OpenRaiser/Intern-Atlas huggingface dataset:
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset
Venue: arXiv cs.CV 2024
Year: 2024

Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison. The source code of this paper will be released on: https://github.com/Event-AHU/OpenEvDET
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model through a unified next-token prediction formulation. To address the large dataset size typically required for image-text alignment, we propose to enhance data efficiency through the design of a vision tokenizer that incorporates semantic information and a progressive multi-stage training procedure. This approach reduces the dataset size to just 15M for pretraining -- over four times fewer than what is typically needed -- while achieving competitive or even superior performance with existing unified MLLMs, such as Janus. Additionally, to promote synergistic enhancement between understanding and generation capabilities, which is under-explored in previous works, we introduce a novel self-enhancing multimodal alignment scheme. This scheme supervises the MLLM to self-assess the consistency between text descriptions and self-generated images, facilitating the model to interpret images more accurately and avoid unrealistic and incorrect predictions caused by misalignment in image generation. Based on extensive experiments, our proposed ILLUME stands out and competes with state-of-the-art unified MLLMs and specialized models across various benchmarks for multimodal understanding, generation, and editing.
Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection
Venue: arXiv cs.CV 2024
Year: 2024

The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research on adversarial attacks has become essential. However, most existing adversarial attacks focus only on GAN-generated facial images detection, struggle to be effective on multi-class natural images and diffusion-based detectors, and exhibit poor invisibility. To fill this gap, we first conduct an in-depth analysis of the vulnerability of AIGC detectors and discover the feature that detectors vary in vulnerability to different post-processing. Then, considering that the detector is agnostic in real-world scenarios and given this discovery, we propose a Realistic-like Robust Black-box Adversarial attack (R$^2$BA) with post-processing fusion optimization. Unlike typical perturbations, R$^2$BA uses real-world post-processing, i.e., Gaussian blur, JPEG compression, Gaussian noise and light spot to generate adversarial examples. Specifically, we use a stochastic particle swarm algorithm with inertia decay to optimize post-processing fusion intensity and explore the detector's decision boundary. Guided by the detector's fake probability, R$^2$BA enhances/weakens the detector-vulnerable/detector-robust post-processing intensity to strike a balance between adversariality and invisibility. Extensive experiments on popular/commercial AIGC detectors and datasets demonstrate that R$^2$BA exhibits impressive anti-detection performance, excellent invisibility, and strong robustness in GAN-based and diffusion-based cases. Compared to state-of-the-art white-box and black-box attacks, R$^2$BA shows significant improvements of 15\%--72\% and 21\%--47\% in anti-detection performance under the original and robust scenario respectively, offering valuable insights for the security of AIGC detection in real-world applications.
Enhancing Prediction Models with Reinforcement Learning
Venue: arXiv cs.IR 2024
Year: 2024

We present a large-scale news recommendation system implemented at Ringier Axel Springer Polska, focusing on enhancing prediction models with reinforcement learning techniques. The system, named Aureus, integrates a variety of algorithms, including multi-armed bandit methods and deep learning models based on large language models (LLMs). We detail the architecture and implementation of Aureus, emphasizing the significant improvements in online metrics achieved by combining ranking prediction models with reinforcement learning. The paper further explores the impact of different models mixing on key business performance indicators. Our approach effectively balances the need for personalized recommendations with the ability to adapt to rapidly changing news content, addressing common challenges such as the cold start problem and content freshness. The results of online evaluation demonstrate the effectiveness of the proposed system in a real-world production environment.
Classifier-free guidance in LLMs Safety
Venue: arXiv cs.LG 2024
Year: 2024

The paper describes LLM unlearning without a retaining dataset, using the ORPO reinforcement learning method with inference enhanced by modified classifier-free guidance. Significant improvement in unlearning, without degradation of the model, is achieved through direct training on synthetic replacement data in CFG-aware training regime, with classifier-free guidance applied during the inference. This article is an extended version of the NeurIPS 2024 LLM-PC submission, which was awarded second prize.
Real-Time Performance Optimization of Travel Reservation Systems Using AI and Microservices
Venue: arXiv cs.SE 2024
Year: 2024

