Miracle-dz/UniFAR-FTUs huggingface dataset:
LULL 1 Retargets TorsinA to the Nuclear Envelope Revealing an Activity That Is Impaired by the DYT 1 Dystonia Mutation

TorsinA (TorA) is an AAA ATPase in the endoplasmic reticulum (ER) lumen that is mutated in early onset DYT1 dystonia. TorA is an essential protein in mice and is thought to function in the nuclear envelope (NE) despite localizing throughout the ER. Here, we report that transient interaction of TorA with the ER membrane protein LULL1 targets TorA to the NE. FRAP and Blue Native PAGE indicate that TorA is a stable, slowly diffusing oligomer in either the absence or presence of LULL1. Increasing LULL1 expression redistributes both wild-type and disease-mutant TorA to the NE, while decreasing LULL1 with shRNAs eliminates intrinsic enrichment of disease-mutant TorA in the NE. When concentrated in the NE, TorA displaces the nuclear membrane proteins Sun2, nesprin-2G, and nesprin-3 while leaving nuclear pores and Sun1 unchanged. Wild-type TorA also induces changes in NE membrane structure. Because SUN proteins interact with nesprins to connect nucleus and cytoskeleton, these effects suggest a new role for TorA in modulating complexes that traverse the NE. Importantly, once concentrated in the NE, disease-mutant TorA displaces Sun2 with reduced efficiency and does not change NE membrane structure. Together, our data suggest that LULL1 regulates the distribution and activity of TorA within the ER and NE lumen and reveal functional defects in the mutant protein responsible for DYT1 dystonia.
Time-Scale Decomposition and Equivalent Rate-Based Marking

Differential equation models for Internet congestion control algorithms have been widely used to understand network dynamics and the design of router algorithms. These models use a fluid approximation for user data traffic and describe the dynamics of the router queue and user adaptation through coupled differential equations. The interaction between the routers and flows occurs through marking, where routers indicate congestion by appropriately marking packets during congestion. In this paper, we show that the randomness due to short and unresponsive flows in the Internet is sufficient to decouple the dynamics of the router queues from those of the end controllers. This implies that a time-scale decomposition naturally occurs such that the dynamics of the router manifest only through their statistical steady-state behavior. We show that this time-scale decomposition implies that a queue-length based marking function (e.g., RED-like and REM-like algorithms, which have no queue averaging, but depend only on the instantaneous queue length) has an equivalent form which depends only on the data arrival rate from the end-systems and does not depend on the queue dynamics. This leads to much simpler dynamics of the differential equation models (there is no queueing dynamics to consider), which enables easier analysis and could be potentially used for low-complexity fast simulation. Using packet-based simulations, we study queue-based marking schemes and their equivalent rate-based marking schemes for different types of controlled sources (i.e., proportional fair and TCP) and queue-based marking schemes. Our results indicate a good match in the rates observed at the intermediate router with the queue-based marking function and the corresponding rate-based approximation. Further, the window size distributions of a typical TCP flow with a queue-based marking function as well as the equivalent rate-based marking function match closely, indicating that replacing a queue-based marking function by its equivalent rate-based function does not statistically affect the end host's behavior
The Tutorbot Corpus — A Corpus for Studying Tutoring Behaviour in Multiparty Face-to-Face Spoken Dialogue

This paper describes a novel experimental setup exploiting state-of-the-art capture equipment to collect a multimodally rich game-solving collaborative multiparty dialogue corpus. The corpus is targeted and designed towards the development of a dialogue system platform to explore verbal and nonverbal tutoring strategies in multiparty spoken interactions. The dialogue task is centered on two participants involved in a dialogue aiming to solve a card-ordering game. The participants were paired into teams based on their degree of extraversion as resulted from a personality test. With the participants sits a tutor that helps them perform the task, organizes and balances their interaction and whose behavior was assessed by the participants after each interaction. Different multimodal signals captured and auto-synchronized by different audio-visual capture technologies, together with manual annotations of the tutors behavior constitute the Tutorbot corpus. This corpus is exploited to build a situated model of the interaction based on the participants temporally-changing state of attention, their conversational engagement and verbal dominance, and their correlation with the verbal and visual feedback and conversation regulatory actions generated by the tutor.
The Picard-Lefschetz theory of complexified Morse functions

Given a closed manifold N and a self-indexing Morse function f: N --> R with up to four distinct Morse indices, we construct a symplectic Lefschetz fibration pi: E --> C which models the complexification of f on the disk cotangent bundle, f_C : D(T*N) --> C, when f is real analytic. By construction, pi: E --> C comes with an explicit regular fiber M and vanishing spheres V_1,. ,V_m in M, one for each critical point of f. Our main result is that (E,pi) is a good model for the complexification (D(T*N),f_C) in the sense that N embeds in E as an exact Lagrangian submanifold, and in addition, pi|N = f and E is homotopy equivalent to N. There are several potential applications in symplectic topology, which we discuss in the introduction.
A selective thyroid hormone β receptor agonist enhances human and rodent oligodendrocyte differentiation

