Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychophysical recovery from pulse-train forward masking in electric hearing.

Psychophysical pulse-train forward-masking (PTFM) recovery functions were measured in fifteen subjects with the Nucleus mini-22 cochlear implant and six subjects with the Clarion cochlear implant. Masker and probe stimuli were 500-Hz trains of 200- or 77-micros/phase biphasic current pulses. Electrode configurations were bipolar for Nucleus subjects and monopolar for Clarion subjects. Masker duration was 320 ms. Probe duration was either 10 ms or 30 ms. Recovery functions were measured for a high-level masker on a middle electrode in all 21 subjects, on apical and basal electrodes in 7 of the Nucleus and 3 of the Clarion subjects, and for multiple masker levels on the middle electrode in 8 Nucleus subjects and 6 Clarion subjects. Recovery functions were described by an exponential process in which threshold shift (in microA) decreased exponentially with increasing time delay between the offset of the masker pulse train and the offset of the probe pulse train. All but 3 of the 21 subjects demonstrated recovery time constants on a middle electrode that were less than 95 ms. The mean time constant for these 18 subjects was 54 ms (s.d. 17 ms). Three other subjects tested on three electrodes exhibited time constants larger than 95 ms from an apical electrode only. Growth-of-masking slopes depended upon time delay, as expected from an exponential recovery process, i.e., progressively shallower slopes were observed at time delays of 10 ms and 50 ms. Recovery of threshold shift (in microA) for PTFM in electrical hearing behaves inthe same way as recovery of threshold shift (in dB) for pure-tone forward masking in acoustic hearing. This supports the concept that linear microamps are the electrical equivalent of acoustic decibels. Recovery from PTFM was not related to speech recognition in a simple manner. Three subjects with prolonged PTFM recovery demonstrated poor speech scores. The remaining subjects with apparently normal PTFM recovery demonstrated speech scores ranging from poor to excellent. Findings suggest that normal PTFM recovery is only one of several factors associated with good speech recognition in cochlear-implant listeners. Comparisons of recovery curves for 10- and 30-ms probe durations in two subjects showed little or no temporal integration at time delays less than 95 ms where recovery functions have steep slopes. The same subjects exhibited large amounts of temporal integration at longer time delays where recovery slopes are more gradual. This suggests that probe detection depends primarily on detection of the final pulses in the probe stimulus and supports the use of offset-to-offset time delays for characterizing PTFM recovery in electric hearing.
Identifying zooplankton community changes between shallow and upper-mesophotic reefs on the Mesoamerican Barrier Reef, Caribbean

Mesophotic coral ecosystems (MCEs, reefs 30–150 m) are understudied, yet the limited research conducted has been biased towards large sessile taxa, such as scleractinian corals and sponges, or mobile taxa such as fishes. Here we investigate zooplankton communities on shallow reefs and MCEs around Utila on the southern Mesoamerican Barrier Reef using planktonic light traps. Zooplankton samples were sorted into broad taxonomic groups. Our results indicate similar taxonomic zooplankton richness and overall biomass between shallow reefs and MCEs. However, the abundance of larger bodied (>2 mm) zooplanktonic groups, including decapod crab zoea, mysid shrimps and peracarid crustaceans, was higher on MCEs than shallow reefs. Our findings highlight the importance of considering zooplankton when identifying broader reef community shifts across the shallow reef to MCE depth gradient.
Clinical Outcome Measures in Spinal Muscular Atrophy

Spinal muscular atrophy is one of the most devastating neurological diseases of childhood. Affected infants and children suffer from often severe muscle weakness caused by degeneration of lower motor neurons in the spinal cord and brainstem. Identification of the causative genetic mutation in most cases has resulted in development of potential treatment strategies. To test these new drugs, clinically feasible outcomes are needed. Several different assessments, validated in spinal muscular atrophy or similar disorders, are being used by national and international research groups; however, their sensitivity to detect change is unknown. Acceptance of a few standardized, easily administered, and functionally meaningful outcomes, applicable to the phenotypic spectrum of spinal muscular atrophy, is needed. Consensus is imperative to facilitate collaboration and explore the ability of these measures to identify the therapeutic effect of disease-modifying agents. Following is an evidence-based review of available clinical outcome measures in spinal muscular atrophy.
Particle deposition and optical response of ITER motional Stark effect diagnostic first mirrors

