Miracle-dz/UniFAR-FTUs huggingface dataset:
Adult Neurogenesis in the Female Mouse Hypothalamus: Estradiol and High-Fat Diet Alter the Generation of Newborn Neurons Expressing Estrogen Receptor α

Visual Abstract Estrogens and leptins act in the hypothalamus to maintain reproduction and energy homeostasis. Neurogenesis in the adult mammalian hypothalamus has been implicated in the regulation of energy homeostasis. Recently, high-fat diet (HFD) and estradiol (E2) have been shown to alter cell proliferation and the number of newborn leptin-responsive neurons in the hypothalamus of adult female mice. The current study tested the hypothesis that new cells expressing estrogen receptor α (ERα) are generated in the arcuate nucleus (ARC) and the ventromedial nucleus of the hypothalamus (VMH) of the adult female mouse, hypothalamic regions that are critical in energy homeostasis. Adult mice were ovariectomized and implanted with capsules containing E2 or oil. Within each hormone group, mice were fed an HFD or standard chow for 6 weeks and treated with BrdU to label new cells. Newborn cells that respond to estrogens were identified in the ARC and VMH, of which a subpopulation was leptin sensitive, indicating that the subpopulation consists of neurons. Moreover, there was an interaction between diet and hormone with an effect on the number of these newborn ERα-expressing neurons that respond to leptin. Regardless of hormone treatment, HFD increased the number of ERα-expressing cells in the ARC and VMH. E2 decreased hypothalamic fibroblast growth factor 10 (Fgf10) gene expression in HFD mice, suggesting a role for Fgf10 in E2 effects on neurogenesis. These findings of newly created estrogen-responsive neurons in the adult brain provide a novel mechanism by which estrogens can act in the hypothalamus to regulate energy homeostasis in females.
Performance evaluation of the various edge detectors and filters for the noisy IR images

Edge detection is an important process of image processing and it is the foundation of pattern recognition, which will affect the later processes. Especially in a noisy image, the edge detection is more important because noise is a common phenomenon in an image. This paper deeply studies the edge detection methods for noisy IR images and also studies the performance of the filters involved in removing the noise. Four basic edge detection operators such as Sobel, Prewitt, Log and Canny were selected and a comparison is done to check the quality of the edge detectors with noisy IR images. The study shows that the Sobel edge detector is best in detecting the edges when compared to other edge detectors. Then, the filters such as Median, Lee and Kuan were used to remove the noise in IR image. After applying all the filters the result shows that Median filter works good among the three filters. The quality of the edge detectors and the filter is calculated using the parameters PSNR and RMSE.
Named Entity Recognition for Vietnamese Documents Using Semi-supervised Learning Method of CRFs with Generalized Expectation Criteria

Named Entity Recognition (NER) is an important, useful task in many natural language processing applications and much previous work in NER has been done in many other languages such as English, Japanese, Chinese However, Vietnamese NER task is still relatively new and challenge due to the characteristics of Vietnamese, the lack of a large annotated corpus This paper presents a new approach for Vietnamese NER -- a semi-supervised training method for Conditional random fields (CRFs) models using generalized expectation criteria to express a preference for parameter settings. We perform several experiments using different feature setting and different training data to show the high performance of this method and compare to the other method.
Test pattern based evaluation of ringing and blur in JPEG and JPEG2000 compressed images

In this paper we present main parameters of the ringing artefact and demonstrate their influence on image fidelity of lossy compressed images. We apply variations of these parameters to create a specific set of test images to highlight the ringing artefact and to be able to evaluate the behaviour of ringing along edges. Therefore we introduce a new ringing metric, that analyzes image profiles of compressed images in terms of global luminance shift, blur and ringing.
The Prague Dependency Treebank: Annotation Structure and Support

The contents of the Prague Dependency Treebank (recently released by the Linguistic Data Consortium in its version 1.0) is described, from morphology to surface syntax to the deep (underlying) syntax layers of annotation. For each layer, the basic assumptions are given, followed by a more detailed description of the annotation scheme. Annotation software currently in use is characterized and its distinguishing features are emphasized. Finally, the checking schema and procedures adopted for the release of the Prague Dependency Treebank version 1.0 are discussed.
A representation of elements in F 2 m enabling unified field arithmetic for elliptic curve cryptography

In this letter we propose a change of representation for elements in F 2 m. The proposed representation is useful for architectures that implement unified Montgomery multiplication in finite fields F 2 m and F p used for elliptic curve cryptography since it transforms a standard F 2 m multiplication into a Montgomery multiplication and comes at virtually no cost in terms of conversion operations.
Using Computational Models To Create a Word Learning Intervention

