arXiv subjects

Sibo Zhang

Publications and source records attributed to Sibo Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Late-Time Emergence of Dark Energy and Its Interaction with Dark Matter

We present an interacting scenario between dark energy (DE) and dark matter (DM), where DE has an emergent nature, that means, DE was absent in the early universe but it becomes effective only at late times. We consider two specific emergent DE models, one with no free parameters and the other featuring two parameters describing the speed and epoch of the transition. We constrain both scenarios using the cosmic microwave background (CMB) measurements from the Planck 2018 release, baryon acoustic oscillations from DESI DR2, and three different compilations of Type Ia supernovae (PantheonPlus, DES-Dovekie, and Union3). Our analysis indicates that current cosmological probes are not able to tightly constrain the speed of the transition. For both scenarios, the posterior distribution of the interaction parameter is shifted away from zero at more than 95\% CL whenever the CMB data are combined with any of these additional probes, with the preferred direction corresponding to a transfer of energy from DE to DM. While CMB alone yields a high value of $H_0$, in agreement with local determinations, this effect is reduced when DESI is added and disappears once supernova data are included. In contrast, the clustering parameter $S_8$ is consistently shifted toward lower values in the combined datasets, and it is correlated with the preference for a negative interaction. However, according to the $\Delta \chi^2_{\rm min}$ and Bayesian evidence, none of the interacting models is favored over $\Lambda$CDM or $w_0w_a$CDM, indicating that the interaction does not rescue these emergent DE models. Our results therefore highlight the limitations of these scenarios in addressing current cosmological tensions, while pointing to the crucial role of future data in further assessing their viability.

astro-ph.CO↗

arXiv2026

Do DESI-DR2 BAO data imply a coupling of dark matter and dark energy?

We revisit an interacting dark matter (DM) -- dark energy (DE) model characterized by the interaction function $Q = \Gamma \rho_x$, where $\Gamma$ is a constant coupling parameter and $\rho_x$ is the energy density of DE. This type of interaction is independent of the Hubble rate or other external parameters, but depends only on the fundamental properties of DE, such as its equation of state (EoS), $w_x$. We pay special attention to $w_x$ and study three distinct interacting scenarios distinguished by the nature of $w_x$, i.e. $w_x =-1$ (when DE corresponds to the vacuum energy), $w_{x} < -1$ (when DE has a phantom behavior), and $w_x > -1$ (quintessential DE). We constrain all of them using the most recent cosmological datasets, including CMB from Planck 2018, BAO from DESI DR2, and three compilations of SNIa (PantheonPlus, Union3, and DESY5). Our analyses reveal that evidence of interaction is supported in scenarios with $w_x =-1$ and $w_x < -1$ when all three datasets are combined, but from the Bayesian evidence analysis, $\Lambda$CDM remains favored over these interacting scenarios. Regarding the $S_8$ parameter, when $w_x > -1$, this interacting scenario leads to mildly lower estimates across all datasets.

astro-ph.CO↗

arXiv2026

Beyond dynamical dark energy: the role of dark sector interactions after DESI DR2

Recent DESI DR2 observations have renewed interest in extensions of the $\Lambda$CDM cosmological model, particularly through indications of a time-varying dark energy equation of state. In this work, we investigate whether such deviations may also involve interactions within the dark sector. We consider an interacting dark energy scenario in which the dark matter density evolves as $\rho_{\rm dm}\propto a^{-3+\delta}$, with the constant $\delta$ quantifying the interaction strength, and allow the dark energy equation of state to be either constant but different from $-1$, or dynamically evolving through the CPL parametrization. The models are constrained using Planck CMB data, DESI DR2 BAO measurements, and three Type Ia supernova compilations: PantheonPlus, Union3, and DES-Dovekie. For the constant equation-of-state case, the inclusion of DESI and supernova data leads to a preference for a small negative interaction parameter, with a significance above $2\sigma$. When dynamical dark energy is allowed, the evidence for interaction becomes weak, while the data favor a quintessence-like evolving dark energy component. In both scenarios, Bayesian model comparison still favors $\Lambda$CDM. Our results show that the inferred role of dark-sector interactions depends strongly on the nature of dark energy, highlighting the importance of jointly testing dark energy dynamics and interactions in the DESI era.

