reduce the sampling rates. The solutions representing the measurements of all users are modeled to possess a low-rank property, and the rank order is the same as the size of the nonzero support of the monitored wide spectrum. Accordingly, a nuclear norm minimization problem is formulated to jointly identify the nonzero support and hence the overall wideband spectrum occupancy. Both tradeoff evaluation and simulation results corroborate that the proposed cooperative sensing technique outperforms traditional averaging-based cooperative schemes given the same sampling costs, because the low-rank property enables efficient utilization and tradeoff of the user diversity in the absence of any channel knowledge.
Teacher Educators: Course Experiences of Bachelor of Education Primary Students.

The purpose of this research was to examine feedback from pre-service primary teachers on their course experiences across the four years of the Bachelor of Education program at the Australian Catholic University (ACU) in metropolitan Sydney, NSW, Australia. The aims and objectives of the study were to identify gaps in knowledge about students; to identify relevant domains in the student experience and to assist with course improvements. It was envisaged that findings may benefit teacher educators in designing better ways to prepare pre-service teachers for their complex future teaching roles in catering for increasingly diverse student populations.
The Role of Power Doppler Ultrasonography as Disease Activity Marker in Rheumatoid Arthritis

Structural damage in rheumatoid arthritis (RA) occurs early if inflammation is not treated promptly. Treatment targeted to reduce inflammation, in particular, that of synovial inflammation in the joints (synovitis), has been recommended as standard treat-to-target recommendations by rheumatologists. The goal is to achieve disease remission (i.e., no disease activity). Several accepted remission criteria have not always equated to the complete absence of true inflammation. Over the last decade, musculoskeletal ultrasonography has been demonstrated to detect subclinical synovitis not appreciated by routine clinical or laboratory assessments, with the Power Doppler modality allowing clinicians to more readily appreciate true inflammation. Thus, targeting therapy to Power Doppler activity may provide superior outcomes compared with treating to clinical targets alone, making it an attractive marker of disease activity in RA. However, more validation on its true benefits such as its benefits to patients in regard to patient related outcomes and issues with standardized training in acquisition and interpretation of power Doppler findings are required.
The Classified Information Procedures Act in the Age of Terrorism: Remodeling CIPA in an Offense-Specific Manner

The Classified Information Procedures Act (CIPA) sets the balancing point between the government’s interest in preventing disclosure of classified information with a criminal defendant’s right to exculpatory material. Although CIPA was originally drafted with espionage cases in mind, the statute has become more commonly associated with terrorism prosecutions. This contextual shift has disrupted CIPA’s interest-balancing formulation by altering the governmental interests at stake. CIPA’s discovery burdens on the defendant are ordinarily constitutionally justified by the strong countervailing state interest in preserving vital national-security information. This concern is less salient with terrorism defendants, who are unlikely to possess state secrets. Accordingly, those defendants may require further reciprocity in discovery procedures to keep the statute within constitutional parameters. This Note examines the ill effects of CIPA’s contextual shift and proposes a set of amendments to alleviate those concerns. Chiefly, this Note suggests an offense-specific CIPA, whereby the procedural mechanisms of the statute are tailored to the offense charged. The three core recommendations of this Note are (1) inclusion of defense counsel in the discovery process and clearer standards to govern discoverability; (2) a limited and qualified declassification requirement in select Foreign Intelligence Surveillance Act cases; and (3) bifurcation of admissibility hearings. Copyright © 2015 Arjun Chandran. † Duke University School of Law, J.D. expected 2015; Duke University, A.B. 2012. I am grateful to Professor Samuel Buell for his insights and guidance in writing this Note; to Supervisory Assistant Federal Public Defender Daniel Ecarius for his valuable mentorship, which cultivated my interest in this subject; and to my colleagues on the Duke Law Journal for their tireless efforts in editing this Note. CHANDRAN IN FR (DO NOT DELETE) 3/23/2015 11:28 AM 1412 DUKE LAW JOURNAL [Vol. 64:1411
Preprocessor conditional removal by simple partial evaluation

Preprocessors with conditionals are often used as software configuration management tools, with preprocessor variables naming configuration aspects. The preprocessor conditionals found in large systems often have complex enabling Boolean equations and nesting. Over long periods, some configuration aspects lose all utility. Removal of all traces of a configuration variable and code dependent on that aspect becomes a significant task if performed manually. An industrial-strength transformation system can remove such configuration aspects in a much shorter period of time, by symbolically simplifying away the configuration aspect. The paper sketches the problem and the required transforms, and discusses a case study involving over a million lines of source code.
Relationship between motor movement and physical fitness with physiological variables