Particle deposition and erosion can affect mirrors used in plasma diagnostics and this is a major concern for future fusion reactors. This subject is analysed for the first and second mirrors of the proposed motional Stark effect edge plasma current diagnostic for ITER. Particle fluxes to the diagnostic module aperture are given by edge plasma/impurity-transport solutions for convective plasma flow for full-power fusion conditions. The MC-Mirror code with input of TRIM-SP results is used to compute in-module direct, reflected and sputtered particle transport. Particles analysed are D–T and He atoms/ions from the plasma, and Fe, Be and W from first wall sputtering and/or in-module sputtering. Many of the results are encouraging for optical diagnostic use in ITER and possibly for post-ITER high duty-factor reactors. The LLNL-4B module design analysed works well in minimizing particle flux to the mirrors, with a factor of ∼200–400 reduction in aperture-to-first-mirror flux. Sputtering erosion/degradation of Mo or Rh coated mirrors by incident D, T and He is negligible. IMD optical effects code analysis shows probably tolerable changes in light reflection and polarization due to mirror beryllium deposition. Tungsten flux to the mirrors is very low. Based on available but limited data, however, there is major concern about the effect of the predicted helium flux on mirror optical properties.
Service Classification Using Adaptive Back-Propagation Neural Network and Semantic Similarity

With the growing population of Web services, the discovery of services is a key to the development of Web services. While extensive researches focus mainly on service matchmaking algorithms, service classification that is also a meaningful approach to accelerating service discovery only receives little attention. In this paper, we propose to use adaptive back-propagation neural network model (BPM) to perform service recognition. During the training process, the feature vectors of training services and their categories are learned by the BPM. The element in the feature vector is the semantic similarity between the feature word in the system dictionary and the occurrence in the feature set for a service. During the recognition process, the characteristics of the test service are analyzed by the BPM and the output shows the category of the test service. Furthermore, the BPM is adapted with correctly recognized test services, which results in better modeling over time. Based on extensive experiments, we show that using the adaptive BPM is a promising way to realize automatic service classification, and the adaptive BPM using semantic similarity as the element in feature vector provides better performance than the general BPM using word frequency
Twitter-TTM: An efficient online topic modeling for Twitter considering dynamics of user interests and topic trends

Latent Dirichlet Allocation (LDA) is a topic model which has been applied to various fields. It has been also applied to user profiling or event summarization on Twitter. In the application of LDA to tweet collection, it generally treats aggregated all tweets of a user as a single document. On the other hand, Twitter-LDA which assumes a single tweet consists of a single topic has been proposed and showed that it is superior to the former way in topic semantic coherence. However, Twitter-LDA has a problem that it is not capable of online inference. In this paper, we extend Twitter-LDA in the following two points. First, we model the generation process of tweets more accurately by estimating the ratio between topic words and general words for each user. Second, we enable it to estimate dynamics of user interests and topic trends in online based on Topic Tracking Model (TTM) which models consumer purchase behaviors.
A hybrid technique for accelerated simulation of ATM networks and network elements

Conventional simulation of cell- or packet-switched networks involves the use of discrete event simulators that model each individual cell through the network, typically called cell-level simulation. Each cells arrival at, or departure from, a network element is represented by an event. However, statistical considerations are such that very large numbers of cells have to be simulated to guarantee the accuracy of the results. This has always caused very long simulation times, often amounting to many hours of real time just to simulate a few minutes of simulated time. In this article we describe a novel methodology for accelerating simulation studies in cell-based communication networks, e.g., ATM, by using a hybrid analytical/simulation combination. The methodology uses a mathematical technique to seperate foreground traffic from background traffic, and focuses on accelerating cell by cell simulation.
Hiring Behavior Models for Online Labor Markets

In an online labor marketplace employers post jobs, receive freelancer applications and make hiring decisions. These hiring decisions are based on the freelancer's observed (e.g., education) and latent (e.g., ability) characteristics. Because of the heterogeneity that appears in the observed characteristics, and the existence of latent ones, identifying and hiring the best possible applicant is a very challenging task. In this work we study and model the employer's hiring behavior. We assume that employers are utility maximizers and make rational decisions by hiring the best possible applicant at hand. Based on this premise, we propose a series of probabilistic models that estimate the hiring probability of each applicant. We train and test our models on more than 600,000 job applications obtained by oDesk.com, and we show evidence that the proposed models outperform currently in-use baselines. To get further insights, we conduct an econometric analysis and observe that the attributes that are strongly correlated with the hiring probability are whether or not the freelancer and the employer have previously worked together, the available information on the freelancer's profile, the countries of the employer and the freelancer and the skillset of the freelancer. Finally, we find that the faster a freelancer applies to an opening, the higher is the probability to get the job.
Innovation and hybrid genres: disturbing social rhythm in legal practice

