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Researcher profile

Jun Yang

Jun Yang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.HEastro-ph.GAastro-ph.EPComputationDistributed, Parallel, and Cluster Computingeess.SYphysics.opticsSystems and ControlArtificial IntelligenceDatabaseseess.ASeess.SPMachine Learningmath.PRMethodologySoundComputer Visionmath.APmath.STphysics.atom-phquant-phRoboticsStatistics TheoryComputation and Languagecond-mat.othercond-mat.quant-gasCryptography and Securityeess.IVhep-phInformation Retrievalmath.NTmath.OAmath.OCmath.RTNeural and Evolutionary Computingphysics.ao-phphysics.app-phphysics.atm-clusphysics.class-ph

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Published work

56 published item(s)

preprint2026arXiv

A reconsideration of quasimonotone variational inequality problems

This paper is based on Tseng's exgradient algorithm for solving variational inequality problems in real Hilbert spaces. Under the assumptions that the cost operator is quasimonotone and Lipschitz continuous, we establish the strong convergence, sublinear convergence, and Q-linear convergence of the algorithm. The results of this paper provide new insights into quasimonotone variational inequality problems, extending and enriching existing results in the literature. Finally, we conduct numerical experiments to illustrate the effectiveness and implementability of our proposed condition and algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ActiShade: Activating Overshadowed Knowledge to Guide Multi-Hop Reasoning in Large Language Models

In multi-hop reasoning, multi-round retrieval-augmented generation (RAG) methods typically rely on LLM-generated content as the retrieval query. However, these approaches are inherently vulnerable to knowledge overshadowing - a phenomenon where critical information is overshadowed during generation. As a result, the LLM-generated content may be incomplete or inaccurate, leading to irrelevant retrieval and causing error accumulation during the iteration process. To address this challenge, we propose ActiShade, which detects and activates overshadowed knowledge to guide large language models (LLMs) in multi-hop reasoning. Specifically, ActiShade iteratively detects the overshadowed keyphrase in the given query, retrieves documents relevant to both the query and the overshadowed keyphrase, and generates a new query based on the retrieved documents to guide the next-round iteration. By supplementing the overshadowed knowledge during the formulation of next-round queries while minimizing the introduction of irrelevant noise, ActiShade reduces the error accumulation caused by knowledge overshadowing. Extensive experiments show that ActiShade outperforms existing methods across multiple datasets and LLMs.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Curator: Efficient Vector Search with Low-Selectivity Filters

Embedding-based dense retrieval has become the cornerstone of many critical applications, where approximate nearest neighbor search (ANNS) queries are often combined with filters on labels such as dates and price ranges. Graph-based indexes achieve state-of-the-art performance on unfiltered ANNS but encounter connectivity breakdown on low-selectivity filtered queries, where qualifying vectors become sparse and the graph structure among them fragments. Recent research proposes specialized graph indexes that address this issue by expanding graph degree, which incurs prohibitively high construction costs. Given these inherent limitations of graph-based methods, we argue for a dual-index architecture and present Curator, a partition-based index that complements existing graph-based approaches for low-selectivity filtered ANNS. Curator builds specialized indexes for different labels within a shared clustering tree, where each index adapts to the distribution of its qualifying vectors to ensure efficient search while sharing structure to minimize memory overhead. The system also supports incremental updates and handles arbitrary complex predicates beyond single-label filters by efficiently constructing temporary indexes on the fly. Our evaluation demonstrates that integrating Curator with state-of-the-art graph indexes reduces low-selectivity query latency by up to 20.9x compared to pre-filtering fallback, while increasing construction time and memory footprint by only 5.5% and 4.3%, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Detection of a Millisecond Periodicity in BATSE Short Gamma-Ray Bursts

Coherent oscillations at kilohertz frequencies have recently been detected in a small number of gamma-ray bursts (GRBs), suggesting quasi-periodic dynamics in their central engines. A prominent example is GRB~230307A, which exhibited a brief, highly coherent, energy-dependent periodic signal interpreted as the possible spin signature of a nascent millisecond magnetar formed after a compact binary merger. Motivated by these developments, we conducted a comprehensive search for similar signals, accounting for both temporal and spectral dependencies, in 532 short GRBs with time-tagged event data recorded by the Burst and Transient Source Experiment (BATSE) onboard the \textit{Compton Gamma-Ray Observatory}. Within this sample, we identify a single statistically significant case: GRB~960616 (BATSE trigger~5502), in which the $\sim$30~ms main emission episode is coherently modulated at 1100~Hz, with the strongest modulation above 320~keV and a fractional amplitude of $\sim$47\%. Assuming the presence of a coherent periodic modulation, we use data-driven Monte Carlo simulations to place an upper limit of $\sim$8\% on the fraction of the total radiated energy that can be modulated by the QPO. This event, exhibiting a periodicity at $\sim$0.91~ms, further supports the possibility that millisecond periodicities can arise during GRBs in merger-driven scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Metropolis-Adjusted Diffusion Models

Sampling from score-based diffusion models incurs bias due to both time discretisation and the approximation of the score function. A common strategy for reducing this bias is to apply corrector steps based on the unadjusted Langevin algorithm (ULA) at each noise level within a predictor-corrector framework. However, ULA is itself a biased sampler, as it discretises a continuous diffusion process. In this work, we consider adjusted Langevin correctors that employ Metropolis--Hastings (MH) or Barker's accept-reject steps to correct for this bias. Since the target density ratio typically required by MH-based algorithms is unavailable, we propose methods that instead utilise the score function to compute the correct acceptance probability. We introduce the first exact method for adjusting Langevin corrections in diffusion models, based on a two-coin Bernoulli factory algorithm. We also propose an efficient approximation based on Simpson's rule that achieves accuracy of order $5/2$ in the step size at near-zero marginal cost. We demonstrate that these procedures improve sample quality on both synthetic and image datasets, yielding consistent gains in Fréchet Inception Distance (FID) on the latter.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Sub-Cauchy Sampling: Escaping the Dark Side of the Moon

We introduce a Markov chain Monte Carlo algorithm based on Sub-Cauchy Projection, a geometric transformation that generalizes stereographic projection by mapping Euclidean space into a spherical cap of a hyper-sphere, referred to as the complement of the dark side of the moon. We prove that our proposed method is uniformly ergodic for sub-Cauchy targets, namely targets whose tails are at most as heavy as a multidimensional Cauchy distribution, and show empirically its performance for challenging high-dimensional problems. The simplicity and broad applicability of our approach open new opportunities for Bayesian modeling and computation with heavy-tailed distributions in settings where most existing methods are unreliable.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Edge Enhanced Image Style Transfer via Transformers

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A distributionally robust optimization approach to two-sided chance constrained stochastic model predictive control with unknown noise distribution

In this work, we propose a distributionally robust stochastic model predictive control (DR-SMPC) algorithm to address the problem of two-sided chance constrained discrete-time linear system corrupted by additive noise. The prevalent mechanism to cope with two-sided chance constraints is the so-called risk allocation approach, which conservatively approximates the two-sided chance constraints with two single chance constraints by applying the Boole's inequality. In this proposed DR-SMPC framework, an exact tractable second-order cone (SOC) approach is adopted to abstract the two-sided chance constraints by considering the first and second moments of the noise. The proposed DR-SMPC algorithm is able to guarantee that the worst-case probability of violating both the upper and lower limits of safety constraints is within the pre-specified maximum probability (PsMP). By flexibly adjusting this PsMP, the feasible region of the initial states can be increased for the SMPC problem. The recursive feasibility and convergence of the proposed DR-SMPC are established rigorously by introducing binary initialization strategy of nominal state. Simulation studies of two practical cases are conducted to demonstrate the effectiveness of the proposed DR-SMPC algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection

Polyphonic sound event localization and detection (SELD) aims at detecting types of sound events with corresponding temporal activities and spatial locations. In this paper, a track-wise ensemble event independent network with a novel data augmentation method is proposed. The proposed model is based on our previous proposed Event-Independent Network V2 and is extended by conformer blocks and dense blocks. The track-wise ensemble model with track-wise output format is proposed to solve an ensemble model problem for track-wise output format that track permutation may occur among different models. The data augmentation approach contains several data augmentation chains, which are composed of random combinations of several data augmentation operations. The method also utilizes log-mel spectrograms, intensity vectors, and Spatial Cues-Augmented Log-Spectrogram (SALSA) for different models. We evaluate our proposed method in the Task of the L3DAS22 challenge and obtain the top ranking solution with a location-dependent F-score to be 0.699. Source code is released.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecast model, which could investigate the variation trend of stability coefficient of tailing dam and issue early warnings. In order to fill the gap, this work presents an hybrid network - Wavelet-based Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), namely Wavelet-CNN-LSTM netwrok for predicting the tailings pond risk. Firstly, we construct the especial nonlinear data processing method to impute the missing value with the numerical inversion (NI) method, which combines correlation analysis, sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new forecasting model was proposed to monitor the saturation line, which is the lifeline of the tailings pond and can directly reflect the stability of the tailings pond. After using the discrete wavelet transform (DWT) to decompose the original saturation line data into 4-layer wavelets and de-noise the data, the CNN was used to identify and learn the spatial structures in the time series, followed by LSTM cells for detecting the long-short-term dependence. Finally, different experiments were conducted to evaluate the effectiveness of our model by comparing it with other state-of-the-art algorithms. The results show that Wavelet-CNN-LSTM achieves the best score both in mean absolute percentage error (MAPE), root-mean-square error (RMSE) and R 2 .

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Adversarial Prefetch: New Cross-Core Cache Side Channel Attacks

Modern x86 processors have many prefetch instructions that can be used by programmers to boost performance. However, these instructions may also cause security problems. In particular, we found that on Intel processors, there are two security flaws in the implementation of PREFETCHW, an instruction for accelerating future writes. First, this instruction can execute on data with read-only permission. Second, the execution time of this instruction leaks the current coherence state of the target data. Based on these two design issues, we build two cross-core private cache attacks that work with both inclusive and non-inclusive LLCs, named Prefetch+Reload and Prefetch+Prefetch. We demonstrate the significance of our attacks in different scenarios. First, in the covert channel case, Prefetch+Reload and Prefetch+Prefetch achieve 782 KB/s and 822 KB/s channel capacities, when using only one shared cache line between the sender and receiver, the largest-to-date single-line capacities for CPU cache covert channels. Further, in the side channel case, our attacks can monitor the access pattern of the victim on the same processor, with almost zero error rate. We show that they can be used to leak private information of real-world applications such as cryptographic keys. Finally, our attacks can be used in transient execution attacks in order to leak more secrets within the transient window than prior work. From the experimental results, our attacks allow leaking about 2 times as many secret bytes, compared to Flush+Reload, which is widely used in transient execution attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Complexity Results for MCMC derived from Quantitative Bounds

This paper considers how to obtain MCMC quantitative convergence bounds which can be translated into tight complexity bounds in high-dimensional {settings}. We propose a modified drift-and-minorization approach, which establishes generalized drift conditions defined in subsets of the state space. The subsets are called the "large sets", and are chosen to rule out some "bad" states which have poor drift property when the dimension of the state space gets large. Using the "large sets" together with a "fitted family of drift functions", a quantitative bound can be obtained which can be translated into a tight complexity bound. As a demonstration, we analyze several Gibbs samplers and obtain complexity upper bounds for the mixing time. In particular, for one example of Gibbs sampler which is related to the James--Stein estimator, we show that the number of iterations required for the Gibbs sampler to converge is constant under certain conditions on the observed data and the initial state. It is our hope that this modified drift-and-minorization approach can be employed in many other specific examples to obtain complexity bounds for high-dimensional Markov chains.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dimension-free Mixing for High-dimensional Bayesian Variable Selection

Yang et al. (2016) proved that the symmetric random walk Metropolis--Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. We propose a novel MCMC sampler using an informed proposal scheme, which we prove achieves a much faster mixing time that is independent of the number of covariates, under the same assumptions. To the best of our knowledge, this is the first high-dimensional result which rigorously shows that the mixing rate of informed MCMC methods can be fast enough to offset the computational cost of local posterior evaluation. Motivated by the theoretical analysis of our sampler, we further propose a new approach called "two-stage drift condition" to studying convergence rates of Markov chains on general state spaces, which can be useful for obtaining tight complexity bounds in high-dimensional settings. The practical advantages of our algorithm are illustrated by both simulation studies and real data analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient Pipeline Planning for Expedited Distributed DNN Training

To train modern large DNN models, pipeline parallelism has recently emerged, which distributes the model across GPUs and enables different devices to process different microbatches in pipeline. Earlier pipeline designs allow multiple versions of model parameters to co-exist (similar to asynchronous training), and cannot ensure the same model convergence and accuracy performance as without pipelining. Synchronous pipelining has recently been proposed which ensures model performance by enforcing a synchronization barrier between training iterations. Nonetheless, the synchronization barrier requires waiting for gradient aggregation from all microbatches and thus delays the training progress. Optimized pipeline planning is needed to minimize such wait and hence the training time, which has not been well studied in the literature. This paper designs efficient, near-optimal algorithms for expediting synchronous pipeline-parallel training of modern large DNNs over arbitrary inter-GPU connectivity. Our algorithm framework comprises two components: a pipeline partition and device mapping algorithm, and a pipeline scheduler that decides processing order of microbatches over the partitions, which together minimize the per-iteration training time. We conduct thorough theoretical analysis, extensive testbed experiments and trace-driven simulation, and demonstrate our scheme can accelerate training up to 157% compared with state-of-the-art designs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-resolution VLBI observations of and modelling the radio emission from the TDE AT2019dsg

A tidal disruption event (TDE) involves the shredding of a star in the proximity of a supermassive black hole (SMBH). The nearby ($\approx$230 Mpc) relatively radio-quiet, thermal emission dominated source AT2019dsg is the first TDE with a potential neutrino association. The origin of non-thermal emission remains inconclusive; possibilities include a relativistic jet or a sub-relativistic outflow. Distinguishing between them can address neutrino production mechanisms. High-resolution very long baseline interferometry 5-GHz observations provide a proper motion of 0.94 $\pm$ 0.65 mas yr$^{-1}$ ($3.2 \pm 2.2~c$; $1-σ$). Modelling the radio emission favors an origin from the interaction between a decelerating outflow (velocity $\approx$ 0.1 $c$) and a dense circum-nuclear medium. The transition of the synchrotron self-absorption frequency through the observation band marks a peak flux density of 1.19 $\pm$ 0.18 mJy at 152.8 $\pm$ 16.2 days. An equipartition analysis indicates an emission region distance of $\geqslant$ 4.7 $\times$ 10$^{16}$ cm, magnetic field strength $\geqslant$ 0.17 G, and number density $\geqslant$ 5.7 $\times$ 10$^{3}$ cm$^{-3}$. The disruption involves a $\approx$ 2 $M_\odot$ star with a penetration factor $\approx 1$ and a total energy output of $\leqslant$ 1.5 $\times$ 10$^{52}$ erg. The outflow is radiatively driven by accretion of stellar debris onto the SMBH. Neutrino production is likely related to the acceleration of protons to PeV energies and the availability of a suitable cross-section at the outflow base. The present study thus helps exclude jet-related origins for non-thermal emission and neutrino production, and constrains non-jetted scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Idealized 2D Cloud-Resolving Simulations for Tidally Locked Habitable Planets

