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We study envy-free up to any item (EFX) allocations on graphs where vertices and edges represent agents and items respectively. An agent is only interested in items that are incident to her and all other items have zero marginal values to her. Christodoulou et al. [EC, 2023] first proposed this setting and studied the case of goods. We extend this setting to the case of mixed manna where an item may be liked or disliked by its endpoint agents. In our problem, an agent has an arbitrary valuation over her incident items such that the items she likes have non-negative marginal values to her and those she dislikes have non-positive marginal values. We provide a complete study of the four notions of EFX for mixed manna in the literature, which differ by whether the removed item can have zero marginal value. We prove that an allocation that satisfies the notion of EFX where the virtually-removed item could always have zero marginal value may not exist and determining its existence is NP-complete, while one that satisfies any of the other three notions always exists and can be computed in polynomial time. We also prove that an orientation (i.e., a special allocation where each edge must be allocated to one of its endpoint agents) that satisfies any of the four notions may not exist, and determining its existence is NP-complete., Comment: Accepted in IJCAI 2024

Relativistic charged particle beam can be used as destructive beam weapons in space for debris removal tasks. The trajectories of charged particles are affected by both electric and magnetic forces in the Earth's magnetic field. In this paper, we firstly analyzed the correlation parameters of the charged particle beam as a weapon when it propagated in the geomagnetic field. Then the models were constructed based on COMSOL Multiphysics and the IGRF model was adopted in the simulation. The gyro-radius and the related uncertainty were analyzed by simulation of the charged particle transport in the geomagnetic field at different altitudes. The charged beam spot radius divergency was also simulated. The magnetic field pinch effect can be found and can limit the beam spreading., Comment: 10 pages, 7 figures

Pre-trained multilingual speech foundation models, like Whisper, have shown impressive performance across different languages. However, adapting these models to new or specific languages is computationally extensive and faces catastrophic forgetting problems. Addressing these issues, our study investigates strategies to enhance the model on new languages in the absence of original training data, while also preserving the established performance on the original languages. Specifically, we first compare various LoRA-based methods to find out their vulnerability to forgetting. To mitigate this issue, we propose to leverage the LoRA parameters from the original model for approximate orthogonal gradient descent on the new samples. Additionally, we also introduce a learnable rank coefficient to allocate trainable parameters for more efficient training. Our experiments with a Chinese Whisper model (for Uyghur and Tibetan) yield better results with a more compact parameter set.

In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-text pair data for fine-tuning and alignment is extremely high. While existing multimodal data augmentation frameworks propose ways to augment image-text pairs, they either suffer from semantic inconsistency between texts and images, or generate unrealistic images, causing knowledge gap with real world examples. To address these issues, we propose Attribute-based Multimodal Data Augmentation (ARMADA), a novel multimodal data augmentation method via knowledge-guided manipulation of visual attributes of the mentioned entities. Specifically, we extract entities and their visual attributes from the original text data, then search for alternative values for the visual attributes under the guidance of knowledge bases (KBs) and large language models (LLMs). We then utilize an image-editing model to edit the images with the extracted attributes. ARMADA is a novel multimodal data generation framework that: (i) extracts knowledge-grounded attributes from symbolic KBs for semantically consistent yet distinctive image-text pair generation, (ii) generates visually similar images of disparate categories using neighboring entities in the KB hierarchy, and (iii) uses the commonsense knowledge of LLMs to modulate auxiliary visual attributes such as backgrounds for more robust representation of original entities. Our empirical results over four downstream tasks demonstrate the efficacy of our framework to produce high-quality data and enhance the model performance. This also highlights the need to leverage external knowledge proxies for enhanced interpretability and real-world grounding.

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP's particular advantages in handling diverse forms of query text. The source code will be publicly available at https://github.com/Gyann-z/FDP., Comment: Accepted by ACM MM 2024

We introduce a novel level crossing in the mass mixing between two QCD axions, one canonical QCD axion and one $Z_{\mathcal N}$ QCD axion. The level crossing can take place at the QCD phase transition critical temperature or slightly before it, depending on the ratio of the axion decay constants $\sim1.69$. The cosmological evolution of the mass eigenvalues in these two cases is similar, however, the transition of axion energy density is completely different. Finally, we estimate the relic density of the QCD axion dark matter. This level crossing may also have some cosmological implications., Comment: 6 pages, 4 figures

