Miracle-dz/UniFAR-FTUs huggingface dataset:
Frictional Resistance Calculations on a Ship using CFD

In the present study, we conducted propeller open water test, resistance test, and propeller, ship hull interaction for a ship‟s resistance and propulsion performance, using computational fluid dynamics techniques, where a K-epsilon, K-omega turbulence viscous models were employed. For convenience of mesh generation, unstructured meshes were used in the propeller region of a ship, where the hull shape is formed of delicate curved surfaces. On the other hand, structured meshes were generated for the remaining part of the hull and its domain, i.e., the region of relatively simple geometry. To facilitate the rotating propeller for propeller a moving reference frame motion type technique was adopted. The computational results were validated by comparing with the existing experimental data. In this work we are interested in predicting the frictional resistance offered to a ship during is motion. To this effect we start off with a consideration of the resistance offered to the bare hull in the absence of the propeller and later extend to the case where the propeller is in-place. The thrust generated by the propeller alone without considering the ship (called open water analysis) is also performed using CFD. FLUENT 6.0®, was used for CFD analysis and for modeling and meshing the packages used are CATIA – V5® and ICEM-CFD® respectively. The open water analysis of the 4-bladed propeller predicted a thrust of 346 kN at 30 rps. The bare hull resistance at 228 kN at 18 m/s, and resistance with propeller in place at 18 m/s was found to be 245 kN. The results predicted by the CFD analysis were found to be suitable for the present HSDS and it is believed that the hydrodynamics design of the propeller is acceptable for the problem at hand.
The efficacy of antidepressants for generalized anxiety disorder: a systematic review and meta-analysis.

OBJECTIVE To investigate the efficacy and acceptability of antidepressants in the treatment of generalized anxiety disorder. METHODS All randomized controlled trials assessing the use of antidepressants in generalized anxiety disorder up to may 2002 were included. Non randomized trials and those that included patients with both generalized anxiety disorder and another Axis I co-morbidity were excluded. Relative risks, weighted mean difference and number needed to treat were estimated. People who died or dropped out were regarded as having had no improvement. RESULTS Antidepressants (imipramine, venlafaxine and paroxetine) were found to be superior to placebo in treating generalized anxiety disorder. The calculated number needed to treat for antidepressants in generalized anxiety disorder was 5.15. Dropout rates did not differ between antidepressants and placebo. CONCLUSION The available evidence suggests that antidepressants would probably be a reasonable treatment for generalized anxiety disorder patients in the clinical context.
Event Relations at the Phonetics/Phonology Interface

Summary In this paper a procedure for the constrnction of event relations at the phonetics/phonolngy interface is presented. The approach goes further than previous formal interpretations of autosegmental phonology in that phonological relations are explicitly related to intervals in actual speech signals as required by a speech recognition system. An event structure containing the temporal relations of overlap, precedence and inclusion is automatically constructed on the basis of an event lattice with time annotations derived from the speech signal. The event structure can be interpreted linguistically as an antosegmental representation with assimilation, hmg components or coarticulation. The theoretical interest of this work lies in its contrilmtion to the solution of the projection problem in speech recognition, since a rigid mapping to segments is not required.
Forward Error Correction in Digital Television Broadcast Systems

Digital television (DTV) broadcasting is in the process of migrating from analogue to digital systems, with regions around the world at different stages of adoption. With some governments looking to auction off the remaining analogue spectrum, deadlines have been set for regions to switch off analogue TV transmission, resulting in growing demand for flexible technologies to enable a smooth transition in the timescales available. Various delivery systems are available for digital TV, but the main ones are satellite, cable, terrestrial, and mobile. Each has a variety of standards and derivatives that are either mature or emerging. These standards aim to ensure interoperability between different vendor’s equipment, including the set-top boxes, cell phones, and other means for DTV reception.
Crowdsourced automatic zoom and scroll for video retargeting

Screen size and display resolution limit the experience of watching videos on mobile devices. The viewing experience can be improved by determining important or interesting regions within the video (called regions of interest, or ROIs) and displaying only the ROIs to the viewer. Previous work focuses on analyzing the video content using visual attention model to infer the ROIs. Such content-based technique, however, has limitations. In this paper, we propose an alternative paradigm to infer ROIs from a video. We crowdsource from a large number of users through their implicit viewing behavior using a zoom and pan interface, and infer the ROIs from their collective wisdom. A retargeted video, consisting of relevant shots determined from historical users behavior, can be automatically generated and replayed to subsequent users who would prefer a less interactive viewing experience. This paper presents how we collect the user traces, infer the ROIs and their dynamics, group the ROIs into shots, and automatically reframe those shots to improve the aesthetics of the video. A user study with 48 participants shows that our automatically retargeted video is of comparable quality to one handcrafted by an expert user
Con gurable Shared Virtual Memory for

