arXiv subjects

Hao He

Publications and source records attributed to Hao He.

At least 73 records · Page 4Linked to original sources

arXiv2024

Dynamic gain and frequency comb formation in exceptional-point lasers

Exceptional points (EPs) -- singularities in the parameter space of non-Hermitian systems where two nearby eigenmodes coalesce -- feature unique properties with applications such as sensitivity enhancement and chiral emission. Existing realizations of EP lasers operate with static populations in the gain medium. By analyzing the full-wave Maxwell--Bloch equations, here we show that in a laser operating sufficiently close to an EP, the nonlinear gain will spontaneously induce a multi-spectral multi-modal instability above a pump threshold, which initiates an oscillating population inversion and generates a frequency comb. The efficiency of comb generation is enhanced by both the spectral degeneracy and the spatial coalescence of modes near an EP. Such an ``EP comb'' has a widely tunable repetition rate, self-starts without external modulators or a continuous-wave pump, and can be realized with an ultra-compact footprint. We develop an exact solution of the Maxwell--Bloch equations with an oscillating inversion, describing all spatiotemporal properties of the EP comb as a limit cycle. We numerically illustrate this phenomenon in a 5-\textmu m-long gain-loss coupled AlGaAs cavity and adjust the EP comb repetition rate from 20 to 27~GHz. This work provides a rigorous spatiotemporal description of the rich laser behaviors that arise from the interplay between the non-Hermiticity, nonlinearity, and dynamics of a gain medium.

physics.optics↗

arXiv2024

What Radio Waves Tell Us about Sleep

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

cs.LG↗

arXiv2024

PHANGS-MeerKAT and MHONGOOSE HI observations of nearby spiral galaxies: physical drivers of the molecular gas fraction, $R_{\mathrm{mol}}$

The molecular-to-atomic gas ratio is crucial to the evolution of the interstellar medium in galaxies. We investigate the balance between the atomic ($Σ_{\rm HI}$) and molecular gas ($Σ_{\rm H2}$) surface densities in eight nearby star-forming galaxies using new high-quality observations from MeerKAT and ALMA (for HI and CO, respectively). We define the molecular gas ratio as $R_{\rm mol} = Σ_{\rm H2} / Σ_{\rm HI}$ and measure how it depends on local conditions in the galaxy disks using multi-wavelength observations. We find that, depending on the galaxy, HI is detected at $>3σ$ out to 20-120 kpc in galactocentric radius ($r_{\rm gal}$). The typical radius at which $Σ_{\rm HI}$ reaches 1~$\rm M_\odot~pc^{-2}$ is $r_{\rm HI}\approx22$~kpc, which corresponds to 1-3 times the optical radius ($r_{25}$). $R_{\rm mol}$ correlates best with the dynamical equilibrium pressure, P$_{\rm DE}$, among potential drivers studied, with a median correlation coefficient of $<ρ>=0.89$. Correlations between $R_{\rm mol}$ and star formation rate, total gas and stellar surface density, metallicity, and $Σ_{\rm SFR}$/P$_{\rm DE}$ are present but somewhat weaker. Our results also show a direct correlation between P$_{\rm DE}$ and $Σ_{\rm SFR}$, supporting self-regulation models. Quantitatively, we measure similar scalings as previous works and attribute the modest differences that we find to the effect of varying resolution and sensitivity. At $r_{\rm gal} {\gtrsim}0.4~r_{25}$, atomic gas dominates over molecular gas, and at the balance of these two gas phases, we find that the baryon mass is dominated by stars, with $Σ_{*} > 5~Σ_{\rm gas}$. Our study constitutes an important step in the statistical investigation of how local galaxy properties impact the conversion from atomic to molecular gas in nearby galaxies.

astro-ph.GA↗

arXiv2024

Unraveling the Mystery of the Low CO-to-H$_2$ Conversion Factor in Starburst Galaxies: RADEX Modeling of the Antennae

