arXiv subjects

Bo Wang

Publications and source records attributed to Bo Wang.

At least 379 records · Page 21Linked to original sources

arXiv2024

SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model

Large language models (LLMs) have shown remarkable achievements across various language tasks.To enhance the performance of LLMs in scientific literature services, we developed the scientific literature LLM (SciLit-LLM) through pre-training and supervised fine-tuning on scientific literature, building upon the iFLYTEK Spark LLM. Furthermore, we present a knowledge service system Spark Research Assistant (SparkRA) based on our SciLit-LLM. SparkRA is accessible online and provides three primary functions: literature investigation, paper reading, and academic writing. As of July 30, 2024, SparkRA has garnered over 50,000 registered users, with a total usage count exceeding 1.3 million.

cs.CL↗

arXiv2024

Segment Anything in Medical Images and Videos: Benchmark and Deployment

Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical data remains unclear. In this work, we first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2) across 11 medical image modalities and videos and point out its strengths and weaknesses by comparing it to SAM1 and MedSAM. Then, we develop a transfer learning pipeline and demonstrate SAM2 can be quickly adapted to medical domain by fine-tuning. Furthermore, we implement SAM2 as a 3D slicer plugin and Gradio API for efficient 3D image and video segmentation. The code has been made publicly available at \url{https://github.com/bowang-lab/MedSAM}.

eess.IV↗

arXiv2024

Theory of nonlinear terahertz susceptibility in ferroelectrics

An analytical theory is developed for predicting the nonlinear susceptibility of ionic polarization to continuous electromagnetic waves in both bulk and strained thin film ferroelectrics. Using a perturbation method for solving the nonlinear equation of motion for ionic polarization within the framework of Landau-Ginzburg-Devonshire theory, the full second-order nonlinear susceptibility tensor is derived as a function of frequency, temperature, and strain. The theory predicts the coexistence of a significantly enhanced second-order dielectric susceptibility and a relatively low dielectric loss in BaTiO3 films with a strain-stabilized monoclinic ferroelectric phase and in a strained SrTiO3 film near its temperature-driven second-order ferroelectric-to-paraelectric phase transition. This work establishes a theoretical framework for predicting and exploiting nonlinear interactions between THz waves and ferroelectric materials, and more generally, suggests exciting opportunities to strain-engineer nonlinear dynamical properties of ferroelectrics beyond the static and quasi-static limits.

cond-mat.mtrl-sci↗

arXiv2024

Galaxy-galaxy lensing data: $f(T)$ gravity challenges General Relativity

We use galaxy-galaxy lensing data to test General Relativity and $f(T)$ gravity at galaxies scales. We consider an exact spherically symmetric solution of $f(T)$ theory which is obtained from an approximate quadratic correction, and thus it is expected to hold for every realistic deviation from General Relativity. Quantifying the deviation by a single parameter $Q$, and following the post-Newtonian approximation, we obtain the corresponding deviation in the gravitational potential, shear component, and effective excess surface density profile. We used five stellar mass samples and divided them into blue and red to test the model dependence on galaxy color, and we modeled the excess surface density (ESD) profiles using the Navarro-Frenk-White (NFW) profiles. Based on the group catalog from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) we finally extract $Q=-2.138^{+0.952}_{-0.516}\times 10^{-5}\,$Mpc$^{-2}$ at $1σ$ confidence. This result indicates that $f(T)$ corrections on top of General Relativity are favored. Finally, we apply information criteria, such as the AIC and BIC ones, and although the dependence of $f(T)$ gravity on the off-center effect implies that its optimality needs to be carefully studied, our analysis shows that $f(T)$ gravity is more efficient in fitting the data comparing to General Relativity and $Λ$CDM paradigm, and thus it offers a challenge to the latter.

