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We completely determine the depth and regularity of symbolic powers of the fiber product of two homogeneous ideals in disjoint sets of variables, given knowledge of the symbolic powers of each factor. Generalizing previous joint work with Vu, we provide exact, characteristic-independent formulas for the depth and regularity of ordinary powers of such fiber products.

This paper advances the understanding of how the size of a machine learning model affects its vulnerability to poisoning, despite state-of-the-art defenses. Given isotropic random honest feature vectors and the geometric median (or clipped mean) as the robust gradient aggregator rule, we essentially prove that, perhaps surprisingly, linear and logistic regressions with $D \geq 169 H^2/P^2$ parameters are subject to arbitrary model manipulation by poisoners, where $H$ and $P$ are the numbers of honestly labeled and poisoned data points used for training. Our experiments go on exposing a fundamental tradeoff between augmenting model expressivity and increasing the poisoners' attack surface, on both synthetic data, and on MNIST & FashionMNIST data for linear classifiers with random features. We also discuss potential implications for source-based learning and neural nets., Comment: 29 pages, 3 figures

Ego-velocity estimation from point cloud measurements of a millimeter-wave frequency-modulated continuous wave (mmWave FMCW) radar has become a crucial component of radar-inertial odometry (RIO) systems. Conventional approaches often perform poorly when the number of point cloud outliers exceeds that of inliers. In this paper, we propose CREVE, an acceleration-based inequality constraints filter that leverages additional measurements from an inertial measurement unit (IMU) to achieve robust ego-velocity estimations. To further enhance accuracy and robustness against sensor errors, we introduce a practical accelerometer bias estimation method and a parameter adaptation rule. The effectiveness of the proposed method is evaluated using five open-source drone datasets. Experimental results demonstrate that our algorithm significantly outperforms three existing state-of-the-art methods, achieving reductions in absolute trajectory error of approximately 53%, 84%, and 35% compared to them., Comment: 7 pages, conference

In this work, we present a numerical method that provides accurate real-time detection for the widths of the fractures in a fractured porous medium based on observational data on porous medium fluid mass and velocity. To achieve this task, an inverse problem is formulated by first constructing a forward formulation based on the reduced fracture model of the diffusion equation. A parameter estimation problem is then performed online by utilizing a direct filter method. Numerical experiments are carried out to demonstrate the accuracy of our method in approximating the target parameters.

Reinforcement Learning (RL) has been shown to be effective and convenient for a number of tasks in robotics. However, it requires the exploration of a sufficiently large number of state-action pairs, many of which may be unsafe or unimportant. For instance, online model-free learning can be hazardous and inefficient in the absence of guarantees that a certain set of desired states will be reached during an episode. An increasingly common approach to address safety involves the addition of a shielding system that constrains the RL actions to a safe set of actions. In turn, a difficulty for such frameworks is how to effectively couple RL with the shielding system to make sure the exploration is not excessively restricted. This work presents a novel safe model-free RL agent called Critic As Lyapunov Function (CALF) and showcases how CALF can be used to improve upon control baselines in robotics in an efficient and convenient fashion while ensuring guarantees of stable goal reaching. The latter is a crucial part of safety, as seen generally. With CALF all state-action pairs remain explorable and yet reaching of desired goal states is formally guaranteed. Formal analysis is provided that shows the goal stabilization-ensuring properties of CALF and a set of real-world and numerical experiments with a non-holonomic wheeled mobile robot (WMR) TurtleBot3 Burger confirmed the superiority of CALF over such a well-established RL agent as proximal policy optimization (PPO), and a modified version of SARSA in a few-episode setting in terms of attained total cost.

Grasp detection is a fundamental robotic task critical to the success of many industrial applications. However, current language-driven models for this task often struggle with cluttered images, lengthy textual descriptions, or slow inference speed. We introduce GraspMamba, a new language-driven grasp detection method that employs hierarchical feature fusion with Mamba vision to tackle these challenges. By leveraging rich visual features of the Mamba-based backbone alongside textual information, our approach effectively enhances the fusion of multimodal features. GraspMamba represents the first Mamba-based grasp detection model to extract vision and language features at multiple scales, delivering robust performance and rapid inference time. Intensive experiments show that GraspMamba outperforms recent methods by a clear margin. We validate our approach through real-world robotic experiments, highlighting its fast inference speed., Comment: 8 pages. Project page: https://airvlab.github.io/grasp-anything/

Let $G$ be $Sp_{2n}$, $SO_{2n}$ or $SO_{2n+1}$ and let $G^\vee$ be its Langlands dual group. Barbash and Vogan based on earlier work of Lusztig and Spaltenstein, define a duality map $D$ that sends nilpotent orbits $\mathbb{O}_{e^\vee} \subset \mathfrak{g}^\vee$ to special nilpotent orbits $\mathbb{O}_e\subset \mathfrak{g}$. In a work by Losev, Mason-Brown and Matvieievskyi, an upgraded version $\tilde{D}$ of this duality is considered, called the refined BVLS duality. $\tilde{D}(\mathbb{O}_{e^\vee})$ is a $G$-equivariant cover $\tilde{\mathbb{O}}_e$ of $\mathbb{O}_e$. Let $S_{{e^\vee}}$ be the nilpotent Slodowy slice of the orbit $\mathbb{O}_{e^\vee}$. The two varieties $X^\vee= S_{e^\vee}$ and $X=$ Spec$(\mathbb{C}[\tilde{\mathbb{O}}_e])$ are expected to be symplectic dual to each other. In this context, a version of the Hikita conjecture predicts an isomorphism between the cohomology ring of the Springer fiber $\mathcal{B}_{e^\vee}$ and the ring of regular functions on the scheme-theoretic fixed point $X^T$ for some torus $T$. This paper verifies the isomorphism for certain pairs $e$ and $e^\vee$. These cases are expected to cover almost all instances in which the Hikita conjecture holds when $e^\vee$ regular in a Levi $\mathfrak{l}^\vee\subset \mathfrak{g}^\vee$. Our results in these cases follow from the relations of three different types of objects: generalized coinvariant algebras, equivariant cohomology rings, and functions on scheme-theoretic intersections. We also give evidence for the Hikita conjecture when $e^\vee$ is distinguished.

