Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender in everyday speech and language: a corpus-based study

This paper presents an exploratory study on the relations between gender and everyday parlance. A “data-mining” approach is used to explore gender-specific characteristics in a large number of spontaneous telephone and face-to-face conversations. Our study focuses on speech rate (speaking rate and articulation rate), disfluencies (filled pauses and repetitions), pronunciation variation (phoneme substitutions, deletions and insertions), and preferences for particular parts of speech. Our study reveals interesting similarities and differences in everyday male and female speech, and proves that data-mining on large spoken language corpora is a promising approach for obtaining information on spontaneous speech phenomena and for generating new hypotheses for research.
Probabilistic Approach Using Joint Clean and Noisy i-Vectors Modeling for Speaker Recognition

Additive noise is one of the main challenges for automatic speaker recognition and several compensation techniques have been proposed to deal with this problem. In this paper, we present a new ”data-driven” denoising technique operating in the i-vector space based on a joint modeling of clean and noisy i-vectors. The joint distribution is estimated using a large set of i-vectors pairs (clean i-vectors and their noisy versions generated artificially) then integrated in an MMSE estimator in the test phase to compute a ”cleaned-up” version of noisy test ivectors. We show that this algorithm achieves up to 80% of relative improvement in EER. We also present a version of the proposed algorithm that can be used to compensate multiple ”unseen” noises. We test this technique on the recently published SITW database and show a significant gain compared to the baseline system performance.
Theta and gamma coordination of hippocampal networks during waking and REM sleep

REM sleep has been considered a paradoxical state because, despite the high behavioral threshold to arousing perturbations, gross physiological patterns in the forebrain resemble those of waking states. To understand how intrahippocampal networks interact during REM sleep, we used 96-site silicon probes to record from different hippocampal subregions and compared the patterns of activity during waking exploration and REM sleep. Dentate/CA3 theta and gamma synchrony was significantly higher during REM sleep compared to active waking. In contrast, gamma power in CA1 and CA3-CA1 gamma coherence showed significant decreases in REM sleep. Changes in unit firing rhythmicity and unit-field coherence specified the local generation of these patterns. Although these patterns of hippocampal network coordination characterized the more common tonic periods of REM sleep (~95% of total REM), we also detected large phasic bursts of local field potential (LFP) power in the dentate molecular layer that were accompanied by transient increases in the firing of dentate and CA1 neurons. In contrast to tonic REM periods, phasic REM epochs were characterized by higher theta and gamma synchrony among the dentate, CA3 and CA1 regions. These data suggest enhanced dentate processing, but limited CA3-CA1 coordination during tonic REM sleep. In contrast, phasic bursts of activity during REM sleep may provide windows of opportunity to synchronize the hippocampal trisynaptic loop and increase output to cortical targets. We hypothesize that tonic REM sleep may support offline mnemonic processing, while phasic bursts of activity during REM may promote memory consolidation.
Investigation of flood routing by a dynamic wave model in trapezoidal channels

The problems of flood wave propagation, in bodies of waters, cause by intense rains or breaking of control structures, represent a great challenge in the mathematical modeling processes. This research concerns about the development and application of a mathematical model based on the Saint Venant’s equations, to study the behavior of the propagation of a flood wave in trapezoidal channels. In these equations, the momentum equation transforms to partial differential equation which has two parameters related to cross-sectional area and discharge of the channel. These new formulas have been solved by using an explicit finite difference scheme. In computation procedure, after computing the discharge from the momentum equation, the cross-sectional area will be obtained from the continuity equation for a given point of channel. To evaluate the behavior of the control variables, several scenarios for the main channel as well as for flood waves are considered and different simulations are performed. The simulations demonstrate that for the same bed width, the peak discharge in trapezoidal channel smaller than in rectangular one at a specific distance along the channel length and so, that roughness coefficient and bed slope of the channel play a strong game on the behavior of the flood wave propagation.The problems of flood wave propagation, in bodies of waters, cause by intense rains or breaking of control structures, represent a great challenge in the mathematical modeling processes. This research concerns about the development and application of a mathematical model based on the Saint Venant’s equations, to study the behavior of the propagation of a flood wave in trapezoidal channels. In these equations, the momentum equation transforms to partial differential equation which has two parameters related to cross-sectional area and discharge of the channel. These new formulas have been solved by using an explicit finite difference scheme. In computation procedure, after computing the discharge from the momentum equation, the cross-sectional area will be obtained from the continuity equation for a given point of channel. To evaluate the behavior of the control variables, several scenarios for the main channel as well as for flood waves are considered and different simulations are performed. The simulatio.
Toward Ontology-based Knowledge Extraction from Web Data with the Lexicalization of Ontology for Korean QA System

