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On-site conversion of organic waste into biogas to satisfy consumer energy demand has the potential to realize energy equality and mitigate climate change reliably. However, existing methods ignore either real-time full supply or methane escape when supply and demand are mismatched. Here, we show an improved design of community biogas production and distribution system to overcome these and achieve full co-benefits in developing economies. We take five existing systems as empirical examples. Mechanisms of synergistic adjusting out-of-step biogas flow rates on both the plant-side and user-side are defined to obtain consumption-to-production ratios of close to 1, such that biogas demand of rural inhabitants can be met. Furthermore, carbon mitigation and its viability under universal prevailing climates are illustrated. Coupled with manure management optimization, Chinese national deployment of the proposed system would contribute a 3.77% reduction towards meeting its global 1.5 °C target. Additionally, fulfilling others' energy demands has considerable decarbonization potential.

Redox-driven reactions involving Mn(II) species adsorbed at Mn(IV) oxide surfaces can release Mn(III) in the form of dissolved Mn(III)-ligand species in natural waters. Using pyrophosphate (PP) as a model ligand, we show that freezing accelerates and enhances Mn(III) formation in the form of Mn(III)-PP complexes. This freeze-promoted reaction is explained by the concentration of Mn(IV) oxides and solutes (Mn(II), Na+, and Cl-) into the minute fractions of liquid water locked between ice (micro)crystals - the Liquid Intergrain Boundary (LIB). Time-resolved freezing experiments at −20 °C showed that Mn(III) yields were greatest at low salt (NaCl) content. In contrast, high salt content promoted Mn(III) formation through chloride complexation, although yields became lower as the cryosalt mineral hydrohalite (NaCl·2H2O) dehydrated the LIB by drawing water into its structure. Consecutive freeze-thaw cycles also showed that dissolved Mn(III) concentrations increased within the very first few minutes of each freezing event. Because each thaw event released unreacted PP previously locked in ice, each sequential freeze-thaw cycle increased Mn(III) yields, until ∼80% of the Mn was converted to Mn(III). This was achieved after only seven cycles. Finally, temperature-resolved freezing experiments down to −50 °C showed that the LIB produced the greatest quantities of Mn(III) at −10 °C, where the volumes were greater. Reactivity was however sustained in ice formed below the eutectic (−21.3 °C), down to −50 °C. We suspect that this sustained reactivity was driven by persistent forms of supercooled water, such as Mn(IV) oxide-bound thin water films. By demonstrating the freeze-driven production of Mn(III) by comproportionation of dissolved Mn(II) and Mn(IV) oxide, this study highlights the potentially important roles these reactions could play in the production of pools of Mn(III) in natural water and sediments of mid- and high-latitudes environments exposed to freeze-thaw episodes

Redox-driven reactions involving Mn(II) species adsorbed at Mn(IV) oxide surfaces can release Mn(III) in the form of dissolved Mn(III)-ligand species in natural waters. Using pyrophosphate (PP) as a model ligand, we show that freezing accelerates and enhances Mn(III) formation in the form of Mn(III)-PP complexes. This freeze-promoted reaction is explained by the concentration of Mn(IV) oxides and solutes (Mn(II), Na+, and Cl-) into the minute fractions of liquid water locked between ice (micro)crystals - the Liquid Intergrain Boundary (LIB). Time-resolved freezing experiments at −20 °C showed that Mn(III) yields were greatest at low salt (NaCl) content. In contrast, high salt content promoted Mn(III) formation through chloride complexation, although yields became lower as the cryosalt mineral hydrohalite (NaCl·2H2O) dehydrated the LIB by drawing water into its structure. Consecutive freeze-thaw cycles also showed that dissolved Mn(III) concentrations increased within the very first few minutes of each freezing event. Because each thaw event released unreacted PP previously locked in ice, each sequential freeze-thaw cycle increased Mn(III) yields, until ∼80% of the Mn was converted to Mn(III). This was achieved after only seven cycles. Finally, temperature-resolved freezing experiments down to −50 °C showed that the LIB produced the greatest quantities of Mn(III) at −10 °C, where the volumes were greater. Reactivity was however sustained in ice formed below the eutectic (−21.3 °C), down to −50 °C. We suspect that this sustained reactivity was driven by persistent forms of supercooled water, such as Mn(IV) oxide-bound thin water films. By demonstrating the freeze-driven production of Mn(III) by comproportionation of dissolved Mn(II) and Mn(IV) oxide, this study highlights the potentially important roles these reactions could play in the production of pools of Mn(III) in natural water and sediments of mid- and high-latitudes environments exposed to freeze-thaw episodes

