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Power analysis is a class of side-channel attacks, where power consumption data is used to infer sensitive information and extract secrets from a system. Traditionally, such attacks required physical access to the target, as well as specialized devices to measure the power consumption with enough precision. The PLATYPUS attack has shown that on-chip power meter capabilities exposed to a software interface might form a new class of power side-channel attacks. This paper presents a software-based power side-channel attack on Apple Silicon M1/M2 platforms, exploiting the System Management Controller (SMC) and its power-related keys, which provides access to the on-chip power meters through a software interface to user space software. We observed data-dependent power consumption reporting from such keys and analyzed the correlations between the power consumption and the processed data. Our work also demonstrated how an unprivileged user mode application successfully recovers bytes from an AES encryption key from a cryptographic service supported by a kernel mode driver in macOS. Furthermore, we discuss the impact of software-based power side-channels in the industry, possible countermeasures, and the overall implications of software interfaces for modern on-chip power management systems., 6 pages, 4 figures, 5 tables

Brain-inspired computing has the potential to change the current paradigm based on the von Neumann architecture, boosting machine learning applications. Signalling in our brains relies on voltage-gated ion channels, which have the electrical behaviour of memristors, a resistor with memory. We use molecular dynamics simulations, continuum models, and electrophysiological experiments to investigate the concept of a bioinspired hydrophobically gated memristive nanopore. We found that hydrophobic gating enables memory by an electrowetting mechanism, for which we formulate simple design rules. We engineered a biological nanopore, to produce the typical hysteresis cycles of a memristor. This route can be used to realise compact, economical, and flexible bioinspired memristors.

In this report, 2 × 2 zero-sum games are studied under the following assumptions: (1) One of the players (the leader) commits to choose its actions by sampling a given probability measure (strategy); (2) The leader announces its action, which is observed by its opponent (the follower) through a binary channel; and (3) the follower chooses its strategy based on the knowledge of the leader’s strategy and the noisy observation of the leader’s action. Under these conditions, the equilibrium is shown to always exist. Interestingly, even subject to noise, observing the actions of the leader is shown to be either beneficial or immaterial for the follower. More specifically, the payoff at the equilibrium of this game is upper bounded by the payoff at the Stackelberg equilibrium (SE) in pure strategies; and lower bounded by the payoff at the Nash equilibrium, which is equivalent to the SE in mixed strategies. Finally, necessary and sufficient conditions for observing the payoff at equilibrium to be equal to its lower bound are presented. Sufficient conditions for the payoff at equilibrium to be equal to its upper bound are also presented.; Dans ce rapport, les jeux à somme nulle 2 × 2 sont étudiés sous les hypothèses suivantes : (1) L’un des joueurs (le meneur) s’engage à choisir ses actions en échantillonnant une mesure de probabilité donnée (stratégie) ; (2) Le meneur annonce son action, qui est observée par son adversaire (le suiveur) à travers un canal binaire ; et (3) le suiveur choisit sa stratégie en fonction de la connaissance de la stratégie du meneur et de l’observation bruitée de l’action du meneur. Dans ces conditions, on montre que l’équilibre existe toujours. Fait intéressant, même sujette au bruit, l’observation des actions du leader s’avère soit bénéfique, soit immatérielle pour le suiveur. Plus précisément, la récompense à l’équilibre de ce jeu est majorée par la récompense à l’équilibre de Stackelberg (SE) en stratégies pures, et minorée par la récompense à l’équilibre de Nash, qui équivaut au SE en stratégies mixtes. Enfin, les conditions nécessaires et suffisantes pour observer que la récompense à l’équilibre est égale à sa borne inférieure sont présentées. Les conditions suffisantes pour observer que la récompense à l’équilibre est égale à sa borne supérieure sont aussi présentées.

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods primarily employ semantic segmentation, which suffers from overconfident incorrect predictions. In this paper, we propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask. Due to the stochastic sampling process of diffusion, our model is capable of sampling multiple possible predictions from the mask distribution, avoiding the problem of overconfident point estimation. Moreover, we develop specialized learning strategies that include an innovative ensemble approach for generating robust predictions and tailored forward diffusion methods for efficient training, specifically for the COD task. Extensive experiments on three COD datasets attest the superior performance of our model compared to existing state-of-the-art methods, particularly on the most challenging COD10K dataset, where our approach achieves 0.019 in terms of MAE.

In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations. Although intermediate representation like gloss has been proven effective, gloss annotations are hard to acquire, especially in large quantities. This limits the domain coverage of translation datasets, thus handicapping real-world applications. To mitigate this problem, we design the Gloss-Free End-to-end sign language translation framework (GloFE). Our method improves the performance of SLT in the gloss-free setting by exploiting the shared underlying semantics of signs and the corresponding spoken translation. Common concepts are extracted from the text and used as a weak form of intermediate representation. The global embedding of these concepts is used as a query for cross-attention to find the corresponding information within the learned visual features. In a contrastive manner, we encourage the similarity of query results between samples containing such concepts and decrease those that do not. We obtained state-of-the-art results on large-scale datasets, including OpenASL and How2Sign. The code and model will be available at https://github.com/HenryLittle/GloFE., ACL 2023 Main Conference (Oral)

