Miracle-dz/UniFAR-FTUs huggingface dataset:
Classical Simulations of Quantum Field Theory in Curved Spacetime I: Fermionic Hawking-Hartle Vacua from a Staggered Lattice Scheme

We numerically compute renormalized expectation values of quadratic operators in a quantum field theory (QFT) of free Dirac fermions in curved two-dimensional (Lorentzian) spacetime. First, we use a staggered-fermion discretization to generate a sequence of lattice theories yielding the desired QFT in the continuum limit. Numerically-computed lattice correlators are then used to approximate, through extrapolation, those in the continuum. Finally, we use so-called point-splitting regularization and Hadamard renormalization to remove divergences, and thus obtain finite, renormalized expectation values of quadratic operators in the continuum. As illustrative applications, we show how to recover the Unruh effect in flat spacetime and how to compute renormalized expectation values in the Hawking-Hartle vacuum of a Schwarzschild black hole and in the Bunch-Davies vacuum of an expanding universe described by de Sitter spacetime. Although here we address a non-interacting QFT using free fermion techniques, the framework described in this paper lays the groundwork for a series of subsequent studies involving simulation of interacting QFTs in curved spacetime by tensor network techniques.
Assessment of AlGaN/AlN superlattices on GaN nanowires as active region of electron-pumped ultraviolet sources

In this paper, we describe the design and characterization of 400 nm long (88 periods) AlxGa1−xN/AlN (0 ≤ x ≤ 0.1) quantum dot superlattices deposited on self-assembled GaN nanowires for application in electron-pumped ultraviolet sources. The optical performance of GaN/AlN superlattices on nanowires is compared with the emission of planar GaN/AlN superlattices with the same periodicity and thickness grown on bulk GaN substrates along the N-polar and metal-polar crystallographic axes. The nanowire samples are less sensitive to nonradiative recombination than planar layers, attaining internal quantum efficiencies (IQE) in excess of 60% at room temperature even under low injection conditions. The IQE remains stable for higher excitation power densities, up to 50 kW cm−2. We demonstrate that the nanowire superlattice is long enough to collect the electron–hole pairs generated by an electron beam with an acceleration voltage VA = 5 kV. At such VA, the light emitted from the nanowire ensemble does not show any sign of quenching under constant electron beam excitation (tested for an excitation power density around 8 kW cm−2 over the scale of minutes). Varying the dot/barrier thickness ratio and the Al content in the dots, the nanowire peak emission can be tuned in the range from 340 to 258 nm.
Sparse and Low-Rank High-Order Tensor Regression via Parallel Proximal Method

Recently, tensor data (or multidimensional array) have been generated in many modern applications, such as functional magnetic resonance imaging (fMRI) in neuroscience and videos in video analysis. Many efforts are made in recent years to predict the relationship between tensor features and univariate responses. However, previously proposed methods either lose structural information within tensor data or have prohibitively expensive time costs, especially for large-scale data with high-order structures. To address such problems, we propose the Sparse and Low-rank Tensor Regression (SLTR) model. Our model enforces sparsity and low-rankness of the tensor coefficient by directly applying $\ell_1$ norm and tensor nuclear norm, such that it preserves structural information of the tensor. To make the solving procedure scalable and efficient, SLTR makes use of the proximal gradient method, which can be easily implemented parallelly. We evaluate SLTR on several simulated datasets and one video action recognition dataset. Experiment results show that, compared with previous models, SLTR can obtain a better solution with much fewer time costs. Moreover, our model's predictions exhibit meaningful interpretations on the video dataset.
Robust, accurate, and efficient: quantum embedding using the Huzinaga level-shift projection operator for complex systems.

Wave function (WF) in density functional theory (DFT) embedding methods provide a framework for performing localized, high accuracy WF calculations on a system, while not incurring the full computational cost of the WF calculation on the full system. In order to effectively partition a system into localized WF and DFT subsystems, we utilize the Huzinaga level-shift projection operator within an absolutely localized basis. In this work, we study the ability of the absolutely localized Huzinaga level-shift projection operator method to study complex WF and DFT partitions, including partitions between multiple covalent bonds, a double bond, and transition metal-ligand bonds. We find that our methodology can accurately describe all of these complex partitions. Additionally, we study the robustness of this method with respect to the WF method, specifically where the embedded systems were described using a multiconfigurational WF method. We found that the method is systematically improvable with respect to both the number of atoms in the WF region and the size of the basis set used, with energy errors less than 1 kcal/mol. Additionally, we calculated the adsorption energy of H2 to a model of an iron metal organic framework (Fe-MOF-74) to within 1 kcal/mol compared to CASPT2 calculations performed on the full model while incurring only a small fraction of the full computational cost. This work demonstrates that the absolutely localized Huzinaga level-shift projection operator method is applicable to very complex systems with difficult electronic structures.
Transflow Learning: Repurposing Flow Models Without Retraining

