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Bo Wang

Publications and source records attributed to Bo Wang.

At least 325 records · Page 18Linked to original sources
arXiv2025

Binary Evolution Pathways to Blue Large-Amplitude Pulsators: Insights from HD 133729

Blue Large-Amplitude Pulsators (BLAPs) represent a recently identified class of pulsating stars distinguished by their short pulsation periods ($2 - 60$ minutes) and asymmetric light curves. This study investigated the evolutionary channel of HD 133729 which is the first confirmed BLAP in a binary system. Using the binary evolution code MESA, we explored various mass ratios and initial orbital periods. Our simulations suggest that a system with a mass ratio $q = 0.30$ undergoing non-conservative mass transfer ($β=0.15$) can reproduce the observed characteristics through the pre-white dwarf Roche lobe overflow channel. Meanwhile, we predict that there are significant helium and nitrogen enhancements on the surface of the main sequence (MS) star. The system will eventually undergo the common envelope phase, leading to a stellar merger. HD 133729 is a unique case as a benchmark, providing crucial insights into the formation mechanism and evolutionary fate of BLAPs with MS companions. This work constrains the elemental abundances of the MS star and has helped our understanding of non-conservative mass transfer in binary evolution.

astro-ph.SR↗
arXiv2025

Cavity as Radio Telescope for Galactic Dark Photon

Dark photons, as a minimal extension of the Standard Model through an additional Abelian gauge group, may propagate relativistically across the galaxy, originating from dark matter decay or annihilation, thereby contributing to a galactic dark photon background. The generation of dark photons typically favors certain polarization modes, which are dependent on the interactions between dark matter and dark photons. We introduce a framework in which a resonant cavity is utilized to detect and differentiate these polarizations, leveraging the daily variation in expected signals due to the anisotropic distribution of dark photons and the rotation of the Earth. We conduct an experimental search using superconducting radio-frequency cavities, noted for their exceptionally high quality factors, proving them to be effective telescopes for observing galactic dark photons. This approach establishes the most stringent limits yet on the kinetic mixing coefficient between dark photons and electromagnetic photons, thereby unveiling a novel avenue for the indirect search for dark matter via multi-messenger astronomy.

hep-ph↗
arXiv2025

Resistive memory-based zero-shot liquid state machine for multimodal event data learning

The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.

cs.ET↗
arXiv2024

Meson correlators in 4d $\mathcal{N}=2$ SCFTs and hints for 8d structures at weak coupling

We study correlators of $\frac{1}{2}$-BPS mesons in two examples of 4d SQCDs with $\mathcal{N}=2$ superconformal symmetry in the planar limit. We focus on the weakly coupled regime and obtain one-loop corrections to $n$-point meson correlators with arbitrary operator dimensions. We show that these corrections can be resumed into generating functions which exhibit emergent 8d structures similar to the ones previously observed at strong coupling via AdS/CFT. These structures of the $\mathcal{N}=2$ theories also resemble the hidden 10d structures in 4d $\mathcal{N}=4$ SYM.

hep-th↗
arXiv2024

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications.

eess.IV↗
arXiv2024

EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection

Detecting Human-Object Interactions (HOI) in zero-shot settings, where models must handle unseen classes, poses significant challenges. Existing methods that rely on aligning visual encoders with large Vision-Language Models (VLMs) to tap into the extensive knowledge of VLMs, require large, computationally expensive models and encounter training difficulties. Adapting VLMs with prompt learning offers an alternative to direct alignment. However, fine-tuning on task-specific datasets often leads to overfitting to seen classes and suboptimal performance on unseen classes, due to the absence of unseen class labels. To address these challenges, we introduce a novel prompt learning-based framework for Efficient Zero-Shot HOI detection (EZ-HOI). First, we introduce Large Language Model (LLM) and VLM guidance for learnable prompts, integrating detailed HOI descriptions and visual semantics to adapt VLMs to HOI tasks. However, because training datasets contain seen-class labels alone, fine-tuning VLMs on such datasets tends to optimize learnable prompts for seen classes instead of unseen ones. Therefore, we design prompt learning for unseen classes using information from related seen classes, with LLMs utilized to highlight the differences between unseen and related seen classes. Quantitative evaluations on benchmark datasets demonstrate that our EZ-HOI achieves state-of-the-art performance across various zero-shot settings with only 10.35% to 33.95% of the trainable parameters compared to existing methods. Code is available at https://github.com/ChelsieLei/EZ-HOI.