Most of knowledge is written in natural language and structured knowledge base includes the partially limited information of them. In QA system perspective, the quality of knowledge base is depends on how it covers the knowledge to answer user’s questions. To deal with this knowledge base construction problem, we define the natural language question sets and answer documents which contains knowledge enough to answer. And we manually generate triples in DBpedia schema to use as a test dataset. In this paper we present a framework for automatically structuring of knowledge from unstructured data and semi-structured data with pattern learning method to cover the knowledge to answer natural language question dataset. Our platform extracts entities and its relations in triple structure, and its schema is based on ontological knowledge representation. To achieve this goal, our pattern learning method is based on lexicalization of ontology. For unstructured data, we lexicalize ontological representation from natural language from its dependency structure. And for semi-structured data, the headers in table are also lexicalized. We have instantiated the system for Korean. To evaluate our system, we build gold standard triple datasets from natural language, and use F-measure and coverage measurement.
Chronic acetylcholinesterase overexpression induces multilevelled aberrations in mouse neuromuscular physiology

Chronic overexpression of the acetylcholine‐hydrolysing enzyme acetylcholinesterase (AChE) is a notable consequence of exposure to anticholinesterase drugs or poisons. However, the physiological consequences for the resultant neuromuscular disfunction have not yet been carefully analysed. Here we report detailed dissection of the different components of neuromuscular function in transgenic mice previously shown to display motor fatigue and altered muscle morphology as a consequence of neuronal overexpression of AChE‐S, the synaptic AChE variant. Transgenic diaphragm muscle presented exaggerated fatigue as a combined consequence of neurotransmission fading and muscle mechanical malfunctioning. In a tetanic stimulation protocol, transgenic muscles rapidly fatigued to a larger extent than wild‐type muscles, when stimulated either directly or via the phrenic nerve. AChE overexpression involved moderate but significant aberrations of synaptic transmission with higher quantal content (measured at 0.2 mm Ca2+, 2.3 mm Mg2+). Furthermore, treatment with the anti‐cholinesterase physostigmine revealed a higher amplitude and half‐decay time of the transgenic quantal postsynaptic response. Our observations imply that elevated levels of neuronal AChE‐S are expected to cause muscle exhaustion due to a combination of modest, multilevelled aberrations in synaptic transmission, muscle function and morphology.
Correlation Filter Learning Toward Peak Strength for Visual Tracking

This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.
On-Line Search for Solving Markov Decision Processes via Heuristic Sampling

In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the computation of an optimal policy that defines an optimal action for every state of the system. For complex MDPs, exact computation of optimal policies is often untractable. Several approaches have been developed to compute near optimal policies for complex MDPs by means of function approximation and simulation. In this paper, we investigate the problem of refining near optimal policies via online search techniques, tackling the local problem of finding an optimal action for a single current state of the system. More precisely we consider an on-line approach based on sampling: at each step, a randomly sampled look-ahead tree is developed to compute the optimal action for the current state. In this work, we propose a search strategy for constructing such trees. Its purpose is to provide good "anytime" profiles: at first, it quickly selects a good action with a high probability and then it smoothly increases the probability of selecting an optimal action.
Cell‐Free Preparation of Functional and Triggerable Giant Proteoliposomes

Heat, we leak: We express a membrane protein outside well-defined giant liposomes obtained by gravity-transferred sucrose-in-oil droplets into a cell-free, reconstituted expression system. We show that the presence of the liposome is necessary during expression for efficient protein insertion into the membrane and that temperature can trigger the resulting membrane function.
Topological segmentation in three-dimensional vector fields

We present a new method for topological segmentation in steady three-dimensional vector fields. Depending on desired properties, the algorithm replaces the original vector field by a derived segmented data set, which is utilized to produce separating surfaces in the vector field. We define the concept of a segmented data set, develop methods that produce the segmented data by sampling the vector field with streamlines, and describe algorithms that generate the separating surfaces. This method is applied to generate local separatrices in the field, defined by a movable boundary region placed in the field. The resulting partitions can be visualized using standard techniques for a visualization of a vector field at a higher level of abstraction.
Understanding, investigating and theorizing inter-organizational collaborations: a focus on paradox

