Miracle-dz/UniFAR-FTUs huggingface dataset:
RSP-Based Analysis for Sparsest and Least $\ell_1$-Norm Solutions to Underdetermined Linear Systems

Recently, the worse-case analysis, probabilistic analysis and empirical justification have been employed to address the fundamental question: When does ℓ1-minimization find the sparsest solution to an underdetermined linear system? In this paper, a deterministic analysis, rooted in the classic linear programming theory, is carried out to further address this question. We first identify a necessary and sufficient condition for the uniqueness of least ℓ1-norm solutions to linear systems. From this condition, we deduce that a sparsest solution coincides with the unique least ℓ1-norm solution to a linear system if and only if the so-called range space property (RSP) holds at this solution. This yields a broad understanding of the relationship between ℓ0- and ℓ1-minimization problems. Our analysis indicates that the RSP truly lies at the heart of the relationship between these two problems. Through RSP-based analysis, several important questions in this field can be largely addressed. For instance, how to efficiently interpret the gap between the current theory and the actual numerical performance of ℓ1-minimization by a deterministic analysis, and if a linear system has multiple sparsest solutions, when does ℓ1-minimization guarantee to find one of them? Moreover, new matrix properties (such as the RSP of order K and the Weak-RSP of order K) are introduced in this paper, and a new theory for sparse signal recovery based on the RSP of order K is established.
A framework for semantic enrichment of sensor data

The increased interest in sensing the environment in which we live has led to the deployment of thousands of sensors which can measure and report its status. In order to raise the impact that sensor networks can have, improving the usability and accessibility of the measurements they provide is an important step. The problem addressed in this paper is that of enrichment of sensor descriptions and measurements in order to provide richer data, i.e., data containing more meaning. We propose a framework for automatizing the process of semantically enriching sensor descriptions and measurements with the purpose of improving the usability and accessibility of sensor data.
Constrained Pure Nash Equilibria in Graphical Games

The rational behavior of non-cooperative players is often formalized by means of the game theoretic notion of Nash equilibrium. However, there are several practical applications (e.g., multi-agent planning, mechanism design, and routing protocols design), where the computation of any Nash equilibrium could not be satisfactory, since we are often interested only in equilibria that satisfy some additional requirements. Even though such Nash equilibria, called constrained Nash equilibria, received a great deal of interest, a comprehensive formalization and a deep investigation of the complexity issues related to their computation are still missing. In this paper, we present a formal framework for specifying and working with these constraints, by focusing on graphical games, where a player p may be directly interested only on part of the other players, called neighbors of p. We study the computational complexity of the main problems arising in this framework for pure strategies. Furthermore, we identify some restrictions on players' interaction that make some of these problems easy, by exploiting the graph representation of the structure of the game, and a generalization of graph acyclicity called treewidth. In these tractable cases, we also provide highly-parallelizable algorithms for the computation of constrained Nash equilibria.
Interactive animation of visual program execution

Software visualization refers to graphical views on computer programs in general. There are only few attempts towards animation systems of visual program execution. Since programming languages nowadays are more or less complete software development environments such tools are extremely desirable also in the area of visual languages. The paper reports on the integration of such an animation tool into the visual language VisaVis taking possible parallel evaluations into account. In order to support abstractness the user can skip possibly less interesting parts of a visual program during the animation. <<ETX>>
Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization

In this paper we propose the generalization of the recently introduced Neighbor Embedding Exploratory Observation Machine (NE- XOM) for dimension reduction and visualization. We provide a gen- eral mathematical framework called Self Organized Neighbor Embedding (SONE). It treats the components, like data similarity measures and neigh- borhood functions, independently and easily changeable. And it enables the utilization of different divergences, based on the theory of Frechet derivatives. In this way we propose a new dimension reduction and visual- ization algorithm, which can be easily adapted to the user specific request and the actual problem. Various dimension reduction techniques have been introduced based on different properties of the original data to be preserved. The spectrum ranges from linear projections of original data, such as in Principal Component Analysis (PCA) or classical Multidimensional Scaling (MDS) to a wide range of locally linear and non-linear approaches, such as Isomap, Locally Linear Embedding (LLE), Local Linear Coordination (LLC), or charting. Stochastic Neighbor Embed- ding (SNE)approximates the probability distribution in the high-dimensional space, defined by neighboring points, with their probability distribution in a lower-dimensional space. A technique called t-SNE was proposed in (10). It is a variation of SNE considering another statistical model assumption for data distributions. Other methods aim at the preservation of the classification ac- curacy in lower dimensions and incorporate the available label information for the embedding, e. g. Linear Discriminant Analysis (LDA) (5) and generaliza- tions thereof and extensions of the Self Organizing Map (SOM) incorporating class labels. For a comprehensive review on nonlinear dimensionality reduction methods, we refer to (7). Recently, the idea of fast and efficient online learning was combined with the high-quality of divergence based optimization, resulting in a new dimension reduction algorithm called Neighbor Embedding XOM (NE- XOM). Its usefulness and comparison with other methods is shown in (3). The authors connected a computational approach to topology learning, the Explo- ration Observation Machine (XOM) as introduced in (12), with the divergence optimization of SNE. In this contribution, we extend the approach proposed in (3), with a mathematical foundation for the generalization of the principle to
Assessment and Analysis on Color Image Classification Techniques of Dermatological Ulcers

