Outlier Robust Adversarial Training

Supervised learning models are challenged by the intrinsic complexities of training data such as outliers and minority subpopulations and intentional attacks at inference time with adversarial samples. While traditional robust learning methods and the recent adversarial training approaches are designed to handle each of the two challenges, to date, no work has been done to develop models that are robust with regard to the low-quality training data and the potential adversarial attack at inference time simultaneously. It is for this reason that we introduce Outlier Robust Adversarial Training (ORAT) in this work. ORAT is based on a bi-level optimization formulation of adversarial training with a robust rank-based loss function. Theoretically, we show that the learning objective of ORAT satisfies the $\mathcal{H}$-consistency in binary classification, which establishes it as a proper surrogate to adversarial 0/1 loss. Furthermore, we analyze its generalization ability and provide uniform convergence rates in high probability. ORAT can be optimized with a simple algorithm. Experimental evaluations on three benchmark datasets demonstrate the effectiveness and robustness of ORAT in handling outliers and adversarial attacks. Our code is available at https://github.com/discovershu/ORAT.Comment: Accepted by The 15th Asian Conference on Machine Learning (ACML 2023

Differentially Private SGDA for Minimax Problems

Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descent (SGD) with differential privacy (DP) constraints, there is little work on understanding the generalization (utility) of SGDA with DP constraints. In this paper, we use the algorithmic stability approach to establish the generalization (utility) of DP-SGDA in different settings. In particular, for the convex-concave setting, we prove that the DP-SGDA can achieve an optimal utility rate in terms of the weak primal-dual population risk in both smooth and non-smooth cases. To our best knowledge, this is the first-ever-known result for DP-SGDA in the non-smooth case. We further provide its utility analysis in the nonconvex-strongly-concave setting which is the first-ever-known result in terms of the primal population risk. The convergence and generalization results for this nonconvex setting are new even in the non-private setting. Finally, numerical experiments are conducted to demonstrate the effectiveness of DP-SGDA for both convex and nonconvex cases

Camera-Based Blind Spot Detection with a General Purpose Lightweight Neural Network

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset. Document type: Articl

MACROD1/LRP16 Enhances LPS-Stimulated Inflammatory Responses by Up-Regulating a Rac1-Dependent Pathway in Adipocytes

Background/Aims: Chronic inflammation contributes to the development of type 2 diabetes mellitus by targeting the insulin receptor substrate protein-1 (IRS-1) signaling pathway. Previous studies showed that Leukemia related protein 16 (LRP16) reduced insulin stimulated glucose uptake in adipocytes by impairing the IRS-1 signaling pathway. We explored the mechanism by which LRP16 promotes the inflammatory response. Methods: We screened LRP16 induced proteins in the lipopolysaccharide (LPS)-stimulated inflammatory response using liquid chromatography-mass spectrometry (LC-MS) and analyzed the potential biological functions of these proteins using online bioinformatics tools. mRNA expression and protein expression of target genes were measured by real time PCR and Western blot, respectively. Results: A total of 390 differentially expressed proteins were identified. The mitogen-activated protein kinase (MAPK) signaling pathway was the primary activated pathway in LRP16-expressing cells. Overexpression of LRP16 activated ERK1/2 and Rac1, which are two key players related to the MAPK signaling pathway. Furthermore, knock down of endogenous LRP16 by RNA interference (RNAi) reduced Rac1 expression, ERK activation, and inflammatory cytokine expression in human adipocytes stimulated by LPS. The stimulatory effect of LRP16 was diminished by suppressing Rac1 expression and treating the cells with the ERK specific inhibitor, PD98059. Conclusion: These findings revealed the functions of LRP16 in promoting the inflammatory response through activating the Rac1-MAPK1/ERK pathway in human adipocytes

GWSpace: a multi-mission science data simulator for space-based gravitational wave detection

Space-based gravitational wave detectors such as TianQin, LISA, and TaiJi have the potential to outperform themselves through joint observation. To achieve this, it is desirable to practice joint data analysis in advance on simulated data that encodes the intrinsic correlation among the signals found in different detectors that operate simultaneously. In this paper, we introduce \texttt{GWSpace}, a package that can simulate the joint detection data from TianQin, LISA, and TaiJi. The software is not a groundbreaking work that starts from scratch. Rather, we use as many open-source resources as possible, tailoring them to the needs of simulating the multi-mission science data and putting everything into a ready-to-go and easy-to-use package. We shall describe the main components, the construction, and a few examples of application of the package. A common coordinate system, namely the Solar System Barycenter (SSB) coordinate system, is utilized to calculate spacecraft orbits for all three missions. The paper also provides a brief derivation of the detection process and outlines the general waveform of sources detectable by these detectors.Comment: 24 pages, 13 figures, GWSpace will be uploaded at https://github.com/TianQinSYSU/GWSpac

The first compound heterozygous mutations in SLC12A3 and PDX1 genes: a unique presentation of Gitelman syndrome with distinct insulin resistance and familial diabetes insights

BackgroundGitelman Syndrome (GS) patients frequently exhibit disrupted glucose metabolism, attributed to hypokalemia, hypomagnesemia and heightened aldosterone. This study delved into the genetic underpinnings linked to insulin resistance and diabetes in a GS patient, contextualized within his family history.MethodsThe hydrochlorothiazide and furosemide loading test were performed to ascertain the presence of GS. Oral glucose tolerance test (OGTT) evaluated glucose metabolism and insulin sensitivity. Whole-exome sequencing, validated by Sanger sequencing, was employed to confirm gene mutations, which were then tracked among the patient’s relatives.ResultsSymptoms and laboratory examination confirmed the clinical diagnosis of GS. Comprehensive whole-exome sequencing, augmented by Sanger sequencing validation, revealed a compound heterozygous mutation within the SLC12A3 gene (c.1108G>C in exon 9, c.676G>A in exon 5 and c.2398G>A in exon 20) in the patient. The OGTT affirmed diabetes and heightened insulin resistance, distinct from previous patients with GS we evaluated. Further genetic analysis identified a missense heterozygous mutation (c.97C>G in exon 1) within the PDX1 gene, inherited from the patient’s diabetic mother without GS. Furthermore, the patient’s brother, with impaired glucose tolerance but regular potassium levels, also bore this mutation, hinting at additional impacts of the PDX1 gene mutation on glucose metabolism regulation beyond the known impacts of GS.ConclusionThis study unveils unprecedented compound heterozygous mutations in the SLC12A3 and PDX1 genes in a GS patient. These findings illuminate the potential complex genetic factors influencing glucose metabolism disruptions in GS.Take-home messageThis research uncovers a novel combination of SLC12A3 and PDX1 gene mutations in a Gitelman Syndrome patient, revealing intricate genetic factors that potentially disrupt glucose metabolism and shedding light on familial diabetes links