Miracle-dz/UniFAR-FTUs huggingface dataset:
On Convergence of General Truncation-Augmentation Schemes for Approximating Stationary Distributions of Markov Chains

In the analysis of Markov chains and processes, it is sometimes conve-nient to replace an unbounded state space with a “truncated” bounded state space. When such a replacement is made, one often wants to know whether the equilibrium behavior of the truncated chain or process is close to that of the untruncated system. For example, such questions arise naturally when considering numerical methods for computing stationary distributions on unbounded state space. In this paper, we study general truncation-augmentation schemes, in which the substochastic truncated “northwest corner” of the transition matrix or kernel is stochasticized (or augmented) arbitrarily. In the presence of a Lyapunov condition involving a coercive function, we show that such schemes are generally convergent in countable state space, provided that the truncation is chosen as a sublevel set of the Lyapunov function. For stochastically monotone Markov chains on Z + , we prove that we can always choose the truncation sets to be of the form { 0 , 1 , . , n } . We then provide suﬃcient conditions for weakly continuous Markov chains under which general truncation-augmentation schemes converge weakly in continuous state space. Finally, we brieﬂy discuss the extension of the theory to continuous time Markov jump processes.
Best Arm Identification in Restless Markov Multi-Armed Bandits

We study the problem of identifying the best arm in a multi-armed bandit environment when each arm is a time-homogeneous and ergodic discrete-time Markov process on a common, finite state space. The state evolution on each arm is governed by the arm’s transition probability matrix (TPM). A decision entity that knows the set of arm TPMs but not the exact mapping of the TPMs to the arms, wishes to find the index of the best arm as quickly as possible, subject to an upper bound on the error probability. The decision entity selects one arm at a time sequentially, and all the unselected arms continue to undergo state evolution (restless arms). For this problem, we derive the first-known problem instance-dependent asymptotic lower bound on the growth rate of the expected time required to find the index of the best arm, where the asymptotics is as the error probability vanishes. Further, we propose a sequential policy that, for an input parameter <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>, forcibly selects an arm that has not been selected for <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> consecutive time instants. We show that this policy achieves an upper bound that depends on <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> and is monotonically non-increasing as <inline-formula> <tex-math notation="LaTeX">$R\to \infty $ </tex-math></inline-formula>. The question of whether, in general, the limiting value of the upper bound as <inline-formula> <tex-math notation="LaTeX">$R\to \infty $ </tex-math></inline-formula> matches with the lower bound, remains open. We identify a special case in which the upper and the lower bounds match. Prior works on best arm identification have dealt with (a) independent and identically distributed observations from the arms, and (b) rested Markov arms, whereas our work deals with the more difficult setting of restless Markov arms.
Searching Far and Long. I. Pilot ALMA 2 mm Follow-up of Bright Dusty Galaxies as a Redshift Filter

A complete census of dusty star-forming galaxies (DSFGs) at early epochs is necessary to constrain the obscured contribution to the cosmic star formation rate density (CSFRD); however, DSFGs beyond z ∼ 4 are both rare and hard to identify from photometric data alone due to degeneracies in submillimeter photometry with redshift. Here, we present a pilot study obtaining follow-up Atacama Large Millimeter Array (ALMA) 2 mm observations of a complete sample of 39 850 μm-bright dusty galaxies in the SSA22 field. Empirical modeling suggests 2 mm imaging of existing samples of DSFGs selected at 850 μm—1 mm can quickly and easily isolate the “needle in a haystack” DSFGs that sit at z > 4 or beyond. Combining archival submillimeter imaging with our measured ALMA 2 mm photometry (1σ ∼ 0.08 mJy beam−1 rms), we characterize the galaxies’ IR spectral energy distributions (SEDs) and use them to constrain redshifts. With available redshift constraints fit via the combination of six submillimeter bands, we identify 6/39 high-z candidates each with >50% likelihood to sit at z > 4, and find a positive correlation between redshift and 2 mm flux density. Specifically, our models suggest the addition of 2 mm to a moderately constrained IR SED will improve the accuracy of a millimeter-derived redshift from Δz/(1 + z) = 0.3 to Δz/(1 + z) = 0.2. Our IR SED characterizations provide evidence for relatively high-emissivity spectral indices (〈β〉 = 2.4 ± 0.3) in the sample. We measure that especially bright (S 850μ m > 5.55 mJy) DSFGs contribute ∼10% to the cosmic-averaged CSFRD from 2 < z < 5, confirming findings from previous work with similar samples.
A new procedure for Selective Inference with the Generalized Linear Lasso

