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Purpose: To investigate the clinical effect and side effects of early addition of small-dose eltrombopag in initial treatment of primary immune thrombocytopenia (ITP) patients with decreased platelet count and hormone reduction, and patients with persistent ITP. Methods: Thirty ITP patients newly diagnosed with poor hormonal levels were recruited, along with 36 patients with persistent ITP. The ITP patients were given prednisone in combination with eltrombopag. Blood routine tests, blood pressure, blood sugar, liver and kidney function, and serum ion levels were monitored every two weeks. Adverse events were recorded at 2, 4, and 8 weeks after eltrombopag treatment. Results: After treatment for 2, 4, and 8 weeks, there was obvious effectiveness in 45.0, 39.4, and 40.9 % of patients, respectively, while effectiveness was good in 22.7, 37.9, and 50 % of patients, respectively. Treatment was ineffective in 31.8, 22.7, and 9.1 % of patients, respectively. In the 8th week, 15 patients (22.7 %) had to use prednisone at a dose of more than 20 mg/day to keep their platelet level above 30 x 109/L, while 7 patients (10.6 %) stopped using prednisone completely. Moreover, 4 patients (6.1 %) had their platelet counts maintained above 30 x 109/L after stopping the use of eltrombopag for more than 2 weeks. Conclusion: The combined use of eltrombopag and low-dose hormone is an effective treatment for ITP patients. It produces only a slight side effect, and it is affordable. A larger population study will be required to validate these findings of this new therapeutic option.

Hyperspectral images (HSI) have a large amount of spectral information reflecting the characteristics of matter, while their spatial resolution is low due to the limitations of imaging technology. Complementary to this are multispectral images (MSI), e.g., RGB images, with high spatial resolution but insufficient spectral bands. Hyperspectral and multispectral image fusion is a technique for acquiring ideal images that have both high spatial and high spectral resolution cost-effectively. Many existing HSI and MSI fusion algorithms rely on known imaging degradation models, which are often not available in practice. In this paper, we propose a deep fusion method based on the conditional denoising diffusion probabilistic model, called DDPM-Fus. Specifically, the DDPM-Fus contains the forward diffusion process which gradually adds Gaussian noise to the high spatial resolution HSI (HrHSI) and another reverse denoising process which learns to predict the desired HrHSI from its noisy version conditioning on the corresponding high spatial resolution MSI (HrMSI) and low spatial resolution HSI (LrHSI). Once the training is completes, the proposed DDPM-Fus implements the reverse process on the test HrMSI and LrHSI to generate the fused HrHSI. Experiments conducted on one indoor and two remote sensing datasets show the superiority of the proposed model when compared with other advanced deep learningbased fusion methods. The codes of this work will be opensourced at this address: https://github.com/shuaikaishi/DDPMFus for reproducibility.

Xueyao Su,1,&ast; Yanyan Huang,1,&ast; Wei Dai,2 Yubo Zhang,3 Lijun Zhang,1 Jiayuan Zhang,3 Ruoyan Gong,1 Jingwen Yu,3 Dan Kang,1 Rumei Xiang,1 Jiaojiao Chen,1 Qiuling Shi1– 3 1School of Public Health, Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, Sichuan, People’s Republic of China; 3State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qiuling Shi, School of Public Health, Chongqing Medical University, No. 1, Yixueyuan Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86-18290585397, Fax +86-28-85420116, Email qshi@cqmu.edu.cnPurpose: This study aimed to evaluate the presence of recall bias when patients retrospectively report cough scores.Patients and Methods: Patients who underwent lung surgery between July 2021 and November 2021 were recruited for this study. We retrospectively assessed the severity of cough within the past 24 hours and the past 7 days using a 0– 10 numerical rating scale. Recall bias was defined as the difference between the scores reported on the two assessments. Patients were grouped based on the longitudinal change in cough scores from pre-operation to 4 weeks after discharge using group-based trajectory models. Using generalized estimating equation to explore the factors influencing recall bias.Results: Overall, 199 patients were analyzed and demonstrated the three distinct trajectories of post-discharge cough: high (21.1%), medium (58.3%), and low (20.6%). Significant recall bias was found in week 2 for the high-trajectory patients (6.26 vs 5.10, P< 0.01) and in week 3 for the medium-trajectory patients (2.88 vs 2.60, P=0.01). Among all recall bias, 41.8% were of underestimation, and 21.7% of overestimation. The high trajectory group (β=1.14, P< 0.01) and measurement interval (β=0.36, P< 0.01) were risk factors for underestimation, while post-discharge time (β=− 0.57, P< 0.01) and measurement interval (β=− 0.13, P=0.02) were protective factors for overestimation.Conclusion: Retrospective assessment of post-discharge cough in patients who underwent lung surgery will introduce recall bias, with a tendency of underestimation. The high-trajectory group, interval time and post-discharge time are influencing factors of recall bias. For patients with severe cough at discharge, a shorter recall periods should be employed for monitoring, due to the large bias that results from a longer recall period.Keywords: patient-reported outcome, lung surgery, cough measurement, recall bias

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.

While traditional distributionally robust optimization (DRO) aims to minimize the maximal risk over a set of distributions, Agarwal and Zhang (2022) recently proposed a variant that replaces risk with excess risk. Compared to DRO, the new formulation -- minimax excess risk optimization (MERO) has the advantage of suppressing the effect of heterogeneous noise in different distributions. However, the choice of excess risk leads to a very challenging minimax optimization problem, and currently there exists only an inefficient algorithm for empirical MERO. In this paper, we develop efficient stochastic approximation approaches which directly target MERO. Specifically, we leverage techniques from stochastic convex optimization to estimate the minimal risk of every distribution, and solve MERO as a stochastic convex-concave optimization (SCCO) problem with biased gradients. The presence of bias makes existing theoretical guarantees of SCCO inapplicable, and fortunately, we demonstrate that the bias, caused by the estimation error of the minimal risk, is under-control. Thus, MERO can still be optimized with a nearly optimal convergence rate. Moreover, we investigate a practical scenario where the quantity of samples drawn from each distribution may differ, and propose a stochastic approach that delivers distribution-dependent convergence rates.

