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Cheng Li

Publications and source records attributed to Cheng Li.

At least 235 records · Page 13Linked to original sources
arXiv2023

The Inhomogeneity Effect III: Weather Impacts on the Heat Flow of Hot Jupiters

The interior flux of a giant planet impacts atmospheric motion, and the atmosphere dictates the interior's cooling. Here we use a non-hydrostatic general circulation model (Simulating Nonhydrostatic Atmospheres on Planets, SNAP) coupled with a multi-stream multi-scattering radiative module (High-performance Atmospheric Radiation Package, HARP) to simulate the weather impacts on the heat flow of hot Jupiters. We found that the vertical heat flux is primarily transported by convection in the lower atmosphere and regulated by dynamics and radiation in the overlying ``radiation-circulation" zone. The temperature inversion occurs on the dayside and reduces the upward radiative flux. The atmospheric dynamics relay the vertical heat transport until the radiation becomes efficient in the upper atmosphere. The cooling flux increases with atmospheric drag due to increased day-night contrast and spatial inhomogeneity. The temperature dependence of the infrared opacity greatly amplifies the opacity inhomogeneity. Although atmospheric circulation could transport heat downward in a narrow region above the radiative-convective boundary, the opacity inhomogeneity effect overcomes the dynamical effect and leads to a larger overall interior cooling than the local simulations with the same interior entropy and stellar flux. The enhancement depends critically on the equilibrium temperature, drag, and atmospheric opacity. In a strong-drag atmosphere hotter than 1600 K, a significant inhomogeneity effect in three-dimensional (3D) models can boost interior cooling several-fold compared to the 1D radiative-convective equilibrium models. This study confirms the analytical argument of the inhomogeneity effect in Zhang (2023a,b). It highlights the importance of using 3D atmospheric models in understanding the inflation mechanisms of hot Jupiters and giant planet evolution in general.

astro-ph.EP↗
arXiv2023

Cross-Modal Object Tracking via Modality-Aware Fusion Network and A Large-Scale Dataset

Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multi-modal imaging platforms are complex and lack real-world applicability. In contrast, near-infrared (NIR) imaging, commonly used in surveillance cameras, can switch between RGB and NIR based on light intensity. However, tracking objects across these heterogeneous modalities poses significant challenges, particularly due to the absence of modality switch signals during tracking. To address these challenges, we propose an adaptive cross-modal object tracking algorithm called Modality-Aware Fusion Network (MAFNet). MAFNet efficiently integrates information from both RGB and NIR modalities using an adaptive weighting mechanism, effectively bridging the appearance gap and enabling a modality-aware target representation. It consists of two key components: an adaptive weighting module and a modality-specific representation module......

cs.CV↗
arXiv2023

Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. Purpose: To build a deep learning method exploring sparse annotations, namely only a single 2D slice label for each 3D training MR image. Population: 3D MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1,377 image slices) are for prostate segmentation. The other 100 (8,800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. Assessment: A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled non-central slices. Segmentation performance on testing set was reported quantitatively and qualitatively. Results: Compared to FS-LCS, MT, UA-MT, DCT-Seg, ICT, and AC-MT, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% vs. ICT B-IoU: 60.3%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% vs. ICT B-IoU: 60.1%).

eess.IV↗
arXiv2023

Determination of CP-violating Higgs couplings with transversely-polarized beams at the ILC

We study possible CP-violation effects of the Higgs to $Z$-boson coupling at a future $e^+ e^-$ collider, e.g. the International Linear Collider (ILC). We find that the azimuthal angular distribution of the muon pair, produced by $e^+ e^- \rightarrow H Z \rightarrow H μ^+ μ^-$, can be sensitive to such a CP-violation effect when we apply initial transversely polarized beams. Based on this angular distribution, we construct a CP sensitive asymmetry and obtain this asymmetry by \texttt{Whizard} simulation. By comparing the SM prediction with 2$σ$ range of this asymmetry, we estimate the discovery limit of the CP-odd coupling in $HZZ$ interaction.

