arXiv subjects

Yifan Zhou

Publications and source records attributed to Yifan Zhou.

At least 145 records · Page 8Linked to original sources

arXiv2024

Ytterbium atom interferometry for dark matter searches

We analyze the projected sensitivity of a laboratory-scale ytterbium atom interferometer to scalar, vector, and axion dark matter signals. A frequency ratio measurement between two transitions in $^{171}$Yb enables a search for variations of the fine-structure constant that could surpass existing limits by a factor of 100 in the mass range $10^{-22}$ eV to $10^{-16}$ eV. Differential accelerometry between Yb isotopes yields projected sensitivities to scalar and vector dark matter couplings that are stronger than the limits set by the MICROSCOPE equivalence principle test, and an analogous measurement in the MAGIS-100 long-baseline interferometer would be more sensitive than previous bounds by factors of 10 or more. A search for anomalous spin torque in MAGIS-100 is projected to reach similar sensitivity to atomic magnetometry experiments. We discuss strategies for mitigating the main systematic effects in each measurement. These results indicate that improved dark matter searches with Yb atom interferometry are technically feasible.

physics.atom-ph↗

arXiv2024

The Retrieved Atmospheric Properties of the Sub-stellar Object VHS 1256 b from Analyzing HST, VLT and JWST Spectra

Motivated by the observed ~30% variations in flux from the L7 dwarf VHS 1256 b, we subjected its time-resolved Hubble Space Telescope (HST) WFC3 spectra (measured in two epochs in 2018 and 2020), as well as medium-resolution Very Large Telescope (VLT) X-shooter and Early Release Science James Webb Space Telescope (JWST) spectra to a suite of both standard Bayesian (nested sampling) and machine-learning (random forest) retrievals. We find that both HST and VLT data require vertically varying abundance profiles of water in order to model the spectra accurately. Despite the large flux variations observed in the HST data, the temporal variability cannot be attributed to a single varying atmospheric property. The retrieved atmospheric quantities are consistent with being invariant across time. However, we find that model grids provide generally poor fits to the measured HST spectra and are unsuitable for quantifying the temporal variability of atmospheric properties. Additionally, our analysis of JWST spectra using model grids indicates consistency in retrieved properties across different wavelength channels. Despite the temporal variability in flux, the retrieved properties between HST and VLT, as well as between HST and JWST, are consistent within the respective posterior uncertainties. Such an outcome bodes well for future retrieval analyses of exoplanetary atmospheres, which are expected to exhibit weaker flux variations.

astro-ph.EP↗

arXiv2024

The only inflated brown dwarf in an eclipsing white dwarf-brown dwarf binary: WD1032+011B

Due to their short orbital periods and relatively high flux ratios, irradiated brown dwarfs in binaries with white dwarfs offer better opportunities to study irradiated atmospheres than hot Jupiters, which have lower planet-to-star flux ratios. WD1032+011 is an eclipsing, tidally locked white dwarf-brown dwarf binary with a 9950 K white dwarf orbited by a 69.7 M$_{Jup}$ brown dwarf in a 0.09 day orbit. We present time-resolved Hubble Space Telescope Wide Field Camera 3 spectrophotometric data of WD1032+011. We isolate the phase-dependent spectra of WD1032+011B, finding a 210 K difference in brightness temperature between the dayside and nightside. The spectral type of the brown dwarf is identified as L1 peculiar, with atmospheric retrievals and comparison to field brown dwarfs showing evidence for a cloud-free atmosphere. The retrieved temperature of the dayside is $1748^{+66}_{-67}$ K, with a nightside temperature of $1555^{+76}_{-62}$ K, showing an irradiation-driven temperature contrast coupled with inefficient heat redistribution from the dayside to the nightside. The brown dwarf radius is inflated, likely due to the constant irradiation from the white dwarf, making it the only known inflated brown dwarf in an eclipsing white dwarf-brown dwarf binary.

astro-ph.SR↗

arXiv2024

Implementation of The Future of Drug Discovery: QuantumBased Machine Learning Simulation (QMLS)

