Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural correlates of viewing photographs of one's own body and another woman's body in anorexia and bulimia nervosa: an fMRI study.

BACKGROUND In spite of many similarities in the psychopathology of anorexia nervosa (AN) and bulimia nervosa (BN), the 2 groups seem to differ in terms of body image disturbances. Therefore, the aim of the present study was to compare neuronal correlates of viewing photographs of one's own body and another woman's body in patients with these forms of eating disorders as well as controls. METHODS We performed functional magnetic resonance imaging while women with AN (n = 13), BN (n = 15) and healthy controls (n = 27) viewed 16 standardized pictures of their own body and another woman's body, taken while the participants were wearing a bikini. RESULTS When viewing their own body, participants with AN and BN showed reduced activity in the inferior parietal lobule compared with healthy women. In response to looking at another woman's body, participants with AN had higher amygdala activity than did those in the BN and control groups. LIMITATIONS The generalizability of the results is limited by the small sample size. CONCLUSION Our data suggest decreased attentional processes in AN and BN toward one's own body, possibly reflecting body-related avoidance behaviour. Enhanced limbic activity elicited by looking at another woman's body in participants with AN might be a neural correlate of stronger emotional activation and enhanced vigilance, possibly resulting from social comparison processes. Our study reveals hints about body image-associated alterations in brain activity, which seem to be more pronounced among women with AN than among those with BN.
Selection functions in doppler planet searches

We present a preliminary analysis of the sensitivity of Anglo-Australian Planet Search data to the orbital parameters of extrasolar planets. To do so, we have developed new tools for the automatic analysis of large-scale simulations of Doppler velocity planet search data. One of these tools is the two-dimensional Keplerian Lomb-Scargle (LS) periodogram that enables the straightforward detection of exoplanets with high eccentricities (something the standard LS periodogram routinely fails to do). We used this technique to redetermine the orbital parameters of HD20782b, with one of the highest known exoplanet eccentricities (e = 0.97 +/- 0.01). We also derive a set of detection criteria that do not depend on the distribution functions of fitted Keplerian orbital parameters (which we show are non-Gaussian with pronounced, extended wings). Using these tools, we examine the selection functions in orbital period, eccentricity and planet mass of Anglo-Australian Planet Search data for three planets with large-scale Monte Carlo like simulations. We find that the detectability of exoplanets declines at high eccentricities. However, we also find that exoplanet detectability is a strong function of epoch-to-epoch data quality, number of observations and period sampling. This strongly suggests that simple parametrizations of the detectability of exoplanets based on `whole-of-survey' metrics may not be accurate. We have derived empirical relationships between the uncertainty estimates for orbital parameters that are derived from least-squares Keplerian fits to our simulations and the true 99 per cent limits for the errors in those parameters, which are larger than equivalent Gaussian limits by the factors of 5-10. We quantify the rate at which false positives are made by our detection criteria, and find that they do not significantly affect our final conclusions. And finally, we find that there is a bias against measuring near-zero eccentricities, which becomes more significant in
Controller complexity reduction for piecewise affine systems through safe region elimination

We consider the class of piecewise affine optimal state feedback control laws applied to discrete-time piecewise affine systems, motivated by recent work on the computation of closed-form MPC controllers. The storage demand and complexity of these optimal closed-form solutions limit their applicability in most real-life situations. In this paper we present a novel algorithm to a posteriori reduce the storage demand and complexity of the closed-form controller without losing closed-loop stability or all time feasibility while guaranteeing a bounded performance decay compared to the optimal solution. The algorithm combines simple polyhedral manipulations with (multi-parametric) linear programming and the effectiveness of the algorithm is demonstrated on a large numerical example.
Distributed stochastic optimization and learning

We consider the problem of distributed stochastic optimization, where each of several machines has access to samples from the same source distribution, and the goal is to jointly optimize the expected objective w.r. t. the source distribution, minimizing: (1) overall runtime; (2) communication costs; (3) number of samples used. We study this problem systematically, highlighting fundamental limitations, and differences versus distributed consensus problems where each machine has a different, independent, objective. We show how the best known guarantees are obtained by an accelerated mini-batched SGD approach, and contrast the runtime and sample costs of the approach with those of other distributed optimization algorithms.
A tighter reachable set bound for linear systems subject to both discrete and distributed delays

The problem of reachable set bounding for a class of linear systems subject to both discrete and distributed delays is addressed in this paper. First, a new criterion is derived to give an ellipsoid which bounds all the states starting from the origin by inputs with peak-values. The constraint with a special structure that appeared in our previous result is removed by combining the Jensen integral inequality and the reciprocally convex approach. In addition, the obtained condition brings a tighter reachable set estimation with lower computational complexity. We also extend the above result to systems with polytopic uncertainties. Finally, an example is presented to illustrate the merit of our proposed method.
On the Convergence Rates of Learning-based Signature Generation Schemes to Contain Self-propagating Malware