cs.CV↗
arXiv2024

EvoWiki: Evaluating LLMs on Evolving Knowledge

Knowledge utilization is a critical aspect of LLMs, and understanding how they adapt to evolving knowledge is essential for their effective deployment. However, existing benchmarks are predominantly static, failing to capture the evolving nature of LLMs and knowledge, leading to inaccuracies and vulnerabilities such as contamination. In this paper, we introduce EvoWiki, an evolving dataset designed to reflect knowledge evolution by categorizing information into stable, evolved, and uncharted states. EvoWiki is fully auto-updatable, enabling precise evaluation of continuously changing knowledge and newly released LLMs. Through experiments with Retrieval-Augmented Generation (RAG) and Contunual Learning (CL), we evaluate how effectively LLMs adapt to evolving knowledge. Our results indicate that current models often struggle with evolved knowledge, frequently providing outdated or incorrect responses. Moreover, the dataset highlights a synergistic effect between RAG and CL, demonstrating their potential to better adapt to evolving knowledge. EvoWiki provides a robust benchmark for advancing future research on the knowledge evolution capabilities of large language models.

cs.CL↗
arXiv2024

Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective

OpenAI o1 represents a significant milestone in Artificial Inteiligence, which achieves expert-level performances on many challanging tasks that require strong reasoning ability.OpenAI has claimed that the main techinique behinds o1 is the reinforcement learining. Recent works use alternative approaches like knowledge distillation to imitate o1's reasoning style, but their effectiveness is limited by the capability ceiling of the teacher model. Therefore, this paper analyzes the roadmap to achieving o1 from the perspective of reinforcement learning, focusing on four key components: policy initialization, reward design, search, and learning. Policy initialization enables models to develop human-like reasoning behaviors, equipping them with the ability to effectively explore solution spaces for complex problems. Reward design provides dense and effective signals via reward shaping or reward modeling, which is the guidance for both search and learning. Search plays a crucial role in generating high-quality solutions during both training and testing phases, which can produce better solutions with more computation. Learning utilizes the data generated by search for improving policy, which can achieve the better performance with more parameters and more searched data. Existing open-source projects that attempt to reproduce o1 can be seem as a part or a variant of our roadmap. Collectively, these components underscore how learning and search drive o1's advancement, making meaningful contributions to the development of LLM.

cs.AI↗
arXiv2024

High-performance thin-film lithium niobate Mach-Zehnder modulator on thick silica buffering layer

High-speed photonic integrated circuits leveraging the thin-film lithium niobate (TFLN) platform present a promising approach to address the burgeoning global data traffic demands. As a pivotal component, TFLN-based electro-optic (EO) Mach-Zehnder modulators (MZMs) should exhibit low driving voltage, broad operation bandwidth, high extinction ration, and low insertion loss. However, the pursuit of both maximal EO overlap integral and minimal microwave loss necessitates a fundamental compromise between driving voltage and operational bandwidth. Here, we demonstrate high-performance TFLN EO MZMs constructed on a 12-μm-thick silica buried layer using periodic capacitively loaded traveling-wave electrodes. In contrast to their counterparts utilizing undercut etched silicon substrates or quartz substrates, our devices exhibit streamlined fabrication processes and enhanced modulation efficiency. Notably, the fabricated MZMs attains a high modulation efficiency of 1.25 Vcm in the telecom C-band, while maintaining a low EO roll-off of 1.3 dB at 67 GHz. Our demonstration offers a pathway to achieving perfect group velocity matching and break the voltage-bandwidth limit in a simplified configuration suitable for volume fabrication, thereby laying foundational groundwork for the advancement of high-performance TFLN MZMs and benefiting the next-generation PICs in optical telecommunication, signal processing and other applications.