1 Understanding, investigating and theorizing inter-organizational collaborations: A focus on paradox Siv Vangen This paper focuses on the role of paradox in understanding, investigating and theorizing the management and governance of inter-organizational relations (IOR). Encouraged by the conference theme to review and evaluate the latest management fads and fashions while keeping sight of the core principles of, and perspectives on, management research and their value, the paper suggests that while paradox may be a somewhat overused and under-defined concept in organization studies (Lewis, 2000) the recent interest in the application of paradox 1 to research on collaboration has real merits.
Mean-variance Portfolio Model with Consumption

Suppose that a market consists of a foreign exchange deposit and a risky stock, the optimal portfolio problem with consumption is formulated under the continuous-time mean- variance frame. By using the stochastic linear-square control theory, the explicit optimal trading strategies and the closed-form efficient frontier are derived. The numerical example shows that with the increase of the consumption rate, the amount invested in the risky stock, the mean terminal wealth, and the variance of the terminal wealth are all decreased.
Frame rate up-conversionwith global-to-local iterative motion compensated interpolation

Video frame rate up conversion (FRUC) has many applications, especially it has been proved to effectively reduce the motion blur problem and improve the visual quality on LCD. In this paper, we propose a global-to-local iterative algorithm to resolve the problems occurring in the existing block based algorithms. In addition, the iterative approach, where interpolated blocks are generated in confident order, is employed in order to find the applying order and combination of different motion compensated interpolation(MCI) algorithms. Experimental results show that the proposed algorithm outperforms other MCI algorithms in perceptual and can generate smooth high quality interpolated frames.
Transactivation of a DR-1 PPRE by a human constitutive androstane receptor variant expressed from internal protein translation start sites

Downstream in-frame start codons produce amino-terminal-truncated human constitutive androstane receptor protein isoforms (ΔNCARs). The ΔNCARs are expressed in liver and in vitro cell systems following translation from in-frame methionine AUG start codons at positions 76, 80, 125, 128, 168 and 265 within the full-length CAR mRNA. The resulting CAR proteins lack the N-terminal DNA-binding domain (DBD) of the receptor, yielding ΔNCAR variants with unique biological function. Although the ΔNCARs maintain full retinoid X receptor alpha (RXRα) heterodimerization capacity, the ΔNCARs are inactive on classical CAR-inducible direct repeat (DR)-4 elements, yet efficiently transactivate a DR-1 element derived from the endogenous PPAR-inducible acyl-CoA oxidase gene promoter. RXRα heterodimerization with CAR1, CAR76 and CAR80 isoforms is necessary for the DR-1 PPRE activation, a function that exhibits absolute dependence on both the respective RXRα DBD and CAR activation (AF)-2 domains, but not the AF-1 or AF-2 domain of RXRα, nor CAR's DBD. A new model of CAR DBD-independent transactivation is proposed, such that in the context of a DR-1 peroxisome proliferator-activated response element, only the RXRα portion of the CAR-RXRα heterodimer binds directly to DNA, with the AF-2 domain of tethered CAR mediating transcriptional activation of the receptor complex.
An ATPG Enhancement for Reducing Scan-Based Test Application Time

We propose a new test application method that can be applied on top of any combinational stuck-at fault ATPG to significantly reduce the test application time in full scan designs. In contrast to any other existing method, our approach effectively utilizes both the shift and reapply basic scan operations. The computational difficulties that arise due to the enormous search space are tackled by efficient, BDD-based, algorithms.
Comprehensive benefit analysis of regional water resources based on multi-objective evaluation

The purpose of the water resources comprehensive benefits analysis is to maximize the comprehensive benefits on the aspects of social, economic and ecological environment. Aiming at the defects of the traditional analytic hierarchy process in the evaluation of water resources, it proposed a comprehensive benefit evaluation of social, economic and environmental benefits index from the perspective of water resources comprehensive benefit in the social system, economic system and environmental system; determined the index weight by the improved fuzzy analytic hierarchy process (AHP), calculated the relative index of water resources comprehensive benefit and analyzed the comprehensive benefit of water resources in Xiangshui County by the multi-objective evaluation model. Based on the water resources data in Xiangshui County, 20 main comprehensive benefit assessment factors of 5 districts belonged to Xiangshui County were evaluated. The results showed that the comprehensive benefit of Xiangshui County was 0.7317, meanwhile the social economy has a further development space in the current situation of water resources.
Position-dependent effects of locked nucleic acid (LNA) on DNA sequencing and PCR primers

