Miracle-dz/UniFAR-FTUs huggingface dataset:
The NCAN gene: schizophrenia susceptibility and cognitive dysfunction

Background Cognitive dysfunction has been recognized as a cardinal feature of schizophrenia. Elucidating the neurobiological substrates of cognitive dysfunction in schizophrenia would help identify the underlying mechanism of this disorder. The rs1064395 single nucleotide polymorphism, within the gene encoding neurocan (NCAN), is reported to be associated with schizophrenia in European populations and may influence brain structure in patients with schizophrenia. Methods In this study, we aimed to explore whether NCAN rs1064395 confers some risk for schizophrenia and cognitive dysfunction in Han Chinese. We recruited 681 patients with schizophrenia and 699 healthy subjects. Two hundred and fifty-four patients were evaluated according to Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Results There were no significant differences in genotype or allele distributions of the rs1064395 polymorphism between the schizophrenia and control groups. Patients showed significantly poorer performance than controls on immediate memory, visuospatial skill, language, attention, delayed memory, and total RBANS score. Patients with the A/A or A/G genotype of rs1064395 had lower scores of immediate memory, visuospatial skill, attention, and total RBANS score than those with the G/G genotype. We performed an expression quantitative trait loci analysis and observed a significant association between rs1064395 and NCAN expression in the frontal (P=0.0022, P=0.022 after Bonferroni correction) and cerebellar cortex (P=0.0032, P=0.032 after Bonferroni correction). Conclusion Our findings indicate that this single nucleotide polymorphism may be a risk factor for cognitive dysfunction in patients with schizophrenia. Further investigations are warranted for validation purposes and to identify the precise mechanism by which rs1064395 influences cognitive performance in patients with schizophrenia.
Fast localization for emergency monitoring and rescue in disaster scenarios based on WSN

Natural disasters, such as earthquake, typhoon and flood, have caused great loss of lives and property each year, which makes emergency monitoring and rescue an imperative problem to be addressed. In this paper, we designed a novel monitoring and rescue system based on wireless sensor network (WSN) for disaster scenarios, which combines environment monitoring, information transmission, and emergency localization. In our system, fast localization through WSN is a crucial technique for searching and rescuing. We proposed an improved weighted centroid algorithm with adaptive error correction to increase localization accuracy of sensor networks with sparse anchors. This method based on received signal strength indication (RSSI) is low cost, expandable, easy to implement, and suitable for disaster rescues.
Postmortem changes in the neuroanatomical characteristics of the primate brain: Hippocampal formation

Comparative studies of the structural organization of the brain are fundamental to our understanding of human brain function. However, whereas brains of experimental animals are fixed by perfusion of a fixative through the vasculature, human or ape brains are fixed by immersion after varying postmortem intervals. Although differential treatments might affect the fundamental characteristics of the tissue, this question has not been evaluated empirically in primate brains. Monkey brains were either perfused or acquired after varying postmortem intervals before immersion‐fixation in 4% paraformaldehyde. We found that the fixation method affected the neuroanatomical characteristics of the monkey hippocampal formation. Soma size was smaller in Nissl‐stained, immersion‐fixed tissue, although overall brain volume was larger as compared to perfusion‐fixed tissue. Nonphosphorylated high‐molecular‐weight neurofilament immunoreactivity was lower in CA3 pyramidal neurons, dentate mossy cells, and the entorhinal cortex, whereas it was higher in the mossy fiber pathway in immersion‐fixed tissue. Serotonin‐immunoreactive fibers were well stained in perfused tissue but were undetectable in immersion‐fixed tissue. Although regional immunoreactivity patterns for calcium‐binding proteins were not affected, intracellular staining degraded with increasing postmortem intervals. Somatostatin‐immunoreactive clusters of large axonal varicosities, previously reported only in humans, were observed in immersion‐fixed monkey tissue. In addition, calretinin‐immunoreactive multipolar neurons, previously observed only in rodents, were found in the rostral dentate gyrus in both perfused and immersion‐fixed brains. In conclusion, comparative studies of the brain must evaluate the effects of fixation on the staining pattern of each marker in every structure of interest before drawing conclusions about species differences. J. Comp. Neurol. 512:27–51, 2009. © 2008 Wiley‐Liss, Inc.
Collaborative Filtering Recommendation on Users’ Interest Sequences