Pharmaceutical compounds often coexist in mixtures rather than as individual entities. However, little is known about their co-adsorption and co-mobility in soil and groundwater. In this study, we investigated the adsorption of a quinolone antibiotic (nalidixic acid, NA) and an anti-inflammatory agent (niflumic acid, NFA) onto two soils from France and Sweden in water-saturated soil columns. Despite its lower hydrophobicity, adsorption of NA is much greater than NFA, which can be ascribed to the presence of both carbonyl and carboxylic groups in NA molecule. The data suggest that adsorption to soil components can mainly take place through hydrogen bonding and surface complexation mechanisms, prevailing over hydrophobic interactions. Accordingly, more sorption of NA and NFA was observed in the Swedish soil because it contains more clay content, and much higher Al and Fe contents than the French soil. Injection of NA/NFA mixture in the column did not modify the breakthrough behavior compared to single systems, although cooperative adsorption was observed under static batch conditions. Ca2+ inhibited NA adsorption by forming a soluble NA-Ca2+ complex but promoted NFA adsorption both in single and binary systems. The mobility in soil columns was well predicted using a new transport model that accounts for both kinetics and binding reactions of NA and NFA to soil constituents. This work will help in accurately predicting the mobility of coexisting pharmaceutical compounds in soils.

Pharmaceutical compounds often coexist in mixtures rather than as individual entities. However, little is known about their co-adsorption and co-mobility in soil and groundwater. In this study, we investigated the adsorption of a quinolone antibiotic (nalidixic acid, NA) and an anti-inflammatory agent (niflumic acid, NFA) onto two soils from France and Sweden in water-saturated soil columns. Despite its lower hydrophobicity, adsorption of NA is much greater than NFA, which can be ascribed to the presence of both carbonyl and carboxylic groups in NA molecule. The data suggest that adsorption to soil components can mainly take place through hydrogen bonding and surface complexation mechanisms, prevailing over hydrophobic interactions. Accordingly, more sorption of NA and NFA was observed in the Swedish soil because it contains more clay content, and much higher Al and Fe contents than the French soil. Injection of NA/NFA mixture in the column did not modify the breakthrough behavior compared to single systems, although cooperative adsorption was observed under static batch conditions. Ca2+ inhibited NA adsorption by forming a soluble NA-Ca2+ complex but promoted NFA adsorption both in single and binary systems. The mobility in soil columns was well predicted using a new transport model that accounts for both kinetics and binding reactions of NA and NFA to soil constituents. This work will help in accurately predicting the mobility of coexisting pharmaceutical compounds in soils.

Recent advancements in neural network quantisation have yielded remarkable outcomes, with three-bit networks reaching state-of-the-art full-precision accuracy in complex tasks. These achievements present valuable opportunities for accelerating neural networks by computing in reduced precision. Implementing it on FPGAs can take advantage of bit-level reconfigurability, which is not available on conventional CPUs and GPUs. Simultaneously, the high data intensity of neural network processing has inspired computing-in-memory paradigms, including on FPGA platforms. By programming the effects of trained model weights as lookup operations in soft logic, the transfer of weight data from memory units can be avoided, alleviating the memory bottleneck. However, previous methods face poor scalability - the high logic utilisation limiting them to small networks/sub-networks of binary models with low accuracy. In this paper, we introduce Table Lookup Multiply-Accumulate (TLMAC) as a framework to compile and optimise quantised neural networks for scalable lookup-based processing. TLMAC clusters and maps unique groups of weights to lookup-based processing elements, enabling highly parallel computation while taking advantage of parameter redundancy. Further place and route algorithms are proposed to reduce LUT utilisation and routing congestion. We demonstrate that TLMAC significantly improves the scalability of previous related works. Our efficient logic mapping and high degree of reuse enables entire ImageNet-scale quantised models with full-precision accuracy to be implemented using lookup-based computing on one commercially available FPGA.

Deep reinforcement learning excels in numerous large-scale practical applications. However, existing performance analyses ignores the unique characteristics of continuous-time control problems, is unable to directly estimate the generalization error of the Bellman optimal loss and require a boundedness assumption. Our work focuses on continuous-time control problems and proposes a method that is applicable to all such problems where the transition function satisfies semi-group and Lipschitz properties. Under this method, we can directly analyze the \emph{a priori} generalization error of the Bellman optimal loss. The core of this method lies in two transformations of the loss function. To complete the transformation, we propose a decomposition method for the maximum operator. Additionally, this analysis method does not require a boundedness assumption. Finally, we obtain an \emph{a priori} generalization error without the curse of dimensionality.