This articles investigates the distribution of the solutions of the generalized linear lasso (GLL), conditional on some selection event. In this framework of post-selection inference (PSI), we provide rigorous deﬁnitions of the selected and saturated models: two diﬀerent paradigms that determine the hypothesis being tested. Based on a conditional Maximum Likelihood Estimator (MLE) approach, we give a procedure to obtain asymptotically valid PSI conﬁdence regions and testing procedures for Generalized Linear Models (GLMs). In a second stage, we focus on the sparse logistic regression and we exhibit conditions ensuring that our conditional MLE method is valid. We present extensive numerical simulations supporting our theoretical results.
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints

Vision-based tactile sensors typically utilize a deformable elastomer and a camera mounted above to provide high-resolution image observations of contacts. Obtaining accurate volumetric meshes for the deformed elastomer can provide direct contact information and benefit robotic grasping and manipulation. This paper focuses on learning to synthesize the volumetric mesh of the elastomer based on the image imprints acquired from vision-based tactile sensors. Synthetic image-mesh pairs and real-world images are gathered from 3D finite element methods (FEM) and physical sensors, respectively. A graph neural network (GNN) is introduced to learn the image-to-mesh mappings with supervised learning. A self-supervised adaptation method and image augmentation techniques are proposed to transfer networks from simulation to reality, from primitive contacts to unseen contacts, and from one sensor to another. Using these learned and adapted networks, our proposed method can accurately reconstruct the deformation of the real-world tactile sensor elastomer in various domains, as indicated by the quantitative and qualitative results.
On the fate of the secondary white dwarf in double-degenerate double-detonation Type Ia supernovae

The progenitor systems and explosion mechanism of Type Ia supernovae are still unknown. Currently favoured progenitors include double-degenerate systems consisting of two carbon-oxygen white dwarfs with thin helium shells. In the double-detonation scenario, violent accretion leads to a helium detonation on the more massive primary white dwarf that turns into a carbon detonation in its core and explodes it. We investigate the fate of the secondary white dwarf, focusing on changes of the ejecta and observables of the explosion if the secondary explodes as well rather than survives. We simulate a binary system of a 1 . 05 M (cid:12) and a 0 . 7 M (cid:12) carbon-oxygen white dwarf with 0 . 03 M (cid:12) helium shells each. We follow the system self-consistently from inspiral to ignition, through the explosion, to synthetic observables. We conﬁrm that the primary white dwarf explodes self-consistently. The helium detonation around the secondary white dwarf, however, fails to ignite a carbon detonation. We restart the simulation igniting the carbon detonation in the secondary white dwarf by hand and compare the ejecta and observables of both explosions. We ﬁnd that the outer ejecta at 𝑣 > 15 000 km s − 1 are indistinguishable. Light curves and spectra are very similar until ∼ 40 d after explosion and the ejecta are much more spherical than violent merger models. The inner ejecta diﬀer signiﬁcantly slowing down the decline rate of the bolometric light curve after maximum of the model with a secondary explosion by ∼ 20 per cent. We expect future synthetic 3D nebular spectra to conﬁrm or rule out either model.
Separate What You Describe: Language-Queried Audio Source Separation

In this paper, we introduce the task of language-queried audio source separation (LASS), which aims to separate a target source from an audio mixture based on a natural language query of the target source (e.g.,"a man tells a joke followed by people laughing"). A unique challenge in LASS is associated with the complexity of natural language description and its relation with the audio sources. To address this issue, we proposed LASS-Net, an end-to-end neural network that is learned to jointly process acoustic and linguistic information, and separate the target source that is consistent with the language query from an audio mixture. We evaluate the performance of our proposed system with a dataset created from the AudioCaps dataset. Experimental results show that LASS-Net achieves considerable improvements over baseline methods. Furthermore, we observe that LASS-Net achieves promising generalization results when using diverse human-annotated descriptions as queries, indicating its potential use in real-world scenarios. The separated audio samples and source code are available at https://liuxubo717.github. io/LASS-demopage.
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However, videos inherently express a much wider gamut of information than texts. Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos. Therefore, for a given text, a retrieval model should focus on the text's most semantically similar video sub-regions to make a more relevant comparison. Yet, most existing works aggregate entire videos with-out directly considering text. Common text-agnostic ag-gregations schemes include mean-pooling or self-attention over the frames, but these are likely to encode misleading vi-sual information not described in the given text. To address this, we propose a cross-modal attention model called X-Pool that reasons between a text and the frames of a video. Our core mechanism is a scaled dot product attention for a text to attend to its most semantically similar frames. We then generate an aggregated video representation conditioned on the text's attention weights over the frames. We evaluate our method on three benchmark datasets of MSR-VTT, MSVD and LSMDC, achieving new state-of-the-art re-sults by up to 12% in relative improvement in Recall@ 1. Our findings thereby highlight the importance of joint text-video reasoning to extract important visual cues according to text. Full code and demo can be found at: layer6ai-labs.github. iolxpooll.
Long-term Evolution of Supercritical Black Hole Accretion with Outflows: A Subgrid Feedback Model for Cosmological Simulations