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.
Asset Prices Under Prospect Theory and Habit Formation

In this paper, we develop a consumption-based asset pricing model motivated by prospect theory, where habit formation determines the endogenous reference point. This exploits the similarity between habit formation and prospect theory. Both emphasize that the investor does not care about the absolute amount of gain or loss, but rather compares the gain or the loss experienced to a benchmark. The results show that when taking people's loss averse attitude over consumption into consideration, our model is capable of resolving the equity premium puzzle.
Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback

Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate auxiliary data from users and items that can be incorporated into the recommendation model to improve recommendations. In this work, we propose an incremental Matrix Co-factorization model with implicit user feedback, considering a real-world data-stream scenario. This model can be seen as an extension of the conventional Matrix Factorization that includes additional dimensions to be decomposed in the common latent factor space. We test our proposal against a baseline algorithm that relies exclusively on interaction data, using prequential evaluation. Our experimental results show a significant improvement in the accuracy of recommendations, after incorporating an additional dimension in three music domain datasets.
Multi-view feature extraction for hyperspectral image classification

We study the multi-view feature extraction (MV-FE) framework for the classification of hyperspectral images acquired from airborne and spaceborne sensors. This type of data is naturally composed by distinct blocks of spectral channels, forming the hypercube. To reduce the dimensionality of the data by taking advantage of this particular structure, an unsupervised multi-view feature extraction method is applied prior to classification. First, a technique to automatically obtain the blocks, based on the global spectral correlation matrix, is applied. Then, the kernel canonical correlation analysis is performed in a multi-view setting (MVkCCA) to find projections of the data blocks in a correlated subspace, gaining thus discriminant power. Experiments using the linear discriminant classifier (LDA) show the appropriateness of adopting a MV-FE approach.
Why people go to unfamiliar areas?: analysis of mobility pattern based on users' familiarity

Human mobility analysis with Location-Based Social Network (LBSN) data is the basis of personalized point-of-interest (POI) recommendations or location-aware advertisements. In addition to personal preference and spatiotemporal factors such as time and distance, personal context has a strong influence on mobility. An individual's familiarity with an area is an interesting context because it can bias the influence of certain factors. For example, the mobility patterns of two persons who have similar preferences are different when their familiarity with the area is different, even in the same area. In this paper, we analyze familiarity's effect on mobility patterns by using over 1.4 million check-ins gathered from Foursquare. The analysis indicates that there is a skewness of the visit time and visited venue distribution in unfamiliar areas. For instance, people go to unfamiliar areas on weekends; and venues for cultural experiences, such as museums, strongly contribute to the motivation of visit.
Using a fleet of slocum battery gliders in a regional scale coastal ocean observatory

Rutgers University is constructing the New Jersey Shelf Observing System (NJSOS), a regional-scale (300 km /spl times/ 300 km) observatory for the coastal ocean which includes the LEO15 site. Spatially extensive surface remote sensing systems (CODAR, satellites) are continuously collecting data in this region. However, only during the month of July in 1998, 1999, 2000 and 2001 were extensive subsurface physical and optical data collected and only in the LEO15 vicinity. The July samplings were labor and boat intensive. Obtaining the subsurface data in the LEO15 area required the use of up to seven boats and manpower to collect, process and analyze the information. To make the collection of subsurface data in the NJSOS more efficient, less expensive and more complete both spatially and temporally, Rutgers University has worked with Webb Research Corporation on their development of the Slocum Glider autonomous underwater vehicles. Currently Rutgers owns a fleet of four Gliders that can be used individually or as a group to collect subsurface data in the observatory region.
Distortions in memory for visual displays