Simulating the quantum dynamics of molecules in the condensed phase represents a longstanding challenge in chemistry. Trapped-ion quantum systems may serve as a platform for the analog-quantum simulation of chemical dynamics that is beyond the reach of current classical-digital simulation. To identify a "quantum advantage" for these simulations, performance analysis of both classical-digital algorithms and analog-quantum simulation on noisy hardware is needed. In this Perspective, we make this comparison for the simulation of model molecular Hamiltonians that describe intrinsically quantum models for molecules that possess linear vibronic coupling, comparing the accuracy and computational cost. We describe several simple Hamiltonians that are commonly used to model molecular systems, which can be simulated with existing or emerging trapped-ion hardware. These Hamiltonians may serve as stepping stones toward the use of trapped-ion simulators beyond the reach of classical-digital methods. Finally, we identify dynamical regimes where classical-digital simulations seem to have the weakest performance compared to analog-quantum simulations. These regimes may provide the lowest hanging fruit to exploit potential quantum advantages., 23 pages, 6 figures

Electron transfer within and between molecules is crucial in chemistry, biochemistry, and energy science. This study describes a quantum simulation method that explores the influence of light polarization on the electron transfer between two molecules. By implementing precise and coherent control among the quantum states of trapped atomic ions, we can induce quantum dynamics that mimic the electron transfer dynamics in molecules. We use $3$-level systems (qutrits), rather than traditional two-level systems (qubits) to enhance the simulation efficiency and realize high-fidelity simulations of electron transfer dynamics. We treat the quantum interference between the electron coupling pathways from a donor with two degenerate excited states to an acceptor and analyze the transfer efficiency. We also examine the potential error sources that enter the quantum simulations. The trapped ion systems have favorable scalings with system size compared to those of classical computers, promising access to electron-transfer simulations of increasing richness., 9 pages, 6 figures

We present the design, implementation, and evaluation of FineIBT: a CFI enforcement mechanism that improves the precision of hardware-assisted CFI solutions, like Intel IBT, by instrumenting program code to reduce the valid/allowed targets of indirect forward-edge transfers. We study the design of FineIBT on the x86-64 architecture, and implement and evaluate it on Linux and the LLVM toolchain. We designed FineIBT's instrumentation to be compact, incurring low runtime and memory overheads, and generic, so as to support different CFI policies. Our prototype implementation incurs negligible runtime slowdowns ($\approx$0%-1.94% in SPEC CPU2017 and $\approx$0%-1.92% in real-world applications) outperforming Clang-CFI. Lastly, we investigate the effectiveness/security and compatibility of FineIBT using the ConFIRM CFI benchmarking suite, demonstrating that our instrumentation provides complete coverage in the presence of modern software features, while supporting a wide range of CFI policies with the same, predictable performance., Accepted at RAID 2023

Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challenges to this field with applications in cell phones, personal computers, autonomous cars, and wireless base stations. Here we list a set of problems, ranging from training, inference, generalization bound, and optimization with some formalism to communicate these challenges with mathematicians, statisticians, and theoretical computer scientists. This is a subjective view of the research questions in deep learning that benefits the tech industry in long run.

Gastric cancer is a tumor type characterized by lymph node metastasis and the invasion of local tissues. There is thus a critical need to clarify the molecular mechanisms governing gastric cancer onset and progression to guide the treatment of this disease. Long non-coding RNAs and mRNA expression profiles associated with early and local advanced gastric cancer were examined through microarray analyses, with GO and KEGG analyses being employed as a means of exploring the functional roles of those long non-coding RNAs and mRNAs that were differentially expressed in gastric cancer. In total, 1005 and 1831 lncRNAs and mRNAs, respectively, were found to be differentially expressed between early and local advanced gastric cancer. GO and KEGG analyses revealed several pathways and processes that were dysregulated, including the RNA transport, ECM-receptor interaction, and mRNA splicing pathways. In co-expression networks, E2F1, E2F4, and STAT2 were identified as key transcriptional regulators of these processes. Moreover, thrombospondin-2 was confirmed as being expressed at high levels in more advanced gastric cancer by both the GEO and TCGA databases. RNA-sequencing analyses of SGC-790 cells transfected to express thrombospondin-2 further revealed this gene to enhance NF-kB and TNF pathway signaling activity. These results offer insight into gastric cancer-related regulatory networks and suggest thrombospondin-2 to be an important oncogene that drives the progression of this deadly cancer type.

We study the problem of imputing missing values in a dataset, which has important applications in many domains. The key to missing value imputation is to capture the data distribution with incomplete samples and impute the missing values accordingly. In this paper, by leveraging the fact that any two batches of data with missing values come from the same data distribution, we propose to impute the missing values of two batches of samples by transforming them into a latent space through deep invertible functions and matching them distributionally. To learn the transformations and impute the missing values simultaneously, a simple and well-motivated algorithm is proposed. Our algorithm has fewer hyperparameters to fine-tune and generates high-quality imputations regardless of how missing values are generated. Extensive experiments over a large number of datasets and competing benchmark algorithms show that our method achieves state-of-the-art performance., ICML 2023 camera-ready version, https://openreview.net/forum?id=WBWb1FU8iz

The universality theorem in quantum computing states that any quantum computational task can be decomposed into a finite set of logic gates operating on one and two qubits. However, the process of such decomposition is generally inefficient, often leading to exponentially many gates to realize an arbitrary computational task. Practical processor designs benefit greatly from availability of multi-qubit gates that operate on more than two qubits to implement the desired circuit. In 1995, Cirac and Zoller proposed a method to realize native multi-qubit controlled-$Z$ gates in trapped ion systems, which has a stringent requirement on ground-state cooling of the motional modes utilized by the gate. An alternative approach, the M\o lmer-S\o rensen gate, is robust against residual motional excitation and has been a foundation for many high-fidelity gate demonstrations. This gate does not scale well beyond two qubits, incurring additional overhead when used to construct many target algorithms. Here, we take advantage of novel performance benefits of long ion chains to realize fully programmable and scalable high-fidelity Cirac-Zoller gates., Comment: 11 pages, 6 figures

