Miracle-dz/UniFAR-FTUs huggingface dataset:
Excitation structure of the hierarchy scheme in the fractional quantum Hall effect.

The hierarchy schemes for the fractional quantum Hall effect are reexamined and it is shown that different schemes all give the same lattice of excitations whose statistics is determined by the norm of the corresponding vector, and hence have equivalent Ginzburg-Landau theories. Similar ideas apply to the anyon liquid. The schemes can be generalized by using different lattices; many inequivalent states can be obtained at any filling factor (or value of the statistics parameter)
A Novel Fabrication Technique for Liquid-Tight Microchannels by Combination of a Paraffin Polymer and a Photo-Curable Silicone Elastomer

The development and growth of microfluidics has been mainly based on various novel fabrication techniques for downsizing and integration of the micro/nano components. Especially, an effective fabrication technique of three-dimensional structures still continues to be strongly required in order to improve device performance, functionality, and device packing density because the conventional lamination-based technique for integrating several two-dimensional components is not enough to satisfy the requirement. Although three-dimensional printers have a high potential for becoming an effective tool to fabricate a three-dimensional microstructure, a leak caused by the roughness of a low-precision structure made by a 3D printer is a critical problem when the microfluidic device is composed of several parts. To build a liquid-tight microchannel on such a low-precision structure, we developed a novel assembly technique in which a paraffin polymer was used as a mold for a microchannel of photo-curable silicone elastomer on a rough surface. The shape and roughness of the molded microchannel was in good agreement with the master pattern. Additionally, the seal performance of the microchannel was demonstrated by an experiment of electrophoresis in the microchannel built on a substrate which has a huge roughness and a joint.
A coverage analysis tool for the effectiveness of software testing

We describe a software testing and analysis tool, called ATAC (Automatic Test Analysis for C), which is developed as a research instrument at Bellcore to measure the effectiveness of testing data. The design, functionality, and usage of ATAC are presented in this paper. Furthermore, to demonstrate the capability and applicability of ATAC, we obtain the 12 program versions of a critical industrial application developed in a recent university/industry N-Version Software project, and use the ATAC tool to analyze and compare coverage of the testing conducted in the program versions. Preliminary results from this investigation show that ATAC could be a powerful testing tool to provide testing metrics and quality control guidance for the certification of high quality software components or systems. It can also assist software reliability researchers and practitioners in searching for the missing link between structure-based testing schemes and software reliability.
Building Science Reading Fluency for Students with Disabilities with Repeated Reading to a Fluency Criterion.

The current study investigated the effects of repeated readings to a fluency criterion with science text for seven students with disabilities using multiple probe multiple baselines across participants. Results indicated students met criteria on four consecutive passages. As shown on Standard Celeration Charts a majority of students’ correct words per minute accelerated while incorrect words per minute decelerated on successive initial, intervention readings demonstrating reading transfer. Students’ reading scores during post-test and maintenance out performed pre-test and baseline readings providing additional measures of reading transfer. The study also suggested a relationship with comprehension; students scored higher on oral retell measures after meeting criterion when compared to initial readings. Overall, repeated readings to a fluency criterion intervention improved science reading fluency for students with disabilities.
The critical role of the linear plasmid lp36 in the infectious cycle of Borrelia burgdorferi

Borrelia burgdorferi, the aetiological agent of Lyme disease, follows a life cycle that involves passage between the tick vector and the mammalian host. To investigate the role of the 36 kb linear plasmid, lp36 (also designated the B. burgdorferi K plasmid), in the infectious cycle of B. burgdorferi, we examined a clone lacking this plasmid, but containing all other plasmids known to be required for infectivity. Our results indicated that lp36 was not required for spirochete survival in the tick, but the clone lacking lp36 demonstrated low infectivity in the mammal. Restoration of lp36 to the mutant strain confirmed that the infectivity defect was due to loss of lp36. Moreover, spirochetes lacking lp36 exhibited a nearly 4‐log increase in ID50 relative to the isogenic lp36+ clone. The infectivity defect of lp36‐minus spirochetes was localized, in part, to loss of the bbk17 (adeC) gene, which encodes an adenine deaminase. This work establishes a vital role for lp36 in the infectious cycle of B. burgdorferi and identifies the bbk17 gene as a component of this plasmid that contributes to mammalian infectivity.
Complex Expression Patterns for Na+,K+ ‐ATPase Isoforms in Retina and Optic Nerve