astro-ph.CO↗

arXiv2026

HierSVA: A Data Synthesis Pipeline, Dataset, and Benchmark for LLM-Driven Hierarchical Hardware Formal Verification

We present HierSVA, an integrated suite that combines a pipeline, dataset, and benchmark for LLM-driven hierarchical hardware formal verification. HierSVA-SP pairs an RTL preprocessing toolchain with an LLM-in-the-loop formal verification flow to produce reference SystemVerilog Assertions (SVA) on hierarchical RTL. Applying it to BaseJump STL yields HierSVA-DS, a dataset of 342 modules, with hierarchy metadata and depths 0--9, accompanied by a deep subset of 28 module-bug pairs with natural-language specifications and bug variants. HierSVA-B decomposes assertion quality into six metric axes: syntax correctness, assertion proof success rate, vacuity, specification faithfulness, mutation coverage, and formal core coverage. Applying HierSVA-B to twelve recent LLMs reveals three findings. First, the module-level compile rate is 67.1\%; among generated assertions in evaluable runs, 82.1\% prove non-vacuously, but the corresponding assertion sets detect only 70.2\% of eligible injected faults and cover 36.2\% of the formal core. Second, on 211 evaluable model--module entries in the deep subset, assertion sets flag buggy RTL with 0.87 recall, but 40\% of predicted-buggy outcomes are false positives on correct RTL, limiting precision to 0.60. Third, agentic mode improves S1-style provability and strength metrics, but gains plateau and oscillate. Codes and artifacts are available at \href{https://github.com/HierSVAAnon/HierSVACodeAndArtifacts}{https://github.com/HierSVAAnon/HierSVACodeAndArtifacts}. Dataset is available at \href{https://huggingface.co/datasets/AnonymousHierSVA/HierSVA}{https://huggingface.co/datasets/AnonymousHierSVA/HierSVA}.

cs.AR↗

arXiv2026

When One-Parameter Dark Energy Makes Neutrinos Physical Again

A puzzling implication of current data interpreted in the $\Lambda$CDM cosmology is the preference for a negative sum of neutrino masses. Moving to $w_0w_a$CDM brings an appreciable fraction of the neutrino mass posterior back to positive values, while the constant equation-of-state dark energy case $w$CDM does not. We investigate a variety of one-parameter dark energy equations of state (DE EoS), each variation with particular physical properties, to understand whether a two-parameter DE EoS is required to bring the neutrino mass positive. The conclusion is that certain one-parameter DE EoS can suffice, implying that the data are pointing toward physical characteristics rather than a broad degeneracy. The required characteristics are identified as phantom dark energy at high redshift, crossing $w=-1$ at lower redshift.

astro-ph.CO↗

arXiv2026

CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies

Reinforcement learning (RL) has achieved notable performance in high-dimensional sequential decision-making tasks, yet remains limited by low sample efficiency, sensitivity to noise, and weak generalization under partial observability. Most existing approaches address these issues primarily through optimization strategies, while the role of architectural priors in shaping representation learning and decision dynamics is less explored. Inspired by structural principles of the cerebellum, we propose a biologically grounded RL architecture that incorporate large expansion, sparse connectivity, sparse activation, and dendritic-level modulation. Experiments on noisy, high-dimensional RL benchmarks show that both the cerebellar architecture and dendritic modulation consistently improve sample efficiency, robustness, and generalization compared to conventional designs. Sensitivity analysis of architectural parameters suggests that cerebellum-inspired structures can offer optimized performance for RL with constrained model parameters. Overall, our work underscores the value of cerebellar structural priors as effective inductive biases for RL.