It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space. Recently, architectures have emerged that allow for more complex manipulations, such as making an image look as though it were from a different class, or painted in a certain style. These methods typically require large amounts of training in order to learn a single class of manipulations. We present Transflow Learning, a method for transforming a pre-trained generative model so that its outputs more closely resemble data that we provide afterwards. In contrast to previous methods, Transflow Learning does not require any training at all, and instead warps the probability distribution from which we sample latent vectors using Bayesian inference. Transflow Learning can be used to solve a wide variety of tasks, such as neural style transfer and few-shot classification.
Optimal Multivariate Tuning with Neuron-Level and Population-Level Energy Constraints

Optimality principles have been useful in explaining many aspects of biological systems. In the context of neural encoding in sensory areas, optimality is naturally formulated in a Bayesian setting as neural tuning which minimizes mean decoding error. Many works optimize Fisher information, which approximates the minimum mean square error (MMSE) of the optimal decoder for long encoding time but may be misleading for short encoding times. We study MMSE-optimal neural encoding of a multivariate stimulus by uniform populations of spiking neurons, under firing rate constraints for each neuron as well as for the entire population. We show that the population-level constraint is essential for the formulation of a well-posed problem having finite optimal tuning widths and optimal tuning aligns with the principal components of the prior distribution. Numerical evaluation of the two-dimensional case shows that encoding only the dimension with higher variance is optimal for short encoding times. We also compare direct MMSE optimization to optimization of several proxies to MMSE: Fisher information, maximum likelihood estimation error, and the Bayesian Cramér-Rao bound. We find that optimization of these measures yields qualitatively misleading results regarding MMSE-optimal tuning and its dependence on encoding time and energy constraints.
THOR 2.0: Major Improvements to the Open-source General Circulation Model

THOR is the first open-source general circulation model (GCM) developed from scratch to study the atmospheres and climates of exoplanets, free from Earth- or solar-system-centric tunings. It solves the general nonhydrostatic Euler equations (instead of the primitive equations) on a sphere using the icosahedral grid. In the current study, we report major upgrades to THOR, building on the work of Mendonça et al. First, while the horizontally explicit and vertically implicit integration scheme is the same as that described in Mendonça et al., we provide a clearer description of the scheme and improve its implementation in the code. The differences in implementation between the hydrostatic shallow, quasi-hydrostatic deep, and nonhydrostatic deep treatments are fully detailed. Second, standard physics modules are added: two-stream, double-gray radiative transfer and dry convective adjustment. Third, THOR is tested on additional benchmarks: tidally locked Earth, deep hot Jupiter, acoustic wave, and gravity wave. Fourth, we report that differences between the hydrostatic and nonhydrostatic simulations are negligible in the Earth case but pronounced in the hot Jupiter case. Finally, the effects of the so-called “sponge layer,” a form of drag implemented in most GCMs to provide numerical stability, are examined. Overall, these upgrades have improved the flexibility, user-friendliness, and stability of THOR.
Type IIP Supernova Progenitors. II. Stellar Mass and Obscuration by the Dust in the Circumstellar Medium

It has been well established from a variety of observations that red supergiants (RSGs) lose a lot of mass in stellar wind. Dust that has formed in this emitted gas over a few decades before core-collapse can lead to substantial extinction and obscure the intrinsic luminosity of the progenitor RSG. This may lead to a difficulty in determining the range of progenitor masses that lead to the different classes of supernovae. Even nearby, well-studied supernovae with pre-explosion observations, such as SN 2013ej, may suffer from this uncertainty in the progenitor mass. We explore here two different masses proposed for its progenitor. We compute their pre-supernova characteristics using Modules for Experiments in Stellar Astrophysics. We show that a non-rotating star with an initial mass of 26 M⊙ would require a considerable amount of circumstellar medium (AV ∼ 3) to obscure its high luminosity given the observed pre-explosion magnitudes detected by the Hubble Space Telescope (HST). Such a high value of visual extinction appears to be inconsistent with that derived for SN 2013ej as well as SN 2003gd in the same host galaxy, M74. In contrast, the evolutionary models of a lower mass (13 M⊙) star are easily accommodated within the observed HST magnitudes. Some of the 26 M⊙ simulations show luminosity variation in the last few years, which could be discriminated by high-cadence and multiband monitoring of supernova candidates in nearby galaxies. We demonstrate that our calculations are well resolved with adequate zoning and evolutionary time steps.
Emission from a Pulsar Wind Nebula: Application to the Persistent Radio Counterpart of FRB 121102

