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We generalize previous diameter estimates and local non-vanishing of volumes for Kaehler metrics to the case of big cohomology classes. In our proof, among other things, we will prove a uniform diameter estimate for a family of smooth Kaehler metrics only involving an integrability condition. We also have to use fine stability properties of complex Monge-Ampere equations with prescribed singularities., Comment: 28 pages, minor changes, comments welcome

This research exploits the applications of reconfigurable intelligent surface (RIS)-assisted multiple input multiple output (MIMO) systems, specifically addressing the enhancement of communication reliability with modulated signals. Specifically, we first derive the analytical downlink symbol error rate (SER) of each user as a multivariate function of both the phase-shift and beamforming vectors. The analytical SER enables us to obtain insights into the synergistic dynamics between the RIS and MIMO communication. We then introduce a novel average SER minimization problem subject to the practical constraints of the transmitted power budget and phase shift coefficients, which is NP-hard. By incorporating the differential evolution (DE) algorithm as a pivotal tool for optimizing the intricate active and passive beamforming variables in RIS-assisted communication systems, the non-convexity of the considered SER optimization problem can be effectively handled. Furthermore, an efficient local search is incorporated into the DE algorithm to overcome the local optimum, and hence offer low SER and high communication reliability. Monte Carlo simulations validate the analytical results and the proposed optimization framework, indicating that the joint active and passive beamforming design is superior to the other benchmarks., Comment: 16 pages, 13 figures, and 1 table . Accepted by TWC

Topological spin textures in magnetic materials beyond two-dimensional skyrmions have attracted attention for electronics beyond CMOS technologies. In particular, three-dimensional (3D) topological spin textures are promising due to the expected complex non-linear dynamics as well as high static and dynamic thermal stability. In multilayer heterostructures, a hybrid chiral skyrmion tube is a well-known example of a 3D topological spin texture, exhibiting an intriguing chirality transition along the thickness direction. This transition progresses from left-handed to right-handed N\'eel-type chirality, passing through a Bloch-type intermediate state. Such an exotic spin configuration potentially exhibits distinctly different dynamics from that of the common skyrmion tube that exhibits a homogeneous chirality; yet these dynamics have not been ascertained so far. Here we reveal the distinct features of current-induced dynamics that result from the hybrid chiral skyrmion tube structure in synthetic antiferromagnetic (SyAFM) multilayers. Strikingly, the SyAFM hybrid chiral skyrmion tubes exhibit a non-reciprocal skyrmion Hall effect in the flow regime. The non-reciprocity can even be tuned by the degree of magnetic compensation in the SyAFM systems. Our theoretical modeling qualitatively corroborates that the non-reciprocity stems from the dynamic oscillation of skyrmion helicity during its current-induced motion. The findings highlight the critical role of the internal degrees of freedom of these complex skyrmion tubes for their current-induced dynamics.

We study how the properties of being reduced, integral domain, and normal, behave under small perturbations of the defining equations of a noetherian local ring. It is not hard to show that the property of being a local integral domain (reduced, normal ring) is not stable under small perturbations in general. We prove that perturbation stability holds in the following situations: (1) perturbation of being an integral domain for factorial excellent Henselian local rings; (2) perturbation of normality for excellent local complete intersections containing a field of characteristic zero; and (3) perturbation of reducedness for excellent local complete intersections containing a field of characteristic zero, and for factorial Nagata local rings., Comment: 15 pages, comments are very well-come

Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a few demonstrations. However, one critical challenge is how to select demonstrations to elicit the full potential of LLMs. In this paper, we propose Curriculum Demonstration Selection (CDS), a novel demonstration selection method for ICL. Instead of merely using similarity, CDS additionally partitions samples by their complexity measurements. Following curriculum learning, CDS then selects demonstrations from easy to difficult. Thus the selected demonstrations cover a wide range of difficulty levels, enabling LLMs to learn from varied complexities within the training set. Experiments demonstrate that our CDS consistently outperforms baseline methods, achieving notable improvements across nine LLMs on three benchmarks. Moreover, CDS proves especially effective in enhancing LLM performance in solving challenging problems., Comment: Accepted at the 40th ACM/SIGAPP Symposium On Applied Computing (SAC 2025), Main Conference

This paper presents a preliminary study of an efficient object tracking approach, comparing the performance of two different 3D point cloud sensory sources: LiDAR and stereo cameras, which have significant price differences. In this preliminary work, we focus on single object tracking. We first developed a fast heuristic object detector that utilizes prior information about the environment and target. The resulting target points are subsequently fed into an extended object tracking framework, where the target shape is parameterized using a star-convex hypersurface model. Experimental results show that our object tracking method using a stereo camera achieves performance similar to that of a LiDAR sensor, with a cost difference of more than tenfold.

