arXiv subjects

Yifan Zhou

Publications and source records attributed to Yifan Zhou.

At least 163 records · Page 9Linked to original sources

arXiv2024

Prioritizing High-Precision Photometric Monitoring of Exoplanet and Brown Dwarf Companions with JWST -- Strategic Exoplanet Initiatives with HST and JWST White Paper

We advocate for the prioritization of high-precision photometric monitoring of exoplanet and brown dwarf companions to detect brightness variability arising from features in their atmospheres. Measurements of photometric variability provide not only an insight into the physical appearances of these companions, but are also a direct probe of their atmospheric structures and dynamics, and yield valuable estimates of their rotation periods. JWST is uniquely capable of monitoring faint exoplanet companions over their full rotation periods, thanks to its inherent stability and powerful high-contrast coronagraphic imaging modes. Rotation period measurements can be further combined with measurements of v sin i obtained using high-resolution spectroscopy to infer the viewing angle of a companion. Photometric monitoring over multiple rotation periods and at multiple epochs will allow both short- and long-term time evolution in variability signals to be traced. Furthermore, the differences between the layers in a companion's atmosphere can be probed by obtaining simultaneous photometric monitoring at different wavelengths through NIRCam dual-band coronagraphy. Overall, JWST will reach the highest sensitivities to variability to date and enable the light curves of substellar companions to be characterised with unprecedented cadence and precision at the sub-percent level.

astro-ph.IM↗

arXiv2024

Atmospheric Retrievals of the Phase-resolved Spectra of Irradiated Brown Dwarfs WD-0137B and EPIC-2122B

We present an atmospheric retrieval analysis of HST/WFC3/G141 spectroscopic phase curve observations of two brown dwarfs, WD-0137B and EPIC-2122B, in ultra-short period orbits around white dwarf hosts. These systems are analogous to hot and ultra-hot Jupiter systems, enabling a unique and high-precision comparison to exoplanet systems. We use the PETRA retrieval suite to test various analysis setups, including joint-phase retrievals, multiple temperature structures, and non-uniform abundances. We find that WD-0137B has a dayside that closely resembles that of other ultra-hot Jupiters with inverted temperature structures and H$^-$ opacity, but quickly transitions to a mostly non-inverted temperature structure on the nightside. Meanwhile, EPIC-2122B's atmosphere remains inverted at all constrained longitudes, with dominant H$^-$ opacity. Retrievals with multiple temperature profiles and non-uniform vertical abundances were generally not statistically justified for this dataset, but retrievals with dayside-dilution factors were found to be justified. Retrieving all phases simultaneously with a linear combination of a dayside and nightside atmosphere was found to be an adequate representation of the entire phase-curve once a longitudinal temperature gradient free parameter was included in the retrieval. Comparing to global circulation models, we attribute behavior in the 1D retrievals to the inclined viewing geometry of the systems, which results in always-visible irradiated and inverted portions of the atmosphere "contaminating" spectra measured from the nightside hemisphere. This study sheds light on the similarities between these irradiated brown dwarf systems and hot and ultra-hot Jupiters, but also their unique differences, including the influence of the inclined viewing geometry.

astro-ph.SR↗

arXiv2024

Time-resolved Hubble Space Telescope Wide Field Camera 3 Spectrophotometry Reveals Inefficient Day-to-Night Heat Redistribution in the Highly Irradiated Brown Dwarf SDSS 1557B

Brown dwarfs in ultra-short period orbits around white dwarfs offer a unique opportunity to study the properties of tidally-locked, fast rotating (1-3 hr), and highly-irradiated atmospheres. Here, we present phase-resolved spectrophotometry of the white dwarf-brown dwarf (WD-BD) binary SDSS 1557, which is the fifth WD-BD binary in our six-object sample. Using the Hubble Space Telescope Wide Field Camera 3 Near-infrared G141 instrument, the 1.1 to 1.7 $μ$m phase curves show rotational modulations with semi-amplitudes of 10.5$\pm$0.1%. We observe a wavelength dependent amplitude, with longer wavelengths producing larger amplitudes, while no wavelength dependent phase shifts were identified. The phase-resolved extracted BD spectra exhibit steep slopes and are nearly featureless. A simple radiative energy redistribution atmospheric model recreates the hemisphere integrated brightness temperatures at three distinct phases and finds evidence for weak redistribution efficiency. Our model also predicts a higher inclination than previously published. We find that SDSS 1557B, the second most irradiated BD in our sample, is likely dominated by clouds on the night side, whereas the featureless day side spectrum is likely dominated by H$^-$ opacity and a temperature inversion, much like the other highly-irradiated BD EPIC2122B.