physics.optics↗
arXiv2024

$η_{c2}(^1D_2)$ and its electromagnetic decays

The spin-singlet state $η_{c2}(^1D_2)$ has not been discovered in experiment and it is the only missing low-excited $D$-wave charmonium, so in this paper, we like to study its properties. Using the Bethe-Salpeter equation method, we obtain its mass as $3828.2$ MeV and its electromagnetic decay widths as $Γ[η_{c2}(1D)\rightarrow h_{c}(1P)γ]=284$ keV, $Γ[η_{c2}(1D)\rightarrow J/ψγ]=1.04$ keV, $Γ[η_{c2}(1D)\rightarrowψ(2S)γ]=3.08$ eV, and $Γ[η_{c2}(1D)\rightarrowψ(3770)γ]=0.143$ keV. {Considering the strong decay widths are estimated to be $Γ(η_{c2}(1D)\toη_c ππ)=144~\rm{keV}$ and $Γ(η_{c2}(1D)\to gg)= 46.1~\rm{keV}$, we obtain the total decay width of $475$ keV for $η_{c2}(1D)$, and point out that the full width is very sensitive to the mass $M_{η_{c2}}$.} In our calculation, the emphasis is put on the relativistic corrections. Our results show that $η_{c2}\rightarrow h_{c}γ$ is the nonrelativistic $E1$ transition dominated $E1+M2+E3$ decay, and $η_{c2}\rightarrow ψγ$ is the $M1+E2+M3+E4$ decay but the relativistic $E2$ transition contributes the most.

hep-ph↗
arXiv2024

Text-Video Multi-Grained Integration for Video Moment Montage

The proliferation of online short video platforms has driven a surge in user demand for short video editing. However, manually selecting, cropping, and assembling raw footage into a coherent, high-quality video remains laborious and time-consuming. To accelerate this process, we focus on a user-friendly new task called Video Moment Montage (VMM), which aims to accurately locate the corresponding video segments based on a pre-provided narration text and then arrange these video clips to create a complete video that aligns with the corresponding descriptions. The challenge lies in extracting precise temporal segments while ensuring intra-sentence and inter-sentence context consistency, as a single script sentence may require trimming and assembling multiple video clips. To address this problem, we present a novel \textit{Text-Video Multi-Grained Integration} method (TV-MGI) that efficiently fuses text features from the script with both shot-level and frame-level video features, which enables the global and fine-grained alignment between the video content and the corresponding textual descriptions in the script. To facilitate further research in this area, we introduce the Multiple Sentences with Shots Dataset (MSSD), a large-scale dataset designed explicitly for the VMM task. We conduct extensive experiments on the MSSD dataset to demonstrate the effectiveness of our framework compared to baseline methods.

cs.CV↗
arXiv2024

The density profile of Milky Way dark matter halo constrained from the OGLE microlensing sky map

We report the detection of a 282 $^{+34}_{-31}$ pc-sized core in the center of Milky Way dark matter halo at $68\%$ confidence level by using the micro-lensing event rate sky map data from the Optical Gravitational Lensing Experiment (OGLE) survey. We apply the spacial information of the microlensing sky map and model it with the detailed Milky Way dark matter halo Core/Cusp profile, and the fraction of dark matter in the form of Mini Dark Matter Structure (MDMS, $f_{\rm MDMS}=Ω_{\rm MDMS}/Ω_{\rm DM}$), e.g. primordial black hole, earth-mass subhalos, floating planets and so on. We find that this sky map can constrain both $f_{\rm MDMS}$ and the core size simultaneously without strong degeneracy while fully considering mass function of Milky Way stellar components from both the bulge and disk.

astro-ph.GA↗
arXiv2024

Octet baryon and heavy meson interactions in chiral effective field theory

We calculate the effective potentials of the octet baryon and heavy meson systems using the chiral effective field theory up to the next-to-leading order. We consider the contact terms, one-pseudoscalar-meson and two-pseudoscalar-meson exchange contributions, facilitating a comprehensive analysis of the short-, mid-, and long-range interactions in these systems. The low energy constants are correlated with those of the $\bar{N}N$ interaction using a quark-level Lagrangian approach. We also incorporate the decuplet baryon contributions in the loop diagrams. Our research provides new insights into several near-threshold charmed baryons [e.g., $Λ_{c}(2940)$, $Ξ_{c}(3055)$, and $Ω_{c}(3188)$, etc.] around $3$ GeV from the hadronic molecular perspective. We also observe several molecular states, referred to as $Ξ_{c}$, within the mass range of $3100-3500$ MeV. Further measurements of their resonance parameters and decay patterns in experiments may help to discriminate the conventional baryon and hadronic molecule explanations for these near-threshold states.