We propose SharpDepth, a novel approach to monocular metric depth estimation that combines the metric accuracy of discriminative depth estimation methods (e.g., Metric3D, UniDepth) with the fine-grained boundary sharpness typically achieved by generative methods (e.g., Marigold, Lotus). Traditional discriminative models trained on real-world data with sparse ground-truth depth can accurately predict metric depth but often produce over-smoothed or low-detail depth maps. Generative models, in contrast, are trained on synthetic data with dense ground truth, generating depth maps with sharp boundaries yet only providing relative depth with low accuracy. Our approach bridges these limitations by integrating metric accuracy with detailed boundary preservation, resulting in depth predictions that are both metrically precise and visually sharp. Our extensive zero-shot evaluations on standard depth estimation benchmarks confirm SharpDepth effectiveness, showing its ability to achieve both high depth accuracy and detailed representation, making it well-suited for applications requiring high-quality depth perception across diverse, real-world environments., Comment: Uncompressed version can be found in https://drive.google.com/file/d/1MG4-d_xDERVBCRfLDolNLnMLLuqd7qRz

In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of constituency and dependency tree pairs that did not adhere to simplified HPSG rules. To attempt to address the issue of the corpora not adhering to simplified HPSG rules, we randomly permuted samples from the training and development sets to make them compliant with simplified HPSG. We then modified the first simplified HPSG Neural Parser for the Penn Treebank by replacing it with the PhoBERT or XLM-RoBERTa models, which can encode Vietnamese texts. We conducted experiments on our modified VietTreebank and VnDT corpora. Our extensive experiments showed that the simplified HPSG Neural Parser achieved a new state-of-the-art F-score of 82% for constituency parsing when using the same predicted part-of-speech (POS) tags as the self-attentive constituency parser. Additionally, it outperformed previous studies in dependency parsing with a higher Unlabeled Attachment Score (UAS). However, our parser obtained lower Labeled Attachment Score (LAS) scores likely due to our focus on arc permutation without changing the original labels, as we did not consult with a linguistic expert. Lastly, the research findings of this paper suggest that simplified HPSG should be given more attention to linguistic expert when developing treebanks for Vietnamese natural language processing., Comment: Accepted at SoICT 2024

Reconfiguration problems involve determining whether two given configurations can be transformed into each other under specific rules. The Token Sliding problem asks whether, given two different set of tokens on vertices of a graph $G$, we can transform one into the other by sliding tokens step-by-step along edges of $G$ such that each resulting set of tokens forms an independent set in $G$. Recently, Ito et al. [MFCS 2022] introduced a directed variant of this problem. They showed that for general oriented graphs (i.e., graphs where no pair of vertices can have directed edges in both directions), the problem remains $\mathsf{PSPACE}$-complete, and is solvable in polynomial time on oriented trees. In this paper, we further investigate the Token Sliding problem on various oriented graph classes. We show that the problem remains $\mathsf{PSPACE}$-complete for oriented planar graphs, split graphs, bipartite graphs and bounded treewidth graphs. Additionally, we present polynomial-time algorithms for solving the problem on oriented cycles and cographs.

We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey (NGVS) and Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey programs, which both used MegaCam on the Canada-France-Hawaii Telescope. We describe the procedure used to select GC candidates and fit the spatial distributions of GCs to a two-dimensional S\'ersic function, which provides effective radii (half number radii) and S\'ersic indices, and estimate background contamination by adding a constant term to the S'ersic function. In cases where a neighboring galaxy affects the estimation of the GC spatial distribution in the target galaxy, we fit two 2D S\'ersic functions, simultaneously. We also investigate the color distributions of GCs in our sample by using Gaussian Mixture Modeling. For GC systems with bimodal color distributions, we divide the GCs into blue and red subgroups and fit their respective spatial distributions with S\'ersic functions. Finally, we measure the total number of GCs based on our fitted S\'ersic function, and calculate the GC specific frequency., Comment: 31 pages, 10 Figures (115 additional figures are available in the ApJS online Journal), 2 Tables, accepted for publication in ApJS

We investigate the synthesis of 340-nm-thick Sc$_x$Al$_{1-x}$N layers with $0 \leq x \leq 0.35$ on AlN-buffered Si(111) by plasma-assisted molecular beam epitaxy. We employ an AlN nucleation layer under conditions giving rise to single-domain N-polar [(000$\bar{1}$)-oriented] layers, as demonstrated by the ($3 \times 3$) pattern observed in reflection high-energy electron diffraction and confirmed by KOH etching. The subsequent growth of pure wurtzite Sc$_x$Al$_{1-x}$N layers with $x \leq 0.1$ is feasible at temperatures $\leq$ 740{\deg}C. However, layers with $x \geq 0.2$ grown at 740{\deg}C develop cracks due the high thermal mismatch between Sc$_x$Al$_{1-x}$N and Si. Lowering the growth temperature to 500{\deg}C not only prevents cracking but also improves the crystallinity of the layers. For Sc$_{0.3}$Al$_{0.7}$N layers grown at 500{\deg}C, additional x-ray reflections due to intermetallic AlSc and Al$_3$Sc inclusions are observed. The formation of these compounds can be inhibited by lowering the temperature further to 300{\deg}C.

