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The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 700 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi25., Comment: Part of the MaCVi 2025 workshop

Efficient decoding of quantum codes is crucial for achieving high-performance quantum error correction. In this paper, we introduce the concept of approximate degenerate decoding and integrate it with ordered statistics decoding (OSD). Previously, we proposed a reliability metric that leverages both hard and soft decisions from the output of belief propagation (BP), which is particularly useful for identifying highly reliable subsets of variables. Using the approach of reliable subset reduction, we reduce the effective problem size. Additionally, we identify a degeneracy condition that allows high-order OSD to be simplified to order-0 OSD. By integrating these techniques, we present an ADOSD algorithm that significantly improves OSD efficiency in the code capacity noise model. We demonstrate the effectiveness of our BP+ADOSD approach through extensive simulations on a varity of quantum codes, including generalized hypergraph-product codes, topological codes, lift-connected surface codes, and bivariate bicycle codes. The results indicate that the BP+ADOSD decoder outperforms existing methods, achieving higher error thresholds and enhanced performance at low error rates. Additionally, we validate the efficiency of our approach in terms of computational time, demonstrating that ADOSD requires, on average, the same amount of time as two to three BP iterations on surface codes at a depolarizing error rate of around $1\%$. All the proposed algorithms are compared using single-threaded CPU implementations., Comment: 16 pages, 10 figures, 6 tables

Functionalities in crystalline materials are determined by 3-dimensional collective interactions of atoms. The confinement of dimensionality in condensed matter provides an exotic research direction to understand the interaction of atoms, thus can be used to tailor or create new functionalities in material systems. In this study, a 2-dimensional transition metal oxide monolayer is constructed inside complex oxide heterostructures based on the theoretical predictions. The electrostatic boundary conditions of oxide monolayer in the heterostructure is carefully designed to tune the chemical, electronic, and magnetic states of oxide monolayer. The challenge of characterizing such an oxide monolayer is overcome by a combination of transmission electron microscopy, x-ray absorption spectroscopy, cross-sectional scanning tunneling microscopy, and electrical transport measurements. An intriguing metal-insulator transition associated with a magnetic transition is discovered in the MnO2 monolayer. This study paves a new route to understand the confinement of dimensionality and explore new intriguing phenomena in condensed matters., Comment: 23 pages, 6 figures

As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model's general decision logic, and inductive illusion, where the victim model's general decision logic is shaped by specific stimuli. The former exploits the model's decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviours. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalises and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios.

Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detecting LLM hallucinations. Recent approaches to document-level synthetic data generation involve iteratively removing sentences from documents and annotating factuality using LLM-based prompts. While effective, this method is computationally expensive for long documents and limited by the LLM's capabilities. In this work, we analyze the differences between existing synthetic training data used in state-of-the-art models and real LLM output claims. Based on our findings, we propose a novel approach for synthetic data generation, CG2C, that leverages multi-hop reasoning on context graphs extracted from documents. Our fact checker model, FactCG, demonstrates improved performance with more connected reasoning, using the same backbone models. Experiments show it even outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size., Comment: NAACL 2025

Play is pivotal in fostering the emotional, social, and cultural dimensions of urban spaces. While generative AI (GAI) potentially supports playful urban interaction, a balanced and critical approach to the design opportunities and challenges is needed. This work develops iWonder, an image-to-image GAI tool engaging fourteen designers in urban explorations to identify GAI's playful features and create design ideas. Fourteen citizens then evaluated these ideas, providing expectations and critical concerns from a bottom-up perspective. Our findings reveal the dynamic interplay between users, GAI, and urban contexts, highlighting GAI's potential to facilitate playful urban experiences through \textit{generative agency}, \textit{meaningful unpredictability}, \textit{social performativity}, and the associated offensive qualities. We propose design considerations to address citizen concerns and the `tourist metaphor' to deepen our understanding of GAI's impact, offering insights to enhance cities' socio-cultural fabric. Overall, this research contributes to the effort to harness GAI's capabilities for urban enrichment.

