Sampling of the Wiener Process for Remote Estimation over a Channel with Unknown Delay Statistics

In this paper, we study an online sampling problem of the Wiener process. The goal is to minimize the mean squared error (MSE) of the remote estimator under a sampling frequency constraint when the transmission delay distribution is unknown. The sampling problem is reformulated into an optional stopping problem, and we propose an online sampling algorithm that can adaptively learn the optimal stopping threshold through stochastic approximation. We prove that the cumulative MSE regret grows with rate $\mathcal{O}(\ln k)$, where $k$ is the number of samples. Through Le Cam's two point method, we show that the worst-case cumulative MSE regret of any online sampling algorithm is lower bounded by $\Omega(\ln k)$. Hence, the proposed online sampling algorithm is minimax order-optimal. Finally, we validate the performance of the proposed algorithm via numerical simulations.Comment: Conference Version: Mobihoc 2022, submitted to IEEE/ACM Transactions on Networkin

A protein network refinement method based on module discovery and biological information

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Network-based centrality approaches are commonly used to identify essential proteins from protein-protein interaction networks (PINs). Unfortunately, these approaches are limited by the poor quality of the underlying PIN data. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data. In this paper, we proposed a network refinement method based on module discovery and biological information to obtain a higher quality PIN. First, to extract the maximal connected subgraph in the PIN and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules based on the homology information, subcellular localization information and topology information within each module, and to construct a more refined network (CM-PIN). To evaluate the effectiveness of the proposed method, we used 10 typical network-based centrality methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR) to compare the overall performance of the CM-PIN with those the refined dynamic protein network (RD-PIN). The experimental results showed that the CM-PIN was optimal in terms of precision-recall curve, jackknife curve and other criteria, and can help to identify essential proteins more accurately

Late-Quaternary paleoearthquakes along the Liulengshan Fault on the northern Shanxi Rift system

The Liulengshan Fault (LLSF), which lies on the northeastern edge of the Ordos Plateau, is a controlling boundary fault in the northern part of the Shanxi Rift system (SRS). The displaced landforms show that the fault has undergone strong and frequent late-Quaternary seismic activities. In 1989 and 1991, two moderate–strong earthquake swarms (Ms=6.1 and Ms=5.8) successively occurred in the LLSF, and GPS velocity shows that the areas are extending at around 1–2 mm/a. However, there is no surface-rupturing earthquake reported on the LLSF in historical records. Thus, the study of paleoseismic history and rupture behavior of paleoearthquakes in late-Quaternary on the LLSF is of fundamental importance for understanding the future seismic risk of this fault. To solve these problems, we conducted paleoseismological trench excavations at two sites on the LLSF to establish its paleoearthquake history. On the basis of the field geological survey and interpretation of high-precision topographic data, we carried out large-scale fault mapping and excavated two trenches in Xujiabao and Luofengwa across the LLSF. Then, four events in the Xujiabao trench and three events in the Luofengwa trench are identified. Finally, combined with radiocarbon dating (C14), optically stimulated luminescence (OSL) and OxCal modeling, we constrained the ages of these events. Together with the previous results of paleoseismology in Yin et al. (1997), we consider that different segments of the LLSF may rupture together at the same time. Therefore, a total of six paleoearthquake events since late-Quaternary have been finally confirmed at 44,151–30881a, 40,163-28045a, 28,233-19215a, 16,742-12915a, 12,788-8252a, and 8203–2300a BP. According to the empirical relationships between moment magnitude and rupture length, the best estimated magnitude is inferred to be in the range between Mw 6.9 and Mw 7.7. Considering the strong late-Quaternary activity and a long earthquake elapsed time, we propose that the LLSF might have a high seismic hazard potential in the near future

DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation

While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework to a wide range of applications is challenging, mainly due to possible correlation shift and diversity shift in the real world. Most of the previous approaches can only solve one specific distribution shift, such as shift across domains or the extrapolation of correlation. To address that, we propose DecAug, a novel decomposed feature representation and semantic augmentation approach for OoD generalization. DecAug disentangles the category-related and context-related features. Category-related features contain causal information of the target object, while context-related features describe the attributes, styles, backgrounds, or scenes, causing distribution shifts between training and test data. The decomposition is achieved by orthogonalizing the two gradients (w.r.t. intermediate features) of losses for predicting category and context labels. Furthermore, we perform gradient-based augmentation on context-related features to improve the robustness of the learned representations. Experimental results show that DecAug outperforms other state-of-the-art methods on various OoD datasets, which is among the very few methods that can deal with different types of OoD generalization challenges.Comment: Accepted by AAAI202

NH3 sensor based on 3D hierarchical flower-shaped n-ZnO/p-NiO heterostructures yields outstanding sensing capabilities at ppb level