Compared to classical optical coherence theory based on Maxwell's electromagnetic theory and Glauber's quantum optical coherence theory based on matrix mechanics formulation of quantum mechanics, quantum optical coherence theory based on Feynman's path integral formulation of quantum mechanics provides a novel tool to study optical coherence. It has the advantage of understanding the connection between mathematical calculations and physical interpretations better. Quantum optical coherence theory based on Feynman's path integral is introduced and reviewed in this paper. Based on the results of transient first-order interference of two independent light beams, it is predicted that the classical model for electric field of thermal light introduced by classical optical textbooks may not be accurate. The physics of two-photon bunching of thermal light and Hong-Ou-Mandel dip of entangled photon pairs is the same, which can be interpreted by constructive and destructive two-photon interference, respectively. Quantum optical coherence theory based on Feynman's path integral is helpful to understand the coherence properties of light, which may eventually lead us to the answer of the question: what is a photon?, Comment: 40 pages, 35 figures

Astrophysical emission lines arising from particle decays can offer unique insights into the nature of dark matter (DM). Using dedicated simulations with background and foreground modeling, we comprehensively demonstrate that the recently launched XRISM space telescope with powerful X-ray spectroscopy capabilities is particularly well-suited to probe decaying DM, such as sterile neutrinos and axion-like particles, in the mass range of few to tens of keV. We analyze and map XRISM's DM discovery potential parameter space by considering Milky Way Galactic DM halo, including establishing an optimal line-of-sight search, as well as dwarf galaxies where we identify Segue 1 as a remarkably promising target. We demonstrate that with only 100 ks exposure XRISM/Resolve instrument is capable of probing the underexplored DM parameter window around few keV and testing DM couplings with sensitivity that exceeds by two orders existing Segue 1 limits. Further, we demonstrate that XRISM/Xtend instrument sensitivity enables discovery of the nature of faint astrophysical X-ray sources, especially in Segue 1, which could shed light on star-formation history. We discuss implications for decaying DM searches with improved detector energy resolution in future experiments., Comment: 12 pages, 9 figures

Code Language Models (CLMs), particularly those leveraging deep learning, have achieved significant success in code intelligence domain. However, the issue of security, particularly backdoor attacks, is often overlooked in this process. The previous research has focused on designing backdoor attacks for CLMs, but effective defenses have not been adequately addressed. In particular, existing defense methods from natural language processing, when directly applied to CLMs, are not effective enough and lack generality, working well in some models and scenarios but failing in others, thus fall short in consistently mitigating backdoor attacks. To bridge this gap, we first confirm the phenomenon of ``early learning" as a general occurrence during the training of CLMs. This phenomenon refers to that a model initially focuses on the main features of training data but may become more sensitive to backdoor triggers over time, leading to overfitting and susceptibility to backdoor attacks. We then analyze that overfitting to backdoor triggers results from the use of the cross-entropy loss function, where the unboundedness of cross-entropy leads the model to increasingly concentrate on the features of the poisoned data. Based on this insight, we propose a general and effective loss function DeCE (Deceptive Cross-Entropy) by blending deceptive distributions and applying label smoothing to limit the gradient to be bounded, which prevents the model from overfitting to backdoor triggers and then enhances the security of CLMs against backdoor attacks. To verify the effectiveness of our defense method, we select code synthesis tasks as our experimental scenarios. Our experiments across various code synthesis datasets, models, and poisoning ratios demonstrate the applicability and effectiveness of DeCE in enhancing the security of CLMs., Comment: Under Review; Waiting for updates

With the proliferation of social media posts in recent years, the need to detect sentiments in multimodal (image-text) content has grown rapidly. Since posts are user-generated, the image and text from the same post can express different or even contradictory sentiments, leading to potential \textbf{sentiment discrepancy}. However, existing works mainly adopt a single-branch fusion structure that primarily captures the consistent sentiment between image and text. The ignorance or implicit modeling of discrepant sentiment results in compromised unimodal encoding and limited performances. In this paper, we propose a semantics Completion and Decomposition (CoDe) network to resolve the above issue. In the semantics completion module, we complement image and text representations with the semantics of the OCR text embedded in the image, helping bridge the sentiment gap. In the semantics decomposition module, we decompose image and text representations with exclusive projection and contrastive learning, thereby explicitly capturing the discrepant sentiment between modalities. Finally, we fuse image and text representations by cross-attention and combine them with the learned discrepant sentiment for final classification. Extensive experiments conducted on four multimodal sentiment datasets demonstrate the superiority of CoDe against SOTA methods., Comment: 8 pages, 6 figures