astro-ph.CO↗

arXiv2024

Constraints on large-scale polarization in northern hemisphere

Present cosmic microwave background (CMB) observations have significantly advanced our understanding of the universe's origin, especially with primordial gravitational waves (PGWs). Currently, ground-based CMB telescopes are mainly located in the southern hemisphere, leaving an untapped potential for observations in the northern hemisphere. In this work, we investigate the perspective of a northern hemisphere CMB polarization telescope (NHT) to detect PGWs and present mock data for such a project. We forecast the detection sensitivity on the tensor-to-scalar ratio r of NHT and compare it with the existed ground-based experiments, also search for optimal experimental configurations that can achieve the best sensitivity of r. Our results indicate that, considering realistic experimental conditions, the first year of NHT observations combined with Planck can achieve a precision of σ(r)= 0.015, reaching the level of BICEP2/Keck, with significant potential for improvement with subsequent instrumentation parameter enhancements.

astro-ph.CO↗

arXiv2024

Topology Optimization of Random Memristors for Input-Aware Dynamic SNN

There is unprecedented development in machine learning, exemplified by recent large language models and world simulators, which are artificial neural networks running on digital computers. However, they still cannot parallel human brains in terms of energy efficiency and the streamlined adaptability to inputs of different difficulties, due to differences in signal representation, optimization, run-time reconfigurability, and hardware architecture. To address these fundamental challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). Signal representation-wise, PRIME employs leaky integrate-and-fire neurons to emulate the brain's inherent spiking mechanism. Drawing inspiration from the brain's structural plasticity, PRIME optimizes the topology of a random memristive spiking neural network without expensive memristor conductance fine-tuning. For runtime reconfigurability, inspired by the brain's dynamic adjustment of computational depth, PRIME employs an input-aware dynamic early stop policy to minimize latency during inference, thereby boosting energy efficiency without compromising performance. Architecture-wise, PRIME leverages memristive in-memory computing, mirroring the brain and mitigating the von Neumann bottleneck. We validated our system using a 40 nm 256 Kb memristor-based in-memory computing macro on neuromorphic image classification and image inpainting. Our results demonstrate the classification accuracy and Inception Score are comparable to the software baseline, while achieving maximal 62.50-fold improvements in energy efficiency, and maximal 77.0% computational load savings. The system also exhibits robustness against stochastic synaptic noise of analogue memristors. Our software-hardware co-designed model paves the way to future brain-inspired neuromorphic computing with brain-like energy efficiency and adaptivity.

cs.ET↗

arXiv2024

Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions

Existing 2D human pose estimation research predominantly concentrates on well-lit scenarios, with limited exploration of poor lighting conditions, which are a prevalent aspect of daily life. Recent studies on low-light pose estimation require the use of paired well-lit and low-light images with ground truths for training, which are impractical due to the inherent challenges associated with annotation on low-light images. To this end, we introduce a novel approach that eliminates the need for low-light ground truths. Our primary novelty lies in leveraging two complementary-teacher networks to generate more reliable pseudo labels, enabling our model achieves competitive performance on extremely low-light images without the need for training with low-light ground truths. Our framework consists of two stages. In the first stage, our model is trained on well-lit data with low-light augmentations. In the second stage, we propose a dual-teacher framework to utilize the unlabeled low-light data, where a center-based main teacher produces the pseudo labels for relatively visible cases, while a keypoints-based complementary teacher focuses on producing the pseudo labels for the missed persons of the main teacher. With the pseudo labels from both teachers, we propose a person-specific low-light augmentation to challenge a student model in training to outperform the teachers. Experimental results on real low-light dataset (ExLPose-OCN) show, our method achieves 6.8% (2.4 AP) improvement over the state-of-the-art (SOTA) method, despite no low-light ground-truth data is used in our approach, in contrast to the SOTA method. Our code will be available at:https://github.com/ayh015-dev/DA-LLPose.

cs.CV↗

arXiv2024

First Scan Search for Dark Photon Dark Matter with a Tunable Superconducting Radio-Frequency Cavity

