Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Method for Making Un-supervised Adaptation of HMM-based Speech Recognition Systems Robust Against Out-of-Domain Data

Major aspects of cognitive science are based on natural language processing utilizing automatic speech recognition (ASR) systems in scenarios of human-computer interaction. In order to improve the accuracy of related HMM-based ASR systems efficient approaches for un-supervised adaptation represent the methodology of choice. The recognition accuracy of speaker-specific recognition systems derived by online acoustic adaptation directly depends on the quality of the adaptation data actually used. It drops significantly if sample data outof-scope (lexicon, acoustic conditions) of the original recognizer generating the necessary annotation is exploited without further analysis. In this paper we present an approach for fast and robust MLLR adaptation based on a rejection model which rapidly evaluates an alternative to existing confidence measures, so-called log-odd scores. These measures are computed as ratio of scores obtained from acoustic model evaluation to those produced by some reasonable background model. By means of log-odd scores threshold based detection and rejection of improper adaptation samples, i.e. out-of-domain data, is realized. By means of experimental evaluations on two challenging tasks we demonstrate the effectiveness of the proposed approach.
Deep Learning for Classification of Hyperspectral Data: A Comparative Review

In recent years, deep-learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less straightforward than with other optical data. This article presents the state of the art of previous machine-learning approaches, reviews the various deeplearning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties that arise in the implementation of deep neural networks for this task. In particular, the issues of spatial and spectral resolution, data volume, and transfer of models from multimedia images to hyperspectral data are addressed. Additionally, a comparative study of various families of network architectures is provided, and a software toolbox is publicly released to allow experimenting with these methods (https://github.com/nshaud/DeepHyperX). This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deeplearning techniques to their own data set.
Mapping hearthstone deck spaces through MAP-elites with sliding boundaries

Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary. This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. Experiments in this paper demonstrate the performance of MESB in Hearthstone. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game.
Integer Programming for Learning Directed Acyclic Graphs from Continuous Data

Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this paper, we study the problem of learning an optimal DAG from continuous observational data. We cast this problem in the form of a mathematical programming model that can naturally incorporate a superstructure to reduce the set of possible candidate DAGs. We use a negative log-likelihood score function with both ℓ 0 and ℓ 1 penalties and propose a new mixed-integer quadratic program, referred to as a layered network (LN) formulation. The LN formulation is a compact model that enjoys as tight an optimal continuous relaxation value as the stronger but larger formulations under a mild condition. Computational results indicate that the proposed formulation outperforms existing mathematical formulations and scales better than available algorithms that can solve the same problem with only ℓ 1 regularization. In particular, the LN formulation clearly outperforms existing methods in terms of computational time needed to find an optimal DAG in the presence of a sparse superstructure.
Two-Qubit Circuit Depth and the Monodromy Polytope

For a native gate set which includes all single-qubit gates, we apply results from symplectic geometry to analyze the spaces of two-qubit programs accessible within a fixed number of gates. These techniques yield an explicit description of this subspace as a convex polytope, presented by a family of linear inequalities themselves accessible via a finite calculation. We completely describe this family of inequalities in a variety of familiar example cases, and as a consequence we highlight a certain member of the ``XY--family'' for which this subspace is particularly large, i.e., for which many two-qubit programs admit expression as low-depth circuits.
TurbuStat: Turbulence Statistics in Python

We present TurbuStat (v1.0): a python package for computing turbulence statistics in spectral-line data cubes. TurbuStat includes implementations of 14 methods for recovering turbulent properties from observational data. Additional features of the software include: distance metrics for comparing two data sets; a segmented linear model for fitting lines with a break point; a two-dimensional elliptical power-law model; multicore fast-Fourier-transform support; a suite for producing simulated observations of fractional Brownian Motion fields, including two-dimensional images and optically thin H i data cubes; and functions for creating realistic world coordinate system information for synthetic observations. This paper summarizes the TurbuStat package and provides representative examples using several different methods. TurbuStat is an open-source package and we welcome community feedback and contributions.
TESS Delivers Its First Earth-sized Planet and a Warm Sub-Neptune

The future of exoplanet science is bright, as Transiting Exoplanet Survey Satellite (TESS) once again demonstrates with the discovery of its longest-period confirmed planet to date. We hereby present HD 21749b (TOI 186.01), a sub-Neptune in a 36 day orbit around a bright (V = 8.1) nearby (16 pc) K4.5 dwarf. TESS measures HD 21749b to be R⊕, and combined archival and follow-up precision radial velocity data put the mass of the planet at M⊕. HD 21749b contributes to the TESS Level 1 Science Requirement of providing 50 transiting planets smaller than 4 R⊕ with measured masses. Furthermore, we report the discovery of HD 21749c (TOI 186.02), the first Earth-sized ( ) planet from TESS. The HD 21749 system is a prime target for comparative studies of planetary composition and architecture in multi-planet systems.
The tidal disruption event AT2017eqx: spectroscopic evolution from hydrogen rich to poor suggests an atmosphere and outflow

