Miracle-dz/UniFAR-FTUs huggingface dataset:
Low complexity soft sphere decoding via optimal radius control

In this paper, we present a low-complexity list sphere search algorithm for achieving near-optimal a posteriori (APP) detection in iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching fixed number of lattice points is inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and derive the optimal sphere radius satisfying this requirement. Through simulations on realistic IDD systems, we show that the proposed method provides considerable complexity savings while maintaining near-optimal performance.
Route Investigation for Secure Routing in Mobile Ad-hoc Networks

Secure routing protocols in mobile ad-hoc networks (MANET) have been extensively studied in recent years. Most routing protocol, however, are based on authentication, cryptographic schemes, security association and key distribution and so on. In this paper, we propose an enhanced MANET routing protocol, which prevents security threats, especially by employing route investigation. When an intermediate node that is on a path between source and destination receives two types of control messages (RREP and RERR), the intermediate node sends them to the source in pure AODV. However, in our method, the node which receives the control messages confirms it using route investigation request (IREQ) and route investigation reply (IREP). Through this investigation procedure, we can gain reliability of path for transmitting data packets in MANET. Simulation results show considerable improvement in the packet delivery ratio and the end-to-end latency at the expense of the moderate increase of control message overhead.
Change-Prone Java Method Prediction by Focusing on Individual Differences in Comment Density

This paper focuses on differences in comment densities among individual programmers, and proposes to adjust the conventional code complexity metric (the cyclomatic complexity) by using the abnormality of the comment density. An empirical study with nine popular open source Java products (including 103,246 methods) shows that the proposed metric performs better than the conventional one in predicting change-prone methods; the proposed metric improves the area under the ROC curve (AUC) by about 3.4% on average. key words: change-prone method, comment density, individual difference, ROC curve, AUC
The College Transition Experience of Students with ADHD.

This qualitative study explored the college transition experience of eight first-year students with Attention Deficit Hyperactivity Disorder at a midwestern research university. Using a three-interview-series model, students participated in structured interviews designed to explore their backgrounds, discuss their current experiences on campus, and discover what they have learned from their college transition experiences. The findings reveal that these students with ADHD did not adequately plan their college transitions, relied heavily on family for assistance with their transition and medical treatment, did not utilize many campus resources available to them, and lacked strategies to manage their ADHD symptoms. Additionally, they found the process of becoming college students to be stressful due to the many responsibilities inherent in the role. Findings also indicate that students relied on medication to perform academically in college; however, they lacked knowledge of medication and treatment options. The results contribute to research addressing individuals with ADHD across the lifespan, particularly in college, as well as research of college students and college environments. Recommendations for practice and future research are discussed. THE COLLEGE TRANSITION EXPERIENCE OF STUDENTS WITH ADHD
The Role of Alpha-Synuclein Oligomerization and Aggregation in Cellular and Animal Models of Parkinson’s Disease

α-synuclein (α-syn) is a synaptic protein in which four mutations (A53T, A30P, E46K and gene triplication) have been found to cause an autosomal dominant form of Parkinson’s disease (PD). It is also the major component of intraneuronal protein aggregates, designated as Lewy bodies (LBs), a prominent pathological hallmark of PD. How α-syn contributes to LB formation and PD is still not well-understood. It has been proposed that aggregation of α-syn contributes to the formation of LBs, which then leads to neurodegeneration in PD. However, studies have also suggested that aggregates formation is a protective mechanism against more toxic α-syn oligomers. In this study, we have generated α-syn mutants that have increased propensity to form aggregates by attaching a CL1 peptide to the C-terminal of α-syn. Data from our cellular study suggest an inverse correlation between cell viability and the amount of α-syn aggregates formed in the cells. In addition, our animal model of PD indicates that attachment of CL1 to α-syn enhanced its toxicity to dopaminergic neurons in an age-dependent manner and induced the formation of Lewy body-like α-syn aggregates in the substantia nigra. These results provide new insights into how α-syn-induced toxicity is related to its aggregation.
Multiobjective Optimization