astro-ph.EP↗

arXiv2024

Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems

This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system. The LE-SESs are networked systems, composed of a set of connected components including: Bayes filters for state estimation, and neural networks for processing sensory input. We study LE-SESs from the perspective of formal verification, which determines the satisfiabilty of a system model against the specified properties. Over LE-SESs, we investigate two key properties -- robustness and resilience -- and provide their formal definitions. To enable formal verification, we reduce the LE-SESs to a novel class of labelled transition systems, named {PO}^2-LTS in the paper, and formally express the properties as constrained optimisation objectives. We prove that the verification problems are NP-complete. Based on {PO}^2-LTS and the optimisation objectives, practical verification algorithms are developed to check the satisfiability of the properties on the LE-SESs. As a major case study, we interrogate a real-world dynamic tracking system which uses a single Kalman Filter (KF) -- a special case of Bayes filter -- to localise and track a ground vehicle. Its perception system, based on convolutional neural networks, processes a high-resolution Wide Area Motion Imagery (WAMI) data stream. Experimental results show that our algorithms can not only verify the properties of the WAMI tracking system but also provide representative examples, the latter of which inspired us to take an enhanced LE-SESs design where runtime monitors or joint-KFs are required. Experimental results confirm the improvement in the robustness of the enhanced design.

cs.RO↗

arXiv2024

Dense Vision Transformer Compression with Few Samples

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for Vision Transformers (ViT) remains largely unexplored, which presents a new challenge. In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block, DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression, which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points, along with lower latency in the compression of ViT and its variants.

cs.CV↗

arXiv2024

BAGS: Blur Agnostic Gaussian Splatting through Multi-Scale Kernel Modeling

Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of Gaussian-Splatting-based methods against various image blur, such as motion blur, defocus blur, downscaling blur, \etc. Under these degradations, Gaussian-Splatting-based methods tend to overfit and produce worse results than Neural-Radiance-Field-based methods. To address this issue, we propose Blur Agnostic Gaussian Splatting (BAGS). BAGS introduces additional 2D modeling capacities such that a 3D-consistent and high quality scene can be reconstructed despite image-wise blur. Specifically, we model blur by estimating per-pixel convolution kernels from a Blur Proposal Network (BPN). BPN is designed to consider spatial, color, and depth variations of the scene to maximize modeling capacity. Additionally, BPN also proposes a quality-assessing mask, which indicates regions where blur occur. Finally, we introduce a coarse-to-fine kernel optimization scheme; this optimization scheme is fast and avoids sub-optimal solutions due to a sparse point cloud initialization, which often occurs when we apply Structure-from-Motion on blurry images. We demonstrate that BAGS achieves photorealistic renderings under various challenging blur conditions and imaging geometry, while significantly improving upon existing approaches.

cs.CV↗

arXiv2024

Scalable Neural Dynamic Equivalence for Power Systems

Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components. However, acquiring thorough power system models can be impractical in real operation due to inaccessible system parameters and privacy of consumers, which necessitate data-driven dynamic equivalencing of unknown subsystems. Learning reliable dynamic equivalent models for the external systems from SCADA and PMU data, however, is a long-standing intractable problem in power system analysis due to complicated nonlinearity and unforeseeable dynamic modes of power systems. This paper advances a practical application of neural dynamic equivalence (NeuDyE) called Driving Port NeuDyE (DP-NeuDyE), which exploits physics-informed machine learning and neural-ordinary-differential-equations (ODE-NET) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The new contributions are threefold: A NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems, saving the effort and expense of acquiring inaccessible system; An introduction of a Physics-Informed NeuDyE learning (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE; and A DP-NeuDyE to reduce the number of inputs required for the training. We conduct extensive case studies on the NPCC system to validate the generalizability and accuracy of both PI-NeuDyE and DP-NeuDyE, which span a multitude of scenarios, differing in the time required for fault clearance, the specific fault locations, and the limitations of data. Test results have demonstrated the scalability and practicality of NeuDyE, showing its potential to be used in ISO and utility control centers for online transient stability analysis and for planning purposes.

eess.SY↗

arXiv2024

FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation

The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However, the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient, resulting in temporal inconsistency. In this paper, we introduce FRESCO, intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance, our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video, significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality, coherent videos, marking a notable improvement over existing zero-shot methods.

cs.CV↗

arXiv2024

Revisiting the Helium and Hydrogen Accretion Indicators at TWA 27B: Weak Mass Flow at Near-Freefall Velocity

