Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressible Magnetohydrodynamic Turbulence : mode coupling , scaling relations , anisotropy , viscosity-damped regime , and astrophysical implications

We present numerical simulations and explore scalings and anisotropy of compressible magnetohydrodynamic (MHD) turbulence. Our study covers both gas pressure dominated (high β) and magnetic pressure dominated (low β) plasmas at different Mach numbers. In addition, we present results for superAlfvenic turbulence and discuss in what way it is similar to the subAlfvenic turbulence. We describe a technique of separating different magnetohydrodynamic (MHD) modes (slow, fast and Alfven) and apply it to our simulations. We show that, for both high and low β cases, Alfven and slow modes reveal the Kolmogorov k spectrum and scale-dependent GoldreichSridhar anisotropy, while fast modes exhibit k spectrum and isotropy. We discuss the statistics of density fluctuations arising from MHD turbulence at different regimes. Our findings entail numerous astrophysical implications ranging from cosmic ray propagation to gamma ray bursts and star formation. In particular, we show that the rapid decay of turbulence reported by earlier researchers is not related to compressibility and mode coupling in MHD turbulence. In addition, we show that magnetic field enhancements and density enhancements are marginally correlated. Addressing the density structure of partially ionized interstellar gas on AU scales, we show that the viscositydamped regime of MHD turbulence that we earlier reported for incompressible flows persists for compressible turbulence and therefore may provide an explanation for those mysterious structures.
Expressing Flood Likelihood: Return Period versus Probability

AbstractThe likelihood of floods and other potentially destructive natural phenomena is often expressed as a return period or recurrence interval, such as a 100-yr flood. However, the expression might give users the impression that the event will occur exactly once within the described period, obscuring the intended probabilistic meaning. If so, users may think a flood is less likely when one has just occurred or more likely when it has not, leading to a “flood is due” effect. This hypothesis was tested experimentally in two studies reported here. Participants were given either a return period or a probability expression and asked to rate flood likelihood and concern. Flood recency was also manipulated. The results from both studies support a flood is due effect when the return period expression is used. In the return period condition alone, participants rated floods as more likely and expressed greater concern when no flood had occurred recently. When no likelihood information was conveyed in the control.
Coevolution of RAC Small GTPases and their Regulators GEF Proteins

RAC proteins are small GTPases involved in important cellular processes in eukaryotes, and their deregulation may contribute to cancer. Activation of RAC proteins is regulated by DOCK and DBL protein families of guanine nucleotide exchange factors (GEFs). Although DOCK and DBL proteins act as GEFs on RAC proteins, DOCK and DBL family members are evolutionarily unrelated. To understand how DBL and DOCK families perform the same function on RAC proteins despite their unrelated primary structure, phylogenetic analyses of the RAC, DBL, and DOCK families were implemented, and interaction patterns that may suggest a coevolutionary process were searched. Interestingly, while RAC and DOCK proteins are very well conserved in humans and among eukaryotes, DBL proteins are highly divergent. Moreover, correlation analyses of the phylogenetic distances of RAC and GEF proteins and covariation analyses between residues in the interacting domains showed significant coevolution rates for both RAC–DOCK and RAC–DBL interactions.
Anonymizing Graphs Against Weight-Based Attacks

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph by having the topological structure and/or basic graph properties as background knowledge. Many previous studies addressing such attack as identity disclosure, however, concentrate on preserving privacy in simple graph data only. In this paper, we consider the identity disclosure problem in weighted graphs. The motivation is that, a weighted graph can introduce much more unique information than its simple version, which makes the disclosure easier. We first formalize a general anonymization model to deal with weight-based attacks. Then two concrete attacks are discussed based on weight properties of a graph, including the sum and the set of adjacent weights for each vertex. We also propose a complete solution for the weight anonymization problem to prevent a graph from both attacks. Our approaches are efficient and practical, and have been validated by extensive experiments on both synthetic and real-world datasets.
Dynamic Recognition of Enviromental Sounds with Recurrent Neural Network *