Cloud is critical for planetary climate and habitability, but it is also one of the most challenging parts of studying planets in and beyond the solar system. Here we use a cloud-resolving model (CRM) with high resolution (2 km) in a two-dimensional (2D) configuration to simulate the clouds and circulation on tidally locked aqua-planets. We find that the substellar area is covered by deep convective clouds, the nightside is dominated by low-level clouds, and these two are linked by a global-scale Walker circulation. We further find that a uniform surface warming causes the substellar cloud width to decrease, but a reduction in day-night surface temperature contrast or an increase in longwave radiative cooling rate causes the substellar cloud width to increase. These relationships can be roughly interpreted based on simple thermodynamic theories. Comparing the results between CRM and global 3D general circulation model (GCM), we find that they show qualitatively consistent results, including the Walker circulation, the substellar clouds, and the responses of the substellar ascending area and strength to changes in surface temperature or in its zonal contrast. But, large quantitative differences exist, such as the magnitude of cloud water path, the cloud width, and their responses to external forcings. These results increase our confidence in using GCMs for modeling exoplanetary climates, although large quantitative uncertainties should always exist. Future work is required to use 3D CRM(s) with realistic radiative transfer and with the Coriolis force to examine the clouds and climate of tidally locked planets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Improved Multi-step FCS-MPCC with Disturbance Compensation for PMSM Drives -- Methods and Experimental Validation

In this paper, an improved multi-step finite control set model predictive current control (FCS-MPCC) strategy with speed loop disturbance compensation is proposed for permanent magnet synchronous machine (PMSM) drives system. A multi-step prediction mechanism is beneficial to significantly improve the steady-state performance of the motor system. While the conventional multi-step prediction has the defect of heavy computational burden, an improved multi-step finite control set model predictive current control (IM MPCC) strategy is proposed by developing a new multi-step prediction mechanism. Furthermore, in order to improve the dynamic response of the system, a disturbance compensation (DC) mechanism based on an extended state observer (ESO) is proposed to estimate and compensate the total disturbance in the speed loop of the PMSM system. Both simulation and experimental results validate the effectiveness of the proposed control strategy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

In-situ probing and stabilizing the power ratio of electro-optic-modulated laser pairs based on VIPA etalon for quantum sensing

Monitoring and stabilizing the power ratio of laser pairs is significant to high-precision atom interferometers, especially as the compact electro-optic modulated all-fiber laser system prevails. In this Letter, we demonstrate a novel method to in-situ probe the relative power of laser pairs and to stabilize the power ratio of two Raman lasers using a high-dispersion virtually imaged phased array (VIPA) etalon. Sub-microsecond resolution on probing laser power transformation during atom interferometer sequence is achieved and the power ratio of two Raman lasers (PRTR) is tightly locked with high bandwidth despite of environmental disturbances, showing an Allan deviation of $4.39\times 10^{-5}$ at 1000 s averaging time. This method provides a novel way to stabilize the PRTR and diagnose the multi-frequency laser systems for atom interferometers and could find potential application in broad quantum sensing scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Is there a sub-parsec-scale jet base in the nearby dwarf galaxy NGC 4395?

NGC 4395 is a dwarf galaxy at a distance of about 4.3 Mpc (scale: ~0.021 pc mas$^{-1}$). It hosts an intermediate-mass black hole (IMBH) with a mass between ~10$^4$ and ~10$^5$ solar masses. The early radio observations of NGC 4395 with the very long baseline interferometry (VLBI) network, High Sensitivity Array (HSA), at 1.4 GHz in 2005 showed that its nucleus has a sub-mJy outflow-like feature (E) extending over 15 mas. To probe the possibility of the feature E as a continuous jet with a base physically coupled with the accretion disc, we performed deep VLBI observations with the European VLBI Network (EVN) at 5 GHz, and analysed the archival data obtained with the HSA at 1.4 GHz in 2008, NSF&#39;s Karl G. Jansky Very Large Array (VLA) at 12-18 GHz and the Atacama Large Millimetre/submillimetre Array (ALMA) at 237 GHz. The feature E displays more diffuse structure in the HSA image of 2008 and has no compact substructure detected in the EVN image. Together with the optically thin steep spectrum and the extremely large angular offset (about 220 mas) from the accurate optical Gaia position, we explain the feature E as nuclear shocks likely formed by the IMBH&#39;s episodic ejection or wide-angle outflow. The VLA and ALMA observations find a sub-mJy pc-scale diffuse feature, possibly tracing a thermal free-free emission region near the IMBH. There is no detection of a jet base at the IMBH position in the VLBI maps. The non-detections give an extremely low luminosity of <=4.7 x 10$^{33}$ erg s$^{-1}$ at 5 GHz and indicate no evidence of a disc-jet coupling on sub-pc scales.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-step dual control for exploration and exploitation in autonomous search with convergence guarantee

Motivated by the recently proposed dual control for exploration and exploitation (DCEE) concept, this paper presents a Multi-Step DCEE (MS-DCEE) framework with guaranteed convergence for autonomous search of a source of airborne dispersion. Different from the existing stochastic model predictive control (SMPC) algorithm and informative path planning (IPP) approaches, the proposed MS-DCEE approach uses the current and future input to not only drive the agent towards the estimated source location (exploitation) but also reduce its estimation uncertainty (exploration) by actively learning the operational environment. Unknown source target position, together with unknown environment, impose significant challenges in establishing the recursive feasibility and the convergence of the proposed algorithm. To address them, with the help of the property of Bayesian estimation, we develop a two-step approach where the unbiasedness of the mean estimation is assumed first and then the randomness of the mean estimate under each collected information sequence is accounted. Based on that, we develop a MS-DCEE scheme with suitable terminal ingredients where recursive feasibility and convergence are guaranteed. Two simulation scenarios are conducted, which show that the proposed MS-DCEE algorithm outperforms the SMPC, the IPP and the single-step DCEE approaches in terms of searching successful rates and efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Next-Best-View Prediction for Active Stereo Cameras and Highly Reflective Objects

Depth acquisition with the active stereo camera is a challenging task for highly reflective objects. When setup permits, multi-view fusion can provide increased levels of depth completion. However, due to the slow acquisition speed of high-end active stereo cameras, collecting a large number of viewpoints for a single scene is generally not practical. In this work, we propose a next-best-view framework to strategically select camera viewpoints for completing depth data on reflective objects. In particular, we explicitly model the specular reflection of reflective surfaces based on the Phong reflection model and a photometric response function. Given the object CAD model and grayscale image, we employ an RGB-based pose estimator to obtain current pose predictions from the existing data, which is used to form predicted surface normal and depth hypotheses, and allows us to then assess the information gain from a subsequent frame for any candidate viewpoint. Using this formulation, we implement an active perception pipeline which is evaluated on a challenging real-world dataset. The evaluation results demonstrate that our active depth acquisition method outperforms two strong baselines for both depth completion and object pose estimation performance.