Most distributed-shared memory(DSM) systems use a global address space which is shared by all processing nodes. Although the global address space is convenient in providing a single address space for programming models, it is applicable to DSM systems with relatively small number of processing nodes. As the system size grows, there is a problem with scalability. To avoid this drawback, a new shared virtual memory management scheme, \CSVM (Conngurable Shared Virtual Memory)," is proposed. CSVM creates a new single address space for a given program which is shared only by the nodes cooperating for the parallel execution of the program. The new scheme, therefore, can be applied to any size of DSM system and even to heterogeneous DSM systems. We have developed two algorithms for the distribution and the management of a shared address space. These algorithms cover both the virtual memory of sequential computer and the shared virtual memory of the traditional DSM system, which means CSVM is reduced to the virtual memory management of sequential computer in executing a sequential program and to the traditional shared virtual memory in executing a parallel program. According to the analytical result, CSVM reduces the message traac of DSM system with global address very eeectively.
Interplay among catecholamine systems: Dopamine binds to α2‐adrenergic receptors in birds and mammals

Dopaminergic and adrenergic receptors are G‐protein‐coupled receptors considered to be different based on their pharmacology and signaling pathways. Some receptor subtypes that are members of one family are actually closer in phylogenetic terms to some subtypes belonging to the other family, suggesting that the pharmacological specificity among these receptors from different families is not perfect. Indeed, evidence is accumulating that one amine can cross‐talk with receptors belonging to the other system. However, most of these observations were collected in vitro using artificial cell models transfected with cloned receptors, so that the occurrence of this phenomenon in vivo as well as its distribution in the central nervous system is not known. In this study the pharmacological basis of possible in vivo interactions between dopamine and α2‐adrenergic receptors was investigated in quail, zebra finches, and rats. Binding competitions showed that dopamine displaces the binding of the selective α2‐adrenergic ligand, [3H]RX821002, in the brain of the three species with an affinity ≈10–28‐fold lower than that of norepinephrine. Dopamine also displaces with an affinity 3‐fold lower than norepinephrine the binding of [3H]RX821002 to human αh2A‐adrenergic receptors expressed in Sf9 cells. The anatomical distribution of this interaction was assessed in brain slices of quail and rat based on autoradiographic methods. Both norepinephrine and dopamine significantly displace [3H]RX821002 binding in all brain nuclei considered. Together, these data provide evidence for an interaction between the dopaminergic and noradrenergic systems in the vertebrate brain, albeit with species variations. J. Comp. Neurol. 511:610–627, 2008. © 2008 Wiley‐Liss, Inc.
Impulsivity and Rapid Decision-Making for Reward

Impulsivity is a feature of many brain disorders. Although often defined as the predisposition to act with an inadequate degree of deliberation, forethought, or control, it has proven difficult to measure. This may in part be due to the fact that it is a multifaceted construct, with impulsive decisions potentially arising as a result of a number of underlying mechanisms. Indeed, a “functional” degree of impulsivity may even promote effective behavior in healthy participants in a way that can be advantageous under certain circumstances. Although many tasks have been developed to study impulsivity, few examine decisions made rapidly, for time-sensitive rewards. In the current study we examine behavior in 59 adults on a manual “Traffic Light” task which requires participants to take risks under time pressure, if they are to maximize reward. We show that behavioral variables that index rapid anticipatory responding in this paradigm are correlated with one, specific self-report measure of impulsivity: “lack of premeditation” on the UPPS Impulsive Behavior Scale. Participants who scored more highly on this subscale performed better on the task. Moreover, anticipatory behavior reduced significantly with age (18–79 years), an effect that continued to be upheld after correction for potential age differences in the ability to judge the timing of responses. Based on these findings, we argue that the Traffic Light task provides a parametric method to study one aspect of impulsivity in health and disease: namely, rapid decision-making in pursuit of risky, time-sensitive rewards.
Protocol and Rationale-The Efficacy of Minocycline as an Adjunctive Treatment for Major Depressive Disorder: A Double Blind, Randomised, Placebo Controlled Trial