The rapid growth of the travel industry has increased the need for real-time optimization in reservation systems that could take care of huge data and transaction volumes. This study proposes a hybrid framework that ut folds an Artificial Intelligence and a Microservices approach for the performance optimization of the system. The AI algorithms forecast demand patterns, optimize the allocation of resources, and enhance decision-making driven by Microservices architecture, hence decentralizing system components for scalability, fault tolerance, and reduced downtime. The model provided focuses on major problems associated with the travel reservation systems such as latency of systems, load balancing and data consistency. It endows the systems with predictive models based on AI improved ability to forecast user demands. Microservices would also take care of different scales during uneven traffic patterns. Hence, both aspects ensure better handling of peak loads and spikes while minimizing delays and ensuring high service quality. A comparison was made between traditional reservation models, which are monolithic and the new model of AI-Microservices. Comparatively, the analysis results state that there is a drastic improvement in processing times where the system uptime and resource utilization proved the capability of AI and the microservices in transforming the travel industry in terms of reservation. This research work focused on AI and Microservices towards real-time optimization, providing critical insight into how to move forward with practical recommendations for upgrading travel reservation systems with this technology.
Framework to coordinate ubiquitous devices with SOA standards
Venue: arXiv cs.SE 2024
Year: 2024

Context: Ubiquitous devices and pervasive environments are in permanent interaction in people's daily lives. In today's hyper-connected environments, it is necessary for these devices to interact with each other, transparently to the users. The problem is analyzed from the different perspectives that compose it: SOA, service composition, interaction, and the capabilities of ubiquitous devices. Problem: Currently, ubiquitous devices can interact in a limited way due to the proprietary mechanisms and protocols available on the market. The few proposals from academia have hardly achieved an impact in practice. This is not in harmony with the situation of the Internet environment and web services, which have standardized mechanisms for service composition. Aim: Apply the principles of SOA, currently standardized and tested in the information systems industry, for the connectivity of ubiquitous devices in pervasive environments. For this, a coordination framework based on these technologies is proposed. Methodology: We apply an adaptation of Design Science in our environment to allow the iterative construction and evaluation of prototypes. For this, a proof of concept is developed on which this methodology and its cycles are based. Results: We built and put into operation a coordination framework for ubiquitous devices based on WS-CDL, along with a proof of concept. In addition, we contribute to the WS-CDL language in order to support the characteristics of specific ubiquitous devices.
Haptics in Micro- and Nano-Manipulation
Venue: arXiv cs.RO 2024
Year: 2024

One of the motivations for the development of wirelessly guided untethered magnetic devices (UMDs), such as microrobots and nanorobots, is the continuous demand to manipulate, sort, and assemble micro-objects with high level of accuracy and dexterity. UMDs can function as microgrippers or manipulators and move micro-objects with or without direct contact. In this case, the UMDs can be directly teleoperated by an operator using haptic tele-manipulation systems. The aim of this chapter is threefold: first, to provide a mathematical framework to design a scaled bilateral tele-manipulation system to achieve wireless actuation of micro-objects using magnetically-guided UMDs; second, to demonstrate closed-loop stability based on absolute stability theory; third, to provide experimental case studies performed on haptic devices to manipulate microrobots and assemble micro-objects. In this chapter, we are concerned with some fundamental concepts of electromagnetics and low-Reynolds number hydrodynamics to understand the stability and performance of haptic devices in micro- and nano-manipulation applications.
Understanding Gradient Descent through the Training Jacobian
Venue: arXiv cs.LG 2024
Year: 2024

We examine the geometry of neural network training using the Jacobian of trained network parameters with respect to their initial values. Our analysis reveals low-dimensional structure in the training process which is dependent on the input data but largely independent of the labels. We find that the singular value spectrum of the Jacobian matrix consists of three distinctive regions: a "chaotic" region of values orders of magnitude greater than one, a large "bulk" region of values extremely close to one, and a "stable" region of values less than one. Along each bulk direction, the left and right singular vectors are nearly identical, indicating that perturbations to the initialization are carried through training almost unchanged. These perturbations have virtually no effect on the network's output in-distribution, yet do have an effect far out-of-distribution. While the Jacobian applies only locally around a single initialization, we find substantial overlap in bulk subspaces for different random seeds. Our code is available at https://github.com/EleutherAI/training-jacobian
TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems
Venue: arXiv cs.LG 2024
Year: 2024

