arXiv subjects

Jun Yang

Publications and source records attributed to Jun Yang.

At least 289 records · Page 16Linked to original sources

arXiv2022

SmartFPS: Neural Network based Wireless-inertial fusion positioning system

The current fusion positioning systems are mainly based on filtering algorithms, such as Kalman filtering or particle filtering. However, the system complexity of practical application scenarios is often very high, such as noise modeling in pedestrian inertial navigation systems, or environmental noise modeling in fingerprint matching and localization algorithms. To solve this problem, this paper proposes a fusion positioning system based on deep learning and proposes a transfer learning strategy for improving the performance of neural network models for samples with different distributions. The results show that in the whole floor scenario, the average positioning accuracy of the fusion network is 0.506m. The experiment results of transfer learning show that the estimation accuracy of the inertial navigation positioning step size and rotation angle of different pedestrians can be improved by 53.3% on average, the Bluetooth positioning accuracy of different devices can be improved by 33.4%, and the fusion can be improved by 31.6%.

eess.SP↗

arXiv2022

Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion

This paper proposes DisCo, an automatic deep learning compilation module for data-parallel distributed training. Unlike most deep learning compilers that focus on training or inference on a single device, DisCo optimizes a DNN model for distributed training over multiple GPU machines. Existing single-device compilation strategies do not work well in distributed training, due mainly to communication inefficiency that they incur. DisCo generates optimized, joint computation operator and communication tensor fusion strategies to enable highly efficient distributed training. A GNN-based simulator is built to effectively estimate per-iteration training time achieved by operator/tensor fusion candidates. A backtracking search algorithm is driven by the simulator, navigating efficiently in the large strategy space to identify good operator/tensor fusion strategies that minimize distributed training time. We compare DisCo with existing DL fusion schemes and show that it achieves good training speed-up close to the ideal, full computation-communication overlap case.

cs.DC↗

arXiv2022

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.

eess.AS↗

arXiv2022

Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmospheres

Precipitation and its response to forcings is an important aspect of planetary climate system. In this study, we examine the strength of precipitation in the experiments with different atmospheric masses and their response to surface warming, using three global atmospheric general circulation models (GCMs) and one regional cloud-resolving model (CRM). We find that precipitation is weaker when atmospheric mass is larger for a given surface temperature. Furthermore, the increasing rate of precipitation with increasing surface temperature under a larger atmospheric mass is smaller than that under a smaller atmospheric mass. These behaviors can be understood based on atmospheric or surface energy balance. Atmospheric mass influences Rayleigh scattering, multiple scattering in the atmosphere, pressure broadening, lapse rate, and thereby precipitation strength. These results have important implications on the climate and habitability of early Earth, early Mars, and exoplanets with oceans.

astro-ph.EP↗

arXiv2022

Cloud Feedback on Earth's Long-term Climate Simulated by a Near-global Cloud-permitting Model

The Sun becomes brighter with time, but Earth's climate is roughly temperate for life during its long-term history; for early Earth, this is known as the Faint Young Sun Problem (FYSP). Besides the carbonate-silicate feedback, recent researches suggest that a long-term cloud feedback may partially solve the FYSP. However, the general circulation models they used cannot resolve convection and clouds explicitly. This study re-investigates the clouds using a near-global cloud-permitting model without cumulus convection parameterization. Our results confirm that a stabilizing shortwave cloud feedback does exist, and its magnitude is $\approx$6 W m$^{-2}$ or 14% of the energy required to offset a 20% fainter Sun than today, or $\approx$10 W m$^{-2}$ or 16% for a 30% fainter Sun. When insolation increases and meanwhile CO$_2$ concentration decreases, low-level clouds increase, acting to stabilize the climate by raising planetary albedo, and vice versa.

physics.ao-ph↗

arXiv2022

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for testing artificial intelligence, various human-computer gaming AI systems (AIs) have been developed such as the Libratus, OpenAI Five and AlphaStar, beating professional human players. The rapid development of human-computer gaming AIs indicate a big step of decision making intelligence, and it seems that current techniques can handle very complex human-computer games. So, one natural question raises: what are the possible challenges of current techniques in human-computer gaming, and what are the future trends? To answer the above question, in this paper, we survey recent successful game AIs, covering board game AIs, card game AIs, first-person shooting game AIs and real time strategy game AIs. Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs. Finally, we hope this brief review can provide an introduction for beginners, and inspire insights for researchers in the field of AI in human-computer gaming.

cs.AI↗

arXiv2022

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.

cs.CR↗

arXiv2022

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 .

eess.SP↗

arXiv2022

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.