In this paper, we investigate the second-order normal quantum fluctuation on the world-sheet of a probe string in the Ba\~nados-Teitelboim-Zanelli (BTZ) black hole. These fluctuations is treated as the projection of Hawking radiation on the worldsheet and indeed modify the action growth of the string. Then in the string field theory/boundary conformal field theory framework, via the boundary vertex operator we study the correlation function of the Schr\"odinger functional of excited fields on the world-sheet and further extract the field's formula. Our study could shed light on the potential connection between complexity growth and correlation function., Comment: 18 pages, 2 figures

Evaluation metrics are crucial in the field of code synthesis. Commonly used code evaluation metrics canbe classified into three types: match-based, semantic-based, and execution-based. Among them, the execution-basedPass@k metric accurately assesses the functionality of predicted code by executing test cases. However, calculatingthis metric requires a significant amount of overhead, necessitating the design of an automated evaluation metric thatcan assess the functionality of predicted code without the need for test cases. Additionally, a good evaluation metricshould be robust, that is the metric can maintain its accuracy even when the predicted code undergoes minor changes.To address these challenges, we propose an automated robust metric, called CodeScore-R, based on UniXcoder andcontrastive learning, for evaluating the functionality of code synthesis. CodeScore-R employs techniques such assketch-based processing, syntactic-equivalent transformations, and mutation testing to effectively mitigate theinterference caused by identifiers, syntax structures, and operators on evaluation results. Experimental resultsdemonstrate that in the tasks of code generation and migration in Java and Python, CodeScore-R outperforms otherevaluation metrics and is more closely aligned with the Pass@k metric, while exhibiting stronger robustness., Comment: in Chinese language, Journal of Computer Research and Development

With the rapid proliferation of mobile devices and data, next-generation wireless communication systems face stringent requirements for ultra-low latency, ultra-high reliability, and massive connectivity. Traditional AI-driven wireless network designs, while promising, often suffer from limitations such as dependency on labeled data and poor generalization. To address these challenges, we present an integration of self-supervised learning (SSL) into wireless networks. SSL leverages large volumes of unlabeled data to train models, enhancing scalability, adaptability, and generalization. This paper offers a comprehensive overview of SSL, categorizing its application scenarios in wireless network optimization and presenting a case study on its impact on semantic communication. Our findings highlight the potentials of SSL to significantly improve wireless network performance without extensive labeled data, paving the way for more intelligent and efficient communication systems.

With the increasing demand for multi-carrier communication in high-mobility scenarios, it is urgent to design new multi-carrier communication waveforms that can resist large delay-Doppler spreads. Various multi-carrier waveforms in the transform domain were proposed for the fast time-varying channels, including orthogonal time frequency space (OTFS), orthogonal chirp division multiplexing (OCDM), and affine frequency division multiplexing (AFDM). Among these, the AFDM is a strong candidate for its low implementation complexity and ability to achieve optimal diversity. This paper unifies the waveforms based on the discrete affine Fourier transform (DAFT) by using the chirp slope factor "k" in the time-frequency representation to construct a unified design framework for high-mobility communications. The design framework is employed to verify that the bit error rate performance of the DAFT-based waveform can be enhanced when the signal-to-noise ratio (SNR) is sufficiently high by adjusting the chirp slope factor "k".

Simultaneous Machine Translation (SiMT) generates target outputs while receiving stream source inputs and requires a read/write policy to decide whether to wait for the next source token or generate a new target token, whose decisions form a \textit{decision path}. Existing SiMT methods, which learn the policy by exploring various decision paths in training, face inherent limitations. These methods not only fail to precisely optimize the policy due to the inability to accurately assess the individual impact of each decision on SiMT performance, but also cannot sufficiently explore all potential paths because of their vast number. Besides, building decision paths requires unidirectional encoders to simulate streaming source inputs, which impairs the translation quality of SiMT models. To solve these issues, we propose \textbf{S}elf-\textbf{M}odifying \textbf{S}tate \textbf{M}odeling (SM$^2$), a novel training paradigm for SiMT task. Without building decision paths, SM$^2$ individually optimizes decisions at each state during training. To precisely optimize the policy, SM$^2$ introduces Self-Modifying process to independently assess and adjust decisions at each state. For sufficient exploration, SM$^2$ proposes Prefix Sampling to efficiently traverse all potential states. Moreover, SM$^2$ ensures compatibility with bidirectional encoders, thus achieving higher translation quality. Experiments show that SM$^2$ outperforms strong baselines. Furthermore, SM$^2$ allows offline machine translation models to acquire SiMT ability with fine-tuning., Comment: Accept to ACL 2024 main conference. 15 pages, 13 figures, 9 tables