Physical fitness is one of the basic needs of human life. Physical fitness means being able to do things daily without feeling tired. The purpose of this study was to investigate the relationship between motor fitness and physical fitness of karate athletes with their physiological characteristics. This research was a kind of relationship and using descriptive and inferential statistics has been attempted to investigate the relationship between motor movement and physical fitness or physiological resting heart rate, heart rate after exercise (Harvard step) and vo2max karate. Data analysis was performed using Pearson statistical method at a significant level of p <0.05 and using SPSS software version 22. The end result of the research is the proof of this hypothesis, one of the success codes in Karate, is better preparedness and more favorable in motor movement and physical fitness readings and physiological variables.
Analysis of flow on a moving flat plate in slip regime by Pseudo-Spectral method

The purpose of this work is to present an efficient low computational cost Pseudo-Spectral (P.S) approach based on the Chebyshev collocation method for solving the Blasius boundary layer equation in slip regime over a moving semi-infinite flat plate. This choice is supported by the excellent reputation of P.S amongst practitioners because of its high accuracy and low computational cost [1,2]. The Maxwell's original first-order model without temperature jump, which is limited to Kn ≤ 0.1 has been used for modeling wall slip velocity. A moving flat plate have been considered in the current work and the effects of wall velocity and slip flow at the wall on the velocity and shear stress profiles have been discussed. In spite of low computational cost in the present study, the results accurately have a good agreement with previous works.
Altered Amygdala Connectivity in Individuals with Chronic Traumatic Brain Injury and Comorbid Depressive Symptoms

Depression is one of the most common psychiatric conditions in individuals with chronic traumatic brain injury (TBI). Though depression has detrimental effects in TBI and network dysfunction is a “hallmark” of TBI and depression, there have not been any prior investigations of connectivity-based neuroimaging biomarkers for comorbid depression in TBI. We utilized resting-state functional magnetic resonance imaging to identify altered amygdala connectivity in individuals with chronic TBI (8 years post-injury on average) exhibiting comorbid depressive symptoms (N = 31), relative to chronic TBI individuals having minimal depressive symptoms (N = 23). Connectivity analysis of these participant sub-groups revealed that the TBI-plus-depressive symptoms group showed relative increases in amygdala connectivity primarily in the regions that are part of the salience, somatomotor, dorsal attention, and visual networks (pvoxel < 0.01, pcluster < 0.025). Relative increases in amygdala connectivity in the TBI-plus-depressive symptoms group were also observed within areas of the limbic–cortical mood-regulating circuit (the left dorsomedial and right dorsolateral prefrontal cortices and thalamus) and the brainstem. Further analysis revealed that spatially dissociable patterns of correlation between amygdala connectivity and symptom severity according to subtypes (Cognitive and Affective) of depressive symptoms (pvoxel < 0.01, pcluster < 0.025). Taken together, these results suggest that amygdala connectivity may be a potentially effective neuroimaging biomarker for comorbid depressive symptoms in chronic TBI.
Paneitz-type operators and applications

To the memory of André Lichnerowicz Given (M, g) a smooth 4-dimensional Riemannian manifold, let S g be the scalar curvature of g, and let Rc g be the Ricci curvature of g. The Paneitz operator, discovered in [21], is the fourth-order operator defined by P 4 g u = 2 g u − div g 2 3 S g g − 2 Rc g du, where g u = −div g ∇u is the Laplacian of u with respect to g. When (M, g) is the 4-dimensional standard unit sphere (S 4 , h), we get that P 4 h u = 2 h u + 2 h u.
The CS Character and Limitations of the Sharpe Ratio