In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve $m\gg 1$ lower level problems and have important applications in machine learning. Designing a stochastic gradient and controlling its variance is more intricate due to the hierarchical sampling of blocks and data and the unique challenge of estimating hyper-gradient. We aim to achieve three nice properties for our algorithm: (a) matching the state-of-the-art complexity of standard BO problems with a single block; (b) achieving parallel speedup by sampling $I$ blocks and sampling $B$ samples for each sampled block per-iteration; (c) avoiding the computation of the inverse of a high-dimensional Hessian matrix estimator. However, it is non-trivial to achieve all of these by observing that existing works only achieve one or two of these properties. To address the involved challenges for achieving (a, b, c), we propose two stochastic algorithms by using advanced blockwise variance-reduction techniques for tracking the Hessian matrices (for low-dimensional problems) or the Hessian-vector products (for high-dimensional problems), and prove an iteration complexity of $O(\frac{m\epsilon^{-3}\mathbb{I}(I

Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projection-free online methods focus on minimizing the static regret, which unfortunately fails to capture the challenge of changing environments. In this paper, we investigate non-stationary projection-free online learning, and choose dynamic regret and adaptive regret to measure the performance. Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence. Then, we improve the upper bound to $\mathcal{O}(T^{3/4}(1+P_T)^{1/4})$ by running multiple $\text{BOGD}_\text{IP}$ algorithms with different step sizes in parallel, and tracking the best one on the fly. Our results are the first general-case dynamic regret bounds for projection-free online learning, and can recover the existing $\mathcal{O}(T^{3/4})$ static regret by setting $P_T = 0$. Furthermore, we propose a projection-free method to attain an $\tilde{\mathcal{O}}(\tau^{3/4})$ adaptive regret bound for any interval with length $\tau$, which nearly matches the static regret over that interval. The essential idea is to maintain a set of $\text{BOGD}_\text{IP}$ algorithms dynamically, and combine them by a meta algorithm. Moreover, we demonstrate that it is also equipped with an $\mathcal{O}(T^{3/4}(1+P_T)^{1/4})$ dynamic regret bound. Finally, empirical studies verify our theoretical findings.

In this paper we investigate the optimal controller synthesis problem, so that the system under the controller can reach a specified target set while satisfying given constraints. Existing model predictive control (MPC) methods learn from a set of discrete states visited by previous (sub-)optimized trajectories and thus result in computationally expensive mixed-integer nonlinear optimization. In this paper a novel MPC method is proposed based on reach-avoid analysis to solve the controller synthesis problem iteratively. The reach-avoid analysis is concerned with computing a reach-avoid set which is a set of initial states such that the system can reach the target set successfully. It not only provides terminal constraints, which ensure feasibility of MPC, but also expands discrete states in existing methods into a continuous set (i.e., reach-avoid sets) and thus leads to nonlinear optimization which is more computationally tractable online due to the absence of integer variables. Finally, we evaluate the proposed method and make comparisons with state-of-the-art ones based on several examples.

In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set. The approach falls within the reachability analysis framework and is based on the computation of inner-approximations of controlled reach-avoid sets(CRSs). Given a target set and a safe set, the controlled reach-avoid set is the set of states such that starting from each of them, there exists at least one controller to ensure that the system can enter the target set while staying inside the safe set before the target hitting time. Therefore, the boundary of the controlled reach-avoid set acts as a barrier, which separating states capable of achieving the reach-avoid objective from those that are not, and thus the computed inner-approximation provides a viable space for the system to achieve the reach-avoid objective. Our approach for synthesizing reach-avoid controllers mainly consists of three steps. We first learn a safe set of states in the unknown environment from sensor measurements based on a support vector machine approach. Then, based on the learned safe set and target set, we compute an inner-approximation of the CRS. Finally, we synthesize controllers online to ensure that the system will reach the target set by evolving inside the computed inner-approximation. The proposed method is demonstrated on a Dubin's car system.

For driving safely and efficiently in highway scenarios, autonomous vehicles (AVs) must be able to predict future behaviors of surrounding object vehicles (OVs), and assess collision risk accurately for reasonable decision-making. Aiming at autonomous driving in highway scenarios, a predictive collision risk assessment method based on trajectory prediction of OVs is proposed in this paper. Firstly, the vehicle trajectory prediction is formulated as a sequence generation task with long short-term memory (LSTM) encoder-decoder framework. Convolutional social pooling (CSP) and graph attention network (GAN) are adopted for extracting local spatial vehicle interactions and distant spatial vehicle interactions, respectively. Then, two basic risk metrics, time-to-collision (TTC) and minimal distance margin (MDM), are calculated between the predicted trajectory of OV and the candidate trajectory of AV. Consequently, a time-continuous risk function is constructed with temporal and spatial risk metrics. Finally, the vehicle trajectory prediction model CSP-GAN-LSTM is evaluated on two public highway datasets. The quantitative results indicate that the proposed CSP-GAN-LSTM model outperforms the existing state-of-the-art (SOTA) methods in terms of position prediction accuracy. Besides, simulation results in typical highway scenarios further validate the feasibility and effectiveness of the proposed predictive collision risk assessment method., manuscript submitted to IEEE Transactions on Intelligent Transportation Systems

The purpose of this study is to investigate the entire evolution process of shales with various total organic contents (TOC) in order to build models for quantitative evaluation of oil and gas yields and establish methods for assessing recoverable oil and gas resources from in-situ conversion of organic matters in shale. Thermal simulation experiments under in-situ conversion conditions were conducted on Chang 73 shales from the Ordos Basin in a semi-open system with large capacity. The results showed that TOC and Ro were the key factors affecting the in-situ transformation potential of shale. The remaining oil and gas yields increased linearly with TOC but inconsistently with Ro. Ro ranged 0.75%–1.25% and 1.05%–2.3%, respectively, corresponding to the main oil generation stage and gas generation stage of shale in-situ transformation. Thus a model to evaluate the remaining oil/gas yield with TOC and Ro was obtained. The TOC of shale suitable for in-situ conversion should be greater than 6%, whereas its Ro should be less than 1.0%. Shales with 0.75% (Ro) could obtain the best economic benefit. The results provided a theoretical basis and evaluation methodology for predicting the hydrocarbon resources from in-situ conversion of shale and for the identification of the optimum “sweet spots”. The assessment of the Chang 73 shale in the Ordos Basin indicated that the recoverable oil and gas resources from in-situ conversion of organic matters in shale are substantial, with oil and gas resources reaching approximately 450×108 t and 30×1012 m3, respectively, from an area of 4.27×104 km2.

