Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Gaussian mixture model framework for speaker adaptation of deep neural network acoustic models

In this paper we investigate the GMM-derived (GMMD) features for adaptation of deep neural network (DNN) acoustic models. The adaptation of the DNN trained on GMMD features is done through the maximum a posteriori (MAP) adaptation of the auxiliary GMM model used for GMMD feature extraction. We explore fusion of the adapted GMMD features with conventional features, such as bottleneck and MFCC features, in two different neural network architectures: DNN and time-delay neural network (TDNN). We analyze and compare different types of adaptation techniques such as i-vectors and feature-space adaptation techniques based on maximum likelihood linear regression (fMLLR) with the proposed adaptation approach, and explore their complementarity using various types of fusion such as feature level, posterior level, lattice level and others in order to discover the best possible way of combination. Experimental results on the TED-LIUM corpus show that the proposed adaptation technique can be effectively integrated into DNN and TDNN setups at different levels and provide additional gain in recognition performance: up to 6% of relative word error rate reduction (WERR) over the strong feature-space adaptation techniques based on maximum likelihood linear regression (fMLLR) speaker adapted DNN baseline, and up to 18% of relative WERR in comparison with a speaker independent (SI) DNN baseline model, trained on conventional features. For TDNN models the proposed approach achieves up to 26% of relative WERR in comparison with a SI baseline, and up 13% in comparison with the model adapted by using i-vectors. The analysis of the adapted GMMD features from various points of view demonstrates their effectiveness at different levels.
An Experiment in Morphological Development for Learning ANN Based Controllers

Morphological development is part of the way any human or animal learns. The learning processes starts with the morphology at birth and progresses through changing morphologies until adulthood is reached. Biologically, this seems to facilitate learning and make it more robust. However, when this approach is transferred to robotic systems, the results found in the literature are inconsistent: morphological development does not provide a learning advantage in every case. In fact, it can lead to poorer results than when learning with a fixed morphology. In this paper we analyze some of the issues involved by means of a simple, but very informative experiment in quadruped walking. From the results obtained an initial series of insights on when and under what conditions to apply morphological development for learning are presented.
Mean-Field Limit for a Class of Stochastic Ergodic Control Problems

We study a family of McKean-Vlasov type ergodic optimal control problems with linear control, and quadratic dependence on control of the cost function. For this class of problems we establish existence and uniqueness of optimal control. We propose an $N$-particles Markovian optimal control problem approximating the McKean-Vlasov one and we prove the convergence in total variation of the law of the former to the law of the latter when $N$ goes to infinity. Some McKean-Vlasov optimal control problems with singular cost function and the relation of these problems with the mathematical theory of Bose-Einstein condensation is also discussed.
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation

Image denoising is a well studied problem with an extensive activity that has spread over several decades. Leading classical denoising methods are typically designed to exploit the inner structure in images by modeling local overlapping patches, and operating in an unsupervised fashion. In contrast, newcomers to this arena are supervised and universal neural-network-based methods that bypass this modeling altogether, targeting the inference goal directly and globally, tending to be deep and parameter heavy.This work proposes a novel lightweight learnable architecture for image denoising, using a combination of supervised and unsupervised training of it, the first aiming for a universal denoiser and the second for an instance adaptation. Our architecture embeds in it concepts taken from classical methods, leveraging patch processing, non-local self-similarity, representation sparsity and a multiscale treatment. Our proposed universal denoiser achieves near state-of-the-art results, while using a small fraction of the typical number of parameters. In addition, we introduce and demonstrate two highly effective ways for further boosting the denoising performance, by adapting this universal network to the input image. The code reproducing the results of this paper is available at https://github.com/grishavak/LIDIA-denoiser.
Night-time Semantic Segmentation with a Large Real Dataset

Although huge progress has been made on semantic segmentation in recent years, most existing works assume that the input images are captured in day-time with good lighting conditions. In this work, we aim to address the semantic segmentation problem of night-time scenes, which has two main challenges: 1) labeled night-time data are scarce, and 2) over- and under-exposures may co-occur in the input night-time images and are not explicitly modeled in existing semantic segmentation pipelines. To tackle the scarcity of night-time data, we collect a novel labeled dataset (named NightCity) of 4,297 real night-time images with ground truth pixel-level semantic annotations. To our knowledge, NightCity is the largest dataset for night-time semantic segmentation. In addition, we also propose an exposure-aware framework to address the night-time segmentation problem through augmenting the segmentation process with explicitly learned exposure features. Extensive experiments show that training on NightCity can significantly improve the performance of night-time semantic segmentation and that our exposure-aware model outperforms the state-of-the-art segmentation methods, yielding top performances on our benchmark dataset.
Accessible precisions for estimating two conjugate parameters using Gaussian probes