The present investigation examines the role of semantic network structure in the mental lexicon of toddlers during language acquisition. The growth principle preferential attachment can be applied to a lexical network; implying networks grow by adding new words based on the connectivity of words already in the network. Aguilar (unpublished) used preferential attachment to create simulations of children’s lexical network growth. The following paper is an extension of Aguilar (unpublished) and applies the same preferential attachment algorithm developed for semantic-based features to real children’s vocabularies. Using real children’s vocabularies offers further insight on what types of words affect a child’s mental lexicon and overall vocabulary. In this experimental study, children were given a custom-made picture book containing sixteen words that have either a highor low-probability of being learned according to the predicted growth of each child’s existing vocabulary. Overall, children in both conditions were able to successfully learn the words in their book as measured by a receptive vocabulary pointing task. In addition, findings show a significant difference in whether or not words are acquired differently for different word-learning populations; depending on what words they already know. Findings suggest children benefit from learning words in an intervention when presented with words they otherwise wouldn't learn. The work presented makes two main contributions. The first contribution is a measure of how well children under the age of two respond to individualized storybook interventions. The second is a test of whether the Aguilar (unpublished) models capture some aspect of actual growth. MENTAL LEXICON INTERVENTION
Do Private Equity Firms Foster Innovation? Evidence from French LBOs

Agency theory has historically presented buyouts as a superior governance framework that generates economic efficiencies in the short term but these transactions might have a negative impact on long-term growth and innovation. In this study, we use a strategic entrepreneurship perspective to argue that private equity firm's extensive network and relationships, and expertise and competencies help managers to innovate. Using a propensity score methodology, we provide an empirical analysis of the innovative efforts of a sample of 89 French manufacturing firms that underwent a buyout between 2001 and 2005. The matching estimates (average treatment on the treated, ATT) of the effect of LBOs on firm level of innovation expenditures in 2006 show no significant differences between LBO targets and comparable companies that did not go through an LBO. In contrast, we find significant effects of LBOs on both service innovation and marketing innovations in design and packaging and product promotion. Results suggest that private equity firms provide marketing capabilities or encourage managers of LBO targets to build new innovation strategies.
Sakurai's Object: characterizing the near-infrared CO ejecta between 2003 and 2007

We present observations of Sakurai’s Object obtained at 1 5µm between 2003 and 2007. By fitting a radiative transfer model to an echelle spectrum o f CO fundamental absorption features around 4. 7µm, we determine the excitation conditions in the line-forming region. We find 12 C/ 13 C = 3.5 +2.0 −1.5 , consistent with CO originating in ejecta processed by the very late thermal pulse, rather than in the pre-existing planetary ne bula. We demonstrate the existence of 2.2 × 10 −6 6 MCO 6 2.7 × 10 −6 M⊙ of CO ejecta outside the dust, forming a highvelocity wind of 500 ± 80 km s −1 . We find evidence for significant weakening of the CO band and cooling of the dust around the central star between 2003 and 2005. The gas and dust temperatures are implausibly high for stellar radiation to be the sole contributor.
Video-Based Traffic Analysis System Using a Hierarchical Feature Point Grouping Approach

This study presents a video-based car detection and tracking algorithm for traffic analysis system. By setting a detection window at the entry of a traffic lane, the algorithm applies a two-layer SVM classifier to detect car-passing events at the detection window. When car-passing event occurs, Harris corners are detected at the detection window and then tracked by optical flow. These tracked corners are then organized by using a hierarchical feature point grouping approach which not only groups the corners which should belong to a single vehicle but also rejects outlier corners. Each vehicle is thus detected and tracked. A variety of traffic parameters such as vehicle count, traffic flow density, vehicle speed detection, and lane change event detection can be further obtained. Experimental results reveal that the proposed method shows better performance than conventional background-subtraction based or virtual-wires based methods on challenging videos such as with traffic jam or at night.
Active noise control: a tutorial review

Active noise control (ANC) is achieved by introducing a cancelling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.
The Art of Modeling Coupled-Field Effects in Microdevices and Microsystems

