Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-Term Facilitation Modulates Size and Timing of the Synaptic Response at the Inner Hair Cell Ribbon Synapse

Inner hair cells (IHCs) in the mammalian cochlea are able to continuously release neurotransmitter in the presence of constant stimuli. Nonetheless, strong synaptic depression is observed over the first few milliseconds of stimulation. This process most likely underlies adaptation in the auditory nerve. In the present study we demonstrate that under certain conditions of stimulation, facilitation can occur at the IHC ribbon synapse. Using simultaneous whole-cell, voltage-clamp recordings from IHCs and afferent fiber endings in excised postnatal rat cochleae, we stimulated IHCs with 2 ms long test depolarizations from a holding potential of −89 mV. Synaptic currents in afferent fibers occurred with high failure rates of ∼50%. However, when a pre-depolarization to values of −55 to −49 mV was implemented before the test pulse, success rates of the synaptic response increased to 100%, the strength of the synaptic response increased ∼2.8-fold, and synaptic latency was reduced by ∼50%. When calcium influx was minimized during pre-depolarization, none of these effects were found, suggesting that calcium influx during pre-depolarizations is required for synaptic conditioning. Similarly, in response to paired-pulse protocols, short term facilitation occurred. The response to the second stimulus increased up to ∼5-fold, and its latency was reduced by up to 35% compared to the response to the first stimulus. We propose that at the IHC resting membrane potential, the ribbon synapse operates in a constantly facilitated mode caused by Ca2+ influx, optimizing the size and timing of the postsynaptic response in auditory nerve fibers.
An intuitive approach to determine test adequacy in safety-critical software

Safety-critical software must adhere to stringent quality standards and is expected to be thoroughly tested. However, exhaustive testing of software is usually impractical. The two main challenges faced by a software testing team are generation of effective test cases and demonstration of testing adequacy. This paper proposes an intuitive and conservative approach to determine the test adequacy in safety-critical software. The approach is demonstrated through a case study: the core temperature monitoring system of a nuclear reactor. We combine conservative test coverage of unique execution path test cases, and the results from mutation testing to determine the test adequacy. Although mutation testing is a powerful technique, the difficulty in identifying equivalent mutants has limited its practical utility. To gain confidence on the computed test adequacy: (i) faults during mutation testing must be induced at all possible execution paths of the code, (ii) properties of unkilled mutants must be studied, and (iii) all equivalent mutants must be detected. In this regard; results of static, dynamic and coverage analysis of the mutants is presented, and a technique to identify the likely equivalent mutants is proposed.
Dynamics and high-energy emission of the flaring HST-1 knot in the M 87 jet

Stimulated by recent observations of a giant radio-to-X-ray synchrotron flare from Hubble Space Telescope (HST)-1, the innermost knot of the M 87 jet, as well as by the detection of a very high energy γ-ray emission from M 87, we investigated the dynamics and multiwavelength emission of the HST-1 region. We study thermal pressure of the hot interstellar medium in M 87 and argue for the presence of a gaseous condensation in its central parts. We postulate that this additional feature is linked to the observed central stellar cusp of the elliptical host. Interaction of the jet with such a feature is likely to result in the formation of a stationary converging/diverging reconfinement/reflected shock structure in the innermost parts of the M 87 jet. We show that for a realistic set of the outflow parameters, a stationary and a flaring part of the HST-1 knot located ∼100 pc away from the active centre can be associated with the decelerated portion of the jet matter placed immediately downstream of the point where the reconfinement shock reaches the jet axis. We discuss a possible scenario explaining a broad-band brightening of the HST-1 region related to the variable activity of the central core. In particular, we show that assuming a previous epoch of the high central black hole activity resulting in ejection of excess particles and photons down along the jet, one may first expect a high-energy flare of HST-1 due to inverse-Comptonization of the nuclear radiation, followed after a few years by an increase in the synchrotron continuum of this region. The synchrotron flare itself could be accompanied by a subsequent inverse-Compton brightening due to upscattering of the ambient (mostly starlight) photons. If this is the case, then the recently observed order-of-magnitude increase in the knot luminosity in all spectral bands could be regarded as an unusual echo of the order-of-magnitude outburst that had happened previously (and could be eventually observed some ∼40 yr ago) in the highly relativistic active core of the M 87 radio galaxy. We show that very high energy γ-ray fluxes expected in a framework of the proposed scenario are consistent with the observed ones.
Face pose estimation with a knowledge-based model