In this paper, we investigate the importance of a defense system's learning rates to fight against the self-propagating class of malware such as worms and bots. To this end, we introduce a new propagation model based on the interactions between an adversary (and its agents) who wishes to construct a zombie army of a specific size, and a defender taking advantage of standard security tools and technologies such as honeypots (HPs) and intrusion detection and prevention systems (IDPSes) in the network environment. As time goes on, the defender can incrementally learn from the collected/observed attack samples (e.g., malware payloads), and therefore being able to generate attack signatures. The generated signatures then are used for filtering next attack traffic and thus containing the attacker's progress in its malware propagation mission. Using simulation and numerical analysis, we evaluate the efficacy of signature generation algorithms and in general any learning-based scheme in bringing an adversary's maneuvering in the environment to a halt as an adversarial containment strategy.
SUMO modification of Rad22, the Schizosaccharomyces pombe homologue of the recombination protein Rad52.

The Schizosaccharomyces pombe rad31 and hus5 genes are required for the DNA damage response, as mutants defective in these genes are sensitive to DNA damaging agents, such as UV and ionising radiation and to the DNA synthesis inhibitor hydroxyurea (HU). Sequence analysis has suggested that rad31 and hus5 encode components of the Pmt3 (SUMO) modification process in S.pombe. We show here that the rad31 null and hus5.62 mutants display reduced levels of Pmt3 modification. We have initiated a search for proteins required for the DNA damage response, which may be modified by Pmt3 and have identified Rad22, the fission yeast homologue of the recombination protein Rad52. Purification of myc + His-tagged Rad22 protein from cells expressing HA-tagged Pmt3 identifies an 83 kDa species which cross-reacts with anti-HA antisera. We show here that Rad22 interacts with Rhp51 and Rpa70 (the fission yeast homologues of Rad51 and the large subunit of RPA, respectively), but that neither of these proteins appears to be responsible for the 83 kDa species. The 83 kDa species is observed when extracts are prepared under both native and denaturing conditions, and is also observed when myc + His-tagged Rad22 and Pmt3 are expressed at wild type levels, suggesting that Rad22 is modified by Pmt3 in vivo. We have established an S.pombe in vitro Pmt3 modification system and have shown that Rad22 and Rhp51 are modified in vitro, but that Rpa70 is not.
Continuous reconstruction, rendering, and editing of point-sampled surfaces

The mathematical representation of surfaces is at the core of most computer graphics problems. In particular, the design of modeling and rendering algorithms for computer graphics applications is largely determined by the specific surface representation that is chosen. A variety of mathematical models has been developed for such applications, ranging from parametric to implicit surfaces. In this thesis, we propose to use nonuniform point samples to continuously reconstruct, render, and interactively edit surfaces. Based on results from classical signal processing theory, we develop core methods for reconstructing, filtering, and resampling nonuniformly point-sampled surfaces. From these concepts, we derive algorithms for high quality, antialiased rendering and interactive surface parameterization. The term point sample denotes a sample of a surface, including information about the surface geometry and the surface appearance. A point-sampled surface consists of a set of point samples without connectivity information, where the points are distributed nonuniformly in space. Point-sampled surfaces are attractive for computer graphics applications because they are easily acquired using range sensing techniques and because they are efficient for modeling and rendering due to their simple structure. This thesis starts by reviewing fundamental signal processing theory, paying particular attention to an analysis of the aliasing phenomenon. We then derive the concept of resampling filters as an efficient antialiasing strategy. Next, we shift our attention to nonuniformly sampled signals and discuss a local filtering approach to nonuniform sampling and reconstruction. Extending these local filtering techniques and combining them with resampling filters, we introduce a parametric representation of point-sampled surfaces. We show how to interpret rendering as resampling a parameterized surface, and we derive an antialiased rendering algorithm for point-sampled surfaces. In addition, we present an adaptation of the algorithm suitable for hardware acceleration, and we present an efficient, hierarchical data structure tailored for point-based rendering. We also develop a resampling filter for volume data and design an efficient splatting algorithm for volume rendering. Finally, we present a framework for interactive editing of point-sampled surfaces. We extend the concept of two-dimensional photo editing to three-dimensional objects by providing two fundamental operations on point-sampled surfaces, namely surface parameterization and surface resampling. Both these operators are based on the parametric representation of point-sampled surfaces. With these building blocks, we show how to implement editing operations such as painting, texturing, carving, displacement mapping, and filtering. Further, we explain a constrained minimum distortion parameterization algorithm that allows the user to perform sophisticated texturing operations. We also provide an efficient strategy for solving the resulting linear equation system using a nested iteration approach.
Knowledge-based program analysis