This paper proposes a model predictive controller for discrete-time linear systems with additive, possibly unbounded, stochastic disturbances and subject to chance constraints. By computing a polytopic probabilistic positively invariant set for constraint tightening with the help of the computation of the minimal robust positively invariant set, the chance constraints are guaranteed, assuming only the mean and covariance of the disturbance distribution are given. The resulting online optimization problem is a standard strictly quadratic programming, just like in conventional model predictive control with recursive feasibility and stability guarantees and is simple to implement. A numerical example is provided to illustrate the proposed method., Comment: This paper was accepted for publication in CDC 2024

The differences in H2O2 production between conventional (CONV) and ultra-high dose rate (UHDR) irradiations in water radiolysis are still not fully understood. The lower levels of this radiolytic species, as a critical end product of water radiolysis, are particularly relevant for investigating the connection between the high-density energy deposition during short-duration physical events (ionizations or excitations) and biological responses of the FLASH effect. In this study, we developed a new Geant4-DNA chemistry model to simulate radiolysis considering the time structure of the irradiation pulse at different absorbed doses to liquid water of 0.01, 0.1, 1, and 2 Gy under 1 MeV electron irradiation. The model allows the description of the beam's temporal structure, including the pulse duration, the pulse repetition frequency, and the pulse amplitude for the different beam irradiation conditions through a wide dose rate range, from 0.01 Gy/s up to about 105 Gy/s, at various oxygen concentrations. The preliminary results indicate a correlation between the temporal structure of the pulses and a significant reduction in the production of reactive oxygen species (ROS) at different dose rates., Comment: 27 pages, 14 figures including 3 figures in appendix

Neural functional networks (NFNs) have recently gained significant attention due to their diverse applications, ranging from predicting network generalization and network editing to classifying implicit neural representation. Previous NFN designs often depend on permutation symmetries in neural networks' weights, which traditionally arise from the unordered arrangement of neurons in hidden layers. However, these designs do not take into account the weight scaling symmetries of $\operatorname{ReLU}$ networks, and the weight sign flipping symmetries of $\operatorname{sin}$ or $\operatorname{tanh}$ networks. In this paper, we extend the study of the group action on the network weights from the group of permutation matrices to the group of monomial matrices by incorporating scaling/sign-flipping symmetries. Particularly, we encode these scaling/sign-flipping symmetries by designing our corresponding equivariant and invariant layers. We name our new family of NFNs the Monomial Matrix Group Equivariant Neural Functional Networks (Monomial-NFN). Because of the expansion of the symmetries, Monomial-NFN has much fewer independent trainable parameters compared to the baseline NFNs in the literature, thus enhancing the model's efficiency. Moreover, for fully connected and convolutional neural networks, we theoretically prove that all groups that leave these networks invariant while acting on their weight spaces are some subgroups of the monomial matrix group. We provide empirical evidences to demonstrate the advantages of our model over existing baselines, achieving competitive performance and efficiency.

We completely determine the asymptotic depth, equivalently, the asymptotic projective dimension of a chain of edge ideals that is invariant under the action of the monoid Inc of increasing functions on the positive integers. Our results and their proofs also reveal surprising combinatorial and topological properties of corresponding graphs and their independence complexes. In particular, we are able to determine the asymptotic behavior of all reduced homology groups of these independence complexes., Comment: 33 pages

Existing work on improving language model reasoning typically explores a single solution path, which can be prone to errors. Inspired by perspective-taking in social studies, this paper introduces DiPT, a novel approach that complements current reasoning methods by explicitly incorporating diversified viewpoints. This approach allows the model to gain a deeper understanding of the problem's context and identify the most effective solution path during the inference stage. Additionally, it provides a general data-centric AI recipe for augmenting existing data to improve their quality for fine-tuning. Our empirical results demonstrate that DiPT can be flexibly integrated into existing methods that focus on a single reasoning approach, enhancing their reasoning performance and stability when presented with paraphrased problems. Furthermore, we illustrate improved context understanding by maintaining the model's safe outputs against "jailbreaking" prompts intentionally designed to bypass safeguards built into deployed models. Lastly, we show that fine-tuning with data enriched with diverse perspectives can boost the reasoning capabilities of the model compared to fine-tuning with raw data alone., Comment: LLM Reasoning with Perspectives, Preprint