BackgroundWith the increasing interest of academics in the application of artificial intelligence to sepsis, thousands of papers on this field had been published in the past few decades. It is difficult for researchers to understand the themes and latest research frontiers in this field from a multi-dimensional perspective. Consequently, the purpose of this study is to analyze the relevant literature in the application of artificial intelligence to sepsis through bibliometrics software, so as to better understand the development status, study the core hotspots and future development trends of this field.MethodsWe collected relevant publications in the application of artificial intelligence to sepsis from the Web of Science Core Collection in 2000 to 2021. The type of publication was limited to articles and reviews, and language was limited to English. Research cooperation network, journals, cited references, keywords in this field were visually analyzed by using CiteSpace, VOSviewer, and COOC software.ResultsA total of 8,481 publications in the application of artificial intelligence to sepsis between 2000 and 2021 were included, involving 8,132 articles and 349 reviews. Over the past 22 years, the annual number of publications had gradually increased exponentially. The USA was the most productive country, followed by China. Harvard University, Schuetz, Philipp, and Intensive Care Medicine were the most productive institution, author, and journal, respectively. Vincent, Jl and Critical Care Medicine were the most cited author and cited journal, respectively. Several conclusions can be drawn from the analysis of the cited references, including the following: screening and identification of sepsis biomarkers, treatment and related complications of sepsis, and precise treatment of sepsis. Moreover, there were a spike in searches relating to machine learning, antibiotic resistance and accuracy based on burst detection analysis.ConclusionThis study conducted a comprehensive and objective analysis of the publications on the application of artificial intelligence in sepsis. It can be predicted that precise treatment of sepsis through machine learning technology is still research hotspot in this field.

Biochar (BC) has garnered extensive attention as a carbon sequestration solution. This study comprehensively reviews of the effects of BC on the buildup, protection, stabilization, and mineralization of soil organic carbon (SOC). The input of BC-derived carbon alters soil carbon quality by changing SOC content and aromaticity. BC also affects processes controlling substrate accessibility, such as physical protection of SOC by soil aggregates and organo-mineral interaction. Changes in soil carbon quality and physicochemical properties further alter carbon source availability and microbial habitat, thereby affecting the microbial decomposition of SOC. Of particular importance is the soil aggregation process, which regulates the distribution and turnover of microbial necromass and SOC fraction in different aggregates. The “microbial carbon pump” concept framework highlights the pivotal role of microbial necromass in the formation of soil stable carbon pool. BC can affect the accumulation and decomposition of microbial necromass carbon in the bulk soil and different aggregates, thereby changing the soil carbon pool composition and stability. The newly proposed “mineral carbon pump” hypothesis further emphasizes the crucial role of organo-mineral interaction in stabilizing SOC. Further research is required to understand the role of viable microbial biomass and dead microbial necromass in SOC turnover, as well as the extent and mechanism of the interaction between BC and soil minerals. Long-term ecosystem effects of BC, as well as the resilience of BC-soil system to climate change, need to be clarified to determine the rationality of BC application in the context of global change.

Supplemental material, sj-docx-1-onc-10.1177_11795549221147993 for Predictive Value of Flank Pain and Gross Hematuria on Long-Term Survival in Patients With Upper Tract Urothelial Carcinoma Treated by Radical Nephroureterectomy by Ke Ning Sun, Jian Hong Wu, Zhi Hao Chen, Yi Jun He, Yi Ling Chen, Jin Zhong Hu and Lu Sheng in Clinical Medicine Insights: Oncology

Additional file 4: Supplementary Table 3.

Additional file 3: Supplementary Table 2.

The one-loop contributions to the flavor changing neutral current decays of the top quark into a light quark and a gauge boson or Higgs boson: $t\rightarrow qV,qh$, with $q$ = $u$ or $c$, $V$ = $\gamma$, $g$ or $Z$, are analyzed in this work in the framework of the minimal R-symmetric supersymmetric standard model. The numerical results show that the gluino or $\rho$-chargino dominates the predictions on BR($t\rightarrow qV,qh$), and the contributions from neutralino or $\chi$-chargino are insignificant. Taking account of the constraints on the squark mixing parameters from $\bar{B}\rightarrow X_s\gamma$ and $B^0_{d,s}\rightarrow \mu^+\mu^-$, the theoretical predictions on BR($t\rightarrow qg$) can be enhanced to be $\mathcal O(10^{-5}-10^{-6})$ and these two processes are very promising to be observed at the HL-LHC and FCC-hh. The values of BR($t\rightarrow q\gamma,qZ,qh$) are predicted to be, at least, four orders of magnitude below the present experimental bounds., Comment: 21 pages,8 figures

Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators' later click is based on models' feedback of annotators' former click. This serial interaction is unable to utilize model's parallelism capabilities. Second, the model has to repeatedly process the image, the annotator's current click, and the model's feedback of the annotator's former clicks at each step of interaction, resulting in redundant computations. For efficient computation, we propose a method named InterFormer that follows a new pipeline to address these issues. InterFormer extracts and preprocesses the computationally time-consuming part i.e. image processing from the existing process. Specifically, InterFormer employs a large vision transformer (ViT) on high-performance devices to preprocess images in parallel, and then uses a lightweight module called interactive multi-head self attention (I-MSA) for interactive segmentation. Furthermore, the I-MSA module's deployment on low-power devices extends the practical application of interactive segmentation. The I-MSA module utilizes the preprocessed features to efficiently response to the annotator inputs in real-time. The experiments on several datasets demonstrate the effectiveness of InterFormer, which outperforms previous interactive segmentation models in terms of computational efficiency and segmentation quality, achieve real-time high-quality interactive segmentation on CPU-only devices.

