Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers

Automatic speech recognition systems have been largely improved in the past few decades and current systems are mainly hybrid-based and end-to-end-based. The recently proposed CTC-CRF framework inherits the data-efficiency of the hybrid approach and the simplicity of the end-to-end approach. In this paper, we further advance CTC-CRF based ASR technique with explorations on modeling units and neural architectures. Specifically, we investigate techniques to enable the recently developed wordpiece modeling units and Conformer neural networks to be succesfully applied in CTC-CRFs. Experiments are conducted on two English datasets (Switchboard, Librispeech) and a German dataset from CommonVoice. Experimental results suggest that (i) Conformer can improve the recognition performance significantly; (ii) Wordpiece-based systems perform slightly worse compared with phone-based systems for the target language with a low degree of grapheme-phoneme correspondence (e.g. English), while the two systems can perform equally strong when such degree of correspondence is high for the target language (e.g. German).
Adversarial Auto-Encoding for Packet Loss Concealment

Communication technologies like voice over IP operate under constrained real-time conditions, with voice packets being subject to delays and losses from the network. In such cases, the packet loss concealment (PLC) algorithm reconstructs missing frames until a new real packet is received. Recently, autoregressive deep neural networks have been shown to surpass the quality of signal processing methods for PLC, specially for long-term predictions beyond 60 ms. In this work, we propose a non-autoregressive adversarial auto-encoder, named PLAAE, to perform real-time PLC in the waveform domain. PLAAE has a causal convolutional structure, and it learns in an auto-encoder fashion to reconstruct signals with gaps, with the help of an adversarial loss. During inference, it is able to predict smooth and coherent continuations of such gaps in a single feed-forward step, as opposed to autoregressive models. Our evaluation highlights the superiority of PLAAE over two classic PLCs and two deep autoregressive models in terms of spectral and intonation reconstruction, perceptual quality, and intelligibility.
Closed-loop convergence for mean field games with common noise

This paper studies the convergence problem for mean field games with common noise. We define a suitable notion of weak mean field equilibria, which we prove captures all subsequential limit points, as $n\to\infty$, of closed-loop approximate equilibria from the corresponding $n$-player games. This extends to the common noise setting a recent result of the first author, while also simplifying a key step in the proof and allowing unbounded coefficients and non-i.i. d. initial conditions. Conversely, we show that every weak mean field equilibrium arises as the limit of some sequence of approximate equilibria for the $n$-player games, as long as the latter are formulated over a broader class of closed-loop strategies which may depend on an additional common signal.
A Catalog of Host Galaxies for WISE-selected AGN: Connecting Host Properties with Nuclear Activity and Identifying Contaminants

We present a catalog of physical properties for galaxies hosting active galactic nuclei (AGN) detected by the Wide-field Infrared Survey Explorer (WISE). By fitting broadband spectral energy distributions of sources in the WISE AGN Catalog with empirical galaxy and AGN templates, we derive photometric redshifts, AGN bolometric luminosities, measures of AGN obscuration, host galaxy stellar masses, and host galaxy star formation rates (SFRs) for 695,273 WISE AGN. The wide-area nature of this catalog significantly augments the known number of obscured AGN out to redshifts z ∼ 3 and will be useful for studies focused on AGN or their host galaxy physical properties. We first show that the most likely non-AGN contaminants are galaxies at redshifts z = 0.2–0.3, with relatively blue W1–W2 colors, and with high specific SFRs for which the dust continuum emission is elevated in the W2 filter. Toward increasingly lower redshifts, WISE AGN host galaxies have systematically lower specific SFRs relative to those of normal star-forming galaxies, likely due to decreased cold gas fractions and the time delay between global star formation and AGN triggering. Finally, WISE AGN obscuration is not strongly correlated with AGN bolometric luminosity but shows a significant negative correlation with the Eddington ratio. This result is consistent with a version of the receding torus model in which the obscuring material is located within the supermassive black hole gravitational sphere of influence and the dust inner radius increases due to radiation pressure.
An algorithmic view of 𝓁2 regularization and some path-following algorithms

