Distributed feedback control on the SIS network model:An impossibility result

This paper considers the deterministic Susceptible-Infected-Susceptible (SIS) epidemic network model, over strongly connected networks. It is well known that there exists an endemic equilibrium (the disease persists in all nodes of the network) if and only if the effective reproduction number of the network is greater than 1. In fact, the endemic equilibrium is unique and is asymptotically stable for all feasible nonzero initial conditions. We consider the recovery rate of each node as a control input. Using results from differential topology and monotone systems, we establish that it is impossible for a large class of distributed feedback controllers to drive the network to the healthy equilibrium (where every node is disease free) if the uncontrolled network has a reproduction number greater than 1. In fact, a unique endemic equilibrium exists in the controlled network, and it is exponentially stable for all feasible nonzero initial conditions. We illustrate our impossibility result using simulations, and discuss the implications on the problem of control over epidemic networks. </p

A network SIS meta-population model with transportation flow

This paper considers a deterministic Susceptible-Infected-Susceptible (SIS) metapopulation model for the spread of a disease in a strongly connected network, where each node represents a large population. Individuals can travel between the nodes (populations). We derive a necessary and sufficient condition for the healthy equilibrium to be the unique equilibrium of the system, and then in fact it is asymptotically stable for all initial conditions (a sufficient condition for exponential stability is also given). If the condition is not satisfied, then there additionally exists a unique endemic equilibrium which is exponentially stable for all nonzero initial conditions. We then consider time-delay in the travel between nodes, and further investigate the role of the mobility rate that governs the flow of individuals between nodes in determining the convergence properties. We find that sometimes, increasing mobility helps the system converge to the healthy equilibrium.</p

TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training

Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities. Due to noises in web-harvested text-image pairs, however, scaling up training data volume in SMCL presents considerable obstacles in terms of computational cost and data inefficiency. To improve data efficiency in VLP, we propose Text-aware Image Mixing (TiMix), which integrates mix-based data augmentation techniques into SMCL, yielding significant performance improvements without significantly increasing computational overhead. We provide a theoretical analysis of TiMixfrom a mutual information (MI) perspective, showing that mixed data samples for cross-modal contrastive learning implicitly serve as a regularizer for the contrastive loss. The experimental results demonstrate that TiMix exhibits a comparable performance on downstream tasks, even with a reduced amount of training data and shorter training time, when benchmarked against existing methods. This work empirically and theoretically demonstrates the potential of data mixing for data-efficient and computationally viable VLP, benefiting broader VLP model adoption in practical scenarios.Comment: Accepted on AAAI202

Experiments of Image Retrieval Using Weak Attributes

Searching images based on descriptions of image attributes is an intuitive process that can be easily understood by humans and recently made feasible by a few promising works in both the computer vision and multimedia communities. In this report, we describe some experiments of image retrieval methods that utilize weak attributes

Acceleration of on-axis and ring-shaped electron beams in wakefields driven by Laguerre-Gaussian pulses

The acceleration of electron beams with multiple transverse structures in wakefields driven by Laguerre-Gaussian pulses has been studied through three-dimensional (3D) particle-in-cell simulations. Under different laser-plasma conditions, the wakefield shows different transverse structures. In general cases, the wakefield shows a donut-like structure and it accelerates the ring-shaped hollow electron beam. When a lower plasma density or a smaller laser spot size is used, besides the donut-like wakefield, a central bell-like wakefield can also be excited. The wake sets in the center of the donut-like wake. In this case, both a central on-axis electron beam and a ring-shaped electron beam are simultaneously accelerated. Further, reducing the plasma density or laser spot size leads to an on-axis electron beam acceleration only. The research is beneficial for some potential applications requiring special pulse beam structures, such as positron acceleration and collimation

COPA: Efficient Vision-Language Pre-training Through Collaborative Object- and Patch-Text Alignment

Vision-Language Pre-training (VLP) methods based on object detection enjoy the rich knowledge of fine-grained object-text alignment but at the cost of computationally expensive inference. Recent Visual-Transformer (ViT)-based approaches circumvent this issue while struggling with long visual sequences without detailed cross-modal alignment information. This paper introduces a ViT-based VLP technique that efficiently incorporates object information through a novel patch-text alignment mechanism. Specifically, we convert object-level signals into patch-level ones and devise a Patch-Text Alignment pre-training task (PTA) to learn a text-aware patch detector. By using off-the-shelf delicate object annotations in 5\% training images, we jointly train PTA with other conventional VLP objectives in an end-to-end manner, bypassing the high computational cost of object detection and yielding an effective patch detector that accurately detects text-relevant patches, thus considerably reducing patch sequences and accelerating computation within the ViT backbone. Our experiments on a variety of widely-used benchmarks reveal that our method achieves a speedup of nearly 88\% compared to prior VLP models while maintaining competitive or superior performance on downstream tasks with similar model size and data scale.Comment: Accepted on ACM MM202

Hallucination Augmented Contrastive Learning for Multimodal Large Language Model

Multi-modal large language models (MLLMs) have been shown to efficiently integrate natural language with visual information to handle multi-modal tasks. However, MLLMs still face a fundamental limitation of hallucinations, where they tend to generate erroneous or fabricated information. In this paper, we address hallucinations in MLLMs from a novel perspective of representation learning. We first analyzed the representation distribution of textual and visual tokens in MLLM, revealing two important findings: 1) there is a significant gap between textual and visual representations, indicating unsatisfactory cross-modal representation alignment; 2) representations of texts that contain and do not contain hallucinations are entangled, making it challenging to distinguish them. These two observations inspire us with a simple yet effective method to mitigate hallucinations. Specifically, we introduce contrastive learning into MLLMs and use text with hallucination as hard negative examples, naturally bringing representations of non-hallucinative text and visual samples closer while pushing way representations of non-hallucinating and hallucinative text. We evaluate our method quantitatively and qualitatively, showing its effectiveness in reducing hallucination occurrences and improving performance across multiple benchmarks. On the MMhal-Bench benchmark, our method obtains a 34.66% /29.5% improvement over the baseline MiniGPT-4/LLaVA. Our code is available on https://github.com/X-PLUG/mPLUG-HalOwl/tree/main/hacl

mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily focus on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collaboration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modality collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experiments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks and achieving state-of-the-art performances with a single generic model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models

Numerical analysis of solid liquid two phase abrasive flow polishing process for three stage variable diameter pipe

With the rapid development of technology and economy, rough machining has not been able to keep pace with the times. Heavy industry is more and more inclined to the field of precision and ultra-precision machining. During that time, abrasive flow machining technology emerged as the times require. The process of micro cutting is achieved through the contact between abrasive particles and workpiece, so that the accuracy of workpiece's inner surface can be polished, and the accuracy of workpiece improved, which is a representative polishing method. Taking the three-order variable diameter tube as the research object, this paper discusses the polishing characteristics of three level variable diameter pipe with solid liquid two phase abrasive flow. The removal pattern of abrasive flow micro cutting is analyzed, with the collision effect between abrasive particles and wall considered and ignored, which provides technical support for abrasive flow polishing variable diameter pipe parts