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preprint2022arXiv

Plancherel Measures of Reductive Adelic Groups and Von Neumann Dimensions

Given a number field $F$ and a reductive group $G$ over $F$, the unitary dual $\hat{G(\mathbb{A}_F)}$ of the adelic group $G(\mathbb{A}_F)$ and the Placherel measure $ν_{G(\mathbb{A}_F)}$ on it can be determined by the Plancherel measure of its local groups $G(F_v)$. Given a subset $X\subset \hat{G(\mathbb{A}_F)}$ of finite Plancherel measure, let $H_X$ be the direct integral of the irreducible representations in $X$. Besides a $G(\mathbb{A}_F)$-module and a $G(F)$-module, $H_X$ is also a module over the group von Neumann algebra $\mathcal{L}(G(F))$, hence there is a canonical dimension $\dim_{\mathcal{L}(G(F))}H_X\in [0,\infty)$. It is proved that the Plancherel measure of $G(\mathbb{A}_F)$ coincides with the dimension over the algebra $\mathcal{L}(G(F))$: $\dim_{\mathcal{L}(G(F))}H_X=ν_{G(\mathbb{A}_F)}(X)$, if $G$ is semisimple, simply connected and $G(\mathbb{A}_F)$ is equipped with the Tamagawa measure.

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preprint2022arXiv

POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes

Maintaining an up-to-date map to reflect recent changes in the scene is very important, particularly in situations involving repeated traversals by a robot operating in an environment over an extended period. Undetected changes may cause a deterioration in map quality, leading to poor localization, inefficient operations, and lost robots. Volumetric methods, such as truncated signed distance functions (TSDFs), have quickly gained traction due to their real-time production of a dense and detailed map, though map updating in scenes that change over time remains a challenge. We propose a framework that introduces a novel probabilistic object state representation to track object pose changes in semi-static scenes. The representation jointly models a stationarity score and a TSDF change measure for each object. A Bayesian update rule that incorporates both geometric and semantic information is derived to achieve consistent online map maintenance. To extensively evaluate our approach alongside the state-of-the-art, we release a novel real-world dataset in a warehouse environment. We also evaluate on the public ToyCar dataset. Our method outperforms state-of-the-art methods on the reconstruction quality of semi-static environments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Probing the progenitor of high-$z$ short-duration GRB 201221D and its possible bulk acceleration in prompt emission

The growing observed evidence shows that the long- and short-duration gamma-ray bursts (GRBs) originate from massive star core-collapse and the merger of compact stars, respectively. GRB 201221D is a short-duration GRB lasting $\sim 0.1$ s without extended emission (EE) at high redshift $z=1.046$. By analyzing data observed with the Swift/BAT and Fermi/GBM, we find that a cutoff power-law model can adequately fit the spectrum with a soft $E_{\rm p}=113^{+9}_{-7}$ keV, and isotropic energy $E_{γ,iso} =1.36^{+0.17}_{-0.14}\times 10^{51}~\rm erg$. In order to reveal the possible physical origin of GRB 201221D, we adopted multi-wavelength criteria (e.g., Amati relation, $\varepsilon$-parameter, amplitude parameter, local event rate density, luminosity function, and properties of the host galaxy), and find that most of the observations of GRB 201221D favor a compact star merger origin. Moreover, we find that $\hatα$ is larger than $2+\hatβ$ in the prompt emission phase which suggests that the emission region is possibly undergoing acceleration during the prompt emission phase with a Poynting-flux-dominated jet.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quantum Computing 2022

Quantum technology is full of figurative and literal noise obscuring its promise. In this overview, we will attempt to provide a sober assessment of the promise of quantum technology with a focus on computing. We provide a tour of quantum computing and quantum technology that is aimed to be comprehensible to scientists and engineers without becoming a popular account. The goal is not a comprehensive review nor a superficial introduction but rather to serve as a useful map to navigate the hype, the scientific literature, and upcoming press releases about quantum technology and quantum computing. We have aimed to cite the most recent topical reviews, key results, and guide the reader away from fallacies and towards active discussions in the current quantum computing literature. The goal of this article was to be pedantic and introductory without compromising on the science.

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preprint2022arXiv

Robust single-sideband-modulated Raman light generation for atom interferometry by FBG-based optical rectangular filtration

Low-phase-noise and pure-spectrum Raman light is vital for high-precision atom interferometry by two-photon Raman transition. A preferred and prevalent solution for Raman light generation is electro-optic phase modulation. However, phase modulation inherently brings in double sidebands, resulting in residual sideband effects of multiple laser pairs beside Raman light in atom interferometry. Based on a well-designed rectangular fiber Bragg grating and an electro-optic modulator, optical single-sideband modulation has been realized at 1560 nm with a stable suppression ratio better than -25 dB despite of intense temperature variations. After optical filtration and frequency doubling, a robust phase-coherent Raman light at 780 nm is generated with a stable SNR of better than -19 dB and facilitates measuring the local gravity successfully. This proposed all-fiber single-sideband-modulated Raman light source, characterized as robust, compact and low-priced, is practical and potential for field applications of portable atom interferometry.

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preprint2022arXiv

Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains

Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from computationally generated spatial recordings to recordings of real-sound scenes. Our system submitted to the DCASE 2022 Task 3 is based on our previous proposed Event-Independent Network V2 (EINV2) with a novel data augmentation method. Our method employs EINV2 with a track-wise output format, permutation-invariant training, and a soft parameter-sharing strategy, to detect different sound events of the same class but in different locations. The Conformer structure is used for extending EINV2 to learn local and global features. A data augmentation method, which contains several data augmentation chains composed of stochastic combinations of several different data augmentation operations, is utilized to generalize the model. To mitigate the lack of real-scene recordings in the development dataset and the presence of sound events being unbalanced, we exploit FSD50K, AudioSet, and TAU Spatial Room Impulse Response Database (TAU-SRIR DB) to generate simulated datasets for training. We present results on the validation set of Sony-TAu Realistic Spatial Soundscapes 2022 (STARSS22) in detail. Experimental results indicate that the ability to generalize to different environments and unbalanced performance among different classes are two main challenges. We evaluate our proposed method in Task 3 of the DCASE 2022 challenge and obtain the second rank in the teams ranking. Source code is released.

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preprint2022arXiv

Thermocline Depth on Water-rich Exoplanets

Water-rich exoplanet is a type of terrestrial planet that is water-rich and its ocean depth can reach tens of to hundreds of kilo-meters with no exposed continents. Due to the lack of exposed continents, neither western boundary current nor coastal upwelling exists, and ocean overturning circulation becomes the most important way to return the nutrients deposited in deep ocean back to the thermocline and to the surface ocean.Here we investigate the depth of the thermocline in both wind-dominated and mixing-dominated systems on water-rich exoplanets using the global ocean model MITgcm. We find that the wind-driven circulation is dominated by overturning cells through Ekman pumping and subduction and by zonal (west--east) circum-longitudinal currents, similar to the Antarctic Circumpolar Current on Earth. The wind-influenced thermocline depth shows little dependence on the ocean depth, and under a large range of parameters, the thermocline is restricted within the upper layers of the ocean. The mixing-influenced thermocline is limited within the upper 10 km of the ocean and can not reach the bottom of the ocean even under extremely strong vertical mixing. The scaling theories for the thermocline depth on Earth are applicable for the thermocline depth on water-rich exoplanets. However, due to the lack of exposed continents, the zonal and meridional flow speeds are not in the same magnitude as that in the oceans of Earth, which results in the scaling relationships for water-rich exoplanets are a little different from that used on Earth.

