Miracle-dz/UniFAR-FTUs huggingface dataset:
Vasoactive Intestinal Polypeptide (VIP)-Expressing Neurons in the Suprachiasmatic Nucleus Provide Sparse GABAergic Outputs to Local Neurons with Circadian Regulation Occurring Distal to the Opening of Postsynaptic GABAA Ionotropic Receptors

GABAergic synaptic transmission plays an important role in resetting and synchronizing circadian rhythms in the suprachiasmatic nucleus (SCN). Although the circadian modulation of intrinsic membrane currents and biochemical signaling have been examined in the SCN, the modulation of specific synaptic pathways within the SCN is unexplored. In addition, little is known about the functional properties of these pathways, including which ones involve GABAA receptors (GABAA-Rs). In brain slices obtained from mice, we examined synaptic responses originating from the SCN neurons expressing vasoactive intestinal peptide (VIP+ neurons). Focusing on the local projection within the ventromedial SCN, we found that VIP+ afferents provided input onto 49% of neurons with a preference for VIP-negative (VIP−) neurons. Responses were mediated by GABAA-Rs. The projection was sparsely connected and preferentially targeted a subset of SCN neurons unrelated to postsynaptic VIP expression. For most aspects of VIP+ network output, there was no circadian regulation. Excitability and spontaneous firing of the presynaptic VIP+ neurons were unchanged between day and night, and their network connectivity and synaptic function up through the evoked synaptic conductance were also unchanged. On the other hand, VIP+ input onto VIP− neurons became less inhibitory at night suggesting a postsynaptic alteration in the coupling of GABAA-R conductances to action potential firing. These data suggest that components of the VIP network and its synaptic output up through GABAA-R opening are invariant during the circadian cycle, but the effect on action potential firing is modulated by postsynaptic processes occurring after GABAA-R channel opening.
Maximum Likelihood Estimation Based Robust Chinese Remainder Theorem for Real Numbers and Its Fast Algorithm

Robust Chinese remainder theorem (CRT) has been recently investigated for both integers and real numbers, where the folding integers are accurately recovered from erroneous remainders. In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum-likelihood estimation (MLE) based CRT when the remainder noises may not necessarily have the same variances. Furthermore, we present a fast algorithm for the MLE based CRT algorithm that only needs to search for the solution among L elements, where L is the number of remainders. Then, a necessary and sufficient condition on the remainder errors for the MLE CRT to be robust is obtained, which is weaker than the existing result. Finally, we compare the performances of the newly proposed algorithm and the existing algorithm in terms of both theoretical analysis and numerical simulations. The results demonstrate that the proposed algorithm not only has a better performance especially when the remainders have different error levels/variances, but also has a much lower computational complexity.
Speech technology for language training and e-inclusion

Efficient language learning is one of the keys to social inclusion. In this paper we present some work aiming at creating a virtual language tutor. The ambition is to create a tutor that can be engaged in many aspects of language learning from detailed pronunciation training to conversational practice. Some of the crucial components of such a system are described. An initial implementation of a stress/quantity training tutor for Swedish will be presented.
The application of dynamic principal component analysis to enhance chunk monitoring of an industrial fluidized-bed reactor

Dynamic principal component analysis(DPCA) is an extension of conventional principal component analysis (PCA) for dealing with multivariate dynamic data. We adopted DPCA to enhance chunk monitoring of an industrial fluidized-bed reactor, overcoming the shortcomings of conventional monitoring schemes. The appropriate methods for application of DPCA were proposed, such as combining parallel analysis and Akaike information criterion (AIC) or Bayesian information criterion @IC) to determine the number of lagged variables, using empirical reference distribution (Em) based non-parametric control limits for the statistics which do not follow theoretical distribution, and adopting data smoothing to reduce the idluence of the noises. A DPCA model for chunk monitoring is constructed using the industrial data and its effectiveness is verified.
SPIDER Attack on a Network of POMDPs: Towards Quality Bounded Solutions

Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are a popular approach for modeling multi-agent systems acting in uncertain domains. Given the significant computational complexity of solving distributed POMDPs, one popular approach has focused on approximate solutions. Though this approach provides for efficient computation of solutions, the algorithms within this approach do not provide any guarantees on the quality of the solutions. A second less popular approach has focused on a global optimal result, but at considerable computational cost. This paper overcomes the limitations of both these approaches by providing SPIDER (Search for Policies In Distributed EnviRonments), which provides quality-guaranteed approximations for distributed POMDPs. SPIDER allows us to vary this quality guarantee, thus allowing us to vary solution quality systematically. SPIDER and its enhancements employ heuristic search techniques for finding a joint policy that satisfies the required bound on the quality of the solution.
Color image database for evaluation of image quality metrics