return on his or her investment with a small risk of incurring a loss; an oncologist prescribes radiotherapy to a cancer patient so as to destroy the tumor without causing damage to healthy organs; an airline manager constructs schedules that incur small salary costs and that ensure smooth operation even in the case of disruptions. All three decision makers (DMs) are in a similar situation—they need to make a decision trying to achieve several conflicting goals at the same time: The highest return investments are in general the riskiest ones, tumors can always be destroyed at the expense of irreversible damage to healthy organs, and the cheapest schedules to operate are ones that leave as little as possible time between flights, wreaking havoc to operations in the case of unexpected delays. Moreover, the investor, the oncologist, and the airline manager are all in a situation where the number of available options or alternatives is very large or even infinite. There are infinitely many ways to invest money and infinitely many possible radiotherapy treatments, but the number of feasible crew schedules is finite, albeit astronomical in practice. The alternatives are therefore described by constraints, rather than explicitly known: the sums invested in every stock must equal the total invested; the radiotherapy treatment must meet physical and clinical constraints; crew schedules must ensure that each flight has exactly one crew assigned to operate it. Mathematically, the alternatives are described by vectors in variable or decision space; the set of all vectors satisfying the constraints is called the feasible set in decision space. The consequences or attributes of the alternatives are described as vectors in objective or outcome space, where outcome (objective) vectors are a function of the decision (variable) vectors. The set of outcomes corresponding to feasible alternatives is called Articles
Individual differences in social information gathering revealed through Bayesian hierarchical models

As studies of the neural circuits underlying choice expand to include more complicated behaviors, analysis of behaviors elicited in laboratory paradigms has grown increasingly difficult. Social behaviors present a particular challenge, since inter- and intra-individual variation are expected to play key roles. However, due to limitations on data collection, studies must often choose between pooling data across all subjects or using individual subjects' data in isolation. Hierarchical models mediate between these two extremes by modeling individual subjects as drawn from a population distribution, allowing the population at large to serve as prior information about individuals' behavior. Here, we apply this method to data collected across multiple experimental sessions from a set of rhesus macaques performing a social information valuation task. We show that, while the values of social images vary markedly between individuals and between experimental sessions for the same individual, individuals also differentially value particular categories of social images. Furthermore, we demonstrate covariance between values for image categories within individuals and find evidence suggesting that magnitudes of stimulus values tend to diminish over time.
Selectivity of TAG-72-targeted adenovirus gene transfer to primary ovarian carcinoma cells versus autologous mesothelial cells in vitro.

Efficient gene transfer by recombinant adenovirus (Ad) vectors depends on expression of CAR and alpha(v) integrin on target cells. Because Ad may also infect nearby nontarget cells expressing these receptors, such as peritoneal mesothelial cells after i.p. injection, we hypothesized that targeting Ad gene delivery to a receptor overexpressed on most ovarian carcinoma cells, such as TAG-72, would enhance the selectivity of Ad gene transfer when used in this context. A monoclonal antibody that has been investigated clinically for immunotherapy and immunodetection of ovarian carcinomas, namely CC49, was used to construct a bispecific conjugate with the Fab fragment of a neutralizing anti-knob mAb to target Ad binding via TAG-72. This conjugate facilitated TAG-72-specific, CAR-independent Ad reporter gene transfer to both ovarian cancer cell lines and primary ovarian cancer cells cultured from malignant ascites fluid. Fab-CC49 was very selective for tumor cells, augmenting Ad gene transfer to primary ovarian cancer cells 2- to 28-fold relative to untargeted Ad, while also decreasing gene transfer to autologous cultured mesothelial cells 4- to 9-fold. These data suggest that targeting Ad via TAG-72 may improve the selectivity of Ad gene transfer for ovarian tumors 8- to 252-fold on i.p. vector injection. These results also define the requirements for a candidate target receptor in the rational design of a targeted Ad vector for ultimate clinical utility, one that selectively infects tumor cells and spares normal cells on i.p. injection. Such a vector may increase gene transfer and decrease the toxicity of Ad vectors, which would improve the therapeutic index of cytotoxic gene therapy for ovarian cancer in clinical trials.
Deformation Lamps: A Projection Technique to Make Static Objects Perceptually Dynamic

Light projection is a powerful technique that can be used to edit the appearance of objects in the real world. Based on pixel-wise modification of light transport, previous techniques have successfully modified static surface properties such as surface color, dynamic range, gloss, and shading. Here, we propose an alternative light projection technique that adds a variety of illusory yet realistic distortions to a wide range of static 2D and 3D projection targets. The key idea of our technique, referred to as (Deformation Lamps), is to project only dynamic luminance information, which effectively activates the motion (and shape) processing in the visual system while preserving the color and texture of the original object. Although the projected dynamic luminance information is spatially inconsistent with the color and texture of the target object, the observer's brain automatically combines these sensory signals in such a way as to correct the inconsistency across visual attributes. We conducted a psychophysical experiment to investigate the characteristics of the inconsistency correction and found that the correction was critically dependent on the retinal magnitude of the inconsistency. Another experiment showed that the perceived magnitude of image deformation produced by our techniques was underestimated. The results ruled out the possibility that the effect obtained by our technique stemmed simply from the physical change in an object's appearance by light projection. Finally, we discuss how our techniques can make the observers perceive a vivid and natural movement, deformation, or oscillation of a variety of static objects, including drawn pictures, printed photographs, sculptures with 3D shading, and objects with natural textures including human bodies.
Global Wire Routing in Two-Dimensional Arrays (Extended Abstract)

