A Unified Framework for Analyzing and Detecting Malicious Examples of DNN Models

Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input can mislead the models to give wrong results. Although defenses against adversarial attacks have been widely studied, research on mitigating backdoor attacks is still at an early stage. It is unknown whether there are any connections and common characteristics between the defenses against these two attacks. In this paper, we present a unified framework for detecting malicious examples and protecting the inference results of Deep Learning models. This framework is based on our observation that both adversarial examples and backdoor examples have anomalies during the inference process, highly distinguishable from benign samples. As a result, we repurpose and revise four existing adversarial defense methods for detecting backdoor examples. Extensive evaluations indicate these approaches provide reliable protection against backdoor attacks, with a higher accuracy than detecting adversarial examples. These solutions also reveal the relations of adversarial examples, backdoor examples and normal samples in model sensitivity, activation space and feature space. This can enhance our understanding about the inherent features of these two attacks, as well as the defense opportunities

Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design LAMP, a framework that integrates a large language model in event prediction. Particularly, the language model performs abductive reasoning to assist an event sequence model: the event model proposes predictions on future events given the past; instructed by a few expert-annotated demonstrations, the language model learns to suggest possible causes for each proposal; a search module finds out the previous events that match the causes; a scoring function learns to examine whether the retrieved events could actually cause the proposal. Through extensive experiments on several challenging real-world datasets, we demonstrate that our framework -- thanks to the reasoning capabilities of large language models -- could significantly outperform the state-of-the-art event sequence models.Comment: NeurIPS 2023 camera-read

Slow equilibrium relaxation in a chiral magnet mediated by topological defects

We performed a pump-probe experiment on the chiral magnet Cu_{2}OSeO_{3} to study the relaxation dynamics of its noncollinear magnetic orders, employing a millisecond magnetic field pulse as the pump and resonant elastic x-ray scattering as the probe. Our findings reveal that the system requires ∼0.2  s to stabilize after the perturbation applied to both the conical and skyrmion lattice phase, which is significantly slower than the typical nanosecond timescale observed in micromagnetics. This prolonged relaxation is attributed to the formation and slow dissipation of local topological defects, such as emergent monopoles. By unveiling the experimental lifetime of these emergent singularities in a noncollinear magnetic system, our study highlights a universal relaxation mechanism in solitonic textures within the slow dynamics regime, offering new insights into topological physics and advanced information storage solutions

Roadmap on spatiotemporal light fields

Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents the holy grail of the human everlasting pursue of ultrafast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally, spatiotemporal light pulses are always treated as spatiotemporally separable wave packet as solution of the Maxwell's equations. In the past decade, however, more generalized forms of spatiotemporally nonseparable solution started to emerge with growing importance for their striking physical effects. This roadmap intends to highlight the recent advances in the creation and control of increasingly complex spatiotemporally sculptured pulses, from spatiotemporally separable to complex nonseparable states, with diverse geometric and topological structures, presenting a bird's eye viewpoint on the zoology of spatiotemporal light fields and the outlook of future trends and open challenges.Comment: This is the version of the article before peer review or editing, as submitted by an author to Journal of Optics. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from i

Relevance of tumor-infiltrating lymphocytes in breast cancer

While breast cancer has not been considered a cancer amenable to immunotherapeutic approaches, recent studies have demonstrated evidence of significant immune cell infiltration via tumor-infiltrating lymphocytes in a subset of patient tumors. In this review we present the current evidence highlighting the clinical relevance and utility of tumor-infiltrating lymphocytes in breast cancer. Retrospective and prospective studies have shown that the presence of tumor-infiltrating lymphocytes is a prognostic marker for higher responses to neoadjuvant chemotherapy and better survival, particularly in triple negative and HER2-positive early breast cancer. Further work is required to determine the immune subsets important in this response and to discover ways of encouraging immune infiltrate in tumor-infiltrating lymphocytes-negative patients

An improvement of embedded zerotree wavelet coding based on compressed sensing

Embedded zero-tree wavelet (EZW) is a kind of image compression algorithm which provides high compression rates fast. In order to improve the quality of reconstructed image after decoding, in this research, we put forward an improvement of embedded zerotree wavelet coding and simulate this new algorithm. This method takes full advantages of the Compressed Sensing (CS) theory and EZW. Simulation results demonstrated that the proposed method improved the quality of the unzipped image significantly. © 2014 IEEE

HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

As we all know, there are many ways to express emotions. Among them, facial emotion recognition, which is widely used in human–computer interaction, psychoanalysis of mental patients, multimedia retrieval, and other fields, is still a challenging task. At present, although convolutional neural network has achieved great success in face emotion recognition algorithms, it has a rising space in effective feature extraction and recognition accuracy. According to a large number of literature studies, histogram of oriented gradient (HOG) can effectively extract face features, and ensemble methods can effectively improve the accuracy and robustness of the algorithm. Therefore, this paper proposes a new algorithm, HOG-ESRs, which improves the traditional ensemble methods to the ensembles with shared representations (ESRs) method, effectively reducing the residual generalization error, and then combining HOG features with ESRs. The experimental results on the FER2013 dataset show that the new algorithm can not only effectively extract features and reduce the residual generalization error, but also improve the accuracy and robustness of the algorithm, the purpose of the study being achieved. The application of HOG-ESRs in facial emotion recognition is helpful to solve the symmetry of edge detection and the deficiency of related methods in an outdoor lighting environment

Damage-Associated Molecular Patterns and Their Signaling Pathways in Primary Blast Lung Injury: New Research Progress and Future Directions

Primary blast lung injury (PBLI) is a common cause of casualties in wars, terrorist attacks, and explosions. It can exist in the absence of any other outward signs of trauma, and further develop into acute lung injury (ALI) or a more severe acute respiratory distress syndrome (ARDS). The pathogenesis of PBLI at the cellular and molecular level has not been clear. Damage-associated molecular pattern (DAMP) is a general term for endogenous danger signals released by the body after injury, including intracellular protein molecules (HMGB1, histones, s100s, heat shock proteins, eCIRP, etc.), secretory protein factors (IL-1β, IL-6, IL-10, TNF-α, VEGF, complements, etc.), purines and pyrimidines and their derived degradation products (nucleic acids, ATP, ADP, UDPG, uric acid, etc.), and extracellular matrix components (hyaluronic acid, fibronectin, heparin sulfate, biglycan, etc.). DAMPs can be detected by multiple receptors including pattern recognition receptors (PRRs). The study of DAMPs and their related signaling pathways, such as the mtDNA-triggered cGAS-YAP pathway, contributes to revealing the molecular mechanism of PBLI, and provides new therapeutic targets for controlling inflammatory diseases and alleviating their symptoms. In this review, we focus on the recent progress of research on DAMPs and their signaling pathways, as well as the potential therapeutic targets and future research directions in PBLI

Vehicle Routing Problem with Soft Time Windows Based on Improved Genetic Algorithm for Fruits and Vegetables Distribution

Fresh fruits and vegetables, perishable by nature, are subject to additional deterioration and bruising in the distribution process due to vibration and shock caused by road irregularities. A nonlinear mathematical model was developed that considered not only the vehicle routing problem with time windows but also the effect of road irregularities on the bruising of fresh fruits and vegetables. The main objective of this work was to obtain the optimal distribution routes for fresh fruits and vegetables considering different road classes with the least amount of logistics costs. An improved genetic algorithm was used to solve the problem. A fruit delivery route among the 13 cities in Jiangsu Province was used as a real analysis case. The simulation results showed that the vehicle routing problem with time windows, considering road irregularities and different classes of toll roads, can significantly influence total delivery costs compared with traditional VRP models. The comparison between four models to predict the total cost and actual total cost in distribution showed that the improved genetic algorithm is superior to the Group-based pattern, CW pattern, and O-X type cross pattern

A symmetric substructuring method for analyzing the natural frequencies of conical origami structures

Conical origami structures are characterized by their substantial out-of-plane stiffness and energy-absorption capacity. Previous investigations have commonly focused on the static characteristics of these lightweight structures. However, the efficient analysis of the natural vibrations of these structures is pivotal for designing conical origami structures with programmable stiffness and mass. In this paper, we propose a novel method to analyze the natural vibrations of such structures by combining a symmetric substructuring method (SSM) and a generalized eigenvalue analysis. SSM exploits the inherent symmetry of the structure to decompose it into a finite set of repetitive substructures. In doing so, we reduce the dimensions of matrices and improve computational efficiency by adopting the stiffness and mass matrices of the substructures in the generalized eigenvalue analysis. Finite element simulations of pin-jointed models are used to validate the computational results of the proposed approach. Moreover, the parametric analysis of the structures demonstrates the influences of the number of segments along the circumference and the radius of the cone on the structural mass and natural frequencies of the structures. Furthermore, we present a comparison between six-fold and four-fold conical origami structures and discuss the influence of various geometric parameters on their natural frequencies. This study provides a strategy for efficiently analyzing the natural vibration of symmetric origami structures and has the potential to contribute to the efficient design and customization of origami metastructures with programmable stiffness