Miracle-dz/UniFAR-FTUs huggingface dataset:
Abdominal fat distribution and its relationship to brain changes: the differential effects of age on cerebellar structure and function: a cross-sectional, exploratory study

Objectives To investigate whether the metabolically important visceral adipose tissue (VAT) relates differently to structural and functional brain changes in comparison with body weight measured as body mass index (BMI). Moreover, we aimed to investigate whether these effects change with age. Design Cross-sectional, exploratory. Setting University Clinic, Integrative Research and Treatment Centre. Participants We included 100 (mean BMI=26.0 kg/m², 42 women) out of 202 volunteers randomly invited by the city's registration office, subdivided into two age groups: young-to-mid-age (n=51, 20–45 years of age, mean BMI=24.9, 24 women) versus old (n=49, 65–70 years of age, mean BMI=27.0, 18 women). Main outcome measures VAT, BMI, subcutaneous abdominal adipose tissue, brain structure (grey matter density), functional brain architecture (eigenvector centrality, EC). Results We discovered a loss of cerebellar structure with increasing VAT in the younger participants, most significantly in regions involved in motor processing. This negative correlation disappeared in the elderly. Investigating functional brain architecture showed again inverse VAT–cerebellum correlations, whereas now regions involved in cognitive and emotional processing were significant. Although we detected similar results for EC using BMI, significant age interaction for both brain structure and functional architecture was only found using VAT. Conclusions Visceral adiposity is associated with cerebellar changes of both structure and function, whereas the regions involved contribute to motor, cognitive and emotional processes. Furthermore, these associations seem to be age dependent, with younger adults’ brains being adversely affected.
Upper bounds for constant-weight codes

Let A(n,d,w) denote the maximum possible number of codewords in an (n,d,w) constant-weight binary code. We improve upon the best known upper bounds on A(n,d,w) in numerous instances for n/spl les/24 and d/spl les/12, which is the parameter range of existing tables. Most improvements occur for d=8, 10, where we reduce the upper bounds in more than half of the unresolved cases. We also extend the existing tables up to n/spl les/28 and d/spl les/14. To obtain these results, we develop new techniques and introduce new classes of codes. We derive a number of general bounds on A(n,d,w) by means of mapping constant-weight codes into Euclidean space. This approach produces, among other results, a bound on A(n,d,w) that is tighter than the Johnson bound. A similar improvement over the best known bounds for doubly-constant-weight codes, studied by Johnson and Levenshtein, is obtained in the same way. Furthermore, we introduce the concept of doubly-bounded-weight codes, which may be thought of as a generalization of the doubly-constant-weight codes. Subsequently, a class of Euclidean-space codes, called zonal codes, is introduced, and a bound on the size of such codes is established. This is used to derive bounds for doubly-bounded-weight codes, which are in turn used to derive bounds on A(n,d,w). We also develop a universal method to establish constraints that augment the Delsarte inequalities for constant-weight codes, used in the linear programming bound. In addition, we present a detailed survey of known upper bounds for constant-weight codes, and sharpen these bounds in several cases. All these bounds, along with all known dependencies among them, are then combined in a coherent framework that is amenable to analysis by computer. This improves the bounds on A(n,d,w) even further for a large number of instances of n, d, and w.
Verifiable Composition of Language Extensions

Domain-specific languages offer a variety of advantages, but their implementation techniques have disadvantages that sometimes prevent their use in practice. Language extension offers a potential solution to some of these problems, but remains essentially unused in practice. It is our contention that the main obstacle to adoption is the lack of any assurance that the compiler composed of multiple independent language extensions will work without the need for additional modifications, or at all. We propose to solve this problem by requiring extensions to independently pass a composition test that will ensure that any such extensions can be safely composed without “glue code,” and we propose to demonstrate that interesting extensions are still possible that satisfy such a test.
Why are people bad at detecting randomness ? A statistical analysis

