arXiv subjects

Jia Liu

Publications and source records attributed to Jia Liu.

At least 217 records · Page 12Linked to original sources

arXiv2024

Enhanced Monochromatic Photon Emission from Millicharged Co-Interacting Dark Matter

We study a millicharged co-interacting dark matter scenario, where the primary dark matter constituent is the dark photon $A'$ and the secondary component is the fermion $χ$. In this model, $χ$ interacts with $A'$ via a $U(1)'$ interaction while being millicharged with respect to normal photons. Our investigation focuses on the oscillation of $A'$ dark matter into photons within the background of $χ$ particles, revealing that the $A'-χ$ scattering rate benefits from a Bose enhancement of the $A'$ final state. As the oscillation production rate is directly linked to the scattering rate, the conversion of $A'$ dark matter into monochromatic photons experiences significant amplification owing to this Bose enhancement, especially when the scattering rate $Γ_{\rm sca}$ approaches the dark photon mass $m_{A'}$. These converted monochromatic photons are detectable through radio telescopes and can induce distortions in the Cosmic Microwave Background (CMB) spectrum. We find that the sensitivity of radio telescopes and the constraints imposed by CMB distortion on the kinetic mixing parameter are notably heightened compared to scenarios without the subdominant millicharged dark matter.

hep-ph↗

arXiv2024

Amplifying Non-Resonant Production of Dark Sector Particles in Scattering Dominance Regime

We investigate the enhancement of dark sector particle production within the scattering dominant regime. These particles typically exhibit a slight mixing with Standard Model particles through various portals, allowing for their generation through in-medium oscillation from Standard Model particle sources. Our analysis reveals that in the scattering dominance regime, with a significantly smaller scattering mean free path $λ_{\rm sca}$ compared to the absorption mean free path $λ_{\rm abs}$, the nonresonant production of sterile states can experience an enhancement by a factor of $λ_{\rm abs}/λ_{\rm sca}$. This phenomenon is demonstrated within the context of kinetic mixing dark photon production at a reactor, precisely satisfying this condition. By incorporating this collisional enhancement, we find that the current sensitivity to the mixing parameter $ε$ for dark photons in the TEXONO experiment can be significantly improved across a range spanning from tens of eV to MeV. This advancement establishes the most stringent laboratory constraint within this mass spectrum for the dark photon. Sterile neutrino production, however, does not exhibit such enhancement, either due to the failure to meet the scattering dominance criterion or the neutrino damping in resonant production.

hep-ph↗

arXiv2024

DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency

The combination of electronic health records (EHR) and medical images is crucial for clinicians in making diagnoses and forecasting prognosis. Strategically fusing these two data modalities has great potential to improve the accuracy of machine learning models in clinical prediction tasks. However, the asynchronous and complementary nature of EHR and medical images presents unique challenges. Missing modalities due to clinical and administrative factors are inevitable in practice, and the significance of each data modality varies depending on the patient and the prediction target, resulting in inconsistent predictions and suboptimal model performance. To address these challenges, we propose DrFuse to achieve effective clinical multi-modal fusion. It tackles the missing modality issue by disentangling the features shared across modalities and those unique within each modality. Furthermore, we address the modal inconsistency issue via a disease-wise attention layer that produces the patient- and disease-wise weighting for each modality to make the final prediction. We validate the proposed method using real-world large-scale datasets, MIMIC-IV and MIMIC-CXR. Experimental results show that the proposed method significantly outperforms the state-of-the-art models. Our implementation is publicly available at https://github.com/dorothy-yao/drfuse.

eess.IV↗

arXiv2024

State Machine of Thoughts: Leveraging Past Reasoning Trajectories for Enhancing Problem Solving

Current Large Language Model-based agents reason within an exploration-evaluation framework, navigating problem-solving processes in a tree-like manner. However, these methods often neglect successful reasoning trajectories once a problem is resolved, leading to inefficient use of these trajectories for future analogous problems. To address this inefficiency, we adopt a state machine to record experience derived from previous reasoning trajectories. Within the state machine, states represent decomposed sub-problems, while state transitions reflect the dependencies among sub-problems. The state machine records both successful and failed trajectories. Utilizing the experience from the state machine, our proposed State Machine of Thoughts (SMoT) selects the most optimal sub-solutions and avoids incorrect ones. Our experiments show that SMoT can significantly improve problem-solving abilities in two exploration-intensive problems: the 24-point game and a taxi navigation reinforcement learning game.

cs.AI↗

arXiv2024

Integration of cognitive tasks into artificial general intelligence test for large models