This paper introduces our method for classifying non-speech environmental sounds. Most existing studies require a huge number of sample data of all target sounds including noises for training stochastic models such as gaussian mixture model. We propose a use of neuro-dynamical model which can be trained with a small amount of data. In this paper, we show the results of preliminary experiments of the proposed model, which enables classification of both known and unknown sound targets.
Scalability of the RAMpage Memory Hierarchy

The RAMpage memory hierarchy is an alternative to the traditional division between cache and main memory: main memory is moved up a level and DRAM is used as a paging device. As the CPU-DRAM speed gap grows, it is expected that the RAMpage approach should become more viable. Results in this paper show that RAMpage scales better than a standard second-level cache, because the number of DRAM references is lower. Further, RAMpage allows the possibility of taking a context switch on a miss, which is shown to further improve scalability. The paper also suggests that memory wall work ought to include the TLB, which can represent a significant fraction of e xecution time. With context switches on misses, the speed improvement at an 8 GHz instruction issue rate is 62% over a standard 2-level cache hierarchy.
R / BioC Exercises and Answers : Linear models and differential expression

Many microarray studies are designed to detect genes associated with different phenotypes, for example, the comparison of cancer tumors to normal cells. In the more complex experiments genetic networks are perturbed with various treatments to understand the effects of these treatments and their interactions with each other in a dynamic cellular network. This tutorial presents some commonly used experimental designs used to explore differential expression of genes between phenotypic groups and their analyses using linear models via the limma package.
A generic approach to the security of multi-threaded programs

Abstract: The security of computation at the level of a specific programming language and the security of complex systems at a more abstract level are two major areas of current security research. With the objective to integrate the two, this article proposes a translation of a timing-sensitive security property for simple multi-threaded programs into a more general security framework. Interestingly, our notion of security for programs is bisimulation-based while the security framework is trace-based. Nevertheless, we show that the translation is sound and complete in the sense that the trace-based specification which results from the translation of a multi-threaded program is secure if and only if the original program is secure. The translation is presented as a two-step process where the first step is independent from the concrete programming language.
InGaAs/InAs heterojunction vertical nanowire tunnel fets fabricated by a top-down approach

We demonstrate for the first time InGaAs/InAs heterojunction single nanowire (NW) vertical tunnel FETs fabricated by a top-down approach. Using a novel III-V dry etch process and gate-source isolation method, we have fabricated 50 nm diameter NW TFETs with a channel length of 60 nm and EOT=1.2 nm. Thanks to the insertion of an InAs notch, high source doping, high-aspect ratio nanowire geometry and scaled gate oxide, an average subthreshold swing (S) of 79 mV/dec at Vds= 0.3 V is obtained over 2 decades of current. On the same device, Ion= 0.27 μA/μm is extracted at Vdd= 0.3 V with a fixed Ioff= 100 pA/μm. This is the highest ON current demonstrated at this OFF current level in NW TFETs containing III-V materials.
Investigating the Neural Correlates of Voice versus Speech-Sound Directed Information in Pre-School Children

Studies in sleeping newborns and infants propose that the superior temporal sulcus is involved in speech processing soon after birth. Speech processing also implicitly requires the analysis of the human voice, which conveys both linguistic and extra-linguistic information. However, due to technical and practical challenges when neuroimaging young children, evidence of neural correlates of speech and/or voice processing in toddlers and young children remains scarce. In the current study, we used functional magnetic resonance imaging (fMRI) in 20 typically developing preschool children (average age = 5.8 y; range 5.2–6.8 y) to investigate brain activation during judgments about vocal identity versus the initial speech sound of spoken object words. FMRI results reveal common brain regions responsible for voice-specific and speech-sound specific processing of spoken object words including bilateral primary and secondary language areas of the brain. Contrasting voice-specific with speech-sound specific processing predominantly activates the anterior part of the right-hemispheric superior temporal sulcus. Furthermore, the right STS is functionally correlated with left-hemispheric temporal and right-hemispheric prefrontal regions. This finding underlines the importance of the right superior temporal sulcus as a temporal voice area and indicates that this brain region is specialized, and functions similarly to adults by the age of five. We thus extend previous knowledge of voice-specific regions and their functional connections to the young brain which may further our understanding of the neuronal mechanism of speech-specific processing in children with developmental disorders, such as autism or specific language impairments.
Central corneal thickness in Latinos.