hep-ph↗
arXiv2023

Criss Cross Nebula: Case study of shock regions with resolved microstructures at scales of $\sim$1000 AU

Using integral field spectroscopy from MaNGA, we study the resolved microstructures in a shocked region in Criss Cross Nebula (CCN), with an unprecedentedly high resolution of $\lesssim$1000 AU. We measure surface brightness maps for 34 emission lines, which can be broadly divided into three categories: (1) the [OIII] $λ$5007-like group including seven high-ionization lines and two [OII] auroral lines which uniformly present a remarkable lane structure, (2) the H$α$ $λ$6563-like group including 23 low-ionization or recombination lines which present a clump-like structure, and (3) [OII] $λ$3726 and [OII] $λ$3729 showing high densities at both the [OIII] $λ$5007 lane and the H$α$ clump. We use these measurements to constrain resolved shock models implemented in MAPPINGS V. We find our data can be reasonably well-fitted by a model which includes a plane-parallel shock with a velocity of $133\pm5$ km/s, plus an isotropic two-dimensional Gaussian component which is likely another clump of gas ionized by photons from the shocked region, and a constant background. We compare the electron density and temperature profiles as predicted by our model with those calculated using observed emission line ratios. We find different line ratios to provide inconsistent temperature maps, and the discrepancies can be attributed to observational effects caused by limited spatial resolution and projection of the shock geometry, as well as contamination of the additional Gaussian component. Implications on shock properties and perspectives on future IFS-based studies of CCN are discussed.

astro-ph.GA↗
arXiv2023

Kinematical coherence between satellite galaxies and host stellar discs for MaNGA & SAMI galaxies

The effect of angular momentum on galaxy formation and evolution has been studied for several decades. Our recent two papers using IllustrisTNG-100 simulation have revealed the acquisition path of the angular momentum from large-scale environment (satellites within hundreds of kpc) through the circum-galactic medium (CGM) to the stellar discs, putting forward the co-rotation scenario across the three distance scales. In real observations, although the rotation signature for the CGM and environmental three-dimensional (3d) angular momentum are difficult to obtain, line-of-sight kinematics of group member galaxies and stellar disc kinematics of central galaxies are available utilizing existing group catalogue data and integral field unit (IFU) data. In this paper, we use (1) the group catalogue of SDSS DR7 and MaNGA IFU stellar kinematic maps and (2) the group catalogue of GAMA DR4 data and SAMI IFU stellar kinematic maps, to test if the prediction above can be seen in real data. We found the co-rotation pattern between stellar discs and satellites can be concluded with 99.7 percent confidence level ($\sim 3σ$) when combining the two datasets. And the random tests show that the signal can be scarcely drawn from random distribution.

astro-ph.GA↗
arXiv2023

Experimental Generation of Spin-Photon Entanglement in Silicon Carbide

A solid-state approach for quantum networks is advantages, as it allows the integration of nanophotonics to enhance the photon emission and the utilization of weakly coupled nuclear spins for long-lived storage. Silicon carbide, specifically point defects within it, shows great promise in this regard due to the easy of availability and well-established nanofabrication techniques. Despite of remarkable progresses made, achieving spin-photon entanglement remains a crucial aspect to be realized. In this paper, we experimentally generate entanglement between a silicon vacancy defect in silicon carbide and a scattered single photon in the zero-phonon line. The spin state is measured by detecting photons scattered in the phonon sideband. The photonic qubit is encoded in the time-bin degree-of-freedom and measured using an unbalanced Mach-Zehnder interferometer. Photonic correlations not only reveal the quality of the entanglement but also verify the deterministic nature of the entanglement creation process. By harnessing two pairs of such spin-photon entanglement, it becomes straightforward to entangle remote quantum nodes at long distance.

quant-ph↗
arXiv2023

The First LHAASO Catalog of Gamma-Ray Sources

We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2A) from January 2020 to September 2022. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering declination from $-$20$^{\circ}$ to 80$^{\circ}$. In total, the catalog contains 90 sources with an extended size smaller than $2^\circ$ and a significance of detection at $> 5σ$. Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy ($E > 100$ TeV) emission at $> 4σ$ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog.

