Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Algorithm for Blind Independence-Based Extraction of a Moving Speaker

Independent Vector Extraction (IVE) is a modification of Independent Vector Analysis (IVA) for Blind Source Extraction (BSE) to a setup in which only one source of interest (SOI) should be separated from a mixture of signals observed by microphones. The fundamental assumption is that the SOI is independent of the other signals. IVE shows reasonable results; however, its basic variant is limited to static sources. To extract a moving source, IVE has recently been extended by considering the Constant Separating Vector (CSV) mixing model. It enables us to estimate a separating filter that extracts the SOI from a wider spatial area through which the source has moved. However, only slow gradient-based algorithms were proposed in the pioneering papers on IVE and CSV. In this paper, we experimentally verify the applicability of the CSV mixing model and propose new IVE methods derived by modifying the auxiliary function-based algorithm for IVA. Piloted Variants are proposed as well for the methods with partially controllable global convergence. The methods are verified under reverberant and noisy conditions using model-based as well as real-world acoustic impulse responses. They are also verified within the CHiME-4 speech separation and recognition challenge. The experiments corroborate the applicability of the CSV mixing model for the blind moving source extraction as well as the improved convergence of the proposed algorithms.
Bringing freedom in variable choice when searching counter-examples in floating point programs

Program verification techniques typically focus on finding counter-examples that violate properties of a program. Constraint programming offers a convenient way to verify programs by modeling their state transformations and specifying searches that seek counter-examples. Floating-point computations present additional challenges for verification given the semantic subtleties of floating point arithmetic. % This paper focuses on search strategies for CSPs using floating point numbers constraint systems and dedicated to program verification. It introduces a new search heuristic based on the global number of occurrences that outperforms state-of-the-art strategies. More importantly, it demonstrates that a new technique that only branches on input variables of the verified program improve performance. It composes with a diversification technique that prevents the selection of the same variable within a fixed horizon further improving performances and reduces disparities between various variable choice heuristics. The result is a robust methodology that can tailor the search strategy according to the sought properties of the counter example.
Analysis of leptin concentrations in oral fluids (saliva and crevicular gingival fluid) and blood in patients with chronic periodontal disease: systematic review of literature

Objective: The objective of this systematic analysis was to perform a qualitative and quantitative synthesis of the literature concerning salivary and serum leptin variations in patients with chronic periodontitis (CP) compared with healthy subjects. Saliva leptin concentration analysis could be a relevant and non-invasive biological test for the evaluation of periodontal disease in both medical and clinical trials, beyond the clinical and radiographic elements. Material and Method: Querying the PubMed and Web of Science databases identified articles that met our inclusion criteria. Quantitative analysis of the literature data was performed with the Review Manager 5.3 software. Results: The qualitative analysis included 14 articles and showed a decrease of salivary leptin (5 studies out of 5) and an increase of serum leptin (11 of 12 studies) in patients with CP compared to unaffected subjects of CP. Quantitative analysis was performed on 4 trials. For salivary leptin, we confirmed a decrease in its level in patients with CP with a standardized mean difference (SMD) of −2.27, 95% CI [−2.68, −1.86]. The difference was highly significant but we detected a very important heterogeneity in this dataset (I2 = 94%). For serum leptin, we also confirmed an increase in its rate in patients with CP with an SMD of 2.18, 95% CI [1.75, 2.61]. The difference was highly significant but the heterogeneity measured in this dataset was also too high (I2 = 95%). Conclusion: The current level of evidence was insufficient to assert an increase in serum leptin and a decrease in salivary leptin in CP patients compared to healthy controls due to a great heterogeneity of the values measured in the studies.
Indoor multiuser visible light communication systems using Hadamard-coded modulation

Visible light communications (VLC) is a short-range optical wireless communication technology that uses light-emitting diodes (LEDs) as lighting devices and data transmitters. This paper describes a multiuser VLC system using Hadamard-coded modulation (HCM) for indoor data transmission. Considering the peak transmitted power limit of the LEDs, a DC-reduced HCM (DCR-HCM) is used to reduce the nonlinear clipping distortion. Since the Hadamard codewords have different bandwidth requirements for a given symbol rate, they can be assigned to users with varying hardware capabilities. Optimally assigning codewords to users is found to significantly improve the average throughput, up to twice higher than a random assignment for a typical scenario. When the number of active users is less than the size of the Hadamard matrix used, more than one codeword can be assigned per user, which further improves the throughput. This paper also examines a scenario where multiple lamps in an indoor space transmit the same data. Since the time of arrival for the received signals emitted from different lamps is different, the Hadamard codes received are no longer orthogonal, resulting in multiple access interference and inter-chip interference. The number of acceptable codewords is computed based on the specific interference experienced in different parts of the indoor space. The spatial distribution of the maximum throughput is also simulated, showing that the ratio of the maximum to the minimum data rate can be as high as 10 when considering the entire area of a typical indoor room. This article is part of the theme issue ‘Optical wireless communication’.
Gallium nitride micro-light-emitting diode structured light sources for multi-modal optical wireless communications systems

