Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Domain Relevance Estimation for Word Sense Disambiguation

This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain category. We use a pre-defined set of categories (we call them domains) which have been previously associated to WORDNET word senses. Given a certain domain, DRE distinguishes between relevant and non-relevant texts by means of a Gaussian Mixture model that describes the frequency distribution of domain words inside a large-scale corpus. Then, an Expectation Maximization algorithm computes the parameters that maximize the likelihood of the model on the empirical data. The correct identification of the domain of the text is a crucial point for Domain Driven Disambiguation, an unsupervised Word Sense Disambiguation (WSD) methodology that makes use of only domain information. Therefore, DRE has been exploited and evaluated in the context of a WSD task. Results are comparable to those of state-ofthe-art unsupervised WSD systems and show that DRE provides an important contribution.
Balancing Centralised Control with Vehicle Autonomy in AGV Systems for Industrial Acceptance

Automated Guided Vehicle (AGV) systems have to fulfil safety requirements and work reliably in order to be cost-effective and gain industry acceptance. Consumers want flexible AGV systems which require autonomous and distributed components to work, but this autonomy is often perceived as a disadvantage and a safety hazard. This work presents ongoing attempts and challenges to the distribution of knowledge and autonomy within AGV fleets while still ensuring safety and efficiency. Acceptance is gained by the integration of expert knowledge and a smoothly adjustable level of AGV autonomy which allows for a balance between centralized control and vehicle autonomy. Results are shown using a 3D physics simulation of a small production site. Keywords–AGV; Robotic; Industry; Safety; Planning; Autonomous; Navigation.
Urine Biomarkers of Risk in the Molecular Etiology of Breast Cancer

Endogenous estrogens can be bio-activated to endogenous carcinogens via formation of estrogen quinones. Estrogen-3,4-quinones react with DNA to form mutagenic depurinating estrogen-DNA adducts. The carcinogenicity of endogenous estrogens is related to unbalanced estrogen metabolism leading to excess estrogen quinones and formation of depurinating DNA adducts. The present studies were initiated to confirm that relatively high levels of depurinating estrogen-DNA adducts are present in women at high risk for breast cancer or diagnosed with the disease. These adducts may be biomarkers for early detection of breast cancer risk. The estrogen metabolites, conjugates and depurinating DNA adducts were identified and quantified by using ultraperformance liquid chromatography/tandem mass spectrometry to analyze urine samples from 40 healthy control women, 40 high-risk women and 40 women with newly diagnosed breast cancer. Estrogen metabolism was shifted from protective methoxylation and conjugation pathways in healthy control women towards activating pathways leading to formation of depurinating DNA adducts in women at high risk or with breast cancer. These results support the hypothesis that breast cancer is initiated by mutations derived from depurination of estrogen-DNA adducts. Therefore, relative levels of depurinating estrogen-DNA adducts could become biomarkers for early detection of breast cancer risk and aid in determining preventive strategies.
Progressive 3D Animated Models for Mobile & Web Uses

To date, more high resolution 3D animated models are required to present important details and fine structures, however, sometimes such high resolution models are un-necessary and undesired,especially on web and mobile phone environments. Though there are many well-known algorithms dealing well on simplifying 3D models, most of them are limited to static ones. Applying these mesh simplification methods to 3D animated models, a good simplified model in a specified pose can be obtained. However, some features of the original animated model, which can be shown in other poses, may be destroyed. In this paper, we propose an automatic method to simplify a 3D animated model which takes the features shown in every poses into account and preserves the geometry details of them. Therefore, a progressive 3D animated model can be generated for mobile or web uses.
Assessment of Ionospheric Models for GNSS During a Year of Solar Maximum

The main objective of this work is to present a methodology to assess the accuracy of any ionospheric model used in Global Navigation Satellite System (GNSS) applications. A number of global and regional models (both in realtime and post-process) will be analyzed during the entire 2014, i.e. near to the last Solar Cycle Maximum, to identify seasonal characteristics. The new method uses as reference values the unambiguous and undifferenced geometry-free combination of carrier-phase measurements from a worldwide distribution of receivers. The differences between the Slant Total Electron
Qualitative mapping of cerebral blood flow and functional localization with echo-planar MR imaging and signal targeting with alternating radio frequency.