While current pharmacotherapies are efficacious, there remain a clear shortfall between symptom remission and functional recovery. With the explosion in our understanding of the biology of these disorders, the time is ripe for the investigation of novel therapies. Recently depression is conceptualized as an immune-inflammatory and nitro-oxidative stress related disorder. Minocycline is a tetracycline antibiotic that has anti-inflammatory, pro-oxidant, glutamatergic, neurotrophic and neuroprotective properties that make it a viable target to explore as a new therapy. This double blind, randomised, placebo controlled adjunctive trial will investigate the benefits of 200 mg/day of minocycline treatment, in addition to any usual treatment, as an adjunctive treatment for moderate-severe major depressive disorder. Sixty adults are being randomised to 12 weeks of treatment (with a 4 week follow-up post-discontinuation). The primary outcome measure for the study is mean change on the Montgomery-Asberg Depression Rating Scale (MADRS), with secondary outcomes including the Social and Occupational Functioning Assessment Scale (SOFAS), Clinical Global Impressions (CGI), Hamilton Rating Scale for Anxiety (HAM-A), Patient Global Impression (PGI), Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q) and Range of Impaired Functioning Tool (LIFE-RIFT). Biomarker analyses will also be conducted at baseline and week 12. The study has the potential to provide new treatment targets, both by showing efficacy with a new class of 'antidepressant' but also through the analysis of biomarkers that may further inform our understanding of the pathophysiology of unipolar depression.
Recent Development of Pd-Based Electrocatalysts for Proton Exchange Membrane Fuel Cells

This review selectively summarizes the latest developments in the Pd-based cataysts for low temperature proton exchange membrane fuel cells, especially in the application of formic acid oxidation, alcohol oxidation and oxygen reduction reaction. The advantages and shortcomings of the Pd-based catalysts for electrocatalysis are analyzed. The influence of the structure and morphology of the Pd materials on the performance of the Pd-based catalysts were described. Finally, the perspectives of future trends on Pd-based catalysts for different applications were considered.
DeMIAN: Deep Modality Invariant Adversarial Network

Obtaining common representations from different modalities is important in that they are interchangeable with each other in a classification problem. For example, we can train a classifier on image features in the common representations and apply it to the testing of the text features in the representations. Existing multi-modal representation learning methods mainly aim to extract rich information from paired samples and train a classifier by the corresponding labels; however, collecting paired samples and their labels simultaneously involves high labor costs. Addressing paired modal samples without their labels and single modal data with their labels independently is much easier than addressing labeled multi-modal data. To obtain the common representations under such a situation, we propose to make the distributions over different modalities similar in the learned representations, namely modality-invariant representations. In particular, we propose a novel algorithm for modality-invariant representation learning, named Deep Modality Invariant Adversarial Network (DeMIAN), which utilizes the idea of Domain Adaptation (DA). Using the modality-invariant representations learned by DeMIAN, we achieved better classification accuracy than with the state-of-the-art methods, especially for some benchmark datasets of zero-shot learning.
Limits to performance spread tuning using adaptive voltage and body biasing

We examine technology constraints on tuning active power and delay using adaptive voltage scaling (AVS) and adaptive body biasing (ABB) design techniques. To serve this purpose, a test circuit was fabricated in a 90 nm triple-well low-power CMOS technology. The presented analysis is based on a ring oscillator running at 488 MHz and a circular shift register with 8 K flip-flops and 50 K gates. Measurement results indicate that it is possible to reach 24.4/spl times/ power savings by 6.1/spl times/ frequency downscaling using AVS, /spl plusmn/24% power and /spl plusmn/22% frequency tuning at nominal conditions using ABB only, 127/spl times/ power savings with 37.4/spl times/ frequency downscaling by combining AVS and ABB.
A Time-based Visualization for Web User Classification in Social Networks

This paper presents a new visual analytics framework for analyzing health-related physical activity data. Existing techniques mostly rely on node-links visualizations to represent the usage patterns as social networks. This work takes a different approach that provides interactive scatter-plot visualizations on classified and time-based data. By providing a flexible visualization that can provide different angles on the multidimensional and classified data, the analyst could have better understanding and insight on web user behavior compared to the traditional social network methods. The effectiveness of our method has been demonstrated with a case study on an online portal system for tracking passive physical activity, called Walk 2.0.
Artificial neural network based identification and classification of images of Bharatanatya gestures