Supplementary Methods, Figures 1-3, Tables 1-2 from EBV-Induced Human CD8+ NKT Cells Suppress Tumorigenesis by EBV-Associated Malignancies

The underlying mechanism of the protective and suppressive role of NKT cells in human tumor immunosurveillance remains to be fully elucidated. We show that the frequencies of CD8+ NKT cells in patients with EBV-associated Hodgkin's lymphoma or nasopharyngeal carcinoma are significantly lower than those in healthy EBV carriers. These CD8+ NKT cells in tumor patients are also functionally impaired. In human-thymus-severe combined immunodeficient (hu-thym-SCID) chimeras, EBV challenge efficiently promotes the generation of IFN-γ–biased CD8+ NKT cells. These cells are strongly cytotoxic, drive syngeneic T cells into a Th1 bias, and enhance T-cell cytotoxicity to EBV-associated tumor cells. Interleukin-4–biased CD4+ NKT cells are predominately generated in unchallenged chimeras. These cells are noncytotoxic, drive syngeneic T cells into a Th2 bias, and do not affect T-cell cytotoxicity. In humanized xenogeneic tumor-transplanted hu-thym-SCID chimeras, adoptive transfer with EBV-induced CD8+ NKT cells significantly suppresses tumorigenesis by EBV-associated malignancies. EBV-induced CD8+ NKT cells are necessary and sufficient to enhance the T-cell immunity to EBV-associated malignancies in the hu-thym-SCID chimeras. CD4+ NKT cells are synergetic with CD8+ NKT cells, leading to a more pronounced T-cell antitumor response in the chimeras cotransferred with CD4+ and CD8+ NKT cells. Thus, immune reconstitution with EBV-induced CD8+ NKT cells could be a useful strategy in management of EBV-associated malignancies. [Cancer Res 2009;69(20):7935–44]

BackgroundLaparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms.MethodsPeripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m2 in the Department of General Surgery of Kunming First People’s Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-Seq data were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases.ResultsWe identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P.ConclusionThis study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.

Dynamic evolution behaviors of dimension-varying control systems often appear in the genetic regulatory network and the vehicle clutch system etc. An interesting and significant study on dimension-varying control systems is how to realize the dimension-varying (the transient dynamics) process smoothly between the different dimensional subsystems. The quotient space approach is considered as an effective tool to model the transient dynamics of dimension-varying control systems.This paper investigates the realization problem of the transient dynamics of dimension-varying control systems. By revealing the structure of the controllable subspace for the linear system on quotient space, we propose the condition for the realization of the transient dynamics of dimension-varying control systems, based on which a new scheme for modeling the transient dynamics is given. Moreover, the proposed scheme justifies the existing result for the strategy for modelling the transient dynamics. A numeric example is given to illustrate our theoretical results., 15 pages,1figure

This paper investigates group distributionally robust optimization (GDRO), with the purpose to learn a model that performs well over $m$ different distributions. First, we formulate GDRO as a stochastic convex-concave saddle-point problem, and demonstrate that stochastic mirror descent (SMD), using $m$ samples in each iteration, achieves an $O(m (\log m)/\epsilon^2)$ sample complexity for finding an $\epsilon$-optimal solution, which matches the $\Omega(m/\epsilon^2)$ lower bound up to a logarithmic factor. Then, we make use of techniques from online learning to reduce the number of samples required in each round from $m$ to $1$, keeping the same sample complexity. Specifically, we cast GDRO as a two-players game where one player simply performs SMD and the other executes an online algorithm for non-oblivious multi-armed bandits. Next, we consider a more practical scenario where the number of samples that can be drawn from each distribution is different, and propose a novel formulation of weighted GDRO, which allows us to derive distribution-dependent convergence rates. Denote by $n_i$ the sample budget for the $i$-th distribution, and assume $n_1 \geq n_2 \geq \cdots \geq n_m$. In the first approach, we incorporate non-uniform sampling into SMD such that the sample budget is satisfied in expectation, and prove the excess risk of the $i$-th distribution decreases at an $O(\sqrt{n_1 \log m}/n_i)$ rate. In the second approach, we use mini-batches to meet the budget exactly and also reduce the variance in stochastic gradients, and then leverage stochastic mirror-prox algorithm, which can exploit small variances, to optimize a carefully designed weighted GDRO problem. Under appropriate conditions, it attains an $O((\log m)/\sqrt{n_i})$ convergence rate, which almost matches the optimal $O(\sqrt{1/n_i})$ rate of only learning from the $i$-th distribution with $n_i$ samples.

To deal with non-stationary online problems with complex constraints, we investigate the dynamic regret of online Frank-Wolfe (OFW), which is an efficient projection-free algorithm for online convex optimization. It is well-known that in the setting of offline optimization, the smoothness of functions and the strong convexity of functions accompanying specific properties of constraint sets can be utilized to achieve fast convergence rates for the Frank-Wolfe (FW) algorithm. However, for OFW, previous studies only establish a dynamic regret bound of $O(\sqrt{T}(1+V_T+\sqrt{D_T}))$ by utilizing the convexity of problems, where $T$ is the number of rounds, $V_T$ is the function variation, and $D_T$ is the gradient variation. In this paper, we derive improved dynamic regret bounds for OFW by extending the fast convergence rates of FW from offline optimization to online optimization. The key technique for this extension is to set the step size of OFW with a line search rule. In this way, we first show that the dynamic regret bound of OFW can be improved to $O(\sqrt{T(1+V_T)})$ for smooth functions. Second, we achieve a better dynamic regret bound of $O((1+V_T)^{2/3}T^{1/3})$ when functions are smooth and strongly convex, and the constraint set is strongly convex. Finally, for smooth and strongly convex functions with minimizers in the interior of the constraint set, we demonstrate that the dynamic regret of OFW reduces to $O(1+V_T)$, and can be further strengthened to $O(\min\{P_T^\ast,S_T^\ast,V_T\}+1)$ by performing a constant number of FW iterations per round, where $P_T^\ast$ and $S_T^\ast$ denote the path length and squared path length of minimizers, respectively.

Stochastically Extended Adversarial (SEA) model is introduced by Sachs et al. [2022] as an interpolation between stochastic and adversarial online convex optimization. Under the smoothness condition, they demonstrate that the expected regret of optimistic follow-the-regularized-leader (FTRL) depends on the cumulative stochastic variance $\sigma_{1:T}^2$ and the cumulative adversarial variation $\Sigma_{1:T}^2$ for convex functions. They also provide a slightly weaker bound based on the maximal stochastic variance $\sigma_{\max}^2$ and the maximal adversarial variation $\Sigma_{\max}^2$ for strongly convex functions. Inspired by their work, we investigate the theoretical guarantees of optimistic online mirror descent (OMD) for the SEA model. For convex and smooth functions, we obtain the same $\mathcal{O}(\sqrt{\sigma_{1:T}^2}+\sqrt{\Sigma_{1:T}^2})$ regret bound, without the convexity requirement of individual functions. For strongly convex and smooth functions, we establish an $\mathcal{O}(\min\{\log (\sigma_{1:T}^2+\Sigma_{1:T}^2), (\sigma_{\max}^2 + \Sigma_{\max}^2) \log T\})$ bound, better than their $\mathcal{O}((\sigma_{\max}^2 + \Sigma_{\max}^2) \log T)$ bound. For \mbox{exp-concave} and smooth functions, we achieve a new $\mathcal{O}(d\log(\sigma_{1:T}^2+\Sigma_{1:T}^2))$ bound. Owing to the OMD framework, we can further extend our result to obtain dynamic regret guarantees, which are more favorable in non-stationary online scenarios. The attained results allow us to recover excess risk bounds of the stochastic setting and regret bounds of the adversarial setting, and derive new guarantees for many intermediate scenarios.