We obtain a stringent upper limit on axion-photon coupling from the 1038 days gamma-ray observations of the TeV blazar Markarian 421. The long-term VHE gamma-ray spectra are measured by the collaborations Fermi-LAT and HAWC from 2015 June to 2018 July. We show the best-fit SEDs of Markarian 421 under the null and axion hypotheses. Then we set the axion-photon limit in the $\{m_a, \, g_{a\gamma}\}$ plane. The 99% $\rm C.L.$ upper limit set by Markarian 421 is $g_{a\gamma} \lesssim 4.0\times 10^{-12} \rm \, GeV^{-1}$ for the axion mass $[1.0\times10^{-9} \, {\rm eV} \lesssim m_a \lesssim 1.0\times10^{-8} \, {\rm eV}]$. It is the most stringent upper limit in this axion mass region., Comment: 3+7 pages, 3 figures

LiteBIRD, a forthcoming JAXA mission, aims to accurately study the microwave sky within the 40-400 GHz frequency range divided into 15 distinct nominal bands. The primary objective is to constrain the CMB inflationary signal, specifically the primordial B-modes. LiteBIRD targets the CMB B-mode signal on large angular scales, where the primordial inflationary signal is expected to dominate, with the goal of reaching a tensor-to-scalar ratio sensitivity of $\sigma_r\sim0.001$. LiteBIRD frequency bands will be split among three telescopes, with some overlap between telescopes for better control of systematic effects. Here we report on the development status of the detector arrays for the Low Frequency Telescope (LFT), which spans the 34-161 GHz range, with 12 bands subdivided between four types of trichroic pixels consisting of lenslet-coupled sinuous antennas. The signal from the antenna is bandpass filtered and sensed by AlMn Transition-Edge Sensors (TES). We provide an update on the status of the design and development of LiteBIRD's LFT LF1 (40-60-78 GHz), LF2 (50-68-89 GHz) pixels. We discuss design choices motivated by LiteBIRD scientific goals. In particular we focus on the details of the optimization of the design parameters of the sinuous antenna, on-chip bandpass filters, cross-under and impedance transformers and all the RF components that define the LF1 and LF2 pixel detection chain. We present this work in the context of the technical challenges and physical constraints imposed by the finite size of the instrument., Comment: 12 pages, 10 figures, 1 table, SPIE 2024

The intermediate-mass-ratio inspirals (IMRIs) may be surrounded by dark matter (DM) minispikes. The dynamical friction from these DM minispike structures can affect the dynamics and the gravitational wave (GW) emission of the IMRIs. We analyze the effects of general dynamical friction, with a particular contribution from DM particles moving faster than the stellar-mass black hole in an eccentric IMRI. The results show that the dynamical friction caused by these DM particles tends to eccentricify the orbit, and the general dynamical friction is able to increase the eccentricity. We also analyze the effects of general dynamical friction on the GW characteristic strain. The results indicate that the peak value of the characteristic strain occurs at higher frequencies as the power law index of DM minispike $\gamma_\mathrm{sp}$ increases. For the first time, a general analytical relation between the frequency peak value of characteristic strain of GWs and $\gamma_\mathrm{sp}$ is established. Using the analytical relation, the presence of DM and its halo density may be determined potentially from future GW data., Comment: 7 pages, 4 figures

The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optimal compression. In this study, we propose an adaptive compression workflow based on Implicit Neural Representation (INR). This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images from real applications that our workflow not only achieved high, controllable compression ratios (e.g., 512x) but also preserved detailed information critical for downstream analysis.