- With the implementation of color image processing methods, the image of dermatological ulcers are analyzed in order to detect the affected area of the skin. The detection of classification rate focus on the application of feature extraction method that segment, classify and analyze the tissue composition of skin lesions or ulcers. Indexing of skin ulcer images was performed based on the statistical texture features derived from the RGB color components. This literature assesses the high level methology for dermatological ulcer image classifier. The classifiers analyzed here are used for labeling the images by the dermatologist used in training and testing of the classifier. The classification performance rate, coverage area of the affected skin is analyzed based on the choice of different algorithms. Classifier uses the algorithm to perform attribute or feature selection that generates the candidate subsets of attributes and evaluate them by using the training and testing schemes, thus creating the computed values of corrected classified image rate up to 90% and assessed coverage area of affected skin up to 0.82.
Reduction of pyramidal and immature hippocampal neurons in pediatric simian immunodeficiency virus infection

Pediatric HIV infection remains a global health crisis with a worldwide infection rate of 2.5 million (WHO, Geneva Switzerland, 2009). Children are much more susceptible to HIV-1 neurological impairments compared with adults, which is exacerbated by coinfections. A major obstacle in pediatric HIV research is sample access. The proposed studies take advantage of ongoing pediatric simian immunodeficiency virus (SIV) pathogenesis and vaccine studies to test the hypothesis that pediatric SIV infection diminishes neuronal populations and neurogenesis in the hippocampus. Newborn rhesus macaques (Macaca mulatta) that received intravenous inoculation of highly virulent SIVmac251 (n=3) or vehicle (control n=4) were used in this study. After a 6–18-week survival time, the animals were euthanized and the brains prepared for quantitative histopathological analysis. Systematic sections through the hippocampus were either Nissl stained or immunostained for doublecortin (DCX+), a putative marker of immature neurons. Using design-based stereology, we report a 42% reduction in the pyramidal neuron population of the CA1, CA2, and CA3 fields of the hippocampus (P<0.05) in SIV-infected infants. The DCX+ neuronal population was also significantly reduced within the dentate gyrus of the hippocampus. The loss of hippocampal neurons and neurogenic capacity may contribute to the rapid neurocognitive decline associated with pediatric HIV infection. These data suggest that pediatric SIV infection, which leads to significant neuronal loss in the hippocampus within 3 months, closely models a subset of pediatric HIV infections with rapid progression.
Balancing with positive feedback: the case for discontinuous control

Experimental observations indicate that positive feedback plays an important role for maintaining human balance in the upright position. This observation is used to motivate an investigation of a simple switch-like controller for postural sway in which corrective movements are made only when the vertical displacement angle exceeds a certain threshold. This mechanism is shown to be consistent with the experimentally observed variations in the two-point correlation for human postural sway. Analysis of first-passage times for this model suggests that this control strategy may slow escape by taking advantage of two intrinsic properties of a stochastic unstable first-order delay differential equation: (i) time delay and (ii) the possibility that the dynamics can be ‘temporarily confined’ near the origin.
An Improved Possibilistic Clustering Based on Differential Algorithm

A possibilistic clustering algorithm called unsupervised possibilistic clustering (UPC) was proposed in a previous paper. Although UPC is sound, the algorithm has the problem of generating coincident clusters. In this paper, we propose a new clustering model called improved unsupervised possibilistic clustering (IUPC) to overcome this weakness of UPC, and an efficient global optimization technique-differential evolution algorithm (DE) is introduced to optimize the proposed model. In IUPC, the optimal cluster centers are searched by the DE algorithm within a fixed feasible region, which is determined by the fuzzy c-means clustering algorithm. IUPC inherits the merits of UPC. In the meanwhile, it avoids the problem of generating coincident clusters by limiting the feasible regions of different clusters disjoint. The contrast experiments with PCM and its variants show the effectiveness of IUPC.
The OSCAR for Generalized Linear Models