astro-ph.HE↗
arXiv2023

Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search

Increasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require carefully planned schedules for performance. However, existing practices often rely on predefined schedules that may not fully exploit the benefits of emerging diverse model-aware operator placement strategies. Handcrafting high-efficiency schedules can be challenging due to the large and varying schedule space. This paper presents Tessel, an automated system that searches for efficient schedules for distributed DNN training and inference for diverse operator placement strategies. To reduce search costs, Tessel leverages the insight that the most efficient schedules often exhibit repetitive pattern (repetend) across different data inputs. This leads to a two-phase approach: repetend construction and schedule completion. By exploring schedules for various operator placement strategies, Tessel significantly improves both training and inference performance. Experiments with representative DNN models demonstrate that Tessel achieves up to 5.5x training performance speedup and up to 38% inference latency reduction.

cs.DC↗
arXiv2023

Very high energy gamma-ray emission beyond 10 TeV from GRB 221009A

The highest energy gamma-rays from gamma-ray bursts (GRBs) have important implications for their radiation mechanism. Here we report for the first time the detection of gamma-rays up to 13 TeV from the brightest GRB 221009A by the Large High Altitude Air-shower Observatory (LHAASO). The LHAASO-KM2A detector registered more than 140 gamma-rays with energies above 3 TeV during 230$-$900s after the trigger. The intrinsic energy spectrum of gamma-rays can be described by a power-law after correcting for extragalactic background light (EBL) absorption. Such a hard spectrum challenges the synchrotron self-Compton (SSC) scenario of relativistic electrons for the afterglow emission above several TeV. Observations of gamma-rays up to 13 TeV from a source with a measured redshift of z=0.151 hints more transparency in intergalactic space than previously expected. Alternatively, one may invoke new physics such as Lorentz Invariance Violation (LIV) or an axion origin of very high energy (VHE) signals.

astro-ph.HE↗
arXiv2023

Large Language Models Understand and Can be Enhanced by Emotional Stimuli

Emotional intelligence significantly impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial general intelligence, exhibiting impressive performance in numerous tasks, it is still uncertain if LLMs can genuinely grasp psychological emotional stimuli. Understanding and responding to emotional cues gives humans a distinct advantage in problem-solving. In this paper, we take the first step towards exploring the ability of LLMs to understand emotional stimuli. To this end, we first conduct automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4. Our tasks span deterministic and generative applications that represent comprehensive evaluation scenarios. Our automatic experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts (which we call "EmotionPrompt" that combines the original prompt with emotional stimuli), e.g., 8.00% relative performance improvement in Instruction Induction and 115% in BIG-Bench. In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks (10.9% average improvement in terms of performance, truthfulness, and responsibility metrics). We provide an in-depth discussion regarding why EmotionPrompt works for LLMs and the factors that may influence its performance. We posit that EmotionPrompt heralds a novel avenue for exploring interdisciplinary knowledge for human-LLMs interaction.

cs.CL↗
arXiv2023

Dark matter phenomenology in Z'2 broken singlet extended 2HDM

Many different approaches have been made to explain the nature of dark matter (DM), but it remains and unsolved mystery of our universe. In this work we examine a type II two-Higgs-doublet model extended by a complex singlet (2HDMS), where the pseudo-scalar component of the singlet acts as a natural DM candidate. The DM candidate is stabilized by a Z'2 symmetry, which is broken spontaneously by the singlet acquiring a vacuum expectation value (vev). This vev in turn causes the scalar component of the singlet to mix with the scalar components of the two doublets, which results in three scalar Higgs particles. Additionally we aim to include an excess around 95 GeV, which was observed at CSM and LEP and can be explained by one of the three scalar Higgs particles. After introducing the model, we apply experimental and theoretical constraints and find a viable benchmark point. We then look into the DM phenomenology as well as collider phenomenology.

hep-ph↗
arXiv2023

Does or did the supernova remnant Cassiopeia A operate as a PeVatron?