We study the long-term evolution of the global structure of axisymmetric accretion flows onto a black hole (BH) at rates substantially higher than the Eddington value ( ṀEdd ), performing 2D hydrodynamical simulations with and without radiative diffusion. In the high-accretion optically thick limit, where the radiation energy is efficiently trapped within the inflow, the accretion flow becomes adiabatic and comprises turbulent gas in the equatorial region and strong bipolar outflows. As a result, the mass inflow rate decreases toward the center as Ṁin∝rp with p ∼ 0.5–0.7 and a small fraction of the inflowing gas feeds the nuclear BH. Thus, super-Eddington accretion is sustained only when a larger amount of gas is supplied from larger radii at ≳100–1000 ṀEdd . The global structure of the flow settles down to a quasi-steady state in millions of the orbital timescale at the BH event horizon, which is ≳10–100 times longer than that addressed in previous (magneto-)RHD simulation studies. Energy transport via radiative diffusion accelerates the outflow near the poles in the inner region but does not change the overall properties of the accretion flow compared to the cases without diffusion. Based on our simulation results, we provide a mechanical feedback model for super-Eddington accreting BHs. This can be applied as a subgrid model in large-scale cosmological simulations that do not sufficiently resolve galactic nuclei, and to the formation of the heaviest gravitational-wave sources via accretion in dense environments.
From snapshots to manifolds – a tale of shear flows

Abstract We propose a novel nonlinear manifold learning from snapshot data and demonstrate its superiority over proper orthogonal decomposition (POD) for shedding-dominated shear flows. Key enablers are isometric feature mapping, Isomap, as encoder and, $K$-nearest neighbours ($K$NN) algorithm as decoder. The proposed technique is applied to numerical and experimental datasets including the fluidic pinball, a swirling jet and the wake behind a couple of tandem cylinders. Analysing the fluidic pinball, the manifold is able to describe the pitchfork bifurcation and the chaotic regime with only three feature coordinates. These coordinates are linked to the vortex-shedding phases and the force coefficients. The manifold coordinates of the swirling jet are comparable to the POD mode amplitudes, yet allow for a more distinct and less noise-sensitive manifold identification. A similar observation is made for the wake of two tandem cylinders. The tandem cylinders are aligned and located at a streamwise distance which corresponds to the transition between the single bluff body and the reattachment regimes of vortex shedding. Isomap unveils these two shedding regimes while the Lissajous plot of the first two POD mode amplitudes features a single circle. The reconstruction error of the manifold model is small compared with the fluctuation level, indicating that the low embedding dimensions contain the coherent structure dynamics. The proposed Isomap–$K$NN manifold learner is expected to be of great importance in estimation, dynamic modelling and control for a large range of configurations with dominant coherent structures.
Generalizing Few-Shot NAS with Gradient Matching

Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search. One-Shot methods tackle this challenge by training one supernet to approximate the performance of every architecture in the search space via weight-sharing, thereby drastically reducing the search cost. However, due to coupled optimization between child architectures caused by weight-sharing, One-Shot supernet's performance estimation could be inaccurate, leading to degraded search outcomes. To address this issue, Few-Shot NAS reduces the level of weight-sharing by splitting the One-Shot supernet into multiple separated sub-supernets via edge-wise (layer-wise) exhaustive partitioning. Since each partition of the supernet is not equally important, it necessitates the design of a more effective splitting criterion. In this work, we propose a gradient matching score (GM) that leverages gradient information at the shared weight for making informed splitting decisions. Intuitively, gradients from different child models can be used to identify whether they agree on how to update the shared modules, and subsequently to decide if they should share the same weight. Compared with exhaustive partitioning, the proposed criterion significantly reduces the branching factor per edge. This allows us to split more edges (layers) for a given budget, resulting in substantially improved performance as NAS search spaces usually include dozens of edges (layers). Extensive empirical evaluations of the proposed method on a wide range of search spaces (NASBench-201, DARTS, MobileNet Space), datasets (cifar10, cifar100, ImageNet) and search algorithms (DARTS, SNAS, RSPS, ProxylessNAS, OFA) demonstrate that it significantly outperforms its Few-Shot counterparts while surpassing previous comparable methods in terms of the accuracy of derived architectures.
Gravitational wave nonlinearities and pulsar-timing array angular correlations