PURPOSE To characterize central corneal thickness (CCT) in Latinos aged 40 or more years. METHODS A population-based cohort of Latinos from two census tracts in La Puente, California, underwent measurements of CCT and intraocular pressure (IOP). CCT was measured with an ultrasonic pachymeter, and IOP was measured by applanation tonometry. One eye of each of 1699 participants was included in the analyses. RESULTS The mean (+/-SD) CCT was 546.9 +/- 33.5 micro m. Older participants (>or=70 years) had significantly thinner CCs compared with participants 40 to 49 years of age (P < 0.05). Eyes with ocular hypertension had thicker CCs than did normal and glaucomatous eyes (P < 0.05). Multivariate adaptive regression spline analyses and analysis of variance contrasting IOP subgroups revealed that eyes with thinner CCs had lower IOP than did eyes with thicker CCs (P < 0.001). The absolute range of interocular differences in CCT in the same subject was as high as 24 micro m. CONCLUSIONS On average, CCT in Latinos was less than that previously reported in whites but greater than that reported in African Americans and Asians. Older Latinos had thinner corneas compared with younger Latinos. Asymmetry in CCT of 25 micro m or more should be evaluated for potential corneal disease. Spline analyses suggest that although the relationship between IOP and CCT is best explained by a nonlinear equation, when measuring IOP with the Goldmann tonometer, it is likely that IOP is underestimated in eyes with thinner CCs and overestimated in eyes with thicker CCs.
Temporal kernel neural network language model

Using neural networks to estimate the probabilities of word sequences has shown significant promise for statistical language modeling. Typical modeling methods include multi-layer neural networks, log-bilinear networks and recurrent neural networks, etc. In this paper, we propose the temporal kernel neural network language model, a variant of models mentioned above. This model explicitly captures long-term dependencies of words with exponential kernel, where the memory of history is decayed exponentially. Additionally, several sentences with variable lengths as a mini-batch are efficiently implemented for speeding up. Experimental results show that the proposed model is very competitive to the recurrent neural network language model and obtains the lower perplexity of 111.6 (more than 10% reduction) than the state-of-the-art results reported in the standard Penn Treebank Corpus. We further apply this model to Wall Street Journal speech recognition task, and observe significant improvements in word error rate.
Congenital Transmission of Trypanosoma cruzi Infection in Argentina

Trypanosoma cruzi, the causative agent of Chagas disease, infects 10–18 million people and may be transmitted to the newborn. Using various data sources, we estimated that nearly 850 congenital cases occurred in Argentina in 1993, or 6.3 expected cases per each reported case in 1994 and in 1994–2001. The congenital transmission of T. cruzi constitutes a sizeable public health problem in the region.
No Load Performance of a Novel Synchronous Permanent Magnet Generator with Soft Magnetic Composite

This paper introduces a new axial field flux synchronous permanent magnet generator for small wind turbine applications. 3D finite element analysis used to investigate, design and optimize a 3-phase 12/10-pole machine analysis used to investigation the back-EMF, open circuit flux linkage, winding inductance, cogging torque etc. After construction of the designed proposed machine the practical results compared with the simulation results. A good compliance between these two criteria were observed. Soft magnetic composite (SMC) material are introducing a new window to construction of both cores and rotors of the electrical machinery. This structure in conjunction with SMC facilitates design and construction and well fitted to the fast prototyping and economic manufacturing, compatible with growing need to reduce both cost and environmental impacts.
An Efficient Causal Protocol with Forward Error Correction for Mobile Distributed Systems