Negation is a fundamental linguistic concept used by humans to convey information that they do not desire. Despite this, there has been minimal research specifically focused on negation within vision-language tasks. This lack of research means that vision-language models (VLMs) may struggle to understand negation, implying that they struggle to provide accurate results. One barrier to achieving human-level intelligence is the lack of a standard collection by which research into negation can be evaluated. This paper presents the first large-scale dataset, Negative Instruction (NeIn), for studying negation within the vision-language domain. Our dataset comprises 530,694 quadruples, i.e., source image, original caption, negative sentence, and target image in total, including 495,694 queries for training and 35,000 queries for benchmarking across multiple vision-language tasks. Specifically, we automatically generate NeIn based on a large, existing vision-language dataset, MS-COCO, via two steps: generation and filtering. During the generation phase, we leverage two VLMs, BLIP and MagicBrush, to generate the target image and a negative clause that expresses the content of the source image. In the subsequent filtering phase, we apply BLIP to remove erroneous samples. Additionally, we introduce an evaluation protocol for negation understanding of image editing models. Extensive experiments using our dataset across multiple VLMs for instruction-based image editing tasks demonstrate that even recent state-of-the-art VLMs struggle to understand negative queries. The project page is: https://tanbuinhat.github.io/NeIn/

Audio-driven talking face generation is a widely researched topic due to its high applicability. Reconstructing a talking face using audio significantly contributes to fields such as education, healthcare, online conversations, virtual assistants, and virtual reality. Early studies often focused solely on changing the mouth movements, which resulted in outcomes with limited practical applications. Recently, researchers have proposed a new approach of constructing the entire face, including face pose, neck, and shoulders. To achieve this, they need to generate through landmarks. However, creating stable landmarks that align well with the audio is a challenge. In this paper, we propose the KFusion of Dual-Domain model, a robust model that generates landmarks from audio. We separate the audio into two distinct domains to learn emotional information and facial context, then use a fusion mechanism based on the KAN model. Our model demonstrates high efficiency compared to recent models. This will lay the groundwork for the development of the audio-driven talking face generation problem in the future.

In response to the ongoing pandemic and health emergency of COVID-19, several models have been used to understand the dynamics of virus spread. Some employ mathematical models like the compartmental SEIHRD approach and others rely on agent-based modeling (ABM). In this paper, a new city-based agent-based modeling approach called COVID19-CBABM is introduced. It considers not only the transmission mechanism simulated by the SEHIRD compartments but also models people movements and their interactions with their surroundings, particularly their interactions at different types of Points of Interest (POI), such as supermarkets. Through the development of knowledge extraction procedures for Safegraph data, our approach simulates realistic conditions based on spatial patterns and infection conditions considering locations where people spend their time in a given city. Our model was implemented in Python using the Mesa-Geo framework. COVID19-CBABM is portable and can be easily extended by adding more complicated scenarios. Therefore, it is a useful tool to assist the government and health authorities in evaluating strategic decisions and actions efficiently against this epidemic, using the unique mobility patterns of each city.

This paper presents a novel Collaborative Cyberattack Detection (CCD) system aimed at enhancing the security of blockchain-based data-sharing networks by addressing the complex challenges associated with noise addition in federated learning models. Leveraging the theoretical principles of differential privacy, our approach strategically integrates noise into trained sub-models before reconstructing the global model through transmission. We systematically explore the effects of various noise types, i.e., Gaussian, Laplace, and Moment Accountant, on key performance metrics, including attack detection accuracy, deep learning model convergence time, and the overall runtime of global model generation. Our findings reveal the intricate trade-offs between ensuring data privacy and maintaining system performance, offering valuable insights into optimizing these parameters for diverse CCD environments. Through extensive simulations, we provide actionable recommendations for achieving an optimal balance between data protection and system efficiency, contributing to the advancement of secure and reliable blockchain networks., Comment: 13 pages

Let $G$ be a simple Lie group. Consider a nilpotent element $e\in \mathfrak{g}$. Let $Z_G(e)$ be the centralizer of $e$ in $G$, and let $A_e:= Z_G(e)/Z_G(e)^{o}$ be its component group. Write $\text{Irr}(\mathcal{B}_e)$ for the set of irreducible components of the Springer fiber $\mathcal{B}_e$. We have an action of $A_e$ on $\text{Irr}(\mathcal{B}_e)$. When $\mathfrak{g}$ is exceptional, we give an explicit description of $\text{Irr}(\mathcal{B}_e)$ as an $A_e$-set. For $\mathfrak{g}$ of classical type, we describe the stabilizers for the $A_e$-action. With this description, we prove a conjecture of Lusztig and Sommers.

This letter addresses a multivariate optimization problem for linear movable antenna arrays (MAAs). Particularly, the position and beamforming vectors of the under-investigated MAA are optimized simultaneously to maximize the minimum beamforming gain across several intended directions, while ensuring interference levels at various unintended directions remain below specified thresholds. To this end, a swarm-intelligence-based firefly algorithm (FA) is introduced to acquire an effective solution to the optimization problem. Simulation results reveal the superior performance of the proposed FA approach compared to the state-of-the-art approach employing alternating optimization and successive convex approximation. This is attributed to the FA's effectiveness in handling non-convex multivariate and multimodal optimization problems without resorting approximations.

A solution was made available for Korean Food lovers who have dietary restrictions to identify the Korean food before consuming. Just by uploading a clear photo of the dish, people can get to know what they are eating. Image processing techniques together with machine learning helped to come up with this solution.

Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality agent datasets and the absence of standard protocols in this area. We introduce and publicly release xLAM, a series of large action models designed for AI agent tasks. The xLAM series includes five models with both dense and mixture-of-expert architectures, ranging from 1B to 8x22B parameters, trained using a scalable, flexible pipeline that unifies, augments, and synthesizes diverse datasets to enhance AI agents' generalizability and performance across varied environments. Our experimental results demonstrate that xLAM consistently delivers exceptional performance across multiple agent ability benchmarks, notably securing the 1st position on the Berkeley Function-Calling Leaderboard, outperforming GPT-4, Claude-3, and many other models in terms of tool use. By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks. Models are available at https://huggingface.co/collections/Salesforce/xlam-models-65f00e2a0a63bbcd1c2dade4, Comment: Technical report for the Salesforce xLAM model series

In this paper, we introduce FC-KAN, a Kolmogorov-Arnold Network (KAN) that leverages combinations of popular mathematical functions such as B-splines, wavelets, and radial basis functions on low-dimensional data through element-wise operations. We explore several methods for combining the outputs of these functions, including sum, element-wise product, the addition of sum and element-wise product, quadratic function representation, and concatenation. In our experiments, we compare FC-KAN with multi-layer perceptron network (MLP) and other existing KANs, such as BSRBF-KAN, EfficientKAN, FastKAN, and FasterKAN, on the MNIST and Fashion-MNIST datasets. A variant of FC-KAN, which uses a combination of outputs from B-splines and Difference of Gaussians (DoG) in the form of a quadratic function, outperformed all other models on the average of 5 independent training runs. We expect that FC-KAN can leverage function combinations to design future KANs. Our repository is publicly available at: https://github.com/hoangthangta/FC_KAN., Comment: 9 pages, 1 figure

Let $G$ be a $\mathbf{W}_{p}$ graph if $n\geq p$ and every $p$ pairwise disjoint independent sets of $G$ are contained within $p$ pairwise disjoint maximum independent sets. In this paper, we establish that every $\mathbf{W}_{p}$ graph $G$ is $p$-quasi-regularizable if and only if $n\geq (p+1)\alpha $, where $\alpha $ is the independence number of $G$. This finding ensures that the independence polynomial of a connected $\mathbf{W}_{p}$ graph $G$ is log-concave whenever $(p+1)\alpha \leq n\leq 2p\alpha +p+1$. Furthermore, we demonstrate that the independence polynomial of the clique corona $G\circ K_{p}$ is invariably log-concave for all $p\geq 1$. As an application, we validate a long-standing conjecture claiming that the independence polynomial of a very well-covered graph is unimodal., Comment: 16 pages, 2 figures

Projection-based integrators are effectively employed in high-precision systems with growing industrial success. By utilizing a projection operator, the resulting projection-based integrator keeps its input-output pair within a designated sector set, leading to unique freedom in control design that can be directly translated into performance benefits. This paper aims to enhance projection-based integrators by incorporating well-crafted linear filters into its structure, resulting in a new class of projected integrators that includes the earlier ones, such as the hybrid-integrator gain systems (with and without pre-filtering) as special cases. The extra design freedom in the form of two filters in the input paths to the projection operator and the internal dynamics allows the controller to break away from the inherent limitations of the linear control design. The enhanced performance properties of the proposed structure are formally demonstrated through a (quasi-linear) describing function analysis, the absence of the gain-loss problem, and numerical case studies showcasing improved time-domain properties. The describing function analysis is supported by rigorously showing incremental properties of the new filtered projection-based integrators thereby guaranteeing that the computed steady-state responses are unique and asymptotically stable., Comment: to be presented at IEEE CDC 2024

The Coulomb impurity problem of graphene, in the absence of a magnetic field, displays discrete scale invariance. Applying a magnetic field introduces a new magnetic length scale $\ell$ and breaks discrete scale invariance. Moreover, a magnetic field is a singular perturbation as it turns complex energies into real energies. Nonetheless, the Coulomb potential must be regularized with a length $R$ at short distances for supercritical impurities. We investigate the structure of the induced density of a filled Landau impurity band in the supercritical regime. The coupling between Landau level states by the impurity potential is nontrivial and can lead to several anomalous effects. First, we find that the peak in the induced density can be located away from the center of the impurity, depending on the characteristics of the Landau impurity bands. Second, the impurity charge is screened, despite the Landau impurity band being filled. Third, anticrossing impurity states lead to additional impurity cyclotron resonances.

Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Computer Vision tasks, including medical image segmentation (MIS). Vision Mamba (VM)-based networks are particularly attractive due to their ability to achieve global receptive fields, similar to Vision Transformers, while also maintaining linear complexity in the number of tokens. However, the existing VM models still struggle to maintain both spatially local and global dependencies of tokens in high dimensional arrays due to their sequential nature. Employing multiple and/or complicated scanning strategies is computationally costly, which hinders applications of SSMs to high-dimensional 2D and 3D images that are common in MIS problems. In this work, we propose Local-Global Vision Mamba, LoG-VMamba, that explicitly enforces spatially adjacent tokens to remain nearby on the channel axis, and retains the global context in a compressed form. Our method allows the SSMs to access the local and global contexts even before reaching the last token while requiring only a simple scanning strategy. Our segmentation models are computationally efficient and substantially outperform both CNN and Transformers-based baselines on a diverse set of 2D and 3D MIS tasks. The implementation of LoG-VMamba is available at \url{https://github.com/Oulu-IMEDS/LoG-VMamba}., Comment: 20 pages