In this paper, an approach for 3D head pose estimation from a monocular sequence is proposed. At the beginning, we employ modified block matching to track face features, including five points (two at inner eyebrow tips, two at eyes and the fifth is nose tip). A matching criterion is designed the effect of matching is improved. To estimate the head pose accurately and simply, we present an algorithm based on the geometry information of the individual face and projective model without the need of any 3D face model and any special marks on user's face. Any people with different facial characters can be adapted to this algorithm. At the end of this paper, some experimental results and conclusion are given.
On the Impact of Spatial Correlation on the Finite Diversity-Multiplexing Tradeoff

We present a tight lower bound on the outage probability of a spatially correlated multielement antenna (MEA) channel. Using this lower bound, an accurate finite-SNR estimate of the diversity-multiplexing tradeoff over a spatially correlated Rayleigh fading channel is derived. This estimate allows gaining insight on the impact of spatial correlation on the diversity-multiplexing tradeoff at finite SNR. As expected, the diversity multiplexing tradeoff is severely degraded as the spatial correlation increases. For example, a MIMO system operating at a transmission rate of R = rlog2(1 + g ldr eta) bps/Hz, where r is the multiplexing gain, g is the array gain and eta is the SNR at each receive antenna, and an SNR of 5 dB in a moderately correlated channel, achieves a better diversity gain than a system operating at an SNR of 10 dB in a highly correlated channel, when r ges0.8. Another interesting point is that the maximum diversity gain is not affected by the correlation, provided that the spatial channel correlation matrix is of full rank.
Modelling and mitigation of time-zero variability in sub-16nm finfet-based STT-MRAM memories

Spin-transfer torque magnetic RAM (STT-MRAM) is one of the most promising non-volatile memory technologies and shows potential as an SRAM replacement. However, targeted for advanced CMOS technologies such as the 14nm FinFET node, time-zero variability is a major concern for these memory technologies. In this paper, we investigate the STT-MRAM variability with respect to different technology scenarios. We show the impact of these variations on the bit error rate of the emerging STT-MRAM memories.
Benefit Generosity and Injury Duration: Quasi-Experimental Evidence from Regression Kinks

In this paper, we investigate the effect of benefit generosity on claim duration and temporary benefits paid among temporary disability claims for workers' compensation. While previous studies have focused on natural experiments created by one-time large changes in minimum or maximum weekly benefits, we exploit variation around a kink in benefit generosity inherent in all workers' compensation systems in the United States. Using administrative data on the universe of injured workers in Oregon, we also find that more-generous benefits leads to longer injuries, but with implied elasticities that are smaller than the average elasticity from previous difference-in-difference studies. Our preferred estimates suggest that a 10-percent increase in benefit generosity leads to a 2- to 4-percent increase in injury duration. We derive similar duration-benefit elasticities when studying changes in benefits paid at the kink. We also introduce the first evidence that more-generous benefits encourage subsequent claim filing.
Photovoltaic DC-DC module integrated converter for novel cascaded and bypass grid connection topologies - design and optimisation

Grid connected photovoltaic (PV) inverters fall into three broad categories - central, string and module integrated converters (MICs). MICs offer many advantages in performance and flexibility, but are at a cost disadvantage. Two alternative novel approaches proposed by the author - cascaded dc-dc MICs and bypass dc-dc MICs - integrate a simple non-isolated intelligent dc-dc converter with each PV module to provide the advantages of dc-ac MICs at a lower cost. A suitable universal 150 W 5 A dc-dc converter design is presented based on two interleaved MOSFET half bridges. Testing shows zero voltage switching (ZVS) keeps losses under 1 W for bi-directional power flows up to 15 W between two adjacent 12 V PV modules for the bypass application, and efficiencies over 94% for most of the operational power range for the cascaded converter application. Based on the experimental results, potential optimizations to further reduce losses are discussed.
Dual-Processing Altruism