Empirical and theoretical works show that the input weights of two-layer neural networks, when initialized with small values, converge towards isolated orientations. This phenomenon, referred to as condensation, indicates that the gradient descent methods tend to spontaneously reduce the complexity of neural networks during the training process. In this work, we elucidate the mechanisms behind the condensation phenomena occurring in the training of three-layer neural networks and distinguish it from the training of two-layer neural networks. Through rigorous theoretical analysis, we establish the blow-up property of effective dynamics and present a sufficient condition for the occurrence of condensation, findings that are substantiated by experimental results. Additionally, we explore the association between condensation and the low-rank bias observed in deep matrix factorization.

This article studies the problem of image segmentation-based semantic communication in autonomous driving. In real traffic scenes, detecting the key objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that of other objects to guarantee driving safety. Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, where image segmentation features of important objects are transmitted to reduce transmission redundancy. First, to accurately extract image semantics, we develop a semantic codec based on Swin Transformer architecture, which expands the perceptual field thus improving the segmentation accuracy. Next, we propose a multi-scale semantic extraction scheme via assigning the number of Swin Transformer blocks for diverse resolution features, thus highlighting the important objects' accuracy. Furthermore, the importance-aware loss is invoked to emphasize the important objects, and an online hard sample mining (OHEM) strategy is proposed to handle small sample issues in the dataset. Experimental results demonstrate that the proposed VIS-SemCom can achieve a coding gain of nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the transmitted data amount by up to 70% with a 60% mIoU, and improve the segmentation intersection over union (IoU) of important objects by 4%, compared to traditional transmission scheme., Comment: 10 pages, 8 figures

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to the quadratic computation complexity of attention in transformers, current in-context RL methods suffer from huge computational costs as the task horizon increases. In contrast, the Mamba model is renowned for its efficient ability to process long-term dependencies, which provides an opportunity for in-context RL to solve tasks that require long-term memory. To this end, we first implement Decision Mamba (DM) by replacing the backbone of Decision Transformer (DT). Then, we propose a Decision Mamba-Hybrid (DM-H) with the merits of transformers and Mamba in high-quality prediction and long-term memory. Specifically, DM-H first generates high-value sub-goals from long-term memory through the Mamba model. Then, we use sub-goals to prompt the transformer, establishing high-quality predictions. Experimental results demonstrate that DM-H achieves state-of-the-art in long and short-term tasks, such as D4RL, Grid World, and Tmaze benchmarks. Regarding efficiency, the online testing of DM-H in the long-term task is 28$\times$ times faster than the transformer-based baselines., Comment: arXiv admin note: text overlap with arXiv:2405.20692. arXiv admin note: text overlap with arXiv:2405.20692; text overlap with arXiv:2305.16554, arXiv:2210.14215 by other authors

This paper presents a comprehensive analysis of critical point sets in two-layer neural networks. To study such complex entities, we introduce the critical embedding operator and critical reduction operator as our tools. Given a critical point, we use these operators to uncover the whole underlying critical set representing the same output function, which exhibits a hierarchical structure. Furthermore, we prove existence of saddle branches for any critical set whose output function can be represented by a narrower network. Our results provide a solid foundation to the further study of optimization and training behavior of neural networks.

In this paper, we advance the understanding of neural network training dynamics by examining the intricate interplay of various factors introduced by weight parameters in the initialization process. Motivated by the foundational work of Luo et al. (J. Mach. Learn. Res., Vol. 22, Iss. 1, No. 71, pp 3327-3373), we explore the gradient descent dynamics of neural networks through the lens of macroscopic limits, where we analyze its behavior as width $m$ tends to infinity. Our study presents a unified approach with refined techniques designed for multi-layer fully connected neural networks, which can be readily extended to other neural network architectures. Our investigation reveals that gradient descent can rapidly drive deep neural networks to zero training loss, irrespective of the specific initialization schemes employed by weight parameters, provided that the initial scale of the output function $\kappa$ surpasses a certain threshold. This regime, characterized as the theta-lazy area, accentuates the predominant influence of the initial scale $\kappa$ over other factors on the training behavior of neural networks. Furthermore, our approach draws inspiration from the Neural Tangent Kernel (NTK) paradigm, and we expand its applicability. While NTK typically assumes that $\lim_{m\to\infty}\frac{\log \kappa}{\log m}=\frac{1}{2}$, and imposes each weight parameters to scale by the factor $\frac{1}{\sqrt{m}}$, in our theta-lazy regime, we discard the factor and relax the conditions to $\lim_{m\to\infty}\frac{\log \kappa}{\log m}>0$. Similar to NTK, the behavior of overparameterized neural networks within the theta-lazy regime trained by gradient descent can be effectively described by a specific kernel. Through rigorous analysis, our investigation illuminates the pivotal role of $\kappa$ in governing the training dynamics of neural networks.