The occurrences of cyber attacks on the power grids have been increasing every year, with novel attack techniques emerging every year. In this paper, we address the critical challenge of detecting novel/zero-day attacks in digital substations that employ the IEC-61850 communication protocol. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC-61850 digital substations, generalization to novel or zero-day attacks remains challenging. We propose an approach that leverages the in-context learning (ICL) capability of the transformer architecture, the fundamental building block of large language models. The ICL approach enables the model to detect zero-day attacks and learn from a few examples of that attack without explicit retraining. Our experiments on the IEC-61850 dataset demonstrate that the proposed method achieves more than $85\%$ detection accuracy on zero-day attacks while the existing state-of-the-art baselines fail. This work paves the way for building more secure and resilient digital substations of the future.

One approach for multilingual data-to-text generation is to translate grammatical configurations upfront from the source language into each target language. These configurations are then used by a surface realizer and in document planning stages to generate output. In this paper, we describe a rule-based NLG implementation of this approach where the configuration is translated by Neural Machine Translation (NMT) combined with a one-time human review, and introduce a cross-language grammar dependency model to create a multilingual NLG system that generates text from the source data, scaling the generation phase without a human in the loop. Additionally, we introduce a method for human post-editing evaluation on the automatically translated text. Our evaluation on the SportSett:Basketball dataset shows that our NLG system performs well, underlining its grammatical correctness in translation tasks.

In this paper, we present a system that uses a Large Language Model (LLM) to perform grammar and spelling correction as a component of Quality Assurance (QA) for texts generated by NLG systems, which is important for text production in real-world scenarios. Evaluating the results of the system on work-in-progress sports news texts in three languages, we show that it is able to deliver acceptable corrections.

Quantum chemistry is among the most promising applications of quantum computing, offering the potential to solve complex electronic structure problems more efficiently than classical approaches. A critical component of any quantum algorithm is the measurement step, where the desired properties are extracted from a quantum computer. This review focuses on recent advancements in quantum measurement techniques tailored for quantum chemistry, particularly within the second quantized framework suitable for current and near-term quantum hardware. We provide a comprehensive overview of measurement strategies developed primarily for the Variational Quantum Eigensolver (VQE) and its derivatives. These strategies address the inherent challenges posed by complexity of the electronic Hamiltonian operator. Additionally, we examine methods for estimating excited states and one- and two-electron properties, extending the applicability of quantum algorithms to broader chemical phenomena. Key aspects of the review include approaches for constructing measurement operators with reduced classical preprocessing and quantum implementation costs, techniques to minimize the number of measurements required for a given accuracy, and error mitigation strategies that leverage symmetries and other properties of the measurement operators. Furthermore, we explore measurement schemes rooted in Quantum Phase Estimation (QPE), which are expected to become viable with the advent of fault-tolerant quantum computing. This review emphasizes foundational concepts and methodologies rather than numerical benchmarks, serving as a resource for researchers aiming to enhance the efficiency and accuracy of quantum measurements in quantum chemistry.

A critical requirement for deep learning models is ensuring their robustness against adversarial attacks. These attacks commonly introduce noticeable perturbations, compromising the visual fidelity of adversarial examples. Another key challenge is that while white-box algorithms can generate effective adversarial perturbations, they require access to the model gradients, limiting their practicality in many real-world scenarios. Existing attack mechanisms struggle to achieve similar efficacy without access to these gradients. In this paper, we introduce GreedyPixel, a novel pixel-wise greedy algorithm designed to generate high-quality adversarial examples using only query-based feedback from the target model. GreedyPixel improves computational efficiency in what is typically a brute-force process by perturbing individual pixels in sequence, guided by a pixel-wise priority map. This priority map is constructed by ranking gradients obtained from a surrogate model, providing a structured path for perturbation. Our results demonstrate that GreedyPixel achieves attack success rates comparable to white-box methods without the need for gradient information, and surpasses existing algorithms in black-box settings, offering higher success rates, reduced computational time, and imperceptible perturbations. These findings underscore the advantages of GreedyPixel in terms of attack efficacy, time efficiency, and visual quality.