Genomes are becoming heavily annotated with important features. Analysis of these features often employs oligonucleotides that hybridize at defined locations. When the defined location lies in a poor sequence context, traditional design strategies may fail. Locked Nucleic Acid (LNA) can enhance oligonucleotide affinity and specificity. Though LNA has been used in many applications, formal design rules are still being defined. To further this effort we have investigated the effect of LNA on the performance of sequencing and PCR primers in AT-rich regions, where short primers yield poor sequencing reads or PCR yields. LNA was used in three positional patterns: near the 5′ end (LNA-5′), near the 3′ end (LNA-3′) and distributed throughout (LNA-Even). Quantitative measures of sequencing read length (Phred Q30 count) and real-time PCR signal (cycle threshold, CT) were characterized using two-way ANOVA. LNA-5′ increased the average Phred Q30 score by 60% and it was never observed to decrease performance. LNA-5′ generated cycle thresholds in quantitative PCR that were comparable to high-yielding conventional primers. In contrast, LNA-3′ and LNA-Even did not improve read lengths or CT. ANOVA demonstrated the statistical significance of these results and identified significant interaction between the positional design rule and primer sequence.
Further testing and development of simulation models for UT inspections of armor

In previous work we introduced an approach for simulating ultrasonic pulse/echo immersion inspections of multi-layer armor panels. Model inputs include the thickness, density, velocity and attenuation of each armor layer, the focal properties of the transducer, and a measured calibration signal. The basic model output is a response-versus-time waveform (ultrasonic A-scan) which includes echoes from all interfaces including those arising from reverberations within layers. Such A-scans can be predicted both for unflawed panels and panels containing a large disbond at any given interface. In this paper we continue our testing of the simulation software, applying it now to an armor panel consisting of SiC ceramic tiles fully embedded in a titanium-alloy matrix. An interesting specimen of such armor became available in which some tile/metal interfaces appear to be well bonded, while others have disbonded areas of various sizes. We compare measured and predicted A-scans for UT inspections, and also demonstrate .
Perspectives On Simulation Using GPSS

A broad overview of the simulation modeling language GPSS is given. The class of problems to which GPSS applies especially well is described, and, commentary on the semantics and syntax of the language is offered. Various GPSS implementations are indicated, and vendor information is given. The GPSS learning-oriented literature is reviewed, and sources comparing GPSS and other simulation languages are cited. Professional GPSS courses are listed. A GPSS model and its output are presented and discussed in an appendix. The GPSS tutorial at the 1989 Winter Simulation Conference will show how one goes about building GPSS models. Paper copies of the tutorial transparencies will be given out at the tutorial.
Inferring Individual Differences Between and Within Exemplar and Decision-Bound Models of Categorization

Different models of categorization are often treated as competing accounts, but specific models are often used to understand individual differences, by estimating individual-level parameters. We develop an approach to understanding categorization that allows for individual differences both between and within models, using two prominent categorization models that make different theoretical assumptions: the Generalized Context Model (GCM) and General Recognition Theory (GRT). We develop a latent-mixture model for inferring whether an individual uses the GCM or GRT, while simultaneously allowing for the use of special-case simpler strategies. The GCM simple strategies involve attending to a single stimulus dimension, while the GRT simple strategies involve using unidimensional decision bounds. Our model also allows for simple contaminant strategies. We apply the model to four previously published categorization experiments, finding large and interpretable individual differences in the use of both models and specific strategies, depending on the nature of the stimuli and category structures.
Dietary patterns and gastric cancer risk: a systematic review and meta-analysis.

BACKGROUND Declines in gastric cancer (GC) incidence and mortality have been related to improvements in diet. It is therefore important to consider dietary patterns. DESIGN We conducted a systematic review and meta-analysis of the literature through Medline and Embase databases. RESULTS We identified 16 papers, of these 9 derived dietary patterns through an a posteriori method, 5 through a priori scores, and 2 used both approaches. Eight studies that used the a posteriori approach were considered for the meta-analysis. A favorable role on GC emerged for the 'Prudent/healthy', with an odds ratio (OR) of 0.75 [95% confidence interval (CI): 0.63-0.90], for the highest versus the lowest category. Similar results emerged for separate anatomical subtypes. An unfavorable role on GC emerged for the 'Western/unhealthy' dietary pattern, with an OR of 1.51 (95% CI: 1.21-1.89). This association was weaker for the distal/NOS (not otherwise specified) category (OR = 1.36) compared with the cardia GC (OR = 2.05). Among the a priori scores, the ORs ranged from 0.2 to 0.7 for the favorable and from 1.8 to 6.9 for the unfavorable ones. CONCLUSION There is a ~2-fold difference in GC risk between a 'Prudent/healthy' diet-rich in fruits and vegetables, and a 'Western/unhealthy' diet-rich in starchy foods, meat and fats.