Dark photons have emerged as promising candidates for dark matter, and their search is a top priority in particle physics, astrophysics, and cosmology. We report the first use of a tunable niobium superconducting radio-frequency cavity for a scan search of dark photon dark matter with innovative data analysis techniques. We mechanically adjusted the resonant frequency of a cavity submerged in liquid helium at a temperature of $2$ K, and scanned the dark photon mass over a frequency range of $1.37$ MHz centered at $1.3$ GHz. Our study leveraged the superconducting radio-frequency cavity's remarkably high quality factors of approximately $10^{10}$, resulting in the most stringent constraints to date on a substantial portion of the exclusion parameter space on the kinetic mixing coefficient $ε$ between dark photons and electromagnetic photons, yielding a value of $ε< 2.2 \times 10^{-16}$.

hep-ex↗

arXiv2024

Dynamic neural network with memristive CIM and CAM for 2D and 3D vision

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based Computing-In-Memory (CIM) and Content-Addressable Memory (CAM) circuits, respectively. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets, which not only achieves accuracy on par with software but also a 48.1% and 15.9% reduction in computational budget. Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption.

cs.AR↗

arXiv2024

Enhanced Second-Harmonic Generation in Thin-Film Lithium Niobate Circular Bragg Nanocavity

Second-order nonlinearity gives rise to many distinctive physical phenomena, e.g., second-harmonic generation, which plays an important role in fundamental science and various applications. Lithium niobate, one of the most widely used nonlinear crystals, exhibits strong second-order nonlinear effects and electro-optic properties. However, its moderate refractive index and etching sidewall angle limit its capability in confining light into nanoscales, restricting its application in nanophotonics. Here, we exploit nanocavities formed by second-order circular Bragg gratings, which support resonant anapole modes to achieve highly enhanced SHG in thin film lithium niobate. The CBG nanocavity exhibits a record-high normalized conversion efficiency of $1.21\times10^{-2}\mathrm{cm^2/GW}$ under the pump intensity of $1.9$ $\mathrm{MW/cm^2}$. An SHG enhancement of $42,000$ is realized compared to TFLN. Besides, we also show s- and p-polarization independent SHG in elliptical Bragg nanocavities. This work could inspire studying nonlinear optics at the nanoscale on TFLN as well as other novel photonic platforms.

physics.optics↗

arXiv2024

Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition

Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios. Classic end-to-end models fused with extra language models perform well, but mainly in data matching scenarios and are gradually approaching a bottleneck. In this work, we introduce Seed-ASR, a large language model (LLM) based speech recognition model. Seed-ASR is developed based on the framework of audio conditioned LLM (AcLLM), leveraging the capabilities of LLMs by inputting continuous speech representations together with contextual information into the LLM. Through stage-wise large-scale training and the elicitation of context-aware capabilities in LLM, Seed-ASR demonstrates significant improvement over end-to-end models on comprehensive evaluation sets, including multiple domains, accents/dialects and languages. Additionally, Seed-ASR can be further deployed to support specific needs in various scenarios without requiring extra language models. Compared to recently released large ASR models, Seed-ASR achieves 10%-40% reduction in word (or character, for Chinese) error rates on Chinese and English public test sets, further demonstrating its powerful performance.

eess.AS↗

arXiv2024

Biomanufacturing Harvest Optimization with Small Data

In biopharmaceutical manufacturing, fermentation processes play a critical role in productivity and profit. A fermentation process uses living cells with complex biological mechanisms, leading to high variability in the process outputs, namely, the protein and impurity levels. By building on the biological mechanisms of protein and impurity growth, we introduce a stochastic model to characterize the accumulation of the protein and impurity levels in the fermentation process. However, a common challenge in the industry is the availability of only a very limited amount of data, especially in the development and early stages of production. This adds an additional layer of uncertainty, referred to as model risk, due to the difficulty of estimating the model parameters with limited data. In this paper, we study the harvesting decision for a fermentation process (i.e., when to stop the fermentation and collect the production reward) under model risk. We adopt a Bayesian approach to update the unknown parameters of the growth-rate distributions, and use the resulting posterior distributions to characterize the impact of model risk on fermentation output variability. The harvesting problem is formulated as a Markov decision process model with knowledge states that summarize the posterior distributions and hence incorporate the model risk in decision-making. Our case studies at MSD Animal Health demonstrate that the proposed model and solution approach improve the harvesting decisions in real life by achieving substantially higher average output from a fermentation batch along with lower batch-to-batch variability.