In this paper, we obtain the following results on dual subspaces of Boolean networks (BNs). For a BN, there is a one-to-one correspondence between partitions of its state-transition graph (STG) and its dual subspaces (i.e., the subspaces generated by a number of Boolean functions of the BN's variables). Moreover, a dual subspace is invariant if and only if the corresponding partition is equitable, i.e., for every two (not necessarily different) cells of the partition, every two states in the former have equally many out-neighbors in the latter. With the help of equitable partitions of an STG, we study the structural properties of the smallest invariant dual subspaces containing a number of Boolean functions. And then, we give algorithms for computing the equitable partition corresponding to the smallest invariant dual subspace containing a given dual subspace. Moreover, we reveal that the unobservable subspace of a BN is the smallest invariant dual subspace containing the output function. We analyze properties of the unobservable subspace by using the obtained structural properties. The graphical representation provides an easier and more intuitive way to characterizing the (smallest) invariant dual subspaces of a BN.

Jingyu Zhang,1,&ast; Yinxia Zhan,2,&ast; Jiaojiao Chen,2 Dan Kang,2 Rumei Xiang,2 Ruoyi Zhang,2 Yubo Zhang,1 Yang Pu,2 Jiayuan Zhang,1 Lijun Zhang,2 Yanyan Huang,2 Ruoyan Gong,2 Xueyao Su,2 Yuxian Nie,1 Qiuling Shi1,2 1State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, People’s Republic of China; 2School of Public Health, Chongqing Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qiuling Shi, State Key Laboratory of Ultrasound in Medicine and Engineering, Chongqing Medical University, No. 1, Medical College Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86-18290585397, Email qshi@cqmu.edu.cnBackground: Patients with hepatobiliary or pancreatic cancers often experience severe symptoms, resulting in a sharp decline in functioning, poor quality of life, and increased mortality risk. Early and effective management of symptoms allows a better quality of life and reduced mortality, depending on the selection of appropriate assessment of specific symptoms for a defined purpose. We aimed to develop a symptom measurement item bank for hepatobiliary or pancreatic cancers.Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was applied to organize this systematic review. The articles validated patient-reported outcome measures (PROMs) for hepatobiliary or pancreatic cancer and published before December 2021 were retrieved from the Web of Science, PubMed, Embase databases and Cochrane Library. Items from the existing PROMs were selected and classified into different patient-reported symptoms based on the concepts and specific underlying constructs of the objects measured.Results: Sixteen unique PROMs were identified across the 29 eligible studies included in our analysis. Items from the literature review (14 PROMs with 421 items for which information was obtained) were selected and classified. As a result of this study, we developed a symptom item bank with 40 patient-reported symptoms and 229 assessment items for hepatobiliary or pancreatic cancer, and fatigue, pain and nausea were the most common symptom items.Conclusion: We developed an item bank to assess the patient-reported symptoms of hepatobiliary or pancreatic cancer. This item bank could allow researchers to select appropriate measures of symptom and provide a basis for the development of a single-item symptom-measurement system.Keywords: patient-reported outcome measures, symptom, item bank, pancreatic carcinoma, hepatocellular carcinoma, cholangiocarcinoma, systematic review

Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compression. Numerous structured pruning methods are already developed that can readily achieve speedups in single-task models, but the pruning of multi-task networks has not yet been extensively studied. In this work, we investigate the effectiveness of structured pruning on multi-task models. We use an existing single-task filter pruning criterion and also introduce an MTL-based filter pruning criterion for estimating the filter importance scores. We prune the model using an iterative pruning strategy with both pruning methods. We show that, with careful hyper-parameter tuning, architectures obtained from different pruning methods do not have significant differences in their performances across tasks when the number of parameters is similar. We also show that iterative structure pruning may not be the best way to achieve a well-performing pruned model because, at extreme pruning levels, there is a high drop in performance across all tasks. But when the same models are randomly initialized and re-trained, they show better results.

Supplemental material, sj-docx-1-npx-10.1177_1934578X231180076 for Integrated Serum Metabolomics and Network Pharmacology Approach to Reveal the Potential Mechanisms of Diwuyanggan Prescription in the Prevention of Acute Liver Injury by Jinlin Xu, Feng Zou, Yuqi Lin, Zhaoxiang Zeng, Yukun Liu, Lijun Zhang, Rongzeng Huang, Xugui Li, Chengwu Song, Yinping Tang and Shuna Jin in Natural Product Communications

In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis., Comment: 24 pages, 8 figures

Learning unnormalized statistical models (e.g., energy-based models) is computationally challenging due to the complexity of handling the partition function. To eschew this complexity, noise-contrastive estimation~(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. However, as found in previous works, NCE may perform poorly in many tasks due to its flat loss landscape and slow convergence. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models from the perspective of compositional optimization. To tackle the partition function, a noise distribution is introduced such that the log partition function can be written as a compositional function whose inner function can be estimated with stochastic samples. Hence, the objective can be optimized by stochastic compositional optimization algorithms. Despite being a simple method, we demonstrate that it is more favorable than NCE by (1) establishing a fast convergence rate and quantifying its dependence on the noise distribution through the variance of stochastic estimators; (2) developing better results for one-dimensional Gaussian mean estimation by showing our objective has a much favorable loss landscape and hence our method enjoys faster convergence; (3) demonstrating better performance on multiple applications, including density estimation, out-of-distribution detection, and real image generation.

In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of using a global temperature parameter $\tau$ ignores the fact that ``not all semantics are created equal", meaning that different anchor data may have different numbers of samples with similar semantics, especially when data exhibits long-tails. First, we propose a new robust contrastive loss inspired by distributionally robust optimization (DRO), providing us an intuition about the effect of $\tau$ and a mechanism for automatic temperature individualization. Then, we propose an efficient stochastic algorithm for optimizing the robust contrastive loss with a provable convergence guarantee without using large mini-batch sizes. Theoretical and experimental results show that our algorithm automatically learns a suitable $\tau$ for each sample. Specifically, samples with frequent semantics use large temperatures to keep local semantic structures, while samples with rare semantics use small temperatures to induce more separable features. Our method not only outperforms prior strong baselines (e.g., SimCLR, CLIP) on unimodal and bimodal datasets with larger improvements on imbalanced data but also is less sensitive to hyper-parameters. To our best knowledge, this is the first methodical approach to optimizing a contrastive loss with individualized temperatures., Comment: 33 pages, 11 figures, accepted by ICML2023