This work investigates the effects of time-varying media, where optical properties change over time, on electromagnetic wave propagation, focusing on plane waves and free-electron evanescent waves. We introduce a switching parameter, $\tau$, to model ultrafast switching in the femtosecond to nanosecond range. For plane-wave incidence at angular frequency $\omega_0$, we derive a generalized expression for the backward-to-forward flux ratio as a function of $\omega_0$ and $\tau$, aligning with recent experimental data and providing a unified interpretation framework. For free-electron incidence, we observe intensity saturation in temporal transition radiation at $I_{\textrm{max}}$ for $\tau \leq \tau_{\textrm{0}}$, with both $I_{\textrm{max}}$ and $\tau_{\textrm{0}}$ depending on electron speed. These results highlight the importance of precise $\tau$ control in experiments to probe time-varying media effectively., Comment: 6 pages, 3 figures

Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder~(MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets., Comment: WACV2025, camera ready with updated reference

We present a novel approach for the construction of interior solutions for the Kerr metric, extending J. Ovalle's foundational work through ellipsoidal coordinate transformations. By deriving a physically plausible interior solution that smoothly matches the Kerr exterior metric, we analyze the energy conditions across various rotation parameters. Our findings reveal anisotropic fluid properties and energy condition behaviors in specific space-time regions, providing insights into the strong-field regime of rotating black holes. The proposed solution offers a more realistic description of rotating black hole interiors, with implications for understanding compact astrophysical objects., Comment: 17 pages

Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised DL models (ResNet50, ViT-base, SwinV2) in detecting ocular and systemic diseases. Methods: We fine-tuned/trained RETFound and three DL models on full datasets, 50%, 20%, and fixed sample sizes (400, 200, 100 images, with half comprising disease cases; for each DR severity class, 100 and 50 cases were used. Fine-tuned models were tested internally using the SEED (53,090 images) and APTOS-2019 (3,672 images) datasets and externally validated on population-based (BES, CIEMS, SP2, UKBB) and open-source datasets (ODIR-5k, PAPILA, GAMMA, IDRiD, MESSIDOR-2). Model performance was compared using area under the receiver operating characteristic curve (AUC) and Z-tests with Bonferroni correction (P<0.05/3). Interpretation: Traditional DL models are mostly comparable to RETFound for ocular disease detection with large datasets. However, RETFound is superior in systemic disease detection with smaller datasets. These findings offer valuable insights into the respective merits and limitation of traditional models and FMs.

Nanoscrolls, radial superlattices formed by rolling up a nanomembrane, exhibit distinct electronic and magneto-transport properties compared to their flat counterparts. In this study, we theoretically demonstrate that the conductance can be precisely enhanced N times by rolling up graphene into an N-turn nanoscroll and applying a longitudinal magnetic field. This tunable positive magnetoconductance stems from the topological winding number which is activated in a carbon nanoscroll with magnetic flux and its maximum value purely increases with the scroll winding number (the number of turns). By integrating material geometry and topology, our work opens the door to artificially creating, customizing, and designing topological materials in rolled-up graphene-like systems., Comment: 5 pages, 4 figures

We show that for a weak $\mathbb{Q}$-Fano threefold $X$ of Picard rank two ($\mathbb{Q}$-factorial with at worst terminal singularities), the anticanonical volume satisfies $-K_X^3\leq72$ except in one case, and the equality holds only if $X=\mathbb{P} (\mathcal{O}_{\mathbb{P}^2}\oplus\mathcal{O}_{\mathbb{P}^2}(3))$. The approach in this article can serve as a general strategy to establish the optimal upper bound of $-K_X^3$ for any canonical Fano threefolds, where the described main result serves as the first step., Comment: 18 pages. Comments are welcome

This paper concerns preservation of velocity and pressure equilibria in smooth, compressible, multicomponent flows in the inviscid limit. First, we derive the velocity-equilibrium and pressure-equilibrium conditions of a standard discontinuous Galerkin method that discretizes the conservative form of the compressible, multicomponent Euler equations. We show that under certain constraints on the numerical flux, the scheme is velocity-equilibrium-preserving. However, standard discontinuous Galerkin schemes are not pressure-equilibrium-preserving. Therefore, we introduce a discontinuous Galerkin method that discretizes the pressure-evolution equation in place of the total-energy conservation equation. Semidiscrete conservation of total energy, which would otherwise be lost, is restored via the correction terms of [Abgrall, J. Comput. Phys., 372, 2018, pp. 640-666] and [Abgrall et al., J. Comput. Phys., 453, 2022, 110955]. Since the addition of the correction terms prevents exact preservation of pressure and velocity equilibria, we propose modifications that then lead to a velocity-equilibrium-preserving, pressure-equilibrium-preserving, and (semidiscretely) energy-conservative discontinuous Galerkin scheme, although there are certain tradeoffs. Additional extensions are also introduced. We apply the developed scheme to smooth, interfacial flows involving mixtures of thermally perfect gases initially in pressure and velocity equilibria to demonstrate its performance in one, two, and three spatial dimensions.