hep-ph↗
arXiv2024

Translating C To Rust: Lessons from a User Study

Rust aims to offer full memory safety for programs, a guarantee that untamed C programs do not enjoy. How difficult is it to translate existing C code to Rust? To get a complementary view from that of automatic C to Rust translators, we report on a user study asking humans to translate real-world C programs to Rust. Our participants are able to produce safe Rust translations, whereas state-of-the-art automatic tools are not able to do so. Our analysis highlights that the high-level strategy taken by users departs significantly from those of automatic tools we study. We also find that users often choose zero-cost (static) abstractions for temporal safety, which addresses a predominant component of runtime costs in other full memory safety defenses. User-provided translations showcase a rich landscape of specialized strategies to translate the same C program in different ways to safe Rust, which future automatic translators can consider.

cs.SE↗
arXiv2024

GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning

Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic diseases. The rapid decrease in next generation sequencing cost has led to an exponential increase in patient-level GV data. This growth poses a challenge for clinicians who must efficiently prioritize patient-specific GVs and integrate them with existing genomic databases to inform patient management. To addressing the interpretation of GVs, genomic foundation models (GFMs) have emerged. However, these models lack standardized performance assessments, leading to considerable variability in model evaluations. This poses the question: How effectively do deep learning methods classify unknown GVs and align them with clinically-verified GVs? We argue that representation learning, which transforms raw data into meaningful feature spaces, is an effective approach for addressing both indexing and classification challenges. We introduce a large-scale Genetic Variant dataset, named GV-Rep, featuring variable-length contexts and detailed annotations, designed for deep learning models to learn GV representations across various traits, diseases, tissue types, and experimental contexts. Our contributions are three-fold: (i) Construction of a comprehensive dataset with 7 million records, each labeled with characteristics of the corresponding variants, alongside additional data from 17,548 gene knockout tests across 1,107 cell types, 1,808 variant combinations, and 156 unique clinically verified GVs from real-world patients. (ii) Analysis of the structure and properties of the dataset. (iii) Experimentation of the dataset with pre-trained GFMs. The results show a significant gap between GFMs current capabilities and accurate GV representation. We hope this dataset will help advance genomic deep learning to bridge this gap.

cs.LG↗
arXiv2024

Brownian spin-locking effect

Brownian systems are characterized by spatiotemporal disorder, which arises from the erratic motion of particles driven by thermal fluctuations. When light interacts with such systems, it typically produces unpolarized and uncorrelated fields. Here, we report the observation of a large-scale spin-locking effect of light within a Brownian medium. In an observation direction perpendicular to the incident wave momentum, scattering naturally divides into two diffusion regions, each associated with an opposite spin from the Brownian nanoparticles. This effect arises from the intrinsic spin-orbit interactions of scattering from individual nanoparticles, which ubiquitously generate radiative spin fields that propagate through the Brownian medium with multiple incoherent scattering. It offers a novel experimental platform for exploring macroscale spin behaviors of diffused light, with potential applications in precision metrology for measuring various nanoparticle properties. Our findings may inspire the study of analogous phenomena for different waves from novel spin-orbit interactions in complex disordered systems.

physics.optics↗
arXiv2024

Nearfield Vortex Dynamics of Supercell Bloch Modes

Densely arranged optical vortices are natural solutions of high-symmetry Bloch modes in photonic crystals. However, strict symmetry constraints limit the potential spatial configurations of nearfield vortices, restricting the control over light-matter interaction. Here, we demonstrate a nearfield vortex dynamic within a supercell photonic crystal. By introducing paired rotations of triangular structures, we achieve high-quality-factor Bloch mode transition from evanescent valley modes, to quasi-bound states in the continuum, frustrated modes, and quasi-valleys. Each stage exhibits distinct nearfield vortex distributions, nonlinear overlap properties, and quality factors, revealing diverse physical behaviors for tailoring light-matter interaction. Notably, the asymmetric vortex configuration of frustrated modes enhances second harmonic generation, driven by an optimized nonlinear overlap factor. Our paired-rotation strategy offers a versatile design framework for creating supercell photonic crystals with unique nearfield vortex properties, presenting promising applications in lasing, nonlinear optics and optical forces.

physics.optics↗