The rapid progress in microsystems technology is increasingly supported by MEMS-specific modeling methodologies and dedicated simulation tools. These do not only allow the visualization of fabrication processes and operational principles, but they also assist the designer in making decisions with a view to finding optimized microstructures under technological and economical constraints. Currently strong efforts are being made towards a predictive "CAD tool box" for top-down and closed-loop simulation of microsystems. We discuss the most important aspects to be focussed on and practicable methodologies for microdevice and system modeling, in particular the consistent treatment of coupled fields and coupled domains required for setting up physically-based models for full system and mixed-mode simulation, and for the reliable validation and calibration of the models.
Rupture model of the 1989 Loma Prieta earthquake from the inversion of strong-motion and broadband teleseismic data

We have used 24 broadband teleseismic and 48 components of local strong-motion velocity records of the 1989 Loma Prieta earthquake in a formal inversion to determine the temporal and spatial distribution of slip. Separate inversions of the teleseismic data (periods of 3 to 30 sec) or strong-motion data (periods of 1 to 5 sec) result in similar models. The data require bilateral rupture with relatively little slip in the region directly updip from the hypocenter. Slip is concentrated in two patches: one centered 6 km northwest of the hypocenter at a depth of 12 km and with a maximum slip of 350 cm, and the other centered about 5 km southeast of the hypocenter at a depth of 16 km and with a maximum slip of 460 cm. The bilateral nature of the rupture results in large amplitude ground motions at sites located along the fault strike, both to the northwest and the southeast. However, the northwestern patch has a larger moment and overall stress drop and is, consequently, the source of the largest ground motion velocities, consistent with the observed recordings. This bilateral rupture also produces relatively modest ground motion amplitudes directly updip from the hypocenter, which is in agreement with the velocity ground motions observed at Corralitos. There is clear evidence of a foreshock (magnitude between 3.5 and 5.0) or a slow rupture nucleation about 2 sec before the main part of the rupture; the origin time implied by strong-motion trigger times is systematically 2 sec later than the time predicted from the high-gain regional network data. The seismic moment obtained from either of the separate data sets or both sets combined is about 3.0 × 1026 dyne-cm and the potency is 0.95 km3.
LIMSI @ WMT12

This paper describes LIMSI's submissions to the shared translation task. We report results for French-English and German-English in both directions. Our submissions use n-code, an open source system based on bilingual n-grams. In this approach, both the translation and target language models are estimated as conventional smoothed n-gram models; an approach we extend here by estimating the translation probabilities in a continuous space using neural networks. Experimental results show a significant and consistent BLEU improvement of approximately 1 point for all conditions. We also report preliminary experiments using an "on-the-fly" translation model.
Distortion estimation for reference frame modification methods

Due to the transmission of encoded video over error prone channels, using error resilient techniques at the encoder has become an essential issue. These techniques try to decrease the impact of transmission errors by using different approaches such as inserting Intra MacroBlocks (MBs), changing the prediction structure, or considering the channel state in selecting the best MB modes. In this work, we make use of the channel aware mode decision scheme used in the Loss Aware Rate Distortion Optimization (LARDO) method while simultaneously using the prediction structure of the Improved Generalized Source Channel Prediction (IGSCP) technique. In order to combine these two schemes, we estimate the end-to-end distortion for the IGSCP prediction structure in the H.264/AVC encoder. Simulation results, using the JSVM software, demonstrate the effectiveness of our technique for different sequences.
Assessing QOL through Natural Language Use 1 Chapter 4 Assessing Quality of Life through Natural Language Use : Implications of Computerized Text Analysis

Quality of life (QOL) is a broad, multiply determined construct with both objective and subjective components. Objective indicators of QOL can include physiological markers of fitness and disease, a nation’s Gross Domestic Products, marital status, church attendance, or even the number of minutes per day spent laughing. But objective indicators do not always reflect one’s own assessment of or satisfaction with life: Cancer patients can exude vitality, rich people can think that they don’t have enough, married people can feel isolated, church goers can experience meaninglessness, and laughter can be a sign of anxiety. It becomes clear, then, that emotional state, belongingness, and cognitive judgments are important in the assessment of QOL. These subjective components of QOL are typically measured by self-report methods, including surveys or interviews. However, self-report methods suffer from their own shortcomings. Surveys are susceptible to response bias. The reference group on which a respondent heavily relies to makes comparative QOL judgments is often unknown. Furthermore, surveys are constrained by what the researcher assumes constitutes QOL. Standardized or open-ended interviews can be appealing in getting participants’ broader world views. The recurring frustration with interviews, however, is in finding reliable and efficient ways of coding them. In this paper, we bring together empirical evidence showing how computerized text analysis programs can assess natural language in order to reveal emotional state, belongingness, and cognitive judgments. We also present data that has linked natural language use to objective indicators of QOL. Our understanding of natural language use within the context of QOL is just in its infancy. Nevertheless, preliminary analyses are beginning to yield some important insights.
Emotion-Based Crowd Representation for Abnormality Detection