PURPOSE To create qualitative maps of cerebral blood flow (CBF) with the EPISTAR (echo-planar imaging and signal targeting with alternating radio frequency) technique. MATERIALS AND METHODS The EPISTAR technique was performed in a pig model of hypercapnia and then tested in 26 volunteers by using various paradigms for cortical activation. Echo-planar images were acquired with and without use of a radio-frequency inversion pulse applied to inflowing arterial spins. A qualitative map of CBF was then created by subtracting the image obtained without the radio-frequency pulse from that obtained with the radio-frequency pulse. RESULTS Progressively more distal portions of the tagged vessels were seen as the inflow time was lengthened until cortical enhancement was seen for inflow times of approximately 1 second or longer. Signal intensity increases from rest to sensorimotor activation ranged from 13% to 193%. CBF changes in the motor strip, primary visual cortex, and the motor area for eye movements were well localized to the cortical gray matter ribbon. CONCLUSION The EPISTAR technique is a rapid, noninvasive means for creating qualitative maps of CBF.
Human-computer interaction through computer vision

In this paper, we describe the work of our group on the use of computer vision to complement existing modes of human-interaction. Our main achievements have been the development of an iris tracker and a hand tracker for HCI similar to a "visual mouse", and a deformable model of a generic face for facial feature extraction, for emotion recognition.
Efficient transfer of selectable and membrane reporter genes in hematopoietic progenitor and stem cells purified from human peripheral blood.

We have utilized highly purified hematopoietic progenitor and stem cells (HPCs, HSCs) from normal peripheral blood to develop methodology for: (a) efficient transfer into HPCs of a non-hematopoietic membrane reporter, i.e., the nerve growth factor receptor complementary DNA; and (b) effective gene transduction of putative HSCs, i.e., cells initiating Dexter-type long-term culture (LTC-ICs). Purified HPCs induced into cycling by growth factors (interleukin 3, interleukin 6, c-kit ligand) were transduced with the N2 retroviral vector containing the neomycin resistance (neor) gene. More than 80% of transduced HPCs were resistant to the toxic G418 level. Thereafter, the HPCs were effectively transduced with the LNSN retroviral vector containing a nerve growth factor receptor complementary DNA; the nerve growth factor receptor was detected on > or = 18% of the transduced HPCs. These experiments provide a new tool from which (a) to monitor expression of a transduced membrane report on hematopoietic cells, particularly at the level of HPCs/HSCs, and (b) to characterize the transduced cells by double- and triple-labeling membrane antigen analysis. Purified HPCs/HSCs grown in Dexter-type LTC were transduced at 1 week by exposure to supernatant N2 retroviral particles in the absence of exogenous hematopoietic growth factors. The procedure, devoid of toxic effects, allowed an efficient neor transduction into LTC-ICs. Thus, we consistently detected neomycin-resistant mRNA in the clonal progeny of HPCs produced in LTC at 5-8 weeks in both the nonadherent and adherent fractions; this timing of expression coincides with that of HPC production by LTC-ICs, thereby indicating the effective transduction of the LTC-ICs. These experiments represent a first step toward development of preclinical models for gene transfer into human peripheral blood HSCs by complex retroviral vectors.
Towards the classification of cancer subtypes by using cascade deep forest model in gene expression data

The classification of cancer subtypes is of great importance in cancer disease diagnosis and therapy. Many supervised learning methods have been applied to classification of cancer subtypes in the past few years, especially of deep learning based methods. Recently, a deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees, and it has been proved that deep forest model has competitive or even better performance than deep neural networks. However, the original deep forest may face under-fitting and ensemble diversity problems when dealing with small sample size, and high-dimension biology data. It is important to improve the deep forest model to work better on small-scale biology data. In this paper, we propose a deep learning model to follow the mission of cancer subtype classification on small-scale biology data sets, which can be viewed as modification of original deep forest model. Our model distinguishes from the original deep forest model with two main contributions: First, a named multi-class-scanning method is proposed to train multiple simple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representations learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests of representations learning, thus to propagate the benefits of discriminative features among layers to improve the overall classification performance. Systematical experiments on both microarray and RNA-seq data sets demonstrate that our method consistently outperforms the most state-of-the-art classification methods in application of cancer subtype classifications.
Java bytecode verification via static single assignment form

Java Virtual Machines (JVMs) traditionally perform bytecode verification by way of an iterative dataflow analysis. Bytecode verification is necessary to ensure type safety because temporary variables in the JVM are not statically typed. We present an alternative verification mechanism that transforms JVM bytecode into Static Single Assignment Form (SSA) and thereby propagates definitions directly to uses. Type checking at control flow merge points can then be performed in a single pass. Our prototype implementation of the new algorithm is faster than the standard JVM bytecode verifier. It has the additional benefit of generating SSA as a side effect, which may be immediately useful for a subsequent dynamic compilation stage.
Tetrahydrobiopterin deficiency increases neuronal vulnerability to hypoxia

