Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation

Machine Speech Chain, which integrates both end-to-end (E2E) automatic speech recognition (ASR) and text-to-speech (TTS) into one circle for joint training, has been proven to be effective in data augmentation by leveraging large amounts of unpaired data. In this paper, we explore the TTS->ASR pipeline in speech chain to do domain adaptation for both neural TTS and E2E ASR models, with only text data from target domain. We conduct experiments by adapting from audiobook domain (LibriSpeech) to presentation domain (TED-LIUM), there is a relative word error rate (WER) reduction of 10% for the E2E ASR model on the TED-LIUM test set, and a relative WER reduction of 51.5% in synthetic speech generated by neural TTS in the presentation domain. Further, we apply few-shot speaker adaptation for the E2E ASR by using a few utterances from target speakers in an unsupervised way, results in additional gains.
Practical Byzantine Reliable Broadcast on Partially Connected Networks

In this paper, we consider the Byzantine reliable broadcast problem on authenticated and partially connected networks. The state-of-the-art method to solve this problem consists in combining two algorithms from the literature. Handling asynchrony and faulty senders is typically done thanks to Gabriel Bracha's authenticated double-echo broadcast protocol, which assumes an asynchronous fully connected network. Danny Dolev's algorithm can then be used to provide reliable communications between processes in the global fault model, where up to f processes among N can be faulty in a communication network that is at least 2f+1-connected. Following recent works that showed how Dolev's protocol can be made more practical thanks to several optimizations, we show that the state-of-the-art methods to solve our problem can be optimized thanks to layer-specific and cross-layer optimizations. Our simulations with the Omnet++ network simulator show that these optimizations can be efficiently combined to decrease the total amount of information transmitted or the protocol's latency (e.g., respectively, −25% and −50% with a 16B payload, N=31 and f=4) compared to the state-of-the-art combination of Bracha's and Dolev's protocols.
Extended Parallel Corpus for Amharic-English Machine Translation

This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the corpus for research purposes. Furthermore, we developed baseline statistical and neural machine translation systems; we trained statistical and neural machine translation models using the corpus. In the experiments, we also used a large monolingual corpus for the language model of statistical machine translation and back-translation of neural machine translation. In the automatic evaluation, neural machine translation models outperform statistical machine translation models by approximately six to seven Bilingual Evaluation Understudy (BLEU) points. Besides, among the neural machine translation models, the subword models outperform the word-based models by three to four BLEU points. Moreover, two other relevant automatic evaluation metrics, Translation Edit Rate on Character Level and Better Evaluation as Ranking, reflect corresponding differences among the trained models.
Fixed-Domain Inference for Gausian Processes with Mat´ern Covariogram on Compact Riemannian Manifolds

: Gaussian processes are widely employed as versatile modeling and predictive tools in spatial statistics, functional data analysis, computer modeling and in diverse applications of machine learning. Such processes have been widely studied over Euclidean spaces, where they are constructed using speciﬁed covariance functions or covariograms. These functions specify valid stochastic processes that can be used to model complex dependencies in spatial statistics and other machine learning contexts. Valid (positive deﬁnite) covariance functions have been extensively studied for Gaussian processes on Euclidean spaces. Such investigations have focused, among other aspects, on the identiﬁability and consistency of covariance parameters as well as the problem of spatial interpolation and prediction within the ﬁxed-domain or in-ﬁll paradigm of asymptotic inference. This manuscript undertakes analogous theoretical developments for Gaussian processes constructed over Riemannian manifolds. We begin by establishing formal notions and conditions for the equivalence of two Gaussian random measures on compact manifolds. We build upon recently introduced Mat´ern covariograms on compact Riemannian manifold, derive the microergodic parameter and formally establish the consistency of maximum likelihood estimators and the asymptotic optimality of the best linear unbiased predictor (BLUP). The circle and sphere are studied as two speciﬁc examples of compact Riemannian manifolds with numerical experiments that illustrate the theory.
Exact Stochastic Second Order Deep Learning

Optimization in Deep Learning is mainly dominated by first-order methods which are built around the central concept of backpropagation. Second-order optimization methods, which take into account the second-order derivatives are far less used despite superior theoretical properties. This inadequacy of second-order methods stems from its exorbitant computational cost, poor performance, and the ineluctable non-convex nature of Deep Learning. Several attempts were made to resolve the inadequacy of second-order optimization without reaching a cost-effective solution, much less an exact solution. In this work, we show that this long-standing problem in Deep Learning could be solved in the stochastic case, given a suitable regularization of the neural network. Interestingly, we provide an expression of the stochastic Hessian and its exact eigenvalues. We provide a closed-form formula for the exact stochastic second-order Newton direction, we solve the non-convexity issue and adjust our exact solution to favor flat minima through regularization and spectral adjustment. We test our exact stochastic second-order method on popular datasets and reveal its adequacy for Deep Learning.
Graph toughness from Laplacian eigenvalues

