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Bo Wang

Publications and source records attributed to Bo Wang.

At least 433 records · Page 24Linked to original sources

Uniform Diophantine approximation with restricted denominators

Let $b\geq2$ be an integer and $A=(a_{n})_{n=1}^{\infty}$ be a strictly increasing subsequence of positive integers with $η:=\limsup\limits_{n\to\infty}\frac{a_{n+1}}{a_{n}}<+\infty$. For each irrational real number $ξ$, we denote by $\hat{v}_{b,A}(ξ)$ the supremum of the real numbers $\hat{v}$ for which, for every sufficiently large integer $N$, the equation $\|b^{a_n}ξ\|<(b^{a_N})^{-\hat{v}}$ has a solution $n$ with $1\leq n\leq N$. For every $\hat{v}\in[0,η]$, let $\hat{\mathcal{V}}_{b,A}(\hat{v})$ ($\hat{\mathcal{V}}_{b,A}^{\ast}(\hat{v})$) be the set of all real numbers $ξ$ such that $\hat{v}_{b,A}(ξ)\geq\hat{v}$ ($\hat{v}_{b,A}(ξ)=\hat{v}$) respectively. In this paper, we give some results of the Hausdorfff dimensions of $\hat{\mathcal{V}}_{b,A}(\hat{v})$ and $\hat{\mathcal{V}}_{b,A}^{\ast}(\hat{v})$. When $η=1$, we prove that the Hausdorfff dimensions of $\hat{\mathcal{V}}_{b,A}(\hat{v})$ and $\hat{\mathcal{V}}_{b,A}^{\ast}(\hat{v})$ are equal to $\left(\frac{1-\hat{v}}{1+\hat{v}}\right)^{2}$ for any $\hat{v}\in[0,1]$. When $η>1$ and $\lim_{n\to\infty}\frac{a_{n+1}}{a_{n}}$ exists, we show that the Hausdorfff dimension of $\hat{\mathcal{V}}_{b,A}(\hat{v})$ is strictly less than $\left(\frac{η-\hat{v}}{η+\hat{v}}\right)^{2}$ for some $\hat{v}$, which is different with the case $η=1$, and we give a lower bound of the Hausdorfff dimensions of $\hat{\mathcal{V}}_{b,A}(\hat{v})$ and $\hat{\mathcal{V}}_{b,A}^{\ast}(\hat{v})$ for any $\hat{v}\in[0,η]$. Furthermore, we show that this lower bound can be reached for some $\hat{v}$.

math.NT↗

Coon unitarity via partial waves or: how I learned to stop worrying and love the harmonic numbers

We present a novel approach to partial-wave unitarity that bypasses a lot of technical difficulties of previous approaches. In passing, we explicitly demonstrate that our approach provides a very suggestive form for the partial-wave coefficients in a natural way. We use the Coon amplitudes to exemplify this method and show how it allows to make important properties such as partial-wave unitarity manifest.

hep-th↗

In-Memory Learning: A Declarative Learning Framework for Large Language Models

The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic. Drawing inspiration from the alignment process observed in intelligent organisms, where declarative memory plays a pivotal role in summarizing past experiences, we propose a novel learning framework. The agents adeptly distill insights from past experiences, refining and updating existing notes to enhance their performance in the environment. This entire process transpires within the memory components and is implemented through natural language, so we character this framework as In-memory Learning. We also delve into the key features of benchmarks designed to evaluate the self-improvement process. Through systematic experiments, we demonstrate the effectiveness of our framework and provide insights into this problem.

cs.CL↗

Solid-Lubrication Performance of Ti3C2Tx -- Effect of Tribo-Chemistry and Exfoliation

Multi-layer Ti3C2Tx coatings have demonstrated an outstanding wear performance with excellent durability due to beneficial tribo-layers formed. However, the involved formation processes dependent on the tribological conditions and coating thickness are yet to be fully explored. Therefore, we spray-coated Ti3C2Tx multi-layer particles onto stainless steel substrates to create coatings with two different thicknesses and tested their solid lubrication performance with different normal loads (100 and 200 mN) and sliding frequencies (1 and 2.4 Hz) using linear-reciprocating ball-on-disk tribometry. We demonstrate that MXenes' tribological performance depends on their initial state (delaminated few-layer vs. multi-layer particles), coating thickness, applied load and sliding frequency. Specifically, the best behavior is observed for thinner multi-layer coatings tested at the lower frequency. In contrast, coatings made of delaminated few-layer MXene are not as effective as their multi-layer counterparts. Our high-resolution interface characterization by transmission electron microscopy revealed unambiguous differences regarding the uniformity and chemistry of the formed tribo-layers as well as the degree of tribo-induced MXenes' exfoliation. Atomistic insights into the exfoliation process and molecular dynamic simulations quantitatively backed up our experimental results regarding coating thickness and velocity dependency. This ultimately demonstrates that MXenes' tribological performance is governed by the underlying tribo-chemistry and their exfoliation ability during rubbing.