Tetrahydrobiopterin (BH4) is an essential co‐factor for nitric oxide synthases (NOS). The aim of the present work was to study whether BH4 deficiency affects the vulnerability of neurones in primary culture to hypoxia. Intracellular BH4 levels were depleted by pre‐incubating neurones with 5 mm 2,4‐diamino‐6‐hydroxypyrimidine (DAHP) for 18 h, after which cells were exposed for 1 h to normoxic or hypoxic conditions. Our results showed that whereas neurones were resistant to hypoxia‐induced cellular damage, BH4 deficiency in neurones led to oxidative stress, mitochondrial depolarization, ATP depletion and necrosis after 1 h of hypoxia. Indeed, hypoxia specifically inhibited mitochondrial complex IV activity in BH4‐deficient neurones. All these effects were counteracted whenneuronal BH4 levels were restored by incubating cells with exogenous BH4 during the hypoxic period. Moreover, hypoxia‐induced damage in BH4‐deficient neurones was prevented when Nω‐nitro‐l‐arginine monomethyl ester (NAME), haemoglobin or superoxide dismutase plus catalase were present during the hypoxic period, suggesting that peroxynitrite might be involved in the process. In fact, BH4 deficiency elicited neuronal NO dysfunction, resulting in an increase in peroxynitrite generation by cells, as shown by the enhancement in tyrosine nitration; this was prevented by supplements of BH4, NAME, haemoglobin or superoxide dismutase plus catalase during hypoxia. Our results suggest that BH4 deficiency converts neuronal NOS into an efficient peroxynitrite synthase, which is responsible for the increase in neuronal vulnerability tohypoxia‐induced mitochondrial damage and necrosis.
Feeding value of wet distillers byproducts for finishing ruminants.

Yearling and calf finishing trials (replicated over 2 yr) evaluated the feeding value of wet distillers byproducts (wet distillers grains and thin stillage). An additional trial estimated the amount of thin stillage bypassing the rumen when consumed by drinking. Yearlings were 5, 10, and 20% more efficient (linear, P < .01; quadratic, P = .05), whereas calves were 2, 6, and 14% more efficient (linear, P < .01) when fed 5.2, 12.6, and 40.0% (DM basis) wet distillers byproducts, respectively, compared with cattle fed a 79% dry-rolled corn diet. Cattle fed 5.2 or 12.6% wet distillers byproducts, or the dry-rolled corn diet, received similar amounts of protein (crude or metabolizable), which exceeded the metabolizable protein requirement of all cattle. Therefore, differences in efficiency were attributed to differences in energy utilization of the diets. Wet distillers byproducts fed at 5.2, 12.6 and 40.0% contributed 80, 62, and 47% more net energy for gain than corn when fed to yearlings and 17, 33, and 29% more net energy for gain than corn when fed to calves, respectively. Wet distillers byproducts averaged 169% the energy value of corn (2.53 Mcal of NEg/kg) when fed to yearlings and 128% the energy value of corn (1.96 Mcal of NEg/kg) when fed to calves. The increased energy values cannot be explained by increased digestibility, but they may be due to a combination of factors (reduced acidosis, increased energy utilization, yeast end products, etc.) that increase the net energy content of distillers byproducts. Approximately 50% of the thin stillage consumed by drinking bypassed ruminal fermentation. (ABSTRACT TRUNCATED AT 250 WORDS)
Extrasynaptic Vesicular Transmitter Release from the Somata of Substantia Nigra Neurons in Rat Midbrain Slices

Substantia nigra neurons release dopamine from their somatodendritic regions. A long-unresolved question is whether this release occurs by exocytosis or by a nonvesicular mechanism. We used carbon fiber microelectrodes in a brainstem slice to assay secretion from single cell bodies that had been cleared of connective tissue. Amperometry at the carbon fiber microelectrodes revealed unitary events in ∼90% of cells in resting conditions. These events had charge integrals ranging from a few femtocoulombs to several hundred femtocoulombs (fC). Local glutamate application enhanced the event frequency by 3.5-fold on average and up to 10-fold in highly responsive cells, although the mean charge integral was not modified. Local application of a high K+-containing saline had effects similar to those of glutamate. The frequency of resting and stimulated amperometric events was much lower at 21–22°C than at 32–35°C. The addition of Cd2+ (50 μm), a blocker of voltage-dependent Ca2+ channels, to the bath solution blocked the stimulatory effects of glutamate. These results suggest that dopamine is released from the somata of substantia nigra neurons by exocytosis and that this mechanism is regulated by neuronal electrical activity. More generally, this study demonstrates the applicability of carbon fiber microelectrodes to the measurement of quantal monoamine secretion in brain slices.
Isomorph-Free Branch and Bound Search for Finite State Controllers