This paper explores the non-adoption of an innovation via the concept of hybrid genres, that is digital genres that emerge from a non-digital material precedent. As instances of innovation these are often resisted because they disturb the order of activity and balance of power relations in a given situation, or require users to make conceptual and physical adaptation efforts that they consider too costly. The authors investigate such issues with a case study of the introduction of a hybrid digital genre, ODR or online dispute resolution, in legal practice
The News As Myth: Fact and Context in Journalism

In non-technical language, this book argues that the myth of the news is its supposed objectivity and that the very forms which presumably guarantee veracity ultimately lead to consistently incomplete and misleading news reports. This book may be of importance to journalists, sociologists, political scientists and mass communication experts both for its analysis of objectivity and subjectivity as well as for its practical demonstration of the means by which misinformation is introduced into "objective" reports. The examples of news stories in this book also provides a series of case studies which may be of particular interest to educators teaching journalism or focusing on the relation between reportage and society at large.
International social policy : welfare regimes in the developed world

Introduction and Acknowledgements P.Alcock& G.Craig The International Context P.Alcock& G.Craig An American Welfare State? J.Clarke with F.F. Piven Canada: One Step Forward, Two Steps Back? E.Lightman& G.Riches Australia: From Wage-Earners to Neo-Liberal State L.Bryson& F. Verity New Zealand: From Early Innovation to Humanising the Market J.Davey& S.Grey The United Kingdom: Constructing a Third Way? P.Alcock Sweden: Between Model and Reality T.Salonen Germany: A Centrist Welfare State at the Crossroads L.Leisering Italy: Still a Pension State? V.Fargion Russia: State Socialism to Marketised Welfare N.Davidova& N.Manning Japan: Constructing the 'Welfare Society' T.Uzuhasi Korea: Rescaling the Developmental Welfare State H.Kwon China: The Art of State and Social Policy Remodelling K.Lin South Africa - Transition Under Pressure F.Lund
A Cost-Effective and Scalable Merge Sorter Tree on FPGAs

Sorting is an important computation kernel used in a lot of fields such as image processing, data compression, and database operation. There have been many attempts to accelerate sorting using FPGAs. Most of them are based on merge sort algorithm. Merge sorter trees are tree-structured architectures for large-scale sorting. If a merge sorter tree with K input leaves merges N elements, merge phases are performed recursively, so its time complexity is O(NlogK(N)). Hence, to achieve higher sorting performance, it is effective to increase the number of input leaves K. However, the hardware resource usage is O(K). It is difficult to efficiently implement a merge sorter tree with many input leaves. Ito et al. have recently proposed an algorithm which can reduce the hardware complexity of a merge sorter tree with K input leaves from O(K) to O(log(K)). However, they only report the evaluation results when K is 8 and 16. In this paper, we propose a cost-effective and scalable merge sorter tree architecture based on their algorithm. We show that our design achieves almost the same performance compared to the conventional design of which the hardware complexity is O(K). We implement a merge sorter tree with 1,024 input leaves on a Xilinx XC7VX485T-2 FPGA and show that the proposed architecture has 52.4x better logic slice utilization with only 1.31x performance degradation compared with the conventional design. We succeed in implementing a very large merge sorter tree with 4,096 input leaves which cannot be implemented using the conventional design. This tree achieves a merging throughput of 149 million 64-bit elements per second while using 1.72% of slices and 7.48% of Block RAMs of the FPGA.
ACQUIRING LEXICAL DATA FROM MACHINE-READABLE DICTIONARY RESOURCES FOR MACHINE TRANSLATION

This paper describes the direct and indirect automatic exploitation of multiple monolingual and bilingual machine-readable resources for acquiring lexical information, in (partial) real-time support of the English-to-German version of the Prologbased machine translation system LMT. A detailed analysis of the structural properties of the lexical resources underlies our exploitation of them for the identification and extraction of syntactic and semantic characterizations of words, both as a subsystem of a machine translation project and as part of an overall enterprise of acquiring a computational lexicon.
Transsynaptic Tracing with Vesicular Stomatitis Virus Reveals Novel Retinal Circuitry