For decades, supernova remnants (SNRs) have been considered the prime sources of Galactic Cosmic rays (CRs). But whether SNRs can accelerate CR protons to PeV energies and thus dominate CR flux up to the knee is currently under intensive theoretical and phenomenological debate. The direct test of the ability of SNRs to operate as CR PeVatrons can be provided by ultrahigh-energy (UHE; $E_γ\geq 100$~TeV) $γ$-rays. In this context, the historical SNR Cassiopeia A (Cas A) is considered one of the most promising target for UHE observations. This paper presents the observation of Cas A and its vicinity by the LHAASO KM2A detector. The exceptional sensitivity of LHAASO KM2A in the UHE band, combined with the young age of Cas A, enabled us to derive stringent model-independent limits on the energy budget of UHE protons and nuclei accelerated by Cas A at any epoch after the explosion. The results challenge the prevailing paradigm that Cas A-type SNRs are major suppliers of PeV CRs in the Milky Way.

astro-ph.HE↗
arXiv2023

Heat-Flux Limited Cloud Activity and Vertical Mixing in Giant Planet Atmospheres with an Application to Uranus and Neptune

Storms operated by moist convection and the condensation of $\rm CH_{4}$ or $\rm H_{2}S$ have been observed on Uranus and Neptune. However, the mechanism of cloud formation, thermal structure, and mixing efficiency of ice giant weather layers remains unclear. In this paper, we show that moist convection is limited by heat transport on giant planets, especially on ice giants where planetary heat flux is weak. Latent heat associated with condensation and evaporation can efficiently bring heat across the weather layer through precipitations. This effect was usually neglected in previous studies without a complete hydrological cycle. We first derive analytical theories and show the upper limit of cloud density is determined by the planetary heat flux and microphysics of clouds but independent of the atmospheric composition. The eddy diffusivity of moisture depends on the heat fluxes, atmospheric composition, and gravity of the planet but is not directly related to cloud microphysics. We then conduct convection- and cloud-resolving simulations with SNAP to validate our analytical theory. The simulated cloud density and eddy diffusivity are smaller than the results acquired from the equilibrium cloud condensation model and mixing length theory by several orders of magnitude but consistent with our analytical solutions. Meanwhile, the mass-loading effect of $\rm CH_{4}$ and $\rm H_{2}S$ leads to superadiabatic and stable weather layers. Our simulations produced three cloud layers that are qualitatively similar to recent observations. This study has important implications for cloud formation and eddy mixing in giant planet atmospheres in general and observations for future space missions and ground-based telescopes.

astro-ph.EP↗
arXiv2023

A Conditional Abundance Matching Method of Extending Simulated Halo Merger Trees to Resolve Low-Mass Progenitors and Sub-halos

We present an algorithm to extend subhalo merger trees in a low-resolution dark-matter-only simulation by conditionally matching them to those in a high-resolution simulation. The algorithm is general and can be applied to simulation data with different resolutions using different target variables. We instantiate the algorithm by a case in which trees from ELUCID, a constrained simulation of $(500h^{-1}{\rm Mpc})^3$ volume of the local universe, are extended by matching trees from TNGDark, a simulation with much higher resolution. Our tests show that the extended trees are statistically equivalent to the high-resolution trees in the joint distribution of subhalo quantities and in important summary statistics relevant to modeling galaxy formation and evolution in halos. The extended trees preserve certain information of individual systems in the target simulation, including properties of resolved satellite subhalos, and shapes and orientations of their host halos. With the extension, subhalo merger trees in a cosmological scale simulation are extrapolated to a mass resolution comparable to that in a higher-resolution simulation carried out in a smaller volume, which can be used as the input for (sub)halo-based models of galaxy formation. The source code of the algorithm, and halo merger trees extended to a mass resolution of $\sim 2 \times 10^8 h^{-1}M_\odot$ in the entire ELUCID simulation, are available.