We present the large-scale distribution and kinematics of cold molecular gas across the compact galaxy group Stephan's Quintet, based on CO(2-1) observations performed with the Atacama Compact Array (ACA) and CO(1-0) data from the Combined Array for Research in Millimeter-wave Astronomy (CARMA). We find coherent structures of molecular gas associated with the galaxies and intra-group medium, which follow the distribution of warm H$_{2}$ previously seen with the James Webb Space Telescope (JWST). CO is associated with a ridge of shocked gas that crosses the galaxy group, and with a spiral arm of the intruding galaxy NGC7318b, which interacts with the intra-group medium along the ridge. Although the ridge contains widespread shocks, turbulent gas, and warm H$_{2}$, the CO lines are narrower than elsewhere in Stephan's Quintet (FWHM~25-65 km/s), indicative of settled cold gas. At a distinctly different velocity, CO is found in the active galaxy NGC7319 and Northern star-forming region SQ-A. A bridge of turbulent molecular gas connects NGC7319 with the ridge, covering a gap of ~700 km/s between these structures. The gas excitation ranges from $L'_{\rm CO(2-1)}$/$L'_{\rm CO(1-0)}$ ~ 0.3 in the bridge and SQ-A, to ~0.5 along the ridge, to near unity in the center of NGC7319. We also detect either a molecular outflow or turbulent molecular gas associated with the radio source in NGC7319. These ACA data are part of a program with the Atacama Large Millimeter/submillimeter Array (ALMA) and JWST to study molecular gas physics from the largest to the smallest scales across the intra-group medium of Stephan's Quintet., Comment: Accepted for publication in ApJ

Naturalistic driving action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a recognition model followed by probability-based post-processing. Nevertheless, the probabilities provided by the recognition model frequently contain confused information causing challenge for post-processing. In this work, we adopt an action recognition model based on self-supervise learning to detect distracted activities and give potential action probabilities. Subsequently, a constraint ensemble strategy takes advantages of multi-camera views to provide robust predictions. Finally, we introduce a conditional post-processing operation to locate distracted behaviours and action temporal boundaries precisely. Experimenting on test set A2, our method obtains the sixth position on the public leaderboard of track 3 of the 2024 AI City Challenge., Comment: Computer Vision and Pattern Recognition Workshop 2024

Malicious attacks on open source software packages are a growing concern. This concern morphed into a panic-inducing crisis after the revelation of the XZ Utils backdoor, which would have provided the attacker with, according to one observer, a "skeleton key" to the internet. This study therefore explores the challenges of preventing and detecting malware in Linux distribution package repositories. To do so, we ask two research questions: (1) What measures have Linux distributions implemented to counter malware, and how have maintainers experienced these efforts? (2) How effective are current malware detection tools at identifying malicious Linux packages? To answer these questions, we conduct interviews with maintainers at several major Linux distributions and introduce a Linux package malware benchmark dataset. Using this dataset, we evaluate the performance of six open source malware detection scanners. Distribution maintainers, according to the interviews, have mostly focused on reproducible builds to date. Our interviews identified only a single Linux distribution, Wolfi OS, that performs active malware scanning. Using this new benchmark dataset, the evaluation found that the performance of existing open-source malware scanners is underwhelming. Most studied tools excel at producing false positives but only infrequently detect true malware. Those that avoid high false positive rates often do so at the expense of a satisfactory true positive. Our findings provide insights into Linux distribution package repositories' current practices for malware detection and demonstrate the current inadequacy of open-source tools designed to detect malicious Linux packages., Comment: 14 pages, 3 figures, 11 tables

We report the discovery of a rare isolated group of five dwarf galaxies located at z = 0.0086 ($D$ = 36 Mpc). All member galaxies are star-forming, blue, and gas-rich with $g-r$ indices ranging from 0.2 to 0.6 mag, and two of them show signs of ongoing mutual interaction. The most massive member of the group has a stellar mass that is half of the Small Magellanic Cloud stellar mass, and the median stellar mass of the group members is 7.87 $\times$ 10$^{7}$ M$_{\odot}$. The derived total dynamical mass of the group is $M_{\rm dyn}$ = 6.02$\times$10$^{10}$ M$_{\odot}$, whereas its total baryonic mass (stellar + HI) is 2.6$\times$10$^{9}$ M$_{\odot}$, which gives us the dynamical to baryonic mass ratio of 23. Interestingly, all galaxies found in the group are aligned along a straight line in the plane of the sky. The observed spatial extent of the member galaxies is 154 kpc, and their relative line-of-sight velocity span is within 75 km s$^{-1}$. Using the spatially resolved optical spectra provided by DESI EDR, we find that three group members share a common rotational direction. With these unique properties of the group and its member galaxies, we discuss the possible importance of such a system in the formation and evolution of dwarf galaxy groups and in testing the theory of large-scale structure formation., Comment: Accepted for publication in ApJL

A recent study of the distribution of dwarf galaxies in the MATLAS sample in galaxy groups revealed an excess of flattened satellite structures, reminiscent of the co-rotating planes of dwarf galaxies discovered in the local Universe. If confirmed, this lends credence to the plane-of-satellite problem and further challenges the standard model of hierarchical structure formation. However, with only photometric data and no confirmation of the satellite membership, the study could not address the plane-of-satellite problem in full detail. Here we present spectroscopic follow-up observations of one of the most promising planes-of-satellites candidates in the MATLAS survey, the satellite system of NGC 474. Employing MUSE at the VLT and full spectrum fitting, we studied 13 dwarf galaxy candidates and confirmed nine to be members of the field around NGC 474. Measuring the stellar populations of all observed galaxies, we find that the MATLAS dwarfs have lower metallicities than the Local Group dwarfs at given luminosity. Two dwarf galaxies may form a pair of satellites based on their close projection and common velocity. Within the virial radius, we do not find a significant plane-of-satellites, however, there is a sub-population of six dwarf galaxies which seem to be anti-correlated in phase-space. Due to the low number of dwarf galaxies, this signal may arise by chance. With over 2000 dwarf galaxy candidates found in the MATLAS survey, this remains an intriguing data set to study the plane-of-satellites problem in a statistical fashion once more follow-up observations have been conducted., Comment: 9 pages, 8 figures, 2 tables. Accepted for publication in Astronomy & Astrophysics (A&A)