We examine the problem of routing wires on a VLSI chip, where the pins to be connected are arranged in a regular rectangular array. We obtain tight bounds for the worst-case "channel-width" needed to route an n × n array, and develop provably good heuristics for the general case. An interesting "rounding algorithm" for obtaining integral approximations to solutions of linear equations is used to show the near-optimality of single-turn routings in the worst-case.
A Multiclass Classification of Cancer Data: Using a Kernel Based Clustering k-NN Support

Support vector machines (SVM) have been prom- ising methods for classification because of their solid ma- thematical foundations which convey several salient proper- ties that other methods hardly provide. However, despite of the prominent properties of SVM, they are not as favored for large-scale data as complexity of SVM is highly depen- dent on the size of a data set. Microarray gene expression data that usually have a large number of dimensions, over thousands of genes, and a small number of samples, e.g., a few tens of patients. This paper presents a noble and efficient approach, Dimen- sionality Reduction (T-test), followed by Clustering kNN Support Vector machines (CK-SVM), which is specially de- signed for handling very large data sets like Microarray gene expression data. The CK-SVM classifies by reflecting the degree of a training data point, as a support vector by using Gaussian function, with K-nearest neighbor (k-NN) and Euclidean distance measure. To add local control prop- erty a simple clustering scheme is implemented, before Gaussian functions are constructed for each cluster. In addi- tion probabilistic SVM out puts are used for extending bi- nary classification to multiclass classification, in a pair wise manner. In this paper a multiclass classification has been applied to cancer data, represented by SRBCT data set.
Pseudomonas syringae pv. phaseolicola isolated from weeds in bean crop fields

Pseudomonas syringae pv. phaseolicola, the causative agent of halo blight in common bean (Phaseolus vulgaris L.), was isolated from weeds associated with bean crops in Spain. The bacterium was recovered from Fumaria sp, Mercurialis annua, Solanum nigrum and Sonchus oleraceus. Ps. s. pv. phaseolicola had previously been isolated from leguminous plants and S. nigrum, but to our knowledge, this is the first time it was recovered from the other three species. The isolates were phenotypically and genetically characterized, and they were compared with isolates recovered from common beans. Five different genotypic profiles were detected by PmeI‐PFGE, two of them being of new description. Weed isolates were as pathogenic on bean plants as bean isolates, but they were not pathogenic on S. nigrum. Regarding the survival of the pathogen in weeds, Ps. s. pv. phaseolicola was isolated from So. oleraceus 11 weeks after the end of the bean crop. These results strongly support the idea of weeds as a potential source of inoculum for halo blight in bean.
Epidemiological Study of Hepatitis A in Plovdiv Region—Bulgaria, 2005-2008

Background: Hepatitis A is a social disease and has great importance for public health in countries with high level of incidence rate. The problem is particularly serious for Bulgaria because the morbidity rate in the country is several times higher in comparison with the other countries of the European Union. The aim of the study is to establish the contemporary epidemiological characteristics of viral hepatitis A in Bulgaria. Methods: Analysis of the incidence rate and the age distribution has been done among two groups of the population differentiated depending on their hygienic living conditions: 1st group—living in bad hygienic conditions; 2nd group—living in good hygienic conditions. A large epidemic outbreak of hepatitis A with 1004 persons of Roma origin has been described. The comprehensive method for epidemiological research and serological methods for determining the diagnosis have been used. To study the risk factors the methods of case-control and multi-factor logistic regression are used. Results: Nine times higher incidence rate has been established in the 1st group 450/100,000 in comparison with the 2nd 50/100,000. The patients from the 1st group are predominantly children aged 5 - 9 years and from the 2nd adults aged 30 - 39 years. The level of significance of the risk factors for the biggest epidemic outbreak has been established—bad personal hygiene is of the greatest importance. The risk for travellers to countries and regions with high endemicity and during epidemic outbreaks has been shown. Conclusions: There are essential differences in the epidemiology of HAV between the groups with different hygienic living conditions. That is why the analyses of the incidence rate and age distribution of hepatitis A have to be done separately for different population groups. Recommendations for providing differentiated measures for prevention and control of hepatitis A among the two groups are given.
Frequency among Enterobacteriaceae of the DNA sequences encoding type 1 pili