We establish an equivalence between the `2-regularized solution path for a convex loss function, and the solution of an ordinary differentiable equation (ODE). Importantly, this equivalence reveals that the solution path can be viewed as the flow of a hybrid of gradient descent and Newton method applying to the empirical loss, which is similar to a widely used optimization technique called trust region method. This provides an interesting algorithmic view of `2 regularization, and is in contrast to the conventional view that the `2 regularization solution path is similar to the gradient flow of the empirical loss. New path-following algorithms based on homotopy methods and numerical ODE solvers are proposed to numerically approximate the solution path. In particular, we consider respectively Newton method and gradient descent method as the basis algorithm for the homotopy method, and establish their approximation error rates over the solution path. Importantly, our theory suggests novel schemes to choose grid points that guarantee an arbitrarily small suboptimality for the solution path. In terms of computational cost, we prove that in order to achieve an -suboptimality for the entire solution path, the number of Newton steps required for the Newton method is O( −1/2), while the number of gradient steps required for the gradient descent method is O ( −1 ln( −1) ) . Finally, we use `2-regularized logistic regression as an illustrating example to demonstrate the effectiveness of the proposed path-following algorithms.
Dynamics and rheology of ring-linear blend semidilute solutions in extensional flow. Part I: Modeling and molecular simulations

We use Brownian dynamics (BD) simulations and single molecule experiments to investigate the influence of topological constraints and hydrodynamic interactions on the dynamics and rheology of solutions of ring-linear polymer blends at the overlap concentration. We find agreement between simulation and experiment in which rings in solution blends exhibit large conformational fluctuations. A subpopulation of rings shows extension overshoots in the startup of the flow, and other populations display tumbling and tank-treading at the steady state. Ring polymer fluctuations increase with the blend fraction of linear polymers and are peaked at a ring Weissenberg number WiR 1:5. On the contrary, linear and ring polymers in pure solutions show a peak in fluctuations at the critical coil-stretch Weissenberg number Wi 1⁄4 0:5. BD simulations show that extension overshoots on the startup of the flow are due to flow-induced intermolecular ring-linear polymer hooks, whereas fluctuations at the steady state are dominated by intermolecular hydrodynamic interactions (HIs). This is supported by simulations of bidisperse linear polymer solution blends, which show similar trends in conformational dynamics between rings and linear polymers with a matched contour length. Compared to BD simulations, single molecule experiments show quantitatively larger fluctuations, which could arise because experiments are performed on higher molecular weight polymers with stronger topological constraints. To this end, we have advanced the understanding of the effects of topological interactions and intermolecular HIs on the dynamics of semidilute ring-linear polymer blend solutions. © 2021 The Society of Rheology. https://doi.org/10.1122/8.0000221
Social Coordination through Altruism in Autonomous Driving

—Despite the leaps in the autonomous driving domain, autonomous vehicles (AVs) are still inefﬁcient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A group of autonomous and human-driven vehicles (HVs) which work together to optimize an altruistic social utility — as opposed to the egoistic individual utility — can co-exist seamlessly and assure safety and efﬁciency on the road. Achieving this mission is challenging in the absence of explicit coordination among agents. Additionally, existence of humans in mixed-autonomy environments create social dilemmas as they are known to be heterogeneous in social preference and their behavior is hard to predict by nature. Formally, we model an AV’s maneuver planning in mixed-autonomy trafﬁc as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using our multi-agent reinforcement learning framework. We introduce a quantitative representation of the AVs’ social value orientation and design a distributed reward structure that induces altruism into their decision making process. Our trained altruistic AVs are able to form alliances, guide the trafﬁc, and affect the behavior of the HVs to handle conﬂictive and competitive driving scenarios. As a case study, we compare egoistic AVs to our altruistic autonomous agents in a highway merging case study and demonstrate a signiﬁcant improvement in the number of successful merges as well as the overall trafﬁc ﬂow and safety.
SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers and propose a novel backbone network called SpectralFormer. Beyond bandwise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layerwise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse “soft” residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixelwise and patchwise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_SpectralFormer for the sake of reproducibility.
Efficient Transformer for Direct Speech Translation