cs.LG↗

arXiv2025

Dark Energy Is Not That Into You: Variable Couplings after DESI DR2 BAO

In interacting dark energy (DE) and dark matter (DM) scenarios, the interaction function typically includes a coupling parameter $\xi$ that quantifies the strength of energy exchange between the dark sectors. While $\xi$ is often assumed to be constant, there is no fundamental reason to exclude a time-dependent coupling, which could provide a more general and realistic description of dark sector dynamics. In this work, we study two widely used interacting models involving pressureless DM and DE, where the coupling parameter is allowed to vary with the scale factor $a$. Specifically, we consider two parametrizations: $\xi(a) = \xi_0 + \xi_a (1-a)$ and $\xi(a) = \xi_0 \left(1 + \frac{1-a}{a^2 + (1-a)^2} \right)$, and constrain them using the latest cosmological observations, including Planck 2018 CMB data, DESI DR2 BAO measurements, and multiple Type Ia supernovae samples. Our results show that one scenario yields evidence for a non-zero interaction at more than 95\% confidence level, while the remaining cases indicate at most mild or inconclusive signs of interaction. These findings highlight the potential of variable coupling models and the importance of continued investigation into the nature of the dark sectors.

astro-ph.CO↗

arXiv2025

Codeword-Segmentation Rate-Splitting Multiple Access and Evaluation under Suboptimal Decoding

Rate-Splitting Multiple Access (RSMA) has been recognized as a promising multiple access technique. We propose a novel architecture for downlink RSMA, namely Codeword-Segmentation RSMA (CS-RSMA). Different from conventional RSMA which splits users' messages into common and private parts before encoding, CS-RSMA encodes the users' messages directly, segments the codewords into common and private parts, and transmits the codeword segments using common and private streams. In addition to the principle of CS-RSMA, a novel performance analysis framework is proposed. This framework utilizes a recent discovery in mismatched decoding under finite-alphabet input and interference, and can better capture the receiver's complexity limits. Precoder optimization under finite alphabets and suboptimal decoders for conventional RSMA and CS-RSMA to maximize the Sum-Rate (SR) and the Max-Min Fairness (MMF) is also addressed. The numerical results reveal the theoretical performance of conventional RSMA and CS-RSMA. We observe that CS-RSMA leads to better performance than conventional RSMA in SR, and similar performance in MMF. Furthermore, a physical-layer implementation of CS-RSMA is proposed and evaluated through link-level simulations. Aside performance benefits, we also demonstrate that CS-RSMA brings significant benefits on the encoding/decoding, control signaling, and retransmission process compared to conventional RSMA.

cs.IT↗

arXiv2025

SIC-Free Rate-Splitting Multiple Access: Constellation-Constrained Optimization and Application to Large-Scale Systems

Rate-Splitting Multiple Access (RSMA) has been recognized as a promising multiple access technique for future wireless communication systems. Recent research demonstrates that RSMA can maintain its superiority without relying on Successive Interference Cancellation (SIC) receivers. In practical systems, SIC-free receivers are more attractive than SIC receivers because of their low complexity and latency. This paper evaluates the theoretical limits of RSMA with and without SIC receivers under finite constellations. We first derive the constellation-constrained rate expressions for RSMA. We then design algorithms based on projected subgradient ascent to optimize the precoders and maximize the weighted sum-rate or max-min fairness among users. To apply the proposed optimization algorithms to large-scale systems, one challenge lies in the exponentially increasing computational complexity brought about by the constellation-constrained rate expressions. In light of this, we propose methods to avoid such computational burden. Numerical results show that, under optimized precoders, SIC-free RSMA leads to minor losses in both weighted sum-rate and max-min fairness in comparison to RSMA with SIC receivers, making it a viable option for future implementations.