Gallium nitride-based light-emitting diodes (LEDs) have revolutionized the lighting industry with their efficient generation of blue and green light. While broad-area (square millimetre) devices have become the dominant LED lighting technology, fabricating LEDs into micro-scale pixels (micro-LEDs) yields further advantages for optical wireless communications (OWC), and for the development of smart-lighting applications such as tracking and imaging. The smaller active areas of micro-LEDs result in high current density operation, providing high modulation bandwidths and increased optical power density. Fabricating micro-LEDs in array formats allows device layouts to be tailored for target applications and provides additional degrees of freedom for OWC systems. Temporal and spatial control is crucial to use the full potential of these micro-scale sources, and is achieved by bonding arrays to pitch-matched complementary metal-oxide-semiconductor control electronics. These compact, integrated chips operate as digital-to-light converters, providing optical signals from digital inputs. Applying the devices as projection systems allows structured light patterns to be used for tracking and self-location, while simultaneously providing space-division multiple access communication links. The high-speed nature of micro-LED array devices, combined with spatial and temporal control, allows many modes of operation for OWC providing complex functionality with chip-scale devices. This article is part of the theme issue ‘Optical wireless communication’.
Text-Based Speaker Identiﬁcation For Multi-Participant Open-Domain Dialogue Systems

Understanding the interactive structure of dialogues, such as turn taking behaviour and change of speakers, is a critical prerequisite for dialogue systems which aim to understand and communicate using natural spoken language. In this work, we take a data-driven approach to learn turn changes and speaker identity from an open-domain corpus of movie scripts. We frame the problem as two distinct clas-siﬁcation tasks and investigate the potential of different machine learning models, including logistic regression and recurrent neural networks. Our results indicate that it is feasible to estimate turn taking and speaker identity with high accuracy
Massive Maritime Path Planning: A Contextual Online Learning Approach

The ocean has been investigated for centuries across the world, and planning the travel path for vessels in the ocean has become a hot topic in recent decades as the increasing development of worldwide business trading. Planning such suitable paths is often based on big data processing in cybernetics, while not many investigations have been done. We attempt to find the optimal path for vessels in the ocean by proposing an online learning dispatch approach on studying the mission–executing–feedback (MEF) model. The proposed approach explores the ocean subdomain (OS) to achieve the largest average traveling feedback for different vessels. It balances the ocean path by a deep and wide search, and considers adaptation for these vessels. Further, we propose a contextual multiarmed bandit-based algorithm, which provides accurate exploration results with sublinear regret and significantly improves the learning speed. The experimental results show that the proposed MEF approach possesses 90% accuracy gain over random exploration and achieves about 25% accuracy improvement over other contextual bandit models on supporting big data online learning pre-eminently.
A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs

Image denoising technologies in a Euclidean domain have achieved good results and are becoming mature. However, in recent years, many real-world applications encountered in computer vision and geometric modeling involve image data defined in irregular domains modeled by huge graphs, which results in the problem on how to solve image denoising problems defined on graphs. In this paper, we propose a novel model for removing mixed or unknown noise in images on graphs. The objective is to minimize the sum of a weighted fidelity term and a sparse regularization term that additionally utilizes wavelet frame transform on graphs to retain feature details of images defined on graphs. Specifically, the weighted fidelity term with $\ell _{1}$ -norm and $\ell _{2}$ -norm is designed based on a analysis of the distribution of mixed noise. The augmented Lagrangian and accelerated proximal gradient methods are employed to achieve the optimal solution to the problem. Finally, some supporting numerical results and comparative analyses with other denoising algorithms are provided. It is noted that we investigate image denoising with unknown noise or a wide range of mixed noise, especially the mixture of Poisson, Gaussian, and impulse noise. Experimental results reported for synthetic and real images on graphs demonstrate that the proposed method is effective and efficient, and exhibits better performance for the removal of mixed or unknown noise in images on graphs than other denoising algorithms in the literature. The method can effectively remove mixed or unknown noise and retain feature details of images on graphs. It delivers a new avenue for denoising images in irregular domains.
Event-Driven H∞-Constrained Control Using Adaptive Critic Learning