Automatic program analysis is regarded here as both the mechanized process of understanding high-level concepts from program text and the use of those concepts to guide program maintenance. A knowledge-based program analysis tool called PAT, which realizes this concept, is described. PAT uses an object-oriented framework to represent programming concepts and a heuristic-based concept-recognition mechanism to derive high-level functional concepts from the source code. As an example, a segment of a much larger program written in a Pascal-like language is analyzed using PAT. <<ETX>>
The Teacher Proximity Continuum: A Conceptual Framework for the Analysis of Teacher-Related Phenomena.

In conjunction with a national study on the induction process for beginning vocational teachers, researchers developed a general conceptual framework for the classification and analysis of teacher-related phenomena. The framework, called the Teacher Proximity Continuum, has proved useful in analysis of negative influences, positive influences, significant events, and assistance provided to teachers. Based on functional distance from the teacher, the continuum has been used to classify over 5,000 events and influences up to this point and appears to hold great promise for utility in continuing analysis. Two samples of beginning vocational teachers participated in the study, 12 first-year teachers and 13 third-year teachers. Data for the analysis were collected from the teachers for a year using nominal group technique, interviews, and daily logs. A total of 281 NGT pro'Jlem statements and 1,777 daily log negative influences were identdfied. The grouping system that emerged was based on the functional proximity to the teacher. Categories or domains of interest were then developed. The Teacher Proximity Continvum consists of eight domains at five levels of functional distance from the teacher. The five distance levels are personal characteristics, professional skills, interpersonal relationships, educational system, and extra system (outside educational system boundaries). The domains are internal, pedagogy, curriculum, program, peer, student, system, and community. The model was valid for the purpose for which it was used, reliable, and inclusive of the events analyzed. (Five references and a figure describing the Teacher Proximity Continuum are included.) (Author/NLA) THE TEACHER PROXIMITY CONTINUUM: A CONCEPTUAL FRAMEWORK FOR THE ANALYSIS OF TEACHER-RELATED PHENOMENA
Some randomized code constructions from group actions

We study in this paper randomized constructions of binary linear codes that are invariant under the action of some group on the bits of the codewords. We study a non-Abelian randomized construction corresponding to the action of the dihedral group on a single copy of itself as well as a randomized Abelian construction based on the action of an Abelian group on a number of disjoint copies of itself. Cyclic codes have been extensively studied over the last 40 years. However, it is still an open question as to whether there exist asymptotically good binary cyclic codes. We argue that by using a slightly more complex group than a cyclic group, namely, the dihedral group, the existence of asymptotically good codes that are invariant under the action of the group on itself can be guaranteed. In particular, we show that, for infinitely many block lengths, a random ideal in the binary group algebra of the dihedral group is an asymptotically good rate-half code with a high probability. We argue also that a random code that is invariant under the action of an Abelian group G of odd order on k disjoint copies of itself satisfies the binary Gilbert-Varshamov (GV) bound with a high probability for rate 1/k under a condition on the family of groups. The underlying condition is in terms of the growth of the smallest dimension of a nontrivial F/sub 2/-representation of the group and is satisfied by roughly most Abelian groups of odd order, and specifically by almost all cyclic groups of prime order.
Proteomics and Immunological Analysis of a Novel Shrimp Allergen, Pen m 21

Shellfish are a common cause of adverse food reactions in hypersensitive individuals and shrimp is one of the most frequently reported causes of allergic reactions. A novel allergen from Penaeus monodon, designated Pen m 2, was identified by two-dimensional immunoblotting using sera from subjects with shrimp allergy, followed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis of the peptide digest. This novel allergen was then cloned and the amino acid sequence deduced from the cDNA sequence. The cloned cDNA encoded a 356-aa protein with an acetylated N terminus at Ala2, identified by postsource decay analysis. Comparison of the Pen m 2 sequence with known protein sequences revealed extensive similarity with arginine kinase (EC 2.7.3.3) from crustaceans. Pen m 2 was purified by anion exchange chromatography and shown to have arginine kinase activity and to react with serum IgE from shrimp allergic patients and induce immediate type skin reactions in sensitized patients. Using Pen m 2-specific antisera and polyclonal sera from shrimp-sensitive subjects in a competitive ELISA inhibition assay, Pen m 2 was identified as a novel cross-reactive Crustacea allergen. This novel allergen could be useful in allergy diagnosis and in the treatment of Crustacea-derived allergic disorders.
The Effects of Taekwondo Training on Peripheral Neuroplasticity-Related Growth Factors, Cerebral Blood Flow Velocity, and Cognitive Functions in Healthy Children: A Randomized Controlled Trial