Osseointegration is crucial to the success of biomedical implants. Additive manufacturing of implants offers a high degree of design freedom, enabling precise control over implant geometry and material composition. Bioactive glass (BG) can substantially enhance bone binding and bioactivity; however, limited research has been conducted on its incorporation into additively manufactured implants. The performance of BG varies depending on the incorporation method, and the spatial and temporal evolution of its integration remains unclear. In this study, we synthesized Ti-6Al-4V/58S BG composites by using the selective laser melting method and systematically compared the effects of BG coating and doping in additively manufactured implants. In vivo histological results from animal tests were statistically analyzed and discussed in terms of osseointegration over 4- and 12-week periods. Bone-to-implant contact (BIC) and bone density (BD) were used as quantitative metrics to evaluate interactions between the implants and surrounding bone. Our findings indicate that both BG-doped and BG-coated implants accelerated bone ingrowth during the early stages of healing. BG-coated implants demonstrated a greater improvement than did pure 3D-printed Ti-6Al-4V implants. However, the effects of BG became nonsignificant during the later healing stage (12 weeks). This study provides a foundation for systematically investigating BG incorporation methods in 3D-printed biomedical implants and their effect on osseointegration.

Stark Many-Body Localization (MBL) is a phenomenon observed in quantum systems in the absence of disorder, where the presence of a linear potential, known as the Stark field, causes the localization. Our study aims to provide novel insight into the properties of Stark MBL and to discover unique entanglement characteristics specific to this phenomenon. The phase diagram analysis reveals different behavior with varying interaction strengths. Furthermore, we highlight the influence of domain wall structures on the breakdown of the entanglement entropy of the system. Moreover, the investigation of Out-of-Time-Ordered Correlator (OTOC) behavior demonstrates distinct responses to interactions based on domain wall configurations. Our findings contribute to a better understanding of Stark MBL and offer valuable insights into the entanglement properties of systems subjected to Stark potentials., Comment: 5 pages, 5 figures

The generalized Brillouin zone (GBZ) has been highly successful in characterizing the topology and band structure of non-Hermitian systems. However, its applicability has been challenged in spatially inhomogeneous settings, where the non-locality of non-Hermitian pumping competes with Wannier-Stark localization and quantum interference, potentially leading to highly non-exponential state accumulation. To transcend this major conceptual bottleneck, we develop a general phase-space GBZ formalism that encodes non-Bloch deformations in both position and momentum space, such as to accurately represent spatially inhomogeneous non-Hermitian pumping. A key new phenomenon is the bifurcation of the phase-space GBZ branches, which allows certain eigenstates to jump abruptly between different GBZ solutions at various points in real space. The freedom in the locations of such jumps opens up an emergent degree of freedom that protects the stability of real spectra and, more impressively, the robustness of a new class of topological zero modes unique to GBZ bifurcation.The response from these novel spectral and GBZ singularities can be readily demonstrated in mature metamaterial platforms such as photonic crystals or circuit arrays, where effective real-space hoppings can be engineered in a versatile manner.Our framework directly generalizes to more complicated unit cells and further hoppings, opening up a vast new arena for exploring unconventional spectral and topological transitions as well as GBZ fragmentation in spatially inhomogeneous non-Hermitian settings.

Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. Although scaling Transformer-based generators has been central to recent advances, the tokenizer component itself is rarely scaled, leaving open questions about how auto-encoder design choices influence both its objective of reconstruction and downstream generative performance. Our work aims to conduct an exploration of scaling in auto-encoders to fill in this blank. To facilitate this exploration, we replace the typical convolutional backbone with an enhanced Vision Transformer architecture for Tokenization (ViTok). We train ViTok on large-scale image and video datasets far exceeding ImageNet-1K, removing data constraints on tokenizer scaling. We first study how scaling the auto-encoder bottleneck affects both reconstruction and generation -- and find that while it is highly correlated with reconstruction, its relationship with generation is more complex. We next explored the effect of separately scaling the auto-encoders' encoder and decoder on reconstruction and generation performance. Crucially, we find that scaling the encoder yields minimal gains for either reconstruction or generation, while scaling the decoder boosts reconstruction but the benefits for generation are mixed. Building on our exploration, we design ViTok as a lightweight auto-encoder that achieves competitive performance with state-of-the-art auto-encoders on ImageNet-1K and COCO reconstruction tasks (256p and 512p) while outperforming existing auto-encoders on 16-frame 128p video reconstruction for UCF-101, all with 2-5x fewer FLOPs. When integrated with Diffusion Transformers, ViTok demonstrates competitive performance on image generation for ImageNet-1K and sets new state-of-the-art benchmarks for class-conditional video generation on UCF-101., Comment: 28 pages, 25 figures, 7 Tables

Recently, diffusion-based blind super-resolution (SR) methods have shown great ability to generate high-resolution images with abundant high-frequency detail, but the detail is often achieved at the expense of fidelity. Meanwhile, another line of research focusing on rectifying the reverse process of diffusion models (i.e., diffusion guidance), has demonstrated the power to generate high-fidelity results for non-blind SR. However, these methods rely on known degradation kernels, making them difficult to apply to blind SR. To address these issues, we present DADiff in this paper. DADiff incorporates degradation-aware models into the diffusion guidance framework, eliminating the need to know degradation kernels. Additionally, we propose two novel techniques: input perturbation and guidance scalar, to further improve our performance. Extensive experimental results show that our proposed method has superior performance over state-of-the-art methods on blind SR benchmarks., Comment: To appear in WACV 2025. Code is available at: https://github.com/ryanlu2240/DADiff

Reliance on anonymity in social media has increased its popularity on these platforms among all ages. The availability of public Wi-Fi networks has facilitated a vast variety of online content, including social media applications. Although anonymity and ease of access can be a convenient means of communication for their users, it is difficult to manage and protect its vulnerable users against sexual predators. Using an automated identification system that can attribute predators to their text would make the solution more attainable. In this survey, we provide a review of the methods of pedophile attribution used in social media platforms. We examine the effect of the size of the suspect set and the length of the text on the task of attribution. Moreover, we review the most-used datasets, features, classification techniques and performance measures for attributing sexual predators. We found that few studies have proposed tools to mitigate the risk of online sexual predators, but none of them can provide suspect attribution. Finally, we list several open research problems., Comment: 17 pages, 3 figures

This study explores speaker-specific features encoded in speaker embeddings and intermediate layers of speech self-supervised learning (SSL) models. By utilising a probing method, we analyse features such as pitch, tempo, and energy across prominent speaker embedding models and speech SSL models, including HuBERT, WavLM, and Wav2vec 2.0. The results reveal that speaker embeddings like CAM++ excel in energy classification, while speech SSL models demonstrate superior performance across multiple features due to their hierarchical feature encoding. Intermediate layers effectively capture a mix of acoustic and para-linguistic information, with deeper layers refining these representations. This investigation provides insights into model design and highlights the potential of these representations for downstream applications, such as speaker verification and text-to-speech synthesis, while laying the groundwork for exploring additional features and advanced probing methods.

Steganography, the art of information hiding, has continually evolved across visual, auditory and linguistic domains, adapting to the ceaseless interplay between steganographic concealment and steganalytic revelation. This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control. Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory. The constraints of maximal robot integrity and minimal motion deviation are established as fundamental principles underlying secrecy. As a proof of concept, we conduct experiments in simulated environments across various manipulation tasks, incorporating robotic embodiments equipped with generalist multimodal policies.

Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they continue to face significant challenges in medical question answering, particularly in understanding domain-specific terminologies and performing complex reasoning. These limitations undermine their effectiveness in critical medical applications. To address these issues, we propose a novel approach incorporating similar case generation within a multi-agent medical question-answering (MedQA) system. Specifically, we leverage the Llama3.1:70B model, a state-of-the-art LLM, in a multi-agent architecture to enhance performance on the MedQA dataset using zero-shot learning. Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data. Experimental results show substantial performance gains over existing benchmark models, with improvements of 7% in both accuracy and F1-score across various medical QA tasks. Furthermore, we examine the model's interpretability and reliability in addressing complex medical queries. This research not only offers a robust solution for medical question answering but also establishes a foundation for broader applications of LLMs in the medical domain.