The digital frequency domain multiplexing (DfMux) technique is widely used for astrophysical instruments with large detector arrays. Detailed detector characterization is required for instrument calibration and systematics control. We conduct the TES complex electrothermal-feedback (ETF) response measurement with the DfMux readout system as follows. By injecting a single sideband signal, we induce modulation in TES power dissipation over a frequency range encompassing the detector response. The modulated current signal induced by TES heating effect is measured, allowing for the ETF response characterization of the detector. With the injection of an upper sideband, the TES readout current shows both an upper and a lower sideband. We model the upper and lower sideband complex ETF response and verify the model by fitting to experimental data. The model not only can fit for certain physical parameters of the detector, such as loop gain, temperature sensitivity, current sensitivity, and time constant, but also enables us to estimate the systematic effect introduced by the multiplexed readout. The method is therefore useful for in-situ detector calibration and for estimating systematic effects during astronomical telescope observations, such as those performed by the upcoming LiteBIRD satellite., Comment: 9 pages, 4 figures, accepted to Journal of Low Temperature Physics

Bone segmentation is an essential step for the preoperative planning of fracture trauma surgery. The automated segmentation of fractured bone from computed tomography (CT) scans remains challenging, due to the large differences of fractures in position and morphology, and also the inherent anatomical characteristics of different bone structures. To alleviate these issues, we propose a cross-scale attention mechanism as well as a surface supervision strategy for fractured bone segmentation in CT. Specifically, a cross-scale attention mechanism is introduced to effectively aggregate the features among different scales to provide more powerful fracture representation. Moreover, a surface supervision strategy is employed, which explicitly constrains the network to pay more attention to the bone boundary. The efficacy of the proposed method is evaluated on a public dataset containing CT scans with hip fractures. The evaluation metrics are Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and Hausdorff distance (95HD). The proposed method achieves an average DSC of 93.36%, ASSD of 0.85mm, 95HD of 7.51mm. Our method offers an effective fracture segmentation approach for the pelvic CT examinations, and has the potential to be used for improving the segmentation performance of other types of fractures.

Causality reveals fundamental principles behind data distributions in real-world scenarios, and the capability of large language models (LLMs) to understand causality directly impacts their efficacy across explaining outputs, adapting to new evidence, and generating counterfactuals. With the proliferation of LLMs, the evaluation of this capacity is increasingly garnering attention. However, the absence of a comprehensive benchmark has rendered existing evaluation studies being straightforward, undiversified, and homogeneous. To address these challenges, this paper proposes a comprehensive benchmark, namely CausalBench, to evaluate the causality understanding capabilities of LLMs. Originating from the causal research community, CausalBench encompasses three causal learning-related tasks, which facilitate a convenient comparison of LLMs' performance with classic causal learning algorithms. Meanwhile, causal networks of varying scales and densities are integrated in CausalBench, to explore the upper limits of LLMs' capabilities across task scenarios of varying difficulty. Notably, background knowledge and structured data are also incorporated into CausalBench to thoroughly unlock the underlying potential of LLMs for long-text comprehension and prior information utilization. Based on CausalBench, this paper evaluates nineteen leading LLMs and unveils insightful conclusions in diverse aspects. Firstly, we present the strengths and weaknesses of LLMs and quantitatively explore the upper limits of their capabilities across various scenarios. Meanwhile, we further discern the adaptability and abilities of LLMs to specific structural networks and complex chain of thought structures. Moreover, this paper quantitatively presents the differences across diverse information sources and uncovers the gap between LLMs' capabilities in causal understanding within textual contexts and numerical domains.

Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diverse optimization scenarios, there is also a rising trend of applying LLMs. However, whether the application of LLMs in the black-box optimization problems is genuinely beneficial remains unexplored. This paper endeavors to offer deep insights into the potential of LLMs in optimization through a comprehensive investigation, which covers both discrete and continuous optimization problems to assess the efficacy and distinctive characteristics that LLMs bring to this field. Our findings reveal both the limitations and advantages of LLMs in optimization. Specifically, on the one hand, despite the significant power consumed for running the models, LLMs exhibit subpar performance in pure numerical tasks, primarily due to a mismatch between the problem domain and their processing capabilities; on the other hand, although LLMs may not be ideal for traditional numerical optimization, their potential in broader optimization contexts remains promising, where LLMs exhibit the ability to solve problems in non-numerical domains and can leverage heuristics from the prompt to enhance their performance. To the best of our knowledge, this work presents the first systematic evaluation of LLMs for numerical optimization. Our findings pave the way for a deeper understanding of LLMs' role in optimization and guide future application of LLMs in a wide range of scenarios.