cond-mat.mtrl-sci↗

A Tampering Risk of Fiber-Based Frequency Synchronization Networks and Its Countermeasures

Fiber optic networks are used worldwide and have been regarded as excellent media for transmitting time-frequency (TF) signals. In the past decades, fiber-based TF synchronization techniques have been extensively studied. Instruments based on these techniques have been successfully applied. With the increasing application of TF synchronization instruments, their security has become an important issue. Unfortunately, the security risks of fiber-based frequency synchronization (FbFS) instruments have been overlooked. This paper proposes a frequency tampering method called "frequency lens". On a 200 km fiber link, we demonstrate a frequency tampering scenario using a frequency lens-enabled frequency tampering module (FTM). On the user side, the frequency value of the recovered 100 MHz signal can be stealthily altered within a range of 100 MHz-100 Hz to 100 MHz+100 Hz, while the frequency dissemination stability of the system remains normal. Related to this tampering risk, potential hazards in three different application scenarios, which rely on precise frequency references, are analyzed. Two countermeasures are also proposed to solve this tampering risk.

physics.ins-det↗

Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings

We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of processing lengthy text inputs with up to 8192 tokens, making them highly versatile for a range of natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. By focusing on bilingual models and introducing a unique multi-task learning objective, we have significantly improved the model performance on STS tasks, which outperforms the capabilities of existing multilingual models in both target language understanding and cross-lingual evaluation tasks. Moreover, our bilingual models are more efficient, requiring fewer parameters and less memory due to their smaller vocabulary needs. Furthermore, we have expanded the Massive Text Embedding Benchmark (MTEB) to include benchmarks for German and Spanish embedding models. This integration aims to stimulate further research and advancement in text embedding technologies for these languages.

cs.CL↗

SplatFlow: Learning Multi-frame Optical Flow via Splatting

The occlusion problem remains a crucial challenge in optical flow estimation (OFE). Despite the recent significant progress brought about by deep learning, most existing deep learning OFE methods still struggle to handle occlusions; in particular, those based on two frames cannot correctly handle occlusions because occluded regions have no visual correspondences. However, there is still hope in multi-frame settings, which can potentially mitigate the occlusion issue in OFE. Unfortunately, multi-frame OFE (MOFE) remains underexplored, and the limited studies on it are mainly specially designed for pyramid backbones or else obtain the aligned previous frame's features, such as correlation volume and optical flow, through time-consuming backward flow calculation or non-differentiable forward warping transformation. This study proposes an efficient MOFE framework named SplatFlow to address these shortcomings. SplatFlow introduces the differentiable splatting transformation to align the previous frame's motion feature and designs a Final-to-All embedding method to input the aligned motion feature into the current frame's estimation, thus remodeling the existing two-frame backbones. The proposed SplatFlow is efficient yet more accurate, as it can handle occlusions properly. Extensive experimental evaluations show that SplatFlow substantially outperforms all published methods on the KITTI2015 and Sintel benchmarks. Especially on the Sintel benchmark, SplatFlow achieves errors of 1.12 (clean pass) and 2.07 (final pass), with surprisingly significant 19.4% and 16.2% error reductions, respectively, from the previous best results submitted. The code for SplatFlow is available at https://github.com/wwsource/SplatFlow.

cs.CV↗

Analytical model and dynamical phase-field simulation of terahertz transmission across ferroelectrics

We theoretically investigate the steady-state transmission of continuous terahertz (THz) wave across a freestanding ferroelectric slab. Based on the Landau-Ginzburg-Devonshire theory of ferroelectrics and the coupled equations of motion for polarization and electromagnetic (EM) waves, we derive the analytical expressions of the frequency- and thickness-dependent dielectric susceptibility and transmission coefficient at the thin slab limit in the harmonic excitation regime. When the slab thickness is much smaller than the THz wavelength in the ferroelectric, the analytical predictions agree well with the numerical simulations from a dynamical phase-field model that incorporates the coupled dynamics of strain, polarization, and EM wave in multiphase systems. At larger thicknesses, the transmission is mainly determined by the frequency-dependent attenuation of THz waves in the ferroelectric and the formation of a standing polarization/THz wave. Our results advance the understanding of the interaction between THz wave and ferroelectrics and suggest the potential of exploiting ferroelectrics to achieve low-heat-dissipation, nonvolatile voltage modulation of THz transmission for high-data-rate wireless communication.