The Research & Development (R&D) phase of drug development is a lengthy and costly process. To revolutionize this process, we introduce our new concept QMLS to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD. For Hit Generation, Machine Learning Molecule Generation (MLMG) generates possible hits according to the molecular structure of the target protein while the Quantum Simulation (QS) filters molecules from the primary essay based on the reaction and binding effectiveness with the target protein. Then, For Lead Optimization, the resultant molecules generated and filtered from MLMG and QS are compared, and molecules that appear as a result of both processes will be made into dozens of molecular variations through Machine Learning Molecule Variation (MLMV), while others will only be made into a few variations. Lastly, all optimized molecules would undergo multiple rounds of QS filtering with a high standard for reaction effectiveness and safety, creating a few dozen pre-clinical-trail-ready drugs. This paper is based on our first paper, where we pitched the concept of machine learning combined with quantum simulations. In this paper we will go over the detailed design and framework of QMLS, including MLMG, MLMV, and QS.

q-bio.BM↗

arXiv2024

Novel Long Distance Free Space Quantum Secure Direct Communication for Web 3.0 Networks

With the advent of Web 3.0, the swift advancement of technology confronts an imminent threat from quantum computing. Security protocols safeguarding the integrity of Web 2.0 and Web 3.0 are growing more susceptible to both quantum attacks and sophisticated classical threats. The article introduces our novel long-distance free-space quantum secure direct communication (LF QSDC) as a method to safeguard against security breaches in both quantum and classical contexts. Differing from techniques like quantum key distribution (QKD), LF QSDC surpasses constraints by facilitating encrypted data transmission sans key exchanges, thus diminishing the inherent weaknesses of key-based systems. The distinctiveness of this attribute, coupled with its quantum mechanics base, protects against quantum computer assaults and advanced non-quantum dangers, harmonizing seamlessly with the untrustworthy tenets of the Web 3.0 age. The focus of our study is the technical design and incorporation of LF QSDC into web 3.0 network infrastructures, highlighting its efficacy for extended-range communication. LF QSDC is based on the memory DL04 protocol and enhanced with our novel Quantum-Aware Low-Density Parity Check (LDPC), Pointing, Acquisition, and Tracking (PAT) technologies, and Atmospheric Quantum Correction Algorithm (AQCA). Utilizing this method not only bolsters the security of worldwide Web 3.0 networks but also guarantees their endurance in a time when quantum and sophisticated classical threats exist simultaneously. Consequently, LF QSDC stands out as a robust security solution, well-suited for Web 3.0 systems amidst the constantly evolving digital environment.

quant-ph↗

arXiv2024

JWST/NIRCam 4-5 $μ$m Imaging of the Giant Planet AF Lep b

With a dynamical mass of $3 \, M_\mathrm{Jup}$, the recently discovered giant planet AF Lep b is the lowest-mass imaged planet with a direct mass measurement. Its youth and spectral type near the L/T transition make it a promising target to study the impact of clouds and atmospheric chemistry at low surface gravities. In this work, we present JWST/NIRCam imaging of AF Lep b. Across two epochs, we detect AF Lep b in F444W ($4.4 \, \mathrm{μm}$) with S/N ratios of $9.6$ and $8.7$, respectively. At the planet's separation of $320 \, \mathrm{mas}$ during the observations, the coronagraphic throughput is ${\approx}7\%$, demonstrating that NIRCam's excellent sensitivity persists down to small separations. The F444W photometry of AF Lep b affirms the presence of disequilibrium carbon chemistry and enhanced atmospheric metallicity. These observations also place deep limits on wider-separation planets in the system, ruling out $1.1 \, M_\mathrm{Jup}$ planets beyond $15.6 \, \mathrm{au}$ ($0.58$ arcsec), $1.1 \, M_\mathrm{Sat}$ planets beyond $27 \, \mathrm{au}$ ($1$ arcsec), and $2.8 \, M_\mathrm{Nep}$ planets beyond $67 \, \mathrm{au}$ ($2.5$ arcsec). We also present new Keck/NIRC2 $L'$ imaging of AF Lep b; combining this with the two epochs of F444W photometry and previous Keck $L'$ photometry provides limits on the long-term $3{-}5 \, \mathrm{μm}$ variability of AF Lep b on months-to-years timescales. AF Lep b is the closest-separation planet imaged with JWST to date, demonstrating that planets can be recovered well inside the nominal (50\% throughput) NIRCam coronagraph inner working angle.