cs.IT↗

arXiv2025

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective

Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information (MI), fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an information-theoretic tool for analyzing communications with suboptimal decoders. In this work, we use mismatched decoding to analyze communications with decoders that treat interference suboptimally, aiming at more accurate performance metrics. Specifically, we consider a finite-alphabet input Gaussian channel under interference, representative of modern systems, where the decoder can be matched (optimal) or mismatched (suboptimal) to the channel. The matched capacity is derived using MI, while a lower bound on the mismatched capacity under various decoding metrics is derived using generalized mutual information (GMI). We show that the decoding metric in the proposed channel model is closely related to the behavior of the demodulator in bit-interleaved coded modulation (BICM) systems. Simulations illustrate that GMI/MI accurately predicts the throughput of BICM-type systems {with various demodulators}. Finally, we extend the channel model and the GMI to multiple antenna cases, with an example of multi-user multiple-input-single-output (MU-MISO) precoder optimization problem considering GMI under different decoding strategies. In short, this work discovers new insights about the impact of interference, proposes novel receivers, and introduces a new design and performance evaluation framework that more accurately captures the effect of interference.

cs.IT↗

arXiv2025

Rate-Splitting Multiple Access for 6G: Prototypes, Experimental Results and Link/System level Simulations

Rate-Splitting Multiple Access (RSMA) is a powerful and versatile physical layer multiple access technique that generalizes and has better interference management capabilities than 5G-based Space Division Multiple Access (SDMA). It is also a rapidly maturing technology, all of which makes it a natural successor to SDMA in 6G. In this article, we describe RSMA's suitability for 6G by presenting: i) link and system level simulations of RSMA's performance gains over SDMA in realistic environments, and (ii) pioneering experimental results that demonstrate RSMA's gains over SDMA for key use cases like enhanced Mobile Broadband (eMBb), and Integrated Sensing and Communications (ISAC). We also comment on the status of standardization activities for RSMA.

eess.SP↗

arXiv2023

Seismic Foundation Model (SFM): a new generation deep learning model in geophysics

While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, including data preparation, model pre-training, and adaption to downstream tasks. From 192 globally collected 3-D seismic volumes, we create a carefully curated dataset of 2,286,422 2-D seismic images. Fully using these unlabeled images, we employ the self-supervised learning to pre-train a Transformer-based Seismic Foundation Model (SFM) for producing all-purpose seismic features that work across various tasks and surveys. Through experiments on seismic facies classification, geobody identification, interpolation, denoising, and inversion, our pre-trained model demonstrates versatility, generalization, scalability, and superior performance over baseline models. Conclusively, we provide a foundation model and vast dataset to advance AI in geophysics, addressing challenges (poor generalization, lacking labels, and repetitive training for task-specified models) of applying AI in geophysics and paving the way for future innovations in geoscience.

physics.geo-ph↗

arXiv2023

Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review

The continuous development of artificial intelligence has a profound impact on biomedicine and other fields, providing new research ideas and technical methods. Brain-inspired computing is an important intersection between multimodal technology and biomedical field. Focusing on the application scenarios of decoding text and speech from brain signals in human-computer interaction, this paper presents a comprehensive review of the brain-inspired computing models based on deep learning (DL), tracking its evolution, application value, challenges and potential research trends. We first reviews its basic concepts and development history, and divides its evolution into two stages: recent machine learning and current deep learning, emphasizing the importance of each stage in the research of brain-inspired computing for human-computer interaction. In addition, the latest progress of deep learning in different tasks of brain-inspired computing for human-computer interaction is reviewed from five perspectives, including datasets and different brain signals, and the application of key technologies in the model is elaborated in detail. Despite significant advances in brain-inspired computational models, challenges remain to fully exploit their capabilities, and we provide insights into possible directions for future academic research. For more detailed information, please visit our GitHub page: https://github.com/ultracoolHub/brain-inspired-computing.

cs.AI↗

arXiv2023

A Survey on Image-text Multimodal Models

With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered widespread attention. Current surveys on image-text multimodal models mainly focus on representative models or application domains, but lack a review on how general technical models influence the development of domain-specific models, which is crucial for domain researchers. Based on this, this paper first reviews the technological evolution of image-text multimodal models, from early explorations of feature space to visual language encoding structures, and then to the latest large model architectures. Next, from the perspective of technological evolution, we explain how the development of general image-text multimodal technologies promotes the progress of multimodal technologies in the biomedical field, as well as the importance and complexity of specific datasets in the biomedical domain. Then, centered on the tasks of image-text multimodal models, we analyze their common components and challenges. After that, we summarize the architecture, components, and data of general image-text multimodal models, and introduce the applications and improvements of image-text multimodal models in the biomedical field. Finally, we categorize the challenges faced in the development and application of general models into external factors and intrinsic factors, further refining them into 2 external factors and 5 intrinsic factors, and propose targeted solutions, providing guidance for future research directions. For more details and data, please visit our GitHub page: \url{https://github.com/i2vec/A-survey-on-image-text-multimodal-models}.