In this contribution, a new image database for testing full-reference image quality assessment metrics is presented. It is based on 1700 test images (25 reference images, 17 types of distortions for each reference image, 4 levels for each type of distortion). Using this image database, 654 observers from three different countries (Finland, Italy, and Ukraine) have carried out about 400000 individual human quality judgments (more than 200 judgments for each distorted image). The obtained mean opinion scores for the considered images can be used for evaluating the performances of visual quality metrics as well as for comparison and for the design of new metrics. The database, with testing results, is freely available.
Workload optimization using SharedDB

This demonstration presents SharedDB, an implementation of a relational database system capable of executing all SQL operators by sharing computation and resources across all running queries. SharedDB sidesteps the traditional query-at-a-time approach and executes queries in batches. Unlike proposed multi-query optimization ideas, in SharedDB queries do not have to contain common subexpressions in order to be part of the same batch, which allows for a higher degree of sharing. By sharing as much as possible, SharedDB avoids repeating parts of computation that is common across all running queries. The goal of this demonstration is to show the ability of shared query execution to a) answer complex and diverse workloads, and b) reduce the interaction among concurrently executed queries that is observed in traditional systems and leads to performance deterioration and instabilities.
Re-Identification Risk versus Data Utility for Aggregated Mobility Research Using Mobile Phone Location Data

Mobile phone location data is a newly emerging data source of great potential to support human mobility research. However, recent studies have indicated that many users can be easily re-identified based on their unique activity patterns. Privacy protection procedures will usually change the original data and cause a loss of data utility for analysis purposes. Therefore, the need for detailed data for activity analysis while avoiding potential privacy risks presents a challenge. The aim of this study is to reveal the re-identification risks from a Chinese city’s mobile users and to examine the quantitative relationship between re-identification risk and data utility for an aggregated mobility analysis. The first step is to apply two reported attack models, the top N locations and the spatio-temporal points, to evaluate the re-identification risks in Shenzhen City, a metropolis in China. A spatial generalization approach to protecting privacy is then proposed and implemented, and spatially aggregated analysis is used to assess the loss of data utility after privacy protection. The results demonstrate that the re-identification risks in Shenzhen City are clearly different from those in regions reported in Western countries, which prove the spatial heterogeneity of re-identification risks in mobile phone location data. A uniform mathematical relationship has also been found between re-identification risk (x) and data (y) utility for both attack models: y = -ax b+c, (a, b, c>0; 0<x<1), where the exponent b increases with the background knowledge of the attackers. The discovered mathematical relationship provides data publishers with useful guidance on choosing the right tradeoff between privacy and utility. Overall, this study contributes to a better understanding of re-identification risks and a privacy-utility tradeoff benchmark for improving privacy protection when sharing detailed trajectory data.
The Asymptotic Behavior of the Nonlinear Estimators of the Diffusion Tensor and Tensor-Derived Quantities with Implications for Group Analysis

Diffusion tensor imaging (DTI) is a quantitative magnetic resonance imaging (MRI) method that is used to study the microstructural properties of white matter in the brain. Tensor-derived quantities, such as the trace and fractional anisotropy (FA), are important for characterizing the normal, diseased, and developing brain. Consequently, determining the statistical properties of the diffusion tensor estimator is important to assess whether the method of estimation is appropriate and to describe how noise in diffusion-weighted images affects the variability in estimates of the tensor and functions of the tensor estimate. Here we derive asymptotic properties of the nonlinear least squares estimator (NLSE) of the diffusion tensor. We show that the NLSE is consistent and asymptotically normal. To illustrate and validate this framework we derive the asymptotic distributions of trace and FA. We show, with simulations, experimental designs that have asymptotic distributions that are very close to the empirical distributions. The methods described in this paper are applied to estimate the variances of trace and FA in a healthy human volunteer. The variance of the trace and FA are found to vary significantly throughout the brain. This renders many popular tests used in group analysis invalid for DTI data. Unequal variances for statistical tests with tensor-derived quantities is discussed.
Crucial Role for Ecto-5′-Nucleotidase (CD73) in Vascular Leakage during Hypoxia