Altruism refers to an other-benefiting behavior that is costly but bears no direct profit to oneself. At least three different forms can be distinguished: help giving, altruistic punishment, and moral courage. We investigated the differential impact of two thinking modes, intuitive (System 1) and rational (System 2), on these three altruistic behaviors. Situational (state-related) thinking style was manipulated via experimental instructions and generally preferred thinking style (trait-related) was assessed via questionnaires. We found that of the subjectively preferred thinking styles (trait), faith in intuition (System 1) promoted sharing and altruistic punishment, whereas need for cognition (System 2) promoted volunteering in a situation that required moral courage. By contrast, we did not find a significant effect of situational thinking style (state) on any of the altruistic behaviors, although manipulation checks were positive. Results elucidate the affective-motivational underpinnings of different types of altruistic behaviors.
SafeRazor: Metastability-Robust Adaptive Clocking in Resilient Circuits

Razor-based circuits can run faster or at a lower voltage than those designed to work at the worst case corner. However, all known implementations are prone to failures due to the non-deterministic timing behavior introduced by metastability, even in the case where sufficient time is left for resolution. This paper analyzes the causes why Razor-based circuits fail and proposes a new scheme combining the Razor principle with stoppable clocks in a GALS setting. This scheme avoids any timing failure due to metastability and does not require any checkpointing or pipeline restarting logic, other than the usual auxiliary latch to store valid data. The experiments show how the Razor principle can be extended to any generic logic circuit, and not just to microprocessors with sophisticated pipeline flush/recovery mechanisms. In this way, the performance/power benefits of Razor can be adopted without the complex architectural changes required by the various Razor schemes in the literature.
From A to Z: Supervised Transfer of Style and Content Using Deep Neural Network Generators

We propose a new neural network architecture for solving single-image analogies - the generation of an entire set of stylistically similar images from just a single input image. Solving this problem requires separating image style from content. Our network is a modified variational autoencoder (VAE) that supports supervised training of single-image analogies and in-network evaluation of outputs with a structured similarity objective that captures pixel covariances. On the challenging task of generating a 62-letter font from a single example letter we produce images with 22.4% lower dissimilarity to the ground truth than state-of-the-art.
A Space Alignment Method for Cold-Start TV Show Recommendations

In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique. On the other hand, an online updating scheme is developed to utilize new user watching behaviors. We present experimental results on a real TV watch behavior data set to demonstrate the significant performance improvement over other state-of-the-art algorithms.
TASC:Topic-Adaptive Sentiment Classification on Dynamic Tweets

Sentiment classification is a topic-sensitive task, i.e., a classifier trained from one topic will perform worse on another. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Moreover, compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics. It minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors' sentiments and sentiment connections derived from“@” mentions of tweets, named as topic-adaptive features. Text and non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. An experiment on 6 topics from published tweet corpuses demonstrates that TASC outperforms other well-known supervised and ensemble classifiers. It also beats those semi-supervised learning methods without feature adaption. Meanwhile, TASC-t can also achieve impressive accuracy and F-score. Finally, with timeline visualization of “river” graph, people can intuitively grasp the ups and downs of sentiments' evolvement, and the intensity by color gradation.
Toward a Better Understanding of the Injured Hippocampus: Multimodal Imaging in Functionally Relevant Substructures