The properties of fast radio bursts (FRBs) indicate that the physical origin of this type of astrophysical phenomenon is related to neutron stars. The first detected repeating source, FRB 121102, is associated with a persistent radio counterpart. In this paper, we propose that this radio counterpart could arise from a pulsar wind nebula powered by a magnetar without surrounding supernova ejecta. Its medium is a stratified structure produced by a progenitor wind. The model parameters are constrained by the spectrum of the counterpart emission, the size of the nebula, and the large but decreasing rotation measure (RM) of the repeating bursts. In addition, the observed dispersion measure is consistent with the assumption that all of the RM comes from the shocked medium.
Music Source Separation in the Waveform Domain

Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments. Contrarily to many audio synthesis tasks where the best performances are achieved by models that directly generate the waveform, the state-of-the-art in source separation for music is to compute masks on the magnitude spectrum. In this paper, we first show that an adaptation of Conv-Tasnet (Luo & Mesgarani, 2019), a waveform-to-waveform model for source separation for speech, significantly beats the state-of-the-art on the MusDB dataset, the standard benchmark of multi-instrument source separation. Second, we observe that Conv-Tasnet follows a masking approach on the input signal, which has the potential drawback of removing parts of the relevant source without the capacity to reconstruct it. We propose Demucs, a new waveform-to-waveform model, which has an architecture closer to models for audio generation with more capacity on the decoder. Experiments on the MusDB dataset show that Demucs beats previously reported results in terms of signal to distortion ratio (SDR), but lower than Conv-Tasnet. Human evaluations show that Demucs has significantly higher quality (as assessed by mean opinion score) than Conv-Tasnet, but slightly more contamination from other sources, which explains the difference in SDR. Additional experiments with a larger dataset suggest that the gap in SDR between Demucs and Conv-Tasnet shrinks, showing that our approach is promising.
The ATLAS 9.0 GHz survey of the extended Chandra Deep Field South: the faint 9.0 GHz radio population

We present a new image of the 9.0 GHz radio emission from the extended Chandra Deep Field South. A total of 181 hours of integration with the Australia Telescope Compact Array has resulted in a 0.276 square degree image with a median sensitivity of $\sim$20 $\mu$Jy/beam rms, for a synthesised beam of 4.0 $\times$ 1.3 arcsec. We present a catalogue of the 9.0 GHz radio sources, identifying 70 source components and 55 individual radio galaxies. Source counts derived from this sample are consistent with those reported in the literature. The observed source counts are also generally consistent with the source counts from simulations of the faint radio population. Using the wealth of multiwavelength data available for this region, we classify the faint 9 GHz population and find that 91% are radio loud AGN, 7% are radio quiet AGN and 2% are star forming galaxies. The 9.0 GHz radio sources were matched to 5.5 and 1.4 GHz sources in the literature and we find a significant fraction of flat or inverted spectrum sources, with 36% of the 9 GHz sources having $\alpha_{5.5GHz}^{9.0GHz}$ $>$ -0.3 (for $S \propto \nu^\alpha$). This flat or inverted population is not well reproduced by current simulations of radio source populations.
Sentiment Analysis On Indian Indigenous Languages: A Review On Multilingual Opinion Mining

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.
Option-critic in cooperative multi-agent systems