stat.ML↗

arXiv2024

Formation of millisecond pulsars with wide orbits

Millisecond pulsars (MSPs) are a kind of radio pulsars with short spin periods, playing a key role in many aspects of stellar astrophysics. In recent years, some more MSPs with wide orbits ($>30\,\rm d$) have been discovered, but their origin is still highly unclear. In the present work, according to an adiabatic power-law assumption for the mass-transfer process, we carried out a large number of complete binary evolution computations for the formation of MSPs with wide orbits through the iron core-collapse supernova (CCSN) channel, in which a neutron star (NS) originating from a CCSN accretes matter from a red-giant (RG) star and spun up to millisecond periods. We found that this channel can form the observed MSPs with wide orbits in the range of $30-1200\,{\rm d}$, in which the WD companions have masses in the range of $0.28-0.55\,\rm M_{\odot}$. We also found that almost all the observed MSPs can be reproduced by this channel in the WD companion mass versus orbital period diagram. We estimate that the Galactic numbers of the resulting MSPs from the CCSN channel are in the range of $\sim 4.8-8.5\times10^{5}$. Compared with the accretion-induced collapse channel, the CCSN channel provides a main way to produce MSPs with wide orbits.

astro-ph.SR↗

arXiv2024

MORPHEUS: Modeling Role from Personalized Dialogue History by Exploring and Utilizing Latent Space

Personalized Dialogue Generation (PDG) aims to create coherent responses according to roles or personas. Traditional PDG relies on external role data, which can be scarce and raise privacy concerns. Approaches address these issues by extracting role information from dialogue history, which often fail to generically model roles in continuous space. To overcome these limitations, we introduce a novel framework \textbf{MO}dels \textbf{R}oles from \textbf{P}ersonalized Dialogue \textbf{H}istory by \textbf{E}xploring and \textbf{U}tilizing Latent \textbf{S}pace (MORPHEUS) through a three-stage training process. Specifically, we create a persona codebook to represent roles in latent space compactly, and this codebook is used to construct a posterior distribution of role information. This method enables the model to generalize across roles, allowing the generation of personalized dialogues even for unseen roles. Experiments on both Chinese and English datasets demonstrate that MORPHEUS enhances the extraction of role information, and improves response generation without external role data. Additionally, MORPHEUS can be considered an efficient fine-tuning for large language models.

cs.CL↗

arXiv2024

From the $P^{N}_ψ$/$P^Λ_{ψs}$ to $\bar{T}^f_{cc}$: symmetry analysis to the interactions of the $(\bar{c}q)(\bar{c}q)$/$(ccq)(\bar{c}q)$/$(ccq)(ccq)$ di-hadron systems

We investigate the interactions of the $(\bar{c}q)(\bar{c}q)$/$(ccq)(\bar{c}q)$/$(ccq)(ccq)$ di-hadron systems based on a contact lagrangian possessing the SU(3) flavor and SU(2) spin symmetries. Under the assumptions of two scenarios for the $J^P$ quantum numbers of the $P_ψ^N(4440)$ and $P_ψ^N(4457)$ states, we obtain the parameters ($\tilde{g}_s$, $\tilde{g}_a$) introduced from this contact lagrangian. Then we include the SU(3) breaking effect by introducing a factor $g_x$, this quantity can be further constrained by the experimental mass of the $P_{ψs}^Λ(4338)$ state. We can reproduce the mass of the $T^f_{cc}(3875)$ state with the parameters extracted from the observed $P_ψ^N$ states, this consistency indicates a unified description of the di-hadron molecular states composed of two heavy-light hadrons. With the same parameters, we discuss the possible mass spectra of the $\bar{T}_{cc}^f$/$P_{ψc}^Λ$/$H_{Ω_{ccc}c}^Λ$ systems. Then we proceed to discuss the existences of the $\bar{T}_{cc\bar{s}}^θ$/$P_{ψcs}^N$/$H_{Ω_{ccc}cs}^N$ states by investigating the SU(3) breaking effects. Our results show that the states in the $\bar{T}_{cc\bar{s}}^θ$/$P_{ψcs}^N$ systems can hardly form bound states, while the states in the $H_{Ω_{ccc}cs}^N$ system can form bound states due to their larger reduced masses.