Hyperspectral images (HSI) with abundant spectral information reflected materials property usually perform low spatial resolution due to the hardware limits. Meanwhile, multispectral images (MSI), e.g., RGB images, have a high spatial resolution but deficient spectral signatures. Hyperspectral and multispectral image fusion can be cost-effective and efficient for acquiring both high spatial resolution and high spectral resolution images. Many of the conventional HSI and MSI fusion algorithms rely on known spatial degradation parameters, i.e., point spread function, spectral degradation parameters, spectral response function, or both of them. Another class of deep learning-based models relies on the ground truth of high spatial resolution HSI and needs large amounts of paired training images when working in a supervised manner. Both of these models are limited in practical fusion scenarios. In this paper, we propose an unsupervised HSI and MSI fusion model based on the cycle consistency, called CycFusion. The CycFusion learns the domain transformation between low spatial resolution HSI (LrHSI) and high spatial resolution MSI (HrMSI), and the desired high spatial resolution HSI (HrHSI) are considered to be intermediate feature maps in the transformation networks. The CycFusion can be trained with the objective functions of marginal matching in single transform and cycle consistency in double transforms. Moreover, the estimated PSF and SRF are embedded in the model as the pre-training weights, which further enhances the practicality of our proposed model. Experiments conducted on several datasets show that our proposed model outperforms all compared unsupervised fusion methods. The codes of this paper will be available at this address: https: //github.com/shuaikaishi/CycFusion for reproducibility.

Friction squeal is an unstable friction-induced vibration with high frequency and high intensity, which is troublesome for both people and environment. Surface topography is an important influencing factors of friction squeal. In this paper, based on introduction of surface topography, a review on the influence of surface topography on friction squeal is conducted from the aspect of experimental analysis, theoretical research and simulation analysis. For the convenience of scholars’ understanding and carrying out relevant research, the influence of surface topography on friction and contact parameters, and the influence of friction and contact parameters on friction squeal are reviewed. Moreover, the generation methods of rough surface are introduced to facilitate the research. Finally, on the basis of a discussion on difficulties and shortcomings of the former research, recommendations are proposed for future research.

Highly infectious illnesses caused by pathogens constitute severe threats to public health and lead to global economic loss. The use of robust and programmable clustered regularly interspaced short palindromic repeat and CRISPR-associated protein (CRISPR-Cas) systems, repurposed from genome-engineering applications has markedly improved traditional nucleic acid detection for precise identification, independently enabling rapid diagnostics of multiplex biomarker with genetic and mutation related to tumors, and microbial pathogens. In this review, we delineate the utility of the current CRISPR-Cas enzyme as biosensors by which these effector toolkits achieve recognition, signaling amplification, and finally, accurate detection. Additionally, we discuss the details of the dominance and hurdles related to expanding this revolutionary technology into an effective and convenient contraption crucial for improving the rational redesign to CRISPR/Cas biosensing. Overall, this review provides an insight into the current status of rapid and POC diagnostic systems by CRISPR/Cas tools.

Background: Cardiovascular diseases (CVD) have been reported in 8%-16% of patients with 2019 coronavirus disease (COVID-19). Digoxin is one of the main drugs to treat CVD. Objective: The clinician conducted therapeutic drug monitoring (TDM) of digoxin according to the drug usage on patients to monitor the concentration of digoxin, so as to avoid its toxic and side effects, and provide a theoretical reference for clinical usage of digoxin in patients with COVID-19. Methods: A method for quantifying digoxin concentration in plasma with ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) was developed. After simple protein precipitation of plasma with methanol, digoxin and its internal standard (digoxin-d3) were detected in the positive ion mode using multiple reaction monitoring. Results: Plasma digoxin in the range of 0.2-10 ng/mL had good linearity. The UPLC-MS/MS method was validated with inter-run accuracies ranging from 91.3% to 107.4% and precision less than 13%. Nine plasma samples (5 at valley concentration and 4 at follow-up after stopping dosing) from three patients with COVID-19 were tested. The mean plasma digoxin concentration was 0.73 ng/mL (ranged from 0 to 1.31 ng/mL). Digoxin was detected at the concentration of 0.93 ng/mL after stopping drug administration for 14 days. Conclusion: In this study, we established a simple UPLC-MS/MS method using protein-precipitation to perform TDM of digoxin in patients with COVID-19, and found that about 56% of digoxin plasma concentration was within the treatment window (0.8-2.0 ng/mL). Digoxin can be remained in the body for nearly 14 days in severe patients with COVID-19 after stopping dosing.

Background: Talin-1 is involved in the invasion and synapse development of the Human Immunodeficiency Virus (HIV). We found that talin-1 was cleaved into a 38 KDa fragment (talin-C) in the Peripheral Blood Mononuclear Cells (PBMCs) of HIV patients; however, the underlying mechanisms remain unknown. Objective: This study aimed to determine the relationship between talin-C and HIV infection and to identify the mechanisms underlying the ability of this protein to influence HIV infection. Methods: PBMCs were derived from HIV-infected patients enrolled in this study. N- and C-terminal peptides matching the potential sequence of talin-C were detected in PBMCs by Multiple Reaction Monitoring (MRM) mass spectrometry. TZM-b1 cells were infected with HIV-1 pseudotyped virus (HIVpp) for different durations to detect the talin-C product. Three stable cell lines overexpressing the talin head (TLN1-H) or TLN1-C or with TLN1 knockdown (shTLN1) were created and infected by HIVpp. The HIV marker protein (P24) was then detected by enzyme-linked immunosorbent assay. Finally, an isobaric tag for relative and absolute quantification (iTRAQ)-based proteomics study was performed to detect the TLN1-C-regulated proteins with or without HIVpp infection in TZM-bl cells. The identified proteins were analyzed by R version 4.0.2 and STRING software (Version: 11.0) (https://string-db.org). Results: N- and C-peptides of talin-C were detected to have higher expression in patients with lower HIV load. Talin-C was produced during HIVpp infection. TLN1-C significantly inhibited HIVpp infection in the TZM-b1 cells. Additionally, a proteomic study found that TLN1-C regulated the expression of 99 proteins in TZM-b1 cells with and without HIVpp infection, respectively. According to Gene Ontology (GO) annotation, proteins with cellular metabolic processes and binding function were found to be enriched. Thirty-four proteins have protein-protein interaction, 19 down- and 15 up-regulated proteins, respectively. Conclusion: Talin-C was produced following HIV infection and was inversely proportional to HIV load. A proteomic study indicated that TLN1-C might be involved in HIV infection through regulating metabolic processes.

We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the behaviour of an ADS in the specified traffic scenario. The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee. Furthermore, we explore the safe and the unsafe parameter space of the traffic scenario for driving hazards. We demonstrate the utility of the proposed approach by evaluating safety properties on the state-of-the-art ADS in literature, with a variety of simulated traffic scenarios.