Additional file 2: Supplementary Table 1.

Cretaceous is the key exploration target layer in the eastern depression of North Yellow Sea basin, which has a good prospect for oil and gas exploration. Its huge oil and gas resource potential has attracted great attention from petroleum geologists. In this study, the main rock types, reservoir space types, petrophysical characteristics and main controlling factors of Cretaceous reservoir are studied through core observation, thin section identification, petrophysical analysis and scanning electron microscope observation. The results indicate that the main rock types of Cretaceous reservoir in the eastern depression of North Yellow Sea basin are lithic arkose, feldspar lithic sandstone, some feldspar sandstone and a small amount of lithic sandstone. The average porosity is 6.9%, and the average permeability is 0.46 × 10−3 μm, so Cretaceous reservoir in the study area has poor petrophysical characteristics and belongs to low porosity and low permeability reservoir. Cretaceous reservoirs in the study area mainly develop in secondary pores, which are dominated by dissolution pores (including intragranular pores, intercrystalline pores and cleavage pores), followed by fractures. The main factors affecting petrophysical characteristics of Cretaceous reservoir in the study area are provenance properties, sedimentation, diagenesis (including compaction, cementation and dissolution) and tectonism. The provenance properties and sedimentation are the prerequisite conditions affecting petrophysical characteristics. Petrophysical characteristics of feldspar sandstone of Cretaceous reservoir in the study area and lithic arkose are better than that of feldspar lithic sandstone. Both compaction and cementation reduce the porosity and permeability of Cretaceous reservoir in the study area and make petrophysical characteristics become poor, whereas the dissolution and tectonism play an important role in improving petrophysical characteristics of Cretaceous reservoir.

In this report, some properties of the set of Nash equilibria (NEs) of 2 × 2 zero-sum games are reviewed. In particular, the cardinality of the set of NEs is given in terms of the entries of the payoff matrix. Moreover, closed-form expressions for the NE strategies and the payoff at the NE (the value of the game) are provided in terms of the entries of the payoff matrix. The results presented in this report are not necessarily new knowledge, as they follow from the definition of the NE after some tedious calculations. Nevertheless this synthetic presentation is original in the literature.; Dans ce rapport, certaines propriétés de l’ensemble des équilibres de Nash (EN) des jeux 2 × 2 à somme nulle sont étudiés. En particulier, la cardinalité de l’ensemble des EN est donnée en termes des entrées de la matrice du jeu. De plus, des expressions explicites pour les stratégies d’équilibre et la valeur du jeu sont présentées. Ces résultats ne sont pas nécessairement des connaissances nouvelles, car ils découlent de la définition de l’EN après quelques calculs. Néanmoins, cette présentation synthétique est originale dans la littérature.

Epilepsy affects more than 70 million people in the world. It is characterized by recurrent spontaneous seizures, and it is related to many neurological, cognitive, and psychosocial consequences. Glutamate neurotransmitter dysfunction has essential functions in the pathophysiology of epilepsy. In this work, bibliometric analysis was conducted to explore the trends, frontiers, and hotspots of the global scientific output of glutamate in epilepsy research in the past 20 years. The Science Citation Index Expanded of the Web of Science Core Collection (WoSCC) was searched to obtain information on publications and records published between 2002 and 2021. VOSviewer and CiteSpace were used to conduct bibliometric and visual analyses on the overall distribution of annual output, major countries, active institutions, journals, authors, commonly cited literature, and keywords. The impact and quality of the papers were assessed using the global citation score (GCS). Four thousand eight hundred ninety-one publications were retrieved in total. During the past two decades, the number of publications (Np) associated with glutamate in epilepsy has risen yearly. The United States has published the most papers; its H-index and number of citations are also the highest. The League of European Research Universities (LERU) was the most productive institution. In 2016, the total score of the paper written by Zhang Y was 854, ranking first. The keywords that appear most frequently are “epilepsy,” “glutamate,” “temporal lobe epilepsy (TLE),” “hippocampus,” and “seizures.” This study showed that although the publications related to epileptic glutamate fluctuated slightly, the Np increased overall. The United States is a great creator and influential country in this field. The first three authors are Eid, T., Aronica, E., and Smolders, I. “spectrum,” “animal model,” “inflammation,” “mutation,” “dysfunction,” and “prefrontal cortex” are increasing research hotspots. By recognizing the most critical indicators (researchers, countries, research institutes, and journals of glutamate release in epilepsy research), the research hotspot of glutamate in epilepsy could help countries, scholars, and policymakers in this field enhance their understanding of the role of glutamate in epilepsy and make decisions.

The objective of this study was to convert inulin to high output fructose with the commercial inulinase in the continuous ultrafiltration membrane bioreactor (UFMB). The effects of operational conditions were explored for the volumetric productivity. The model equations of kinetics and membrane fouling were also founded. The stable fructose production with the volumetric productivity was attained at the optimum condition of inulin concentration (100 g/L), E/S ratio (40 U/g) and residence time (20 min). The kinetics results showed that the experimental data obtained were fit to the model equations. Preliminary experimental results were attained the fundamental membrane fouling mechanism. These results demonstrated its feasibility and availability for efficient fructose production, which were beneficial to provide theoretical basis for further researches. The operational conditions were explored for the volumetric productivity. Model equations and parameters of kinetics were founded and obtained. The membrane fouling mechanism of UFMB system was founded.