astro-ph.GA↗
arXiv2023

Automated Evaluation of Personalized Text Generation using Large Language Models

Personalized text generation presents a specialized mechanism for delivering content that is specific to a user's personal context. While the research progress in this area has been rapid, evaluation still presents a challenge. Traditional automated metrics such as BLEU and ROUGE primarily measure lexical similarity to human-written references, and are not able to distinguish personalization from other subtle semantic aspects, thus falling short of capturing the nuances of personalized generated content quality. On the other hand, human judgments are costly to obtain, especially in the realm of personalized evaluation. Inspired by these challenges, we explore the use of large language models (LLMs) for evaluating personalized text generation, and examine their ability to understand nuanced user context. We present AuPEL, a novel evaluation method that distills three major semantic aspects of the generated text: personalization, quality and relevance, and automatically measures these aspects. To validate the effectiveness of AuPEL, we design carefully controlled experiments and compare the accuracy of the evaluation judgments made by LLMs versus that of judgements made by human annotators, and conduct rigorous analyses of the consistency and sensitivity of the proposed metric. We find that, compared to existing evaluation metrics, AuPEL not only distinguishes and ranks models based on their personalization abilities more accurately, but also presents commendable consistency and efficiency for this task. Our work suggests that using LLMs as the evaluators of personalized text generation is superior to traditional text similarity metrics, even though interesting new challenges still remain.

cs.CL↗
arXiv2023

Hyperbolic phonon-polariton electroluminescence in graphene-hBN van der Waals heterostructures

Phonon-polaritons are electromagnetic waves resulting from the coherent coupling of photons with optical phonons in polar dielectrics. Due to their exceptional ability to confine electric fields to deep subwavelength scales with low loss, they are uniquely poised to enable a suite of applications beyond the reach of conventional photonics, such as sub-diffraction imaging and near-field energy transfer. The conventional approach to exciting phonon-polaritons through optical methods, however, necessitates costly mid-infrared and terahertz coherent light sources along with near-field scanning probes, and generally leads to low excitation efficiency due to the substantial momentum mismatch between phonon-polaritons and free-space photons. Here, we demonstrate that under proper conditions, phonon-polaritons can be excited all-electrically by flowing charge carriers. Specifically, in hexagonal boron nitride (hBN)/graphene heterostructures, by electrically driving charge carriers in ultra-high-mobility graphene out of equilibrium, we observe bright electroluminescence of hBN's hyperbolic phonon-polaritons (HPhPs) at mid-IR frequencies. The HPhP electroluminescence shows a temperature and carrier density dependence distinct from black-body or super-Planckian thermal emission. Moreover, the carrier density dependence of HPhP electroluminescence spectra reveals that HPhP electroluminescence can arise from both inter-band transition and intra-band Cherenkov radiation of charge carriers in graphene. The HPhP electroluminescence offers fundamentally new avenues for realizing electrically-pumped, tunable mid-IR and THz phonon-polariton lasers, and efficient cooling of electronic devices.

cond-mat.mes-hall↗
arXiv2023

CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving

Even though the collaboration between traditional and neuromorphic event cameras brings prosperity to frame-event based vision applications, the performance is still confined by the resolution gap crossing two modalities in both spatial and temporal domains. This paper is devoted to bridging the gap by increasing the temporal resolution for images, i.e., motion deblurring, and the spatial resolution for events, i.e., event super-resolving, respectively. To this end, we introduce CrossZoom, a novel unified neural Network (CZ-Net) to jointly recover sharp latent sequences within the exposure period of a blurry input and the corresponding High-Resolution (HR) events. Specifically, we present a multi-scale blur-event fusion architecture that leverages the scale-variant properties and effectively fuses cross-modality information to achieve cross-enhancement. Attention-based adaptive enhancement and cross-interaction prediction modules are devised to alleviate the distortions inherent in Low-Resolution (LR) events and enhance the final results through the prior blur-event complementary information. Furthermore, we propose a new dataset containing HR sharp-blurry images and the corresponding HR-LR event streams to facilitate future research. Extensive qualitative and quantitative experiments on synthetic and real-world datasets demonstrate the effectiveness and robustness of the proposed method. Codes and datasets are released at https://bestrivenzc.github.io/CZ-Net/.

cs.CV↗