Efficient real-time solvers for forward and inverse problems are essential in engineering and science applications. Machine learning surrogate models have emerged as promising alternatives to traditional methods, offering substantially reduced computational time. Nevertheless, these models typically demand extensive training datasets to achieve robust generalization across diverse scenarios. While physics-based approaches can partially mitigate this data dependency and ensure physics-interpretable solutions, addressing scarce data regimes remains a challenge. Both purely data-driven and physics-based machine learning approaches demonstrate severe overfitting issues when trained with insufficient data. We propose a novel Tikhonov autoencoder model-constrained framework, called TAE, capable of learning both forward and inverse surrogate models using a single arbitrary observation sample. We develop comprehensive theoretical foundations including forward and inverse inference error bounds for the proposed approach for linear cases. For comparative analysis, we derive equivalent formulations for pure data-driven and model-constrained approach counterparts. At the heart of our approach is a data randomization strategy, which functions as a generative mechanism for exploring the training data space, enabling effective training of both forward and inverse surrogate models from a single observation, while regularizing the learning process. We validate our approach through extensive numerical experiments on two challenging inverse problems: 2D heat conductivity inversion and initial condition reconstruction for time-dependent 2D Navier-Stokes equations. Results demonstrate that TAE achieves accuracy comparable to traditional Tikhonov solvers and numerical forward solvers for both inverse and forward problems, respectively, while delivering orders of magnitude computational speedups.
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Venue: arXiv cs.CV 2024
Year: 2024

With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existing practices rely on powerful but costly large language models (LLMs) or multimodal language models (MLMs) to produce instruction data. These are often prone to hallucinations, licensing issues and the generation process is often hard to scale and interpret. In this work, we present a programmatic approach that employs scene graphs as symbolic representations of images and human-written programs to systematically synthesize vision-centric instruction data. Our approach ensures the interpretability and controllability of the data generation process and scales efficiently while maintaining factual accuracy. By implementing a suite of 24 single-image, 14 multi-image instruction generators, and a scene graph generation pipeline, we build a scalable, cost-effective system: ProVision which produces diverse question-answer pairs concerning objects, attributes, relations, depth, etc., for any given image. Applied to Visual Genome and DataComp datasets, we generate over 10 million instruction data points, ProVision-10M, and leverage them in both pretraining and instruction tuning stages of MLMs. When adopted in the instruction tuning stage, our single-image instruction data yields up to a 7% improvement on the 2D split and 8% on the 3D split of CVBench, along with a 3% increase in performance on QBench2, RealWorldQA, and MMMU. Our multi-image instruction data leads to an 8% improvement on Mantis-Eval. Incorporation of our data in both pre-training and fine-tuning stages of xGen-MM-4B leads to an averaged improvement of 1.6% across 11 benchmarks.
Accurate Performance Modeling And Uncertainty Analysis of Lossy Compression in Scientific Applications
Venue: arXiv cs.PF 2024
Year: 2024

Scientific applications typically generate large volumes of floating-point data, making lossy compression one of the most effective methods for data reduction, thereby lowering storage requirements and improving performance in large-scale applications. However, variations in compression time can significantly impact overall performance improvement, due to inaccurate scheduling, workload imbalances, etc. Existing approaches rely on empirical methods to predict the compression performance, which often lack interpretability and suffer from limitations in accuracy and generalizability. In this paper, we propose surrogate models for predicting the compression time of prediction-based lossy compression and provide a detailed analysis of the factors influencing time variability with uncertainty analysis. Our evaluation shows that our solution can accuratly predict the compression time with 5% average error across six scientific datasets. It also provides accurate 95% confidence interval, which is essential for time-sensitive scheduling and applications.
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems
Venue: arXiv cs.LG 2024
Year: 2024