Problem statement: Causal ordering is used in Mobile Distributed Systems (MDS) to reduce the non-determinism induced by four main aspects: host mobility, asynchronous execution, unpredictable communication delays and unreliable communication channels. Some causal protocols have been proposed for MDS. All of these protocols in order to ensure the causal order in unreliable channels use the method of Automatic Repeat Request (ARQ). They detect a lost message and carry out the retransmission of this message. This approach was not recommended in a real time mobile distributed system because it increased the transmission delay of the data and the overhead sent in the communication channels. Approach: In this study, we proposed a protocol that ensured the causal order of messages in unreliable and asynchronous MDS. In our protocol, the detection and recovery of lost messages was achieved by the method of Forward Error Correction (FEC) in a distributed form. One interesting aspect of our Causal-FEC protocol was that the redundant information sent in the wired and wireless communication channels is dynamically adapted to the behavior of the system. Results: Our protocol was efficient in terms of the overhead attached per message, the computational cost and the storage control information at a mobile host. Conclusion: The present study is one of the first works on causal algorithms based on forward error recovery in mobile networks.
Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm

Quantum effects are a natural phenomenon and just like evolution, or immune processes, can serve as an inspiration for the design of computing algorithms. This study illustrates how a real-valued quantum-inspired evolutionary algorithm(QEA) can be constructed and examines the utility of the resulting algorithm on an important real-world problem, namely the calibration of an Option Pricing model. The results from the algorithm are shown to be robust and sensitivity analysis is carried out on the algorithm parameters, suggesting that there is useful potential to apply QEA to this domain.
Run-time Measurement of COSMIC Functional Size for Java Business Applications: Initial Results

The issue of "Functional Size Measurement" is increasing in importance for software project management. It is considerably time-consuming and costly when functional size measurement is manually done. In this study, automatic measurement of COSMIC functional size for three-tier Java business applications at rum-time is aimed. Functional size measurement is performed by using a self-developed "Measurement Library" for functional processes that are triggered via graphical user interface of the application. The library has been utilized by being imported from a simple student registration system, and the application size has been measured automatically. It has been found that the COSMIC functional sizes measured automatically and calculated manually are 92% approximate.
Fuzzy Answer Set Programming with Literal Preferences

In the current approaches to fuzzy answer set pro- gramming (FASP) one can state preferences amongst rules to denote their relative importance. However, in many situations we need more complex preferences such as those in the answer set optimization framework proposed by Brewka for crisp answer set programming. Unfortunately, these complex preferences do not readily fit into the current FASP approaches. In this paper we propose a language to state such preferences and show that programs with these preferences can be translated into equivalent general fuzzy answer set program- ming (gFASP) programs. This not only provides an implementation method, but also shows that this extension can be added as syntactic
Combining implicit measures and information foraging theory to improve web search

The massive distributed, dynamical and evolutionary characteristics of World Wide Web inspire us study it drawing on the Information Foraging Theory which assumes that people prefer yield more useful information per unit cost. Understanding the value of implicit measures is also important to help World Wide Web users search more effectively. Borrowing idea from information foraging theory we propose a method to estimate the Web page information gain based on the implicit measures analysis. We developed an experimental search platform to record the URLs of the pages be browsed, the time spent reading, the time spent scrolling and the number of links be clicked. We analyzed these data using multiple linear regression modeling and obtained a regression function which could be used in calculating the information profitability of the Web pages. Then we regrouped the Web pages searched out by the descending order of the Web page information profitability. We also reported experimental data to show that the search results be regrouped can increase searching efficiency. The findings suggest that the combination of information foraging theory and implicit measures analysis support effective Web-search interaction for everyone.
A Collective Biological Processing Algorithm for ECG Signals

We establish and explore an analogy between hunting by packs of agents and signal processing. We present a version of adaptive ‘Hunting Swarm’ algorithm (HSA), apply it to EKG signals, and investigate the influence of the model parameters on the filtering of stationary and nonstationary noise. We show that results obtained with the HSA filter may outperform results obtained with several other filters.
Comparison Analysis of a Biomedical Image for Compression Using Various Transform Coding Techniques