Objective To observe the regulation of Panx1 on ATP/IP3 signaling pathway and its mechanism during cisplatin-induced apoptosis of lung adenocarcinoma cells. Methods Human lung adenocarcinoma cell line A549 was used as the research object and carbenoxolone (CBX) was used as a drug interference tool. A549 cells were divided into normal control group, the CBX group, the cisplatin (DDP) group and the CBX+DDP group. MTT assay and Annexin V/PI assay were used to detect the survival and apoptosis rates of A549 cells. The relative concentrations of extracellular adenosine triphosphate (ATP) and intracellular inositol triphosphate (IP3) were measured by Chemiluminescence and ELISA. Results Compared with DDP group, the cell survival rate of CBX+DDP group increased, while the early and late apoptotic rates and the release concentration of extracellular ATP and intracellular IP3 decreased (all P < 0.01). Conclusion Panx1 can increase the sensitivity of lung adenocarcinoma cells to cisplatin by regulating the ATP/IP3 signaling pathway.

Personal sound zone systems require to know the acoustic transfer functions (ATFs) between the loudspeakers and matching microphones, which in general have to be measured in advance. As the measurement is affected by such factors as sensors' position mismatches and environmental changes, deviation of the measured ATF-s from the true ones is unavoidable and such perturbation may lead to significant performance degradation in sound zone production. In this work, a method is developed for robust acoustic contrast control, which simultaneously considers the norm-bounded uncertainties and a positive semidefinite constraint to ensure the optimization problem to have a solution. Since the problem is non-convex, the polynomial time difference-of-convex functions (POTDC) algorithm is adopted to identify the optimal solution. Simulations are performed to validate the effectiveness of the proposed method and it demonstrates a performance superior to other existing robust control methods.

BackgroundThis study aims to screen out differentially expressed genes (DEGs) regulated by BRCA1-associated protein 1 (BAP1) in osteosarcoma cells, and to analyze their biological functions.MethodsThe microarray dataset GSE23035 of BAP1-knockdown osteosarcoma cells was obtained from Gene Expression Omnibus (GEO) database, consisting of shControl, shBAP1#1 and shBAP1#2 samples. The DEGs between the BAP1-knockdown osteosarcoma cells and the untreated osteosarcoma cells were screened with limma package, and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene Set Enrichment Analysis (GSEA) was also performed for the three groups of samples. Hub genes in a protein-protein interaction (PPI) network of DEGs was filtered, and then subjected to prognostic analysis and correlation analysis with BAP1 in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Besides, the correlation between BAP1 and biological processes/pathways was analyzed by Gene Set Variation Analysis (GSVA) method and the correlation between BAP1 and immune infiltration by CIBERSORT and ESTIMATE methods. The roles of BAP1 in regulating proliferation and epithelial-mesenchymal transition (EMT) were validated by CCK-8 and western blot.Results58 upregulated DEGs and 81 downregulated DEGs were obtained with |logFC| ≥ 1 and adj.p < 0.05. Cell cycle, DNA repair, and focal adhesion were associated with BAP1 in datasets. Further, BAP1 was negatively correlated with naïve CD4 T cells infiltration. In vitro, BAP1 inhibited proliferation and EMT.ConclusionBAP1 might be a tumor suppressor in osteosarcoma and a promising therapeutic target.

Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively studied to improve the convergence rates of stochastic non-convex optimization, which usually maintain and update a sequence of estimators for a single function across iterations. What if we need to track multiple functional mappings across iterations but only with access to stochastic samples of $\mathcal{O}(1)$ functional mappings at each iteration? There is an important application in solving an emerging family of coupled compositional optimization problems in the form of $\sum_{i=1}^m f_i(g_i(\mathbf{w}))$, where $g_i$ is accessible through a stochastic oracle. The key issue is to track and estimate a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$ blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their stochastic values and Jacobians. To improve the complexity for solving these problems, we propose a novel stochastic method named Multi-block-Single-probe Variance Reduced (MSVR) estimator to track the sequence of $\mathbf g(\mathbf{w})$. It is inspired by STORM but introduces a customized error correction term to alleviate the noise not only in stochastic samples for the selected blocks but also in those blocks that are not sampled. With the help of the MSVR estimator, we develop several algorithms for solving the aforementioned compositional problems with improved complexities across a spectrum of settings with non-convex/convex/strongly convex/Polyak-{\L}ojasiewicz (PL) objectives. Our results improve upon prior ones in several aspects, including the order of sample complexities and dependence on the strong convexity parameter. Empirical studies on multi-task deep AUC maximization demonstrate the better performance of using the new estimator.

The dataset is composed of the theoretically optimized structures, energetics and electronic structures information of more than two hundreds of tetrahedral-bonding hybrid heterostructured semiconductors (HHSs). We have given the related information to reproduce the calculations by using the plane-wave pseudopotential methods within the density functional theory with the Perdew-Burke-Ernzerhof exchange-correlation functional as implemented in the Vienna Ab-initio Simulation Package. The file directory is organized as below: |-.Input-files |-.HHSs-CIF |-.Tetrahedral-Bonding HHSs |-.Inorganic_Semiconductors |-.Organic_Molecules |-.HHSs-INCAR |-.HHSs-POTCAR |-.a-[CaAs]1en ->POTCAR …… |-.Output-data |- Formation-energy_bandgap_information.docx |-Relaxed_structure_information.xlsx We expect this database will serve as a valuable resource for the scientific community to further explorethe tetrahedral-bonding hybrid heterostructured semiconductors

Based on 991 groups of analysis data of shale samples from the Lower Member of the Cretaceous Eagle Ford Formation of 1317 production wells and 72 systematic coring wells in the U.S. Gulf Basin, the estimated ultimate recovery (EUR) of shale oil and gas of the wells are predicted by using two classical EUR estimation models, and the average values predicted excluding the effect of engineering factors are taken as the final EUR. Key geological factors controlling EUR of shale oil and gas are fully investigated. The reservoir capacity, resources, flow capacity and fracability are the four key geological parameters controlling EUR. The storage capacity of shale oil and gas is directly controlled by total porosity and hydrocarbon-bearing porosity, and indirectly controlled by total organic carbon (TOC) and vitrinite reflectance (Ro). The resources of shale oil and gas are controlled by hydrocarbon-bearing porosity and effective shale thickness etc. The flow capacity of shale oil and gas is controlled by effective permeability, crude oil density, gas-oil ratio, condensate oil-gas ratio, formation pressure gradient, and Ro. The fracability of shale is directly controlled by brittleness index, and indirectly controlled by clay content in volume. EUR of shale oil and gas is controlled by six geological parameters: it is positively correlated with effective shale thickness, TOC and fracture porosity, negatively correlated with clay content in volume, and increases firstly and then decreases with the rise of Ro and formation pressure gradient. Under the present upper limit of horizontal well fracturing effective thickness of 65 m and the lower limit of EUR of 3×104 m3, when TOC