Objective: Liquid-liquid phase separation (LLPS) is a functional unit formed by specific molecules. It lacks a membrane and has been reported to play a crucial role in tumor drug resistance and growth by regulating gene expression and drug distribution. However, whether LLPS could be used to predict cancer prognosis was not clear. This study aimed to construct a prognostic model for breast cancer based on LLPS-correlated genes (LCGs).Methods: LCGs were identified using the PhaSepDB, gene expression profile and clinical characteristics of breast cancer were obtained from TCGA and cBioportal. The PanCancer Atlas (TCGA) cohort was used as the training cohort to construct the prognostic model, while the Nature 2012 and Nat Commun 2016 (TCGA) cohort and GEO data were used as test cohort to perform external verification. Data analysis was performed with R4.2.0 and SPSS20.0.Results: We identified 140 prognosis-related LCGs (pLCGs) (p< 0.01) in all cohorts, 240 pLCGs (p< 0.01) in the luminal cohort, and 28 pLCGs (p< 0.05) in the triple-negative breast cancer (TNBC) cohort. Twelve genes in all cohorts (training cohort: 5/10-year ROC values were 0.76 and 0.77; verification cohort: 5/10-year ROC values were 0.61 and 0.58), eight genes in the luminal cohort (training cohort: 5/10-year ROC values were 0.79 and 0.75; verification cohort: 5/10-year ROC values were 0.62 and 0.62), and four genes in the TNBC cohort (training cohort: 5/10-year ROC values were 0.73 and 0.79; verification cohort: 5/10-year ROC values were 0.55 and 0.54) were screened out to construct the prognostic prediction model. The 17-gene risk-score was constructed in all cohorts (1/3/5-year ROC values were 0.88, 0.83, and 0.81), and the 11-gene risk-score was constructed in the luminal cohort (1/3/5-year ROC values were 0.67, 0.85 and 0.84), and the six-gene risk-score was constructed in the TNBC cohort (1/3/5-year ROC value were 0.87, 0.88 and 0.81). Finally, the risk-score and clinical factors were applied to construct nomograms in all cohorts (1/3/5-year ROC values were 0.89, 0.79 and 0.75, C-index = 0.784), in the luminal cohort (1/3/5-year ROC values were 0.84, 0.83 and 0.85, C-index = 0.803), and in the TNBC cohort (1/3/5-year ROC values were 0.95, 0.84 and 0.77, C-index = 0.847).Discussion: This study explored the roles of LCGs in the prediction of breast cancer prognosis.

The service performance of concrete structures in a water environment differs from that in a normal environment. An accurate evaluation of the mechanical properties and service status of wet concrete is related to the reliable design and safe operation of concrete structures, e.g., hydraulics, marine engineering, bridges, and tunnels. To promote the application of new and high-performance concrete to complex water environments and grasp the future development trend, the research progress on the service performance of concrete in water environments was reviewed worldwide. Starting from the internal water content of concrete, the existing research is combed, the influence of water content, water pressure, and loading rate on the static and dynamic characteristics of concrete in a water environment is summarized, and the influence mechanism is analyzed. The literature review demonstrates that the static compressive strength of wet concrete is lower than that of dry concrete; however, the elastic modulus improves. With an increase in the strain rate, the compressive strength of wet and dry concretes improves, and the rate sensitivity of the wet concrete is greater than that of the normal concrete. Pore structure characteristics mainly affect the static strength of the wet concrete. The improvement in the dynamic strength of the wet concrete is caused by the combined effect of the concrete rate sensitivity, “Stefan” effect, and water pressure.

In the search for green and sustainable development today, a good model of development for society is using social resources reasonably. According to the exploration of the supply and demand of electricity and the development trend of the new energy vehicle market, we have developed a feasibility study on the reverse charging of new energy vehicle batteries. By the study, we will use the new energy vehicle as an energy storage medium to regulate the supply and demand of the electricity market, establishing a systematic regulation network and leading the development of related industries and promoting the concept of resource conservation and green and sustainable development together all the time on the strategy of carbon peak and neutrality goals

Traction power fluctuations have economic and environmental effects on high-speed railway system (HSRS). The combination of energy storage system (ESS) and HSRS shows a promising potential for utilization of regenerative braking energy and peak shaving and valley filling. This paper studies a hybrid energy storage system (HESS) for traction substation (TS) which integrates super-capacitor (SC) and vanadium redox battery (VRB). According to the characteristics of the traction load under actual operating conditions, an energy management strategy with fixed-period control (FPC) is proposed, which fully leverages the periodicity and regularity of HSRS operation. To achieve the optimal size, economic feasibility is selected as the optimization objective, which is fully assessed in terms of Net Present Value (NPV). The optimization constraints are formulated in which the Discrete Fourier Transform (DFT) is performed for power allocation between SC and VRB. Besides, an improved mutation-based particle swarm optimization (IMBPSO) is proposed to efficiently solve the optimization and enhance convergence performance. Finally, combined with the measured traction load data, the effectiveness of the FPC energy management strategy is verified and the optimal scale of the HESS is provided.

C30H48O4, orthorhombic, P212121 (no. 19), a = 12.34133(14) Å, b = 16.50759(15) Å, c = 26.8804(2) Å, V = 5476.23(9) Å3, Z = 8, Rgt(F) = 0.0422, wRref(F2) = 0.0990, T = 293 K.

