Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilingual Knowledge Extraction Using Chunk Alignment

In this paper, we propose a new method for effectively acquiring bilingual knowledge by exploiting the dependency relations among the aligned chunks and words. We use a monolingual dependency parser to automatically obtain dependency parses of target language using chunk and word alignment. For reducing the computational complexity of structural alignment, we use a bilingual dictionary and adopt a divide-and-conquer strategy. By sharing the dependency relations of a given source sentence, we automatically obtain a dependency parse of a target sentence that is structurally consistent with the source sentence. Moreover, we extract bilingual knowledge bases from translation correspondences of singletons to surface verb subcategorization patterns by exploiting the bilingual dependency relations. To acquire reliable ones, we take a stepwise filtering method based on statistical test.
Classi er Combination and Feature Sele tion for Land-Cover Mapping from High-Resolution Airborne Dual-Band SAR Data

We study feature selection and classification for a land-cov er mapping task from airborne high resolution AeS-1 data in r ada Xand P-Band. The studied feature selection methods are the well-established sequential addition of features to an ini tially empty feature set and genetic algorithm search, both based o n statistical distance measures, and exhaustive evaluatio n based on actual classifier performance. It was observed, that diff erent criteria and search strategies come up with different subsets of features. We present results of combined classifications de rived from classifiers trained on different subsets of featu res. The considered combination strategies are product, sum, maxim um and majority rules. Combination turned out to bring signi ficant improvement. The task of discriminating two forest classes , three classes of agricultural area, two classes of built-u p area and a specific class devoted to radar imaging ambiguities for a test site located in Switzerland provided a satisfying res ult for machine classification from radar data.
Peripheral SLC6A4 DNA Methylation Is Associated with In Vivo Measures of Human Brain Serotonin Synthesis and Childhood Physical Aggression

The main challenge in addressing the role of DNA methylation in human behaviour is the fact that the brain is inaccessible to epigenetic analysis in living humans. Using positron emission tomography (PET) measures of brain serotonin (5-HT) synthesis, we found in a longitudinal sample that adult males with high childhood-limited aggression (C-LHPA) had lower in vivo 5-HT synthesis in the orbitofrontal cortex (OBFC). Here we hypothesized that 5-HT alterations associated with childhood aggression were linked to differential DNA methylation of critical genes in the 5-HT pathway and these changes were also detectable in peripheral white blood cells. Using pyrosequencing, we determined the state of DNA methylation of SLC6A4 promoter in T cells and monocytes isolated from blood of cohort members (N = 25) who underwent a PET scan, and we examined whether methylation status in the blood is associated with in vivo brain 5-HT synthesis. Higher levels of methylation were observed in both T cells and monocytes at specific CpG sites in the C-LHPA group. DNA methylation of SLC6A4 in monocytes appears to be associated more reliably with group membership than T cells. In both cell types the methylation state of these CpGs was associated with lower in vivo measures of brain 5-HT synthesis in the left and right lateral OBFC (N = 20) where lower 5-HT synthesis in C-LHPA group was observed. Furthermore, in vitro methylation of the SLC6A4 promoter in a luciferase reporter construct suppresses its transcriptional activity supporting a functional role of DNA methylation in SLC6A4 promoter regulation. These findings indicate that state of SLC6A4 promoter methylation is altered in peripheral white blood cells of individuals with physical aggression during childhood. This supports the relevance of peripheral DNA methylation for brain function and suggests that peripheral SLC6A4 DNA methylation could be a marker of central 5-HT function.
Distinct Genomic Integration of MLV and SIV Vectors in Primate Hematopoietic Stem and Progenitor Cells