CO emission has been widely used as a tracer of molecular gas mass. However, it is a long-standing issue to accurately constrain the CO-to-H$_2$ conversion factor ($α_{\mathrm{CO}}$) that converts CO luminosity to molecular gas mass, especially in starburst galaxies. We present the first resolved $α_{\mathrm{CO}}$ modeling results with multiple ALMA CO and $^{13}$CO transition observations at both giant molecular cloud (GMC) scale at 150 pc and kpc scale for one of the closest starburst mergers, the Antennae. By combining our CO modeling results and measurements of 350 GHz dust continuum, we find that most GMCs in the Antennae have $α_{\mathrm{CO}}$ values $\sim$4 times smaller than the commonly adopted Milky Way value (4.3). We find $α_{\mathrm{CO}}$ at GMC scales shows a strong dependence on CO intensity, $^{13}$CO/CO ratio and GMC velocity dispersion, which is consistent with various theoretical and simulation predictions. Specifically, we suggest that the $^{13}$CO/CO line ratio and the velocity dispersion can be used to infer $α_{\mathrm{CO}}$ in starburst regions. By applying our modeled $α_{\mathrm{CO}}$ in GMC analyses, we find that GMCs in the Antennae are less gravitationally bound than in normal spiral galaxies, which is more consistent with what is predicted by merger simulations. At kpc scale, we find that our modeled $α_{\mathrm{CO}}$ values are smaller than the modeled $α_{\mathrm{CO}}$ at GMC scale by 40%, which can be due to inclusion of a diffuse gas component with lower $α_{\mathrm{CO}}$ values. We find a similar correlation of $α_{\mathrm{CO}}$ and CO intensity at kpc scales to that at GMC scales.

astro-ph.GA↗

arXiv2024

Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Control

Research on video generation has recently made tremendous progress, enabling high-quality videos to be generated from text prompts or images. Adding control to the video generation process is an important goal moving forward and recent approaches that condition video generation models on camera trajectories make strides towards it. Yet, it remains challenging to generate a video of the same scene from multiple different camera trajectories. Solutions to this multi-video generation problem could enable large-scale 3D scene generation with editable camera trajectories, among other applications. We introduce collaborative video diffusion (CVD) as an important step towards this vision. The CVD framework includes a novel cross-video synchronization module that promotes consistency between corresponding frames of the same video rendered from different camera poses using an epipolar attention mechanism. Trained on top of a state-of-the-art camera-control module for video generation, CVD generates multiple videos rendered from different camera trajectories with significantly better consistency than baselines, as shown in extensive experiments. Project page: https://collaborativevideodiffusion.github.io/.

cs.CV↗

arXiv2024

Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models

Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought prompting). However, along with testing on more complex reasoning, these methods also expose problems such as invalid reasoning and fictional reasoning paths. In this paper, we develop \textit{Hypothesis Testing Prompting}, which adds conclusion assumptions, backward reasoning, and fact verification during intermediate reasoning steps. \textit{Hypothesis Testing prompting} involves multiple assumptions and reverses validation of conclusions leading to its unique correct answer. Experiments on two challenging deductive reasoning datasets ProofWriter and RuleTaker show that hypothesis testing prompting not only significantly improves the effect, but also generates a more reasonable and standardized reasoning process.

cs.CL↗

arXiv2024

Logical Negation Augmenting and Debiasing for Prompt-based Methods

Prompt-based methods have gained increasing attention on NLP and shown validity on many downstream tasks. Many works have focused on mining these methods' potential for knowledge extraction, but few explore their ability to make logical reasoning. In this work, we focus on the effectiveness of the prompt-based methods on first-order logical reasoning and find that the bottleneck lies in logical negation. Based on our analysis, logical negation tends to result in spurious correlations to negative answers, while propositions without logical negation correlate to positive answers. To solve the problem, we propose a simple but effective method, Negation Augmenting and Negation Debiasing (NAND), which introduces negative propositions to prompt-based methods without updating parameters. Specifically, these negative propositions can counteract spurious correlations by providing "not" for all instances so that models cannot make decisions only by whether expressions contain a logical negation. Experiments on three datasets show that NAND not only solves the problem of calibrating logical negation but also significantly enhances prompt-based methods of logical reasoning without model retraining.

cs.CL↗

arXiv2024

Hidden Gems on a Ring: Infant Massive Clusters and Their Formation Timeline Unveiled by ALMA, HST, and JWST in NGC 3351

We study young massive clusters (YMCs) in their embedded "infant" phase with $\sim0.\!^{\prime\prime}1$ ALMA, HST, and JWST observations targeting the central starburst ring in NGC 3351, a nearby Milky Way analog galaxy. Our new ALMA data reveal 18 bright and compact (sub-)millimeter continuum sources, of which 8 have counterparts in JWST images and only 6 have counterparts in HST images. Based on the ALMA continuum and molecular line data, as well as ancillary measurements for the HST and JWST counterparts, we identify 14 sources as infant star clusters with high stellar and/or gas masses (${\sim}10^5\;\mathrm{M_\odot}$), small radii (${\lesssim}\,5\;\mathrm{pc}$), large escape velocities ($6{-}10\;\mathrm{km/s}$), and short free-fall times ($0.5{-}1\;\mathrm{Myr}$). Their multiwavelength properties motivate us to divide them into four categories, likely corresponding to four evolutionary stages from starless clumps to exposed HII region-cluster complexes. Leveraging age estimates for HST-identified clusters in the same region, we infer an evolutionary timeline going from $\sim$1-2 Myr before cluster formation as starless clumps, to $\sim$4-6 Myr after as exposed HII region-cluster complexes. Finally, we show that the YMCs make up a substantial fraction of recent star formation across the ring, exhibit an non-uniform azimuthal distribution without a very coherent evolutionary trend along the ring, and are capable of driving large-scale gas outflows.