The toughness t(G) of a graph G = (V, E) is defined as t(G) = min { |S| c(G−S) } , in which the minimum is taken over all S ⊂ V such that G − S is disconnected, where c(G − S) denotes the number of components of G − S. We present two tight lower bounds for t(G) in terms of the Laplacian eigenvalues and provide strong support for a conjecture for a better bound which, if true, implies both bounds, and improves and generalizes known bounds by Alon, Brouwer, and the first author. As applications, several new results on perfect matchings, factors and walks from Laplacian eigenvalues are obtained, which leads to a conjecture about Hamiltonicity and Laplacian eigenvalues.
Black hole mass measurement using ALMA observations of [CI] and CO emissions in the Seyfert 1 galaxy NGC 7469

We present a supermassive black hole (SMBH) mass measurement in the Seyfert 1 galaxy NGC 7469 using Atacama LargeMillimeter/submillimeter Array (ALMA) observations of the atomic-[CI](1-0) and molecular-12CO(1-0) emission lines at the spatial resolution of ≈ 0. ′′3 (or ≈ 100 pc). These emissions reveal that NGC 7469 hosts a circumnuclear gas disc (CND) with a ring-like structure and a two-arm/bisymmetric spiral pattern within it, surrounded by a starbursting ring. The CND has a relatively low σgas/V ≈ 0.35 (r . 0. ′′5) and≈ 0.19 (r > 0. ′′5), suggesting that the gas is dynamically settled and suitable for dynamically deriving the mass of its central source. As is expected from X-ray dominated region (XDR) effects that dramatically increase an atomic carbon abundance by dissociating CO molecules, we suggest that the atomic [CI](1-0) emission is a better probe of SMBH masses than CO emission in AGNs. Our dynamical model using the [CI](1-0) kinematics yields a MBH = 1.78+2.69 −1.10 × 10 7 M and M/LF547M = 2.25+0.40 −0.43 (M /L ). The model using the 12CO(1-0) kinematics also gives a consistent MBH with a larger uncertainty, up to an order of magnitude, i.e. MBH = 1.60+11.52 −1.45 × 10 7 M . This newly dynamical MBH is ≈ 2 times higher than the mass determined from the reverberation mapped (RM) method using emissions arising in the unresolved broad-line region (BLR). Given this new MBH, we are able to constrain the specific RM dimensionless scaling factor of f = 7.2+4.2 −3.4 for the AGN BLR in NGC 7469. The gas within the unresolved BLR thus has a Keplerian virial velocity component and the inclination of i ≈ 11.0◦+2.2 −2.5, confirming its face-on orientation in a Seyfert 1 AGN by assuming a geometrically thin BLR model.
Possible existence of time machines in a five-dimensional spacetime

The idea of constructing a time machine is not new and even received a boost thanks to the realization that a traversable wormhole could be converted to a time machine. This also implied that one could only travel back to the time of the conversion. This paper addresses these issues, as well as the concomitant causality violations, by starting with a physically acceptable model, a spacetime that is anti-de Sitter due to an extra time-like dimension, thereby allowing the existence of closed time-like curves. By assuming that the extra dimension is independent of the radial coordinate, the wormhole retains its basic geometric properties regard-less of its location and can therefore serve as a shortcut for any closed time-like curve, which, in turn, can extend indeﬁnitely into the past. The same independence of the radial coordinate suggests that the wormhole could connect a region in the present to a local region that (1) lies in the past and (2) does not contain any closed time-like curves. Previous studies have suggested that if such a region is suﬃciently localized, it may be possible to avoid a causality violation.
Calibration and validation of the lunar exploration neutron detector (LEND) observations for the study of the moon volatiles

This paper reviews improved calibration methods for the Lunar Reconnaissance Orbiter Lunar Exploration Neutron Detector. We cross calibrated the set of LEND observations and models of its detectors physical geometry and composition against the McKinney Apollo 17 era measured neutron flux, Lunar Prospector Neutron Spectrometer epithermal neutron observations, Earth based Galactic Cosmic Ray observations and altitude dependent models of the Moon neutron emission flux. Our neutron transport modeling of the LEND system with the Geant4 software package allows us to fully decompose the varying contributions of lunar, spacecraft and instrument dependent sources of neutrons and charged particles during the LEND mission. With this improved calibration, we can now fully predict every observation from the eight helium 3 detectors and the expected total and partial count rates of neutrons and charged particles for the entirety of LEND now ten plus year observation campaign at the Moon. The study has resulted in an improved calibration for all detectors. The high spatial resolution of LEND collimated and uncollimated sensors are illustrated using the neutron suppression region associated with the south polar Cabeus permanent shadowed region.
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems

Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged in Automatic Speech Recognition (ASR) technologies. The callsign used to address an airplane is an essential part of all ATCo-pilot communications. We propose a two-steps approach to add contextual knowledge during semi-supervised training to reduce the ASR system error rates at recognizing the part of the utterance that contains the callsign. Initially, we represent in a WFST the contextual knowledge (i.e. air-surveillance data) of an ATCo-pilot communication. Then, during Semi-Supervised Learning (SSL) the contextual knowledge is added by second-pass decoding (i.e. lattice re-scoring). Results show that `unseen domains' (e.g. data from airports not present in the supervised training data) are further aided by contextual SSL when compared to standalone SSL. For this task, we introduce the Callsign Word Error Rate (CA-WER) as an evaluation metric, which only assesses ASR performance of the spoken callsign in an utterance. We obtained a 32.1% CA-WER relative improvement applying SSL with an additional 17.5% CA-WER improvement by adding contextual knowledge during SSL on a challenging ATC-based test set gathered from LiveATC.
What Fermilab $(g-2)_{\mu}$ experiment tells us about discovering SUSY at HL-LHC and HE-LHC

Using an artificial neural network we explore the parameter space of supergravity grand unified models consistent with the combined Fermilab E989 and Brookhaven E821 data on (g−2)μ. Within an extended mSUGRA model with non-universal gaugino masses the analysis indicates that the region favored by the data is the one generated by gluino-driven radiative breaking of the electroweak symmetry (g̃SUGRA). This region naturally leads to a split sparticle spectrum with light sleptons and weakinos but heavy squarks, with the stau and the chargino as the lightest charged particles. We show that if the entire deviation from the Standard Model (g− 2)μ arises from supersymmetry, then supersymmetry is discoverable at HL-LHC and HE-LHC via production and decay of sleptons and sneutrinos within the optimal integrated luminosity of HL-LHC and with a smaller integrated luminosity at HE-LHC. The effect of CP phases on the muon anomaly is investigated and the parameter space of CP phases excluded by the Fermilab constraint is exhibited.
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification

Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus’ vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model’s run-time. To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy.
How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic Study Using Neural Metaphor Detection

Metaphors are widely used in political rhetoric as an effective framing device. While the efficacy of specific metaphors such as the war metaphor in political discourse has been documented before, those studies often rely on small number of hand-coded instances of metaphor use. Larger-scale topic-agnostic studies are required to establish the general persuasiveness of metaphors as a device, and to shed light on the broader patterns that guide their persuasiveness. In this paper, we present a large-scale data-driven study of metaphors used in political discourse. We conduct this study on a publicly available dataset of over 85K posts made by 412 US politicians in their Facebook public pages, up until Feb 2017. Our contributions are threefold: we show evidence that metaphor use correlates with ideological leanings in complex ways that depend on concurrent political events such as winning or losing elections; we show that posts with metaphors elicit more engagement from their audience overall even after controlling for various socio-political factors such as gender and political party affiliation; and finally, we demonstrate that metaphoricity is indeed the reason for increased engagement of posts, through a fine-grained linguistic analysis of metaphorical vs. literal usages of 513 words across 70K posts.
Reflection on modern methods: competing risks versus multi-state models

Survival competing risks models are very useful for studying the incidence of diseases whose occurrence competes with other possible diseases or health conditions. These models perform properly when working with terminal events, such as death, that imply the conclusion of the corresponding study. But they do not allow the treatment of scenarios with non-terminal competing events that may occur sequentially. Multi-state models are complex survival models. They focus on pathways defined by the temporal and sequential occurrence of numerous events of interest and thus they are suitable for connecting competing non-terminal events as well as to manage other survival scenarios with higher complexity. We discuss competing risks within the framework of multi-state models and clarify the usefulness of both models for analysing epidemiological data. We highlight the power of multi-state models through a real-world study of recurrent hip fracture from Bayesian inferential methodology.
Media Cloud: Massive Open Source Collection of Global News on the Open Web