Nerve conduction within the mammalian central nervous system is made efficient by oligodendrocyte‐derived myelin. Historically, thyroid hormones have a well described role in regulating oligodendrocyte differentiation and myelination during development; however, it remains unclear which thyroid hormone receptors are required to drive these effects. This is a question with clinical relevance since nonspecific thyroid receptor stimulation can produce deleterious side‐effects. Here we report that GC‐1, a thyromimetic with selective thyroid receptor β action and a potentially limited side‐effect profile, promotes in vitro oligodendrogenesis from both rodent and human oligodendrocyte progenitor cells. In addition, we used in vivo genetic fate tracing of oligodendrocyte progenitor cells via PDGFαR‐CreER;Rosa26‐eYFP double‐transgenic mice to examine the effect of GC‐1 on cellular fate and find that treatment with GC‐1 during developmental myelination promotes oligodendrogenesis within the corpus callosum, occipital cortex and optic nerve. GC‐1 was also observed to enhance the expression of the myelin proteins MBP, CNP and MAG within the same regions. These results indicate that a β receptor selective thyromimetic can enhance oligodendrocyte differentiation in vitro and during developmental myelination in vivo and warrants further study as a therapeutic agent for demyelinating models. GLIA 2014;62:1513–1529
Pfortran: a parallel dialect of Fortran

IPfortran is a language designed to facilitate the programming of multi-process, data-parallel applications [BCS91]. Based on a message-passing paradigm, IPfortran extends Fortran with a small set of extensions. The duality of the send and receive operations is encapsulated in an infix operator (Section 2) or reducing functions (Section 3). With system-dependent message-passing pushed out of sight, code is streamlined and development time reduced. Errors in writing message-passing code are reduced by leaving the generation of the message-passing logic to the translator.
Unsupervised Phoneme Segmentation Using Transformed Cepstrum Features

One of the basic problems in speech engineering is phoneme segmentation, that is, to divide a speech stream into a string of phonemes. Automatic Speech Recognition (ASR) models often require reliable phoneme segmentation in the initial training phase, and Text-to-Speech (TTS) systems need a large speech database with correct phoneme segmentation information for improving the performance. Human speech is a smoothly changing continuous signal. Unlike written language, speech signals don’t include explicit marks, such as space, for segmentation. Moreover, there usually does not exist abrupt changes in speech signals due to the temporal constraints of vocal tract motions. The difficulty of phoneme segmentation comes from co-articulation of speech sounds, where acoustic realization of one phoneme may blend or fuse with its adjacent sounds. This phenomenon can even exist at a distance of two or more phonemes. All these facts make automatic phoneme segmentation a challenging problem. Previous approaches to phoneme segmentation can be divided into two categories: supervised and unsupervised segmentation. In the first case, both the linguistic contents and the the acoustic models of phonemes are available. Thus the segmentation problem can be reduced to align speech signals with a string of acoustic models. Perhaps the most famous approach of this category is HMM-based force alignment [2]. The second category of method tries to perform phonetic segmentation without using any prior knowledge on linguistic contents and acoustic models. The approach of this paper belongs to the 2nd class. The unsupervised segmentation is similar to the situation that infants acquire speech [11]. Infants don’t have acoustic and linguistic models for segmentation. However, psychological facts indicate that infants become able to segment speech according to acoustic difference between speech sounds and cluster speech segments into categories [8]. It is only by this procedure that infants can gradually construct the speech model of their native languages. Most of the previous approaches to this problem focus on detecting the change points of speech
The Demographics of Mail Search and their Application to Query Suggestion

Web mail search is an emerging topic, which has not been the object of as many studies as traditional Web search. In particular, little is known about the characteristics of mail searchers and of the queries they issue. We study here the characteristics of Web mail searchers, and explore how demographic signals such as location, age, gender, and inferred income, influence their search behavior. We try to understand for instance, whether women exhibit different mail search patterns than men, or whether senior people formulate more precise queries than younger people. We compare our results, obtained from the analysis of a Yahoo Web mail search query log, to similar work conducted in Web and Twitter search. In addition, we demonstrate the value of the user's personal query log, as well as of the global query log and of the demographic signals, in a key search task: dynamic query auto-completion. We discuss how going beyond users' personal query logs (their search history) significantly improves the quality of suggestions, in spite of the fact that a user's mailbox is perceived as being highly personal. In particular, we note the striking value of demographic features for queries relating to companies/organizations, thus verifying our assumption that query completion benefits from leveraging queries issued by ``people like me". We believe that demographics and other such global features can be leveraged in other mail applications, and hope that this work is a first step in this direction.
A reality check of Base Station Spatial Distribution in mobile networks