astro-ph.GA↗

arXiv2024

Cloud-Scale Molecular Gas Properties of the Antennae Merger: A Comparative Study with PHANGS-ALMA Galaxies and NGC 3256

We present observations of the central 9 kpc of the Antennae merger (NGC 4038/9) at 55 pc resolution in the CO 2-1 line obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). We use a pixel-based analysis to compare the gas properties in the Antennae to those in 70 nearby spiral galaxies from the PHANGS-ALMA survey, as well as the merger and nearest luminous infrared galaxy NGC 3256. Compared to PHANGS galaxies at matched spatial resolution, the molecular gas in the Antennae exhibits some of the highest surface densities, velocity dispersions, peak brightness temperatures, and turbulent pressures. However, the virial parameters in the Antennae are consistent with many of the PHANGS galaxies. NGC 3256 has similar gas surface densities but higher nuclear velocity dispersions than the Antennae, as well as higher system-wide peak brightness temperatures and virial parameters. NGC 3256 is at a later stage in the merging process than the Antennae, which may result in more intense merger-driven gas flows that could drive up the turbulence in the gas. The high virial parameters in NGC 3256 may indicate that this increased turbulence is suppressing future star formation as NGC 3256 moves out of the starburst phase. In comparison, the relatively normal virial parameters in the Antennae may imply that it is about to undergo a new burst of star formation.

astro-ph.GA↗

arXiv2024

Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models

Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, the crucial role of problem context might have been neglected. Accurate recognition of inputs is fundamental for solving mathematical tasks, as ill-formed problems could potentially mislead LLM's reasoning. In this study, we propose a new approach named Problem Elaboration Prompting (PEP) to enhance the mathematical capacities of LLMs. Specifically, PEP decomposes and elucidates the problem context before reasoning, therefore enhancing the context modeling and parsing efficiency. Experiments across datasets and models demonstrate promising performances: (1) PEP demonstrates an overall enhancement in various mathematical tasks. For instance, with the GPT-3.5 model, PEP exhibits improvements of 9.93% and 8.80% on GSM8k through greedy decoding and self-consistency, respectively. (2) PEP can be easily implemented and integrated with other prompting methods. (3) PEP shows particular strength in handling distraction problems.

cs.CL↗

arXiv2024

Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection

In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations. Despite the great success of ICL, the limitation of the demonstration number may lead to demonstration bias, i.e. the input-label mapping induced by LLMs misunderstands the task's essence. Inspired by human experience, we attempt to mitigate such bias through the perspective of the inter-demonstration relationship. Specifically, we construct Comparable Demonstrations (CDs) by minimally editing the texts to flip the corresponding labels, in order to highlight the task's essence and eliminate potential spurious correlations through the inter-demonstration comparison. Through a series of experiments on CDs, we find that (1) demonstration bias does exist in LLMs, and CDs can significantly reduce such bias; (2) CDs exhibit good performance in ICL, especially in out-of-distribution scenarios. In summary, this study explores the ICL mechanisms from a novel perspective, providing a deeper insight into the demonstration selection strategy for ICL.

cs.CL↗

arXiv2024

Randomized algorithms for precise measurement of differentially-private, personalized recommendations

Personalized recommendations form an important part of today's internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and engaging content. However, many users today are skeptical of platforms that personalize recommendations, in part due to historically careless treatment of personal data and data privacy. Now, businesses that rely on personalized recommendations are entering a new paradigm, where many of their systems must be overhauled to be privacy-first. In this article, we propose an algorithm for personalized recommendations that facilitates both precise and differentially-private measurement. We consider advertising as an example application, and conduct offline experiments to quantify how the proposed privacy-preserving algorithm affects key metrics related to user experience, advertiser value, and platform revenue compared to the extremes of both (private) non-personalized and non-private, personalized implementations.

cs.CR↗

arXiv2024

Understanding LLMs: A Comprehensive Overview from Training to Inference

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.