We present the first full description of Media Cloud, an open source platform based on crawling hyperlink structure in operation for over 10 years, that for many uses will be the best way to collect data for studying the media ecosystem on the open web. We document the key choices behind what data Media Cloud collects and stores, how it processes and organizes these data, and its open API access as well as user-facing tools. We also highlight the strengths and limitations of the Media Cloud collection strategy compared to relevant alternatives. We give an overview two sample datasets generated using Media Cloud and discuss how researchers can use the platform to create their own datasets.
Generalized Approach to Matched Filtering using Neural Networks

Gravitational wave science is a pioneering field with rapidly evolving data analysis methodology currently assimilating and inventing deep learning techniques. The bulk of the sophisticated flagship searches of the field rely on the time-tested matched filtering principle within their core. In this paper, we make a key observation on the relationship between the emerging deep learning and the traditional techniques: matched filtering is formally equivalent to a particular neural network. This means that a neural network can be constructed analytically to exactly implement matched filtering, and can be further trained on data or boosted with additional complexity for improved performance. Moreover, we show that the proposed neural network architecture can outperform matched filtering, both with or without knowledge of a prior on the parameter distribution. When a prior is given, the proposed neural network can approach the statistically optimal performance. We also propose and investigate two different neural network architectures MNet-Shallow and MNet-Deep, both of which implement matched filtering at initialization and can be trained on data. MNet-Shallow has simpler structure, while MNet-Deep is more flexible and can deal with a wider range of distributions. Our theoretical findings are corroborated by experiments using real LIGO data and synthetic injections, where our proposed methods significantly outperform matched filtering at false positive rates above $5\times 10^{-3}\%$. The fundamental equivalence between matched filtering and neural networks allows us to define a"complexity standard candle"to characterize the relative complexity of the different approaches to gravitational wave signal searches in a common framework. Finally, our results suggest new perspectives on the role of deep learning in gravitational wave detection.
De-rendering the World’s Revolutionary Artefacts

Recent works have shown exciting results in unsupervised image de-rendering—learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision. However, many of these assume simplistic material and lighting models. We propose a method, termed RADAR, that can recover environment illumination and surface materials from real single-image collections, relying neither on explicit 3D supervision, nor on multi-view or multi-light images. Specifically, we focus on rotationally symmetric artefacts that exhibit challenging surface properties including specular reflections, such as vases. We introduce a novel self-supervised albedo discriminator, which allows the model to recover plausible albedo without requiring any ground-truth during training. In conjunction with a shape reconstruction module exploiting rotational symmetry, we present an end-to-end learning framework that is able to de-render the world’s revolutionary artefacts. We conduct experiments on a real vase dataset and demonstrate compelling decomposition results, allowing for applications including free-viewpoint rendering and relighting. More results and code at: https://sorderender.github. io/.
On the size distribution of spots within sunspot groups

The size distribution of sunspots provides key information about the generation and emergence processes of the solar magnetic field. Previous studies of size distribution have primarily focused on either the whole group or individual spot areas. In this paper we investigate the organisation of spot areas within sunspot groups. In particular, we analysed the ratio (R) of the area of the biggest spot (Abig_spot) inside a group, to the total area of that group (Agroup). We used sunspot observations from Kislovodsk, Pulkovo, and Debrecen observatories, together covering solar cycles 17–24. We find that at the time when the group area reaches its maximum, the single biggest spot in a group typically occupies about 60% of the group area. For half of all groups, R lies in the range between roughly 50% and 70%. We also find R to change with Agroup, such that R reaches a maximum of about 0.65 for groups with Agroup ≈ 200 μHem and then remains at about 0.6 for larger groups. Our findings imply a scale-invariant emergence pattern, providing an observational constraint on the emergence process. Furthermore, extrapolation of our results to larger sunspot groups may have a bearing on the giant unresolved starspot features found in Doppler images of highly active Sun-like stars. Our results suggest that such giant features are composed of multiple spots, with the largest spot occupying roughly 55–75% of the total group area (i.e., the area of the giant starspots seen in Doppler images).
3D Shape Generation and Completion through Point-Voxel Diffusion

We propose a novel approach for probabilistic generative modeling of 3D shapes. Unlike most existing models that learn to deterministically translate a latent vector to a shape, our model, Point-Voxel Diffusion (PVD), is a unified, probabilistic formulation for unconditional shape generation and conditional, multi-modal shape completion. PVD marries denoising diffusion models with the hybrid, point-voxel representation of 3D shapes. It can be viewed as a series of denoising steps, reversing the diffusion process from observed point cloud data to Gaussian noise, and is trained by optimizing a variational lower bound to the (conditional) likelihood function. Experiments demonstrate that PVD is capable of synthesizing high-fidelity shapes, completing partial point clouds, and generating multiple completion results from single-view depth scans of real objects.