We present and analyse a new tidal disruption event (TDE), AT2017eqx at redshift z = 0.1089, discovered by Pan-STARRS and ATLAS. The position of the transient is consistent with the nucleus of its host galaxy; the spectrum shows a persistent blackbody temperature T ≳ 20 000 K with broad H i and He ii emission; and it peaks at a blackbody luminosity of L ≈ 1044 erg s−1. The lines are initially centred at zero velocity, but by 100 d, the H i lines disappear while the He ii develops a blueshift of ≳ 5000 km s−1. Both the early- and late-time morphologies have been seen in other TDEs, but the complete transition between them is unprecedented. The evolution can be explained by combining an extended atmosphere, undergoing slow contraction, with a wind in the polar direction becoming visible at late times. Our observations confirm that a lack of hydrogen a TDE spectrum does not indicate a stripped star, while the proposed model implies that much of the diversity in TDEs may be due to the observer viewing angle. Modelling the light curve suggests AT2017eqx resulted from the complete disruption of a solar-mass star by a black hole of ∼106.3 M⊙. The host is another Balmer-strong absorption galaxy, though fainter and less centrally concentrated than most TDE hosts. Radio limits rule out a relativistic jet, while X-ray limits at 500 d are among the deepest for a TDE at this phase.
Low-index-contrast waveguide bend based on truncated Eaton lens implemented by graded photonic crystals

Low-index contrast waveguides such as silica waveguides are an indispensable part of passive integrated optical components. Reducing the bending radius of silica waveguides with low bend loss is vital in miniaturizing silica planar light-wave circuits. The Eaton lens is a gradient index lens that can bend parallel light rays by 90°, 180°, or 360°. We present a low-loss and compact 90° waveguide bend with a radius of 9 μm designed by truncating the Eaton lens. The performance of the designed bend, implemented by graded photonic crystal, was evaluated by the full-wave two-dimensional finite element method. The average bend loss in the C-band is 0.9 dB, while the average bend loss for the wavelength range of 1260–1675 nm is 1.05 dB. The proposed design strategy can be applied to other low-index contrast waveguides.
Speech Emotion Recognition Using Multi-hop Attention Mechanism

In this paper, we are interested in exploiting textual and acoustic data of an utterance for the speech emotion classification task. The baseline approach models the information from audio and text independently using two deep neural networks (DNNs). The outputs from both the DNNs are then fused for classification. As opposed to using knowledge from both the modalities separately, we propose a framework to exploit acoustic information in tandem with lexical data. The proposed framework uses two bi-directional long short-term memory (BLSTM) for obtaining hidden representations of the utterance. Furthermore, we propose an attention mechanism, referred to as the multi-hop, which is trained to automatically infer the correlation between the modalities. The multi-hop attention first computes the relevant segments of the textual data corresponding to the audio signal. The relevant textual data is then applied to attend parts of the audio signal. To evaluate the performance of the proposed system, experiments are performed in the IEMOCAP dataset. Experimental results show that the proposed technique outperforms the state-of-the-art system by 6.5% relative improvement in terms of weighted accuracy.
Studies of Stented Arteries and Left Ventricular Diastolic Dysfunction Using Experimental and Clinical Analysis with Data Augmentation