This article considers an event-driven <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control problem of continuous-time nonlinear systems with asymmetric input constraints. Initially, the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula>-constrained control problem is converted into a two-person zero-sum game with the discounted nonquadratic cost function. Then, we present the event-driven Hamilton–Jacobi–Isaacs equation (HJIE) associated with the two-person zero-sum game. Meanwhile, we develop a novel event-triggering condition making Zeno behavior excluded. The present event-triggering condition differs from the existing literature in that it can make the triggering threshold non-negative without the requirement of properly selecting the prescribed level of disturbance attenuation. After that, under the framework of adaptive critic learning, we use a single critic network to solve the event-driven HJIE and tune its weight parameters by using historical and instantaneous state data simultaneously. Based on the Lyapunov approach, we demonstrate that the uniform ultimate boundedness of all the signals in the closed-loop system is guaranteed. Finally, simulations of a nonlinear plant are presented to validate the developed event-driven <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control strategy.
Effect of antidepressant therapy on executive Effect of antidepressant therapy on executive function after stroke* function after stroke*

Declaration of interest None. Fundingdetailed in Acknowledgements. Executive functions are mental abilities that allow the individual to respond appropriately to unfamiliar and complex situations and support several cognitive, emotional and social capacities. Impairment of executive functions occurs in the majority of stroke patients (Lesser et al, 1996), and may slow rehabilitation and return to premorbid levels of psychosocial adjustment. Antidepressant treatment improves outcome following stroke, independently of depression. Nortriptyline and fluoxetine improve activities of daily living (Narushima & Robinson, 2003) and mortality (Jorge et al, 2003), and sertraline improves morbidity (Rasmussen et al, 2003). Antidepressants may exert outcome-improving effects on the brain in at least two ways: one is through modulation of corticalstriato-pallido-thalamo-cortical pathways subsequent to action on the raphe nuclei, locus ceruleus and ventral tegmental area (Alexopoulos et al, 2000); another is through reorganisation of neural circuitry favoured by their activity on brain-derived neurotrophic factor (BDNF) (Saarelainen et al, 2003). This evidence prompted our study hypothesis that antidepressants improve frontal executive function following stroke. This hypothesis has not been tested before. The positive effect of treatment with antidepressants on the cognitive abilities of older adults (Allard et al, 2003) and the identification of similar mechanisms of executive deficit in late life and stroke (Coffey et al, 1988a,b) further supported the rationale behind this study. We predicted that antidepressant treatment would improve executive dysfunction independently of depression.
C POVERTY AND MACROECONOMIC PERFORMANCE ACROSS SPACE , RACE , AND FAMILY STRUCTURE *

We examined the effects of macroeconomic performance and social policy on the extent and depth of poverty in America using state-level panel data from the 1981–2000 waves of the Current Population Survey. We found that a strong macroeconomy at both the state and national levels reduced both the number of families who were living in poverty and the severity of poverty. The magnitude and source of these antipoverty effects, however, were not uniform across family structures and racial groups or necessarily over time. While gains in the eradication of poverty, in general, were tempered by rising wage inequality, simulations indicated that female-headed families and families that were headed by black persons experienced substantial reductions in poverty in the 1990s largely because of the growth in median wages. An auxiliary time-series analysis suggests that the expansions in the federal Earned Income Tax Credit of the 1990s accounted for upward of 50% of the reduction in after-tax income deprivation.
The Dragonfly Galaxy

The Dragonfly Galaxy (MRC 0152-209), at redshift z∼ 2, is one of the most vigorously star-forming radio galaxies in the Universe. What triggered its activity? We present ALMA Cycle 2 observations of cold molecular CO(6−5) gas and dust, which reveal that this is likely a gas-rich triple merger. It consists of a close double nucleus (separation ∼4 kpc) and a weak CO-emitter at ∼10 kpc distance, all of which have counterparts in HST/NICMOS imagery. The hyper-luminous starburst and powerful radio-AGN were triggered at this precoalescent stage of the merger. The CO(6−5) traces dense molecular gas in the central region, and complements existing CO(1−0) data, which reveal more widespread tidal debris of cold gas. We also find ∼1010 M of molecular gas with enhanced excitation at the highest velocities. At least 20−50% of this high-excitation, high-velocity gas shows kinematics that suggests it is being displaced and redistributed within the merger, although with line-of-sight velocities of |v|< 500 km s−1, this gas will probably not escape the system. The processes that drive the redistribution of cold gas are likely related to either the gravitational interaction between two kpc-scale discs, or starburst/AGN-driven outflows. We estimate that the rate at which the molecular gas is redistributed is at least Ṁ∼ 1200± 500 M yr−1, and could perhaps even approach the star formation rate of ∼3000± 800 M yr−1. The fact that the gas depletion and gas redistribution timescales are similar implies that dynamical processes can be important in the evolution of massive high-z galaxies.
New Evidence on the Soft Budget Constraint: Chinese Environmental Policy Effectiveness in Private versus SOEs