Errors in detecting randomness are often understood in terms of biases and misconceptions, but we propose and provide evidence for a nested-process account that characterizes the contribution of the inherent statistical difficulty of the problem. The account uses a Bayesian statistical analysis to reveal that a random process is contained or nested within a range of systematic processes. The consequence is that randomly generated data are still reasonably likely to have come from a systematic process, and thus only weakly diagnostic of a random process and easy to misidentify. Experiments 1 and 2 show that the low accuracy in judging whether a sequence of coin flips is random (or biased towards heads or tails) is due to the weak evidence provided by random sequences. While randomness judgments were less accurate than nonnested judgments in the same task domain, once the strength of the evidence available was statistically equated, accuracy on these non-nested judgments was reduced and was no longer significantly different. Experiment 3 extended this finding to assessing whether a sequence was random or exhibited sequential dependence, and found that the nested feature of randomness caused errors in addition to those stemming from known misconceptions. Overall, judgment accuracy was better predicted by whether or not a judgment involved a nested process than whether or not it concerned a random process.
Classification of ECG using chaotic models

Chaotic analysis has been shown to be useful in a variety of medical applications, particularly in cardiology. Chaotic parameters have shown potential in the identification of diseases, especially in the analysis of biomedical signals like electrocardiogram (ECG). In this work, underlying chaos in ECG signals has been analyzed using various non-linear techniques. First, the ECG signal is processed through a series of steps to extract the QRS complex. From this extracted feature, bit-to-bit interval (BBI) and instantaneous heart rate (IHR) have been calculated. Then some nonlinear parameters like standard deviation, and coefficient of variation and nonlinear techniques like central tendency measure (CTM), and phase space portrait have been determined from both the BBI and IHR. Standard database of MIT-BIH is used as the reference data where each ECG record contains 650000 samples. CTM is calculated for both BBI and IHR for each ECG record of the database. A much higher value of CTM for IHR is observed for eleven patients with normal beats with a mean of 0.7737 and SD of 0.0946. On the contrary, the CTM for IHR of eleven patients with abnormal rhythm shows low value with a mean of 0.0833 and SD 0.0748. CTM for BBI of the same eleven normal rhythm records also shows high values with a mean of 0.6172 and SD 0.1472. CTM for BBI of eleven abnormal rhythm records show low values with a mean of 0.0478 and SD 0.0308. Phase space portrait also demonstrates visible attractor with little dispersion for a healthy person’s ECG and a widely dispersed plot in 2-D plane for the ailing person’s ECG. These results indicate that ECG can be classified based on this chaotic modeling which works on the nonlinear dynamics of the system. Index Terms — ECG, CTM, Poincaré plot, ANOVA
An Extension of the WAM for Hybrid Interval Solvers

This paper discusses the implementation of DecLIC, an extension of the clp(fd) language to handle interval constraints. The main solving capabilities of DecLIC are based on the use of two different levels of local consistency, namely hull-consistency and boxφ-consistency, a slight yet computationally relevant generalization of box-consistency. Experimental evidence of the qualities and drawbacks of constraint solving based on these two notions is provided and benchmark comparisons, carried out with a prototype of DecLIC, show that combination permits, on certain cases, relevant speed-ups in the resolution process. The compiler of clp(fd) translates CLP programs to C code via the Warren Abstract Machine (WAM). The extension of the WAM instruction set necessary to support the new features of DecLIC is described, along with the new internal structures.
Influence of sound immersion and communicative interaction on the Lombard effect.