The use of neurotropic viruses as transsynaptic tracers was first described in the 1960s, but only recently have such viruses gained popularity as a method for labeling neural circuits. The development of retrograde monosynaptic tracing vectors has enabled visualization of the presynaptic sources onto defined sets of postsynaptic neurons. Here, we describe the first application of a novel viral tracer, based on vesicular stomatitis virus (VSV), which directs retrograde transsynaptic viral spread between defined cell types. We use this virus in the mouse retina to show connectivity between starburst amacrine cells (SACs) and their known synaptic partners, direction-selective retinal ganglion cells, as well as to discover previously unknown connectivity between SACs and other retinal ganglion cell types. These novel connections were confirmed using physiological recordings. VSV transsynaptic tracing enables cell type-specific dissection of neural circuitry and can reveal synaptic relationships among neurons that are otherwise obscured due to the complexity and density of neuropil.
P ERFORMANCE FACTORS OF CLOUD COMPUTING DATA CENTERS USING [ ( M / G / 1 ) : ( ∞ / GDM ODEL ) ] QUEUING SYSTEMS

The ever-increasing status of the cloud computing hypothesis and the budding concept of federated cloud computing have enthused research efforts towards intellectual cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Cloud computing is a novel paradigm for the provision of computing infrastructure, which aims to shift the location of the computing infrastructure to the network in order to reduce the maintenance costs of hardware and software resources. Cloud computing systems vitally provide access to large pools of resources. Resources provided by cloud computing systems hide a great deal of services from the user through virtualization. In this paper, the cloud data center is modelled as queuing system with a single task arrivals and a task request buffer of infinite capacity.
Collection of Fine Particles by Novel Wet Electrostatic Precipitator

A novel wet electrostatic precipitator is designed for control of fine aerosol from humid effluent gases. It operates on the principle of unipolar particle charging in a DC corona discharge in a high velocity (> 20 m/s) ionizing stage and aerosol precipitation in the low velocity (< 3 m/s) collection stage under the field of particle own space charge. The high gas velocity in the ionizing section diminishes the corona suppression at high particle number concentrations of H2SO4 and HCl aerosol.
ANALYTICAL THEORY OF THE DESTRUCTION TERMS IN DISSIPATION RATE TRANSPORT EQUATIONS

Modeled dissipation rate transport equations are often derived by invoking various hypotheses to close correlations in the corresponding exact equations. D. C. Leslie suggested that these models might be derived instead from Kraichnan''s wavenumber space integrals for inertial range transport power. This suggestion is applied to the destruction terms in the dissipation rate equations for incompressible turbulence, buoyant turbulence, rotating incompressible turbulence, and rotating buoyant turbulence. Model constants like C_epsilon 2 are expressed as integrals; convergence of these integrals implies the absence of Reynolds number dependence in the corresponding destruction term. The dependence of C_epsilon 2 on rotation rate emerges naturally; sensitization of the modeled dissipation rate equation to rotation is not required. A buoyancy related effect which is absent in the exact transport equation for temperature variance dissipation, but which sometimes improves computational predictions, also arises naturally. Both the presence of this effect and the appropriate time scale in the modeled transport equation depend on whether Bolgiano or Kolmogorov inertial range scaling applies. A simple application of these methods leads to a preliminary dissipation rate equation for rotating buoyant turbulence.
A Strategy to Optimize Resource Allocation in Auction-Based Cloud Markets

Current markets of cloud resources are mainly based on a fixed pricing model. In the recent literature several market-based resource allocation models and algorithms have been proposed, showing that dynamic pricing models could result more profitable for both providers and consumers. In this paper we propose a market of resources where the demand and the offer of resources is matched in auction-based sales. Specifically, we looked at this market from the perspective of the provider, who needs a strategy to allocate at best their unused computing capacity. We proposed an adaptive strategy that, suitably customized to the provider's business objective, help them to maximize the revenue in the context of procurement auctions. Furthermore, the impact of resource overbooking onto the utilization level of cloud data centers has been analyzed by means of extensive simulations.
STATISTICAL METHODS FOR 1 ESTIMATING HISTORICAL FIRE 2 FREQUENCY FROM MULTIPLE FIRE 3 SCAR DATA

7 The paper considers the statistical analysis of fire-interval charts 8 based on fire-scar data. Estimation of the fire interval (expected time 9 between scar-registering fires at any location) by maximum likelihood 10 is presented. Because of the fact that fires spread, causing a lack of 11 independence in scar registration at distinct sites, an over-dispersed 12 binomial model is used leading to a two-variable quasi-likelihood func13 tion. From this, point estimates, standard errors and approximate 14 confidence intervals for fire interval and related quantities can be de15 rived. Methods of testing for the significance of spatial and temporal 16 differences are also discussed. A simple example using artificial data 17 is given to illustrate the computational steps involved, and an analysis 18 of real fire-scar data is presented. 19
Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) metaheuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while μAnt-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman's statistical test.