During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, aimed at fulfilling the testing needs of large models with enhanced capabilities. The cognitive science-inspired AGI tests encompass the full spectrum of intelligence facets, including crystallized intelligence, fluid intelligence, social intelligence, and embodied intelligence. To assess the multidimensional intelligence of large models, the AGI tests consist of a battery of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

cs.AI↗

arXiv2024

Impact of baryonic feedback on HSC Y1 weak lensing non-Gaussian statistics

Baryonic feedback is a major systematic in weak lensing cosmology. Its most studied effect is the suppression of the lensing power spectrum, a second-order statistic, on small scales. Motivated by the growing interest in statistics beyond the second order, we investigate the effect of baryons on lensing non-Gaussian statistics and the resulting biases in the matter clustering amplitude $S_8 = σ_8\sqrt{Ω_m/0.3}$. We focus on the Subaru Hyper Suprime-Cam Year 1 (HSC-Y1) data which, with its high source number density, closely resembles those expected from the upcoming Euclid and Rubin LSST. We study four non-Gaussian statistics -- peak counts, minimum counts, the probability distribution function, and the scattering transform -- in addition to the usual power spectrum. We first estimate the biases in $S_8$ using mock observations built from the IllustrisTNG and BAHAMAS hydrodynamical simulations and theoretical models built from dark matter-only simulations. We find up to $1σ$ bias in $S_8$ when the smallest scales (2 arcmin) and the highest feedback level are considered. We then analyze the HSC-Y1 data and compare the $S_8$ obtained for each statistic with different smoothing scales or scale cuts. As we expect that baryons mostly affect the small scales, comparing the results obtained from including and excluding small scales can indicate the level of impact from baryons. With HSC data, we find only minor ($\leq0.5σ$) differences in $S_8$ for all statistics, even when considering very small scales (2 arcmin). Our results suggest that the effect of baryons is insignificant at the level of HSC-Y1 down to 2~arcmin for all statistics examined here, or it is canceled by other scale-dependent systematics.

astro-ph.CO↗

arXiv2024

Velocity recostruction with graph neural networks

In this work, we seek to improve the velocity reconstruction of clusters by using Graph Neural Networks -- a type of deep neural network designed to analyze sparse, unstructured data. In comparison to the Convolutional Neural Network (CNN) which is built for structured data such as regular grids, GNN is particularly suitable for analyzing galaxy catalogs. In our GNNs, galaxies as represented as nodes that are connected with edges. The galaxy positions and properties -- stellar mass, star formation rate, and total number of galaxies within 100~\mpc -- are combined to predict the line-of-sight velocity of the clusters. To train our networks, we use mock SDSS galaxies and clusters constructed from the Magneticum hydrodynamic simulations. Our GNNs reach a precision in reconstructed line-of-sight velocity of $Δv$=163 km/s, outperforming by $\approx$10\% the perturbation theory~($Δv$=181 km/s) or the CNN~($Δv$=179 km/s). The stellar mass provides additional information, improving the precision by $\approx$6\% beyond the position-only GNN, while other properties add little information. Our GNNs remain capable of reconstructing the velocity field when redshift-space distortion is included, with $Δv$=210 km/s which is again 10\% better than CNN with RSD. Finally, we find that even with an impressive, nearly 70\% increase in galaxy number density from SDSS to DESI, our GNNs only show an underwhelming 2\% improvement, in line with previous works using other methods. Our work demonstrates that, while the efficiency in velocity reconstruction may have plateaued already at SDSS number density, further improvements are still hopeful with new reconstruction models such as the GNNs studied here.

astro-ph.CO↗

arXiv2024

Probing CP Violation in Dark Sector through the Electron Electric Dipole Moment

The Two Higgs Doublet Model (2HDM) stands as a promising framework for exploring physics beyond the Standard Model (SM). Within this context, we explore the possibility that the two Higgs doublets may serve as a window into CP-violating dark sectors, neutral under the SM gauge groups. Specifically, our focus is on investigating the electric dipole moment (EDM) of the electron, generated solely by CP violation in the dark sector. We present a general formula for the electron EDM, without specifying the structure of the dark sectors, and discuss the current constraints on various dark sector models. It is noteworthy that even in the case of a CP-conserving 2HDM, the resulting electron EDM is capable of reaching the current experimental limit, with CP violation arising exclusively from the dark sectors. Furthermore, we introduce a heavy dark sector (HDS) approximation for the analytic calculation of the EDM, assuming that the dark sector particles are much heavier than the physical states in the 2HDM. This approximation yields simplified analytic results that are consistent with the full numerical calculations.