Several pulsar-timing array (PTA) collaborations are ﬁnding tantalising hints for a stochastic gravitational wave background signal in the nano-Hertz regime. So far, though, no convincing evidence for the expected Hellings-Downs quadrupolar correlations has been found. While this issue might get ﬁxed at the light of more accurate, forthcoming data, it is important to keep an eye open on diﬀerent possibilities, and explore scenarios able to produce diﬀerent types of PTA angular correlations. We point out that a stationary non-Gaussian component to the gravitational wave background can modulate the 2-point PTA overlap reduction function, adding contributions that can help in ﬁtting the angular distribution of PTA data. We discuss possible sources for such non-Gaussian signal in terms of cosmological processes occurring after inﬂation ends, and we investigate further tests for this idea.
Design strategies for controlling neuron-connected robots using reinforcement learning

Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-dependent behavior here is deﬁned as behavior that is not the result of a simple sensory-motor coupling, but rather based on an understanding of the task goal. This paper proposes design principles for training neuron-connected robots based on task goals to achieve context-dependent behavior. First, we employ deep reinforcement learning (RL) to enable training that accounts for goal achievements. Second, we propose a neuron simulator as a probability distribution based on recorded neural data, aiming to represent physiologically valid neural dynamics while avoiding complex modeling with high computational costs. Furthermore, we propose to update the simulators during the training to bridge the gap between the simulation and the real settings. The experiments showed that the robot gradually learned context-dependent behaviors in pole balancing and robot navigation tasks. Moreover, the learned policies were valid for neural simulators based on novel neural data, and the task performance increased by updating the simulators during training. These results suggest the effectiveness of the proposed design principle for the context-dependent behavior of neuron-connected robots.
Neural Vocoder is All You Need for Speech Super-resolution

Speech super-resolution (SR) is a task to increase speech sampling rate by generating high-frequency components. Existing speech SR methods are trained in constrained experimental settings, such as a fixed upsampling ratio. These strong constraints can potentially lead to poor generalization ability in mismatched real-world cases. In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios. NVSR consists of a mel-bandwidth extension module, a neural vocoder module, and a post-processing module. Our proposed system achieves state-of-the-art results on the VCTK multi-speaker benchmark. On 44.1 kHz target resolution, NVSR outperforms WSRGlow and Nu-wave by 8% and 37% respectively on log spectral distance and achieves a significantly better perceptual quality. We also demonstrate that prior knowledge in the pre-trained vocoder is crucial for speech SR by performing mel-bandwidth extension with a simple replication-padding method. Samples can be found in https://haoheliu.github. io/nvsr.
mc-BEiT: Multi-choice Discretization for Image BERT Pre-training

Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing the continuous visual signals into discrete vision tokens using a pre-learned dVAE. Despite a feasible solution, the improper discretization hinders further improvements of image pre-training. Since image discretization has no ground-truth answers, we believe that the masked patch should not be assigned with a unique token id even if a better tokenizer can be obtained. In this work, we introduce an improved BERT-style image pre-training method, namely mc-BEiT, which performs MIM proxy tasks towards eased and refined multi-choice training objectives. Specifically, the multi-choice supervision for the masked image patches is formed by the soft probability vectors of the discrete token ids, which are predicted by the off-the-shelf image tokenizer and further refined by high-level inter-patch perceptions resorting to the observation that similar patches should share their choices. Extensive experiments on classification, segmentation, and detection tasks demonstrate the superiority of our method, e.g., the pre-trained ViT-B achieves 84.1% top-1 fine-tuning accuracy on ImageNet-1K classification, 49.2% AP^b and 44.0% AP^m of object detection and instance segmentation on COCO, 50.8% mIOU on ADE20K semantic segmentation, outperforming the competitive counterparts. The code will be available at https://github.com/lixiaotong97/mc-BEiT.
The effect of multiple features on the power spectrum in two-field inflation

We extend our previous work on the enhancement of the curvature spectrum during inflation to the two-field case. We identify the slow-roll parameter η as the quantity that can trigger the rapid growth of perturbations. Its two components, η ∥ along the background trajectory and η ⊥ perpendicular to it, remain small during most of the evolution, apart from short intervals during which they take large, positive or negative, values. The typical reason for the appearance of strong features in η ∥ is sharp steps or inflection points in the inflaton potential, while η ⊥ grows large during sharp turns in field space. We focus on the additive effect of several features leading to the resonant growth of the curvature spectrum. Three or four features in the evolution of η are sufficient in order to induce an enhancement of the power spectrum by six or seven orders of magnitude, which can lead to the significant production of primordial black holes and stochastic gravitational waves. A big part of our study focuses on understanding the evolution of the perturbations and the resulting spectra through analytic means. The presence of multiple features in the background evolution points to a more complex inflationary paradigm, which is also more natural in the multi-field case. The critical examination of this possibility is within the reach of experiment.