Approximate matrix multiplication with limited space has received ever-increasing attention due to the emergence of large-scale applications. Recently, based on a popular matrix sketching algorithm---frequent directions, previous work has introduced co-occuring directions (COD) to reduce the approximation error for this problem. Although it enjoys the space complexity of $O((m_x+m_y)\ell)$ for two input matrices $X\in\mathbb{R}^{m_x\times n}$ and $Y\in\mathbb{R}^{m_y\times n}$ where $\ell$ is the sketch size, its time complexity is $O\left(n(m_x+m_y+\ell)\ell\right)$, which is still very high for large input matrices. In this paper, we propose to reduce the time complexity by exploiting the sparsity of the input matrices. The key idea is to employ an approximate singular value decomposition (SVD) method which can utilize the sparsity, to reduce the number of QR decompositions required by COD. In this way, we develop sparse co-occuring directions, which reduces the time complexity to $\widetilde{O}\left((\nnz(X)+\nnz(Y))\ell+n\ell^2\right)$ in expectation while keeps the same space complexity as $O((m_x+m_y)\ell)$, where $\nnz(X)$ denotes the number of non-zero entries in $X$. Theoretical analysis reveals that the approximation error of our algorithm is almost the same as that of COD. Furthermore, we empirically verify the efficiency and effectiveness of our algorithm.

The determinization of a nondeterministic B\"uchi automaton (NBA) is a fundamental construction of automata theory, with applications to probabilistic verification and reactive synthesis. The standard determinization constructions, such as the ones based on the Safra-Piterman's approach, work on the whole NBA. In this work we propose a divide-and-conquer determinization approach. To this end, we first classify the strongly connected components (SCCs) of the given NBA as inherently weak, deterministic accepting, and nondeterministic accepting. We then present how to determinize each type of SCC independently from the others; this results in an easier handling of the determinization algorithm that takes advantage of the structure of that SCC. Once all SCCs have been determinized, we show how to compose them so to obtain the final equivalent deterministic Emerson-Lei automaton, which can be converted into a deterministic Rabin automaton without blow-up of states and transitions. We implement our algorithm in a our tool COLA and empirically evaluate COLA with the state-of-the-art tools Spot and OWL on a large set of benchmarks from the literature. The experimental results show that our prototype COLA outperforms Spot and OWL regarding the number of states and transitions., Comment: This is an extended version of our CAV'22 paper

The saturating counter is the basic module of the dynamic branch predictor, which involves the core technique to improve instruction level parallelism performance in modern processors. However, most studies focus on the performance improvement and hardware consumption of saturating counters, while ignoring the security problems they may cause. In this paper, we creatively propose to study and design saturating counters from the defense perspective of differential privacy, so that attackers cannot distinguish the states that saturating counters are in and further infer sensitive information. To obtain theoretical guarantees, we use Markov chain to formalize the attack algorithm applied to the saturating counter, investigate into the optimal attack strategy and calculate the probability of successful attack. Furthermore, we find that the attacker is able to accurately guess the branch execution of the victim's process in the existing saturating counters. To avoid this, we design a new probabilistic saturating counter, which generalizes the existing conventional and probabilistic saturating counters. The guarantee of differential privacy is applied to deduce parameters of the new saturating counters so that the security requirement can be satisfied. We also theoretically calculate the misprediction rate when the saturating counter reaches the steady state. The experimental results on testing programs show that the calculated theoretical results agree with the experimental performances. Compared with the existing conventional and probabilistic saturating counters, when the parameters of our designed models are selected appropriately, the new saturating counters can not only ensure similar operational performance, but also establish strict security guarantee.

To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. From the learned model, we can infer the corresponding PAC-model robustness property. The innovation of our work is the integration of model learning into PAC robustness analysis: that is, we construct a PAC guarantee on the model level instead of sample distribution, which induces a more faithful and accurate robustness evaluation. This is in contrast to existing statistical methods without model learning. We implement our method in a prototypical tool named DeepPAC. As a black-box method, DeepPAC is scalable and efficient, especially when DNNs have complex structures or high-dimensional inputs. We extensively evaluate DeepPAC, with 4 baselines (using formal verification, statistical methods, testing and adversarial attack) and 20 DNN models across 3 datasets, including MNIST, CIFAR-10, and ImageNet. It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN. Also, its results are consistent with existing DNN testing work like DeepGini.

International audience; Phase Change Materials are materials that exhibit a large optical change in their complex index of refraction upon switching between their various electronic or structural states. As such, they offer a promising way to implement tunability and reconfigurability in the next generation of nano-photonic devices. In this contribution, we will review our recent studies on spin-crossover molecular compounds that can be evaporated and micro-structured. We will also report their use to make resonant and broadband thermo-optic modulators operating in the visible wavelength range respectively based on metal-mirror Fabry-Perot interferometers and frustrated total internal reflection systems.

A previously published study showed that stress may interfere with associative aversive learning and facilitate mood-related disorders. However, whether emotional stress alone affects aversive learning is unknown. Using three chamber-vicarious social defeat stress (3C-VSDS) model mice, we investigated the effect of emotional stress on aversive learning. An important origin of dopamine (DA) neurons, the zona incerta (ZI), is expected to be a novel target for the modulation of aversive learning. However, less is known about the circuit mechanism of ZIDA neurons in aversive learning. Here, we subjected mice to a fear-conditioning system (FCS) and observed an increased calcium activity of ZI TH+ neurons in aversive expectation during the conditioning phase, especially during the late stage of the conditional stimulus (CS) when CS and unconditional stimulus (US) pairings were used. Optogenetic inhibition of ZI TH+ neurons at the late stage of CS disrupted conditioned fear learning in mice. We further identified a TH+ projection from the ZI to the basomedial amygdala (BMA) and found that optogenetic inhibition of the ZI-BMA circuit could also block aversive learning. Finally, we used 3C-VSDS mice as a model of emotional stress. We found that the 3C-VSDS model mice demonstrated reduced aversive expectation associated with ZI TH+ neurons in the late stage of CS and impaired aversive learning in FCS. Optogenetic activation of ZI-BMA TH+ projections in the late stage of CS significantly reversed the aversive FCS learning disability of 3C-VSDS model mice. These data suggest that a TH+ circuit from the ZI to the BMA is required for aversive expectation, both at baseline and in 3C-VSDS-induced aversive learning deficits and that this circuit is a potential target for the modulation of aversive learning. Low activity of ZI-BMA TH+ projections is one reason for 3C-VSDS-induced aversive learning deficits.