astro-ph.EP↗

arXiv2024

Implementation Guidelines and Innovations in Quantum LSTM Networks

The rapid evolution of artificial intelligence has driven interest in Long Short-Term Memory (LSTM) networks for their effectiveness in processing sequential data. However, traditional LSTMs are limited by issues such as the vanishing gradient problem and high computational demands. Quantum computing offers a potential solution to these challenges, promising advancements in computational efficiency through the unique properties of qubits, such as superposition and entanglement. This paper presents a theoretical analysis and an implementation plan for a Quantum LSTM (qLSTM) model, which seeks to integrate quantum computing principles with traditional LSTM networks. While the proposed model aims to address the limitations of classical LSTMs, this study focuses primarily on the theoretical aspects and the implementation framework. The actual architecture and its practical effectiveness in enhancing sequential data processing remain to be developed and demonstrated in future work.

quant-ph↗

arXiv2024

Quantum-Powered Personalized Learning

This paper explores the transformative potential of quantum computing in the realm of personalized learning. Traditional machine learning models and GPU-based approaches have long been utilized to tailor educational experiences to individual student needs. However, these methods face significant challenges in terms of scalability, computational efficiency, and real-time adaptation to the dynamic nature of educational data. This study proposes leveraging quantum computing to address these limitations. We review existing personalized learning systems, classical machine learning methods, and emerging quantum computing applications in education. We then outline a protocol for data collection, privacy preservation using quantum techniques, and preprocessing, followed by the development and implementation of quantum algorithms specifically designed for personalized learning. Our findings indicate that quantum algorithms offer substantial improvements in efficiency, scalability, and personalization quality compared to classical methods. This paper discusses the implications of integrating quantum computing into educational systems, highlighting the potential for enhanced teaching methodologies, curriculum design, and overall student experiences. We conclude by summarizing the advantages of quantum computing in education and suggesting future research directions.

quant-ph↗

arXiv2024

Rebalancing Multi-Label Class-Incremental Learning

Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve suboptimal performance due to the oversight of the positive-negative imbalance problem, which manifests at both the label and loss levels because of the task-level partial label issue. The imbalance at the label level arises from the substantial absence of negative labels, while the imbalance at the loss level stems from the asymmetric contributions of the positive and negative loss parts to the optimization. To address the issue above, we propose a Rebalance framework for both the Loss and Label levels (RebLL), which integrates two key modules: asymmetric knowledge distillation (AKD) and online relabeling (OR). AKD is proposed to rebalance at the loss level by emphasizing the negative label learning in classification loss and down-weighting the contribution of overconfident predictions in distillation loss. OR is designed for label rebalance, which restores the original class distribution in memory by online relabeling the missing classes. Our comprehensive experiments on the PASCAL VOC and MS-COCO datasets demonstrate that this rebalancing strategy significantly improves performance, achieving new state-of-the-art results even with a vanilla CNN backbone.

cs.CV↗

arXiv2024

Confidence Self-Calibration for Multi-Label Class-Incremental Learning

The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable. This issue leads to a proliferation of false-positive errors due to erroneously high confidence multi-label predictions, exacerbating catastrophic forgetting within the disjoint label space. In this paper, we aim to refine multi-label confidence calibration in MLCIL and propose a Confidence Self-Calibration (CSC) approach. Firstly, for label relationship calibration, we introduce a class-incremental graph convolutional network that bridges the isolated label spaces by constructing learnable, dynamically extended label relationship graph. Then, for confidence calibration, we present a max-entropy regularization for each multi-label increment, facilitating confidence self-calibration through the penalization of over-confident output distributions. Our approach attains new state-of-the-art results in MLCIL tasks on both MS-COCO and PASCAL VOC datasets, with the calibration of label confidences confirmed through our methodology.