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preprint2022arXiv

Understanding Queries by Conditional Instances

A powerful way to understand a complex query is by observing how it operates on data instances. However, specific database instances are not ideal for such observations: they often include large amounts of superfluous details that are not only irrelevant to understanding the query but also cause cognitive overload; and one specific database may not be enough. Given a relational query, is it possible to provide a simple and generic &#34;representative&#34; instance that (1) illustrates how the query can be satisfied, (2) summarizes all specific instances that would satisfy the query in the same way by abstracting away unnecessary details? Furthermore, is it possible to find a collection of such representative instances that together completely characterize all possible ways in which the query can be satisfied? This paper takes initial steps towards answering these questions. We design what these representative instances look like, define what they stand for, and formalize what it means for them to satisfy a query in &#34;all possible ways.&#34; We argue that this problem is undecidable for general domain relational calculus queries, and develop practical algorithms for computing a minimum collection of such instances subject to other constraints. We evaluate the efficiency of our approach experimentally, and show its effectiveness in helping users debug relational queries through a user study.

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preprint2021arXiv

A long-lived compact jet in the black hole X-ray binary candidate AT2019wey

AT2019wey is a transient discovered by the Asteroid Terrestrial-impact Last Alert System (ATLAS) survey in December of 2019. Follow-up optical, radio, and X-ray observations led to classification of this source as a Galactic black hole X-ray binary. We carried out one-epoch 6.7 GHz European VLBI (Very Long Baseline Interferometry) Network (EVN) and two-epoch multiple-frequency (1.6, 4.5, 6.7 GHz) Very Long Baseline Array (VLBA) observations within a year after its discovery. These observations reveal a fading and flat-spectrum radio source with no discernible motion. These features suggest the detection of a compact jet. The source appears resolved at milliarcsecond scales, and the source angular size versus frequency trend is consistent with scatter broadening. This allows us to constrain the lower limit of the source distance to 6 kpc, if the scattering medium is in a Galactic spiral arm. For a source location larger than 3 kpc, the estimated upper limit of the peculiar velocity, relative to the local standard of rest, suggests the asymmetric natal kick may have occurred during the black hole formation stage.

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preprint2021arXiv

Active Disturbance Rejection Control Design with Suppression of Sensor Noise Effects in Application to DC-DC Buck Power Converter

The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this work, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a DC-DC buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise over-amplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications.

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preprint2021arXiv

On Ambrosetti-Malchiodi-Ni conjecture on two-dimensional smooth bounded domains: clustering concentration layers

We consider the clustering concentration on curves for solutions to the problem $$ \varepsilon^2 {\mathrm {div}}\big( \nabla_{{\mathfrak a}(y)} u\big)- V(y)u+u^p\, =\, 0, \quad u>0 \quad\mbox{in }Ω, \qquad \nabla_{{\mathfrak a}(y)} u\cdot ν\, =\, 0\quad\mbox{on } \partial Ω, $$ where $Ω$ is a bounded domain in $\mathbb R^2$ with smooth boundary, the exponent $p$ is greater than $1$, $\varepsilon>0$ is a small parameter, $V$ is a uniformly positive smooth potential on $\barΩ$, and $ν$ denotes the outward normal of $\partial Ω$. For two positive smooth functions ${\mathfrak a}_1(y), {\mathfrak a}_2(y)$ on $\barΩ$, the operator $\nabla_{{\mathfrak a}(y)}$ is given by $$ \nabla_{{\mathfrak a}(y)} u=\Bigg({\mathfrak a}_1(y)\frac{\partial u}{\partial y_1}, \, {\mathfrak a}_2(y)\frac{\partial u}{\partial y_2}\Bigg). $$

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preprint2021arXiv

Planetary climate under extremely high vertical diffusivity

Planets with large moon(s) or those in the habitable zone of low-mass stars may experience much stronger tidal force and tide-induced ocean mixing than that on Earth. Thus, the vertical diffusivity (or, more precisely, diapycnal diffusivity) on such planets, which represents the strength of vertical mixing in the ocean, would be greater than that on Earth. In this study, we explore the effects of extremely high diffusivity on the ocean circulation and surface climate of Earth-like planets in one asynchronous rotation orbit. The response of planetary climate to 10 and 100 times greater vertical diffusivity than that found on Earth is investigated using a fully coupled atmosphere-ocean general circulation model. In order to perform a clear comparison with the climate of modern Earth, Earth&#39;s orbit, land-sea configuration, and present levels of greenhouse gases are included in the simulations. We find that a larger vertical diffusivity intensifies the meridional overturning circulation (MOC) in the ocean, which transports more heat to polar regions and melts sea ice there. Feedback associated with sea ice, clouds, and water vapor act to further amplify surface warming. When the vertical diffusivity is 10 (100) times the present-day value, the magnitude of MOC increases by $\approx$3 (18) times, and the global-mean surface temperature increases by $\approx$4$^{\circ}$C (10$^{\circ}$C). This study quantifies the climatic effect of an extremely strong vertical diffusivity and confirms an indirect link between planetary orbit, tidal mixing, ocean circulation, and surface climate. Our results suggest a moderate effect of varying vertical ocean mixing on planetary climate.

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preprint2021arXiv

Structural and spectral properties of Galactic plane variable radio sources

In the time domain, the radio sky in particular along the Galactic plane direction may vary significantly because of various energetic activities associated with stars, stellar and supermassive black holes. Using multi-epoch Very Large Array surveys of the Galactic plane at 5.0 GHz, Becker et al. (2010) presented a catalogue of 39 variable radio sources in the flux density range 1-70 mJy. To probe their radio structures and spectra, we observed 17 sources with the very-long-baseline interferometric (VLBI) imaging technique and collected additional multi-frequency data from the literature. We detected all of the sources at 5 GHz with the Westerbork Synthesis Radio Telescope, but only G23.6644-0.0372 with the European VLBI Network (EVN). Together with its decadal variability and multi-frequency radio spectrum, we interpret it as an extragalactic peaked-spectrum source with a size of <~10 pc. The remaining sources were resolved out by the long baselines of the EVN because of either strong scatter broadening at the Galactic latitude <1 deg or intrinsically very extended structures on centi-arcsec scales. According to their spectral and structural properties, we find that the sample has a diverse nature. We notice two young H II regions and spot a radio star and a candidate planetary nebula. The rest of the sources are very likely associated with radio active galactic nuclei (AGN). Two of them also displays arcsec-scale faint jet activity. The sample study indicates that AGN are commonplace even among variable radio sources in the Galactic plane.

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preprint2020arXiv

A Dielectric Metasurface Optical Chip for the Generation of Cold Atoms

Compact and robust cold atom sources are increasingly important for quantum research, especially for transferring cutting-edge quantum science into practical applications. In this letter, we report on a novel scheme that utilizes a metasurface optical chip to replace the conventional bulky optical elements used to produce a cold atomic ensemble with a single incident laser beam, which is split by the metasurface into multiple beams of the desired polarization states. Atom numbers $~10^7$ and temperatures (about 35 $μ$K) of relevance to quantum sensing are achieved in a compact and robust fashion. Our work highlights the substantial progress towards fully integrated cold atom quantum devices by exploiting metasurface optical chips, which may have great potential in quantum sensing, quantum computing and other areas.

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preprint2020arXiv

A parsec-scale radio jet launched by the central intermediate-mass black hole in the dwarf galaxy SDSS J090613.77+561015.2?