Aside from mean return, the Sharpe ratio probably is the financial world’s most ubiquitous statistic (Sharpe 1966, 1994). Introduced forty years ago and widely used in portfolio construction and performance measurement of conventional equity and bond investments, the Sharpe ratio now is, to the chagrin of its creator, also routinely reported by hedge fund and fund of hedge fund managers. This paper is the first of two that examine the limitations of the Sharpe ratio and introduces new statistics that provide simple and robust tests of financial data for indications that use of the Sharpe ratio is inappropriate. We provide a practical substitute for the Sharpe ratio using standard dispersion - a replacement for standard deviation - as a preferred common unit of risk. In this paper, we examine the conditions under which one may expect the Sharpe ratio to reasonably measure and compare risk-adjusted returns in financial markets. This boils down to a single assumption: that standard deviation provides a common unit of risk among the various returns distributions. We show that, across a variety of randomly selected financial instruments such as equities, mutual funds, and equity indexes in developed and emerging markets, as well as hedge funds and hedge fund indexes, standard deviation fails to provide a common unit of risk. Next we introduce the first CS character, a statistic that provides a simple test for the validity of the assumption that standard deviation provides a common unit of risk. Applied to our examples, the first CS character confirms observations and provides a simple test for the applicability of standard deviation as a risk measure. We discuss the first CS character’s genesis from our research into the properties of Omega functions and the natural statistics they generate. Then we consider the first CS character’s remarkable sampling properties and provide examples of its use that rely on these properties. In the second paper, we outline the mathematical background of CS characters and standard dispersion. Replacing standard deviation with standard dispersion provides our generalization of the Sharpe ratio. We show in a number of examples that using standard dispersion produces significantly different results from the Sharpe ratio and that these results will impact portfolio construction and performance measurement.
Portable Vision-Based HCIA Real-time Hand Mouse System on Handheld Devices

This paper aims to create a lightweight version of vision-based HCI (Human Computer Interaction) for emerging portable/handheld devices. We manage to provide a steerable interface so that a user can manipulate his/her handheld device on a projected screen on a plan surface (figure 1) without the intervention of a " mouse " or a " keyboard ". By means of the idea, we hope to realize a part of the pervasive world [1] by an intuitive way of user input interface. Unlike previous works [4,5,8], which use hand gesture recognition for the same purpose, we use an intuitive method to detect hand motion by its MHI (motion history images)[2]. In this paper, we will introduce an efficient method for the hand motion recognition so that it can fit to a handheld device. With the system, what users need to do then is just install this software program on their handheld device and then they have a vision-based steerable interface.
Planning multi-robot formation with improved poly-clonal artificial immune algorithm

In this paper, a novel algorithm to solve multi-robot formation path planning problem is proposed. A combination of the leader-follower and improved poly-clonal artificial immune algorithm is used to derive the formation architecture. The formation of multi-robot is maintained through controlling the distance and angle between leader and followers. Robots reach the desired positions and avoid obstacles with improved poly-clonal artificial immune algorithm. Artificial immune network has been widely used in obstacles avoidance with the strong searching ability and learning ability. Improved poly-clonal artificial immune algorithm increases the diversity of antibodies. Concentration of every antibody is computed based on the algorithm. Only the antibody with the highest concentration is selected to act on robot. Meanwhile, formation control system changes the leader temporarily when the original followers encounter with obstacles. Extensive experiments show that the proposed algorithm effectively maintains the formation and successfully avoids obstacles. Simulations validate the effectiveness and stability of the proposed algorithm.
Oral Cancer Prevention and Control – the Approach of the World Health Organization

Available online xxxx Keywords: Cancer epidemiology Chronic disease risk factors Oral cancer intervention Oral cancer surveillance National cancer policy World Health Organization s u m m a r y Cancer is one of the most common causes of morbidity and mortality today. It is estimated that around 43% of cancer deaths are due to tobacco use, unhealthy diets, alcohol consumption, inactive lifestyles and infection. Low-income and disadvantaged groups are generally more exposed to avoidable risk factors such as environmental carcinogens, alcohol, infectious agents, and tobacco use. These groups also have less access to the health services and health education that would empower them to make decisions to protect and improve their own health. Oro-pharyngeal cancer is significant component of the global burden of cancer. Tobacco and alcohol are regarded as the major risk factors for oral cancer. The population attributable risks of smoking and alcohol consumption have been estimated to 80% for males, 61% for females, and 74% overall. The evidence that smokeless tobacco causes oral cancer was confirmed recently by the International Agency for Research on Cancer. Studies have shown that heavy intake of alcoholic beverages is associated with nutrient deficiency, which appears to contribute independently to oral carcinogenesis. Oral cancer is preventable through risk factors intervention. Prevention of HIV infection will also reduce the incidence of HIV/AIDS-related cancers such as Kaposi sarcoma and lym-phoma. The WHO Global Oral Health Programme is committed to work for country capacity building in oral cancer prevention, inter-country exchange of information and experiences from integrated approaches in prevention and health promotion, and the development of global surveillance systems for oral cancer and risk factors. The WHO Global Oral Health Programme has established a global surveillance system of oral cavity cancer in order to assess risk factors and to help the planning of effective national intervention programmes. Epidemiological data on oral cancer (ICD-10: C00-C08) incidence and mortality are stored in the Global Oral Health Data Bank. In 2007, the World Health Assembly (WHA) passed a resolution on oral health for the first time in 25 years, which also considers oral cancer prevention. The resolution WHA60 A16 URGES Member states-To take steps to ensure that prevention of oral cancer is an integral part of national cancer-control programmes, and to involve oral-health professionals or primary health care personnel with relevant training in oral health in detection, early diagnosis and treatment ;-The WHO Global Oral Health Programme …
Pulmonary hypertension in systemic sclerosis and systemic lupus erythematosus

