Distributed Learning for Stochastic Generalized Nash Equilibrium Problems

This work examines a stochastic formulation of the generalized Nash equilibrium problem (GNEP) where agents are subject to randomness in the environment of unknown statistical distribution. We focus on fully-distributed online learning by agents and employ penalized individual cost functions to deal with coupled constraints. Three stochastic gradient strategies are developed with constant step-sizes. We allow the agents to use heterogeneous step-sizes and show that the penalty solution is able to approach the Nash equilibrium in a stable manner within $O(\mu_\text{max})$, for small step-size value $\mu_\text{max}$ and sufficiently large penalty parameters. The operation of the algorithm is illustrated by considering the network Cournot competition problem

On the Compressed-Oracle Technique, and Post-Quantum Security of Proofs of Sequential Work

We revisit the so-called compressed oracle technique, introduced by Zhandry for analyzing quantum algorithms in the quantum random oracle model (QROM). To start off with, we offer a concise exposition of the technique, which easily extends to the parallel-query QROM, where in each query-round the considered algorithm may make several queries to the QROM in parallel. This variant of the QROM allows for a more fine-grained query-complexity analysis. Our main technical contribution is a framework that simplifies the use of (the parallel-query generalization of) the compressed oracle technique for proving query complexity results. With our framework in place, whenever applicable, it is possible to prove quantum query complexity lower bounds by means of purely classical reasoning. More than that, for typical examples the crucial classical observations that give rise to the classical bounds are sufficient to conclude the corresponding quantum bounds. We demonstrate this on a few examples, recovering known results (like the optimality of parallel Grover), but also obtaining new results (like the optimality of parallel BHT collision search). Our main target is the hardness of finding a $q$-chain with fewer than $q$ parallel queries, i.e., a sequence $x_0, x_1,\ldots, x_q$ with $x_i = H(x_{i-1})$ for all $1 \leq i \leq q$. The above problem of finding a hash chain is of fundamental importance in the context of proofs of sequential work. Indeed, as a concrete cryptographic application of our techniques, we prove that the "Simple Proofs of Sequential Work" proposed by Cohen and Pietrzak remains secure against quantum attacks. Such an analysis is not simply a matter of plugging in our new bound; the entire protocol needs to be analyzed in the light of a quantum attack. Thanks to our framework, this can now be done with purely classical reasoning

THE EFFECTS OF EXTERNAL LOAD ON LOWER EXTREMiTY ELECTROMYOGRAPHY AMPLITUDE DURING COUNTERMOVEMENT JUMP

The purpose of this study was to investigate the effects of different loads on the mean electromyography (EMG) amplitude of the gluteus maximus, biceps fernoris, vastus medialis, gastrocnemius, soleus, and tibialis anterior during the deceleration phase and the acceleration phase of the countermovement jumps (CMJ). Ten male physical education students performed different CMJs with and without an external load (0,2.5,5.0, 7.5, or 10.0 kg hold in arms). The results s h o w the amplitude of the gluteus maximus with load of 7.5 kg was higher than with load of 2.5 kg during the deceleration phase (p < .05), and the amplitude of the soleus with load of 10.0 kg was higher than with load of 2.5 kg during the acceleration phase (p < .05). It indicated that the activities of lower limb muscles were not influenced by the relative lower of external loading during CMJ

Accumulation of epicardial fat rather than visceral fat is an independent risk factor for left ventricular diastolic dysfunction in patients undergoing peritoneal dialysis