Systematic errors in perception and memory present a challenge to theories of perception and memory and to applied psychologists interested in overcoming them as well. A number of systematic errors in memory for maps and graphs are reviewed, and they are accounted for by an analysis of the perceptual processing presumed to occur in comprehension of maps and graphs. Visual stimuli, like verbal stimuli, are organized in comprehension and memory. For visual stimuli, the organization is a consequence of perceptual processing, which is bottom-up or data-driven in its earlier stages, but top-down and affected by conceptual knowledge later on. Segregation of figure from ground is an early process, and figure recognition later; for both, symmetry is a rapidly detected and ecologically valid cue. Once isolated, figures are organized relative to one another and relative to a frame of reference. Both perceptual (e.g., salience) and conceptual factors (e.g., significance) seem likely to affect selection of a reference frame. Consistent with the analysis, subjects perceived and remembered curves in graphs and rivers in maps as more symmetric than they actually were. Symmetry, useful for detecting and recognizing figures, distorts map and graph figures alike. Top-down processes also seem to operate in that calling attention to the symmetry vs. asymmetry of a slightly asymmetric curve yielded memory errors in the direction of the description. Conceptual frame of reference effects were demonstrated in memory for lines embedded in graphs. In earlier work, the orientation of map figures was distorted in memory toward horizontal or vertical. In recent work, graph lines, but not map lines, were remembered as closer to an imaginary 45 deg line than they had been. Reference frames are determined by both perceptual and conceptual factors, leading to selection of the canonical axes as a reference frame in maps, but selection of the imaginary 45 deg as a reference frame in graphs.
Full-fledged algebraic XPath processing in Natix

We present the first complete translation of XPath into an algebra, paving the way for a comprehensive, state-of-the-art XPath (and later on, XQuery) compiler based on algebraic optimization techniques. Our translation includes all XPath features such as nested expressions, position-based predicates and node-set functions. The translated algebraic expressions can be executed using the proven, scalable, iterator-based approach, as we demonstrate in form of a corresponding physical algebra in our native XML DBMS Natix. A first glance at performance results shows that even without further optimization of the expressions, we provide a competitive evaluation technique for XPath queries.
Single-phase single-stage photovoltaic generation system based on a ripple correlation control maximum power point tracking

A maximum power point tracking algorithm for single-stage converters connecting photovoltaic (PV) panels to a single-phase grid is presented in this paper. The algorithm is based on the application of the "ripple correlation control" using as perturbation signals the current and voltage low-frequency oscillations introduced in the PV panels by the single-phase utility grid. The proposed control technique allows the generation of sinusoidal grid currents with unity power factor. The algorithm has been developed to allow an array of PV modules to be connected to the grid by using a single-stage converter. This simple structure yields higher efficiency and reliability when compared with standard solutions based on double-stage converter configurations. The proposed maximum power point tracking algorithm has been numerically simulated and experimentally verified by means of a converter prototype connected to a single-phase grid. The results are presented in the paper, showing the effectiveness of the proposed system.
Active Object Recognition in Parametric Eigenspace

We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to resolve the classification result obtained from a single view. The approach uses an appearance based object representation, namely the parametric eigenspace, and augments it by probability distributions. This captures possible variations in the input images due to errors in the pre-processing chain or the imaging system. Furthermore, the use of probability distributions gives us a gauge to view planning. View planning is shown to be of great use in reducing the number of images to be captured when compared to a random strategy.
Review of low actuation voltage RF MEMS electrostatic switches based on metallic and carbon alloys

Radio frequency micro electro mechanical systems (RF MEMS) have enabled a new generation of devices that bring many advantages due to their very high performances. There are many incentives for the integration of the RF MEMS switches and electronic devices on the same chip. However, the high actuation voltage of RF MEMS switches compared to electronic devices poses a major problem. By reducing the actuation voltage of the RF MEMS switch, it is possible to integrate it into current electronic devices. Lowering the actuation voltage will have an impact on RF parameters of the RF MEMS switches. This investigation focuses on recent progress in reducing the actuation voltage with an emphasis on a modular approach that gives acceptable design parameters. A number of rules that should be considered in design and fabrication of low actuation RF MEMS switches are suggested.
Interpolation and the chirp transform: DSP meets optics

This paper considers the problem of interpolating a signal from one uniformly-spaced grid to another, where the grid spacings may be related by an arbitrary, irrational factor. Noting that interpolation is the digital equivalent of magnification, we begin by reviewing optical systems for magnification and "computation" of the chirp Fourier transform. This route suggests several analog schemes for magnification, which can be discretized to produce algorithms for interpolation. We then derive one of these algorithms from first principles, using a digital signal processing perspective. The result is an important, but forgotten, algorithm for interpolation first suggested as an application of the chirp-z transform by Rabiner, Schafer, and Rader (1969). Unlike the earlier derivation, our approach is direct-we do not make use of Bluestein's trick of completing the square. In addition, our approach identifies parameters under user control that can be optimized for best performance.
Ehd 3 , a regulator of vesicular trafficking , is silenced in gliomas and functions as a tumor suppressor by controlling cell cycle arrest and apoptosis