The population of intermediate-mass black holes (IMBHs) in nearby dwarf galaxies plays an important &#34;ground truth&#34; role in exploring black hole formation and growth in the early Universe. In the dwarf elliptical galaxy SDSS J090613.77+561015.2 (z=0.0465), an accreting IMBH has been revealed by optical and X-ray observations. Aiming to search for possible radio core and jet associated with the IMBH, we carried out very long baseline interferometry (VLBI) observations with the European VLBI Network (EVN) at 1.66 GHz. Our imaging results show that there are two 1-mJy components with a separation of about 52 mas (projected distance 47 pc) and the more compact component is located within the 1-sigma error circle of the optical centroid from available Gaia astrometry. Based on their positions, elongated structures and relatively high brightness temperatures, as well as the absence of star-forming activity in the host galaxy, we argue that the radio morphology originates from the jet activity powered by the central IMBH. The existence of the large-scale jet implies that violent jet activity might occur in the early epochs of black hole growth and thus help to regulate the co-evolution of black holes and galaxies.

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preprint2020arXiv

A two-sided but significantly beamed jet in the supercritical accretion quasar IRAS F11119+3257

Highly accreting quasars are quite luminous in the X-ray and optical regimes. While, they tend to become radio quiet and have optically thin radio spectra. Among the known quasars, IRAS F11119+3257 is a supercritical accretion source because it has a bolometric luminosity above the Eddington limit and extremely powerful X-ray outflows. To probe its radio structure, we investigated its radio spectrum between 0.15 and 96.15 GHz and performed very-long-baseline interferometric (VLBI) observations with the European VLBI Network (EVN) at 1.66 and 4.93 GHz. The deep EVN image at 1.66 GHz shows a two-sided jet with a projected separation about two hundred parsec and a very high flux density ratio of about 290. Together with the best-fit value of the integrated spectral index of -1.31+/-0.02 in the optically thin part, we infer that the approaching jet has an intrinsic speed at least 0.57 times of the light speed. This is a new record among the known all kinds of super-Eddington accreting sources and unlikely accelerated by the radiation pressure. We propose a scenario in which IRAS F11119+3257 is an unusual compact symmetric object with a small jet viewing angle and a radio spectrum peaking at 0.53+/-0.06 GHz mainly due to the synchrotron self-absorption.

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preprint2020arXiv

Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads

The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) parallelize training tasks onto multiple devices. However, these approaches always rely on specific deep learning frameworks and requires elaborate manual design, which make it difficult to maintain and share between different type of models. In this paper, we propose Auto-MAP, a framework for exploring distributed execution plans for DNN workloads, which can automatically discovering fast parallelization strategies through reinforcement learning on IR level of deep learning models. Efficient exploration remains a major challenge for reinforcement learning. We leverage DQN with task-specific pruning strategies to help efficiently explore the search space including optimized strategies. Our evaluation shows that Auto-MAP can find the optimal solution in two hours, while achieving better throughput on several NLP and convolution models.

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preprint2020arXiv

Catching butterflies in the sky: Extended catalog of winged or X-shaped radio sources from the latest FIRST data release

We present a catalog of 290 &#34;winged&#34; or X-shaped radio galaxies (XRGs) extracted from the latest (2014 December 17) data release of the &#34;Very Large Array Faint Images of the Radio Sky at Twenty centimeter.&#34; We have combined these radio images with their counterparts in the TIFR GMRT sky survey at 150 MHz, in an attempt to identify any low surface brightness radio emission present in these sources. This has enabled us to assemble a sample of 106 &#34;strong&#34; XRG candidates and 184 &#34;probable&#34; XRG candidates whose XRG designation needs to be verified by further observations. The present sample of 290 XRG candidates is almost twice as large as the number of XRGs currently known. Twenty-five of our 290 XRG candidates (9 &#34;strong&#34; and 16 &#34;probable&#34;) are identified as quasars. Double-peaked narrow emission lines are seen in the optical spectra of three of the XRG candidates (two &#34;strong&#34; and one &#34;probable&#34;). Nearly 90% of the sample is located in the FR II domain of the Owen-Ledlow diagram. A few of the strong XRG candidates have a rather flat radio spectrum (spectral index alpha flatter than -0.3) between 150 MHz and 1.4 GHz, or between 1.4 and 5 GHz. Since this is not expected for lobe-dominated extragalactic radio sources (like nearly all known XRGs), these sources are particularly suited for follow-up radio imaging and near-simultaneous measurement of the radio spectrum.

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preprint2020arXiv

Clustering of Boundary Interfaces for an inhomogeneous Allen-Cahn equation on a smooth bounded domain

We consider the inhomogeneous Allen-Cahn equation $$ ε^2Δu\,+\,V(y)(1-u^2)\,u\,=\,0\quad \mbox{in}\ Ω, \qquad \frac {\partial u}{\partial ν}\,=\,0\quad \mbox{on}\ \partial Ω, $$ where $Ω$ is a bounded domain in ${\mathbb R}^2$ with smooth boundary $\partialΩ$ and $V(x)$ is a positive smooth function, $ε>0$ is a small parameter, $ν$ denotes the unit outward normal of $\partialΩ$. For any fixed integer $N\geq 2$, we will show the existence of a clustered solution $u_ε$ with $N$-transition layers near $\partial Ω$ with mutual distance $O(ε|\ln ε|)$, provided that the generalized mean curvature $\mathcal{H} $ of $\partialΩ$ is positive and $ε$ stays away from a discrete set of values at which resonance occurs. Our result is an extension of those (with dimension two) by A. Malchiodi, W.-M. Ni, J. Wei in Pacific J. Math. (Vol. 229, 2007, no. 2, 447-468) and A. Malchiodi, J. Wei in J. Fixed Point Theory Appl. (Vol. 1, 2007, no. 2, 305-336)

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preprint2020arXiv

DAPPLE: A Pipelined Data Parallel Approach for Training Large Models

It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an effective approach for improving device utilization. However, there are still several tricky issues to address: improving computing efficiency while ensuring convergence, and reducing memory usage without incurring additional computing costs. We propose DAPPLE, a synchronous training framework which combines data parallelism and pipeline parallelism for large DNN models. It features a novel parallelization strategy planner to solve the partition and placement problems, and explores the optimal hybrid strategy of data and pipeline parallelism. We also propose a new runtime scheduling algorithm to reduce device memory usage, which is orthogonal to re-computation approach and does not come at the expense of training throughput. Experiments show that DAPPLE planner consistently outperforms strategies generated by PipeDream&#39;s planner by up to 3.23x under synchronous training scenarios, and DAPPLE runtime outperforms GPipe by 1.6x speedup of training throughput and reduces the memory consumption of 12% at the same time.

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preprint2020arXiv

DaSGD: Squeezing SGD Parallelization Performance in Distributed Training Using Delayed Averaging

The state-of-the-art deep learning algorithms rely on distributed training systems to tackle the increasing sizes of models and training data sets. Minibatch stochastic gradient descent (SGD) algorithm requires workers to halt forward/back propagations, to wait for gradients aggregated from all workers, and to receive weight updates before the next batch of tasks. This synchronous execution model exposes the overheads of gradient/weight communication among a large number of workers in a distributed training system. We propose a new SGD algorithm, DaSGD (Local SGD with Delayed Averaging), which parallelizes SGD and forward/back propagations to hide 100% of the communication overhead. By adjusting the gradient update scheme, this algorithm uses hardware resources more efficiently and reduces the reliance on the low-latency and high-throughput inter-connects. The theoretical analysis and the experimental results show its convergence rate O(1/sqrt(K)), the same as SGD. The performance evaluation demonstrates it enables a linear performance scale-up with the cluster size.