As part of the BISTRO survey, we present JCMT 850 $\mu$m polarimetric observations towards the Orion Integral-Shaped Filament (ISF) that covers three portions known as OMC-1, OMC-2, and OMC-3. The magnetic field threading the ISF seen in the JCMT POL-2 map appears as a tale of three: pinched for OMC-1, twisted for OMC-2, and nearly uniform for OMC-3. A multi-scale analysis shows that the magnetic field structure in OMC-3 is very consistent at all the scales, whereas the field structure in OMC-2 shows no correlation across different scales. In OMC-1, the field retains its mean orientation from large to small scales, but shows some deviations at small scales. Histograms of relative orientations between the magnetic field and filaments reveal a bimodal distribution for OMC-1, a relatively random distribution for OMC-2, and a distribution with a predominant peak at 90$^\circ$ for OMC-3. Furthermore, the magnetic fields in OMC-1 and OMC-3 both appear to be aligned perpendicular to the fibers, which are denser structures within the filament, but the field in OMC-2 is aligned along with the fibers. All these suggest that gravity, turbulence, and magnetic field are each playing a leading role in OMC-1, 2, and 3, respectively. While OMC-2 and 3 have almost the same gas mass, density, and non-thermal velocity dispersion, there are on average younger and fewer young stellar objects in OMC-3, providing evidence that a stronger magnetic field will induce slower and less efficient star formation in molecular clouds., Comment: published in the ApJ Letters

We introduce a family of high-rate amplitude-damping (AD) Shor Codes, designed to effectively correct AD errors while maintaining immunity to collective coherent (CC) errors. The proposed $[[(w+1)(w+K), K]]$ AD codes can approximately correct up to $w$ AD errors, with flexible parameters $(w, K)$, and we provide a rigorous proof that these codes satisfy the approximate quantum error correction conditions. These AD Shor codes employ structured stabilizer measurement schemes, allowing efficient detection of AD errors using local operations and ancillary qubits. By concatenating these codes with the dual-rail code, we construct a class of CC-AD Shor codes that inherit the advantageous properties of the AD Shor codes., Comment: 4 pages, 2 figures, 2 tables

Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions -- two representative soft matter systems -- we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems., Comment: 9 pages, 8 figures

License plate detection (LPD) is essential for traffic management, vehicle tracking, and law enforcement but faces challenges like variable lighting and diverse font types, impacting accuracy. Traditionally reliant on image processing and machine learning, the field is now shifting towards deep learning for its robust performance in various conditions. Current methods, however, often require tailoring to specific regional datasets. This paper proposes a dual deep learning strategy using a Faster R-CNN for detection and a CNN-RNN model with Connectionist Temporal Classification (CTC) loss and a MobileNet V3 backbone for recognition. This approach aims to improve model performance using datasets from Ontario, Quebec, California, and New York State, achieving a recall rate of 92% on the Centre for Pattern Recognition and Machine Intelligence (CENPARMI) dataset and 90% on the UFPR-ALPR dataset. It includes a detailed error analysis to identify the causes of false positives. Additionally, the research examines the role of font features in license plate (LP) recognition, analyzing fonts like Driver Gothic, Dreadnought, California Clarendon, and Zurich Extra Condensed with the OpenALPR system. It discovers significant performance discrepancies influenced by font characteristics, offering insights for future LPD system enhancements. Keywords: Deep Learning, License Plate, Font Evaluation, Comment: 12 pages, 5 figures. This is the pre-Springer final accepted version. The final version is published in Springer, Lecture Notes in Computer Science (LNCS), Volume 14731, 2024. Springer Version of Record