The theme work presented in this paper is adetailed analysis of various transforms like Discrete Cosinetransform, Singular Value Decomposition, Discrete Hadamardtransform, Slant transform, Discrete Haar transform whichare applied to a set of considered biomedical images to achieveimage compression. The operations on images are performedin transform domain where the DC coefficients are stored andtruncation operation is performed by setting correspondingthreshold to achieve desired PSNR to maintain the quality ofreconstruction. In this paper, the biomedical images aresubjected to all compression schemes mentioned by settingPSNR to 25dB and 30dB. The reconstruction qualities for boththe results (25dB and 30dB) are tabulated and detailed analysisis done based on the quality of reconstruction which throwslight on optimal transform. The metrics used for analysis areMean Square Error, Peak Signal to Noise ratio, StructuralSimilarity Index, Compression ratio, Energy Compaction and Auto-Correlation.
A MELHORA DA RECONSOLIDAÇÃO DA MEMÓRIA INDUZIDA PORESPERMIDINA ENVOLVE A PROTEÍNA CINASE DEPENDENTE DE CÁLCIO

Dissertation of Master’s degree Post Graduation Program in Pharmacology Federal University of Santa Maria, RS, Brazil SPERMIDINE-INDUCED IMPROVEMENT OF RECONSOLIDATION OF MEMORY INVOLVES CALCIUM-DEPENDENT PROTEIN KINASE AUTHOR: Bruna Amanda Girardi ADVISOR: Dra Maribel Antonello Rubin Place and date of defense: Santa Maria, July 21, 2015. The reactivation of a memory results in its destabilization, requiring a process of memory reconsolidation to maintain it. Spermidine is an endogenous aliphatic amine with polycationic structure that modulates N-methyl-D-aspartate (NMDA) receptor activity and improves memory. Recent evidence suggests that systemic administration of spermidine improves the reconsolidation of fear memory. Here we determined whether the calcium-dependent protein kinase (PKC) signaling pathway is involved in the improvement of fear memory reconsolidation induced by intrahippocampal (ih) administration of spermidine in rats. Male Wistar rats were trained in a fear conditioning apparatus using a 0.4 mA footshock as unconditioned stimulus. Twenty-four hours after training, animals were re-exposed to the apparatus in the absence of shock (reactivation session). Immediately after the reactivation session, spermidine (2-200 ρmol/site); the PKC inhibitor, 3-[1(dimethylaminopropyl)indol-3-yl]-4-(indol-3-yl) maleimide hydrochloride (GF 109203X, 0.3 30 ρg/site); the antagonist of the polyamine-binding site at the NMDA receptor, arcaine (0.2 200 ρmol/site) or the PKC activator, phorbol 12-myristate 13-acetate (PMA, 0.02 2 nmol/site) were injected intra-hipocampally (i.h.). Testing was carried out in the same apparatus, twenty-four hours after reactivation. Freezing scores at testing were considered a measure of memory. While the post-reactivation administration of spermidine (20 and 200 ρmol/site) improved, GF 109203X (1, 10 and 30 ρg/site) impaired memory reconsolidation. GF 109203X (0.3 ρg/site) prevented spermidine (200 ρmol/site)-induced improvement of memory reconsolidation. The post-reactivation administration of arcaine (200 pmol/site) impaired and PMA (2 nmol/site) improved memory reconsolidation. PMA (0.2 nmol/site) prevented arcaine (200 ρmol/site)-induced impairment of memory reconsolidation. The protein synthesis inhibitor anisomycin (2 μg/site) prevented spermidine (200 ρmol/site)-induced improvement of memory reconsolidation. These drugs had no effect on memory if they were administered in the absence of reactivation. These results suggest that the enhancement of memory reconsolidation induced by the administration of spermidine involves PKC activation.