cs.CV↗

arXiv2024

Task Success is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors

Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences. Their full power is better harnessed when the models are coupled with external verifiers and the final solutions are derived iteratively or progressively according to the verification feedback. In the context of embodied AI, verification often solely involves assessing whether goal conditions specified in the instructions have been met. Nonetheless, for these agents to be seamlessly integrated into daily life, it is crucial to account for a broader range of constraints and preferences beyond bare task success (e.g., a robot should grasp bread with care to avoid significant deformations). However, given the unbounded scope of robot tasks, it is infeasible to construct scripted verifiers akin to those used for explicit-knowledge tasks like the game of Go and theorem proving. This begs the question: when no sound verifier is available, can we use large vision and language models (VLMs), which are approximately omniscient, as scalable Behavior Critics to catch undesirable robot behaviors in videos? To answer this, we first construct a benchmark that contains diverse cases of goal-reaching yet undesirable robot policies. Then, we comprehensively evaluate VLM critics to gain a deeper understanding of their strengths and failure modes. Based on the evaluation, we provide guidelines on how to effectively utilize VLM critiques and showcase a practical way to integrate the feedback into an iterative process of policy refinement. The dataset and codebase are released at: https://guansuns.github.io/pages/vlm-critic.

cs.AI↗

arXiv2024

Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal Mapping

Fine-Grained Object Detection (FGOD) is a critical task in high-resolution aerial image analysis. This letter introduces Orthogonal Mapping (OM), a simple yet effective method aimed at addressing the challenge of semantic confusion inherent in FGOD. OM introduces orthogonal constraints in the feature space by decoupling features from the last layer of the classification branch with a class-wise orthogonal vector basis. This effectively mitigates semantic confusion and enhances classification accuracy. Moreover, OM can be seamlessly integrated into mainstream object detectors. Extensive experiments conducted on three FGOD datasets (FAIR1M, ShipRSImageNet, and MAR20) demonstrate the effectiveness and superiority of the proposed approach. Notably, with just one line of code, OM achieves a 4.08% improvement in mean Average Precision (mAP) over FCOS on the ShipRSImageNet dataset. Codes are released at https://github.com/ZhuHaoranEIS/Orthogonal-FGOD.

cs.CV↗

arXiv2024

Enabling Stateful Behaviors for Diffusion-based Policy Learning

While imitation learning provides a simple and effective framework for policy learning, acquiring consistent actions during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation at data curation stage or altering the model itself, both of which do not fully address the scalability of consistent action generation. To overcome this limitation, we introduce the Diff-Control policy, which utilizes a diffusion-based model to learn the action representation from a state-space modeling viewpoint. We demonstrate that we can reduce diffusion-based policies' uncertainty by making it stateful through a Bayesian formulation facilitated by ControlNet, leading to improved robustness and success rates. Our experimental results demonstrate the significance of incorporating action statefulness in policy learning, where Diff-Control shows improved performance across various tasks. Specifically, Diff-Control achieves an average success rate of 72% and 84% on stateful and dynamic tasks, respectively. Project page: https://github.com/ir-lab/Diff-Control

cs.RO↗

arXiv2024

The JWST Weather Report from the Nearest Brown Dwarfs I: multi-period JWST NIRSpec + MIRI monitoring of the benchmark binary brown dwarf WISE 1049AB