Editor's Note: These short, critical reviews of recent papers in the Journal, written exclusively by graduate students or postdoctoral fellows, are intended to summarize the important findings of the paper and provide additional insight and commentary. For more information on the format and purpose of the Journal Club, please see Review of Voets et al. In addition to being a focus of cellular and cognitive neuroscience research due to its involvement in multiple cognitive processes , the hippocampus is in the spotlight of clinical neuroscience because of its vulnerability to a broad range of neuropsy-chiatric disorders (Small et al., 2011). Nevertheless, the mechanisms contributing to the cognitive impact of hippocam-pal injury and potential subsequent memory reorganization are still poorly understood, because the literature, while abundant, is sometimes perplexing. Part of the confusion arises from the diversity of measures (volume, connectivity, task-related activity, glucose metabolism, etc.) obtained from neuroimaging techniques to assess hippocampal integrity. Although assessing different aspects of hippocampal integrity is a potential strength, existing findings are difficult to compare and integrate because the relationships between these measures and the mechanisms they reflect are poorly characterized. Furthermore, some studies have shown counterintuitive results such as increases of hippocampal activation or con-nectivity in the face of pathology, and it is unclear whether these reflect compensatory (i.e., beneficial) mechanisms relying on hippocampal plasticity, or aberrant functioning reflecting underlying pathological processes (Maruszak and Thuret, 2014). In addition, the majority of previous research has considered the hip-pocampus as a unitary entity, whereas much evidence indicates that the hip-pocampus should be parcellated into functional subunits (Maruszak and Thuret, 2014). Indeed, animal studies indicate that hippocampal efferents vary along its anteri-or–posterior axis (Aggleton, 2012), consistent with human neuroimaging data showing differential functional connectivity and activity between anterior and posterior hippocampal regions (Poppenk et al., 2013). This gradient is thus expected to influence the functional impact of diseases targeting specific parts of the hip-pocampus (Small et al., 2011). Exploring these substructures separately could give us more insight into hippocampal function and disorders, and potentially help us reconcile divergent findings. In a recent article published in The Journal of Neuroscience, Voets et al. (2014) took these concerns into account when assessing the hippocampus and related networks in patients with unilateral hip-pocampal injury due to temporal lobe epilepsy. The authors' approach has several strengths and novel approaches to addressing these pitfalls. They aimed to provide a comprehensive view of …
Hit Songs’ Sentiments Harness Public Mood & Predict Stock Market

This work explores the relationship between the sentiment of lyrics in Billboard Top 100 songs, stocks, and a consumer confidence index. We hypothesized that sentiment of Top 100 songs could be representative of public mood and correlate to stock market changes as well. We analyzed the sentiment for polarity and mood in terms of seven dimensions. We gathered data from 2008 to 2013 and found statistically significant correlations between lyrical sentiment polarity and DJIA closing values and between anxiety in lyrics and consumer confidence. We also found strong Granger-causal relationships involving anxiety, hope, anger, and both societal indicators. Finally, we introduced a vector autoregression model with time lag which is able to capture stock and consumer confidence indices (R=.97, p<.001 and R=0.72, p<.01 respectively).
Entropy and MDL discretization of continuous variables for Bayesian belief networks

An efficient algorithm for partitioning the range of a continuous variable to a discrete number of intervals, for use in the construction of Bayesian belief networks (BBNs), is presented here. The partitioning minimizes the information loss, relative to the number of intervals used to represent the variable. Partitioning can be done prior to BBN construction or extended for repartitioning during construction. Prior partitioning allows either Bayesian or minimum descriptive length (MDL) metrics to be used to guide BBN construction. Dynamic repartitioning, during BBN construction, is done with a MDL metric to guide construction. The methods are demonstrated with data from two epidemiological studies and these results are compared for all of the methods. The use of the partitioning algorithm resulted in more sparsely connected BBNs, than with binary partitioning, with little information loss from mapping continuous variables into discrete ones. ©2000 John Wiley & Sons, Inc.
Experts and idiots : an ethnography of epistemic practices in Dutch social health insurance

What is and is not covered by basic health insurance? How many departments of emergency care are needed to be able to offer quality care throughout the Netherlands? This dissertation describes research into the way in which decisions about the insurance package and the quality of care in the social health insurance are justified. The researcher carried out several-years-worth of ethnographic participatory observations at the National Health Care Institute, which is responsible for ensuring that high-quality healthcare remains affordable and accessible to all Dutch people. This research shows that policymakers use different forms of knowledge - statistical knowledge, clinical knowledge, patient knowledge while making complex policy decisions, but that not all forms of knowledge are regarded as equally relevant, credible and reliable. This dissertation examines the question to what extent the existing knowledge practices of policy makers are adequate and socially representative. Following the philosopher Stengers, the character of the 'idiot' serves to argue that public learning processes are only possible if individuals, with their confused presence, question existing knowledge practices.
Supporting agility in software development projects - defining a project ontology

The popularity of agile software development methods, and agile software project management has been accompanied by significant successes in the delivery of software of business value and quality to client organizations, but has also given rise to more pressing difficulties especially in the support of remotely located teams, and distributed or multi-team development activities. The question of whether or not agile methods, which imply small, focused teams, can be successful in dasiabigpsila projects also arises. This paper discusses the essential elements of agile methods, and agile project management methods, and discusses possible applications of ontology-based project support mechanisms, within the application of the digital ecosystem concept.