The recently developed affine frequency division multiplexing (AFDM) can achieve full diversity in doubly selective channels, providing a comprehensive sparse representation of the delay-Doppler domain channel. Thus, accurate channel estimation is feasible by using just one pilot symbol. However, traditional AFDM channel estimation schemes necessitate the use of guard intervals (GI) to mitigate data-pilot interference, leading to spectral efficiency degradation. In this paper, we propose a GI-free pilot-aided channel estimation algorithm for AFDM systems, which improves spectral efficiency significantly. To mitigate the interference between the pilot and data symbols caused by the absence of GI, we perform joint interference cancellation, channel estimation, and signal detection iterately. Simulation results show that the bit error rate (BER) performance of the proposed method can approach the ideal case with perfect channel estimation.

We explore the new physics phenomena of gravidynamics governed by the inhomogeneous spin gauge symmetry based on the gravitational quantum field theory. Such a gravidynamics enables us to derive the generalized Einstein equation and an equation beyond it. To simplify the analyses, we linearize the dynamic equations of gravitational interaction by keeping terms up to the leading order in the dual gravigauge field. We then apply the linearized dynamic equations into two particular gravitational phenomena. First, we consider the linearized equations in the absence of source fields, which is shown to have five physical propagating polarizations as gravitational waves, i.e., two tensor modes, two vector modes, and one scalar, instead of two tensor polarizations in the general relativity. Second, we examine the Newtonian limit in which the gravitational fields and the matter source distribution are weak and static. By deriving the associated Poisson equation, we obtain the exact relation of the fundamental interaction coupling in the gravidynamics with the experimentally measured Newtonian constant. We also make use of nonrelativistic objects and relativistic photons to probe the Newtonian field configurations. In particular, the experiments from the gravitational deflection of light rays and the Shapiro time delay can place stringent constraints on the linearized gravidynamics in the gravitational quantum field theory., Comment: 7 pages, 1 figure

This paper investigates the robust asymptotic stabilization of a linear time-invariant (LTI) system by a static feedback with a static state quantization. It is shown that the controllable LTI system can be stabilized to zero in a finite time by means of a nonlinear feedback with a quantizer having a limited (finite) number of values (quantization seeds) even when all parameters of the controller and the quantizer are time-invariant. The control design is based on generalized homogeneity. A homogeneous spherical quantizer is introduced. The static homogeneous feedback is shown to be local (or global) finite-time stabilizer for the linear system (dependently of the system matrix). The tuning rules for both the quantizer and the feedback law are obtained in the form of Linear Matrix Inequalities (LMIs). The closed-loop system is proven to be robust with respect to some bounded matched and vanishing mismatched perturbations. Theoretical results are supported by numerical simulations. \

We show that the Koszul functor of a homologically smooth graded gentle algebra can be realized as the half rotation in a geometric model. As a byproduct, we prove an intersection-dim formula involving the Koszul functor., Comment: 29 pages, 17 figures. Any comments are welcome

Existing scene text spotters are designed to locate and transcribe texts from images. However, it is challenging for a spotter to achieve precise detection and recognition of scene texts simultaneously. Inspired by the glimpse-focus spotting pipeline of human beings and impressive performances of Pre-trained Language Models (PLMs) on visual tasks, we ask: 1) "Can machines spot texts without precise detection just like human beings?", and if yes, 2) "Is text block another alternative for scene text spotting other than word or character?" To this end, our proposed scene text spotter leverages advanced PLMs to enhance performance without fine-grained detection. Specifically, we first use a simple detector for block-level text detection to obtain rough positional information. Then, we finetune a PLM using a large-scale OCR dataset to achieve accurate recognition. Benefiting from the comprehensive language knowledge gained during the pre-training phase, the PLM-based recognition module effectively handles complex scenarios, including multi-line, reversed, occluded, and incomplete-detection texts. Taking advantage of the fine-tuned language model on scene recognition benchmarks and the paradigm of text block detection, extensive experiments demonstrate the superior performance of our scene text spotter across multiple public benchmarks. Additionally, we attempt to spot texts directly from an entire scene image to demonstrate the potential of PLMs, even Large Language Models (LLMs)., Comment: 12 pages, 8 figures

Cosmic-ray (CR) positrons are relatively rare due to its secondary origin and thus sensitive to exotic contributions. Primordial black holes (PBHs) with masses above $\sim 5\times10^{14}\,\mathrm{g}$ can be stable sources of CR positrons due to Hawking radiation. We show that the CR positron flux measured by AMS-02 can place stringent constraints on the energy fraction of PBHs relative to that of dark matter $f_{\text{PBH}}$. Making use of the state-of-the-art models for CR propagation in both the Galaxy and heliosphere, we obtain conservative upper limit of $f_{\text{PBH}}\lesssim3\times 10^{-4}$ at $M_{\mathrm{PBH}}\simeq2\times 10^{16}$ g, which improves the previous constraints obtained from the Voyager CR all-electron data by around an order of magnitude.