PURPOSE To examine the influence of sound immersion techniques and speech production tasks on speech adaptation in noise. METHOD In Experiment 1, we compared the modification of speakers' perception and speech production in noise when noise is played into headphones (with and without additional self-monitoring feedback) or over loudspeakers. We also examined how this sound immersion effect depends on noise type (broadband or cocktail party) and level (from 62 to 86dB SPL). In Experiment 2, we compared the modification of acoustic and lip articulatory parameters in noise when speakers interact or not with a speech partner. RESULTS Speech modifications in noise were greater when cocktail party noise was played in headphones than over loudspeakers. Such an effect was less noticeable in broadband noise. Adding a self-monitoring feedback into headphones reduced this effect but did not completely compensate for it. Speech modifications in noise were greater in interactive situation and concerned parameters that may not be related to voice intensity. CONCLUSIONS The results support the idea that the Lombard effect is both a communicative adaptation and an automatic regulation of vocal intensity. The influence of auditory and communicative factors has some methodological implications on the choice of appropriate paradigms to study the Lombard effect.
Dependent Inductive and Coinductive Types are Fibrational Dialgebras

In this paper, I establish the categorical structure necessary to interpret dependent inductive and coinductive types. It is well-known that dependent type theories a la Martin-Lof can be interpreted using fibrations. Modern theorem provers, however, are based on more sophisticated type systems that allow the definition of powerful inductive dependent types (known as inductive families) and, somewhat limited, coinductive dependent types. I define a class of functors on fibrations and show how data type definitions correspond to initial and final dialgebras for these functors. This description is also a proposal of how coinductive types should be treated in type theories, as they appear here simply as dual of inductive types. Finally, I show how dependent data types correspond to algebras and coalgebras, and give the correspondence to dependent polynomial functors.
Platelet-activating factor: a candidate human immunodeficiency virus type 1-induced neurotoxin

The pathogenesis of central nervous system disease during human immunodeficiency virus type 1 (HIV-1) infection revolves around productive viral infection of brain macrophages and microglia. Neuronal losses in the cortex and subcortical gray matter accompany macrophage infection. The question of how viral infection of brain macrophages ultimately leads to central nervous system (CNS) pathology remains unanswered. Our previous work demonstrated high-level production of tumor necrosis factor alpha, interleukin 1 beta, arachidonic acid metabolites, and platelet-activating factor (PAF) from HIV-infected monocytes and astroglia (H. E. Gendelman, P. Genis, M. Jett, and H. S. L. M. Nottet, in E. Major, ed., Technical Advances in AIDS Research in the Nervous System, in press; P. Genis, M. Jett, E. W. Bernton, H. A. Gelbard, K. Dzenko, R. Keane, L. Resnick, D. J. Volsky, L. G. Epstein, and H. E. Gendelman, J. Exp. Med. 176:1703-1718, 1992). These factors, together, were neurotoxic. The relative role(s) of each of these candidate neurotoxins in HIV-1-related CNS dysfunction was not unraveled by these initial experiments. We now report that PAF is produced during HIV-1-infected monocyte-astroglia interactions. PAF was detected at high levels in CSF of HIV-1-infected patients with immunosuppression and signs of CNS dysfunction. The biologic significance of the results for neurological disease was determined by addition of PAF to cultures of primary human fetal cortical or rat postnatal retinal ganglion neurons. Here, PAF at concentrations of > or = 300 pg/ml produced neuronal death. The N-methyl-D-aspartate receptor antagonist MK-801 or memantine partially blocked the neurotoxic effects of PAF. The identification of PAF as an HIV-1-induced neurotoxin provides new insights into how HIV-1 causes neurological impairment and how it may ultimately be ameliorated.
Hemoglobin Yamagata : HbAlc 검사 중 검출된 혈색소 변이 Hemoglobin Yamagata : Hemoglobin Variant Detected by HbA 1 c Test