In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart. Initially, we explore the quality-diversity trade-off between SwiftBrush and SD Turbo: the former excels in image diversity, while the latter excels in image quality. This observation motivates our proposed modifications in the training methodology, including better weight initialization and efficient LoRA training. Moreover, our introduction of a novel clamped CLIP loss enhances image-text alignment and results in improved image quality. Remarkably, by combining the weights of models trained with efficient LoRA and full training, we achieve a new state-of-the-art one-step diffusion model, achieving an FID of 8.14 and surpassing all GAN-based and multi-step Stable Diffusion models. The project page is available at https://swiftbrushv2.github.io., Comment: Accepted to ECCV'24

The ability to estimate 3D movements of users over edge computing-enabled networks, such as 5G/6G networks, is a key enabler for the new era of extended reality (XR) and Metaverse applications. Recent advancements in deep learning have shown advantages over optimization techniques for estimating 3D human poses given spare measurements from sensor signals, i.e., inertial measurement unit (IMU) sensors attached to the XR devices. However, the existing works lack applicability to wireless systems, where transmitting the IMU signals over noisy wireless networks poses significant challenges. Furthermore, the potential redundancy of the IMU signals has not been considered, resulting in highly redundant transmissions. In this work, we propose a novel approach for redundancy removal and lightweight transmission of IMU signals over noisy wireless environments. Our approach utilizes a random Gaussian matrix to transform the original signal into a lower-dimensional space. By leveraging the compressive sensing theory, we have proved that the designed Gaussian matrix can project the signal into a lower-dimensional space and preserve the Set-Restricted Eigenvalue condition, subject to a power transmission constraint. Furthermore, we develop a deep generative model at the receiver to recover the original IMU signals from noisy compressed data, thus enabling the creation of 3D human body movements at the receiver for XR and Metaverse applications. Simulation results on a real-world IMU dataset show that our framework can achieve highly accurate 3D human poses of the user using only $82\%$ of the measurements from the original signals. This is comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster.

Our algorithm GNN: Graph Neural Network and Large Language Model for Data Discovery inherit the benefits of \cite{hoang2024plod} (PLOD: Predictive Learning Optimal Data Discovery), \cite{Hoang2024BODBO} (BOD: Blindly Optimal Data Discovery) in terms of overcoming the challenges of having to predefine utility function and the human input for attribute ranking, which helps prevent the time-consuming loop process. In addition to these previous works, our algorithm GNN leverages the advantages of graph neural networks and large language models to understand text type values that cannot be understood by PLOD and MOD, thus making the task of predicting outcomes more reliable. GNN could be seen as an extension of PLOD in terms of understanding the text type value and the user's preferences, not only numerical values but also text values, making the promise of data science and analytics purposes.

This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. The architectural configurations under consideration encompass the original VAE baseline, the VAE with a Gaussian Random Field prior (VAE-GRF), and the VAE incorporating a vision transformer (ViT-VAE). The findings reveal that ViT-VAE exhibits exemplary performance across various scenarios, whereas VAE-GRF may necessitate more intricate hyperparameter tuning to attain its optimal performance state. Additionally, to mitigate the propensity for over-reliance on results derived from the widely used MVTec dataset, this paper leverages the recently-public MiAD dataset for benchmarking. This deliberate inclusion seeks to enhance result competitiveness by alleviating the impact of domain-specific models tailored exclusively for MVTec, thereby contributing to a more robust evaluation framework. Codes is available at https://github.com/endtheme123/VAE-compare.git., Comment: 6 pages; accepted to IEEE ICCE 2024 for poster presentation

We propose a nonlinear filter for noise removal based on the Laplacian for 1D and 2D data. The method utilizes the solution to a fourth-order nonlinear PDE involving the Laplacian for data reconstruction. Evolution equations are introduced to solve this fourth-order nonlinear equation. Numerical experiments show that the new filter preserves discontinuities while filtering out noise. The restored data are piecewise linear and avoid the staircase effect commonly observed with total variation denoising methods., Comment: 10 pages, 7 figures

We develop a novel method for strong-laser-field physics based on the combination of the semiclassical Herman-Kluk propagator and the strong-field approximation and demonstrate its high accuracy on the calculations of photoelectron momentum distribution (PMD) for atoms and molecules in intense lasers. For rescattered electrons, we show that for a given time that electron tunnels to the continuum, there are typically multiple trajectories that lead to the same final momentum in the high-energy region. These trajectories start with slightly different initial transverse momenta and carry different phases giving rise to the interference structures in the PMD, which can also be associated with the laser-free electron-ion differential cross section. This is in contrast to the well-known long and short trajectories, which result in different interference patterns. Our results can be used to extend current capabilities of the laser-induced electron diffraction and other ultrafast imaging and strong-field spectroscopic techniques., Comment: 5 pages, 4 figures

Accurate prediction of 3D semantic occupancy from 2D visual images is vital in enabling autonomous agents to comprehend their surroundings for planning and navigation. State-of-the-art methods typically employ fully supervised approaches, necessitating a huge labeled dataset acquired through expensive LiDAR sensors and meticulous voxel-wise labeling by human annotators. The resource-intensive nature of this annotating process significantly hampers the application and scalability of these methods. We introduce a novel semi-supervised framework to alleviate the dependency on densely annotated data. Our approach leverages 2D foundation models to generate essential 3D scene geometric and semantic cues, facilitating a more efficient training process. Our framework exhibits notable properties: (1) Generalizability, applicable to various 3D semantic scene completion approaches, including 2D-3D lifting and 3D-2D transformer methods. (2) Effectiveness, as demonstrated through experiments on SemanticKITTI and NYUv2, wherein our method achieves up to 85% of the fully-supervised performance using only 10% labeled data. This approach not only reduces the cost and labor associated with data annotation but also demonstrates the potential for broader adoption in camera-based systems for 3D semantic occupancy prediction.