DNSSEC, a DNS security extension, is essential to accurately translating domain names to IP addresses. Digital signatures provide the foundation for this reliable translation, however, the evolution of 'Quantum Computers' has made traditional digital signatures vulnerable. In light of this, NIST has recently selected potential post-quantum digital signatures that can operate on conventional computers and resist attacks made with Quantum Computers. Since these post-quantum digital signatures are still in their early stages of development, replacing pre-quantum digital signature schemes in DNSSEC with post-quantum candidates is risky until the post-quantum candidates have undergone a thorough security analysis. Given this, herein, we investigate the viability of employing 'Double-Signatures' in DNSSEC, combining a post-quantum digital signature and a classic one. The rationale is that double-signatures will offer protection against quantum threats on conventional signature schemes as well as unknown non-quantum attacks on post-quantum signature schemes, hence even if one fails the other provides security guarantees. However, the inclusion of two signatures in the DNSSEC response message doesn't bode well with the maximum allowed size of DNSSEC responses (i.e., 1232B, a limitation enforced by MTU of physical links). To counter this issue, we leverage a way to do application-layer fragmentation of DNSSEC responses with two signatures. We implement our solution on top of OQS-BIND and through experiments show that the addition of two signatures in DNSSEC and application-layer fragmentation of all relevant resource records and their reassembly does not have any substantial impact on the efficiency of the resolution process and thus is suitable for the interim period at least until the quantum computers are fully realized.

In magnetic systems, angular momentum is carried by the spin and orbital degrees of freedom. Non-local devices can be used to study angular momentum transport. They consist of parallel heavy-metal nanowires placed on top of magnetic insulators like yttrium iron garnet (YIG), facilitating the transmission of information by magnons, generated by the accumulation of spin at the interface, created via the Spin Hall Effect (SHE) and detected via the inverse SHE (iSHE). It has been demonstrated that these processes have comparable efficiencies when the role of the detector and injector is reversed, which points to reciprocity of the processes. However, we show that by adding Ru as a source of direct and inverse orbital Hall effect (OHE), the system no longer exhibits this reciprocity. Specifically, the generation of magnons via the combination of SHE and OHE and detection via the iSHE is found to be about 35% more efficient than the inverse process for our system.

The accurate alignment of 3D woodblock geometrical models with 2D orthographic projection images presents a significant challenge in the digital preservation of Vietnamese cultural heritage. This paper proposes a unified image processing algorithm to address this issue, enhancing the registration quality between 3D woodblock models and their 2D representations. The method includes determining the plane of the 3D character model, establishing a transformation matrix to align this plane with the 2D printed image plane, and creating a parallel-projected depth map for precise alignment. This process minimizes disocclusions and ensures that character shapes and strokes are correctly positioned. Experimental results highlight the importance of structure-based comparisons to optimize alignment for large-scale Han-Nom character datasets. The proposed approach, combining density-based and structure-based methods, demonstrates improved registration performance, offering an effective normalization scheme for digital heritage preservation.

Localization and navigation are two crucial issues for mobile robots. In this paper, we propose an approach for localization and navigation systems for a differential-drive robot based on monocular SLAM. The system is implemented on the Robot Operating System (ROS). The hardware includes a differential-drive robot with an embedded computing platform (Jetson Xavier AGX), a 2D camera, and a LiDAR sensor for collecting external environmental information. The A* algorithm and Dynamic Window Approach (DWA) are used for path planning based on a 2D grid map. The ORB_SLAM3 algorithm is utilized to extract environmental features, providing the robot's pose for the localization and navigation processes. Finally, the system is tested in the Gazebo simulation environment and visualized through Rviz, demonstrating the efficiency and potential of the system for indoor localization and navigation of mobile robots., Comment: In The 25th National Conference on Electronics, Communications and Information Technology (REV-ECIT 2022), Hanoi, Vietnam. in Vietnamese language

In the context of modern life, particularly in Industry 4.0 within the online space, emotions and moods are frequently conveyed through social media posts. The trend of sharing stories, thoughts, and feelings on these platforms generates a vast and promising data source for Big Data. This creates both a challenge and an opportunity for research in applying technology to develop more automated and accurate methods for detecting stress in social media users. In this study, we developed a real-time system for stress detection in online posts, using the "Dreaddit: A Reddit Dataset for Stress Analysis in Social Media," which comprises 187,444 posts across five different Reddit domains. Each domain contains texts with both stressful and non-stressful content, showcasing various expressions of stress. A labeled dataset of 3,553 lines was created for training. Apache Kafka, PySpark, and AirFlow were utilized to build and deploy the model. Logistic Regression yielded the best results for new streaming data, achieving 69,39% for measuring accuracy and 68,97 for measuring F1-scores., Comment: 6 pages, 4 figures