Hemoglobin (Hb) Yamagata is a rare Hb variant, which has been reported only twice-one case each in Japan and Korea. This variant arises from a Lys Asn substitution due to a mutation of AAA to AAC or AAT at codon 133 of the beta-globin gene. This study reports the third case of a patient detected with Hb Yamagata [HBB: c.399A>T; p.Lys133Asn] and discusses the effect of this variant on HbA1c measurement. This variant was detected in a 70-yr-old Korean man with diabetes mellitus during a routine follow-up. The HbA1c concentration determined using Variant ll Turbo (Bio-Rad, USA) was abnormally high at 47.9%. It was impossible to measure the HbA1c level accurately using Variant ll Thalassemia Mode (Bio-Rad, USA). However, the HbA1c levels analyzed by HLC-723 G7 (Tosoh, Japan), Cobas Integra (Roche, Switzerland) and NycoCard (Axis-Shield, Norway) were 5.0%, 8.0%, and 7.9%, respectively. This study shows that Hb Yamagata interferes with the accurate measurement of HbA1c levels in a diabetic patient. Taking these findings into consideration, we think that an immunoassay or affinity chromatography can be used as an alternate method for measuring the HbA1c level in a patient with this variant. In conclusion, a patient can be inferred to have an Hb variant if the HbA1c concentration is abnormally high or low or if there is a discrepancy between the results obtained using different methods, and if the clinical status of the patient suggests the presence of abnormal Hb. Subsequently, the HbA1c values can be determined by methods based on different principles. (Korean J Lab Med 2009;29:536-40)
A Fungal Metallothionein Is Required for Pathogenicity of Magnaporthe grisea

The causal agent of rice blast disease, the ascomycete fungus Magnaporthe grisea, infects rice (Oryza sativa) plants by means of specialized infection structures called appressoria, which are formed on the leaf surface and mechanically rupture the cuticle. We have identified a gene, Magnaporthe metallothionein 1 (MMT1), which is highly expressed throughout growth and development by M. grisea and encodes an unusual 22–amino acid metallothionein-like protein containing only six Cys residues. The MMT1-encoded protein shows a very high affinity for zinc and can act as a powerful antioxidant. Targeted gene disruption of MMT1 produced mutants that show accelerated hyphal growth rates and poor sporulation but had no effect on metal tolerance. Mmt1 mutants are incapable of causing plant disease because of an inability to bring about appressorium-mediated cuticle penetration. Mmt1 appears to be distributed in the inner side of the cell wall of the fungus. These findings indicate that Mmt1-like metallothioneins may play a novel role in fungal cell wall biochemistry that is required for fungal virulence.
Distributed Adaptive Convex Optimization on Directed Graphs via Continuous-Time Algorithms

This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is proposed to minimize a differentiable global objective function. By introducing dynamic coupling gains and updating the coupling gains using relative information of system states, the nonconvexity of local objective functions, unknown network connectivity, and the uncertain dynamics caused by locally Lipschitz gradients are tackled concurrently. Consequently, the global asymptotic convergence is established when the global objective function is strongly convex and the gradients of local objective functions are only locally Lipschitz. When the communication graph is strongly connected and weight-balanced, the algorithm is independent of any global information. Then, the algorithm is naturally extended to unbalanced directed graphs by using the left eigenvector of the Laplacian matrix associated with the zero eigenvalue. Several numerical simulations are presented to verify the results.
Intention-based Prediction for Pedestrians and Vehicles in Unstructured Environments

: Motion prediction for holonomic objects in unstructured environments is an ambitious task due to their high freedom of movement compared with non-holonomic objects. In this paper, we present a method for inferring the future goal of holonomic objects by a heuristic generation of target points (tp) and following discriminating decision making. The target points are generated, in a manner that covers the most common motion hypotheses like ”following” or ”staying”, safety relevant motion hypotheses like ”crossing future ego trajectories” or the ”movement to special points of interest”, e.g. gained from a map. Subsequently, for each considered object a trajectory to the inferred target point will be planned. Finally, the uncertainty of the trajectory is estimated by applying a Kalman Filter with a dynamically adjusted process noise matrix. An additional beneﬁt of this concept is its ability to cope with a different quality of context knowledge, so it can produce sound results even at poor structured environments.
Data Utility Maximization When Leveraging Crowdsensing in Machine Learning