In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, 1999). First, we address the planning problem for the decentralized POMDP represented by the multi-agent system, by introducing a \emph{common information approach}. We use the notion of \emph{common beliefs} and broadcasting to solve an equivalent centralized POMDP problem. Then, we propose the Distributed Option Critic (DOC) algorithm, which uses centralized option evaluation and decentralized intra-option improvement. We theoretically analyze the asymptotic convergence of DOC and build a new multi-agent environment to demonstrate its validity. Our experiments empirically show that DOC performs competitively against baselines and scales with the number of agents.
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation. This is challenging as the two input spaces are heterogeneous and can be impacted differently by domain shift. In xMUDA, modalities learn from each other through mutual mimicking, disentangled from the segmentation objective, to prevent the stronger modality from adopting false predictions from the weaker one. We evaluate on new UDA scenarios including day-to-night, country-to-country and dataset-to-dataset, leveraging recent autonomous driving datasets. xMUDA brings large improvements over uni-modal UDA on all tested scenarios, and is complementary to state-of-the-art UDA techniques. Code is available at https://github.com/valeoai/xmuda.
Metastability phenomena in two-dimensional rectangular lattices with nearest-neighbour interaction

We study analytically the dynamics of two-dimensional rectangular lattices with periodic boundary conditions. We consider anisotropic initial data supported on one low-frequency Fourier mode. We show that, in the continuous approximation, the resonant normal form of the system is given by integrable PDEs. We exploit the normal form in order to prove the existence of metastability phenomena for the lattices. More precisely, we show that the energy spectrum of the normal modes attains a distribution in which the energy is shared among a packet of low-frequencies modes; such distribution remains unchanged up to the time-scale of validity of the continuous approximation.
Securing Smart Contract On The Fly

We present Solythesis, a source to source Solidity compiler which takes a smart contract code and a user specified invariant as the input and produces an instrumented contract that rejects all transactions that violate the invariant. The design of Solythesis is driven by our observation that the consensus protocol and the storage layer are the primary and the secondary performance bottlenecks of Ethereum, respectively. Solythesis operates with our novel delta update and delta check techniques to minimize the overhead caused by the instrumented storage access statements. Our experimental results validate our hypothesis that the overhead of runtime validation, which is often too expensive for other domains, is in fact negligible for smart contracts. The CPU overhead of Solythesis is only 0.12% on average for our 23 benchmark contracts.
Counting Hamilton cycles in Dirac hypergraphs

A tight Hamilton cycle in a k-uniform hypergraph (k-graph) G is a cyclic ordering of the vertices of G such that every set of k consecutive vertices in the ordering forms an edge. Rödl, Ruciński and Szemerédi proved that for $k\ge 3$ , every k-graph on n vertices with minimum codegree at least $n/2+o(n)$ contains a tight Hamilton cycle. We show that the number of tight Hamilton cycles in such k-graphs is ${\exp(n\ln n-\Theta(n))}$ . As a corollary, we obtain a similar estimate on the number of Hamilton ${\ell}$ -cycles in such k-graphs for all ${\ell\in\{0,\ldots,k-1\}}$ , which makes progress on a question of Ferber, Krivelevich and Sudakov.
Minimization of Weighted Completion Times in Path-based Coflow Scheduling

Coflow scheduling models communication requests in parallel computing frameworks where multiple data flows between shared resources need to be completed before computation can continue. In this paper, we introduce Path-based Coflow Scheduling, a generalized problem variant that considers coflows as collections of flows along fixed paths on general network topologies with node capacity restrictions. For this problem, we minimize the coflows' total weighted completion time. We show that flows on paths in the original network can be interpreted as hyperedges in a hypergraph and transform the path-based scheduling problem into an edge scheduling problem on this hypergraph. We present a $(2\lambda + 1)$-approximation algorithm when node capacities are set to one, where $\lambda$ is the maximum number of nodes in a path. For the special case of simultaneous release times for all flows, our result improves to a $(2\lambda)$-approximation. Furthermore, we generalize the result to arbitrary node constraints and obtain a $(2\lambda\Delta + 1)$- and a $(2\lambda\Delta)$-approximation in the case of general and zero release times, where $\Delta$ captures the capacity disparity between nodes.
X-Ray Sobolev Variational Auto-Encoders

The quality of the generative models (Generative adversarial networks, Variational Auto-Encoders, .) depends heavily on the choice of a good probability distance. However some popular metrics lack convenient properties such as (geodesic) convexity, fast evaluation and so on. To address these shortcomings, we introduce a class of distances that have built-in convexity. We investigate the relationship with some known paradigms (sliced distances, reproducing kernel Hilbert spaces, energy distances). The distances are shown to posses fast implementations and are included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality results on standard generative datasets.