Self-supervised learning has driven significant progress in learning from single-subject, iconic images. However, there are still unanswered questions about the use of minimally-curated, naturalistic video data, which contain dense scenes with many independent objects, imbalanced class distributions, and varying object sizes. In this paper, we propose a novel approach that combines an invariance-based SSL objective on pooled representations with a dense SSL objective that enforces equivariance to optical flow warping. Our findings indicate that a unified objective applied at multiple feature scales is essential for learning effective image representations from high-resolution, naturalistic videos. We validate our approach on the BDD100K driving video dataset and the Walking Tours first-person video dataset, demonstrating its ability to capture spatial understanding from a dense objective and semantic understanding via a pooled representation objective., Comment: Project page: https://poodle-ssl.github.io

Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is important to understand people's emotions and the life incidents they experience before and after a disaster strikes. In this case study, we collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification like previous methods \cite{math11244910}, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster, offering critical insights for disaster preparedness and response strategies.

We consider the class of stationary-increment harmonizable stable processes with infinite control measure, which most notably includes real harmonizable fractional stable motions. We give conditions for the integrability of the paths of such processes with respect to a finite, absolutely continuous measure and derive the distributional characteristics of the path integral with respect to said measure. The convolution of the path of a stationary-increment harmonizable stable process with a suitable measure yields a real stationary harmonizable stable process with finite control measure. This allows us to construct consistent estimators for the index of stability as well as the kernel function in the integral representation of a stationary increment harmonizable stable process (up to a constant factor). For real harmonizable fractional stable motions consistent estimators for the index of stability and its Hurst parameter are given. These are computed directly from the periodogram frequency estimates of the smoothed process.

Molecular dynamics triggered by interaction with light often involve the excitation of several electronic, vibrational, and rotational states. Characterizing the resulting coupled electronic and nuclear wave packet motion represents a severe challenge, even for small polyatomic systems. In this Letter, we demonstrate how the interplay between vibrational, rotational, and electronic degrees of freedom governs the evolution of molecular wave packets in the low-lying states of strong-field-ionized sulfur dioxide. Using time-resolved Coulomb explosion imaging (CEI) in combination with quantum mechanical wave packet simulations, we directly map bending vibrations of the molecule, show how the vibrational wave packet is influenced by molecular alignment, and elucidate the role of the coupling between the two lowest electronic states of the cation. A conical intersection between these states couples the bending and asymmetric stretching coordinates, which is clearly reflected in the correlated fragment momenta. Our results suggest that multi-coincident CEI represents an efficient experimental tool for characterizing coupled electronic and nuclear motion in polyatomic molecules.

The realization of electron-spin resonance at the single-atom level using scanning tunneling microscopy has opened new avenues for coherent quantum sensing and quantum state manipulation at the ultimate size limit. This allows to build many-body Hamiltonians and the study of their complex physical behavior. Recently, a novel qubit platform has emerged from this field, raising questions about the driving mechanism from single-atom magnets. In this work, we demonstrate how single-atom magnets can be used to drive a nearby single spin qubit efficiently, while also addressing critical aspects related to the optimization of experimental parameters.

Representation Misdirection for Unlearning (RMU), which steers model representation in the intermediate layer to a target random representation, is an effective method for large language model (LLM) unlearning. Despite its high performance, the underlying cause and explanation remain underexplored. In this paper, we first theoretically demonstrate that steering forget representations in the intermediate layer reduces token confidence, causing LLMs to generate wrong or nonsense responses. Second, we investigate how the coefficient influences the alignment of forget-sample representations with the random direction and hint at the optimal coefficient values for effective unlearning across different network layers. Third, we show that RMU unlearned models are robust against adversarial jailbreak attacks. Last, our empirical analysis shows that RMU is less effective when applied to the middle and later layers in LLMs. To resolve this drawback, we propose Adaptive RMU -- a simple yet effective alternative method that makes unlearning effective with most layers. Extensive experiments demonstrate that Adaptive RMU significantly improves the unlearning performance compared to prior art while incurring no additional computational cost., Comment: 15 pages, 5 figures, 8 tables

On Wikipedia, articles are categorized to aid readers in navigating content efficiently. The manual creation of new categories can be laborious and time-intensive. To tackle this issue, we built language models to translate Wikipedia categories from English to Vietnamese with a dataset containing 15,000 English-Vietnamese category pairs. Subsequently, small to medium-scale Transformer pre-trained models with a sequence-to-sequence architecture were fine-tuned for category translation. The experiments revealed that OPUS-MT-en-vi surpassed other models, attaining the highest performance with a BLEU score of 0.73, despite its smaller model storage. We expect our paper to be an alternative solution for translation tasks with limited computer resources., Comment: 5 pages, 1 figure