With the increasingly wide adoption of crowdsensing services, we can leverage the crowd to obtain labeled data instances for training machine learning models. In this paper, we focus on the critical problem that which data instances should be collected to maximize the performance of the trained model under the budget limit. Solving this problem is nontrivial because of the unclear relationship between the performance of the trained model and the data collection process, NP-hardness of the problem and the online arrival of workers. To overcome these challenges, we first propose a crowdsensing framework with multiple rounds of data collecting and model training. The framework is based on the stream-based batch-mode active learning. According to the framework, we come up with a novel data utility model to measure the contribution of a data batch to the performance of the learning model. The data utility model combines uncertainty and weighted density to measure the contribution of one instance. Finally, we propose an online algorithm to select a data batch in each round. The algorithm achieves fairness, computational efficiency and a competitive ratio 0.1218 when the ratio of the largest contribution of one data instance to the optimal offline total data utility is infinitely small. Through evaluations based on a real data set, we demonstrate the efficiency of our data utility model and our online algorithm.
Software architecture design and development of multi-layer highly modular platform using intelligent components for dynamic big data analytics

This paper presents the design, development, and implementation of multi-Layer modular dynamic data analytics software platform using intelligent components. The reference software architecture proposed in this paper has been used to build the software robot called YaMasuta. YaMasuta is a software application that continuously analyzes massive financial data to produce investment decisions to aid human analysts. The objective is to investigate and test an optimal and efficient design of big data analytic software platform and designated as a reference software architecture platform for dynamic big data analytics' applications in different fields. The software platform of dynamic big data analytics will be presented at seven layers: The data acquisition layer, the data filtering layer, the data semantic layer, the intelligence layer, the model layer, the controller layer, and the dynamic presentation layer. Moreover, in this paper, we will present our chained asynchronous intelligent software components structure. The software robot and all of the intelligent software components described in this paper have been designed, developed, and deployed online at yamasuta.com for financial big data analytics and at yamasutaanalytics.apphb. com for medical morbidity analytics. Currently, the software robot YaMasuta for investment decision analytics covers financial data analytics of over 80,000 financial instruments (Stocks, FOREX, and Futures) in the largest 57 financial exchanges in 33 countries.
Preferences for Redistribution

This paper discusses what determines the preferences of individuals for redistribution. We review the theoretical literature and provide a framework to incorporate various effects previously studied separately in the literature. We then examine empirical evidence for the US, using the General Social Survey, and for a large set of countries, using the World Values Survey. The paper reviews previously found results and provides several new ones. We emphasize, in particular, the role of historical experiences, cultural factors and personal history as determinants of preferences for equality or tolerance for inequality.
Latent Space Sparse and Low-Rank Subspace Clustering

We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these representations. Efficient optimization methods are proposed and their non-linear extensions based on kernel methods are presented. Various experiments show that the proposed methods perform better than many competitive subspace clustering methods.
IMPORTANCE OF EVALUATING EFFECTIVENESS OF HEAD-UP DISPLAYS FOR WEAPON PLATFORMS

US Army Research Laboratory researchers designed a head-up display (HUD) that overlays driving information on a switchable vision block. Bradley and Stryker drivers were surveyed for recommendations to incorporate in the prototype. A simulation experiment was conducted to evaluate a HUD versus a traditional head-down display (HDD). Results about driving task performance were mixed, with the HUD associated with poorer performance in some measures and the HDD with others. Participants detected significantly more roadside improvised explosive devices (IEDs) with the HDD than with the HUD; they also detected the IEDs from a farther distance with the HDD than with the HUD. However, participants’ gauge monitoring performance was better with the HUD than with the HDD. The WSTIAC Journal, Volume 11, Number 1 3 http://wstiac.alionscience. com http://wstiac.alionscience. com/quarterly J.Y. C. Chen