TWA 27B (2M1207b) is the first directly-imaged planetary-mass (MP ~ 5 MJ) companion (Chauvin et al. 2004) and was observed at 0.9--5.3 micron with JWST/NIRSpec (Luhman et al. 2023). To understand the accretion properties of TWA 27B, we search for continuum-subtracted near-infrared helium and hydrogen emission lines and measure their widths and luminosities. We detect the He I triplet at 4.3 sigma and all Paschen-series lines covered by NIRSpec (Pa alpha, Pa beta, Pa gamma, Pa delta) at 4--5 sigma. The three brightest Brackett-series lines (Br alpha, Br beta, Br gamma) as well as Pf gamma and Pf delta are tentative detections at 2--3 sigma. We provide upper limits on the other hydrogen lines, including on H alpha through Hubble Space Telescope archival data. Three lines can be reliably deconvolved to reveal an intrinsic width Delta v = 67+-9 km/s, which is 60% of the surface freefall velocity. The line luminosities seem significantly too high to be due to chromospheric activity. Converting line luminosities to an accretion rate yields Mdot ~ 5e-9 MJ/yr when using scaling relationships for planetary masses, and Mdot ~ 0.1e-9 MJ/yr with extrapolated stellar scalings. Several of these lines represent first detections at an accretor of such low mass. The weak accretion rate implies that formation is likely over. This analysis shows that JWST can be used to measure low line-emitting mass accretion rates onto planetary-mass objects, motivates deeper searches for the mass reservoir feeding TWA 27B, and hints that other young directly-imaged objects might -- hitherto unbeknownst -- also be accreting.

astro-ph.EP↗

arXiv2024

AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation

Due to privacy or patent concerns, a growing number of large models are released without granting access to their training data, making transferring their knowledge inefficient and problematic. In response, Data-Free Knowledge Distillation (DFKD) methods have emerged as direct solutions. However, simply adopting models derived from DFKD for real-world applications suffers significant performance degradation, due to the discrepancy between teachers' training data and real-world scenarios (student domain). The degradation stems from the portions of teachers' knowledge that are not applicable to the student domain. They are specific to the teacher domain and would undermine students' performance. Hence, selectively transferring teachers' appropriate knowledge becomes the primary challenge in DFKD. In this work, we propose a simple but effective method AuG-KD. It utilizes an uncertainty-guided and sample-specific anchor to align student-domain data with the teacher domain and leverages a generative method to progressively trade off the learning process between OOD knowledge distillation and domain-specific information learning via mixup learning. Extensive experiments in 3 datasets and 8 settings demonstrate the stability and superiority of our approach. Code available at https://github.com/IshiKura-a/AuG-KD .

cs.LG↗

arXiv2024

Deep Pa$β$ Imaging of the Candidate Accreting Protoplanet AB Aur b

Giant planets grow by accreting gas through circumplanetary disks, but little is known about the timescale and mechanisms involved in the planet assembly process because few accreting protoplanets have been discovered. Recent visible and infrared (IR) imaging revealed a potential accreting protoplanet within the transition disk around the young intermediate-mass Herbig Ae star, AB Aurigae (AB Aur). Additional imaging in H$α$ probed for accretion and found agreement between the line-to-continuum flux ratio of the star and companion, raising the possibility that the emission source could be a compact disk feature seen in scattered starlight. We present new deep Keck/NIRC2 high-contrast imaging of AB Aur to characterize emission in Pa$β$, another accretion tracer less subject to extinction. Our narrow band observations reach a 5$σ$ contrast of 9.6 mag at 0.6$''$, but we do not detect significant emission at the expected location of the companion, nor from other any other source in the system. Our upper limit on Pa$β$ emission suggests that if AB Aur b is a protoplanet, it is not heavily accreting or accretion is stochastic and was weak during the observations.

astro-ph.EP↗

arXiv2024

An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction

This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show potential in medical data but suffer from issues like overfitting and lack of interpretability due to imbalanced and scarce data. Traditional Gray Wolf Optimization (GWO) also has its drawbacks, such as a lack of population diversity and premature convergence. This paper addresses these problems by introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid function. This algorithm was extensively compared to four leading algorithms using six well-known test functions, outperforming them effectively. Moreover, by utilizing the ACGWO, we increase the robustness and generalization of the neural network, resulting in more interpretable predictions. Applied to the publicly accessible Cleveland Heart Disease dataset, our technique surpasses ten other methods, achieving 86.8% accuracy, indicating its potential for efficient heart disease prediction in the clinical setting.