We report results from 8 hours of JWST/MIRI LRS spectroscopic monitoring directly followed by 7 hours of JWST/NIRSpec prism spectroscopic monitoring of the benchmark binary brown dwarf WISE 1049AB, the closest, brightest brown dwarfs known. We find water, methane, and CO absorption features in both components, including the 3.3 $μ$m methane absorption feature and a tentative detection of small grain ($<$ 1$μ$m) silicate absorption at $>$8.5 $μ$m in WISE 1049A. Both components vary significantly ($>$1$\%$), with WISE 1049B displaying larger variations than WISE 1049A. Using K-means clustering, we find three main transition points in wavelength for both components of the binary: 1) change in behavior at $\sim$2.3 $μ$m coincident with a CO absorption bandhead, 2) change in behavior at 4.2 $μ$m, close to the CO fundamental band at $λ>$ 4.4 $μ$m, and 3) change in behavior at 8.3-8.5 $μ$m, potentially corresponding to silicate absorption. We interpret the lightcurves observed with both NIRSpec and MIRI as likely stemming from 1) a deep pressure level driving the double-peaked variability seen in WISE 1049B at wavelengths $<$2.3 $μ$m and $>$8.5 $μ$m, 2) an intermediate pressure level shaping the lightcurve morphology between 2.3 and 4.2 $μ$m, and 3) a higher-altitude pressure level producing single-peaked and plateaued lightcurve behavior between 4.2 and 8.5 $μ$m.

astro-ph.SR↗

arXiv2024

Physics-Informed AI Inverter

This letter devises an AI-Inverter that pilots the use of a physics-informed neural network (PINN) to enable AI-based electromagnetic transient simulations (EMT) of grid-forming inverters. The contributions are threefold: (1) A PINN-enabled AI-Inverter is formulated; (2) An enhanced learning strategy, balanced-adaptive PINN, is devised; (3) extensive validations and comparative analysis of the accuracy and efficiency of AI-Inverter are made to show its superiority over the classical electromagnetic transient programs (EMTP).

eess.SY↗

arXiv2024

The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems II: A 1 to 20 Micron Spectrum of the Planetary-Mass Companion VHS 1256-1257 b

We present the highest fidelity spectrum to date of a planetary-mass object. VHS 1256 b is a $<$20 M$_\mathrm{Jup}$ widely separated ($\sim$8\arcsec, a = 150 au), young, planetary-mass companion that shares photometric colors and spectroscopic features with the directly imaged exoplanets HR 8799 c, d, and e. As an L-to-T transition object, VHS 1256 b exists along the region of the color-magnitude diagram where substellar atmospheres transition from cloudy to clear. We observed VHS 1256~b with \textit{JWST}'s NIRSpec IFU and MIRI MRS modes for coverage from 1 $μ$m to 20 $μ$m at resolutions of $\sim$1,000 - 3,700. Water, methane, carbon monoxide, carbon dioxide, sodium, and potassium are observed in several portions of the \textit{JWST} spectrum based on comparisons from template brown dwarf spectra, molecular opacities, and atmospheric models. The spectral shape of VHS 1256 b is influenced by disequilibrium chemistry and clouds. We directly detect silicate clouds, the first such detection reported for a planetary-mass companion.

astro-ph.EP↗

arXiv2024

Transparent Object Depth Completion

The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties. These properties lead to gaps and inaccuracies in the depth maps of the transparent objects captured by depth sensors. To address this issue, we propose an end-to-end network for transparent object depth completion that combines the strengths of single-view RGB-D based depth completion and multi-view depth estimation. Moreover, we introduce a depth refinement module based on confidence estimation to fuse predicted depth maps from single-view and multi-view modules, which further refines the restored depth map. The extensive experiments on the ClearPose and TransCG datasets demonstrate that our method achieves superior accuracy and robustness in complex scenarios with significant occlusion compared to the state-of-the-art methods.

cs.CV↗

arXiv2024

Delta Tensor: Efficient Vector and Tensor Storage in Delta Lake

The exponential growth of artificial intelligence (AI) and machine learning (ML) applications has necessitated the development of efficient storage solutions for vector and tensor data. This paper presents a novel approach for tensor storage in a Lakehouse architecture using Delta Lake. By adopting the multidimensional array storage strategy from array databases and sparse encoding methods to Delta Lake tables, experiments show that this approach has demonstrated notable improvements in both space and time efficiencies when compared to traditional serialization of tensors. These results provide valuable insights for the development and implementation of optimized vector and tensor storage solutions in data-intensive applications, contributing to the evolution of efficient data management practices in AI and ML domains in cloud-native environments

cs.DC↗