hep-ph↗

arXiv2024

Data Distribution Dynamics in Real-World WiFi-Based Patient Activity Monitoring for Home Healthcare

This paper examines the application of WiFi signals for real-world monitoring of daily activities in home healthcare scenarios. While the state-of-the-art of WiFi-based activity recognition is promising in lab environments, challenges arise in real-world settings due to environmental, subject, and system configuration variables, affecting accuracy and adaptability. The research involved deploying systems in various settings and analyzing data shifts. It aims to guide realistic development of robust, context-aware WiFi sensing systems for elderly care. The findings suggest a shift in WiFi-based activity sensing, bridging the gap between academic research and practical applications, enhancing life quality through technology.

eess.SP↗

arXiv2024

Searching for Ultralight Dark Matter Conversion in Solar Corona using Low Frequency Array Data

Ultralight dark photons and axions are well-motivated hypothetical dark matter candidates. Both dark photon dark matter and axion dark matter can resonantly convert into electromagnetic waves in the solar corona when their mass is equal to the solar plasma frequency. The resultant electromagnetic waves appear as monochromatic signals within the radio-frequency range with an energy equal to the dark matter mass, which can be detected via radio telescopes for solar observations. Here we show our search for converted monochromatic signals in the observational data collected by the high-sensitivity Low Frequency Array (LOFAR) telescope and establish an upper limit on the kinetic mixing coupling between dark photon dark matter and photon, which can reach values as low as $10^{-13}$ within the frequency range of $30-80$ MHz. This limit represents an improvement of approximately one order of magnitude better than the existing constraint from the cosmic microwave background observation. Additionally, we derive an upper limit on the axion-photon coupling within the same frequency range, which is better than the constraints from Light-Shining-through-a-Wall experiments while not exceeding the CERN Axion Solar Telescope (CAST) experiment or other astrophysical bounds.

hep-ph↗

arXiv2024

Multi-granularity Correspondence Learning from Long-term Noisy Videos

Existing video-language studies mainly focus on learning short video clips, leaving long-term temporal dependencies rarely explored due to over-high computational cost of modeling long videos. To address this issue, one feasible solution is learning the correspondence between video clips and captions, which however inevitably encounters the multi-granularity noisy correspondence (MNC) problem. To be specific, MNC refers to the clip-caption misalignment (coarse-grained) and frame-word misalignment (fine-grained), hindering temporal learning and video understanding. In this paper, we propose NOise Robust Temporal Optimal traNsport (Norton) that addresses MNC in a unified optimal transport (OT) framework. In brief, Norton employs video-paragraph and clip-caption contrastive losses to capture long-term dependencies based on OT. To address coarse-grained misalignment in video-paragraph contrast, Norton filters out the irrelevant clips and captions through an alignable prompt bucket and realigns asynchronous clip-caption pairs based on transport distance. To address the fine-grained misalignment, Norton incorporates a soft-maximum operator to identify crucial words and key frames. Additionally, Norton exploits the potential faulty negative samples in clip-caption contrast by rectifying the alignment target with OT assignment to ensure precise temporal modeling. Extensive experiments on video retrieval, videoQA, and action segmentation verify the effectiveness of our method. Code is available at https://lin-yijie.github.io/projects/Norton.

cs.CV↗

arXiv2024

Federated Multi-Objective Learning

In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed nature and data privacy needs of such multi-agent multi-task learning applications. This motivates us to propose a new federated multi-objective learning (FMOL) framework with multiple clients distributively and collaboratively solving an MOO problem while keeping their training data private. Notably, our FMOL framework allows a different set of objective functions across different clients to support a wide range of applications, which advances and generalizes the MOO formulation to the federated learning paradigm for the first time. For this FMOL framework, we propose two new federated multi-objective optimization (FMOO) algorithms called federated multi-gradient descent averaging (FMGDA) and federated stochastic multi-gradient descent averaging (FSMGDA). Both algorithms allow local updates to significantly reduce communication costs, while achieving the {\em same} convergence rates as those of their algorithmic counterparts in the single-objective federated learning. Our extensive experiments also corroborate the efficacy of our proposed FMOO algorithms.

cs.LG↗

arXiv2024

A dynamical neural network approach for distributionally robust chance constrained Markov decision process

In this paper, we study the distributionally robust joint chance constrained Markov decision process. {Utilizing the logarithmic transformation technique,} we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set. To cope with the non-convexity and improve the robustness of the solution, we propose a dynamical neural network approach to solve the reformulated optimization problem. Numerical results on a machine replacement problem demonstrate the efficiency of the proposed dynamical neural network approach when compared with the sequential convex approximation approach.