We consider the spatial distribution of Base Stations (BSs) in different scenarios of a European country, with the goal of finding the best theoretical distribution fitting the real data. We focus on a set of possible distributions, including: Poisson, Weibull, generalized Pareto, Lognormal and α-Stable. Our results show that the Lognormal is the best distribution in a rural scenario, while the α-Stable is the most realistic one in an urban case. This finding is confirmed for different sample areas, and for most of cellular technologies (i.e., 2G and 3G).
The development of all the systems is based on the MOSES toolkit

This paper presents a work related to reordering when dealing with translation using pivot languages. Different pivot strategies are presented in order to compare their translation quality on a ChineseSpanish task. A novel method to generate reordering weights automatically for a language pair that do not share parallel corpus is presented. Experiments which show that the strategy outperforms the cascade approach for pivot translation are reviewed.
Reply to comment by P . Teatini et al . on “ Recent subsidence of the Venice Lagoon from continuous GPS and interferometric synthetic aperture radar ”

[1] Unlike most published scientific comments, the comment submitted by P. Teatini, L. Tosi and T. Strozzi on Bock et al. [2012] does not dispute the scientific merits of our study, but rather reinforces its approach and main results regarding Venice subsidence. Their main points appear to be that our study (1) did not cite the two last papers that they published [Tosi et al., 2010; Teatini et al., 2012a], (2) received considerable attention worldwide for a rather small level of subsidence, (3) used a methodology that is not “original,” (4) misrepresented some of their earlier work, and (5) published uncertainties that were too optimistic.
Content distribution system based on segmented network coding

The network communication applying network coding, in which the intermediate peers firstly encode the information from source, and then forward the encoded data which will be decoded by the destination peers where the original information gets restored. Previous methods indicate that we can improve throughput and stability of a network, fasten the expanding-velocity of copies, and reduce the download time. Although network coding technology is developing so fast, the critical issue is still how to promote encoding and decoding rates and to decrease the complexity, on which we focus in the field. In this paper, we propose an approach based on segmented network coding which is applied to P2P Content Distribution System (P2PCDS). We implement a P2PCDS based on segmented network coding. Through the experiment, we choose the number of encoding data as a seed, and make a comparison between the P2PCDS with and without network coding in term of average download time and in the resist of churn. The resist of churn in our system is raised by 12.5 % and it also shortens average download time. And the experiment shows it's better to adapt P2PCDS under the active Mesh network architecture.
The role of digital networks in promoting collaboration and innovation

The huge investment worldwide in telecommunications infrastructure and Grid technology is being partly justified by the potential for digital networks to promote collaboration and innovation. Australia's Department of Education, Science and Training (DEST) states on its recentlyestablished e-Research Consultation website (DEST, 2005) that "e-Research has the potential to usher in a new era of innovation and help overcome the tyranny of distance by allowing our researchers to collaborate at the national and international level. " The theoretical and empirical bases for this claim are not clear. In particular, while it seems reasonable to assume that the existence of digital networks are are necessary condition for innovation into todays research environment, are digital networks sufficient for innovation? The answer, of course, is no. Careful attention needs to be paid to network structure (not just the physical structure, but the social structure) and governance if Australia's investment in telecommunications infrastructure to support e-research is to lead to a “new era of innovation”. In this paper, a review of research into social capital, economic models of innovation and the structure of the World Wide Web (WWW) is presented. It is argued that these research strands are relevant for assessing the role of digital networks in promoting collaboration and innovation. They may also be important for the design of appropriate governance structures for digital networks.
APPLICATION OF GIS AND REMOTE SENSING IN LAND USE STATISTICS

Systematic and comprehensive compilation of land use statistics is necessary for planned development of agriculture, forests, grasslands, rural settlements, urban spreads, industries and other land based programmes and activities. The need for optimizing land use in an integrated manner has become particularly relevant in recent years as a consequence of compelling and conflicting demands of growing population, increasing land degradation and thus sharply declining man land ratio. The land use planning is a means of optimum utilization of natural resources, specially, the land which is one of the most important resource, based on its related factors like socio-economic condition, soil type, climate, water availability etc. for production as a whole. Naturally, the effective land use planning of particular region needs information related to various input factors of production.
The Canonical Representation of Proteins : Application to Remote Homology Recognition