We study a new variant of graph coloring by adding a connectivity constraint. A path in a vertex-colored graph is called conflict-free if there is a color that appears exactly once on its vertices. A connected graph $G$ is said to be strongly conflict-free vertex-connection $k$-colorable if $G$ admits a vertex $k$-coloring such that any two distinct vertices of $G$ are connected by a conflict-free $shortest$ path. Among others, we show that deciding whether a given graph is strongly conflict-free vertex-connection $3$-colorable is NP-complete even when restricted to $3$-colorable graphs with diameter $3$, radius $2$ and domination number $3$, and, assuming the Exponential Time Hypothesis (ETH), cannot be solved in $2^{o(n)}$ time on such restricted input graphs with $n$ vertices. This hardness result is quite strong when compared to the ordinary $3$-COLORING problem: it is known that $3$-COLORING is solvable in polynomial time in graphs with bounded domination number, and assuming ETH, cannot be solved in $2^{o(\sqrt{n})}$ time in $n$-vertex graphs with diameter $3$ and radius $2$. On the positive side, we point out that a strong conflict-free vertex-connection coloring with minimum color number of a given split graph or a co-bipartite graph can be computed in polynomial time., Comment: The full version of a COCOON 2024 paper

Quantum computing has emerged as a powerful tool for solving complex problems intractable for classical computers, particularly in popular fields such as cryptography, optimization, and neurocomputing. In this paper, we present a new quantum-based approach named the Hierarchical Quantum Control Gates (HQCG) method for efficient understanding of Functional Magnetic Resonance Imaging (fMRI) data. This approach includes two novel modules: the Local Quantum Control Gate (LQCG) and the Global Quantum Control Gate (GQCG), which are designed to extract local and global features of fMRI signals, respectively. Our method operates end-to-end on a quantum machine, leveraging quantum mechanics to learn patterns within extremely high-dimensional fMRI signals, such as 30,000 samples which is a challenge for classical computers. Empirical results demonstrate that our approach significantly outperforms classical methods. Additionally, we found that the proposed quantum model is more stable and less prone to overfitting than the classical methods., Comment: Accepted to IEEE Workshop on Signal Processing Systems (SiPS 2024)

SN 2023ixf was discovered in M101 within a day of explosion and rapidly classified as a Type II supernova with flash features. Here we present ultraviolet (UV) spectra obtained with the Hubble Space Telescope 14, 19, 24, and 66 days after explosion. Interaction between the supernova ejecta and circumstellar material (CSM) is seen in the UV throughout our observations in the flux of the first three epochs and asymmetric Mg II emission on day 66. We compare our observations to CMFGEN supernova models that include CSM interaction ($\dot{M}<10^{-3}$ M$_{\odot}$ yr$^{-1}$) and find that the power from CSM interaction is decreasing with time, from $L_{\rm sh}\approx5\times10^{42}$ erg s$^{-1}$ to $L_{\rm sh}\approx1\times10^{40}$ erg s$^{-1}$ between days 14 and 66. We examine the contribution of individual atomic species to the spectra on days 14 and 19, showing that the majority of the features are dominated by iron, nickel, magnesium, and chromium absorption in the ejecta. The UV spectral energy distribution of SN 2023ixf sits between that of supernovae which show no definitive signs of CSM interaction and those with persistent signatures assuming the same progenitor radius and metallicity. Finally, we show that the evolution and asymmetric shape of the Mg II $\lambda\lambda$ 2796, 2802 emission are not unique to SN 2023ixf. These observations add to the early measurements of dense, confined CSM interaction, tracing the mass-loss history of SN 2023ixf to $\sim33$ yr prior to the explosion and the density profile to a radius of $\sim5.7\times10^{15}$ cm. They show the relatively short evolution from a quiescent red supergiant wind to high mass loss., Comment: Accepted ApJL

We calculate the three-loop Wilson coefficients of all physically relevant dimension-four operators, i.e. $G_{\mu\nu}^a G^{a,\mu\nu}$, $m_i\bar q_j q_j$ and $m_i m_j m_k^2$, in the short-distance expansion of the time-ordered product of a pair of gauge-singlet vector, axial-vector, scalar and pseudo-scalar currents. The results are given for a general non-Abelian gauge theory with arbitrary (compact semi-simple) gauge group and $n_f$ light fermion flavors (quarks) in a common arbitrary representation of the gauge group, which includes QCD as a special case. In particular, we allow for arbitrary flavor contents of each of the currents. For the axial-vector current the included contributions from so-called singlet diagrams are consistent with the one-loop axial anomaly., Comment: 34 pages, 2 figures, 2 appendices, 1 ancillary file

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar $M$-pulse amplitude modulation ($M$-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex non-convex problem due to the non-convexity of the objective function. To tackle the problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima to the original non-convex optimization problem. The FA-based approach, however, suffers from high computational complexity. Therefore, we propose a low-complexity design based on zero-forcing (ZF) precoding, which is solved using an alternating optimization (AO) approach. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. Some insights into the optimal symbol distributions of the two joint design approaches are also provided.

We study the competitive facility location problem, where a firm aims to establish new facilities in a market already occupied by competitors. In this problem, customer behavior is crucial for making optimal location decisions. We explore a general class of customer choice models, known as the cross-nested logit (CNL) model, which is recognized for its flexibility and generality in predicting people's choice behavior. To explore the problem, we first demonstrate that it is NP-hard, even when there is only one customer class. We further show that this hardness result is tight, as the facility location problem under any simpler choice models (such as the logit or nested logit) is polynomial-time solvable when there is one customer class. To tackle the resulting facility location problem, we demonstrate that the objective function under a general cross-nested structure is not concave. Interestingly, we show that by a change of variables, the objective function can be converted to a convex program (i.e., a maximization problem with a concave objective and convex constraints), enabling it to be solved to optimality via an outer-approximation algorithm. Extensive experiments show the efficiency of our approach and provide analyses on the benefits of using the cross-nested model in the facility location context.