We develop a compact four-port superconducting switch with a tunable operating frequency in the range of 4.8 GHz -- 7.3 GHz. Isolation between channel exceeds 20~dB over a bandwidth of several hundred megahertz, exceeding 40 dB at some frequencies. The footprint of the device is $80\times420~\mu$m. The tunability requires only a global flux bias without either permanent magnets or micro-electromechanical structures. As the switch is superconducting, the heat dissipation during operation is negligible. The device can operate at up to -80~dBm, which is equal to $2.5\times 10^6$ photons at 6 GHz per microsecond. The device show a possibility to be operated as a beamsplitter with tunable splitting ratio., Comment: 9 pages, 5 figures

Visual text, a pivotal element in both document and scene images, speaks volumes and attracts significant attention in the computer vision domain. Beyond visual text detection and recognition, the field of visual text processing has experienced a surge in research, driven by the advent of fundamental generative models. However, challenges persist due to the unique properties and features that distinguish text from general objects. Effectively leveraging these unique textual characteristics is crucial in visual text processing, as observed in our study. In this survey, we present a comprehensive, multi-perspective analysis of recent advancements in this field. Initially, we introduce a hierarchical taxonomy encompassing areas ranging from text image enhancement and restoration to text image manipulation, followed by different learning paradigms. Subsequently, we conduct an in-depth discussion of how specific textual features such as structure, stroke, semantics, style, and spatial context are seamlessly integrated into various tasks. Furthermore, we explore available public datasets and benchmark the reviewed methods on several widely-used datasets. Finally, we identify principal challenges and potential avenues for future research. Our aim is to establish this survey as a fundamental resource, fostering continued exploration and innovation in the dynamic area of visual text processing.

Achieving real-time and accuracy on embedded platforms has always been the pursuit of road segmentation methods. To this end, they have proposed many lightweight networks. However, they ignore the fact that roads are "stuff" (background or environmental elements) rather than "things" (specific identifiable objects), which inspires us to explore the feasibility of representing roads with low-level instead of high-level features. Surprisingly, we find that the primary stage of mainstream network models is sufficient to represent most pixels of the road for segmentation. Motivated by this, we propose a Low-level Feature Dominated Road Segmentation network (LFD-RoadSeg). Specifically, LFD-RoadSeg employs a bilateral structure. The spatial detail branch is firstly designed to extract low-level feature representation for the road by the first stage of ResNet-18. To suppress texture-less regions mistaken as the road in the low-level feature, the context semantic branch is then designed to extract the context feature in a fast manner. To this end, in the second branch, we asymmetrically downsample the input image and design an aggregation module to achieve comparable receptive fields to the third stage of ResNet-18 but with less time consumption. Finally, to segment the road from the low-level feature, a selective fusion module is proposed to calculate pixel-wise attention between the low-level representation and context feature, and suppress the non-road low-level response by this attention. On KITTI-Road, LFD-RoadSeg achieves a maximum F1-measure (MaxF) of 95.21% and an average precision of 93.71%, while reaching 238 FPS on a single TITAN Xp and 54 FPS on a Jetson TX2, all with a compact model size of just 936k parameters. The source code is available at https://github.com/zhouhuan-hust/LFD-RoadSeg., Comment: 11 pages, 7 figures, IEEE TITS