The main aim of recognising gestures is to build a system that can identify human gestures that are specific and then to use them to put forth desired information to the device. By using mathematical algorithms, human gestures can be interpreted. This is referred to as Gesture Recognition. Mudra is an expressive form of gesture that is mainly used in Indian classical dance form where the gesture is in visual form so as to connect with the audience. Different positions of body parts make an expressive and meaningful mudra which can be both static and dynamic. Here the project makes an attempt to identify the hand gesture/mudra using Image-processing and also Pattern Recognition methods. An attempt in computer aided recognition of Bharatnatya Mudras is made using Image classification and processing techniques using Artificial Neural Network. The entry that gives the least difference to the feature of a mudra is the match for the input image considered. Finally, the system also provides the health benefit of the identified mudra.
Proposal and Evaluation of Safe and Efficient Log Signature Scheme for the Preservation of Evidence

In recent years, the requirements for the preservation of evidence have increased for important log data, such as the data in the planned common number identification system in Japan. One of the proposed evidence preservation methods, the hysteresis signature scheme, reflects previously summarized data with a new digital signature of the log data. However, it takes a long time for this scheme to verify signatures. Therefore, we propose a new hybrid signature scheme that is based on the existing united signature scheme and the hysteresis signature scheme. In evaluations under various conditions, we found that the hybrid signature scheme is the most efficient.
Iterative selection using orthogonal regression techniques

High dimensional data are nowadays encountered in various branches of science. Variable selection techniques play a key role in analyzing high dimensional data. Generally two approaches for variable selection in the high dimensional data setting are considered—forward selection methods and penalization methods. In the former, variables are introduced in the model one at a time depending on their ability to explain variation and the procedure is terminated at some stage following some stopping rule. In penalization techniques such as the least absolute selection and shrinkage operator (LASSO), as optimization procedure is carried out with an added carefully chosen penalty function, so that the solutions have a sparse structure. Recently, the idea of penalized forward selection has been introduced. The motivation comes from the fact that the penalization techniques like the LASSO give rise to closed form expressions when used in one dimension, just like the least squares estimator. Hence one can repeat such a procedure in a forward selection setting until it converges. The resulting procedure selects sparser models than comparable methods without compromising on predictive power. However, when the regressor is high dimensional, it is typical that many predictors are highly correlated. We show that in such situations, it is possible to improve stability and computational efficiency of the procedure further by introducing an orthogonalization step. At each selection step, variables potentially available to be selected in the model are screened on the basis of their correlation with variables already in the model, thus preventing unnecessary duplication. The new strategy, called the Selection Technique in Orthogonalized Regression Models (STORM), turns out to be extremely successful in reducing the model dimension further and also leads to improved predicting power. We also consider an aggressive version of the STORM, where a potential predictor will be permanently removed from further consideration if its regression coefficient is estimated as zero at any stage. We shall carry out a detailed simulation study to compare the newly proposed method with existing ones and analyze a real dataset. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013
Haplotype of N-Acetyltransferase 1 and 2 and Risk of Pancreatic Cancer

We examined the association between N-acetyltransferase 1 and 2 (NAT1 and NAT2) haplotype and risk of pancreatic cancer by genotyping eight NAT1 and seven NAT2 single nucleotide polymorphisms in 532 patients and in 581 healthy controls (all non-Hispanic white) who were recruited at M. D. Anderson Cancer Center from January 2000 to December 2006. Haplotypes were reconstructed by using an expectation-maximization algorithm. Odds ratios and 95% confidence intervals were estimated by using unconditional logistic regression models. Covariates included age (continuous variable), sex, pack-year of smoking (categorical), and history of diabetes when appropriate. NAT1 and NAT2 genotype was mutually adjusted. The prevalence of haplotype NAT1*10-NAT2*6A was 4.3% versus 2.7% (P = 0.06) and NAT1*11-NAT2*6A was 1.2% versus 0.4% (P = 0.05) in patients and controls, respectively. The diplotype NAT1*10/*10 or NAT1*10/*11 and NAT2*6A/any slow allele was associated with a higher risk of pancreatic cancer compared with other diplotypes (multivariate odds ratio, 4.15; 95% confidence interval, 1.15-15.00; P = 0.03). NAT2 slow genotype were associated with increased risk of pancreatic cancer among heavy smokers and among individuals with a history of diabetes. We for the first time found that rare NAT1*10 or NAT1*11-NAT2*6A diplotype may be an “at-risk” genetic variant for pancreatic cancer. The NAT2*6A/any slow acetylation genotype may be a predisposing factor for pancreatic cancer among diabetics with smoking exposure. Our observations must be confirmed in larger independent studies. (Cancer Epidemiol Biomarkers Prev 2007;16(11):2379–86)