The Octagonal Selection and Clustering Algorithm in Regression (OSCAR) proposed by Bondell and Reich (2008) has the attractive feature that highly correlated predictors can obtain exactly the same coecient yielding clustering of predictors. Estimation methods are available for linear regression models. It is shown how the OSCAR penalty can be used within the framework of generalized linear models. An algorithm that solves the corresponding maximization problem is given. The estimation method is investigated in a simulation study and the usefulness is demonstrated by an example from water engineering.
Localization Algorithms of Underwater Wireless Sensor Networks: A Survey

In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs.
High quality goal connection for nonholonomic obstacle navigation allowing for drift using dynamic potential fields

The problem we address in this paper is how to plan and execute high quality paths for robots subject to nonholonomic constraints while navigating obstacles in 2D space. The navigation is to be carried out continuously at speed and may be subject to drift that is not predictable a priori. The problem raises the challenge of adaptively maintaining a smooth robust path of low computational cost. The algorithm is complete in providing feasible paths connecting to the goal in cluttered environments without global maps or positioning while also optimising the path curvature in free space. The approach is a generic gradient-based methodology set in dynamic potential fields that are not subject to fixed local minima or other misdirecting surface features of static fields. Multiple planning and execution cycles are interleaved to allow frequent updates for dealing with unanticipated obstacles and drift. We present our methodology and demonstrate experimental results for simulated robots. The results show that low curvature paths are found that robustly connect to the goal under perturbation through a sequence of fast adaptive replanning.
Integer factorization and Discrete Logarithm problem are neither in P nor NP-complete

Though integer factorization and discrete logarithm problem are both practically and theoretically important, the computational complexity of these problems remained unknown. By comparing integer factorization problem with a problem in P and NP-complete problems, I show that the decision problem version of integer factorization problem is neither in P nor NP-complete. In addition, integer factorization problem is shown to be as hard as discrete logarithm problem (mod n). From this result, the conclusion that P 6= NP is reached.
A new CCII-based pipelined analog to digital converter

This paper proposes a new pipelined analog to digital converter (ADC) based on second-generation current conveyors (CCII). Two main building blocks of the pipelined ADC, sample-and-hold (S/H) circuit and multiplying digital-to-analog converter (MDAC) are constructed of CCII instead of operational amplifiers (OA). Simulation results show that the proposed CCII-based pipelined ADC can work at 10 MHz with an 8-bit resolution. The DNL is within -0.4 LSB and 0.5 LSB and INL is within -0.4 LSB and 0.7 LSB, respectively. The ADC is realized in TSMC 0.35 /spl mu/m CMOS technology and consumed 29 mW under a 3.3 V power supply. The core size is 0.85/spl times/0.85 mm/sup 2/.
Transplantation: Polyomavirus Nephropathy and the Risk of Specific Immunosuppression Regimens

BK virus is ubiquitously present in the latent state in humans, and awareness of the importance of BK polyomavirus is emerging among the kidney transplant community. First discovered in 1971 in the urine of a renal transplant recipient, BK virus nephropathy (BKVN) has come to be recognized as a significant cause of genitourinary disease and potential graft loss in the kidney transplant patient. In this review, we discuss the risk factors, available methods of diagnosis and therapeutic monitoring, and current approaches to therapy of BKVN.
Production of Alumina Microparts by Powder Injection Molding

In this experimental work, injection molding of alumina using a binder system based on high-density polyethylene and paraffin wax was performed to produce dental brackets. The use of a microinjection molding machine, permitting a careful control of the volume injected into the microcavity, allowed to produce dental brackets with complex shape and high aspect ratio. The organic component was removed by a combination of solvent and thermal debinding. After sintering at 1600°C, the final parts retained the green-part shape, with high surface finish. The density achieved (99.8%) was higher than that for similar parts obtained by uniaxial compaction.
University of Huddersfield Repository Exploring portable negative pressure wound therapy devices in the community