In crowd behavior understanding, a model of crowd behavior need to be trained using the information extracted from video sequences. Since there is no ground-truth available in crowd datasets except the crowd behavior labels, most of the methods proposed so far are just based on low-level visual features. However, there is a huge semantic gap between low-level motion/appearance features and high-level concept of crowd behaviors. In this paper we propose an attribute-based strategy to alleviate this problem. While similar strategies have been recently adopted for object and action recognition, as far as we know, we are the first showing that the crowd emotions can be used as attributes for crowd behavior understanding. The main idea is to train a set of emotion-based classifiers, which can subsequently be used to represent the crowd motion. For this purpose, we collect a big dataset of video clips and provide them with both annotations of "crowd behaviors" and "crowd emotions". We show the results of the proposed method on our dataset, which demonstrate that the crowd emotions enable the construction of more descriptive models for crowd behaviors. We aim at publishing the dataset with the article, to be used as a benchmark for the communities.
Energy Characterization and Optimization of Embedded Data Mining Algorithms: A Case Study

Data mining has been flourishing in the information-based world. In data mining, the DTW-kNN framework is widely applied for classification in miscellaneous application domains. Most of the studies in the DTW-kNN framework focus on accuracy and speedup. However, with increasingly emphasis on applications of mobile and embedded systems, energy efficiency becomes an urgent consideration in data mining algorithm design. In this paper, we present our work on energy characterization and optimization of data mining algorithms. Through a case study of the DTW-kNN framework, we investigate multiple existing strategies to improve the energy efficiency without any loss of algorithm accuracy. To the best of our knowledge, this is the first work about energy characterization and optimization of data mining algorithms on embedded computing testbeds. All the experiments are implemented on a developed energy measurement testbed. The experimental results indicate that the distance matrix calculation is the bottleneck of the DTW-kNN framework, which accounts for 89.14% on average of the total energy. With several optimized methods, the reduction of the total energy in the DTW-kNN framework can reach as much as 74.6%.
EUROPEAN FINANCIAL MANAGEMENT

The EFM Award for Scholarship in Financial Research recognizes the researchers who, through their article published in the EUROPEAN FINANCIAL MANAGEMENT, have most contributed to our understanding of an important area of financial management. The winners of the EFM Top Download Award are selected by the number of downloads through Blackwell Synergy and other online hosts.
Long-term transmit point association for coordinated multipoint transmission by stochastic optimization

We consider the problem of long-term transmit point (TP) association in a heterogeneous network where CoMP (Coordinated Multiple Point) transmission scheme is supported. More specifically, the TP association is designed for a relatively long time period where only channel statistics is available, and each user equipment(UE) could be associated with a number of TPs. We formulate the problem as a two-stage stochastic optimization problem with ℓ1-norm regularization, and propose a stochastic sparse WMMSE (SS-WMMSE) algorithm to solve it. The simulation results show that the proposed algorithm significantly outperforms the traditional deterministic approach.
A Novel Proof of the Heine-Borel Theorem

Every beginning real analysis student learns the classic Heine-Borel theorem, that the interval (0,1) is compact. In this article, we present a proof of this result that doesn't involve the standard techniques such as constructing a sequence and appealing to the completeness of the reals. We put a metric on the space of infinite binary sequences and prove that compactness of this space follows from a simple combinatorial lemma. The Heine-Borel theorem is an immediate corollary. 1. The Heine-Borel theorem
The histological grading of HCC using fusion images

The histological grading of Hepatocellular Carcinoma is essential to prognosis and treatment planning. Providing a quantitative analysis by machine vision is desired for a determination of the grading result. However, the cells on the biopsy are not all in the some depth of focus under the microscope. Some cells in the images captured by machine may become a blur with a small variance of focus. These cells may not be segmented from images or segmented into a undesized shape and thus affect the grading results. Consequently, an “all-in-focus image” is very useful to the grading of Hepatocellular Carcinoma performed by the machine. In this paper, we proposed an image fusion approach based on the wavelet-based focus measure to fuse two images with different depth of focus into one image, which contains much more in-depth focus cells. In our experiments, we demonstrated that the fused images not only provide clear appearance of cells but also higher accuracy of grading than original images.