This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods. Our key observation is that for the industrial product images, the normal image patches could find a relatively large number of similar patches in other unlabeled images, while the abnormal ones only have a few similar patches. We leverage such a discriminative characteristic to design a novel zero-shot AC/AS method by Mutual Scoring (MuSc) of the unlabeled images, which does not need any training or prompts. Specifically, we perform Local Neighborhood Aggregation with Multiple Degrees (LNAMD) to obtain the patch features that are capable of representing anomalies in varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to leverage the unlabeled test images to assign the anomaly score to each other. Furthermore, we present an optimization approach named Re-scoring with Constrained Image-level Neighborhood (RsCIN) for image-level anomaly classification to suppress the false positives caused by noises in normal images. The superior performance on the challenging MVTec AD and VisA datasets demonstrates the effectiveness of our approach. Compared with the state-of-the-art zero-shot approaches, MuSc achieves a $\textbf{21.1%}$ PRO absolute gain (from 72.7% to 93.8%) on MVTec AD, a $\textbf{19.4%}$ pixel-AP gain and a $\textbf{14.7%}$ pixel-AUROC gain on VisA. In addition, our zero-shot approach outperforms most of the few-shot approaches and is comparable to some one-class methods. Code is available at https://github.com/xrli-U/MuSc., Comment: Accepted at ICLR2024

We study the optical appearance of Schwarzschild-de Sitter and Reissner-Nordstr\"{o}m-de Sitter black holes viewed by distant observers inside cosmological horizons. Unlike their asymptotically flat counterparts, due to the positive cosmological constant, there are outermost stable circular orbits in the spacetimes, resulting in significant outer edges in the images. Besides, when the Reissner-Nordstr\"{o}m-de Sitter black hole has a stable Cauchy horizon, the photons from the preceding companion universe can be received by the observer in our universe. These rays create a multi-ring structure in the image. Since the stable Cauchy horizon violates the strong cosmic censorship conjecture, this novel image shed some light on the test of the conjecture by astronomical observations., Comment: 15 pages, 15 figures, references and figures are updated, accepted by Phys. Rev. D

In this study, we investigate the pseudospectrum and spectrum (in)stability of quantum corrected Schwarzschild black hole. Methodologically, we use the hyperboloidal framework to cast the quasinormal mode (QNM) problem into an eigenvalue problem associated with a non-selfadjoint operator, and then the spectrum and pseudospectrum are depicted. Besides, the invariant subspace method is exploited to improve the computational efficiency for pseudospectrum. The investigation into the spectrum (in)stability entails two main aspects. On the one hand, we calculate the spectra of the quantum corrected black hole, then by the means of the migration ratio, the impact of the quantum correction effect on the Schwarzschild black hole has been studied. The results indicate that the so-called ``migration ratio instability" will occur for small black holes with small angular momentum number l. In the eikonal limit, the migration ratios remain the same for each overtone. On the other hand, we study the spectrum (in)stability of the quantum corrected black hole by directly adding some particular perturbations into the effective potential, where perturbations are located at the event horizon and null infinity, respectively. There are two interesting observations under the same perturbation energy norm. First, perturbations at infinity are more capable of generating spectrum instability than those at the event horizon. Second, we find that the peak distribution can lead to the instability of QNM spectrum more efficiently than the average distribution., Comment: 23 pages, 10 figures. This manuscript has been accepted for publication as a regular article in SCPMA

Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile robots. Successful solutions have many applications in multiple scenes. One of the exceptions is the reflective ground. In this case, the reflections on the floor resemble the true world, which confuses the obstacle discovery and leaves navigation unsuccessful. We argue that the key to this problem lies in obtaining discriminative features for reflections and obstacles. Note that obstacle and reflection can be separated by the ground plane in 3D space. With this observation, we firstly introduce a pre-calibration based ground detection scheme that uses robot motion to predict the ground plane. Due to the immunity of robot motion to reflection, this scheme avoids failed ground detection caused by reflection. Given the detected ground, we design a ground-pixel parallax to describe the location of a pixel relative to the ground. Based on this, a unified appearance-geometry feature representation is proposed to describe objects inside rectangular boxes. Eventually, based on segmenting by detection framework, an appearance-geometry fusion regressor is designed to utilize the proposed feature to discover the obstacles. It also prevents our model from concentrating too much on parts of obstacles instead of whole obstacles. For evaluation, we introduce a new dataset for Obstacle on Reflective Ground (ORG), which comprises 15 scenes with various ground reflections, a total of more than 200 image sequences and 3400 RGB images. The pixel-wise annotations of ground and obstacle provide a comparison to our method and other methods. By reducing the misdetection of the reflection, the proposed approach outperforms others. The source code and the dataset will be available at https://github.com/XuefengBUPT/IndoorObstacleDiscovery-RG., Comment: International Journal of Computer Vision (IJCV) 2023. Project Page: https://xuefeng-cvr.github.io/IODRG