We analyse the precision limits for simultaneous estimation of a pair of conjugate parameters in a displacement channel using Gaussian probes. Having a set of squeezed states as an initial resource, we compute the Holevo Cramer-Rao bound to investigate the best achievable estimation precisions if only passive linear operations are allowed to be performed on the resource prior to probing the channel. The analysis reveals the optimal measurement scheme and allows us to quantify the best precision for one parameter when the precision of the second conjugate parameter is fixed. To estimate the conjugate parameter pair with equal precision, our analysis shows that the optimal probe is obtained by combining two squeezed states with orthogonal squeezing quadratures on a 50:50 beam splitter. If different importance are attached to each parameter, then the optimal mixing ratio is no longer 50:50. Instead it follows a simple function of the available squeezing and the relative importance between the two parameters.
clDice - a Topology-Preserving Loss Function for Tubular Structure Segmentation

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, the topology is their most important characteristic, e.g. preserving connectedness: in case of vascular networks, missing a connected vessel entirely alters the blood-flow dynamics. We introduce a novel similarity measure termed clDice, which is calculated on the intersection of the segmentation masks and their (morphological) skeletons. Crucially, we theoretically prove that clDice guarantees topological correctness for binary 2D and 3D segmentation. Extending this, we propose a computationally efficient, differentiable soft-clDice as a loss function for training arbitrary neural segmentation networks. We benchmark the soft-clDice loss for segmentation on four public datasets (2D and 3D). Training on soft-clDice leads to segmentation with more accurate connectivity information, higher graph similarity, and better volumetric scores.
Data-driven contact structures: From homogeneous mixing to multilayer networks

The modeling of the spreading of communicable diseases has experienced significant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare different scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and finally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are different. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population.
Multimodal trajectory optimization for motion planning

Existing motion planning methods often have two drawbacks: (1) goal configurations need to be specified by a user, and (2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.
An XMM-Newton X-Ray View of Supernova Remnant W49B: Revisiting Its Recombining Plasmas and Progenitor Type

We present a comprehensive X-ray spectroscopy and imaging study of supernova remnant W49B using archival XMM-Newton observations. The overionization state of the shocked ejecta in W49B is clearly indicated by the radiative recombination continua of Si XIV, S XV, and Fe XXV, combined with the Ly$\alpha$ lines of Ca and Fe. The line flux images of W49B indicate high emission measures of the central bar-like region for almost all the emission lines, while the equivalent width maps reveal a stratified structure for the metal abundance distributions. The global spectrum of W49B is well reproduced by a model containing one collisional ionization equilibrium (CIE) plasma component and two recombining plasma (RP) components. The CIE plasma represents the shocked interstellar medium, which dominates the X-ray emitting volume in W49B with a mass $\sim450M_\odot$. The two RP components with a total mass $\sim4. 6M_\odot$ are both dominated by the ejecta material, but characterized by different electron temperatures ($\sim1.60$ keV and $\sim0.64$ keV). The recombination ages of the RP components are estimated as $\sim6000$ yr and $\sim3400$ yr, respectively. We then reveal the possibility of a thermal conduction origin for the high-temperature RP in W49B by calculating the conduction timescale. The metal abundance ratios of the ejecta in W49B are roughly consistent with a core-collapse explosion model with a $\lesssim15M_\odot$ progenitor, except for a rather high Mn/Fe. A Type Ia origin can explain the Mn abundance, while it predicts much higher ejecta masses than observed values for all the metal species considered in our analysis.
Building a COVID-19 Vulnerability Index