Hierarchical three-dimensional (3D) flower-like n-ZnO/p-NiO heterostructures with various ZnxNiy molar ratios (Zn5Ni1, Zn2Ni1, Zn1Ni1, Zn1Ni2 and Zn1Ni5) were synthesized by a facile hydrothermal method. Their crystal phase, surface morphology, elemental composition and chemical state were comprehensively investigated by XRD, SEM, EDS, TEM and XPS techniques. Gas sensing measurements were conducted on all the as-developed ZnxNiy-based sensors toward ammonia (NH3) detection under various working temperatures from 160 to 340 °C. In particular, the as-prepared Zn1Ni2 sensor exhibited superior NH3 sensing performance under optimum working temperature (280 °C) including high response (25 toward 100 ppm), fast response/recovery time (16 s/7 s), low detection limit (50 ppb), good selectivity and long-term stability. The enhanced NH3 sensing capabilities of Zn1Ni2 sensor could be attributed to both the specific hierarchical structure which facilitates the adsorption of NH3 molecules and produces much more contact sites, and the improved gas response characteristics of p-n heterojunctions. The obtained results clear demonstrated that the optimum n-ZnO/p-NiO heterostructure is indeed very promising sensing material toward NH3 detection for different applications

Spike 1 trimer, a nanoparticle vaccine against porcine epidemic diarrhea virus induces protective immunity challenge in piglets

Porcine epidemic diarrhea virus (PEDV) is considered the cause for porcine epidemic diarrhea (PED) outbreaks and hefty losses in pig farming. However, no effective commercial vaccines against PEDV mutant strains are available nowadays. Here, we constructed three native-like trimeric candidate nanovaccines, i.e., spike 1 trimer (S1-Trimer), collagenase equivalent domain trimer (COE-Trimer), and receptor-binding domain trimer (RBD-Trimer) for PEDV based on Trimer-Tag technology. And evaluated its physical properties and immune efficacy. The result showed that the candidate nanovaccines were safe for mice and pregnant sows, and no animal death or miscarriage occurred in our study. S1-Trimer showed stable physical properties, high cell uptake rate and receptor affinity. In the mouse, sow and piglet models, immunization of S1-Trimer induced high-level of humoral immunity containing PEDV-specific IgG and IgA. S1-Trimer-driven mucosal IgA responses and systemic IgG responses exhibited high titers of virus neutralizing antibodies (NAbs) in vitro. S1-Trimer induced Th1-biased cellular immune responses in mice. Moreover, the piglets from the S1-Trimer and inactivated vaccine groups displayed significantly fewer microscopic lesions in the intestinal tissue, with only one and two piglets showing mild diarrhea. The viral load in feces and intestines from the S1-Trimer and inactivated vaccine groups were significantly lower than those of the PBS group. For the first time, our data demonstrated the protective efficacy of Trimer-Tag-based nanovaccines used for PEDV. The S1-Trimer developed in this study was a competitive vaccine candidate, and Trimer-Tag may be an important platform for the rapid production of safe and effective subunit vaccines in the future

Tau‐mediated synaptic dysfunction is coupled with HCN channelopathy

INTRODUCTION: In tauopathies, altered tau processing correlates with impairments in synaptic density and function. Changes in hyperpolarization‐activated cyclic nucleotide‐gated (HCN) channels contribute to disease‐associated abnormalities in multiple neurodegenerative diseases. METHODS: To investigate the link between tau and HCN channels, we performed histological, biochemical, ultrastructural, and functional analyses of hippocampal tissues from Alzheimer's disease (AD), age‐matched controls, Tau35 mice, and/or Tau35 primary hippocampal neurons. RESULTS: Expression of specific HCN channels is elevated in post mortem AD hippocampus. Tau35 mice develop progressive abnormalities including increased phosphorylated tau, enhanced HCN channel expression, decreased dendritic branching, reduced synapse density, and vesicle clustering defects. Tau35 primary neurons show increased HCN channel expression enhanced hyperpolarization‐induced membrane voltage “sag” and changes in the frequency and kinetics of spontaneous excitatory postsynaptic currents. DISCUSSION: Our findings are consistent with a model in which pathological changes in tauopathies impact HCN channels to drive network‐wide structural and functional synaptic deficits. Highlights: - Hyperpolarization‐activated cyclic nucleotide‐gated (HCN) channels are functionally linked to the development of tauopathy. - Expression of specific HCN channels is elevated in the hippocampus in Alzheimer's disease and the Tau35 mouse model of tauopathy. - Increased expression of HCN channels in Tau35 mice is accompanied by hyperpolarization‐induced membrane voltage “sag” demonstrating a detrimental effect of tau abnormalities on HCN channel function. - Tau35 expression alters synaptic organization, causing a loosened vesicle clustering phenotype in Tau35 mice