Cardiovascular diseases are among the leading causes of deaths worldwide, but the fluid mechanics of many of these conditions and the devices used to treat them are only partially understood. This goal of this dissertation was to develop new experimental techniques that would enable translational research into two of these conditions. The first set of experiments examined in-vitro the changes in Wall Shear Stress (WSS) and Oscillatory Shear Index (OSI) caused by the implantation of coronary stents into the arteries of the heart using Particle Image Velocimetry. These experiments featured one-to-one scaling, commercial stents, and realistic flow and pressure waveforms, and are believed to be the most physiologically accurate stent experiments to date. This work revealed distinct differences in WSS and OSI between the different stent designs tested, and showed that changes in implantation configuration also affected these hemodynamic parameters. Also, the production of vortices near the stent struts during flow reversal was noted, and an inverse correlation between WSS and OSI was described. The second set of experiments investigated Left Ventricular Diastolic Dysfunction (LVDD) using phase contrast magnetic resonance imaging (pcMRI). Using this technique, ten patients with and without LVDD were scanned and a 2D portrait of blood flow through their heart was obtained. To augment this data, pressure fields were calculated from the velocity data using an omni-directional pressure integration scheme coupled with a proper-orthogonal decompositionbased smoothing. This technique was selected from a variety of methods from the literature based on an extensive error analysis and comparison. With this coupled information, it was observed that healthy patients exhibited different flow patterns than diseased patients, and had stronger pressure differences during early filling. In particular, the ratio of early filling pressure to late filling pressure was a statistically significant predictor of diastolic dysfunction. Based on these observations, a novel hypothesis was presented that related the motion of the heart walls to the observed flow patterns and pressure gradients, which may explain the differences observed clinically between healthy and diseased patients.
Quorum-based Asynchronous Power-saving Protocols for Ieee 802.11 Ad Hoc Networks *

This paper investigates the power mode management problem for an IEEE 802.11-based mobile ad hoc network (MANET) that allows mobile hosts to tune to the power-saving (PS) mode. There are two major issues that need to be addressed in this problem: (a) wakeup prediction and (b) neighbor discovery. The former is to deliver buffered packets to a PS host at the right time when its radio is turned on. The latter is to monitor the environment change under a mobile environment. One costly, and not scalable, solution is to time-synchronize all hosts. Another possibility is to design asynchronous protocols as proposed by Tseng et al. in [25]. In this paper, we adopt the latter approach and correlate this problem to the quorum system concept. We identify a rotation closure property for quorum systems. It is shown that any quorum system that satisfies this property can be translated to an asynchronous power-saving protocol for MANETs. Thus, the result bridges the classical quorum system design problem in the area of distributed systems to the power mode management problem in the area of mobile ad hoc networks. We derive a lower bound for quorum sizes for any quorum system that satisfies the rotation closure property. We identify a group of quorum systems that are optimal or near optimal in terms of quorum sizes, which can be translated to efficient asynchronous power-saving protocols. We also propose a new e-torus quorum system, which can be translated to an adaptive protocol that allows designers to trade hosts' neighbor sensibility for power efficiency. Simulation experiments are conducted to evaluate and compare the proposed protocols.
Deep Packet Inspection in FPGAs via Approximate Nondeterministic Automata

Deep packet inspection via regular expression (RE) matching is a crucial task of network intrusion detection systems (IDSes), which secure Internet connection against attacks and suspicious network traffic. Monitoring high-speed computer networks (100 Gbps and faster) in a single-box solution demands that the RE matching, traditionally based on finite automata (FAs), is accelerated in hardware. In this paper, we describe a novel FPGA architecture for RE matching that is able to process network traffic beyond 100 Gbps. The key idea is to reduce the required FPGA resources by leveraging approximate nondeterministic FAs (NFAs). The NFAs are compiled into a multi-stage architecture starting with the least precise stage with a high throughput and ending with the most precise stage with a low throughput. To obtain the reduced NFAs, we propose new approximate reduction techniques that take into account the profile of the network traffic. Our experiments showed that using our approach, we were able to perform matching of large sets of REs from SNORT, a popular IDS, on unprecedented network speeds.
Alterations in Structural Correlation Networks with Prior Concussion in Collision-Sport Athletes

Several studies have used structural correlation networks, derived from anatomical covariance of brain regions, to analyze neurologic changes associated with multiple sclerosis, schizophrenia and breast cancer [1][2]. Graph-theoretical analyses of human brain structural networks have consistently shown the characteristic of small-worldness that reflects a network with both high segregation and high integration. A large neuroimaging literature on football players, with and without history of concussion, has shown both functional and anatomical changes. Here we use graph-based topological properties of anatomical correlation networks to study the effect of prior concussion in collision-sport athletes. 40 high school collision-sport athletes (23 male football, 17 female soccer; CSA) without self-reported history of concussion (HOC-), 18 athletes (13 male football, 5 female soccer) with self-reported history of concussion (HOC+) and 24 healthy controls (19 male, 5 female; CN) participated in imaging sessions before the beginning of a competition season. The extracted residual volumes for each group were used for building the correlation networks and their small-worldness, , is calculated. The small-worldness of CSA without prior history of concussion, , is significantly greater than that of controls, . CSA with prior history have significantly higher (vs. 95% confidence interval) small-worldness compared to HOC+, over a range of network densities. The longer path lengths in HOC+ group could indicate disrupted neuronal integration relative to healthy controls.
Some constraints for the extensions of the degenerate Ferrari and Ibanez solution