Extracellular adenosine has been widely implicated in adaptive responses to hypoxia. The generation of extracellular adenosine involves phosphohydrolysis of adenine nucleotide intermediates, and is regulated by the terminal enzymatic step catalyzed by ecto-5′-nucleotidase (CD73). Guided by previous work indicating that hypoxia-induced vascular leakage is, at least in part, controlled by adenosine, we generated mice with a targeted disruption of the third coding exon of Cd73 to test the hypothesis that CD73-generated extracellular adenosine functions in an innate protective pathway for hypoxia-induced vascular leakage. Cd73 −/− mice bred and gained weight normally, and appeared to have an intact immune system. However, vascular leakage was significantly increased in multiple organs, and after subjection to normobaric hypoxia (8% O2), Cd73 −/− mice manifested fulminant vascular leakage, particularly prevalent in the lung. Histological examination of lungs from hypoxic Cd73 −/− mice revealed perivascular interstitial edema associated with inflammatory infiltrates surrounding larger pulmonary vessels. Vascular leakage secondary to hypoxia was reversed in part by adenosine receptor agonists or reconstitution with soluble 5′-nucleotidase. Together, our studies identify CD73 as a critical mediator of vascular leakage in vivo.
Accretion Discs in Blazars

The characteristic properties of blazars (rapid variability, strong polarization and high brightness) are widely attributed to a powerful relativistic jet oriented close to our line of sight. Despite the spectral energy distributions being strongly jet-dominated, a 'big blue bump' has been recently detected in the sources known as flat spectrum radio quasars (FSRQs). These new data provide a unique opportunity to observationally test coupled jet-disc accretion models in these extreme sources. In particular, as energy and angular momentum can be extracted by a jet magnetically coupled to the accretion disc, the thermal disc emission spectrum may be modified from that predicted by the standard model for disc accretion. We compare the theoretically predicted jet-modified accretion disc spectra against the new observations of the 'big blue bump' in FSRQs. We find mass accretion rates that are higher, typically by a factor of 2, than predicted by standard accretion disc theory. Furthermore, our results predict that the high-redshift blazars PKS 0836+710, PKS 2149-307, B2 0743+25 and PKS 0537-286 may be predominantly powered by a low- or moderate-spin (a ≲ 0.6) black hole with high-mass accretion rates M a ≈ 50-200 M ⊙ yr ―1 , while 3C 273 harbours a rapidly spinning black hole (a = 0.97) with M a ≈ 20 M ⊙ yc ―1 . We also find that the black hole masses in these high-redshift sources must be ≳5 x 10 9 M ⊙ .
Elephant: the file system that never forgets

Modern file systems associate the deletion of a file with the release of the storage associated with that file, and file writes with the irrevocable change of file contents. We propose that this model of file system behavior is a relic of the past, when disk storage was a scarce resource. We believe that the correct model should ensure that all user actions are revocable. Deleting a file should change only the name space and file writes should overwrite no old data. The file system, not the user should control storage allocation using a combination of user specified policies and information gleaned from file-edit histories to determine which old versions of a file to retain and for how long. The paper presents the Elephant file system, which provides users with a new contract: Elephant will automatically retain all important versions of the users' files. Users name previous file versions by combining a traditional pathname with a time when the desired version of a file or directory existed. Elephant manages storage at the granularity of a file or groups of files using user-specified retention policies. This approach contrasts with checkpointing file systems such as Plan-9 AFS, and WAFL, that periodically generate efficient checkpoints of entire file systems and thus restrict retention to be guided by a single policy for all files within that file system. We also report on the Elephant prototype, which is implemented as a new Virtual File System in the FreeBSD kernel.
Using the Unravel Program Slicing Tool to Evaluate High Integrity Software

This paper describes a program slicing tool, unravel, that can assist in the evaluation of high integrity software by using program slices to extract a single computation from a program for examination and test. The tool, available through the National Institute of Standards and Technology, can currently be used to evaluate software written in ANSI C and is designed such that other languages can be added.
Evaluating and predicting user engagement change with degraded search relevance

User engagement in search refers to the frequency for users (re-)using the search engine to accomplish their tasks. Among factors that affected users' visit frequency, relevance of search results is believed to play a pivotal role. While multiple work in the past has demonstrated the correlation between search success and user engagement based on longitudinal analysis, we examine this problem from a different perspective in this work. Specifically, we carefully designed a large-scale controlled experiment on users of a large commercial Web search engine, in which users were separated into control and treatment groups, where users in treatment group were presented with search results which are deliberate degraded in relevance. We studied users' responses to the relevance degradation through tracking several behavioral metrics (such as query per user, click per session) over an extended period of time both during and following the experiment. By quantifying the relationship between user engagement and search relevance, we observe significant differences between user's short-term search behavior and long-term engagement change. By leveraging some of the key findings from the experiment, we developed a machine learning model to predict the long term impact of relevance degradation on user engagement. Overall, our model achieves over 67% of accuracy in predicting user engagement drop. Besides, our model is also capable of predicting engagement change for low-frequency users with very few user signals. We believe that insights from this study can be leveraged by search engine companies to detect and intervene search relevance degradation and to prevent long term user engagement drop.
Molecular and Functional Characterization of a Novel Cardiac-Specific Human Tropomyosin Isoform