Murine leukemia virus (MLV)-derived vectors are widely used for hematopoietic stem cell (HSC) gene transfer, but lentiviral vectors such as the simian immunodeficiency virus (SIV) may allow higher efficiency transfer and better expression. Recent studies in cell lines have challenged the notion that retroviruses and retroviral vectors integrate randomly into their host genome. Medical applications using these vectors are aimed at HSCs, and thus large-scale comprehensive analysis of MLV and SIV integration in long-term repopulating HSCs is crucial to help develop improved integrating vectors. We studied integration sites in HSCs of rhesus monkeys that had been transplanted 6 mo to 6 y prior with MLV- or SIV-transduced CD34+ cells. Unique MLV (491) and SIV (501) insertions were compared to a set of in silico-generated random integration sites. While MLV integrants were located predominantly around transcription start sites, SIV integrants strongly favored transcription units and gene-dense regions of the genome. These integration patterns suggest different mechanisms for integration as well as distinct safety implications for MLV versus SIV vectors.
Detection of Broken Outer-Cage Bars for Double-Cage Induction Motors Under the Startup Transient

Unlike single-cage rotor fault detection, fast Fourier transform (FFT)-based steady-state spectrum analysis techniques can fail to detect outer-cage faults in double-cage induction motors due to the small outer-cage current under running conditions. Double-cage motors are typically employed in applications that require loaded starts. This makes the outer cage vulnerable to fatigue failure since it must withstand the high starting current and long startup time frequently. However, there are only a few publications that investigate detection techniques specifically for double-cage motors. In this paper, considering that the influence of the faulty outer cage is strong at startup due to the large outer-cage current, detection of outer-cage faults under the startup transient is investigated. A discrete-wavelet-transform-based method is proposed as a viable solution to the detection of outer-cage faults for double-cage motors. An experimental study on fabricated copper double-cage induction motors shows that the proposed method provides sensitive and reliable detection of double-cage rotor faults compared to FFT.
Utilizing relevance feedback in fusion-based retrieval

Work on using relevance feedback for retrieval has focused on the single retrieved list setting. That is, an initial document list is retrieved in response to the query and feedback for the most highly ranked documents is used to perform a second search. We address a setting wherein the list for which feedback is provided results from fusing several intermediate retrieved lists. Accordingly, we devise methods that utilize the feedback while exploiting the special characteristics of the fusion setting. Specifically, the feedback serves two different, yet complementary, purposes. The first is to directly rank the pool of documents in the intermediate lists. The second is to estimate the effectiveness of the intermediate lists for improved re-fusion. In addition, we present a meta fusion method that uses the feedback for these two purposes simultaneously. Empirical evaluation demonstrates the merits of our approach. As a case in point, the retrieval performance is substantially better than that of using the relevance feedback as in the single list setting. The performance also substantially transcends that of a previously proposed approach to utilizing relevance feedback in fusion-based retrieval.
GeDiKe : An attempt to reduce the complexity of distributed programming

Although distributed processing, in principle, provides speed up and access to high performance resources, it will be little used if people could not easily exploit it for their applications. In this way, a lot of network application developers attempted to fac ilitate development of specific distributed applications using different mechanisms. On the other hand, there are many common modules in different distributed applications that of course, their implementation may differ accordingly. Iintending to reduce the complexity of converting a centralized application to a distributed one, we have introduced a generic distributed kernel (GeDiKe) in this paper. Users can save their design in XML format defined by themselves and then customize and initialize GeDiKe to execute their algorithms. The results of execution will be summarized in other XML files and can be processed by any application. GeDiKe has a modular architecture. Each module is a flexible and replaceable unit and has a definite interface through which other modules can interact. The architecture utilizes the benefits of XML, Extensible Markup Language, in modeling the tasks. Processing XML is much easier than any other conventional language used for modeling distributed applications.
Towards a Plan Library for Household Robots

This paper describes the structure for a plan library of a service robot intended to perform household chores. The plans in the library are particularly designed to enable reliable, flexible, and efficient robot control, to learn control heuristics, to generalize the plans to cope with new objects and situations. We believe that plans with these characteristics are required for competent autonomous robots performing skilled manipulation tasks in human environments.
Recursion and Recursion- Like Structure in Ensembles of Neural Elements