Environmental perception forms the basis of intelligent driving systems and is a prerequisite for path planning and vehicle control. Among them, dynamic multi-obstacle tracking is the key to environmental perception. In order to solve the problem of a large amount of correlation calculations and false correlations in the process of dynamic multi-obstacle tracking, and to obtain more accurate surrounding environment information, this paper first designs an obstacle data correlation algorithm based on improving the joint probabilistic data-association algorithm. Then, in order to solve the problem of obstacle movement mobility and the poor filtering effect of a single model, the interacting multiple model is designed to complete the filtering of multiple behavior patterns of obstacles. An obstacle state estimation algorithm based on the unscented Kalman filter is designed to solve the nonlinear problem of obstacle motion. Finally, an experimental prototype is built and tested. The results show that the data association algorithm designed in this paper can complete the data association of obstacles at different times, and there is no problem of obstacle loss and association error. The average running time of each frame is 51.63 ms. The result comparison between the proposed method and the traditional method shows that the proposed method is more effective.

A key technique of machine learning and computer vision is to embed discrete weighted graphs into continuous spaces for further downstream processing. Embedding discrete hierarchical structures in hyperbolic geometry has proven very successful since it was shown that any weighted tree can be embedded in that geometry with arbitrary low distortion. Various optimization methods for hyperbolic embeddings based on common models of hyperbolic geometry have been studied. In this paper, we consider Hilbert geometry for the standard simplex which is isometric to a vector space equipped with the variation polytope norm. We study the representation power of this Hilbert simplex geometry by embedding distance matrices of graphs. Our findings demonstrate that Hilbert simplex geometry is competitive to alternative geometries such as the Poincar\'e hyperbolic ball or the Euclidean geometry for embedding tasks while being fast and numerically robust., Comment: 19 pages, 11 figures

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the entire distribution of the total return rather than only its expectation. The empirical success of distributional RL is determined by the representation of return distributions and the choice of distribution divergence. In this paper, we propose a new class of \textit{Sinkhorn distributional RL~(SinkhornDRL)} algorithm that learns a finite set of statistics, i.e., deterministic samples, from each return distribution and then uses Sinkhorn iterations to evaluate the Sinkhorn distance between the current and target Bellmen distributions. Sinkhorn divergence features as the interpolation between the Wasserstein distance and Maximum Mean Discrepancy~(MMD). SinkhornDRL finds a sweet spot by taking advantage of the geometry of optimal transport-based distance and the unbiased gradient estimate property of MMD. Finally, compared to state-of-the-art algorithms, SinkhornDRL's competitive performance is demonstrated on the suit of 55 Atari games., arXiv admin note: text overlap with arXiv:2110.03155

Graph contrastive learning attracts/disperses node representations for similar/dissimilar node pairs under some notion of similarity. It may be combined with a low-dimensional embedding of nodes to preserve intrinsic and structural properties of a graph. In this paper, we extend the celebrated Laplacian Eigenmaps with contrastive learning, and call them COntrastive Laplacian EigenmapS (COLES). Starting from a GAN-inspired contrastive formulation, we show that the Jensen-Shannon divergence underlying many contrastive graph embedding models fails under disjoint positive and negative distributions, which may naturally emerge during sampling in the contrastive setting. In contrast, we demonstrate analytically that COLES essentially minimizes a surrogate of Wasserstein distance, which is known to cope well under disjoint distributions. Moreover, we show that the loss of COLES belongs to the family of so-called block-contrastive losses, previously shown to be superior compared to pair-wise losses typically used by contrastive methods. We show on popular benchmarks/backbones that COLES offers favourable accuracy/scalability compared to DeepWalk, GCN, Graph2Gauss, DGI and GRACE baselines., Accepted by NeurIPS 2021. Includes the main paper and the supplementary material. OpenReview: https://openreview.net/forum?id=iLn-bhP-kKH

Additional file 1: Figure S1. Transmission electron microscopy (TEM) image of the TP NPs at pH 7.4. Scale bar, 100 nm. Figure S2. Biological transmission electron microscopy (Bio-TEM) image of the TPC@M2 NPs at pH 7.4. Scale bar, 200 nm. Figure S3. The results of dynamic light scattering (DLS) of the TPC@M2 NPs after incubation at 37 ℃ in PBS for different times (n = 3). Results are presented as means ± s.d. *P < 0.05, **P < 0.01, and ***P < 0.001 determined by Student’s t test. Figure S4. The results of hemolytic experiment of the TPC NPs and TPC@M2 NPs after incubation at 37 ℃ in red cell suspension for 12 h. Figure S4. The results of hemolytic experiment of the TPC NPs and TPC@M2 NPs after incubation at 37℃ in red cell suspension for 12 h. Figure S5. The O2 concentration changes in H2O2 solutions (750 μM) after incubation of various concentrations of TPC@M2 NPs. Figure S6. The relative enzymatic activity changes of free catalase and TPC@M2 NPs after protease K digestion for different periods. Figure S7. Mean fluorescence intensity (MFI) of Fe2+ levels in BV-2 cells after different treatment (n = 3). Results are presented as means ± s.d. *P < 0.05, **P < 0.01, and ***P < 0.001 determined by Student’s t test. Figure S8. Representative flow cytometric analysis of the M2 biomarkers (CD206+) and (b) the M1 biomarkers (CD16/32+) on the BV-2 cells treated with different groups (n = 3). Figure S9. The cell viabilities of rat primary neuronal cells and PC-12 cells treated with different concentrations of the TPC@M2 NPs for 24 h (n = 6). Figure S10. Mean fluorescence intensity (MFI) of PC-12 cells uptaked with different treatments (n = 3). Results are presented as means ± s.d. *P < 0.05, **P < 0.01, and ***P < 0.001 determined by Student’s t test. Figure S11. Brain iron content at days 3 of MCAO rats in different treatment group (n = 5). Results are presented as means ± s.d. *P < 0.05, **P < 0.01, and ***P < 0.001 determined by Student’s t test. Figure S12. Major liver function indexes (A), kidney function indexes (B) and blood routine indexes (C) of MCAO rats in different treatment group (n = 5). Results are presented as means ± s.d. *P < 0.05, **P < 0.01, and ***P < 0.001 determined by Student’s t test.