The success of effectively managing open wounds using Negative Pressure Wound Therapy has been explored and discussed within the literature. Case studies and clinical reviews support the use of this therapy in managing a range of wound types, yet very few scientific studies that fully support its effectiveness. There is evidence to suggest that the use of negative pressure in the community settings has been restricted, due to financial pressures aimed at reducing the cost of wound care.Yet it has been argued Negative Pressure Wound Therapy can provide excellent symptom management, reduce the frequency of dressing changes and can provide a cost effective alternative to traditional wound therapies due to faster healing times, leading to a reduction in overall treatment costs. Negative Pressure use within community environments is increasing as length of hospital in patient stay decreases, and many patients who would have traditionally been admitted to an acute setting with a complex or highly exuding wound, are managed by community nurses. This paper presents a narrative review surrounding Negative Pressure Wound Therapy, identifies safety precautions that require consideration and explores the application of smaller/ disposable Negative Pressure Wound Therapy systems that are now available. a study exploring quality of life experienced by patients undergoing negative pressure wound therapy to patients receiving standard wound care that there were no real differences in quality of life scores recorded by patients over the 12 week period. they did identify that during the first 2 weeks of the application of therapy, patients in the NPWT group reported an increase in the social life domain. Quality of life outcomes in this study was measured using the Cardiff Wound Impact Schedule. a condition-specific quality of life tool, giving a profile of scores for Physical Symptoms and Daily Living, Social Life, Wellbeing, and overall quality of
Comparison of Self-Rated Health Among Latina Immigrants in a Southern U.S. City and a National Sample

Background:In the United States, Latinos often report fair/poor self-rated health, which is an indicator for increased morbidity and mortality. Foreign-born Latinos in new immigrant destinations, such as the south, may rate their health more poorly than their counterparts elsewhere in the United States, because of the factors associated with migration and settlement in these communities. Methods:The authors assessed foreign-born Latinas' self-rated health in Birmingham, Alabama (n = 765), and compared it with that of foreign-born Latinas in the National Health Interview Survey (NHIS; n = 8,746). Birmingham participants were matched to Latinas in the NHIS using propensity scores. The authors examined factors associated with reporting worse health using ordered logistic regression and inverse probability of treatment weights. Results:After propensity score matching, 47.6% of foreign-born Latinas in the Birmingham study reported their health as fair/poor compared with 17.9% of foreign-born Latinas in the NHIS (P < 0.001). The association between being Mexican born versus from other countries with poorer health was stronger in the Birmingham study (odds ratio: 4.46 [95% CI: 1.91–10.4]) than in the NHIS (odds ratio: 1.09 [95% CI: 1.08–1.09]). Shorter durations of U.S. residence were associated with better health for Latinas in the NHIS but not those in Birmingham. Conclusions:In this study of Latina immigrants in a new settlement community in the south, women reported worse health than foreign-born Latinas in other U.S. regions, suggesting they may be at increased risk of adverse health outcomes. Future studies are needed to better understand the factors associated with these differences to reduce morbidity and mortality burdens.
Distributed Low-Latency Out-of-Order Event Processing for High Data Rate Sensor Streams

Event-based Systems (EBS) are used to detect and analyze meaningful events in surveillance, sports, finances and many other areas. With rising data and event rates and with correlations among these events, sequential event processing becomes infeasible and needs to be distributed. Existing approaches cannot deal with the ubiquity of out-of-order event arrival that is introduced by network delays when distributing EBS. Order-less event processing may result in a system failure. We present a low-latency approach based on K-slack that achieves ordered event processing on high data rate sensor and event streams without a-priori knowledge. Slack buffers are dynamically adjusted to fit the disorder in the streams without using local or global clocks. The middleware transparently reorders the event input streams so that events can still be aggregated and processed to a granularity that satisfies the demands of the application. On a Realtime Locating System (RTLS) our system performs accurate low-latency event detection under the predominance of out-of-order event arrival and with a close to linear performance scale-up when the system is distributed over several threads and machines.
Can IEEE 802.11p and Wi-Fi coexist in the 5.9GHz ITS band ?

To support the increasing need for WiFi gigabit links, the IEEE 802.11ac technology introduced 80MHz and 160MHz channels, which led to an extended WiFi channelization at 5GHz, spanning over the ISM band up to 5.9GHz. However, 5.9GHz also accommodates the 70MHz spectrum reserved for vehicular traffic safety-related applications using IEEE 802.11p technology, forcing both technologies to coexist. In this paper, we highlight the inherent differences between these two technologies and formulate the coexistence challenge on a basic urban scenario. We present and evaluate two coexistence protocols proposed by the WiFi community. Lastly, we illustrate WiFi potential ‘harmful' interferences to safety-related ITS applications and propose improvements for a better coexistence.
Fast and Accurate Time Delay Estimation in Noisy Environments

The problem of time delay estimation (TDE) in the presence ofzero mean white Gaussian noise is addressed. A novel and efficient algorithm for obtaining Conditional Maximum Likelihood (CML) estimates of delays, attenuation factors and the number of paths from a transmitter to a receiver is introduced. This approach is quite suitable for the cases in which time delay difference is less than the duration of signal autocorrelation.