Connectionist models have used general principles to model phenomena across many mental domains. Thus, they seem promising for uniting diverse syntactic phenomena (e.g., language, music, action). A challenge has been understanding how recursion can work in neurons. Headway has been made in training Recurrent Neural Networks (RNNs) to process languages which can be handled by the use of one or more symbol-counting stacks (e.g., ab, aABb, abc). Success with exponential state growth languages, in which stacks function as full-fledged sequence memories (e.g., palindrome languages, certain natural language relative clause constructions), has not been as great. [24] introduces Fractal Learning Neural Networks (FLNNS), showing that they can learn some exponential state growth languages with high accuracy. The current paper analyzes the performance of these FLNNs clarifying the relationship between their imperfect, but nevertheless structurally insightful, neural recursive encoding, and the perfect recursive encodings of symbolic devices.
The Apollonian structure of Bianchi groups

We study the orbit of $\widehat{\mathbb{R}}$ under the M\"obius action of the Bianchi group $\operatorname{PSL}_2(\mathcal{O}_K)$ on $\widehat{\mathbb{C}}$, where $\mathcal{O}_K$ is the ring of integers of an imaginary quadratic field $K$. The orbit $\mathcal{S}_K$, called a Schmidt arrangement, is a geometric realisation, as an intricate circle packing, of the arithmetic of $K$. We give a simple geometric characterisation of certain subsets of $\mathcal{S}_K$ generalizing Apollonian circle packings, and show that $\mathcal{S}_K$, considered with orientations, is a disjoint union of all primitive integral such $K$-Apollonian packings. These packings are described by a new class of thin groups of arithmetic interest called $K$-Apollonian groups. We make a conjecture on the curvatures of these packings, generalizing the local-to-global conjecture for Apollonian circle packings.
Comparison of mixture formation in two combustion modes in directly-injection diesel

This paper simulates the density and temperature fields in the process of mixture formation with two combustion modes in a Diesel engine. The computational simulations and experiments show that in the normal combustion mode, the density and temperature of mixtures change greatly in the combusting sprays so that there are premixed combustion and diffusion combustion simultaneously, which results in high NOX and smoke emissions, low CO and HC emissions. Homogeneous charge compression ignition (HCCI) combustion takes place by early-injection to form the stratified homogeneous mixtures, which results in very low NOX and smoke emissions, high CO and unburned fuel emissions.
A Consistent Test for Misspeci cation in PolychotomousResponse Models

We propose a test for misspeciication in polychotomous response models such as the conditional or the multinomial logit model. The test is based on comparing (a smoothed version of) the link functions of the parametric polychotomous model under consideration with a nonparametric estimate. If the parametric model is correct the nonparametric estimates should diier from (the smooth of) the hypothesized links only by random sampling error. If however the parametric model is misspeciied then systematic diierences should arise. We derive the asymptotic distribution of the test statistic under the null hypothesis, show that it is consistent against semiparametric alternatives and investigate its local power properties. A small Monte Carlo study investigates the nite sample performance of the test. I am greatly indebted to Joel Horowitz for many helpful suggestions and disussions. Helpful comments were also made
Abelian Varieties, RCFTs, Attractors, and Hitchin Functional in Two Dimensions

We consider a generating function for the number of conformal blocks in rational conformal field theories with an even central charge c on a genus g Riemann surface. It defines an entropy functional on the moduli space of conformal field theories and is captured by the gauged WZW model whose target space is an abelian variety. We study a special coupling of this theory to two-dimensional gravity. When c=2g, the coupling is non-trivial due to the gravitational instantons, and the action of the theory can be interpreted as a two-dimensional analog of the Hitchin functional for Calabi-Yau manifolds. This gives rise to the effective action on the moduli space of Riemann surfaces, whose critical points are attractive and correspond to Jacobian varieties admitting complex multiplication. The theory that we describe can be viewed as a dimensional reduction of topological M-theory.
Article in Press Computational Statistics and Data Analysis a Framework for Modelling Overdispersed Count Data, including the Poisson-shifted Generalized Inverse Gaussian Distribution