There exist two extensions to the interaction region of the Ferrari and Ibanez solution. However, no one gives the corresponding conditions for each extension. To solve this problem, we have introduced quantum effects to constrain these extensions and given the corresponding conditions. By calculation, the Schwarzschild black hole can form after gravitational waves colliding whose expected energy is small.
Antisymmetric magnetoresistance in van der Waals Fe3GeTe2/graphite/Fe3GeTe2 trilayer heterostructures

The observation of an antisymmetric magnetoresistance in a trilayer van der Waals heterostructure Fe3GeTe2/graphite/Fe3GeTe2. With no requirements for lattice matching, van der Waals (vdW) ferromagnetic materials are rapidly establishing themselves as effective building blocks for next-generation spintronic devices. We report a hitherto rarely seen antisymmetric magnetoresistance (MR) effect in vdW heterostructured Fe3GeTe2 (FGT)/graphite/FGT devices. Unlike conventional giant MR (GMR), which is characterized by two resistance states, the MR in these vdW heterostructures features distinct high-, intermediate-, and low-resistance states. This unique characteristic is suggestive of underlying physical mechanisms that differ from those observed before. After theoretical calculations, the three-resistance behavior was attributed to a spin momentum locking induced spin-polarized current at the graphite/FGT interface. Our work reveals that ferromagnetic heterostructures assembled from vdW materials can exhibit substantially different properties to those exhibited by similar heterostructures grown in vacuum. Hence, it highlights the potential for new physics and new spintronic applications to be discovered using vdW heterostructures.
Towards a Semantic Measure of the Execution Time in Call-by-Value lambda-Calculus (Long Version)

We investigate the possibility of a semantic account of the execution time (i.e. the number of beta v-steps leading to the normal form, if any) for the shuffling calculus, an extension of Plotkin's untyped call-by-value lambda-calculus. For this purpose, we use a linear logic based denotational model that can be seen as a non-idempotent intersection type system: relational semantics. Our investigation is inspired by similar ones for linear logic proof-nets and untyped call-by-name lambda-calculus. We first prove a qualitative result: a (possibly open) term is normalizable for weak reduction (which does not reduce under abstractions) if and only if its interpretation is not empty. We then show that the size of type derivations can be used to measure the execution time. Finally, we show that, differently from the case of linear logic and call-by-name lambda-calculus, the quantitative information enclosed in type derivations does not lift to types (i.e. to the interpretation of terms). In order to get a genuine semantic measure of execution time in a call-by-value setting, we conjecture that a refinement of its syntax and operational semantics is needed.
Graph-based Inpainting for 3D Dynamic Point Clouds

With the development of depth sensors and 3D laser scanning techniques, 3D dynamic point clouds have attracted increasing attention as a format for the representation of 3D objects in motion, with applications in various fields such as 3D immersive tele-presence, navigation, animation, gaming and virtual reality. However, dynamic point clouds usually exhibit holes of missing data, mainly due to the fast motion, the limitation of acquisition techniques and complicated structure. Further, point clouds are defined on irregular non-Euclidean domain, which is challenging to address with conventional methods for regular data. Hence, leveraging on graph signal processing tools, we propose an efficient dynamic point cloud inpainting method, exploiting both the inter-frame coherence and the intra-frame self-similarity in 3D dynamic point clouds. Specifically, for each frame in a point cloud sequence, we first split it into cubes of fixed size as the processing unit, and treat cubes with holes inside as target cubes. Secondly, we take advantage of the intra-frame self-similarity in the target frame, by globally searching for the most similar cube to each target cube as the intra-source cube. Thirdly, we exploit the inter-frame coherence among every three consecutive frames, by searching the corresponding cubes in the previous and subsequent frames for each target cube as the inter-source cubes, which contains most nearest neighbors of the target cube in the relative location. Finally, we formulate dynamic point cloud inpainting as an optimization problem based on both intra- and inter-source cubes, which is regularized by the graph-signal smoothness prior. Experimental results show that the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.
The Ex-Ante View of Recommender System Design

Recommender systems (RS) are traditionally deployed in environments where users are uncertain about their preferences and thus face a problem of choice under uncertainty, but most popular design approaches ignore this fact. We argue that predicting and modeling consumer choice in these contexts can improve the usefulness of RS and reframe the RS problem as providing useful information to help reduce user uncertainty as opposed to simply predicting user preferences. Using a theoretical model, we show how this insight can be utilized to design RS that mitigate negative consequences such as filter bubble and user-homogenization effects as well as to better understand the role that RS play in contributing to these phenomena.