math.RT↗

arXiv2022

The helical vortex filaments of Ginzburg-Landau system in ${\mathbb R}^3$

We consider the following coupled Ginzburg-Landau system in ${\mathbb R}^3$ \begin{align*} \begin{cases} -ε^2 Δw^+ +\Big[A_+\big(|w^+|^2-{t^+}^2\big)+B\big(|w^-|^2-{t^-}^2\big)\Big]w^+=0, \\[3mm] -ε^2 Δw^- +\Big[A_-\big(|w^-|^2-{t^-}^2\big)+B\big(|w^+|^2-{t^+}^2\big)\Big]w^-=0, \end{cases} \end{align*} where $w=(w^+, w^-)\in \mathbb{C}^2$ and the constant coefficients satisfy $$ A_+, A_->0,\quad B^2 0, \quad {t^+}^2+{ t^-}^2=1. $$ If $B<0$, then for every $ε$ small enough, we construct a family of entire solutions $w_ε(\tilde{z}, t)\in \mathbb{C}^2$ in the cylindrical coordinates $(\tilde{z}, t)\in \mathbb{R}^2 \times \mathbb{R}$ for this system via the approach introduced by J. Dávila, M. del Pino, M. Medina and R. Rodiac in {\tt arXiv:1901.02807}. These solutions are $2π$-periodic in $t$ and have multiple interacting vortex helices. The main results are the extensions of the phenomena of interacting helical vortex filaments for the classical (single) Ginzburg-Landau equation in $\mathbb{R}^3$ which has been studied in {\tt arXiv:1901.02807}. Our results negatively answer the Gibbons conjecture \cite{Gibbons conjecture} for the Allen-Cahn equation in Ginzburg-Landau system version, which is an extension of the question originally proposed by H. Brezis.

math.AP↗

arXiv2022

Concurrent Active Learning in Autonomous Airborne Source Search: Dual Control for Exploration and Exploitation

In this paper, a concurrent learning framework is developed for source search in an unknown environment using autonomous platforms equipped with onboard sensors. Distinct from the existing solutions that require significant computational power for Bayesian estimation and path planning, the proposed solution is computationally affordable for onboard processors. A new concept of concurrent learning using multiple parallel estimators is proposed to learn the operational environment and quantify estimation uncertainty. The search agent is empowered with dual capability of exploiting current estimated parameters to track the source and probing the environment to reduce the impacts of uncertainty, namely Concurrent Learning based Dual Control for Exploration and Exploitation (CL-DCEE). In this setting, the control action not only minimises the tracking error between future agent's position and estimated source location, but also the uncertainty of predicted estimation. More importantly, the rigorous proven properties such as the convergence of CL-DCEE algorithm are established under mild assumptions on noises, and the impact of noises on the search performance is examined. Simulation results are provided to validate the effectiveness of the proposed CL-DCEE algorithm. Compared with the information-theoretic approach, CL-DCEE not only guarantees convergence, but produces better search performance and consumes much less computational time.

eess.SY↗

arXiv2022

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.

cs.RO↗

arXiv2022

Robust millisecond coherence times of erbium electron spins

Erbium-doped solids are prime candidates for optical quantum communication networks due to erbium's telecom C-band emission. A long-lived electron spin of erbium with millisecond coherence time is highly desirable for establishing entanglement between adjacent quantum repeater nodes while long-term storage of the entanglement could rely on transferring to erbium's second-long coherence nuclear spins. Here we report GHz-range electron spin transitions of $^{167}\mathrm{Er}^{3+}$ in yttrium oxide ($\mathrm{Y_2O_3}$) matrix with coherence times that are consistently longer than a millisecond. Instead of addressing field-specific Zero First-Order Zeeman transitions, we probe weakly mixed electron spins with the field orientation along the lower g-factors. Using pulsed electron spin resonance spectroscopy, we find paramagnetic impurities are the dominant source of decoherence, and by polarizing them we achieve a Hahn echo spin $\mathrm{T_2}$ up to 1.46 ms, and a coherence time up to 7.1 ms after dynamical decoupling. These coherence lifetimes are among the longest found in crystalline hosts especially those with nuclear spins. We further enhance the coherence time beyond conventional dynamical decoupling, using customized sequences to simultaneously mitigate spectral diffusion and Er-Er dipolar interactions. Despite nuclear and impurity spins in the host, this work shows that long-lived erbium spins comparable to non-nuclear spin hosts can be achieved. Our study not only establishes $^{167}\mathrm{Er}^{3+}$: $\mathrm{Y_2 O_3}$ as a significantly promising quantum memory platform but also provides a general guideline for engineering long-lived erbium spins in a variety of host materials for quantum technologies.

quant-ph↗

arXiv2022

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.

astro-ph.EP↗

arXiv2022

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'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'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.

astro-ph.HE↗

arXiv2022

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.

astro-ph.HE↗

arXiv2022

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.

astro-ph.EP↗

arXiv2022

Atmospheric Overturning Circulation on Dry, Tidally-locked Rocky Planets is Mainly Driven by Radiative Cooling

In this study, we examine the driving mechanism for the atmospheric overturning circulation on dry, tidally-locked rocky planets without the condensation of water vapor or other species. We find that the main driving process is the radiative cooling of CO2 (or other non-condensable greenhouse gases) rather than CO2 greenhouse warming or stellar radiation. Stellar radiation is the ultimate mechanism but not the direct mechanism. Due to the combination of the uneven distribution in the stellar radiation and effective horizontal energy transports in the free troposphere, there is strong temperature inversion in the area away from the substellar region. This inversion makes CO2 to have a radiative cooling effect rather than a radiative warming effect for the atmosphere, same as that in the stratosphere of Earth's atmosphere. This cooling effect produces negative buoyancy and drives large-scale downwelling, supporting the formation of a global-scale overturning circulation. If CO2 is excluded from the atmosphere, the overturning circulation becomes very weak, regardless the level of stellar radiation. This mechanism is completely different from that for the atmospheric overturning circulation on Earth or on moist, tidally-locked rocky planets, where latent heat release and/or baroclinic instability are the dominated mechanisms. Our study improves the understanding of the atmospheric circulation on tidally-locked exoplanets and also on other dry planets, such as Venus and Mars in the solar system.

astro-ph.EP↗