The measurement of decay parameters is one of the important goals of particle physics experiments, and the measurement serves as a probe to search for evidence of CP violation in baryonic decays. The experimental results will help advance existing theoretical research and establish new experimental objectives. In this paper, we formulate the asymmetric parameters that characterize parity violation, and then derive formulas for the measurement of CP violation. The formulae for the joint angular distribution of the full decay chain as well as the polarization observable of $\Xi ^ 0 $, $\bar { \Xi } ^ 0 $, $\Lambda $ and $\bar { \Lambda } $ are also provided for experiments. Lastly, we evaluated the sensitivity of two asymmetric parameters: $\alpha _ { \Xi ^ 0 \to \Lambda \pi ^ 0 } $ (abbreviated as $\alpha _ { \Xi ^ 0 } $) and $\alpha _ { \bar { \Xi } ^ 0 \to \bar { \Lambda } \gamma } $ (abbreviated as $\alpha_ {\bar{\Xi}^0 } $) for future experimental measurement., Comment: 8 pages, 5 figures

Yubo Li,1,2,* Chengming Li,1,2,* Tao Luo,3,* Tian Yue,4 Wenjing Xiao,5 Ling Yang,1,2 Zaiyuan Zhang,6 Fei Han,3,* Pan Long,3 Yonghe Hu6 1School of Clinical Medicine, Chengdu University of TCM, Chengdu, People’s Republic of China; 2Basic Medical Laboratory, The General Hospital of Western Theater Command, Chengdu, People’s Republic of China; 3Department of Ophthalmology, The General Hospital of Western Theater Command, Chengdu, People’s Republic of China; 4School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of China; 5Department of Pharmacy, The General Hospital of Western Theater Command, Chengdu, People’s Republic of China; 6College of Medicine, Southwest Jiaotong University, Chengdu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Pan Long, Department of Ophthalmology, The General Hospital of Western Theater Command, Rongdu Avenue #270, Chengdu, People’s Republic of China, Tel +86-181-9125-6132, Email longpan1005@qq.com Yonghe Hu, College of Medicine, Southwest Jiaotong University, No. 111, North First section of the Second Ring Road, Chengdu, People’s Republic of China, Tel +86-138-8059-6789, Email huyonghezyy@163.comAbstract: With the increasing of altitude activities from low-altitude people, the study of high altitude cerebral edema (HACE) has been revived. HACE is a severe acute mountain sickness associated with exposure to hypobaric hypoxia at high altitude, often characterized by disturbance of consciousness and ataxia. As for the pathogenesis of HACE, previous studies suggested that it might be related to the disorder of cerebral blood flow, the destruction of blood-brain barrier and the injury of brain parenchyma cells caused by inflammatory factors. In recent years, studies have confirmed that the imbalance of REDOX homeostasis is also involved in the pathogenesis of HACE, which mainly leads to abnormal act

In-memory deep learning executes neural network models where they are stored, thus avoiding long-distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency. The use of emerging memory technology (EMT) promises to increase density, energy, and performance even further. However, EMT is intrinsically unstable, resulting in random data read fluctuations. This can translate to nonnegligible accuracy loss, potentially nullifying the gains. In this article, we propose three optimization techniques that can mathematically overcome the instability problem of EMT. They can improve the accuracy of the in-memory deep learning model while maximizing its energy efficiency. Experiments show that our solution can fully recover most models' state-of-the-art (SOTA) accuracy and achieves at least an order of magnitude higher energy efficiency than the SOTA.