Type 1 pili, characterized by mannose-inhibitable agglutination of fowl or guinea pig erythrocytes, have been found throughout the family Enterobacteriaceae. A radiolabeled probe was prepared from a restriction endonuclease-digested fragment of the Escherichia coli pil operon and used to detect homologous DNA sequences in 236 bacteria representing 11 genera of Enterobacteriaceae. Only isolates identified as E. coli or Shigella spp. exhibited homology. In contrast, mannose-sensitive hemagglutination was observed in nine genera. Probe DNA did not hybridize to plasmid DNA, indicating a chromosomal location for the pil operon. Analysis of restriction nuclease-digested whole-cell DNA from 60 E. coli and two Shigella sp. isolates indicated that internal sequences were conserved in most strains, but that changes in flanking sequences in the chromosome were common.
High-Order Statistics of Microtexton for HEp-2 Staining Pattern Classification

This study addresses the classification problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Generally, the method used for IIF analysis remains subjective, and depends too heavily on the experience and expertise of the physician. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. However, it still has large gap in recognition rates to the physical experts' one. This paper explores an approach in which the discriminative features of HEp-2 cell images in IIF are extracted and then, the patterns of the HEp-2 cell are identified using machine learning techniques. Motivated by the progress in the research field of computer vision, as a result of which small local pixel pattern distributions can now be highly discriminative, the proposed strategy employs a parametric probability process to model local image patches (textons: microstructures in the cell image) and extract the higher-order statistics of the model parameters for the image description. The proposed strategy can adaptively characterize the microtexton space of HEp-2 cell images as a generative probability model, and discover the parameters that yield a better fitting of the training space, which would lead to a more discriminant representation for the cell image. The simple linear support vector machine is used for cell pattern identification because of its low computational cost, in particular for large-scale datasets. Experiments using the open HEp-2 cell dataset used in the ICIP2013 contest validate that the proposed strategy can achieve a much better performance than the widely used local binary pattern (LBP) histogram and its extensions, rotation invariant co-occurrence LBP, and pairwise rotation invariant co-occurrence LBP, and that the achieved recognition error rate is even very significantly below the observed intralaboratory variability.
The Effect of Maternal Death on the Health of the Husband and Children in a Rural Area of China: A Prospective Cohort Study

Objective To examine the effects of maternal death on the health of the index child, the health and educational attainment of the older children, and the mental health and quality of life of the surviving husband. Methods A cohort study including 183 households that experienced a maternal death matched to 346 households that experienced childbirth but not a maternal death was conducted prospectively between June 2009 and October 2011 in rural China. Data on household sociodemographic characteristics, physical and mental health were collected using a quantitative questionnaire and medical examination at baseline and follow-up surveys. Multivariate linear regression, logistic regression models and difference-in-difference (DID) were used to compare differences of outcomes between two groups. Findings The index children who experienced the loss of a mother had a significantly higher likelihood of dying, abandonment and malnutrition compared to children whose mothers survived at the follow-up survey. The risk of not attending school on time and dropping out of school among older children in the affected group was higher than those in the control group during the follow-up. Husbands whose wife died had significantly lower EQ-5D index and EQ-VAS both at baseline and at follow-up surveys compared to those without experiencing a wife’s death, suggesting an immediate and sustained poorer mental health quality of life among the surviving husbands. Also the prevalence of posttraumatic stress disorder (PTSD) was 72.6% at baseline and 56.2% at follow-up among husbands whose wife died. Conclusions Maternal death has multifaceted and spillover effects on the physical and mental health of family members that are sustained over time. Programmes that reduce maternal mortality will mitigate repercussions on surviving family members are critical and needed.
Minimization of Torque Ripple in Brushless DC Motor Drives

Torque characteristics of brushless dc motors are studied in terms of the EMF and the feed currents for two feeding schemes: two-phase feeding and three-phase feeding. Various techniques for minimizing torque ripple are considered and discussed. For three-phase feeding systems, a selective harmonics elimination technique is studied and its effectiveness is evaluated.
Safe Adaptive Importance Sampling

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - enjoy favorable theoretical properties, but are typically computationally infeasible. In this paper we propose an efficient approximation of gradient-based sampling, which is based on safe bounds on the gradient. The proposed sampling distribution is (i) provably the best sampling with respect to the given bounds, (ii) always better than uniform sampling and fixed importance sampling and (iii) can efficiently be computed - in many applications at negligible extra cost. The proposed sampling scheme is generic and can easily be integrated into existing algorithms. In particular, we show that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy significant a speed-up under the novel scheme. The proven efficiency of the proposed sampling is verified by extensive numerical testing.