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preprint2020arXiv

Drift, Minorization, and Hitting Times

The &#34;drift-and-minorization&#34; method, introduced and popularized in (Rosenthal, 1995; Meyn and Tweedie, 1994; Meyn and Tweedie, 2012), remains the most popular approach for bounding the convergence rates of Markov chains used in statistical computation. This approach requires estimates of two quantities: the rate at which a single copy of the Markov chain &#34;drifts&#34; towards a fixed &#34;small set&#34;, and a &#34;minorization condition&#34; which gives the worst-case time for two Markov chains started within the small set to couple with moderately large probability. In this paper, we build on (Oliveira, 2012; Peres and Sousi, 2015) and our work (Anderson, Duanmu, Smith, 2019a; Anderson, Duanmu, Smith, 2019b) to replace the &#34;minorization condition&#34; with an alternative &#34;hitting condition&#34; that is stated in terms of only one Markov chain, and illustrate how this can be used to obtain similar bounds that can be easier to use.

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preprint2020arXiv

Effect of Sea-ice Drift on the Onset of Snowball Climate on Rapidly Rotating Aqua-planets

Previous studies have shown that sea-ice drift effectively promote the onset of a globally ice-covered snowball climate for paleo Earth and for tidally locked planets around low-mass stars. Here, we investigate whether sea-ice drift can influence the stellar flux threshold for a snowball climate onset on rapidly rotating aqua-planets around a Sun-like star. Using a fully coupled atmosphere--land--ocean--sea-ice model with turning on or off sea-ice drift, a circular orbit with no eccentricity (e=0) and an eccentric orbit (e=0.2) are examined. When sea-ice drift is turned off, the stellar flux threshold for the snowball onset is 1250--1275 and 1173--1199 W m^-2 for e=0 and 0.2, respectively. The difference is mainly due to the poleward retreat of sea ice and snow edges when the planet is close to the perihelion in the eccentric orbit. When sea-ice drift is turned on, the respective stellar flux threshold is 1335--1350 and 1250--1276 W m^-2. These mean that sea-ice drift increases the snowball onset threshold by ~80 W m^-2 for both e=0 and 0.2, promoting the formation of a snowball climate state. We further show that oceanic dynamics have a small effect, <26 W m^-2, on the snowball onset threshold. This is because oceanic heat transport becomes weaker and weaker as the sea ice edge is approaching the equator. These results imply that sea-ice dynamics are important for the climate of planets close to the outer edge of the habitable zone, but oceanic heat transport is less important.

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preprint2020arXiv

Environment-aware Reconfigurable Noise Suppression

The paper proposes an efficient, robust, and reconfigurable technique to suppress various types of noises for any sampling rate. The theoretical analyses, subjective and objective test results show that the proposed noise suppression (NS) solution significantly enhances the speech transmission index (STI), speech intelligibility (SI), signal-to-noise ratio (SNR), and subjective listening experience. The STI and SI consists of 5 levels, i.e., bad, poor, fair, good, and excellent. The most common noisy condition is of SNR ranging from -5 to 8 dB. For the input SNR between -5 and 2.5 dB, the proposed NS improves the STI and SI from &#34;fair&#34; to &#34;good&#34;. For the input SNR between 2.5 to 8 dB, the STI and SI are improved from &#34;good&#34; to &#34;excellent&#34; by the proposed NS. The proposed NS can be adopted in both capture and playback paths for voice over internet protocol, voice-trigger, and automatic speech recognition applications.

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preprint2020arXiv

Evolving parsec-scale radio structure in the most distant blazar known

Blazars are a sub-class of quasars with Doppler boosted jets oriented close to the line of sight, and thus efficient probes of supermassive black hole growth and their environment, especially at high redshifts. Here we report on Very Long Baseline Interferometry observations of a blazar J0906+6930 at z = 5.47, which enabled the detection of polarised emission and measurement of jet proper motion at parsec scales. The observations suggest a less powerful jet compared with the general blazar population, including lower proper motion and bulk Lorentz factor. This coupled with a previously inferred high accretion rate indicate a transition from an accretion radiative power to a jet mechanical power based transfer of energy and momentum to the surrounding gas.While alternative scenarios could not be fully ruled out, our results indicate a possibly nascent jet embedded in and interacting with a dense medium resulting in a jet bending.

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preprint2020arXiv

Intelligent Optimization of Diversified Community Prevention of COVID-19 using Traditional Chinese Medicine

Traditional Chinese medicine (TCM) has played an important role in the prevention and control of the novel coronavirus pneumonia (COVID-19), and community prevention has become the most essential part in reducing the spread risk and protecting populations. However, most communities use a uniform TCM prevention program for all residents, which violates the &#34;treatment based on syndrome differentiation&#34; principle of TCM and limits the effectiveness of prevention. In this paper, we propose an intelligent optimization method to develop diversified TCM prevention programs for community residents. First, we use a fuzzy clustering method to divide the population based on both modern medicine and TCM health characteristics; we then use an interactive optimization method, in which TCM experts develop different TCM prevention programs for different clusters, and a heuristic algorithm is used to optimize the programs under the resource constraints. We demonstrate the computational efficiency of the proposed method and report its successful application to TCM-based prevention of COVID-19 in 12 communities in Zhejiang province, China, during the peak of the pandemic.

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preprint2020arXiv

Optical coherence of Er$^{3+}$:Y$_2$O$_3$ ceramics for telecommunication quantum technologies

We report an optical homogeneous linewidth of 580 $\pm$ 20 Hz of Er$^{3+}$:Y$_2$O$_3$ ceramics at millikelvin temperatures, narrowest so far in rare-earth doped ceramics. We show slow spectral diffusion of $\sim$2 kHz over a millisecond time scale. Temperature, field dependence of optical coherence and spectral diffusions reveal the remaining dephasing mechanism as elastic two-level systems in polycrystalline grain boundaries and superhyperfine interactions of Er$^{3+}$ with nuclear spins. In addition, we perform spectral holeburning and measure up to 5 s hole lifetimes. These spectroscopic results put Er$^{3+}$:Y$_2$O$_3$ ceramics as a promising candidate for telecommunication quantum memories and light-matter interfaces.

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preprint2020arXiv

Optimal Scaling of Random-Walk Metropolis Algorithms on General Target Distributions

One main limitation of the existing optimal scaling results for Metropolis--Hastings algorithms is that the assumptions on the target distribution are unrealistic. In this paper, we consider optimal scaling of random-walk Metropolis algorithms on general target distributions in high dimensions arising from practical MCMC models from Bayesian statistics. For optimal scaling by maximizing expected squared jumping distance (ESJD), we show the asymptotically optimal acceptance rate $0.234$ can be obtained under general realistic sufficient conditions on the target distribution. The new sufficient conditions are easy to be verified and may hold for some general classes of MCMC models arising from Bayesian statistics applications, which substantially generalize the product i.i.d. condition required in most existing literature of optimal scaling. Furthermore, we show one-dimensional diffusion limits can be obtained under slightly stronger conditions, which still allow dependent coordinates of the target distribution. We also connect the new diffusion limit results to complexity bounds of Metropolis algorithms in high dimensions.