Supplemental material, sj-docx-1-tag-10.1177_17562848221140662 for First-line treatments for advanced hepatocellular carcinoma: a network meta-analysis and cost-effectiveness analysis in China and the United States by Ke-Xin Sun, Shan-Shan Cao, Feng-Hao Shi, Yue Guan, Meng Tang, Mei-na Zhao, Yu-Fan Jian, Bin Cui, Zhi-Yan Li, Jing-Wen Wang, Feng Yu and Yi Ding in Therapeutic Advances in Gastroenterology

We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning~(RL) that learns the whole return distribution compared with only its expectation in classical RL. Despite its success to obtain superior performance, we still have a poor understanding of how the value distribution in distributional RL works. In this study, we analyze the optimization benefits of distributional RL by leverage of additional value distribution information over classical RL in the Neural Fitted Z-Iteration~(Neural FZI) framework. To begin with, we demonstrate that the distribution loss of distributional RL has desirable smoothness characteristics and hence enjoys stable gradients, which is in line with its tendency to promote optimization stability. Furthermore, the acceleration effect of distributional RL is revealed by decomposing the return distribution. It turns out that distributional RL can perform favorably if the value distribution approximation is appropriate, measured by the variance of gradient estimates in each environment for any specific distributional RL algorithm. Rigorous experiments validate the stable optimization behaviors of distributional RL, contributing to its acceleration effects compared to classical RL. The findings of our research illuminate how the value distribution in distributional RL algorithms helps the optimization., Comment: arXiv admin note: text overlap with arXiv:2110.03155

Gaussian differential privacy (GDP) is a single-parameter family of privacy notions that provides coherent guarantees to avoid the exposure of sensitive individual information. Despite the extra interpretability and tighter bounds under composition GDP provides, many widely used mechanisms (e.g., the Laplace mechanism) inherently provide GDP guarantees but often fail to take advantage of this new framework because their privacy guarantees were derived under a different background. In this paper, we study the asymptotic properties of privacy profiles and develop a simple criterion to identify algorithms with GDP properties. We propose an efficient method for GDP algorithms to narrow down possible values of an optimal privacy measurement, $\mu$ with an arbitrarily small and quantifiable margin of error. For non GDP algorithms, we provide a post-processing procedure that can amplify existing privacy guarantees to meet the GDP condition. As applications, we compare two single-parameter families of privacy notions, $\epsilon$-DP, and $\mu$-GDP, and show that all $\epsilon$-DP algorithms are intrinsically also GDP. Lastly, we show that the combination of our measurement process and the composition theorem of GDP is a powerful and convenient tool to handle compositions compared to the traditional standard and advanced composition theorems.

Developmental dysplasia of the hip (DDH) is common among Chinese infants, but a lack of large-scale, multi-center epidemiological studies has made it difficult to characterize the risk factors associated with this disease.This multi-center cohort study included 19,833 Chinese infants aged 14 days to 6 months. A multi-center ultrasound protocol was used to diagnose hip abnormalities, and epidemiological data of the infants were collected through questionnaires. Categorical variables were expressed as percentages and compared using χ2 test. Multivariate analysis was performed through logistic regression. Of 19,833 infants, 345 had DDH (1.7%). DDH incidence was higher in female infants (n = 279) than in male infants (n = 66) (χ2 = 95.89, P

We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias. Our technique builds on the recent analysis of improper loss functions whose optimization can correct any twist in prediction, unfairness being treated as a twist. In the post-processing, we learn a wrapper function which we define as an $\alpha$-tree, which modifies the prediction. We provide two generic boosting algorithms to learn $\alpha$-trees. We show that our modification has appealing properties in terms of composition of $\alpha$-trees, generalization, interpretability, and KL divergence between modified and original predictions. We exemplify the use of our technique in three fairness notions: conditional value-at-risk, equality of opportunity, and statistical parity; and provide experiments on several readily available datasets., Comment: Published in Advances in Neural Information Processing Systems 35 (NeurIPS 2022)

In this paper, $2\times2$ zero-sum games are studied under the following assumptions: $(1)$ One of the players (the leader) commits to choose its actions by sampling a given probability measure (strategy); $(2)$ The leader announces its action, which is observed by its opponent (the follower) through a binary channel; and $(3)$ the follower chooses its strategy based on the knowledge of the leader's strategy and the noisy observation of the leader's action. Under these conditions, the equilibrium is shown to always exist. Interestingly, even subject to noise, observing the actions of the leader is shown to be either beneficial or immaterial for the follower. More specifically, the payoff at the equilibrium of this game is upper bounded by the payoff at the Stackelberg equilibrium (SE) in pure strategies; and lower bounded by the payoff at the Nash equilibrium, which is equivalent to the SE in mixed strategies.Finally, necessary and sufficient conditions for observing the payoff at equilibrium to be equal to its lower bound are presented. Sufficient conditions for the payoff at equilibrium to be equal to its upper bound are also presented., Comment: Accepted by 2023 IEEE Int. Symp. on Information Theory (ISIT)