1Department of Pathology, Wayne State University School of Medicine, Detroit, MI 48201, USA, 2Obesity Research Center, College of Medicine, King Saud University, Riyadh 11461, Kingdom of Saudi Arabia, 3Montréal Centre for Experimental Therapeutics in Cancer, Segal Cancer Centre, Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, Montréal, Québec H3T1E2, Canada, 4Department of Oncology, McGill University, Montréal, Québec H2W1S6, Canada and 5Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI 48201, USA
Parallel semantic processing in reading revisited: effects of translation equivalents in bilingual readers

ABSTRACT Previous research has failed to establish semantic parafoveal-on-foveal effects during reading. As an explanation, we theorise that sentence reading engages a sentence-level representation that prevents semantic parafoveal-foveal integration. Putting this account to the test, we examined parafoveal-foveal influences both in- and outside a sentence reading setting. Optimising chances of establishing parafoveal-on-foveal effects, we used translation-equivalent word pairs with French-English bilingual participants. Experiment 1 provided no evidence for semantic parafoveal-on-foveal integration during sentence reading, but some evidence that semantic information had been extracted in parallel from multiple words. Experiments 2 and 3 employed a flanker paradigm in which participants semantically categorised English foveal target words, while these were flanked by the French translation or an unrelated French word (stimulus on-time 170 ms). Performance was drastically better with translation flankers, suggesting that readers can integrate semantic information across multiple words when the task does not require a strict separation of higher-order information.
CERES cloud property retrievals from imagers on TRMM, Terra, and Aqua

The micro- and macrophysical properties of clouds play a crucial role in Earth’s radiation budget. The NASA Clouds and Earth’s Radiant Energy System (CERES) is providing simultaneous measurements of the radiation and cloud fields on a global basis to improve the understanding and modeling of the interaction between clouds and radiation at the top of the atmosphere, at the surface, and within the atmosphere. Cloud properties derived for CERES from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites are compared to ensure consistency between the products to ensure the reliability of the retrievals from multiple platforms at different times of day. Comparisons of cloud fraction, height, optical depth, phase, effective particle size, and ice and liquid water paths from the two satellites show excellent consistency. Initial calibration comparisons are also very favorable. Differences between the Aqua and Terra results are generally due to diurnally dependent changes in the clouds. Additional algorithm refinement is needed over the polar regions for Aqua and at night over those same areas for Terra. The results should be extremely valuable for model validation and improvement and for improving our understanding of the relationship between clouds and the radiation budget.
Dempster-Shafer Fusion Based Gender Recognition for Speech Analysis Applications

Speech signals carry valuable information about the speaker including age, gender, and emotional state. Gender information can act as a vital preprocessing ingredient for enhancing speech analysis applications like adaptive human-machine interfaces, multi-modal security applications, and sophisticated intent and context analysis based forensic systems. In uncontrolled environments like telephone speech applications, the gender recognition system should be adaptive, accurate, and robust to noisy environments. This paper presents a reasoning method governed by Dempster-Shafer theory of evidence for automatic gender recognition from telephone speech. The proposed method uses mel-frequency cepstral coefficients with a support vector machine to generate the initial prediction results for individual speech segments. The reasoning scheme collects and validates results from support vector machine and treats convincing predictions as valid evidence. It is argued that the consideration of valid evidence in the reasoning process improves recognition performance by avoiding unconvincing classification results. Experiments conducted on large speech datasets reveal the superiority of the proposed gender recognition scheme for speech analysis applications.
Smooth Ambiguity Aversion in the Small and in the Large

In this paper I use the smooth ambiguity model developed by Klibanoff, Marinacci, and Mukerji (2005) to define the concepts of ambiguity and uncertainty premia in a way analogous to what Pratt (1964) did in the risk theory literature. I show that those concepts may be useful to quantify the effect ambiguity has on the welfare of economic agents. I also provide local approximations of these premia and show the link that exists between them when comparing different degrees of ambiguity aversion not only in the small, but also in the large.