A variety of methods of modelling overdispersed count data are compared. The methods are classified into three main categories. The first category are ad hoc methods (i.e. pseudo-likelihood, (extended) quasi-likelihood, double exponential family distributions). The second category are discretized continuous distributions and the third category are observational level random effects models (i.e. mixture models comprising explicit and non-explicit continuous mixture models and finite mixture models). The main focus of the paper is a family of mixed Poisson distributions defined so that its mean µ is an explicit parameter of the distribution. This allows easier interpretation when µ is modelled using explanatory variables and provides a more orthogonal parameterization to ease model fitting. Specific three parameter distributions considered are the Sichel and Delaporte distributions. A new four parameter distribution, the Poisson-shifted generalized inverse Gaussian distribution is introduced, which includes the Sichel and Delaporte distributions as a special and a limiting case respectively. A general formula for the derivative of the likelihood with respect to µ, applicable to the whole family of mixed Poisson distributions considered, is given. Within the framework introduced here all parameters of the distributions are modelled as parametric and/or nonparametric (smooth) functions of explanatory variables. This provides a very flexible way of modelling count data. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models.
Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research

Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one hand, inference attack studies explore the types of personal information that can be revealed and the methods used to extract it. An additional risk is that geosocial media data are collected massively for research purposes, and the processing or publication of these data may further compromise individual privacy. On the other hand, consistent and increasing research on location protection measures promises solutions that mitigate disclosure risks. In this paper, we examine recent research efforts on the spectrum of privacy issues related to geosocial network data and identify the contributions and limitations of these research efforts. Furthermore, we provide protection recommendations to researchers that share, anonymise, and store social media data or publish scientific results.
Expedited Benchmarking of Social Networking Actions with Faster Data Load Techniques

To benchmark and rate a data store, one might be required to re peatedly load the same benchmark database onto the data store. This may constitute a significa nt portion of the benchmarking process. This paper focuses on the BG benchmark (www.bgbenchmark.or g) and presents three techniques to expedite loading of its database. These include generating the disk image of the database once and reusing it many times, restoring the updated data items to thei r original value in between experiments, and maintaining in-memory state of the database across differe nt experiments to avoid repeated loading of the database all together. BG may use a hybrid of the proposed techniques when evaluating a data store. When evaluating MongoDB with a million member BG database, we show these techniques expedite BG’s rating of MongoDB from 4 months (123 days) of continuous r nning to less than 11 days for the first rating experiment. Subsequent ratings of MongoDB w ith different workloads using the same database will be much faster, in the order of hours (less than 1 day).
Liquid-Phase Synthesis of 2′-Methyl-RNA on a Homostar Support through Organic-Solvent Nanofiltration

Due to the discovery of RNAi, oligonucleotides (oligos) have re-emerged as a major pharmaceutical target that may soon be required in ton quantities. However, it is questionable whether solid-phase oligo synthesis (SPOS) methods can provide a scalable synthesis. Liquid-phase oligo synthesis (LPOS) is intrinsically scalable and amenable to standard industrial batch synthesis techniques. However, most reported LPOS strategies rely upon at least one precipitation per chain extension cycle to separate the growing oligonucleotide from reaction debris. Precipitation can be difficult to develop and control on an industrial scale and, because many precipitations would be required to prepare a therapeutic oligonucleotide, we contend that this approach is not viable for large-scale industrial preparation. We are developing an LPOS synthetic strategy for 2′-methyl RNA phosphorothioate that is more amenable to standard batch production techniques, using organic solvent nanofiltration (OSN) as the critical scalable separation technology. We report the first LPOS-OSN preparation of a 2′-Me RNA phosphorothioate 9-mer, using commercial phosphoramidite monomers, and monitoring all reactions by HPLC, 31P NMR spectroscopy and MS.
Williams long-term expression hematopoietic cells : effect of selectable marker sequences on Retroviral gene transfer of human adenosine deaminase in murine