hep-ph↗

arXiv2024

LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement

This paper introduces the innovative "LLMs-as-Instructors" framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller target models. Inspired by the theory of "Learning from Errors", this framework employs an instructor LLM to meticulously analyze the specific errors within a target model, facilitating targeted and efficient training cycles. Within this framework, we implement two strategies: "Learning from Error," which focuses solely on incorrect responses to tailor training data, and "Learning from Error by Contrast", which uses contrastive learning to analyze both correct and incorrect responses for a deeper understanding of errors. Our empirical studies, conducted with several open-source models, demonstrate significant improvements across multiple benchmarks, including mathematical reasoning, coding abilities, and factual knowledge. Notably, the refined Llama-3-8b-Instruction has outperformed ChatGPT, illustrating the effectiveness of our approach. By leveraging the strengths of both strategies, we have attained a more balanced performance improvement on both in-domain and out-of-domain benchmarks. Our code can be found at https://yingjiahao14.github.io/LLMs-as-Instructors-pages/.

cs.CL↗

arXiv2024

Jina CLIP: Your CLIP Model Is Also Your Text Retriever

Contrastive Language-Image Pretraining (CLIP) is widely used to train models to align images and texts in a common embedding space by mapping them to fixed-sized vectors. These models are key to multimodal information retrieval and related tasks. However, CLIP models generally underperform in text-only tasks compared to specialized text models. This creates inefficiencies for information retrieval systems that keep separate embeddings and models for text-only and multimodal tasks. We propose a novel, multi-task contrastive training method to address this issue, which we use to train the jina-clip-v1 model to achieve the state-of-the-art performance on both text-image and text-text retrieval tasks.

cs.CL↗

arXiv2024

RGB-Sonar Tracking Benchmark and Spatial Cross-Attention Transformer Tracker

Vision camera and sonar are naturally complementary in the underwater environment. Combining the information from two modalities will promote better observation of underwater targets. However, this problem has not received sufficient attention in previous research. Therefore, this paper introduces a new challenging RGB-Sonar (RGB-S) tracking task and investigates how to achieve efficient tracking of an underwater target through the interaction of RGB and sonar modalities. Specifically, we first propose an RGBS50 benchmark dataset containing 50 sequences and more than 87000 high-quality annotated bounding boxes. Experimental results show that the RGBS50 benchmark poses a challenge to currently popular SOT trackers. Second, we propose an RGB-S tracker called SCANet, which includes a spatial cross-attention module (SCAM) consisting of a novel spatial cross-attention layer and two independent global integration modules. The spatial cross-attention is used to overcome the problem of spatial misalignment of between RGB and sonar images. Third, we propose a SOT data-based RGB-S simulation training method (SRST) to overcome the lack of RGB-S training datasets. It converts RGB images into sonar-like saliency images to construct pseudo-data pairs, enabling the model to learn the semantic structure of RGB-S-like data. Comprehensive experiments show that the proposed spatial cross-attention effectively achieves the interaction between RGB and sonar modalities and SCANet achieves state-of-the-art performance on the proposed benchmark. The code is available at https://github.com/LiYunfengLYF/RGBS50.

cs.CV↗