The advent of Transformer-based models has surpassed the barriers of text. When working with speech, we must face a problem: the sequence length of an audio input is not suitable for the Transformer. To bypass this problem, a usual approach is adding strided convolutional layers, to reduce the sequence length before using the Transformer. In this paper, we propose a new approach for direct Speech Translation, where thanks to an efficient Transformer we can work with a spectrogram without having to use convolutional layers before the Transformer. This allows the encoder to learn directly from the spectrogram and no information is lost. We have created an encoder-decoder model, where the encoder is an efficient Transformer -- the Longformer -- and the decoder is a traditional Transformer decoder. Our results, which are close to the ones obtained with the standard approach, show that this is a promising research direction.
Graphane: a two-dimensional hydrocarbon

We predict the stability of an extended two-dimensional hydrocarbon on the basis of first-principles total-energy calculations. The compound that we call graphane is a fully saturated hydrocarbon derived from a single graphene sheet with formula CH. All of the carbon atoms are in $s{p}^{3}$ hybridization forming a hexagonal network and the hydrogen atoms are bonded to carbon on both sides of the plane in an alternating manner. Graphane is predicted to be stable with a binding energy comparable to other hydrocarbons such as benzene, cyclohexane, and polyethylene. We discuss possible routes for synthesizing graphane and potential applications as a hydrogen storage material and in two-dimensional electronics.
A Theoretical Framework of Chorus Wave Excitation

We propose a self‐consistent theoretical framework of chorus wave excitation, which describes the evolution of the whistler fluctuation spectrum as well as the suprathermal electron distribution function. The renormalized hot electron response is cast in the form of a Dyson‐like equation, which then leads to evolution equations for nonlinear fluctuation growth and frequency shift. This approach allows us to analytically derive for the first time exactly the same expression for the chorus chirping rate originally proposed by Vomvoridis et al. (1982). Chorus chirping is shown to correspond to maximization of wave particle power exchange, where each individual wave belonging to the whistler wave packet is characterized by small nonlinear frequency shift. We also show that different interpretations of chorus chirping proposed in published literature have a consistent reconciliation within the present theoretical framework, which further illuminates the analogy with similar phenomena in fusion plasmas and free electron laser physics.
Strain-induced phase diagram of the 
S=32
 Kitaev material 
CrSiTe3

The interplay among anisotropic magnetic terms, such as the bond-dependent Kitaev interactions and single-ion anisotropy, plays a key role in stabilizing the finite-temperature ferromagnetism in the two-dimensional compound $\rm{CrSiTe_3}$. While the Heisenberg interaction is predominant in this material, a recent work shows that it is rather sensitive to the compressive strain, leading to a variety of phases, possibly including a sought-after Kitaev quantum spin liquid [C. Xu, \textit{et. al.}, Phys. Rev. Lett. \textbf{124}, 087205 (2020)]. To further understand these states, we establish the quantum phase diagram of a related bond-directional spin-$3/2$ model by the density-matrix renormalization group method. As the Heisenberg coupling varies from ferromagnetic to antiferromagnetic, three magnetically ordered phases, i.e., a ferromagnetic phase, a $120^\circ$ phase and an antiferromagnetic phase, appear consecutively. All the phases are separated by first-order phase transitions, as revealed by the kinks in the ground-state energy and the jumps in the magnetic order parameters. However, no positive evidence of the quantum spin liquid state is found and possible reasons are discussed briefly.
Robustifying Multi-hop QA through Pseudo-Evidentiality Training

This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to not only answer right, but also with right reasoning chains. An existing direction is to annotate reasoning chains to train models, requiring expensive additional annotations. In contrast, we propose a new approach to learn evidentiality, deciding whether the answer prediction is supported by correct evidences, without such annotations. Instead, we compare counterfactual changes in answer confidence with and without evidence sentences, to generate “pseudo-evidentiality” annotations. We validate our proposed model on an original set and challenge set in HotpotQA, showing that our method is accurate and robust in multi-hop reasoning.
VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis

This paper describes a variational auto-encoder based non-autoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequence-to-sequence architecture can generate high-quality speech, but their sequential decoding process can be time-consuming. Recently, non-autoregressive TTS (NAR-TTS) models have been shown to be more efficient with the parallel decoding process. However, these NAR-TTS models rely on phoneme-level durations to generate a hard alignment between the text and the spectrogram. Obtaining duration labels, either through forced alignment or knowledge distillation, is cumbersome. Furthermore, hard alignment based on phoneme expansion can degrade the naturalness of the synthesized speech. In contrast, the proposed model of VAENAR-TTS is an end-to-end approach that does not require phoneme-level durations. The VAENAR-TTS model does not contain recurrent structures and is completely non-autoregressive in both the training and inference phases. Based on the VAE architecture, the alignment information is encoded in the latent variable, and attention-based soft alignment between the text and the latent variable is used in the decoder to reconstruct the spectrogram. Experiments show that VAENAR-TTS achieves state-of-the-art synthesis quality, while the synthesis speed is comparable with other NAR-TTS models.
Dynamic ordered panel logit models

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper presents sufficient conditions for the moment conditions to identify the common parameters of the model, namely the regression coefficients, the autoregressive parameters, and the threshold parameters. The availability of moment conditions suggests that these common parameters can be estimated using the generalized method of moments, and the paper documents the performance of this estimator using Monte Carlo simulations and an empirical illustration to self-reported health status using the British Household Panel Survey.
On boundedness of semistable sheaves

A BSTRACT . We give a new simple proof of boundedness of the family of semistable sheaves with ﬁxed numerical invariants on a ﬁxed smooth projective variety. In characteristic zero our method gives a quick proof of Bogomolov’s inequality for semistable sheaves on a smooth projective variety of any dimension ≥ 2 without using any restriction theorems.
Cognitive Contagion: How to model (and potentially counter) the spread of fake news

Understanding the spread of false or dangerous beliefs – so-called mis/disinformation – through a population has never seemed so urgent to many. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. We introduce a cognitive contagion model that combines a network science approach with an internal cognitive model of the individual agents, affecting what they believe, and what they pass on. We show that the model, even with a very discrete and simplistic belief function to capture cognitive dissonance, both adds expressive power over existing disease-based contagion models, and qualitatively demonstrates the appropriate belief update phenomena at the individual level. Moreover, we situate our cognitive contagion model in a larger public opinion diffusion (POD) model, which attempts to capture the role of institutions or media sources in belief diffusion – something that is often left out. We conduct an analysis of the POD model with our simple cognitive dissonance-sensitive update function across various graph topologies and institutional messaging patterns. We demonstrate that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID misinformation public opinion polls. The overall model sets up a preliminary framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of misinformation and “alternative facts. ”
Poly-NL: Linear Complexity Non-local Layers with Polynomials

Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions. Such pairwise functions underpin the effectiveness of non-local layers, but also determine a complexity that scales quadratically with respect to the input size both in space and time. This is a severely limiting factor that practically hinders the applicability of non-local blocks to even moderately sized inputs. Previous works focused on reducing the complexity by modifying the underlying matrix operations, however in this work we aim to retain full expressiveness of non-local layers while keeping complexity linear. We overcome the efficiency limitation of non-local blocks by framing them as special cases of 3rd order polynomial functions. This fact enables us to formulate novel fast Non-Local blocks, capable of reducing the complexity from quadratic to linear with no loss in performance, by replacing any direct computation of pairwise similarities with element-wise multiplications. The proposed method, which we dub as"Poly-NL", is competitive with state-of-the-art performance across image recognition, instance segmentation, and face detection tasks, while having considerably less computational overhead.
Decreasing Incomes Increase Selfishness

We use a controlled laboratory experiment to study the causal impact of income de-creases within a time period on redistribution decisions at the end of that period, in an environment where we keep fixed the sum of incomes over the period. First, we investigate the effect of a negative income trend (intra-personal decrease), which means a decreasing income compared to one’s recent past. Second, we investigate the effect of a negative income trend relative to the income trend of another person (inter-personal decrease). If intra-personal or inter-personal decreases create dissatisfaction for an individual, that person may become more selfish to obtain compensation. We formalize both effects in a multi-period model augmenting a standard model of in equality aversion. Overall, conditional on exhibiting sufficiently-strong social preferences, we find that individuals indeed behave more selfishly when they experience decreasing in-comes. While many studies examine the effect of income inequality on redistribution decisions, we delve into the history behind one’s income to isolate the effect of income changes.