cond-mat.mes-hall↗

Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents

Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essential for tasks like information retrieval, semantic clustering, and text re-ranking, most existing open-source models, especially those built on architectures like BERT, struggle to represent lengthy documents and often resort to truncation. One common approach to mitigate this challenge involves splitting documents into smaller paragraphs for embedding. However, this strategy results in a much larger set of vectors, consequently leading to increased memory consumption and computationally intensive vector searches with elevated latency. To address these challenges, we introduce Jina Embeddings 2, an open-source text embedding model capable of accommodating up to 8192 tokens. This model is designed to transcend the conventional 512-token limit and adeptly process long documents. Jina Embeddings 2 not only achieves state-of-the-art performance on a range of embedding-related tasks in the MTEB benchmark but also matches the performance of OpenAI's proprietary ada-002 model. Additionally, our experiments indicate that an extended context can enhance performance in tasks such as NarrativeQA.

cs.CL↗

A narrow-band parameterization for the stochastic gravitational wave background

In light of the non-perturbative resonance effects that may occur during inflation, we introduce a parametrization for the power spectrum of the stochastic gravitational wave background (SGWB) characterized by narrow-band amplification. We utilize the universal $Ω_\text{GW}\propto k^3$ infrared limit, applicable to a wide array of gravitational wave sources, to devise a robust yet straightforward parameterization optimized for Markov Chain Monte Carlo (MCMC) analyses. This parameterization is demonstrated through select examples where its application is pertinent, and we discuss the advantages of this approach over traditional parametrizations for narrow-band scenarios. To evaluate the sensitivity of our proposed model parameters, we apply a mock likelihood based on the CMB-Stage4 data. Furthermore, we explicate the computational process for the mapping relationship between the foundational model parameters and our parameterized framework, using a two-field inflation model that resonantly amplifies gravitational waves (GWs) as an example.

astro-ph.CO↗

Quantum Phase Transitions in Optomechanical Systems

In this letter, we investigate the ground state properties of an optomechanical system consisting of a coupled cavity and mechanical modes. An exact solution is given when the ratio $η$ between the cavity and mechanical frequencies tends to infinity. This solution reveals a coherent photon occupation in the ground state by breaking continuous or discrete symmetries, exhibiting an equilibrium quantum phase transition (QPT). In the $U(1)$-broken phase, an unstable Goldstone mode can be excited. In the model featuring $Z_2$ symmetry, we discover the mutually (in the finite $η$) or unidirectionally (in $η\rightarrow \infty$) dependent relation between the squeezed vacuum of the cavity and mechanical modes. In particular, when the cavity is driven by a squeezed field along the required squeezing parameter, it enables modifying the region of $Z_2$-broken phase and significantly reducing the coupling strength to reach QPTs. Furthermore, by coupling atoms to the cavity mode, the hybrid system can undergo a QPT at a hybrid critical point, which is cooperatively determined by the optomechanical and light-atom systems. These results suggest that this optomechanical system complements other phase transition models for exploring novel critical phenomena.

quant-ph↗

Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording

Relating speech to EEG holds considerable importance but is challenging. In this study, a deep convolutional network was employed to extract spatiotemporal features from EEG data. Self-supervised speech representation and contextual text embedding were used as speech features. Contrastive learning was used to relate EEG features to speech features. The experimental results demonstrate the benefits of using self-supervised speech representation and contextual text embedding. Through feature fusion and model ensemble, an accuracy of 60.29% was achieved, and the performance was ranked as No.2 in Task 1 of the Auditory EEG Challenge (ICASSP 2024). The code to implement our work is available on Github: https://github.com/bobwangPKU/EEG-Stimulus-Match-Mismatch.