cs.LG↗

arXiv2023

DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing

Diffusion models have achieved remarkable image generation quality surpassing previous generative models. However, a notable limitation of diffusion models, in comparison to GANs, is their difficulty in smoothly interpolating between two image samples, due to their highly unstructured latent space. Such a smooth interpolation is intriguing as it naturally serves as a solution for the image morphing task with many applications. In this work, we present DiffMorpher, the first approach enabling smooth and natural image interpolation using diffusion models. Our key idea is to capture the semantics of the two images by fitting two LoRAs to them respectively, and interpolate between both the LoRA parameters and the latent noises to ensure a smooth semantic transition, where correspondence automatically emerges without the need for annotation. In addition, we propose an attention interpolation and injection technique and a new sampling schedule to further enhance the smoothness between consecutive images. Extensive experiments demonstrate that DiffMorpher achieves starkly better image morphing effects than previous methods across a variety of object categories, bridging a critical functional gap that distinguished diffusion models from GANs.

cs.CV↗

arXiv2023

Software-Defined Virtual Synchronous Condenser

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

eess.SY↗

arXiv2023

Autoencoder with Group-based Decoder and Multi-task Optimization for Anomalous Sound Detection

In industry, machine anomalous sound detection (ASD) is in great demand. However, collecting enough abnormal samples is difficult due to the high cost, which boosts the rapid development of unsupervised ASD algorithms. Autoencoder (AE) based methods have been widely used for unsupervised ASD, but suffer from problems including 'shortcut', poor anti-noise ability and sub-optimal quality of features. To address these challenges, we propose a new AE-based framework termed AEGM. Specifically, we first insert an auxiliary classifier into AE to enhance ASD in a multi-task learning manner. Then, we design a group-based decoder structure, accompanied by an adaptive loss function, to endow the model with domain-specific knowledge. Results on the DCASE 2021 Task 2 development set show that our methods achieve a relative improvement of 13.11% and 15.20% respectively in average AUC over the official AE and MobileNetV2 across test sets of seven machines.

cs.SD↗

arXiv2023

Multimodal Learning of Soft Robot Dynamics using Differentiable Filters

Differentiable Filters, as recursive Bayesian estimators, possess the ability to learn complex dynamics by deriving state transition and measurement models exclusively from data. This data-driven approach eliminates the reliance on explicit analytical models while maintaining the essential algorithmic components of the filtering process. However, the gain mechanism remains non-differentiable, limiting its adaptability to specific task requirements and contextual variations. To address this limitation, this paper introduces an innovative approach called α-MDF (Attention-based Multimodal Differentiable Filter). α-MDF leverages modern attention mechanisms to learn multimodal latent representations for accurate state estimation in soft robots. By incorporating attention mechanisms, α-MDF offers the flexibility to tailor the gain mechanism to the unique nature of the task and context. The effectiveness of α-MDF is validated through real-world state estimation tasks on soft robots. Our experimental results demonstrate significant reductions in state estimation errors, consistently surpassing differentiable filter baselines by up to 45% in the domain of soft robotics.

cs.RO↗

arXiv2023

Recovering simulated planet and disk signals using SCALES aperture masking

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) instrument is a lenslet-based integral field spectrograph that will operate at 2 to 5 microns, imaging and characterizing colder (and thus older) planets than current high-contrast instruments. Its spatial resolution for distant science targets and/or close-in disks and companions could be improved via interferometric techniques such as sparse aperture masking. We introduce a nascent Python package, NRM-artist, that we use to design several SCALES masks to be non-redundant and to have uniform coverage in Fourier space. We generate high-fidelity mock SCALES data using the scalessim package for SCALES' low spectral resolution modes across its 2 to 5 micron bandpass. We include realistic noise from astrophysical and instrument sources, including Keck adaptive optics and Poisson noise. We inject planet and disk signals into the mock datasets and subsequently recover them to test the performance of SCALES sparse aperture masking and to determine the sensitivity of various mask designs to different science signals.

astro-ph.IM↗

arXiv2023

Simulating medium-spectral-resolution exoplanet characterization with SCALES angular/reference differential imaging

SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) is a 2 - 5 micron high-contrast lenslet-based integral field spectrograph (IFS) designed to characterize exoplanets and their atmospheres. The SCALES medium-spectral-resolution mode uses a lenslet subarray with a 0.34 x 0.36 arcsecond field of view which allows for exoplanet characterization at increased spectral resolution. We explore the sensitivity limitations of this mode by simulating planet detections in the presence of realistic noise sources. We use the SCALES simulator scalessim to generate high-fidelity mock observations of planets that include speckle noise from their host stars, as well as other atmospheric and instrumental noise effects. We employ both angular and reference differential imaging as methods of disentangling speckle noise from the injected planet signals. These simulations allow us to assess the feasibility of speckle deconvolution for SCALES medium resolution data, and to test whether one approach outperforms another based on planet angular separations and contrasts.

astro-ph.IM↗