cs.CL↗

arXiv2023

Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent

Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales. Tackling all these problems once could be arduous for LLMs, thus leading to confusion in generation. In this work, we explore the potential of enhancing LLMs with agents by meticulous decomposition and modeling of mathematical reasoning process. Specifically, we propose a formal description of the mathematical solving and extend LLMs with an agent-based zero-shot framework named $\bf{P}$lanner-$\bf{R}$easoner-$\bf{E}$xecutor-$\bf{R}$eflector (PRER). We further provide and implement two MathAgents that define the logical forms and inherent relations via a pool of actions in different grains and orientations: MathAgent-M adapts its actions to LLMs, while MathAgent-H aligns with humankind. Experiments on miniF2F and MATH have demonstrated the effectiveness of PRER and proposed MathAgents, achieving an increase of $12.3\%$($53.9\%\xrightarrow{}66.2\%$) on the MiniF2F, $9.2\%$ ($49.8\%\xrightarrow{}59.0\%$) on MATH, and $13.2\%$($23.2\%\xrightarrow{}35.4\%$) for level-5 problems of MATH against GPT-4. Further analytical results provide more insightful perspectives on exploiting the behaviors of LLMs as agents.

cs.AI↗

arXiv2023

Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis

Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research. However, despite numerous empirical researches on the combination of LLMs and game theory, the capability boundaries of LLMs in game theory remain unclear. In this research, we endeavor to systematically analyze LLMs in the context of game theory. Specifically, rationality, as the fundamental principle of game theory, serves as the metric for evaluating players' behavior -- building a clear desire, refining belief about uncertainty, and taking optimal actions. Accordingly, we select three classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to analyze to what extent LLMs can achieve rationality in these three aspects. The experimental results indicate that even the current state-of-the-art LLM (GPT-4) exhibits substantial disparities compared to humans in game theory. For instance, LLMs struggle to build desires based on uncommon preferences, fail to refine belief from many simple patterns, and may overlook or modify refined belief when taking actions. Therefore, we consider that introducing LLMs into game experiments in the field of social science should be approached with greater caution.

cs.AI↗

arXiv2023

Holistic Evaluation of GPT-4V for Biomedical Imaging

In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain. We assess GPT-4V's performance across 16 medical imaging categories, including radiology, oncology, ophthalmology, pathology, and more. Tasks include modality recognition, anatomy localization, disease diagnosis, report generation, and lesion detection. The extensive experiments provide insights into GPT-4V's strengths and weaknesses. Results show GPT-4V's proficiency in modality and anatomy recognition but difficulty with disease diagnosis and localization. GPT-4V excels at diagnostic report generation, indicating strong image captioning skills. While promising for biomedical imaging AI, GPT-4V requires further enhancement and validation before clinical deployment. We emphasize responsible development and testing for trustworthy integration of biomedical AGI. This rigorous evaluation of GPT-4V on diverse medical images advances understanding of multimodal large language models (LLMs) and guides future work toward impactful healthcare applications.

eess.IV↗

arXiv2023

ReTR: Modeling Rendering Via Transformer for Generalizable Neural Surface Reconstruction

Generalizable neural surface reconstruction techniques have attracted great attention in recent years. However, they encounter limitations of low confidence depth distribution and inaccurate surface reasoning due to the oversimplified volume rendering process employed. In this paper, we present Reconstruction TRansformer (ReTR), a novel framework that leverages the transformer architecture to redesign the rendering process, enabling complex render interaction modeling. It introduces a learnable $\textit{meta-ray token}$ and utilizes the cross-attention mechanism to simulate the interaction of rendering process with sampled points and render the observed color. Meanwhile, by operating within a high-dimensional feature space rather than the color space, ReTR mitigates sensitivity to projected colors in source views. Such improvements result in accurate surface assessment with high confidence. We demonstrate the effectiveness of our approach on various datasets, showcasing how our method outperforms the current state-of-the-art approaches in terms of reconstruction quality and generalization ability. $\textit{Our code is available at }$ https://github.com/YixunLiang/ReTR.

cs.CV↗

arXiv2023

Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation

This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and perform the group-text alignment. Nevertheless, these methods suffer from a granularity inconsistency regarding the usage of group tokens, which are aligned in the all-to-one v.s. one-to-one manners during the training and inference phases, respectively. We argue that this discrepancy arises from the lack of elaborate supervision for each group token. To bridge this granularity gap, this paper explores explicit supervision for the group tokens from the prototypical knowledge. To this end, this paper proposes the non-learnable prototypical regularization (NPR) where non-learnable prototypes are estimated from source features to serve as supervision and enable contrastive matching of the group tokens. This regularization encourages the group tokens to segment objects with less redundancy and capture more comprehensive semantic regions, leading to increased compactness and richness. Based on NPR, we propose the prototypical guidance segmentation network (PGSeg) that incorporates multi-modal regularization by leveraging prototypical sources from both images and texts at different levels, progressively enhancing the segmentation capability with diverse prototypical patterns. Experimental results show that our proposed method achieves state-of-the-art performance on several benchmark datasets. The source code is available at https://github.com/Ferenas/PGSeg.

cs.CV↗