eess.SP↗

Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces

Attention mechanisms have been widely used to capture long-range dependencies among nodes in Graph Transformers. Bottlenecked by the quadratic computational cost, attention mechanisms fail to scale in large graphs. Recent improvements in computational efficiency are mainly achieved by attention sparsification with random or heuristic-based graph subsampling, which falls short in data-dependent context reasoning. State space models (SSMs), such as Mamba, have gained prominence for their effectiveness and efficiency in modeling long-range dependencies in sequential data. However, adapting SSMs to non-sequential graph data presents a notable challenge. In this work, we introduce Graph-Mamba, the first attempt to enhance long-range context modeling in graph networks by integrating a Mamba block with the input-dependent node selection mechanism. Specifically, we formulate graph-centric node prioritization and permutation strategies to enhance context-aware reasoning, leading to a substantial improvement in predictive performance. Extensive experiments on ten benchmark datasets demonstrate that Graph-Mamba outperforms state-of-the-art methods in long-range graph prediction tasks, with a fraction of the computational cost in both FLOPs and GPU memory consumption. The code and models are publicly available at https://github.com/bowang-lab/Graph-Mamba.

cs.LG↗

A Novel PML-type Technique for Acoustic Scattering Problems based on A Real Coordinate Transformation

It is known that any {\em real coordinate transformation} (RCT) to compress waves in an unbounded domain into a bounded domain results in infinite oscillations that cannot be resolved by any grid-based method. In this paper, we intend to show that it is viable if the outgoing waves are compressed along the radial direction and the resulting oscillatory pattern is extracted explicitly. We therefore construct a perfectly matched layer (PML)-type technique for domain reduction of wave scattering problems using RCT, termed as real compressed layer (RCL). Different from all existing approaches, the RCL technique has two features: (i) the RCL-equation only involves real-valued coefficients, which is more desirable for computation and analysis; and (ii) the layer is not ``artificial'' in the sense that the computed field in the layer can recover the outgoing wave of the original scattering problem in the unbounded domain. Here we demonstrate the essential idea and performance of the RCL for the two-dimensional Helmholtz problem with a bounded scatterer, but this technique can be extended to three dimensions in a similar setting.

math.NA↗

Newton polytopes of dual $k$-Schur polynomials

Rado's theorem about permutahedra and dominance order on partitions reveals that each Schur polynomial is M-convex, or equivalently, it has a saturated Newton polytope and this polytope is a generalized permutahedron as well. In this paper we show that the support of each dual $k$-Schur polynomial indexed by a $k$-bounded partition coincides with that of the Schur polynomial indexed by the same partition, and hence the two polynomials share the same saturated Newton polytope. The main result is based on our recursive algorithm to generate a semistandard $k$-tableau for a given shape and $k$-weight. As consequences, we obtain the M-convexity of dual $k$-Schur polynomials, affine Stanley symmetric polynomials and cylindric skew Schur polynomials.

math.CO↗

Accelerating Patch Validation for Program Repair with Interception-Based Execution Scheduling

Long patch validation time is a limiting factor for automated program repair (APR). Though the duality between patch validation and mutation testing is recognized, so far there exists no study of systematically adapting mutation testing techniques to general-purpose patch validation. To address this gap, we investigate existing mutation testing techniques and identify five classes of acceleration techniques that are suitable for general-purpose patch validation. Among them, mutant schemata and mutant deduplication have not been adapted to general-purpose patch validation due to the arbitrary changes that third-party APR approaches may introduce. This presents two problems for adaption: 1) the difficulty of implementing the static equivalence analysis required by the state-of-the-art mutant deduplication approach; 2) the difficulty of capturing the changes of patches to the system state at runtime. To overcome these problems, we propose two novel approaches: 1) execution scheduling, which detects the equivalence between patches online, avoiding the static equivalence analysis and its imprecision; 2) interception-based instrumentation, which intercepts the changes of patches to the system state, avoiding a full interpreter and its overhead. Based on the contributions above, we implement ExpressAPR, a general-purpose patch validator for Java that integrates all recognized classes of techniques suitable for patch validation. Our large-scale evaluation with four APR approaches shows that ExpressAPR accelerates patch validation by 137.1x over plainvalidation or 8.8x over the state-of-the-art approach, making patch validation no longer the time bottleneck of APR. Patch validation time for a single bug can be reduced to within a few minutes on mainstream CPUs.

cs.SE↗

Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models (LLMs) on text generation and reasoning, we first elicit multimodal debate between LLMs to generate the explanations derived from the contradictory arguments. Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes. In this way, our model is empowered to perform dialectical reasoning over intricate and implicit harm-indicative patterns, utilizing multimodal explanations originating from both harmless and harmful arguments. Extensive experiments on three public meme datasets demonstrate that our harmful meme detection approach achieves much better performance than state-of-the-art methods and exhibits a superior capacity for explaining the meme harmfulness of the model predictions.

cs.CL↗

A DenseNet-based method for decoding auditory spatial attention with EEG

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.3% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNet

eess.SP↗