Training robot policies in simulation is becoming increasingly popular; nevertheless, a precise, reliable, and easy-to-use tactile simulator for contact-rich manipulation tasks is still missing. To close this gap, we develop TacEx -- a modular tactile simulation framework. We embed a state-of-the-art soft-body simulator for contacts named GIPC and vision-based tactile simulators Taxim and FOTS into Isaac Sim to achieve robust and plausible simulation of the visuotactile sensor GelSight Mini. We implement several Isaac Lab environments for Reinforcement Learning (RL) leveraging our TacEx simulation, including object pushing, lifting, and pole balancing. We validate that the simulation is stable and that the high-dimensional observations, such as the gel deformation and the RGB images from the GelSight camera, can be used for training. The code, videos, and additional results will be released online https://sites.google.com/view/tacex., Comment: 11 pages, accepted at "CoRL Workshop on Learning Robot Fine and Dexterous Manipulation: Perception and Control"

This paper concerns the numerical approximation for the invariant distribution of Markovian switching L\'evy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear growth drift and diffusion coefficients.

In this paper, we present a new space-time Petrov-Galerkin-like method. This method utilizes a mixed formulation of Tensor Train (TT) and Quantized Tensor Train (QTT), designed for the spectral element discretization (Q1-SEM) of the time-dependent convection-diffusion-reaction (CDR) equation. We reformulate the assembly process of the spectral element discretized CDR to enhance its compatibility with tensor operations and introduce a low-rank tensor structure for the spectral element operators. Recognizing the banded structure inherent in the spectral element framework's discrete operators, we further exploit the QTT format of the CDR to achieve greater speed and compression. Additionally, we present a comprehensive approach for integrating variable coefficients of CDR into the global discrete operators within the TT/QTT framework. The effectiveness of the proposed method, in terms of memory efficiency and computational complexity, is demonstrated through a series of numerical experiments, including a semi-linear example.

Warning: this paper contains content that may be offensive or upsetting Hate speech moderation on global platforms poses unique challenges due to the multimodal and multilingual nature of content, along with the varying cultural perceptions. How well do current vision-language models (VLMs) navigate these nuances? To investigate this, we create the first multimodal and multilingual parallel hate speech dataset, annotated by a multicultural set of annotators, called Multi3Hate. It contains 300 parallel meme samples across 5 languages: English, German, Spanish, Hindi, and Mandarin. We demonstrate that cultural background significantly affects multimodal hate speech annotation in our dataset. The average pairwise agreement among countries is just 74%, significantly lower than that of randomly selected annotator groups. Our qualitative analysis indicates that the lowest pairwise label agreement-only 67% between the USA and India-can be attributed to cultural factors. We then conduct experiments with 5 large VLMs in a zero-shot setting, finding that these models align more closely with annotations from the US than with those from other cultures, even when the memes and prompts are presented in the dominant language of the other culture. Code and dataset are available at https://github.com/MinhDucBui/Multi3Hate., Comment: Preprint

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model's ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR., Comment: ACCV 2024 accepted

We propose a tamed-adaptive Milstein scheme for stochastic differential equations in which the first-order derivatives of the coefficients are locally H\"older continuous of order $\alpha$. We show that the scheme converges in the $L_2$-norm with a rate of $(1+\alpha)/2$ over both finite intervals $[0, T]$ and the infinite interval $(0, +\infty)$, under certain growth conditions on the coefficients.

Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure. The server might function maliciously. Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients. They can poison the local model before submitting to the server. Either type of attack, on the server or the client side, would severely degrade learning accuracy. We propose FedBlock, a novel blockchain-based FL framework that addresses both of these security risks. FedBlock is uniquely desirable in that it involves only smart contract programming, thus deployable atop any blockchain network. Our framework is substantiated with a comprehensive evaluation study using real-world datasets. Its robustness against backdoor attacks is competitive with the literature of FL backdoor defense. The latter, however, does not address the server risk as we do., Comment: This paper has been accepted as a full paper for the IEEE Special Session Federated Learning on Big Data 2024 (IEEE BigData 2024)

Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, especially noticeable with large objects. Additionally, unreliable mask predictions stemming from superpoint mask prediction further compound this issue. To address these challenges, we propose a novel framework called MSTA3D. It leverages multi-scale feature representation and introduces a twin-attention mechanism to effectively capture them. Furthermore, MSTA3D integrates a box query with a box regularizer, offering a complementary spatial constraint alongside semantic queries. Experimental evaluations on ScanNetV2, ScanNet200 and S3DIS datasets demonstrate that our approach surpasses state-of-the-art 3D instance segmentation methods., Comment: 14 pages, 9 figures, 7 tables, conference

This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models, this framework enables lightweight student models, particularly ResNet8 and ResNet16, to surpass 90% in accuracy, precision, and recall, adhering to the stringent confidence thresholds necessary for reliable classification tasks. Unlike conventional KD methods that rely on static weight distribution, our adaptive weighting mechanism responds to each teacher model's confidence, allowing student models to prioritize more credible sources of knowledge dynamically. Remarkably, ResNet8 delivers substantial efficiency gains, achieving a 97.5% reduction in parameters, a 96.7% decrease in FLOPs, an 86.2% cut in power consumption, and a 63.5% increase in inference speed over MobileViT. This significant optimization of complexity and resource demands establishes ResNet8 as an optimal candidate for EO tasks, combining robust performance with feasibility in deployment. The confidence-based, adaptable KD approach underscores the potential of dynamic distillation strategies to yield high-performing, resource-efficient models tailored for satellite-based EO applications. The reproducible code is accessible on our GitHub repository., Comment: Under revisions

Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce DELTA, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predictions. Unlike existing methods, which are limited by computational inefficiency or sparse tracking, DELTA delivers dense 3D tracking at scale, running over 8x faster than previous methods while achieving state-of-the-art accuracy. Furthermore, we explore the impact of depth representation on tracking performance and identify log-depth as the optimal choice. Extensive experiments demonstrate the superiority of DELTA on multiple benchmarks, achieving new state-of-the-art results in both 2D and 3D dense tracking tasks. Our method provides a robust solution for applications requiring fine-grained, long-term motion tracking in 3D space., Comment: Project Page: https://snap-research.github.io/DELTA/

In recent years, motion planning for urban self-driving cars (SDV) has become a popular problem due to its complex interaction of road components. To tackle this, many methods have relied on large-scale, human-sampled data processed through Imitation learning (IL). Although effective, IL alone cannot adequately handle safety and reliability concerns. Combining IL with Reinforcement learning (RL) by adding KL divergence between RL and IL policy to the RL loss can alleviate IL's weakness but suffer from over-conservation caused by covariate shift of IL. To address this limitation, we introduce a method that combines IL with RL using an implicit entropy-KL control that offers a simple way to reduce the over-conservation characteristic. In particular, we validate different challenging simulated urban scenarios from the unseen dataset, indicating that although IL can perform well in imitation tasks, our proposed method significantly improves robustness (over 17\% reduction in failures) and generates human-like driving behavior., Comment: submitted to IEEE Open Journal of Intelligent Transportation Systems

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning., Comment: 8 Pages, 5 figures, Magazine. arXiv admin note: substantial text overlap with arXiv:2409.15246

Scanning probe microscopy (SPM) is a powerful technique for mapping nanoscale surface properties through tip-sample interactions. Thermal scanning-probe lithography (tSPL) is an advanced SPM variant that uses a silicon tip on a heated cantilever to sculpt and measure polymer films with nanometer precision. The surfaces produced by tSPL-smooth topographic landscapes-allow mathematically defined contours to be fabricated on the nanoscale, enabling sophisticated functionalities for photonic, electronic, chemical, and biological technologies. Evaluating the physical effects of a landscape requires fitting arbitrary mathematical functions to SPM datasets, however, this capability does not exist in standard analysis programs. Here, we provide an open-source software package (FunFit) to fit analytical functions to SPM data and develop a fabrication and characterization protocol based on this analysis. We demonstrate the benefit of this approach by patterning periodic and quasiperiodic landscapes in a polymer resist with tSPL, which we transfer to hexagonal boron nitride (hBN) flakes with high fidelity via reactive-ion etching. The topographic landscapes in polymers and hBN are measured with tSPL and atomic force microscopy (AFM), respectively. Within the FunFit program, the datasets are corrected for artefacts, fit with analytical functions, and compared, providing critical feedback on the fabrication procedure. Beyond application to tSPL, this protocol can improve analysis, reproducibility, and process development for a broad range of SPM experiments. The protocol can be performed within a working day by an inexperienced user, where fabrication and characterization take a few hours and software analysis takes a few minutes.

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-assisted multiple-input and multiple-output (MIMO)-based autoencoder, where an adversary employs encoded and decoded datasets to create adversarial perturbation and fool the system. Because of the complex and dynamic data structures, adversarial attacks are not unique, each having its own benefits. We, therefore, propose three algorithms generating adversarial examples and perturbations to attack the RIS-MIMO-based autoencoder, exploiting the gradient descent and allowing for flexibility via varying the input dimensions. Numerical results show that the proposed adversarial attack-based algorithm significantly degrades the system performance regarding the symbol error rate compared to the jamming attacks., Comment: 5 pages, 2 figures. Accepted by WCL

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal data mining. State-of-the-art forecasting is achieved by deep-learning approaches due to their ability to contend with complex spatio-temporal dynamics. However, existing methods assume the input is fixed-topology road networks and static traffic time series. These assumptions fail to align with urbanization, where time series are collected continuously and road networks evolve over time. In such settings, deep-learning models require frequent re-initialization and re-training, imposing high computational costs. To enable much more efficient training without jeopardizing model accuracy, we propose the Topological Evolution-aware Framework (TEAM) for traffic forecasting that incorporates convolution and attention. This combination of mechanisms enables better adaptation to newly collected time series, while being able to maintain learned knowledge from old time series. TEAM features a continual learning module based on the Wasserstein metric that acts as a buffer that can identify the most stable and the most changing network nodes. Then, only data related to stable nodes is employed for re-training when consolidating a model. Further, only data of new nodes and their adjacent nodes as well as data pertaining to changing nodes are used to re-train the model. Empirical studies with two real-world traffic datasets offer evidence that TEAM is capable of much lower re-training costs than existing methods are, without jeopardizing forecasting accuracy., Comment: 16 pages. An extended version of "TEAM: Topological Evolution-aware Framework for Traffic Forecasting" accepted at PVLDB 2025

