On the Resilience of Traffic Networks under Non-Equilibrium Learning

We investigate the resilience of learning-based \textit{Intelligent Navigation Systems} (INS) to informational flow attacks, which exploit the vulnerabilities of IT infrastructure and manipulate traffic condition data. To this end, we propose the notion of \textit{Wardrop Non-Equilibrium Solution} (WANES), which captures the finite-time behavior of dynamic traffic flow adaptation under a learning process. The proposed non-equilibrium solution, characterized by target sets and measurement functions, evaluates the outcome of learning under a bounded number of rounds of interactions, and it pertains to and generalizes the concept of approximate equilibrium. Leveraging finite-time analysis methods, we discover that under the mirror descent (MD) online-learning framework, the traffic flow trajectory is capable of restoring to the Wardrop non-equilibrium solution after a bounded INS attack. The resulting performance loss is of order $\tilde{\mathcal{O}}(T^{\beta})$ ($-\frac{1}{2} \leq \beta < 0 )$), with a constant dependent on the size of the traffic network, indicating the resilience of the MD-based INS. We corroborate the results using an evacuation case study on a Sioux-Fall transportation network.Comment: 8 pages, 3 figures, with a technical appendi

Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study

\textit{Intelligent Navigation Systems} (INS) are exposed to an increasing number of informational attack vectors, which often intercept through the communication channels between the INS and the transportation network during the data collecting process. To measure the resilience of INS, we use the concept of a Wardrop Non-Equilibrium Solution (WANES), which is characterized by the probabilistic outcome of learning within a bounded number of interactions. By using concentration arguments, we have discovered that any bounded feedback delaying attack only degrades the systematic performance up to order $\tilde{\mathcal{O}}(\sqrt{{d^3}{T^{-1}}})$ along the traffic flow trajectory within the Delayed Mirror Descent (DMD) online-learning framework. This degradation in performance can occur with only mild assumptions imposed. Our result implies that learning-based INS infrastructures can achieve Wardrop Non-equilibrium even when experiencing a certain period of disruption in the information structure. These findings provide valuable insights for designing defense mechanisms against possible jamming attacks across different layers of the transportation ecosystem.Comment: Preprint under revie

Sound of Ikebana: Creation of Media Art Based on Fluid Dynamics

We have been working on the creation of media art, utilizing technologies. As fluids create beautiful forms under various conditions, we have been trying to utilize fluid dynamics as a basis for creating media art. However, most of the visualization results of the fluid dynamics show only stable fluid behaviors and lack of unstable or, in other words, unpredictable behaviors that would be significant in the creation of art. To create various unstable or unpredictable fluid behaviors, we have developed and introduced a new method called "Sound Vibration Form (SVF)" to control fluid behaviors and created amedia art called "Sound of Ikebana." Interestingly, people find and feel Japanese beauty in media art, although itis created based on a natural phenomenon. This paper proposes the basic concept of media art based on fluid dynamics, describes details of the SVF to create unpredictable fluid dynamics-based phenomena, and a new media art called "Sound of Ikebana" created utilizing SVF. Also, we will discuss the relationship between Japanese beauty and physical phenomena represented by fluid dynamics

3D Modeling and 3D Materialization of Fluid Art That Occurs in Very Short Time

19th IFIP TC 14 International Conference, ICEC 2020, Xi'an, China, November 10–13, 2020.Proceedings of ICEC2020We have been creating artworks called “liquid art” utilizing liquid dynamics phenomena. One of the liquid artworks is “Sound of Ikebana” which is created by giving sound vibration to color paints and shooting the phenomenon by a high-speed camera, which has been evaluated as “the artwork includes Japanese beauty.” To investigate further why it is evaluated in such a way and also to seek the possibility of its application in society, we tried to materialize it into 3D objects. As the phenomenon occurs in a very short time of less than one second, we have developed a specific experimental environment consisting of multiple high-speed cameras surrounding a speaker where the phenomenon occurs. Among various technologies to reconstruct the 3D model from multiple 2D images, we have chosen a method called Phase-Only Correlation and developed a 3D mesh model of a snapshot of “Sound of Ikebana.” Also using a 3D printer we have successfully obtained 3D materialized “Sound of Ikebana.

A First Order Meta Stackelberg Method for Robust Federated Learning (Technical Report)

Recent research efforts indicate that federated learning (FL) systems are vulnerable to a variety of security breaches. While numerous defense strategies have been suggested, they are mainly designed to counter specific attack patterns and lack adaptability, rendering them less effective when facing uncertain or adaptive threats. This work models adversarial FL as a Bayesian Stackelberg Markov game (BSMG) between the defender and the attacker to address the lack of adaptability to uncertain adaptive attacks. We further devise an effective meta-learning technique to solve for the Stackelberg equilibrium, leading to a resilient and adaptable defense. The experiment results suggest that our meta-Stackelberg learning approach excels in combating intense model poisoning and backdoor attacks of indeterminate types

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Creation of Liquid Art under Microgravity

[第20回情報科学技術フォーラム: FIT2021] 2021.8.25(水)-27(金), オンライン開催