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preprint2020arXiv

Small Sensitivity of the Simulated Climate of Tidally Locked Aquaplanets to Model Resolution

Tidally locked terrestrial planets around low-mass stars are the prime targets of finding potentially habitable exoplanets. Several atmospheric general circulation models have been employed to simulate their possible climates, however, model intercomparisons showed that there are large differences in the results of the models even when they are forced with the same boundary conditions. In this paper, we examine whether model resolution contributes to the differences. Using the atmospheric general circulation model ExoCAM coupled to a 50-m slab ocean, we examine three different horizontal resolutions (440 km * 550 km, 210 km * 280 km, and 50 km * 70 km in latitude and longitude) and three different vertical resolutions (26, 51, and 74 levels) under the same dynamical core and the same schemes of radiation, convection and clouds. Among the experiments, the differences are within 5 K in global-mean surface temperature and within 0.007 in planetary albedo. These differences are from cloud feedback, water vapor feedback, and the decreasing trend of relative humidity with increasing resolution. Relatively small-scale downdrafts between upwelling columns over the substellar region are better resolved and the mixing between dry and wet air parcels and between anvil clouds and their environment are enhanced as the resolution is increased. These reduce atmospheric relative humidity and high-level cloud fraction, causing a lower clear-sky greenhouse effect, a weaker cloud longwave radiation effect, and subsequently a cooler climate with increasing model resolution. Overall, the sensitivity of the simulated climate of tidally locked aquaplanets to model resolution is small.

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preprint2020arXiv

SOAC: The Soft Option Actor-Critic Architecture

The option framework has shown great promise by automatically extracting temporally-extended sub-tasks from a long-horizon task. Methods have been proposed for concurrently learning low-level intra-option policies and high-level option selection policy. However, existing methods typically suffer from two major challenges: ineffective exploration and unstable updates. In this paper, we present a novel and stable off-policy approach that builds on the maximum entropy model to address these challenges. Our approach introduces an information-theoretical intrinsic reward for encouraging the identification of diverse and effective options. Meanwhile, we utilize a probability inference model to simplify the optimization problem as fitting optimal trajectories. Experimental results demonstrate that our approach significantly outperforms prior on-policy and off-policy methods in a range of Mujoco benchmark tasks while still providing benefits for transfer learning. In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.

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preprint2020arXiv

Spectral Inference under Complex Temporal Dynamics

We develop unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and practitioners to visually evaluate the magnitude and pattern of the evolutionary power spectra with asymptotically accurate statistical guarantee. The SCR also serves as a unified tool for a wide range of statistical inference problems in time-frequency analysis ranging from tests for white noise, stationarity and time-frequency separability to the validation for non-stationary linear models.

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preprint2020arXiv

The hyperluminous, dust-obscured quasar W2246-0526 at z=4.6: detection of parsec-scale radio activity

WISE J224607.56$-$052634.9 (W2246-0526) is a hyperluminous ($L_{\rm bol}\approx 1.7\times 10^{14}~L_\odot$), dust-obscured and radio-quiet quasar at redshift $z=4.6$. It plays a key role in probing the transition stage between dusty starbursts and unobscured quasars in the co-evolution of galaxies and supermassive black holes (SMBHs). To search for the evidence of the jet activity launched by the SMBH in W2246-0526, we performed very long baseline interferometry (VLBI) observations of its radio counterpart with the European VLBI Network (EVN) plus the enhanced Multi Element Remotely Linked Interferometer Network (e-MERLIN) at 1.66 GHz and the Very Long Baseline Array (VLBA) at 1.44 and 1.66 GHz. The deep EVN plus e-MERLIN observations detect a compact (size $\leq32$ pc) sub-mJy component contributing about ten percent of its total flux density, which spatially coincides with the peak of dust continuum and [C II] emissions. Together with its relatively high brightness temperature ($\geq8\times10^{6}$ K), we interpret the component as a consequence of non-thermal radio activity powered by the central SMBH, which likely originates from a stationary jet base. The resolved-out radio emission possibly come from a diffuse jet, quasar-driven winds, or both, while the contribution by star formation activity is negligible. Moreover, we propose an updated geometry structure of its multi-wavelength active nucleus and shed light on the radio quasar selection bias towards the blazars at $z>4$.

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preprint2020arXiv

TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). Motivations and protocol version 1.0

Upcoming telescopes such as the James Webb Space Telescope (JWST), or the Extremely Large Telescope (ELTs), may soon be able to characterize, through transmission, emission or reflection spectroscopy, the atmospheres of rocky exoplanets orbiting nearby M dwarfs. One of the most promising candidates is the late M dwarf system TRAPPIST-1 which has seven known transiting planets for which Transit Timing Variation (TTV) measurements suggest that they are terrestrial in nature, with a possible enrichment in volatiles. Among these seven planets, TRAPPIST-1e seems to be the most promising candidate to have habitable surface conditions, receiving ~66 % of the Earth&#39;s incident radiation, and thus needing only modest greenhouse gas inventories to raise surface temperatures to allow surface liquid water to exist. TRAPPIST-1e is therefore one of the prime targets for JWST atmospheric characterization. In this context, the modeling of its potential atmosphere is an essential step prior to observation. Global Climate Models (GCMs) offer the most detailed way to simulate planetary atmospheres. However, intrinsic differences exist between GCMs which can lead to different climate prediction and thus observability of gas and/or cloud features in transmission and thermal emission spectra. Such differences should preferably be known prior to observations. In this paper we present a protocol to inter-compare planetary GCMs. Four testing cases are considered for TRAPPIST-1e but the methodology is applicable to other rocky exoplanets in the Habitable Zone. The four test cases included two land planets composed with a modern Earth and pure CO2 atmospheres, respectively, and two aqua planets with the same atmospheric compositions. Currently, there are four participating models (LMDG, ROCKE-3D, ExoCAM, UM), however this protocol is intended to let other teams participate as well.

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preprint2019arXiv

Learning to Sample: Counting with Complex Queries

We study the problem of efficiently estimating counts for queries involving complex filters, such as user-defined functions, or predicates involving self-joins and correlated subqueries. For such queries, traditional sampling techniques may not be applicable due to the complexity of the filter preventing sampling over joins, and sampling after the join may not be feasible due to the cost of computing the full join. The other natural approach of training and using an inexpensive classifier to estimate the count instead of the expensive predicate suffers from the difficulties in training a good classifier and giving meaningful confidence intervals. In this paper we propose a new method of learning to sample where we combine the best of both worlds by using sampling in two phases. First, we use samples to learn a probabilistic classifier, and then use the classifier to design a stratified sampling method to obtain the final estimates. We theoretically analyze algorithms for obtaining an optimal stratification, and compare our approach with a suite of natural alternatives like quantification learning, weighted and stratified sampling, and other techniques from the literature. We also provide extensive experiments in diverse use cases using multiple real and synthetic datasets to evaluate the quality, efficiency, and robustness of our approach.

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preprint2019arXiv

Study on Asymmetric Diffraction of Acoustic Parity-Time-Symmetric Gratings Using Rigorous Coupled-Wave Analysis

In PT-symmetric gratings, asymmetric diffraction can be generated by modulating the ratio between imaginary and real refractive indices. In this paper, a rigorous coupled-wave analysis (RCWA) has been developed to analyze the diffraction properties of acoustic PT-symmetric gratings with two kinds of modulating approaches, including modulating the effective modulus and the effective density. Asymmetric diffraction with both Bragg incident angles and perpendicular incident angles is discussed by using the RCWA method. Results show that the modulation ratio for the diffraction vanishing point changes with the modulation amplitude differently for two kinds of modulating approaches. Moreover, the sound energy will be weaken or be enhanced at Bragg incident angles depending on the sign of incident angles and the modulation ratio.

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