Background— Tropomyosin (TM), an essential actin-binding protein, is central to the control of calcium-regulated striated muscle contraction. Although TPM1&agr; (also called &agr;-TM) is the predominant TM isoform in human hearts, the precise TM isoform composition remains unclear. Methods and Results— In this study, we quantified for the first time the levels of striated muscle TM isoforms in human heart, including a novel isoform called TPM1&kgr;. By developing a TPM1&kgr;-specific antibody, we found that the TPM1&kgr; protein is expressed and incorporated into organized myofibrils in hearts and that its level is increased in human dilated cardiomyopathy and heart failure. To investigate the role of TPM1&kgr; in sarcomeric function, we generated transgenic mice overexpressing cardiac-specific TPM1&kgr;. Incorporation of increased levels of TPM1&kgr; protein in myofilaments leads to dilated cardiomyopathy. Physiological alterations include decreased fractional shortening, systolic and diastolic dysfunction, and decreased myofilament calcium sensitivity with no change in maximum developed tension. Additional biophysical studies demonstrate less structural stability and weaker actin-binding affinity of TPM1&kgr; compared with TPM1&agr;. Conclusions— This functional analysis of TPM1&kgr; provides a possible mechanism for the consequences of the TM isoform switch observed in dilated cardiomyopathy and heart failure patients.
Using genetic algorithms in word-vector optimisation

Word vectors and sets of words are used in a wide range of text-based applications. Yet these word sets are often chosen on an ad hoc basis. In this study, we examine two text-based applications that use word sets and in both cases find that classification performance can be optimised using a fairly simple genetic algorithm. The first study is in authorship attribution, the second one is sentiment analysis and in both cases classification precision can be improved using a genetic algorithm. In authorship attribution, in recent years the trend has been towards ever larger word vectors [1,2]. We suggest that this might be a counter-productive step as it can easily lead to inaccuracy caused by overfitting or vector-space sparsity (the curse of dimensionality). In sentiment analysis precision is the main issue as rates of greater than 80–85% are not easy to achieve.
Managing Cable TV Migration to IP – Part 1 Series Introduction : Market Drivers and Tech Challenges Efficient Content Processing for Adaptive Video Delivery

Introduction Adaptive delivery technologies are dynamic, flexible alternatives to older, static infrastructure technology outsourcing (ITO) service delivery models. The ability of adaptive delivery technologies to leverage unmanaged networks for delivery purposes, in particular, improves system efficiency and enhances the performance of networked services. Our discussion will focus on video delivery, one of the services that can benefit from the advantages adaptive delivery technologies have over static delivery models. We will emphasize how service providers can use adaptive delivery technology to deploy unified video processing workflows, which they can use to manage large-scale video delivery over unmanaged networks. Unified video processing workflows provide end-to-end support and management for ingesting, securing, and delivering content to a wide range of devices. Active management of adaptive delivery also allows service providers to maintain their network capacity while utilizing unified processing workflows to offer new services and formats incrementally. Service providers, as a result, can improve the quality and scope of their services, significantly improving their subscribers' entertainment experiences. Finally, we will describe the process for validating adaptive profiles and compare the results we have obtained in relation to several different adaptive delivery models. In this section, we present an overview of the architecture and basic video processing infrastructure used by an over-the-top video provider using Adaptive Bit Rate (ABR) video streaming. Delivery of video over an unmanaged network, like the public Internet, is based on best-effort delivery. Therefore the quality of user experience is impacted by various factors including the latency and congestion in the network, and uncontrolled variations of these network parameters over time. In spite of these limitations on-line video distribution has become prevalent recently and the quality of the video being distributed is ratcheting up slowly, especially because of the increase in last-mile access bandwidth. Most on-line video distributors leverage some flavor of ABR streaming technology to adapt video delivery to vagaries of varying network conditions. Most commercial over-the-top video delivery services (Netflix, Hulu, Amazon Prime, etc.) available today deliver on-demand content such as movies and TV shows, and not 24x7 live content. Exceptions to this are popular sporting events like the Olympics that are delivered over the Internet directly by the programmers. On the other hand, MVPDs (multichannel video programming distributors) have started to deliver live content using ABR (adaptive bitrate) streaming techniques to secondary viewing devices both 2 inside and outside the home, details of which will be presented …