cs.CL↗

arXiv2023

Rate-Splitting Multiple Access: Finite Constellations, Receiver Design, and SIC-free Implementation

Rate-Splitting Multiple Access (RSMA) has emerged as a novel multiple access technique that enlarges the achievable rate region of Multiple-Input Multiple-Output (MIMO) broadcast channels with linear precoding. In this work, we jointly address three practical but fundamental questions: (1) How to exploit the benefit of RSMA under finite constellations? (2) What are the potential and promising ways to implement RSMA receivers? (3) Can RSMA still retain its superiority in the absence of successive interference cancellers (SIC)? To address these concerns, we first propose low-complexity precoder designs taking finite constellations into account and show that the potential of RSMA is better achieved with such designs than those assuming Gaussian signalling. We then consider some practical receiver designs that can be applied to RSMA. We notice that these receiver designs follow one of two principles: (1) SIC: cancelling upper layer signals before decoding the lower layer and (2) non-SIC: treating upper layer signals as noise when decoding the lower layer. In light of this, we propose to alter the precoder design according to the receiver category. Through link-level simulations, the effectiveness of the proposed precoder and receiver designs are verified. More importantly, we show that it is possible to preserve the superiority of RSMA over Spatial Domain Multiple Access (SDMA), including SDMA with advanced receivers, even without SIC at the receivers. Those results therefore open the door to competitive implementable RSMA strategies for 6G and beyond communications.

cs.IT↗

arXiv2022

Construction Site Safety Monitoring and Excavator Activity Analysis System

With the recent advancements in deep learning and computer vision, the AI-powered construction machine such as autonomous excavator has made significant progress. Safety is the most important section in modern construction, where construction machines are more and more automated. In this paper, we propose a vision-based excavator perception, activity analysis, and safety monitoring system. Our perception system could detect multi-class construction machines and humans in real-time while estimating the poses and actions of the excavator. Then, we present a novel safety monitoring and excavator activity analysis system based on the perception result. To evaluate the performance of our method, we collect a dataset using the Autonomous Excavator System (AES) including multi-class of objects in different lighting conditions with human annotations. We also evaluate our method on a benchmark construction dataset. The results showed our YOLO v5 multi-class objects detection model improved inference speed by 8 times (YOLO v5 x-large) to 34 times (YOLO v5 small) compared with Faster R-CNN/ YOLO v3 model. Furthermore, the accuracy of YOLO v5 models is improved by 2.7% (YOLO v5 x-large) while model size is reduced by 63.9% (YOLO v5 x-large) to 93.9% (YOLO v5 small). The experimental results show that the proposed action recognition approach outperforms the state-of-the-art approaches on top-1 accuracy by about 5.18%. The proposed real-time safety monitoring system is not only designed for our Autonomous Excavator System (AES) in solid waste scenes, it can also be applied to general construction scenarios.

cs.CV↗

arXiv2022

Text2Video: Text-driven Talking-head Video Synthesis with Personalized Phoneme-Pose Dictionary

With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.

cs.CV↗

arXiv2020

CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception

This paper reviews the CVPR 2019 challenge on Autonomous Driving. Baidu's Robotics and Autonomous Driving Lab (RAL) providing 150 minutes labeled Trajectory and 3D Perception dataset including about 80k lidar point cloud and 1000km trajectories for urban traffic. The challenge has two tasks in (1) Trajectory Prediction and (2) 3D Lidar Object Detection. There are more than 200 teams submitted results on Leaderboard and more than 1000 participants attended the workshop.

cs.CV↗