Three genetically distinct isozymes of the catalytic subunit of the Na,K‐ATPase have been detected and have been designated α1, α2, and α3. To determine whether their expression is restricted to identifiable neurons and glia, specific monoclonal antibodies were used for immunofluorescent localization in the rat retina and optic nerve. The patterns of staining were markedly different, suggesting differences in cellular localization. Photoreceptor inner segments and optic nerve fibers expressed predominantly α3. Müller glia in the retina and astrocytes in the optic nerve expressed α1 and α2. Isolated, dissociated bipolar, horizontal, and Müller cells expressed different isozymes separately or in combination. The complexity of staining of neurons and their axons and dendrites suggested that Na,K‐ATPase isozyme expression is not stereotyped, but is tailored to the ion transport needs of individual cell types, and targeted to specified membrane domains.
Using Rank Aggregation for Expert Search in Academic Digital Libraries

The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence. This paper explores the usage of unsupervised rank aggregation methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure of the citation patterns for the community of experts, and from profile information about the experts. We specifically experimented two unsupervised rank aggregation approaches well known in the information retrieval literature, namely CombSUM and CombMNZ. Experiments made over a dataset of academic publications for the area of Computer Science attest for the adequacy of these methods.
Children and Youth with Autism Spectrum Disorders: The Search for Effective Methods

The current attention to children and youth with autism spectrum disorders (ASD) is no less than astonishing. Media coverage of ASD clearly illustrates that the increasing and interminably enigmatic conditions of autism and ASD hold strong interest for the general public in addition to the professional community. Television specials, newspaper articles, and magazine feature stories on autism are widespread (see, for example, Newsweek, March 24, 2008; Time, May 15, 2006). That the fashionand celebrity-focused magazine Town and Country chose to publish an article on autism (Guernsey, 2006), including prevalence data, diagnostic markers, etiology debates, and information on treatment choices, speaks volumes about the extent to which matters related to ASD have captured public interest. Multiple explanations have been set forth to explain the intense interest in issues and themes connected with ASD. Among these reasons is the increased prevalence of autismrelated disabilities. The Centers for Disease Control and Prevention (2008) has estimated that approximately 1 in 150 children will fall somewhere on the autism spectrum. That prevalence estimate is a dramatic increase over the 4 to 5 per 10,000 estimate of a decade ago (Lotter, 1966). I clearly recall a conversation with an academic dean in the early stages of my higher education career regarding the advisability of my conducting research on learners with autism. The Dean counseled me not to invest substantial research time and effort with students who have autism, arguing that the disability affected so few individuals that I would have difficulty locating sufficient numbers of subjects to conduct research and that the topic of autism was so esoteric that it would be of interest to only a few individuals. Obviously, interest in matters pertaining to ASD has grown exponentially. More important, the impact that autism-related disabilities is having on families, schools, and communities is profound. ASD is currently far more common than Down syndrome, juvenile diabetes, and childhood cancer (Centers for Disease Control and Prevention, 2008); thus, it is particularized, and the aggregate impact is profound (Simpson & Myles, 2008).
VQABQ: Visual Question Answering by Basic Questions

Taking an image and question as the input of our method, it can output the text-based answer of the query question about the given image, so called Visual Question Answering (VQA). There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the basic questions of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the text-based answer of the main question. We formulate the basic questions generation problem as a LASSO optimization problem, and also propose a criterion about how to exploit these basic questions to help answer main question. Our method is evaluated on the challenging VQA dataset and yields state-of-the-art accuracy, 60.34% in open-ended task.
Data Driven and Discriminative Projections for Large-Scale Cover Song Identification

The predominant approach to computing document similarity in web scale applications proceeds by encoding taskspecific invariance in a vectorized representation, such that the relationship between items can be computed efficiently by a simple scoring function, e.g. Euclidean distance. Here, we improve upon previous work in large-scale cover song identification by using data-driven projections at different time-scales to capture local features and embed summary vectors into a semantically organized space. We achieve this by projecting 2D-Fourier Magnitude Coefficients (2DFMCs) of beat-chroma patches into a sparse, high dimensional representation which, due to the shift invariance properties of the Fourier Transform, is similar in principle to convolutional sparse coding. After aggregating these local beat-chroma projections, we apply supervised dimensionality reduction to recover an embedding where distance is useful for cover song retrieval. Evaluating on the Million Song Dataset, we find our method outperforms the current state of the art overall, but significantly so for top-k metrics, which indicate improved usability.
Differentiation of Bartonella-like isolates at the species level by PCR-restriction fragment length polymorphism in the citrate synthase gene

The citrate synthase gene (gltA) of Bartonella henselae was cloned and sequenced to compare genetic divergence among alpha and gamma branches of the class Proteobacteria and to develop enhanced genotypic reagents for B. henselae identification. B. henselae gltA is 1,293 nucleotides in length and 63 to 66% homologous with corresponding gene sequences of Rickettsia prowazekii, Escherichia coli, and Coxiella burnetii. The observed genetic variability suggests that gltA sequences can provide a useful means for studying moderate divergence among related bacteria. Oligonucleotides specific for B. henselae gltA were evaluated for the ability to prime PCR amplification within the alpha and gamma branches of the proteobacteria. Under the conditions used, only B. henselae, Bartonella quintana, and R. prowazekii template DNAs yielded amplification products (approximately 380 bp). DNAs from 28 Bartonella-like isolates of feline origin were amplified by B. henselae primers and analyzed for restriction fragment length polymorphism. The resulting patterns for all 28 isolates were similar or identical to that of the recognized B. henselae strain. Current studies are aimed at optimization of PCR conditions for specificity and sensitivity of amplification of Bartonella sequences from clinical isolates.
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees

Much of the recent work on dependency parsing has been focused on solving inherent combinatorial problems associated with rich scoring functions. In contrast, we demonstrate that highly expressive scoring functions can be used with substantially simpler inference procedures. Specifically, we introduce a sampling-based parser that can easily handle arbitrary global features. Inspired by SampleRank, we learn to take guided stochastic steps towards a high scoring parse. We introduce two samplers for traversing the space of trees, Gibbs and Metropolis-Hastings with Random Walk. The model outperforms state-of-the-art results when evaluated on 14 languages of non-projective CoNLL datasets. Our sampling-based approach naturally extends to joint prediction scenarios, such as joint parsing and POS correction. The resulting method outperforms the best reported results on the CATiB dataset, approaching performance of parsing with gold tags. 1
Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations

We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license at www.ukp. tu-darmstadt.de/data/verb-relations/. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization.
Online Learning Approaches in Computer Assisted Translation

We present a novel online learning approach for statistical machine translation tailored to the computer assisted translation scenario. With the introduction of a simple online feature, we are able to adapt the translation model on the fly to the corrections made by the translators. Additionally, we do online adaption of the feature weights with a large margin algorithm. Our results show that our online adaptation technique outperforms the static phrase based statistical machine translation system by 6 BLEU points absolute, and a standard incremental adaptation approach by 2 BLEU points absolute.
Toward Modeling the Learner's Personality Using Educational Games

Learner﻿modeling﻿is﻿a﻿crucial﻿step﻿in﻿the﻿learning﻿personalization﻿process. ﻿It﻿allows﻿taking﻿into﻿ consideration﻿the﻿learner’s﻿profile﻿to﻿make﻿the﻿learning﻿process﻿more﻿efficient. ﻿Most﻿studies﻿refer﻿ to﻿an﻿explicit﻿method,﻿namely﻿questionnaire,﻿to﻿model﻿learners. ﻿Questionnaires﻿are﻿time﻿consuming﻿ and﻿may﻿not﻿be﻿motivating﻿for﻿learners. ﻿Thus,﻿this﻿paper﻿presents﻿a﻿new﻿approach﻿for﻿Modeling﻿the﻿ learner’s﻿Personality,﻿introvert/extrovert,﻿using﻿Educational﻿Games﻿(MoPEG). ﻿The﻿proposed﻿approach﻿ includes﻿three﻿phases,﻿namely﻿design﻿of﻿the﻿educational﻿game,﻿collection﻿of﻿traces﻿and﻿personality﻿ prediction. ﻿To﻿evaluate﻿the﻿efficiency﻿of﻿MoPEG,﻿an﻿experiment﻿was﻿conducted﻿with﻿thirty﻿learners﻿ where﻿their﻿personality﻿modeling﻿results﻿using﻿MoPEG﻿are﻿compared﻿to﻿the﻿Big﻿Five﻿Inventory﻿ (BFI)﻿questionnaire. ﻿The﻿obtained﻿results﻿showed﻿that﻿the﻿accuracy﻿level﻿of﻿MoPEG﻿is﻿significant﻿ with﻿an﻿80%﻿of﻿similarity. ﻿Besides,﻿based﻿on﻿the﻿Kappa﻿method,﻿used﻿by﻿psychologists﻿to﻿measure﻿ the﻿similarity﻿of﻿two﻿instruments. ﻿The﻿obtained﻿results﻿highlighted﻿an﻿agreement﻿degree﻿of﻿MoPEG﻿ with﻿the﻿level﻿“good”.
Energy Efficiency Analysis of p-Persistent CSMA and the Effect of Sleeping Periods in a Distributed Sensor Topology

A typical wireless sensor network (WSN) operates under the harsh constraints of limited power resources, which poses challenging problems in its network design process. As a result, the energy consumption characteristics of the WSN need to be precisely studied across different layers of the communication protocol stack. In this paper, we present a thorough investigation on the energy consumption of p-persistent carrier sense multiple access (CSMA) based medium access control (MAC) layer in a distributed single hop WSN setting. Specifically, we develop an analytical framework to evaluate the energy efficiency of p-persistent CSMA for non-saturated conditions, suggesting parameters that can maximize the efficiency. Furthermore, we investigate the effect of the addition of sleeping periods by examining the resultant interplay between a sensor node's lifetime extension and the amount of information it can successfully exchange.
Document Generation with Hierarchical Latent Tree Models