In the context of globalization and the rapid expansion of the digital economy, anti-money laundering (AML) has become a crucial aspect of financial oversight, particularly in cross-border transactions. The rising complexity and scale of international financial flows necessitate more intelligent and adaptive AML systems to combat increasingly sophisticated money laundering techniques. This paper explores the application of unsupervised learning models in cross-border AML systems, focusing on rule optimization through contrastive learning techniques. Five deep learning models, ranging from basic convolutional neural networks (CNNs) to hybrid CNNGRU architectures, were designed and tested to assess their performance in detecting abnormal transactions. The results demonstrate that as model complexity increases, so does the system's detection accuracy and responsiveness. In particular, the self-developed hybrid Convolutional-Recurrent Neural Integration Model (CRNIM) model showed superior performance in terms of accuracy and area under the receiver operating characteristic curve (AUROC). These findings highlight the potential of unsupervised learning models to significantly improve the intelligence, flexibility, and real-time capabilities of AML systems. By optimizing detection rules and enhancing adaptability to emerging money laundering schemes, this research provides both theoretical and practical contributions to the advancement of AML technologies, which are essential for safeguarding the global financial system against illicit activities.
NP-hardness and a PTAS for the Euclidean Steiner Line Problem
Venue: arXiv cs.CG 2024
Year: 2024

The Euclidean Steiner Tree Problem (EST) seeks a minimum-cost tree interconnecting a given set of terminal points in the Euclidean plane, allowing the use of additional intersection points. In this paper, we consider two variants that include an additional straight line $γ$ with zero cost, which must be incorporated into the tree. In the Euclidean Steiner fixed Line Problem (ESfL), this line is given as input and can be treated as a terminal. In contrast, the Euclidean Steiner Line Problem (ESL) requires determining the optimal location of $γ$. Despite recent advances, including heuristics and a 1.214-approximation algorithm for both problems, a formal proof of NP-hardness has remained open. In this work, we close this gap by proving that both the ESL and ESfL are NP-hard. Additionally, we prove that both problems admit a polynomial-time approximation scheme (PTAS), by demonstrating that approximation algorithms for the EST can be adapted to the ESL and ESfL with appropriate modifications. Specifically, we show ESfL$\leq_{\text{PTAS}}$EST and ESL$\leq_{\text{PTAS}}$EST, i.e., provide a PTAS reduction to the EST.
Static Key Attention in Vision
Venue: arXiv cs.CV 2024
Year: 2024

The success of vision transformers is widely attributed to the expressive power of their dynamically parameterized multi-head self-attention mechanism. We examine the impact of substituting the dynamic parameterized key with a static key within the standard attention mechanism in Vision Transformers. Our findings reveal that static key attention mechanisms can match or even exceed the performance of standard self-attention. Integrating static key attention modules into a Metaformer backbone, we find that it serves as a better intermediate stage in hierarchical hybrid architectures, balancing the strengths of depth-wise convolution and self-attention. Experiments on several vision tasks underscore the effectiveness of the static key mechanism, indicating that the typical two-step dynamic parameterization in attention can be streamlined to a single step without impacting performance under certain circumstances.
A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp
Venue: arXiv cs.RO 2024
Year: 2024

The anthropomorphism of grasping process significantly benefits the experience and grasping efficiency of prosthetic hand wearers. Currently, prosthetic hands controlled by signals such as brain-computer interfaces (BCI) and electromyography (EMG) face difficulties in precisely recognizing the amputees' grasping gestures and executing anthropomorphic grasp processes. Although prosthetic hands equipped with vision systems enables the objects' feature recognition, they lack perception of human grasping intention. Therefore, this paper explores the estimation of grasping gestures solely through visual data to accomplish anthropopathic grasping control and the determination of grasping intention within a multi-object environment. To address this, we propose the Spatial Geometry-based Gesture Mapping (SG-GM) method, which constructs gesture functions based on the geometric features of the human hand grasping processes. It's subsequently implemented on the prosthetic hand. Furthermore, we propose the Motion Trajectory Regression-based Grasping Intent Estimation (MTR-GIE) algorithm. This algorithm predicts pre-grasping object utilizing regression prediction and prior spatial segmentation estimation derived from the prosthetic hand's position and trajectory. The experiments were conducted to grasp 8 common daily objects including cup, fork, etc. The experimental results presented a similarity coefficient $R^{2}$ of grasping process of 0.911, a Root Mean Squared Error ($RMSE$) of 2.47\degree, a success rate of grasping of 95.43$\%$, and an average duration of grasping process of 3.07$\pm$0.41 s. Furthermore, grasping experiments in a multi-object environment were conducted. The average accuracy of intent estimation reached 94.35$\%$. Our methodologies offer a groundbreaking approach to enhance the prosthetic hand's functionality and provides valuable insights for future research.