Pulmonary arterial hypertension (PAH) is a severe manifestation of systemic sclerosis (SSc) and systemic lupus erythematosus (SLE). Due to improvements in the understanding of the pathogenesis of these diseases, improved methodological rigour in the conduct of epidemiological studies and the advent of successful therapies, our understanding of SSc-PAH and SLE-PAH has evolved considerably. In this review we will review the current evidence regarding the prevalence, prognostic factors and survival estimates for SSc-PAH and SLE-PAH. In doing so, we will compare and contrast these two diseases, highlight clinically useful features, discuss methodological limitations of existing data, and draw attention to areas where research is needed.
Exploiting sparsity during the detection of high-order QAM signals in large dimension MIMO systems

This paper proposes a detector for large-scale multiple-input multiple-output (MIMO) systems for 16-QAM constellation and channel knowledge at the receiver. The detector is composed of multiple stages. During the first stage, linear MMSE filter is employed and nearest neighbor quantization is performed resulting in a sub-optimal estimate. In the second stage, the residual in the measurement vector is calculated and the subsequent detector works on the error vector which has additional structure. The error vector is often sparse (has few non-zero components) with the all-zero and lowest energy errors having the largest priors. Large number of antennas reduces the dependencies between error and noise vectors and allows the residual detection problem to be modeled as a linear inverse problem with sparse regularizer. The familiar sparse structure motivates the application of Sparse Bayesian Learning method in the detection. The resulting detector shows promise: the SNR gain over MMSE receiver is ~ 10 dB at a bit error rate (BER) of 10-3 for 16-QAM 16 × 16 system.
Part-Time Undergraduate Student Funding and Financial Support

This chapter provides a critique of the 2012/13 reforms of part-time student finances in England, and of the 2011White Paper Students at the Heart of the system more generally. It argues that the new loans introduced for part-time students in 2012/12 are more likely to re-enforce or perpetuate existing injustices rather than eradicate them and have contributed to a fall in enrolments, contrary to the governments’ intentions. Alternative policies are proposed, to improve the financial aid part-time students receive.
Feedback Control of Stable , Non-minimum-phase , Nonlinear Processes

A nonlinear control law is presented for stable, multiple-input, multiple-output processes, whether their delay-free part is minimumor non-minimum-phase. It is derived by exploiting the connections between continuous-time model-predictive control and input-output linearization. The differential-geometric, control law induces a linear closed-loop response approximately. It has a few tunable parameters (one for each controlled output), and thus, is easily tuned.
An energy‐based AMG coarsening strategy

Algebraic multigrid (AMG) is an iterative method that is often optimal for solving the matrix equations that arise in a wide variety of applications, including discretized partial differential equations. It automatically constructs a sequence of increasingly smaller matrix problems that hopefully enables efficient resolution of all scales present in the solution. The methodology is based on measuring how a so‐called algebraically smooth error value at one point depends on its value at another. Such a concept of strength of connection is well understood for operators whose principal part is an M‐matrix; however, the strength concept for more general matrices is not yet clearly understood, and this lack of knowledge limits the scope of AMG applicability. The purpose of this paper is to motivate a general definition of strength of connection, discuss its implementation, and present the results of initial numerical experiments. Copyright © 2006 John Wiley & Sons, Ltd.
Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification

In this paper, we present correlated logistic (CorrLog) model for multilabel image classification. CorrLog extends conventional logistic regression model into multilabel cases, via explicitly modeling the pairwise correlation between labels. In addition, we propose to learn the model parameters of CorrLog with elastic net regularization, which helps exploit the sparsity in feature selection and label correlations and thus further boost the performance of multilabel classification. CorrLog can be efficiently learned, though approximately, by regularized maximum pseudo likelihood estimation, and it enjoys a satisfying generalization bound that is independent of the number of labels. CorrLog performs competitively for multilabel image classification on benchmark data sets MULAN scene, MIT outdoor scene, PASCAL VOC 2007, and PASCAL VOC 2012, compared with the state-of-the-art multilabel classification algorithms.