This paper analyses the efficiency of a set of environmental measures introduced by the 11th FYP (Five Years Plan) in China in 2006, using a rich and unique dataset borrowed from the Ministry of Environmental Protection (MEP) and from the State Environmental Protection Agency (SEPA). The objective is to provide new evidence of the Soft Budget Constraint (SBC), which is a key concept coined by Janos Kornai. The main finding is that TCZ (Two Control Zone) cities are successful in bringing down the emission of SO2, and more importantly that this success is driven by the private sector. Sectors dominated by State-Owned Enterprises (SOEs) are less sensitive to the environmental target-based evaluation system, by a factor of 42%. We also find that one channel, through which this adjustment takes place, is Total Factor Productivity (TFP), but not in the case of SOEs. We interpret these results as pointing to the evidence of a still ongoing SBC surrounding Chinese SOEs.
Data Augmentation Based on Distributed Expressions in Text Classification Tasks

We propose a data augmentation method that combines Doc2vec and Label spreading in text classiﬁcation tasks. The feature of our approach is the use of unlabeled samples, which are easier to obtain than labeled samples. We use them as an aid to the classiﬁcation model to improve the accuracy of its prediction. We used this method to classify several text data sets including the natural language branch of the AIWolf contest. As a result of the experiments, we conﬁrmed that the prediction accuracy is improved by applying our proposed method.
Structure-Preserving Diﬀerentiation of Functional Networks in Design Optimization and Optimal Control

Model-based design of complete technical processes or systems is increasingly more often realized by modular development approaches like MATLAB and SIMULINK. They enable engineering teams from various disciplines easily to cooperate. Subsystems are encapsulated into blocks of information, total models are formed by nesting and linking single modules or blocks. A profound knowledge of the details of the respective subsystem is no longer necessary for system design and integration, a new level of abstraction is introduced. For multidisciplinary design optimization and optimal system control – both in a mathematically strict sense – the knowledge of the model system alone is not suﬃcient. The need of sensitivity information makes it necessary to compute ﬁrst and second order derivates of the model equations, optimal control of those systems subject to multiple constraints requires the calculation of even higher order derivatives. Analytical diﬀerentiation of complex models is extremely time consuming and error-prone. Numerical diﬀerentiation suﬀers from inaccuracy and high computational costs. Algo-rithmic or automatic diﬀerentiation techniques destroy the original structure of the model and thus show a reduced eﬃciency in case of higher order derivatives. In this paper, a modiﬁed approach is presented combining analytical and automatic diﬀerentiation while preserving the modular structure of the model. The analytical derivatives of a single (sub-)module are typically available with a tolerable eﬀort. The information of a single module is forwarded via local transfer matrices, which encode the linking structure between two modules. It is shown that the model’s global topology can be computed from this local information: One can determine those components of the model input, which aﬀect a particular output component, if only the local dependencies are known. The proposed algorithm works especially well for hierarchical models with a nested module structure. Only the derivatives of the module lowest level have to be available. The sensitivity information is automatically propagated through the diﬀerent levels. Two algorithmic implementations of this approach have been realized: An elegant recursion for each (sub)module and a more eﬃcient forward propagation of eﬃciently calculated intermediate values. As an industrial application, contributions to aerobatic airplanes in the context of the Red Bull AirRaces are presented. The full 6 DoF airplane model is split into its dynamic layers: position, translation, attitude and rotation. The layers are modularly formulated as sets of blocks, each representing a distinct eﬀect (e
X-ray and c-ray propagation in bent crystals with flat and cylindrical surfaces

Università di Torino, Dipartimento di Fisica Generale ‘A. Avogadro’, via P. Giuria 1, Torino, Italy, NDSSL – Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Research Building XV (0477), 1880 Pratt Drive, Blacksburg, VA, USA, INRiM – Istituto Nazionale di Ricerca Metrologica, Strada delle Cacce 91, Torino, Italy, and Museo Storico della Fisica e Centro Studi e Ricerche ‘Enrico Fermi’, Compendio Viminale, 00184 Roma, Italy. Correspondence e-mail: apolloni@vbi.vt. edu
VIRTUAL REALITY AND DASH

Virtual Reality (VR) has lately gained significant attention primarily driven by the recent market availability of consumer devices, such as mobile phone-based Head Mounted Displays (HMDs). Apart from classic gaming applications, the delivery of 360° video is considered as another major use and is expected to be ubiquitous in the near future. However, the delivery and decoding of high-resolution 360° videos in desirable quality is a challenging task due to network limitations and constraints on available end device decoding and processing. In this paper, we focus on aspects of 360° video streaming and provide an overview and discussion of possible solutions as well as considerations for future VR video streaming applications. This paper mainly focuses on the status of the first standardization activities to support interoperable 360° video streaming. More specifically, MPEG's ongoing work on Omnidirectional MediA Format (OMAF) is introduced -- aiming at harmonization of VR video platforms and applications. The paper also discusses the integration in MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH), which is considered a cornerstone of 360° video streaming services with OMAF content. In the context of the general OMAF service architecture, three distinct delivery approaches and considerations for content protection are discussed.