Primordial black holes (PBHs) are the natural candidate of dark matter (DM) as they only interact gravitationally and can evade any experiments on earth. In the standard semiclassical calculation of Hawking radiation, PBHs with mass below $10^{15}\rm g$ evaporated by now and therefore cannot be DM. However, the recently-discovered quantum memory burden effect can significantly suppress the evaporation of PBHs after the half-decay time. This quantum effect could open up a new mass window below $10^{10} \rm g$ where PBHs can still exist today and be DM. In this paper, we compute the gravitational wave (GW) signals associated with the formation of PBHs in this new mass window. We consider two formation scenarios: PBHs formed from inflationary perturbation and PBHs formed from collapse of Fermi-balls in a first-order phase transition (FOPT). GWs produced from these two scenarios have distinct features and, while the GW from inflation peaks at high frequency, the GW from FOPT peaks at lower frequency that can be within the reach of future experiments., Comment: 20 pages, 4 figures, 1 table

Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure of network organization is hub node identification. Hubs are specialized nodes within a network that link distinct brain units corresponding to specialized functional processes. Conventional methods for identifying hub nodes utilize different types of centrality measures and participation coefficient to profile various aspects of nodal importance. These methods solely rely on the functional connectivity networks constructed from functional magnetic resonance imaging (fMRI), ignoring the structure-function coupling in the brain. In this paper, we introduce a graph signal processing (GSP) based hub detection framework that utilizes both the structural connectivity and the functional activation to identify hub nodes. The proposed framework models functional activity as graph signals on the structural connectivity. Hub nodes are then detected based on the premise that hub nodes are sparse, have higher level of activity compared to their neighbors, and the non-hub nodes' activity can be modeled as the output of a graph-based filter. Based on these assumptions, an optimization framework, GraFHub, is formulated to learn the coefficients of the optimal polynomial graph filter and detect the hub nodes. The proposed framework is evaluated on both simulated data and resting state fMRI (rs-fMRI) data from Human Connectome Project (HCP)., Comment: 13 pages, 4 figures

Current Serverless abstractions (e.g., FaaS) poorly support non-functional requirements (e.g., QoS and constraints), are provider-dependent, and are incompatible with other cloud abstractions (e.g., databases). As a result, application developers have to undergo numerous rounds of development and manual deployment refinements to finally achieve their desired quality and efficiency. In this paper, we present Object-as-a-Service (OaaS) -- a novel serverless paradigm that borrows the object-oriented programming concepts to encapsulate business logic, data, and non-functional requirements into a single deployment package, thereby streamlining provider-agnostic cloud-native application development. We also propose a declarative interface for the non-functional requirements of applications that relieves developers from daunting refinements to meet their desired QoS and deployment constraint targets. We realized the OaaS paradigm through a platform called Oparaca and evaluated it against various real-world applications and scenarios. The evaluation results demonstrate that Oparaca can enhance application performance by 60X and improve reliability by 50X through latency, throughput, and availability enforcement -- all with remarkably less development and deployment time and effort., Comment: Accepted at ACM Symposium of Cloud Computing (SoCC '24)

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalography (EEG). Over the past decade, AAD has undergone continuous development, driven by its promising application in neuro-steered hearing devices. Most AAD algorithms are relying on the increase in neural entrainment to the envelope of attended speech, as compared to unattended speech, typically using a two-step approach. First, the algorithm predicts representations of the attended speech signal envelopes; second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. In this study, we proposed a novel end-to-end neural network architecture, named AADNet, which combines these two stages into a direct approach to address the AAD problem. We compare the proposed network against the traditional approaches, including linear stimulus reconstruction, canonical correlation analysis, and an alternative non-linear stimulus reconstruction using two different datasets. AADNet shows a significant performance improvement for both subject-specific and subject-independent models. Notably, the average subject-independent classification accuracies from 56.1 % to 82.7 % with analysis window lengths ranging from 1 to 40 seconds, respectively, show a significantly improved ability to generalize to data from unseen subjects. These results highlight the potential of deep learning models for advancing AAD, with promising implications for future hearing aids, assistive devices, and clinical assessments., Comment: 11 pages, 6 figures

Identifying auditory attention by comparing auditory stimuli and corresponding brain responses, is known as auditory attention decoding (AAD). The majority of AAD algorithms utilize the so-called envelope entrainment mechanism, whereby auditory attention is identified by how the envelope of the auditory stream drives variation in the electroencephalography (EEG) signal. However, neural processing can also be decoded based on endogenous cognitive responses, in this case, neural responses evoked by attention to specific words in a speech stream. This approach is largely unexplored in the field of AAD but leads to a single-word auditory attention decoding problem in which an epoch of an EEG signal timed to a specific word is labeled as attended or unattended. This paper presents a deep learning approach, based on EEGNet, to address this challenge. We conducted a subject-independent evaluation on an event-based AAD dataset with three different paradigms: word category oddball, word category with competing speakers, and competing speech streams with targets. The results demonstrate that the adapted model is capable of exploiting cognitive-related spatiotemporal EEG features and achieving at least 58% accuracy on the most realistic competing paradigm for the unseen subjects. To our knowledge, this is the first study dealing with this problem., Comment: 5 pages, 3 figures

