Co-design of Control and Scheduling in Networked Systems under Denial-of-Service attacks

We consider the joint design of control and scheduling under stochastic Denial-of-Service (DoS) attacks in the context of networked control systems. A sensor takes measurements of the system output and forwards its dynamic state estimates to a remote controller over a packet-dropping link. The controller determines the optimal control law for the process using the estimates it receives. An attacker aims at degrading the control performance by increasing the packet-dropout rate with a DoS attack towards the sensor-controller channel. Assume both the controller and the attacker are rational in a game-theoretic sense. We establish a partially observable stochastic game to derive the optimal joint design of scheduling and control. Using dynamic programming we prove that the control and scheduling policies can be designed separately without sacrificing optimality, making the problem equivalent to a complete information game. We employ Nash Q-learning to solve the problem and prove that the solution is guaranteed to constitute an $\epsilon$-Nash equilibrium. Numerical examples are provided to illustrate the tradeoffs between control performance and communication cost.Comment: 9 pages, 4 figure

Spectral Unsupervised Domain Adaptation for Visual Recognition

Unsupervised domain adaptation (UDA) aims to learn a well-performed model in an unlabeled target domain by leveraging labeled data from one or multiple related source domains. It remains a great challenge due to 1) the lack of annotations in the target domain and 2) the rich discrepancy between the distributions of source and target data. We propose Spectral UDA (SUDA), an efficient yet effective UDA technique that works in the spectral space and is generic across different visual recognition tasks in detection, classification and segmentation. SUDA addresses UDA challenges from two perspectives. First, it mitigates inter-domain discrepancies by a spectrum transformer (ST) that maps source and target images into spectral space and learns to enhance domain-invariant spectra while suppressing domain-variant spectra simultaneously. To this end, we design novel adversarial multi-head spectrum attention that leverages contextual information to identify domain-variant and domain-invariant spectra effectively. Second, it mitigates the lack of annotations in target domain by introducing multi-view spectral learning which aims to learn comprehensive yet confident target representations by maximizing the mutual information among multiple ST augmentations capturing different spectral views of each target sample. Extensive experiments over different visual tasks (e.g., detection, classification and segmentation) show that SUDA achieves superior accuracy and it is also complementary with state-of-the-art UDA methods with consistent performance boosts but little extra computation

Jlinks: a novel isoform abundance estimation method using splice junctions

Transcripts, or interchangeably referred to as isoforms, have been well known to be involved in many important biological pathways and disease mechanisms such as cancer and mental disorders. Understanding the roles of isoforms calls for precise quantification of isoform expression abundances from RNA-Seq reads. Yet state-of-the-art isoform quantification methods yield weak estimation accuracy, especially for datasets that undergo a wide range of isoform expression levels. Here we present a novel isoform quantification algorithm called Jlinks, designed to estimate isoform abundances using splice junctions. The key distinguishing feature of Jlinks is that it treats each isoform as a “link” of splice junctions and converts the abundance estimation problem into obtaining an optimal solution for a linear system. We demonstrate that Jlinks outperforms existing isoform quantification methods in both speed and accuracy

Regional differences and sources of organochlorine pesticides in soils surrounding chemical industrial parks

Concentrations of organochlorine pesticides (OCPs; dichlorodiphenyltrichloroethanes (DDTs), hexachlorocyclohexanes (HCHs), hexachlorobenzene (HCB)) were investigated in 105 soil samples collected in vicinity of the chemical industrial parks in Tianjin, China. OCP concentrations significantly varied in the study area, high HCH and DDT levels were found close to the chemical industrial parks. The intensity of agricultural activity and distance from the potential OCP emitters have important influences on the OCP residue distributions. Principal component analysis indicates that HCH pollution is a mix of historical technical HCH and current lindane pollution and DDT pollution input is only due to technical DDT sources. The significant correlations of OCP compounds reveal that HCHs, DDTs and HCB could have some similar sources of origin

Identification of sources of elevated concentrations of polycyclic aromatic hydrocarbons in an industrial area in Tianjin, China

The concentrations of 16 polycyclic aromatic hydrocarbons (PAHs) were determined by gas chromatography equipped with a mass spectrometry detector in 105 topsoil samples from an industrial area around Bohai Bay, Tianjin in the North of China. Results demonstrated that concentrations of PAHs in 104 soil samples from this area ranged from 68.7 to 5,590 ng g (-aEuro parts per thousand 1) dry weight with a mean of a16PAHs 814 +/- 813 ng g (-aEuro parts per thousand 1), which suggests that there exists mid to high levels of PAH contamination. The concentration of a16PAHs in one soil sample from Tianjin Port was exceptionally high (48,700 ng g (-aEuro parts per thousand 1)). Ninety-three of the 105 soil samples were considered to be contaminated with PAHs (> 200 ng g (-aEuro parts per thousand 1)), and 25 were heavily polluted (> 1,000 ng g (-aEuro parts per thousand 1)). The sites with high PAHs concentration are mainly distributed around chemical industry parks and near highways. Two low molecular weight PAHs, naphthalene and phenanthrene, were the dominant components in the soil samples, which accounted for 22.1% and 10.7% of the a16PAHs concentration, respectively. According to the observed molecular indices, house heating in winter, straw stalk combustion in open areas after harvest, and petroleum input were common sources of PAHs in this area, while factory discharge and vehicle exhaust were the major sources around chemical industrial parks and near highways. Biological processes were probably another main source of low molecular weight PAHs

SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching

In this paper, we address the challenge of matching semantically similar keypoints across image pairs. Existing research indicates that the intermediate output of the UNet within the Stable Diffusion (SD) can serve as robust image feature maps for such a matching task. We demonstrate that by employing a basic prompt tuning technique, the inherent potential of Stable Diffusion can be harnessed, resulting in a significant enhancement in accuracy over previous approaches. We further introduce a novel conditional prompting module that conditions the prompt on the local details of the input image pairs, leading to a further improvement in performance. We designate our approach as SD4Match, short for Stable Diffusion for Semantic Matching. Comprehensive evaluations of SD4Match on the PF-Pascal, PF-Willow, and SPair-71k datasets show that it sets new benchmarks in accuracy across all these datasets. Particularly, SD4Match outperforms the previous state-of-the-art by a margin of 12 percentage points on the challenging SPair-71k dataset

Stealthy hacking and secrecy of controlled state estimation systems with random dropouts

We study the maximum information gain that an adversary may obtain through hacking without being detected. Consider a dynamical process observed by a sensor that transmits a local estimate of the system state to a remote estimator according to some reference transmission policy across a packet-dropping wireless channel equipped with acknowledgments (ACK). An adversary overhears the transmissions and proactively hijacks the sensor to reprogram its transmission policy. We define perfect secrecy as keeping the averaged expected error covariance bounded at the legitimate estimator and unbounded at the adversary. By analyzing the stationary distribution of the expected error covariance, we show that perfect secrecy can be attained for unstable systems only if the ACK channel has no packet dropouts. In other situations, we prove that independent of the reference policy and the detection methods, perfect secrecy is not attainable. For this scenario, we formulate a constrained Markov decision process to derive the optimal transmission policy that the adversary should implement at the sensor, and devise a Stackelberg game to derive the optimal reference policy for the legitimate estimator.Comment: 16 pages, 6 figure