http://bloodjournal.hematologylibrary. org/site/misc/rights.xhtml#repub_requests Information about reproducing this article in parts or in its entirety may be found online at: http://bloodjournal.hematologylibrary. org/site/misc/rights.xhtml#reprints Information about ordering reprints may be found online at: http://bloodjournal.hematologylibrary. org/site/subscriptions/index.xhtml Information about subscriptions and ASH membership may be found online at:
Predictability by construction

The Predictable Assembly from Certifiable Components (PACC) team at the Software Engineering Institute has developed a vehicle to create component-based systems that have predictable behavior prior to implementation. The project has been used to successfully predict performance and safety properties of real-time systems, but the concepts and technology can be applied to predict other properties (e.g., reliability, security).
Reinforcing robot perception of multi-modal events through repetition and redundancy and repetition and redundancy

For a robot to be capable of development, it must be able to explore its environment and learn from its experiences. It must nd (or create) opportunities to experience the unfamiliar in ways that reveal properties valid beyond the immediate context. In this paper, we develop a novel method for using the rhythm of everyday actions as a basis for identifying the characteristic appearance and sounds associated with objects, people, and the robot itself. Our approach is to identify and segment groups of signals in individual modalities (sight, hearing, and proprioception) based on their rhythmic variation, then to identify and bind causally-related groups of signals across dieren t modalities. By including proprioception as a modality, this cross-modal binding method applies to the robot itself, and we report a series of experiments in which the robot learns about the characteristics of its own body.
Conserved BK Channel-Protein Interactions Reveal Signals Relevant to Cell Death and Survival

The large-conductance Ca2+-activated K+ (BK) channel and its β-subunit underlie tuning in non-mammalian sensory or hair cells, whereas in mammals its function is less clear. To gain insights into species differences and to reveal putative BK functions, we undertook a systems analysis of BK and BK-Associated Proteins (BKAPS) in the chicken cochlea and compared these results to other species. We identified 110 putative partners from cytoplasmic and membrane/cytoskeletal fractions, using a combination of coimmunoprecipitation, 2-D gel, and LC-MS/MS. Partners included 14-3-3γ, valosin-containing protein (VCP), stathmin (STMN), cortactin (CTTN), and prohibitin (PHB), of which 16 partners were verified by reciprocal coimmunoprecipitation. Bioinformatics revealed binary partners, the resultant interactome, subcellular localization, and cellular processes. The interactome contained 193 proteins involved in 190 binary interactions in subcellular compartments such as the ER, mitochondria, and nucleus. Comparisons with mice showed shared hub proteins that included N-methyl-D-aspartate receptor (NMDAR) and ATP-synthase. Ortholog analyses across six species revealed conserved interactions involving apoptosis, Ca2+ binding, and trafficking, in chicks, mice, and humans. Functional studies using recombinant BK and RNAi in a heterologous expression system revealed that proteins important to cell death/survival, such as annexinA5, γ-actin, lamin, superoxide dismutase, and VCP, caused a decrease in BK expression. This revelation led to an examination of specific kinases and their effectors relevant to cell viability. Sequence analyses of the BK C-terminus across 10 species showed putative binding sites for 14-3-3, RAC-α serine/threonine-protein kinase 1 (Akt), glycogen synthase kinase-3β (GSK3β) and phosphoinositide-dependent kinase-1 (PDK1). Knockdown of 14-3-3 and Akt caused an increase in BK expression, whereas silencing of GSK3β and PDK1 had the opposite effect. This comparative systems approach suggests conservation in BK function across different species in addition to novel functions that may include the initiation of signals relevant to cell death/survival.