BACKGROUND: Symptoms of heart failure with preserved left ventricular systolic function are common among patients undergoing peritoneal dialysis (PD). Epicardial fat (EpF) is an ectopic fat depot with possible paracrine or mechanical effects on myocardial function. The aim of our current study is to assess the association between EpF and Left ventricular diastolic dysfunction (LVDD) in patients undergoing PD and to clarify the relationships among EpF, inflammation, and LVDD in this population. METHODS: This was a cross-sectional study of 149 patients with preserved left ventricular systolic function who were undergoing PD. LVDD was diagnosed (according to the European Society of Cardiology guidelines) and EpF thickness measured by echocardiography. The patients without LVDD were used as controls. The serum inflammatory biomarker high-sensitivity C-reactive protein (hsCRP) was measured. The location and amount of adipose tissue were assessed by computed tomography (CT) at the level of the fourth lumbar vertebra. RESULTS: Subjects with LVDD had higher levels of hsCRP, more visceral and peritoneal fat, and thicker EpF (all p < 0.001) than controls. Visceral adipose tissue, hsCRP, and EpF all correlated significantly (p < 0.05) with LVDD. Multivariate regression analysis rendered the relationship between visceral adipose tissue and LVDD insignificant, whereas EpF was the most powerful determinant of LVDD (odds ratio = 2.41, 95% confidence interval = 1.43–4.08, p < 0.01). EpF thickness also correlated significantly with the ratio of transmitral Doppler early filling velocity to tissue Doppler early diastolic mitral annular velocity (E/e’; r = 0.27, p < 0.01). CONCLUSION: EpF thickness is significantly independently associated with LVDD in patients undergoing PD and may be involved in its pathogenesis

Two highly similar DEAD box proteins, OsRH2 and OsRH34, homologous to eukaryotic initiation factor 4AIII, play roles of the exon junction complex in regulating growth and development in rice

Accession numbers and proteins homologous to eIF4A. (DOCX 18 kb

Group Signatures and Accountable Ring Signatures from Isogeny-based Assumptions

Group signatures are an important cryptographic primitive providing both anonymity and accountability to signatures. Accountable ring signatures combine features from both ring signatures and group signatures, and can be directly transformed to group signatures. While there exists extensive work on constructing group signatures from various post-quantum assumptions, there has not been any using isogeny-based assumptions. In this work, we propose the first construction of isogeny-based group signatures, which is a direct result of our isogeny-based accountable ring signature. This is also the first construction of accountable ring signatures based on post-quantum assumptions. Our schemes are based on the decisional CSIDH assumption (D-CSIDH) and are proven secure under the random oracle model (ROM)

Mitigating Bias for Question Answering Models by Tracking Bias Influence

Models of various NLP tasks have been shown to exhibit stereotypes, and the bias in the question answering (QA) models is especially harmful as the output answers might be directly consumed by the end users. There have been datasets to evaluate bias in QA models, while bias mitigation technique for the QA models is still under-explored. In this work, we propose BMBI, an approach to mitigate the bias of multiple-choice QA models. Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance. If the influenced instance is more biased, we derive that the query instance is biased. We then use the bias level detected as an optimization objective to form a multi-task learning setting in addition to the original QA task. We further introduce a new bias evaluation metric to quantify bias in a comprehensive and sensitive way. We show that our method could be applied to multiple QA formulations across multiple bias categories. It can significantly reduce the bias level in all 9 bias categories in the BBQ dataset while maintaining comparable QA accuracy

WiRiS: Transformer for RIS-Assisted Device-Free Sensing for Joint People Counting and Localization using Wi-Fi CSI

Channel State Information (CSI) is widely adopted as a feature for indoor localization. Taking advantage of the abundant information from the CSI, people can be accurately sensed even without equipped devices. However, the positioning error increases severely in non-line-of-sight (NLoS) regions. Reconfigurable intelligent surface (RIS) has been introduced to improve signal coverage in NLoS areas, which can re-direct and enhance reflective signals with massive meta-material elements. In this paper, we have proposed a Transformer-based RIS-assisted device-free sensing for joint people counting and localization (WiRiS) system to precisely predict the number of people and their corresponding locations through configuring RIS. A series of predefined RIS beams is employed to create inputs of fingerprinting CSI features as sequence-to-sequence learning database for Transformer. We have evaluated the performance of proposed WiRiS system in both ray-tracing simulators and experiments. Both simulation and real-world experiments demonstrate that people counting accuracy exceeds 90%, and the localization error can achieve the centimeter-level, which outperforms the existing benchmarks without employment of RIS