In most probabilistic topic models, a document is viewed as a collection of tokens and each token is a variable whose values are all the words in a vocabulary. One exception is hierarchical latent tree models (HLTMs), where a document is viewed as a binary vector over the vocabulary and each word is regarded as a binary variable. The use of word variables allows the detection and representation of patterns of word co-occurrences and co-occurrences of those patterns qualitatively using multiple levels of latent variables, and naturally leads to a method for hierarchical topic detection. In this paper, we assume that an HLTM has been learned from binary data and we extend it to take word frequencies into consideration. The idea is to replace each binary word variable with a real-valued variable that represents the relative frequency of the word in a document. A document generation process is proposed and an algorithm is given for estimating the model parameters by inverting the generation process. Empirical results show that our method significantly outperforms the commonly-used LDA-based methods for hierarchical topic detection, in terms of model quality and meaningfulness of topics and topic hierarchies.
A Behavioral Similarity Metric for Semantic Workflows Based on Semantic Task Adjacency Relations With Importance

For modern enterprises and organizations, new business workflow can be constructed by reusing already available similar workflows in the repository. Workflow reuse is an important method for implementing business workflow management. Semantic workflows contain control-flow, data-flow, and semantic information relevant to a domain, which facilitates workflow reuse and adaptation. A similarity metric for semantic workflows is important for achieving workflow reuse. However, the existing similarity metrics for semantic workflows focus on workflow structures while ignoring their behaviours, which affect the quality of retrieved similar semantic workflows. Therefore, this paper proposes a behavioral similarity metric for semantic workflows based on semantic task adjacency relations with importance (ISTARs) that incorporate domain knowledge. First, ISTARs that involve semantic tasks and importance of semantic task adjacency relations are defined, and the ISTARs set is used to express the behaviour of the semantic workflow. The ISTAR similarity proposal is based on the similarity between two ISTARs sets and represents the similarity between semantic workflows. The ISTAR distance deduced from ISTAR similarity satisfies the properties of distance metrics. An experimental evaluation revealed that the proposed ISTAR similarity resulted in more effective retrieval of similar semantic workflows than did existing popular behavioral similarity measures.
Rearrangement of the dendritic morphology of the neurons from prefrontal cortex and hippocampus after subthalamic lesion in Sprague–Dawley rats

Several studies in rodents have suggested the inactivation of the subthalamic nucleus (STN) as an alternative strategy to Parkinson's disease (PD) treatment. The STN is part of the basal ganglia and plays an important role in the motor function; however, recent data suggest that this structure has a critical role in the cognitive function of the limbic system. The STN receives direct projection from the prefrontal cortex (PFC), structure interconnected with the hippocampus and both structures send excitatory projections to the nucleus accumbens (NAcc). Here, we determined whether and which changes occurred 4 weeks after a STN lesion in the dendritic morphology of pyramidal neurons of the layers 3 and 5 of the PFC and basolateral amygdala, neurons of the ventral hippocampus, and the medium spiny neurons of the NAcc and caudate‐putamen. Dendritic morphology was measured using the Golgi‐Cox procedure followed by Sholl analysis. We also evaluated the effects of STN lesion on locomotor behavior assessed by an open field test, social interaction, acoustic startle response, prepulse inhibition, and locomotor activity induced by a novel environment and amphetamine. We found that STN damage induced a deficit in locomotion measured by open field test with neuronal hypertrophy in PFC (layer 5) and reduced spinogenesis in CA1 ventral hippocampus and PFC (layer 3). Taken together, these data suggest that the behavioral and morphological effects of STN lesion are, at least partially, mediated by limbic subregions with possible consequences for cognitive‐related behaviors observed in PD treatment. Synapse 68:114–126, 2014. © 2013 Wiley Periodicals, Inc.
To my parents

In this thesis I take a modular approach to cortical function. I investigate how the cerebral cortex may realise a number of basic computational tasks, within the framework of its generic architecture. I present novel mechanisms for certain assumed computational capabilities of the cerebral cortex, building on the established notions of attractor memory and sparse coding. A sparse binary coding network for generating efficient representations of sensory input is presented. It is demonstrated that this network model well reproduces the simple cell receptive field shapes seen in the primary visual cortex and that its representations are efficient with respect to storage in associative memory. I show how an autoassociative memory, augmented with dynamical synapses, can function as a general sequence learning network. I demonstrate how an abstract attractor memory system may be realised on the microcircuit level – and how it may be analysed using tools similar to those used experimentally. I outline some predictions from the hypothesis that the macroscopic connectivity of the cortex is optimised for attractor memory function. I also discuss methodological aspects of modelling in computational neuroscience.