Supplemental material, sj-docx-1-tag-10.1177_17562848221140662 for First-line treatments for advanced hepatocellular carcinoma: a network meta-analysis and cost-effectiveness analysis in China and the United States by Ke-Xin Sun, Shan-Shan Cao, Feng-Hao Shi, Yue Guan, Meng Tang, Mei-na Zhao, Yu-Fan Jian, Bin Cui, Zhi-Yan Li, Jing-Wen Wang, Feng Yu and Yi Ding in Therapeutic Advances in Gastroenterology

In this report, some properties of the set of Nash equilibria (NEs) of $2 \times 2$ zero-sum games are reviewed. In particular, the cardinality of the set of NEs is given in terms of the entries of the payoff matrix. Moreover, closed-form expressions for the NE strategies and the payoff at the NE (the value of the game) are provided in terms of the entries of the payoff matrix. The results presented in this report are not necessarily new knowledge, as they follow from the definition of the NE after some tedious calculations. Nevertheless this synthetic presentation is original in the literature.

Aiming at some problems existing in the current quality of service (QoS) mechanism of large-scale networks (i.e. poor scalability, coarse granularity for provided service levels, poor fairness between different service levels, and improving delay performance at the expense of sacrificing some resource utilization), the paper puts forward the idea and thoery of particle access. In the proposed particle access mechanism, the network first granulates the information flow (that is, the information flow is subdivided into information particles, each of which is given its corresponding attributes), and allocates access resources to the information particle group which is composed of all the information particles to be transmitted, so as to ensure that the occupied bandwidth resources is minimized on the premise of meeting the delay requirements of each information particle. Moreover, in the paper, the concepts of both information particle and information particle group are defined; Basic properties of the minimum reachable access bandwidth of an information particle group are analyzed; The influences of time attribute and attribute of bearing capacity of an information particle group on the minimum reachable access bandwidth are analyzed; Finally, an effective method for the calculation of the minimum reachable access bandwidth of an information particle group is given, and a particle access algorithm based on dynamically adjusting the minimum reachable access bandwidth is proposed. The research of the paper pave a new way for further improving QoS mechanisms of large-scale networks, and lay the corresponding theoretical foundation.

TaB2-SiC coating modified by different content of MoSi2 was fabricated on graphite substrate with SiC inner coating by liquid phase sintering to elevate the anti-oxidation capability of the TaB2-SiC coatings. As compared to the sample with the TaB2—40wt%SiC coating, the coating sample modified with MoSi2 exhibited a weight gain trend at lower temperatures, the fastest weight loss rate went down by 76%, and the relative oxygen permeability value reduced from about 1% to near 0. More importantly, the large amount of SiO2 glass phase produced over the coating during oxidation was in contact with the modification of MoSi2, which was proved to be beneficial to the dispersion of Ta-oxides. A concomitantly formed continuous Ta-Si-O-B compound glass layer showed excellent capacity to prevent oxygen penetration. However, when the TaB2 content was sacrificed to increase the MoSi2 content, the relative oxygen permeability of the coating increased instead of decreased. Thus, on the basis of ample TaB2 content, increasing the MoSi2 content of the coating is conducive to reducing the relative oxygen permeability of the coatings in a broad temperature region.

Background A sense of parental competence and satisfaction during the transition to parenthood can have a tremendous impact on the quality of parenting behaviors, with social support being an important facilitator. Aim To examine parental role competence and satisfaction of Chinese mothers and fathers in the early postpartum period with regard to social support. Methods A cross-sectional study was conducted in a regional teaching hospital in Guangzhou, China. The study was conducted between June 5 and November 16, 2015. One hundred and eighty parental pairs at 6–8 weeks after birth completed the Parenting Sense of Competence Scale, Perceived Social Support Scale, and socio-demographic questionnaires. Results Parental role competence and satisfaction of Chinese mothers and fathers were at a moderate level, affected each other and there were no significant differences between the mothers and the fathers. The Competence Scale scores had a significant positive correlation with social support. Multiple regression analysis revealed two variables that predicted maternal Competence Scale scores: maternal social support and the paternal Competence Scale scores. Paternal social support and maternal Competence Scale scores contributed significantly to paternal Competence Scale scores. Conclusion Parental role competence and satisfaction of mothers and fathers were at a moderate level and affected by the parenting partner. To improve parental role competence and satisfaction, health care professionals should develop strategies that impact the whole family and not just a single individual. Supportive parenting programs should be implemented for both mothers and fathers.

Increasing railway traffic and energy utilization issues prompt electrified railway systems to be more economical, efficient and sustainable. As regenerative braking energy in railway systems has huge potential for optimized utilization, a lot of research has been focusing on how to use the energy efficiently and gain sustainable benefits. The energy storage system is an alternative because it not only deals with regenerative braking energy but also smooths drastic fluctuation of load power profile and optimizes energy management. In this work, we propose a co-phase traction power supply system with super capacitor (CSS_SC) for the purpose of realizing the function of energy management and power quality management in electrified railways. Besides, the coordinated control strategy is presented to match four working modes, including traction, regenerative braking, peak shaving and valley filling. A corresponding simulation model is built in MATLAB/Simulink to verify the feasibility of the proposed system under dynamic working conditions. The results demonstrate that CSS_SC is flexible to deal with four different working conditions and can realize energy saving within the allowable voltage unbalance of 0.008% in simulation in contrast to 1.3% of the standard limit. With such a control strategy, the performance of super capacitor is controlled to comply with efficiency and safety constraints. Finally, a case study demonstrates the improvement in power fluctuation with the valley-to-peak ratio reduced by 20.3% and the daily load factor increased by 17.9%.

Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.