Homology establishes the evolutionary relationship among different organisms, and the biological properties of uncharacterized proteins can often be inferred from those of homologous proteins. Our ability to detect subtle similarities between proteins depends strongly on the representations we employ for proteins. Sequence and structure are two possible representations of proteins that hinge directly on molecular information. Structure is often more conserved than sequence and detecting structural similarity may help infer function beyond what is possible with sequence analysis, especially when the sequences have diverged greatly. However, structural information is sparse and available for only a small part of the protein space. We explore a technique similar to the distance-profile technique described in [1]. Our method maps every protein to a high-dimensional feature space based on its similarity or association profile with respect to a library of representative proteins or protein families. This representation induces a new metric between proteins based on the distance between their feature vectors. The key elements of our approach reside in processing the feature vectors through noise reduction, pvalue conversion followed by normalization. The resulting representation has the ability to recover structure in noisy data and boost weak signals.
Hide Markov Model Based Intrusion Detection and Response for Manets

Wireless ad hoc network is highly vulnerable to malicious attacks because of its special characteristics. Attack prevention measures, such as authentication and encryption, can be used as the first line of defense for reducing the possibilities of attacks. Intrusion detection is the second mechanism to detect and response the attacks, especially those newer ones. This paper proposed an anomaly intrusion detection method using Hide Markov Model to detect the abnormal network behaviors. Experiment shows that the IDS model can detect the attacks with high veracity.
Extracting topics in texts: Towards a fuzzy logic approach

The paper presents a preliminary investigation of potential methods for extracting semantic views of text contents, which go beyond standard statistical indexation. The aim is to build kinds of fuzzily weighted structured images of semantic contents. A preliminary step consists in identifying the different types of relations (is-a, part-of, related-to, synonymy, domain, glossary relations) that exist between the words of a text, using some general ontology such as WordNet. Then taking advantage of these relations, different types of fuzzy clusters of words can be built. Moreover, apart from its frequency of occurrence, the importance of a word may be also evaluated through some estimate of its specificity. The size of the clusters, the frequency and the specificity of their words are indications that enable us to build a fuzzy set of sets of words that progressively "emerge" from a text, as being representative of its contents. The ideas advocated in the paper and their potential usefulness are illustrated on a running example. It is expected that obtaining a better representation of the semantic contents of texts may help to better retrieve the texts that are relevant with respect to a given query, and to give some indication of what the text is about to a potential reader.
Articles: Robust Understanding in Multimodal Interfaces

Multimodal grammars provide an effective mechanism for quickly creating integration and understanding capabilities for interactive systems supporting simultaneous use of multiple input modalities. However, like other approaches based on hand-crafted grammars, multimodal grammars can be brittle with respect to unexpected, erroneous, or disfluent input. In this article, we show how the finite-state approach to multimodal language processing can be extended to support multimodal applications combining speech with complex freehand pen input, and evaluate the approach in the context of a multimodal conversational system (MATCH). We explore a range of different techniques for improving the robustness of multimodal integration and understanding. These include techniques for building effective language models for speech recognition when little or no multimodal training data is available, and techniques for robust multimodal understanding that draw on classification, machine translation, and sequence edit methods. We also explore the use of edit-based methods to overcome mismatches between the gesture stream and the speech stream.
Enhancing object detection performance by integrating motion objectness and perceptual organization

In this paper we propose a method to improve the performance of motion detection algorithms by estimating the probability that a detected blob (i.e. a group of pixels identified as foreground) is actually an object of interest. The system exploits “objectness” and perceptual organization to estimate general properties of real-world objects such as convexity, symmetry, well-defined boundary, visual contrast and cohesiveness. The measures of these properties are given as input to a naive Bayes classifier, which is trained to distinguish objects of interest from false positives. The system was trained and tested on two “real-life” environments (underwater and vehicular monitoring) and the results showed an increase of the performance of four state-of-art motion detection algorithms of about 15%. We also tested our approach on the CAVIAR dataset and although the system was not trained on that specific object class (people) it was able to increase the object detection performance of about 10%.
Revisiting Hawes: Social Capital and Racial Disparity in Incarceration Rates

Over the past twenty-five years, the U.S prison system has experienced continuous substantial increment in the prison population. At the same time, incarceration rates have risen by more than 300%, a phenomenon that many analysts have referred to as mass incarceration (DeFina and Hannon, 2013). This study investigates the various divergent impacts of social capital on policy egalitarianism in state outcomes. Notably, this paper reviews the relationships connecting social capital and incarceration rates, while analyzing the level of racial disparities in incarceration rates in the American states using a state-level panel data spanning 1980 to 2015. Building on work by Hawes (2017), we present a theoretical description and empirical examination for how social capital functions uniquely under different racial contexts using a newer dataset. The results suggest that there is a positive correlation between social capital and the incarceration of many African American which is more profound in some states than others.