Although regular Taekwondo (TKD) training has been reported to be effective for improving cognitive function in children, the mechanism underlying this improvement remains unclear. The purpose of the present study was to observe changes in neuroplasticity-related growth factors in the blood, assess cerebral blood flow velocity, and verify the resulting changes in children’s cognitive function after TKD training. Thirty healthy elementary school students were randomly assigned to control (n = 15) and TKD (n = 15) groups. The TKD training was conducted for 60 min at a rating of perceived exertion (RPE) of 11–15, 5 times per week, for 16 weeks. Brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF), and insulin-like growth factor-1 (IGF-1) levels were measured by blood sampling before and after the training, and the cerebral blood flow velocities (peak systolic [MCAs], end diastolic [MCAd], mean cerebral blood flow velocities [MCAm], and pulsatility index [PI]) of the middle cerebral artery (MCA) were measured using Doppler ultrasonography. For cognitive function assessment, Stroop Color and Word Tests (Word, Color, and Color-Word) were administered along with other measurements. The serum BDNF, VEGF, and IGF-1 levels and the Color-Word test scores among the sub-factors of the Stroop Color and Word Test scores were significantly higher in the TKD group after the intervention (p < 0.05). On the other hand, no statistically significant differences were found in any factors related to cerebral blood flow velocities, or in the Word test and Color test scores (p > 0.05). Thus, 16-week TKD training did not significantly affect cerebral blood flow velocities, but the training may have been effective in increasing children’s cognitive function by inducing an increase in the levels of neuroplasticity-related growth factors.
Self‐training author name disambiguation for information scarce scenarios

We present a novel 3‐step self‐training method for author name disambiguation—SAND (self‐training associative name disambiguator)—which requires no manual labeling, no parameterization (in real‐world scenarios) and is particularly suitable for the common situation in which only the most basic information about a citation record is available (i.e., author names, and work and venue titles). During the first step, real‐world heuristics on coauthors are able to produce highly pure (although fragmented) clusters. The most representative of these clusters are then selected to serve as training data for the third supervised author assignment step. The third step exploits a state‐of‐the‐art transductive disambiguation method capable of detecting unseen authors not included in any training example and incorporating reliable predictions to the training data. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation, demonstrate that our proposed method outperforms all representative unsupervised author grouping disambiguation methods and is very competitive with fully supervised author assignment methods. Thus, different from other bootstrapping methods that explore privileged, hard to obtain information such as self‐citations and personal information, our proposed method produces topnotch performance with no (manual) training data or parameterization and in the presence of scarce information.
Factors affecting Polish nurses' willingness to recommend the hospital as a place of care.

BACKGROUND Nurses constitute the major professional group offering constant hospital patients' care. Willingness to recommend their hospital reflects confidence in the offered care, satisfaction and identification with the work place. The aim of the present study has been to investigate which elements of hospital environment and nurse personal related factors predict recommendation of the hospital as a place of care by employed nurses. MATERIAL AND METHODS Cross-sectional, correlation study was, based on 1723 self-reported, anonymous questionnaires of nurses working in 30 acute hospitals. Data was analyzed using the logistic regression model, with general estimation equations. RESULTS About 25% of nurses were unwilling to recommend their hospital as the place of care. The odds ratio (OR) of the lack of willingness to recommend the hospital was related to assessment of patients' safety (OR = 0.28, 95% confidence interval (CI): 0.18-0.46, p = 0.00), decrease in the quality of patient care during the preceding year (OR = 0.62, 95% CI: 0.41-0.93, p = 0.02), overall work conditions (OR = 0.35, 95% CI: 0.22-0.57, p = 0.00), weak cooperation between nurses and physicians (OR = 0.37, 95% CI: 0.25-0.54, p = 0.00), poor work schedule flexibility (OR = 0.74, 95% CI: 0.55- 0.99, p = 0.04) and educational opportunities (OR = 0.71, 95% CI: 0.54-0.95, p = 0.02) and the level of nurses depersonalization (OR = 1.78, 95% CI: 1.18-1.68, p = 0.00). CONCLUSIONS The hospital manager should consider strategies which improve patients' safety and the staff working conditions. Thanks to that they will also achieve better and more competitive image of the hospital in the local community. Med Pr 2016;67(4):447-454.