math.OC↗

arXiv2023

Chance constrained nonlinear fractional programming with random benchmark

This paper studies the chance constrained fractional programming with a random benchmark. We assume that the random variables on the numerator follow the Gaussian distribution, and the random variables on the denominator and the benchmark follow a joint discrete distribution. Under some mild assumptions, we derive a convex reformulation of chance constrained fractional programming. For practical use, we apply piecewise linear and tangent approximations to the quantile function. We conduct numerical experiments on a main economic application problem.

math.OC↗

arXiv2023

SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field

In this paper, we propose SpectralNeRF, an end-to-end Neural Radiance Field (NeRF)-based architecture for high-quality physically based rendering from a novel spectral perspective. We modify the classical spectral rendering into two main steps, 1) the generation of a series of spectrum maps spanning different wavelengths, 2) the combination of these spectrum maps for the RGB output. Our SpectralNeRF follows these two steps through the proposed multi-layer perceptron (MLP)-based architecture (SpectralMLP) and Spectrum Attention UNet (SAUNet). Given the ray origin and the ray direction, the SpectralMLP constructs the spectral radiance field to obtain spectrum maps of novel views, which are then sent to the SAUNet to produce RGB images of white-light illumination. Applying NeRF to build up the spectral rendering is a more physically-based way from the perspective of ray-tracing. Further, the spectral radiance fields decompose difficult scenes and improve the performance of NeRF-based methods. Comprehensive experimental results demonstrate the proposed SpectralNeRF is superior to recent NeRF-based methods when synthesizing new views on synthetic and real datasets. The codes and datasets are available at https://github.com/liru0126/SpectralNeRF.

cs.CV↗

arXiv2023

Watermarking for Neural Radiation Fields by Invertible Neural Network

To protect the copyright of the 3D scene represented by the neural radiation field, the embedding and extraction of the neural radiation field watermark are considered as a pair of inverse problems of image transformations. A scheme for protecting the copyright of the neural radiation field is proposed using invertible neural network watermarking, which utilizes watermarking techniques for 2D images to achieve the protection of the 3D scene. The scheme embeds the watermark in the training image of the neural radiation field through the forward process in the invertible network and extracts the watermark from the image rendered by the neural radiation field using the inverse process to realize the copyright protection of both the neural radiation field and the 3D scene. Since the rendering process of the neural radiation field can cause the loss of watermark information, the scheme incorporates an image quality enhancement module, which utilizes a neural network to recover the rendered image and then extracts the watermark. The scheme embeds a watermark in each training image to train the neural radiation field and enables the extraction of watermark information from multiple viewpoints. Simulation experimental results demonstrate the effectiveness of the method.

cs.CR↗

arXiv2023

Cosmological constraints from HSC Y1 lensing convergence PDF

We utilize the probability distribution function (PDF) of normalized convergence maps reconstructed from the Subaru Hyper Suprime-Cam (HSC) Y1 shear catalogue, in combination with the power spectrum, to measure the matter clustering amplitude $S_8=σ_8\sqrt{Ω_m/0.3}$. The large-scale structure's statistical properties are incompletely described by the traditional two-point statistics, motivating our investigation of the PDF -- a complementary higher-order statistic. By defining the PDF over the standard deviation-normalized convergence map we are able to isolate the non-Gaussian information. We use tailored simulations to compress the data vector and construct a likelihood approximation. We mitigate the impact of survey and astrophysical systematics with cuts on smoothing scales, redshift bins, and data vectors. We find $S_8=0.860^{+0.066}_{-0.109}$ from the PDF alone and $S_8=0.798^{+0.029}_{-0.042}$ from the combination of PDF and power spectrum (68% CL). The PDF improves the power spectrum-only constraint by about 10%.

astro-ph.CO↗

arXiv2023

Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images

Change detection has always been a concerned task in the interpretation of remote sensing images. It is essentially a unique binary classification task with two inputs, and there is a change relationship between these two inputs. At present, the mining of change relationship features is usually implicit in the network architectures that contain single-branch or two-branch encoders. However, due to the lack of artificial prior design for change relationship features, these networks cannot learn enough change semantic information and lose more accurate change detection performance. So we propose a network architecture NAME for the explicit mining of change relation features. In our opinion, the change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively. In addition, we design the continuous change relation (CCR) branch to further obtain the continuous and detail change relation features to improve the change discrimination capability of the model. The experimental results show that our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection on four public very high-resolution (VHR) remote sensing datasets. Our source code is available at https://github.com/DalongZ/NAME.

cs.CV↗