COVID-19 is an acute respiratory disease that has been classified as a pandemic by the World Health Organization. Information regarding this particular disease is limited, however, it is known to have high mortality rates, particularly among individuals with preexisting medical conditions. Creating models to identify individuals who are at the greatest risk for severe complications due to COVID-19 will be useful to help for outreach campaigns in mitigating the diseases worst effects. While information specific to COVID-19 is limited, a model using complications due to other upper respiratory infections can be used as a proxy to help identify those individuals who are at the greatest risk. We present the results for three models predicting such complications, with each model having varying levels of predictive effectiveness at the expense of ease of implementation.
Certified Defenses for Adversarial Patches

Adversarial patch attacks were recently recognized as the most practical threat model against real-world computer vision systems. Most published defenses against patch attacks are based on preprocessing input images to mitigate adversarial noise. The first contribution of this paper is a set of experiments demonstrating that such defense strategies can easily be broken by white-box adversaries. Motivated by this finding, we present an extension of certified defense algorithms and propose significantly faster variants for robust training against patch attacks. Finally, we experiment with different patch shapes for testing, and observe that robustness to such attacks transfers surprisingly well.
Inference and Influence of Large-Scale Social Networks Using Snapshot Population Behaviour without Network Data

Population behaviours, such as voting and vaccination, depend on social networks. Social networks can differ depending on behaviour type and are typically hidden. However, we do often have large-scale behavioural data, albeit only snapshots taken at one timepoint. We present a method that jointly infers large-scale network structure and a networked model of human behaviour using only snapshot population behavioural data. This exploits the simplicity of a few-parameter, geometric socio-demographic network model and a spin-based model of behaviour. We illustrate, for the EU Referendum and two London Mayoral elections, how the model offers both prediction and the interpretation of our homophilic inclinations. Beyond offering the extraction of behaviour-specific network-structure from large-scale behavioural datasets, our approach yields a crude calculus linking inequalities and social preferences to behavioural outcomes. We give examples of potential network-sensitive policies: how changes to income inequality, a social temperature and homophilic preferences might have reduced polarisation in a recent election.
Simultaneous Downlink Data Transmission and Uplink Channel Estimation with Reduced Complexity Full Duplex MIMO Radios

In this paper, we study Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) radios for simultaneous data communication and control information exchange. Capitalizing on a recently proposed FD MIMO architecture combining digital transmit and receive beamforming with reduced complexity multi-tap analog Self-Interference (SI) cancellation, we propose a novel transmission scheme exploiting channel reciprocity for joint downlink beamformed information data communication and uplink channel estimation through training data transmission. We adopt a general model for pilot-assisted channel estimation and present a unified optimization framework for all involved FD MIMO design parameters. Our representative Monte Carlo simulation results for an example algorithmic solution for the beamformers as well as for the analog and digital SI cancellation demonstrate that the proposed FD-based joint communication and control scheme provides 1.4×- the downlink rate of its half duplex counterpart. This performance improvement is achieved with 50% reduction in the hardware complexity for the analog canceller than conventional FD MIMO architectures with fully connected analog cancellation.
Exceptionally large anomalous Hall effect due to anticrossing of spin-split bands in the antiferromagnetic half-Heusler compound TbPtBi

We have investigated the magnetotransport properties and the topological electronic structure of the half-Heusler compound TbPtBi. Our experiments reveal an exceptionally large anomalous Hall effect (AHE) in the canted antiferromagnetic state of TbPtBi with the anomalous Hall angle reaching $\ensuremath{\sim}0.68\ensuremath{-}0.76$, which is a few times larger than the previously reported record in GdPtBi. First-principles electronic structure and the associated anomalous Hall conductivity were computed in order to interpret the experimental results. Our analysis shows that the AHE in TbPtBi does not originate from the Weyl points but that it is driven by the large net Berry curvature produced by the anticrossing of spin-split bands near the Fermi level in TbPtBi.
HD 145263: Spectral Observations of Silica Debris Disk Formation via Extreme Space Weathering?