The recent proliferation of smart-phones and other wearable devices has lead to a surge of new mobile applications. Partially observable Markov decision processes provide a natural framework to design applications that continuously make decisions based on noisy sensor measurements. However, given the limited battery life, there is a need to minimize the amount of online computation. This can be achieved by compiling a policy into a finite state controller since there is no need for belief monitoring or online search. In this paper, we propose a new branch and bound technique to search for a good controller. In contrast to many existing algorithms for controllers, our search technique is not subject to local optima. We also show how to reduce the amount of search by avoiding the enumeration of isomorphic controllers and by taking advantage of suitable upper and lower bounds. The approach is demonstrated on several benchmark problems as well as a smart-phone application to assist persons with Alzheimer's to wayfind.
Modeling of Two-Dimensional Electron Gas Sheet in FDTD Method

The technique for implementation of two-dimensional electron gas (2DEG) sheet in a finite-difference time-domain (FDTD) scheme is proposed. The modified electromagnetic field update equations are derived differing from the standard Yee equations by the tangential components of the electric field in the 2DEG plane. The reliability of the proposed technique is checked applying it to the simple problem-the electromagnetic wave reflection dependence on the surface conductivity of 2DEG sheet overcovering wholly cross-section of the rectangular waveguide, and comparing the obtained results with the available analytical solution.
XRCE ’ s participation to ImagEval

This document describes XRCE’s participation to Imageval, more specifically to the mixed Text-Image search. After reviewing stateof-the-art methods to exploit the correlations between texts and images in multimedia retrieval, we will examine the single-media search components and describe how we have combined them in the framework of ImagEval. It appeared that, with our current settings and the Imageval corpus, no “early fusion” approach gave significantly better results than a “late fusion” method, so that this paper is mainly dedicated to the latter approach. In this track, exploiting textual information with the Language Modelling approach alone already offered very satisfying performance, much larger than purely visual search. Still, late fusion was able to increase monomedia results by more than 10% (relative), showing the usefulness of combining both types of information, even if the purely visual retrieval component gives relatively poor results.
A Heuristic Approach to Portfolio Optimization

Constraints on downside risk, measured by shortfall probability, expected shortfall, semi-variance etc., lead to optimal asset allocations which differ from the meanvariance optimum. The resulting optimization problem can become quite complex as it exhibits multiple local extrema and discontinuities, in particular if we also introduce constraints restricting the trading variables to integers, constraints on the holding size of assets or on the maximum number of different assets in the portfolio. In such situations classical optimization methods fail to work efficiently and heuristic optimization techniques can be the only way out. The paper shows how a particular optimization heuristic, called threshold accepting, can be successfully used to solve complex portfolio choice problems.
Sub-Sentential Alignment Using Substring Co-Occurrence Counts

In this paper, we will present an efficient method to compute the co-occurrence counts of any pair of substring in a parallel corpus, and an algorithm that make use of these counts to create sub-sentential alignments on such a corpus. This algorithm has the advantage of being as general as possible regarding the segmentation of text.
An on-chip 96.5% current efficiency CMOS linear regulator

A proposed linear regulator uses a flexible control technique of output current (FCOC) to achieve 96.5% efficiency. The FCOC technique drives a flexible output current according to the output current variation and stable output voltage supply. The linear regulator fabricated by 1.2 /spl mu/m CMOS process occupies 0.423 mm/sup 2/. The fabricated linear regulator achieves 96.5% current efficiency and less than 6.81 mVpp output voltage fluctuation at an output current frequency from 1.8 Hz to 100 MHz.
Track selection in Multifunction Radars: Nash and correlated equilibria

We consider a track selection problem for multi-target tracking in a multifunction radar network from a game-theoretic perspective. The problem is formulated as a non-cooperative game. The radars are considered to be players in this game with utilities modeled using a proper tracking accuracy criterion and their strategies are the observed targets whose number is known. Initially, for the problem of coordination, the Nash equilibria are characterized and, in order to find equilibria points, a distributed algorithm based on the best-response dynamics is proposed. Afterwards, the analysis is extended to the case of partial target observability and radar connectivity and heterogeneous interests among radars. The solution concept of correlated equilibria is employed and a distributed algorithm based on the regret-matching is proposed. The proposed algorithms are shown to perform well compared to the centralized approach of significantly higher complexity.
MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation. Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ‘learning to rank’. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy. Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI. Availability and implementation: The software is available upon request. Contact: zhusf@fudan.edu. cn