The Permian Lucaogou Formation of the Jimsar Sag in the Junggar Basin, NW China, is one of the largest areas of oil exploration and exploitation in the lacustrine shale sequence in China. Oil is commercially extracted from this markedly heterogeneous formation, which is characterized by frequently interbedded shale and tight reservoirs, although producible intervals within the Lucaogou Formation remain unresolved. This study focused on the Jimsar Sag reservoirs to investigate petrological and mineralogical characteristics, source rock and reservoir physical properties, and the molecular biogeochemistry of core extracts and crude oils. The i-C18/n-C18 and Pr/n-C19 ratios of two-step ultrasonic extracts were applied to infer whether the oil is produced from shale or tight reservoirs, taking into account solvent polarity, molecular characteristics of n-alkanes and isoprenoids, and physical properties of the reservoir. The experimental results indicated that the shallower pay zone is mainly produced from tight reservoirs, while in deeper zone with organic-matter maturity above 1.0%, some of the oil is produced from shale. The reservoir properties in organic-rich shale with vitrinite reflectance (Ro) exceeding 1.0% are improved by pore interconnectivity, and oil mobility is enhanced by high gas/oil ratios, which favors production of free-phase hydrocarbons. Such zones may become major prospects for shale oil exploration and production. The results of the present study can potentially apply to sweet-spot identification and development optimization for the Lucaogou shale and other lacustrine shale sequences.

Since 2008, many academics have increasingly paid attention to blockchain technology from different perspectives. In general, researchers desire to achieve global blockchain systems within a sustainable manufacturing domain; however, a number of technical challenges have come to exist in the recent decade, for instance, consensus algorithms and computing paradigms that can meet the privacy protection requirements of manufacturing systems. Therefore, an integrated decision-making framework called Pythagorean fuzzy-entropy-rank sum-Combined Compromise Solution (PF-entropy-RS-CoCoSo) is developed in this study, including two main phases. In the first phase, the PF-entropy-RS method is applied to obtain the subjective and objective weights of criteria to evaluate the technical challenges of transforming blockchain technology for a sustainable manufacturing paradigm in the Industry 4.0 era. The PF-CoCoSo model is then utilized in the second phase to assess the preferences of organizations over different technical challenges of the blockchain technology transformation for the sustainable manufacturing paradigm in the Industry 4.0 era. An empirical case study is taken to assess the main technical challenges of blockchain technology transformation for the sustainable manufacturing paradigm. Furthermore, a comparison analysis and a sensitivity investigation are made to demonstrate the superiority of the developed framework. © 2023 Elsevier Inc.

Yan Song &ast;, Lijun Zhang &ast;, Wenhui Wang Department of Nursing, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, 210000, People’s Republic of China&ast; These authors contributed equally to this workCorrespondence: Yan Song, Department of Nursing, The Second Hospital of Nanjing University of Chinese Medicine, Nanjing, 210000, People’s Republic of China, Tel +86025-83626512, Fax +86025-83626060, Email songyan_sy01@outlook.comObjective: The aim of this study is to improve the effectiveness of training in putting on and removing personal protective equipment (PPE) during COVID-19.Methods: An information–motivation–behavioral skills (IMB) model intervention team was established with the adoption of the IMB model to intervene in putting on and removing PPE by medical staff during COVID-19. Specifically, the information intervention was strengthened through the formulation of the hospital manual for PPE application. In the present study’s five-stage motivational interview, the personal motivation and social motivation of medical staff were deeply understood, helping the medical staff to improve their belief in the importance of correct PPE application and to establish the correct attitude toward PPE application. In terms of behavioral skills, there are numerous issues that could interfere with the establishment of proper PPE application, including material supply, double duty, simultaneous supervision of entering and leaving, continuous supervision, video supervision, and nosocomial infection inspection. The scores relating to PPE application knowledge and self-efficacy as well as the PPE usage qualification rate were calculated in the control group and the IMB intervention group and subsequently compared.Results: For the control group and the IMB intervention group, the scores for PPE application knowledge were 87.78 ± 10.46 and 95.56 ± 9.06 points, respectively. For self-efficacy, the scores were 25.19 ± 0.97 and 33.79 ± 2.05 points, and the PPE usage qualification rates were 64.8% and 90.0%, respectively. The differences in all the scores were statistically significant between the two groups.Conclusion: The application of the IMB model could improve the relevant knowledge relating to PPE application, strengthen the belief in the importance of and motivation for correct PPE application, improve the qualification rate for PPE application, and provide a theoretical and practical basis for reducing the occurrence of nosocomial infection.Keywords: information–motivation–behavioral skills model, COVID-19, personal protective equipment

Sepsis is a disease with a high morbidity and mortality rate. At present, there is a lack of ideal biomarker prognostic models for sepsis and promising studies using prognostic models to predict and guide the clinical use of medications. In this study, 71 differentially expressed genes (DEGs) were obtained by analyzing single-cell RNA sequencing (scRNA-seq) and transcriptome RNA-seq data, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses were performed on these genes. Then, a prognosis model with CCL5, HBD, IFR2BP2, LTB, and WFDC1 as prognostic signatures was successfully constructed after univariate LASSO regression analysis and multivariate Cox regression analysis. Kaplan–Meier (K-M) survival analysis, receiver operating characteristic (ROC) time curve analysis, internal validation, and principal component analysis (PCA) further validated the model for its high stability and predictive power. Furthermore, based on a risk prediction model, gene set enrichment analysis (GSEA) showed that multiple cellular functions and immune function signaling pathways were significantly different between the high- and low-risk groups. In-depth analysis of the distribution of immune cells in healthy individuals and sepsis patients using scRNA-seq data revealed immunosuppression in sepsis patients and differences in the abundance of immune cells between the high- and low-risk groups. Finally, the genetic targets of immunosuppression-related drugs were used to accurately predict the potential use of clinical agents in high-risk patients with sepsis.

NDCG, namely Normalized Discounted Cumulative Gain, is a widely used ranking metric in information retrieval and machine learning. However, efficient and provable stochastic methods for maximizing NDCG are still lacking, especially for deep models. In this paper, we propose a principled approach to optimize NDCG and its top-$K$ variant. First, we formulate a novel compositional optimization problem for optimizing the NDCG surrogate, and a novel bilevel compositional optimization problem for optimizing the top-$K$ NDCG surrogate. Then, we develop efficient stochastic algorithms with provable convergence guarantees for the non-convex objectives. Different from existing NDCG optimization methods, the per-iteration complexity of our algorithms scales with the mini-batch size instead of the number of total items. To improve the effectiveness for deep learning, we further propose practical strategies by using initial warm-up and stop gradient operator. Experimental results on multiple datasets demonstrate that our methods outperform prior ranking approaches in terms of NDCG. To the best of our knowledge, this is the first time that stochastic algorithms are proposed to optimize NDCG with a provable convergence guarantee. Our proposed methods are implemented in the LibAUC library at https://libauc.org/., 32 pages, 12 figures; Accepted by ICML2022