We report here time-domain infrared spectroscopy and optical photometry of the HD 145263 silica-rich circumstellar-disk system taken from 2003 through 2014. We find an F4V host star surrounded by a stable, massive 1022–1023 kg (MMoon to MMars) dust disk. No disk gas was detected, and the primary star was seen rotating with a rapid ∼1.75 day period. After resolving a problem with previously reported observations, we find the silica, Mg-olivine, and Fe-pyroxene mineralogy of the dust disk to be stable throughout and very unusual compared to the ferromagnesian silicates typically found in primordial and debris disks. By comparison with mid-infrared spectral features of primitive solar system dust, we explore the possibility that HD 145263's circumstellar dust mineralogy occurred with preferential destruction of Fe-bearing olivines, metal sulfides, and water ice in an initially comet-like mineral mix and their replacement by Fe-bearing pyroxenes, amorphous pyroxene, and silica. We reject models based on vaporizing optical stellar megaflares, aqueous alteration, or giant hypervelocity impacts as unable to produce the observed mineralogy. Scenarios involving unusually high Si abundances are at odds with the normal stellar absorption near-infrared feature strengths for Mg, Fe, and Si. Models involving intense space weathering of a thin surface patina via moderate (T < 1300 K) heating and energetic ion sputtering due to a stellar super-flare from the F4V primary are consistent with the observations. The space-weathered patina should be reddened, contain copious amounts of nanophase Fe, and should be transient on timescales of decades unless replenished.
Magnetic field induced tunability of spin Hamiltonians: Resonances and Efimov states in 
Yb2Ti2O7

Universality is a powerful concept that arises from the divergence of a characteristic length scale. For condensed matter systems, this length scale is typically the correlation length, which diverges at critical points separating two different phases. Few-particle systems exhibit a simpler form of universality when the $s$-wave scattering length diverges. A prominent example of universal phenomena is the emergence of an infinite tower of three-body bound states obeying discrete scale invariance, known as the Efimov effect, which has been subject to extensive research in chemical, atomic, nuclear and particle physics. In principle, these universal phenomena can also emerge in the excitation spectrum of condensed matter systems, such as quantum magnets [1]. However, the limited tunability of the effective inter-particle interaction relative to the kinetic energy has precluded so far their observation. Here we demonstrate that a high degree of magnetic-field-induced tunability can also be achieved in quantum magnets with strong spin-orbit coupling: a two-magnon resonance condition can be achieved in Yb$_2$Ti$_2$O$_7$ with a field of $\sim$ 13 T along the [110] direction, which leads to the formation of Efimov states in the three-magnon spectrum of this material. Raman scattering experiments can reveal the field-induced two-magnon resonance, as well as the Efimov three-magnon bound states that emerge near the resonance condition.
Deep Affinity Net: Instance Segmentation via Affinity

Most of the modern instance segmentation approaches fall into two categories: region-based approaches in which object bounding boxes are detected first and later used in cropping and segmenting instances; and keypoint-based approaches in which individual instances are represented by a set of keypoints followed by a dense pixel clustering around those keypoints. Despite the maturity of these two paradigms, we would like to report an alternative affinity-based paradigm where instances are segmented based on densely predicted affinities and graph partitioning algorithms. Such affinity-based approaches indicate that high-level graph features other than regions or keypoints can be directly applied in the instance segmentation task. In this work, we propose Deep Affinity Net, an effective affinity-based approach accompanied with a new graph partitioning algorithm Cascade-GAEC. Without bells and whistles, our end-to-end model results in 32.4% AP on Cityscapes val and 27.5% AP on test. It achieves the best single-shot result as well as the fastest running time among all affinity-based models. It also outperforms the region-based method Mask R-CNN.
Effect of floating flexible plate on the dynamics of flow over a mild-slope

Hydrodynamic instability of a gravity-driven flow down an inclined plane is investigated in the presence of a floating elastic plate which rests on the top surface of the flow. Linear instability of the system with respect to infinitesimal disturbances is captured using normal-mode analysis. The critical conditions for instability are obtained analytically utilizing the long-wave approximation and small film aspect ratio. Further, the bifurcation of the nonlinear evolution equation is analyzed using weakly nonlinear stability analysis. The Orr-Sommerfeld system for the perturbed flow is derived, and it is solved numerically using the spectral collocation method. The behaviour of the marginal stability curves and temporal growth of the unstable waves are portrayed for a range of dimensionless flow parameters. Moreover, the pressure acting on the surface and shearing stress are calculated and analysed for various structural and flow configurations. The study reveals that critical structural parameters such as rigidity and mass per unit length play a crucial role in suppressing and facilitating the unstable surface waves of the flow. Numerical observations imply that the floating elastic plate helps to stabilize the surface flow and to damp the high amplitude waves.