Predicting program behavior without execution is a crucial and challenging task in software engineering. Traditional models often struggle to capture the dynamic dependencies and interactions within code. This paper introduces a novel machine learning-based framework called CodeFlow, designed to predict code coverage and detect runtime errors through Dynamic Dependencies Learning. By utilizing control flow graphs (CFGs), CodeFlow represents all possible execution paths and the relationships between different statements, providing a comprehensive understanding of program behavior. CodeFlow constructs CFGs to depict execution paths and learns vector representations for CFG nodes, capturing static control-flow dependencies. Additionally, it learns dynamic dependencies through execution traces, which reflect the impacts among statements during execution. This approach enables accurate prediction of code coverage and effective identification of runtime errors. Empirical evaluations demonstrate significant improvements in code coverage prediction accuracy and effective localization of runtime errors, outperforming existing models.

We revisit the Fujii-Wetterich model [Phys.Rev.D 26, 2580 (1982) and Nucl.Phys.B 302, 645 (1988)] which allows the Higgs doublet to couple with a "cosmon" scalar $\chi$ of the background spacetime as $\chi^2\,\Phi^2$. Upon the SSB of the $SU(2)$ gauge, the VEV of the Higgs doublet is proportional to the field $\chi$. Fujii and Wetterich employed this linkage to make particle masses dependent on $\chi$. We shall present an $\textit alternative$ mechanism: at a given point $x^*$, the prevailing Higgs VEV will be used to $\textit construct$ a quantum of action $\hbar_*$ and a speed of light $c_*$ in association with $\chi(x^*)$. Specifically, each open set vicinity of a given point $x^*$ on the manifold is equipped with a replica of the Glashow-Weinberg-Salam action operating with its own effective values of $\hbar_*$ and $c_*$, whereas particle masses induced via Higgs SSB remain independent of $\chi(x^*)$. Our mechanism unambiguously generates the dependencies $\hbar_*\propto\chi^{-1/2}(x^*)$ and $c_*\propto\chi^{1/2}(x^*)$, causing these "fundamental constants" to vary along with the dynamical field $\chi$ across the manifold. For late-time cosmology, a varying $c$ along the trajectory of light waves from distant supernovae towards Earth renders the classic Lema\^itre redshift formula $1+z=a^{-1}$ inapplicable. Using the dependency $c_*\propto\chi^{1/2}(x^*)$, we derive the new (variable-$c$) Lema\^itre redshift relation and apply it to analyze the Pantheon Catalog of SneIa $\textit without$ invoking the dark energy hypothesis. Key consequences are: (1) Accounting for the Pantheon Catalog with a fit exceeding the quality of the $\Lambda$CDM model; (2) Explaining the late-time cosmic acceleration based on variable $c$, eliminating the need for dark energy; (3) Revitalizing Blanchard-Douspis-Rowan-Robinson-Sarkar's CMB power spectrum analysis that bypassed dark energy [A&A 412, 35 (2003)]., Comment: 14 pages, 3 figures

Osteoarthritis (OA) is the most common musculoskeletal disease, which has no cure. Knee OA (KOA) is one of the highest causes of disability worldwide, and it costs billions of United States dollars to the global community. Prediction of KOA progression has been of high interest to the community for years, as it can advance treatment development through more efficient clinical trials and improve patient outcomes through more efficient healthcare utilization. Existing approaches for predicting KOA, however, are predominantly static, i.e. consider data from a single time point to predict progression many years into the future, and knee level, i.e. consider progression in a single joint only. Due to these and related reasons, these methods fail to deliver the level of predictive performance, which is sufficient to result in cost savings and better patient outcomes. Collecting extensive data from all patients on a regular basis could address the issue, but it is limited by the high cost at a population level. In this work, we propose to go beyond static prediction models in OA, and bring a novel Active Sensing (AS) approach, designed to dynamically follow up patients with the objective of maximizing the number of informative data acquisitions, while minimizing their total cost over a period of time. Our approach is based on Reinforcement Learning (RL), and it leverages a novel reward function designed specifically for AS of disease progression in more than one part of a human body. Our method is end-to-end, relies on multi-modal Deep Learning, and requires no human input at inference time. Throughout an exhaustive experimental evaluation, we show that using RL can provide a higher monetary benefit when compared to state-of-the-art baselines.

Fuzzy dark matter (FDM) is a compelling candidate for dark matter, offering a natural explanation for the structure of diffuse low-mass haloes. However, the canonical FDM model with a mass of $10^{-22}~{\rm eV}$ encounters challenges in reproducing the observed diversity of dwarf galaxies, except for possibly scenarios where strong galactic feedback is invoked. The introduction of multiple-field FDM can provide a potential resolution to this diversity issue. The theoretical plausibility of this dark matter model is also enhanced by the fact that multiple axion species with logarithmically-distributed mass spectrum exist as a generic prediction of string theory. In this paper we consider the axiverse hypothesis and investigate non-linear structure formation in the two-field fuzzy dark matter (2FDM) model. Our cosmological simulation with an unprecedented resolution and self-consistent initial conditions reveals the diverse structures of dark matter haloes in the 2FDM model for the first time. Depending on the formation time and local tidal activities, late-time haloes can host solitons of nested cores or solitons of one dominant species., Comment: 9 pages, 5 figures. Comments welcome!