The research on supercurrent diodes has surged rapidly due to their potential applications in electronic circuits at cryogenic temperatures. To unlock this functionality, it is essential to find supercurrent diodes that can work consistently at zero magnetic field and under ubiquitous stray fields generated in electronic circuits. However, a supercurrent diode with robust field tolerance is currently lacking. Here, we demonstrate a field-resilient supercurrent diode by incorporating a multiferroic material into a Josephson junction. We first observed a pronounced supercurrent diode effect at zero magnetic field. More importantly, the supercurrent rectification persists over a wide and bipolar magnetic field range beyond industrial standards for field tolerance. By theoretically modeling a multiferroic Josephson junction, we unveil that the interplay between spin-orbit coupling and multiferroicity underlies the unusual field resilience of the observed diode effect. This work introduces multiferroic Josephson junctions as a new field-resilient superconducting device for cryogenic electronics., Comment: Preprint, 33 pages, 4 main figures, 10 extended data figures

Cs\'{a}ji, Jungers, and Blondel prove that while a PageRank optimization problem with edge selection constraints is NP-hard, it can be solved optimally in polynomial time for the unconstrained case. This theoretical result is accompanied by several observations, which we leverage to develop valid inequalities in polynomial time for this class of NP-hard problems.

We propose a nested quantum dot structure for improved control of entanglement induced by the Heisenberg exchange between an electron and a qubit with relative motion. The entanglement is quantified by the mutual information (MI). The electron, initially prepared in the ground state, generally produces greater entanglement when excited to the scattering state compared to remaining in the bound state. In the bound state, the final entanglement oscillates as a function of the qubit speed and can be tuned accordingly. In the case of long-range interaction, the normalized exchange distribution leads to substantial final entanglement, independent of the qubit moving direction, indicating that even very weak but prolonged exchange can still generate significant entanglement. In the case of short-range interaction, different moving directions lead to varying MI values. We also consider the scenario without the nested dot and find that the same maximum (among all times) MI is pre-determined solely by the initial angle between the spins. In this case, the entanglement exhibits different growth characteristics during different phases. The saturation of the MI mimics that of a strict zero-dimensional quantum dot, where exchange and time are combined into a single parameter, the amount of interaction., Comment: 7 pages, 3 figures

We consider the thermalization hypothesis of pure states in quantum Ising chain with $Z_2$ symmetry, XXZ chain with $U(1)$ symmetry, and XXX chain with $SU(2)$ symmetries. Two kinds of pure states are considered: the energy eigenstates and the typical states evolved unitarily from the random product states for a long enough period. We further group the typical states by their expectation values of the conserved charges and consider the fine-grained thermalization hypothesis. We compare the locally (subsystem) reduced states of typical states/eigenstates with the ones of the corresponding thermal ensemble states. Besides the usual thermal ensembles such as the (micro-)canonical ensemble without conserved charges and the generalized Gibbs ensemble (GGE) with all conserved charges included, we also consider the so-called partial-GGEs (p-GGEs), which include only part of the conserved charges in the thermal ensemble. Moreover, in the framework of p-GGE, the Hamiltonian and other conserved charges are on an equal footing. The introduction of p-GGEs extends quantum thermalization to a more general scope. The validity of the subsystem thermalization hypothesis can be quantified by the smallness of the relative entropy of the reduced states obtained from the GGE/p-GGE and the typical states/eigenstates. We examine the validity of the thermalization hypothesis by numerically studying the relative entropy demographics. We show that the thermalization hypothesis holds generically for the small enough subsystems for various p-GGEs. Thus, our framework extends the universality of quantum thermalization.

The problem of subliminal communication has been addressed in various forms of steganography, primarily relying on visual, auditory and linguistic media. However, the field faces a fundamental paradox: as the art of concealment advances, so too does the science of revelation, leading to an ongoing evolutionary interplay. This study seeks to extend the boundaries of what is considered a viable steganographic medium. We explore a steganographic paradigm, where hidden information is communicated through the episodes of multiple agents interacting with an environment. Each agent, acting as an encoder, learns a policy to disguise the very existence of hidden messages within actions seemingly directed toward innocent objectives. Meanwhile, an observer, serving as a decoder, learns to associate behavioural patterns with their respective agents despite their dynamic nature, thereby unveiling the hidden messages. The interactions of agents are governed by the framework of multi-agent reinforcement learning and shaped by feedback from the observer. This framework encapsulates a game-theoretic dilemma, wherein agents face decisions between cooperating to create distinguishable behavioural patterns or defecting to pursue individually optimal yet potentially overlapping episodic actions. As a proof of concept, we exemplify action steganography through the game of labyrinth, a navigation task where subliminal communication is concealed within the act of steering toward a destination. The stego-system has been systematically validated through experimental evaluations, assessing its distortion and capacity alongside its secrecy and robustness when subjected to simulated passive and active adversaries.

Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical catalogs from Hubble Space Telescope with new radio observations from the Karl G. Jansky Very Large Array to search for any evidence of accreting black holes in young massive clusters in the nearby galaxy M51. We find that of 43 bright ($L_X > 10^{38}$ erg/s) X-ray point sources in M51, 24 had probable matches to objects including possible associated star clusters in the HST Legacy Extragalactic UV Survey catalog, seven of which were classified as contaminants (background galaxies or foreground stars). We explore the optical properties of the remaining 17 sources, including cluster age and mass estimates, and search for radio counterparts in the 8-12 GHz band. The lack of radio counterparts to X-ray sources we know to be associated with young massive clusters in M51 suggests that we do not significantly detect hard-state IMBHs ~ 10^4 Msun or above. However, more sensitive radio facilities like the Square Kilometre Array and next generation Very Large Array may be able to provide evidence for IMBHs with masses down to ~ 10^3 Msun., Comment: 18 pages, 7 figures, accepted to ApJ

Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle.

Second-order topological superconductors host Majorana corner modes (MCMs), which are confined to specific corners of the system. This spatial restriction presents challenges for manipulating and relocating MCMs. We propose a novel protocol for dynamically controlling the movement of time-reversal symmetric MCMs in a heterostructure consisting of a 2D Weyl semimetal and a $d$-wave superconductor. By leveraging the energy asymmetry of topological edge states in the 2D Weyl semimetal, the position of MCMs can be effectively tuned via chemical potential adjustments. We further introduce a device architecture that integrates multiple heterostructure blocks, each controlled by independent gate voltages, to enable the adiabatic movement and exchange of MCMs. This approach demonstrates a robust mechanism for Majorana manipulation and provides a scalable framework for future experimental studies of topological quantum computation., Comment: 5 figures

Large Language Models (LLMs) have demonstrated significant capabilities in machine translation. However, their translation quality is sometimes questioned, as the generated outputs may deviate from expressions typically used by native speakers. These deviations often arise from differences in sentence structure between language systems. To address this issue, we propose ParaAlign Translator, a method that fine-tunes LLMs to paraphrase sentences, aligning their structures with those of the target language systems. This approach improves the performance of subsequent translations. Experimental results demonstrate that the proposed method enhances the LLaMA-3-8B model's performance in both resource-rich and low-resource scenarios and achieves parity with or surpassing the much larger LLaMA-3-70B model.

Patterned nanomagnet arrays (PNAs) have been shown to exhibit a strong geometrically frustrated dipole interaction. Some PNAs have also shown emergent domain wall dynamics. Previous works have demonstrated methods to physically probe these magnetization dynamics of PNAs to realize neuromorphic reservoir systems that exhibit chaotic dynamical behavior and high-dimensional nonlinearity. These PNA reservoir systems from prior works leverage echo state properties and linear/nonlinear short-term memory of component reservoir nodes to map and preserve the dynamical information of the input time-series data into nondelay spatial embeddings. Such mappings enable these PNA reservoir systems to imitate and predict/forecast the input time series data. However, these prior PNA reservoir systems are based solely on the nondelay spatial embeddings obtained at component reservoir nodes. As a result, they require a massive number of component reservoir nodes, or a very large spatial embedding (i.e., high-dimensional spatial embedding) per reservoir node, or both, to achieve acceptable imitation and prediction accuracy. These requirements reduce the practical feasibility of such PNA reservoir systems. To address this shortcoming, we present a mixed delay/nondelay embeddings-based PNA reservoir system. Our system uses a single PNA reservoir node with the ability to obtain a mixture of delay/nondelay embeddings of the dynamical information of the time-series data applied at the input of a single PNA reservoir node. Our analysis shows that when these mixed delay/nondelay embeddings are used to train a perceptron at the output layer, our reservoir system outperforms existing PNA-based reservoir systems for the imitation of NARMA 2, NARMA 5, NARMA 7, and NARMA 10 time series data, and for the short-term and long-term prediction of the Mackey Glass time series data.