Statutory law retrieval is a typical problem in legal language processing, that has various practical applications in law engineering. Modern deep learning-based retrieval methods have achieved significant results for this problem. However, retrieval systems relying on semantic and lexical correlations often exhibit limitations, particularly when handling queries that involve real-life scenarios, or use the vocabulary that is not specific to the legal domain. In this work, we focus on overcoming this weaknesses by utilizing the logical reasoning capabilities of large language models (LLMs) to identify relevant legal terms and facts related to the situation mentioned in the query. The proposed retrieval system integrates additional information from the term--based expansion and query reformulation to improve the retrieval accuracy. The experiments on COLIEE 2022 and COLIEE 2023 datasets show that extra knowledge from LLMs helps to improve the retrieval result of both lexical and semantic ranking models. The final ensemble retrieval system outperformed the highest results among all participating teams in the COLIEE 2022 and 2023 competitions., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

We prove appropriate generic vanishing theorems for singular varieties, generalizing the well-known generic vanishing theorem by Green and Lazarsfeld in [GL87] and the generic vanishing theorem of Nakano type in [PS13]. Our theorem explains the counterexample of Hacon and Kov\'acs in [HK15]., Comment: 23 pages. Comments are welcome!

Motivated by the optimality principles for non-subdifferentiable optimization problems, we introduce new relative subdifferentials and examine some properties for relatively lower semicontinuous functions including $\epsilon$-regular subdifferential and limiting subdifferential relative to a set. The fuzzy sum rule for the relative $\epsilon$-regular subdifferentials and the sum rule for the relative limiting subdifferentials are established. We utilize these relative subdifferentials to establish optimality conditions for non-subdifferentiable optimization problems under mild constraint qualifications. Examples are given to demonstrate that the optimality conditions obtained work better and sharper than some existing results. We also provide different versions of mean value theorems via the relative subdifferentials and employ them to characterize the equivalences between the convexity relative to a set and the monotonicity of the relative subdifferentials of a non-subdifferentiable function.

Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR), especially where training and testing data reside in different domains. The efficacy of existing UDA approaches tends to degrade when there is a large gap between the source and target domains. To deal with this problem, gradually shifting or progressively learning to shift from domain to domain is the key issue. In this paper, we introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process. The objective is to partition the training data into subsets so that the progressively self-trained model can adapt to gradual changes. We stratify the training data by evaluating the proximity of each data sample to both the source and target domains. We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples. Extensive experiments on benchmark scene-text datasets show that our approach significantly improves the performance of baseline (source-trained) STR models., Comment: [WACV 2025] 15 pages, 12 figures, 5 tables, include supplementary materials, source code: https://github.com/KhaLee2307/StrDA

Service robots that work alongside humans in a shared environment need a navigation system that takes into account not only physical safety but also social norms for mutual cooperation. In this paper, we introduce a motion planning system that includes human states such as positions and velocities and their personal space for social-aware navigation. The system first extracts human positions from the LiDAR and the RGB-D camera. It then uses the Kalman filter to fuse that information for human state estimation. An asymmetric Gaussian function is then employed to model human personal space based on their states. This model is used as the input to the dynamic window approach algorithm to generate trajectories for the robot. Experiments show that the robot is able to navigate alongside humans in a dynamic environment while respecting their physical and psychological comfort., Comment: In Proceedings of 2024, the 7th International Conference on Control, Robotics and Informatics (ICCRI 2024)

We propose a quantitative direct method of proving the local stability for the trivial solution of a rough differential equation and of its regular discretization scheme. Using Doss-Sussmann technique and stopping time analysis, we prove that the trivial solution of the rough system is exponentially stable as long as the noise is small. The same conclusions hold for the regular discretization scheme with small noise and small step size. Our results are significantly stronger than \cite[Theorem 14]{garrido-atienzaetal} and \cite[Theorem 18]{GABSch18} and can be applied to non-flat bounded or linear noises.

The centimeter-scale cyborg insects have a potential advantage for application in narrow environments where humans cannot operate. To realize such tasks, researchers have developed a small printed-circuit-board (PCB) which an insect can carry and control it. The electronic components usually remain bare on the board and the whole board is mounted on platform animals, resulting in uneven morphology of whole cyborg with sharp edges. It is well known that streamlined body shape in artificial vehicles or robots contributes to effective locomotion by reducing drag force in media. However, little is known how the entire body shape impacts on locomotor performance of cyborg insect. Here, we developed a 10 mm by 10 mm board which provided electrical stimulation via Sub-GHz communication and investigated the impact of physical arrangement of the board using Madagascar hissing cockroach. We compared the success rate of gap negotiation between the cyborg with mounted board and implanted board and found the latter outperformed the former. We demonstrated our cyborg cockroach with implanted board could follow faithfully to the locomotion command via antennal or cercal stimulation and traverse a narrow gap like air vent cover. In contrast to the conventional arrangement, our cyborg insects are suitable for application in a concealed environment.

In the times we live in today, humanity faces unprecedented environmental challenges. The emergence of artificial intelligence (AI) has opened new doors in our collective efforts to address our planet's pressing problems; however, many have doubts on the actual extent of impact that AI have on the environment. In particular, AI also assisting dirty production is a drawback that is largely absent from the literature. To investigate the impact of AI on the environment, we establish mathematical models to model the economy and the production process of goods based on outdated and advanced technologies. The secondary results are stated in the form of lemmas, the main results are stated in the form of theorems. From the theorems we conclude that AI may not on its own prevent an environmental disaster, a reason of which is its concurrent contribution to dirty production. With temporary government intervention, however, AI is able to avert an environmental disaster.