Introduction: The Medium Energy X-ray telescope (ME) is a collimated X-ray telescope onboard the Insight hard X-ray modulation telescope (Insight-HXMT) satellite. It has 1728 Si-PIN pixels readout using 54 low noise application-specific integrated circuits (ASICs). ME covers the energy range of 5-30 keV and has a total detection area of 952 cm2. The typical energy resolution of ME at the beginning of the mission is 3 keV at 17.8 keV (Full Width at Half Maximum, FWHM) and the time resolution is 255 us. In this study, we present the in-orbit performance of ME in its first 5 years of operation. Methods: The performance of ME was monitored using onboard radioactive sources and astronomical X-ray objects. ME carries six 241Am radioactive sources for onboard calibration, which can continuously illuminate the calibration pixels. The long-term performance evolution of ME can be quantified using the properties of the accumulated spectra of the calibration pixels. In addition, observations of the Crab Nebula and the pulsar were used to check the long-term evolution of the detection efficiency as a function of energy. Conclusion: After 5 years of operation, 742 cm2 of the Si-PIN pixels were still working normally. The peak positions of 241Am emission lines gradually shifted to the high energy region, implying a slow increase in ME gain of 1.43%. A comparison of the ME spectra of the Crab Nebula and the pulsar shows that the E-C relations and the redistribution matrix file are still acceptable for most data analysis works, and there is no detectable variation in the detection efficiency.

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of \textit{Insight}-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first $\sim$1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from $\sim$ 10 keV to $\sim$ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy ($E_{\rm iso}$) of $\bf \sim 1.5 \times 10^{55}$ erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at $\sim950$ s from the afterglow starting time $T_{AG}$, which corresponds to a jet opening angle of $\sim {0.7^\circ} \ (\eta_\gamma n)^{1/8}$, where $n$ is the ambient medium density in units of $\rm cm^{-3}$ and $\eta_\gamma$ is the ratio between $\gamma$-ray energy and afterglow kinetic energy. The beaming-corrected total $\gamma$-ray energy $E_{\gamma}$ is $\sim 1.15 \times10^{51} \ (\eta_\gamma n)^{1/4}$ erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement., Comment: Submitted to National Science Review. This paper is under press embargo, contact the corresponding author for details

Zitian Liang,1,* Ding Chen,2,* Ye Jiang,1 Zhikang Su,2 Yixing Pi,2 Tao Luo,2 Qianzhou Jiang,1 Li Yang,1 Lvhua Guo2 1Department of Endodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, Guangdong, 510182, People’s Republic of China; 2Department of Prosthodontics, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, Guangdong, 510182, People’s Republic of China*These authors contributed equally to this workCorrespondence: Li Yang; Lvhua Guo, Affiliated Stomatology Hospital of Guangzhou Medical University, No. 195 West Dongfeng Road, Guangzhou, 510182, People’s Republic of China, Tel +86-020-81299942, Email 2019686001@gzhmu.edu.cn; 2010686002@gzhmu.edu.cnIntroduction: Effective infection control without irritating the pulp tissue is the key to successful vital pulp therapy. Developing a novel antibacterial biomaterial that promotes dentin regeneration for pulp capping is thus a promising strategy for enhancing vital pulp therapy.Methods: Lithium-doped mesoporous nanoparticles (Li-MNPs) were synthesized using an alkali-catalyzed sol-gel method. The particle size, elemental distribution, surface morphology, pore structure, and ion release from Li-MNPs were measured. Human dental pulp stem cells (hDPSCs) and Streptococcus mutans (S. mutans) were used to evaluate the biological effects of Li-MNPs. In addition, a dental pulp exposure mouse model was used to evaluate the regenerative effects of Li-MNPs.Results: Li-MNPs had a larger surface area (221.18 m2/g), a larger pore volume (0.25 cm3/g), and a smaller particle size (520.92 ± 35.21 nm) than MNPs. The in vitro in

We study the (2+1)-dimensional continuum model for the evolution of stepped epitaxial surface under long-range elastic interaction proposed by Xu and Xiang (SIAM J. Appl. Math. 69 (2009), 1393–1414). The long-range interaction term and the two length scales in this model makes PDE analysis challenging. Moreover, unlike in the (1+1)-dimensional case, there is a nonconvexity contribution in the total energy in the (2+1)-dimensional case, and it is not easy to prove that the solution is always in the well-posed regime during the evolution. In this paper, we propose a modified (2+1)-dimensional continuum model based on the underlying physics. This modification fixes the problem of possible ill-posedness due to the nonconvexity of the energy functional. We prove the existence and uniqueness of both the static and dynamic solutions and derive a minimum energy scaling law for them. We show that the minimum energy surface profile is mainly attained by surfaces with step meandering instability. This is essentially different from the energy scaling law for the (1+1)-dimensional epitaxial surfaces under elastic effects attained by step bunching surface profiles. We also discuss the transition from the step bunching instability to the step meandering instability in (2+1)-dimensions.

The phenomenon of distinct behaviors exhibited by neural networks under varying scales of initialization remains an enigma in deep learning research. In this paper, based on the earlier work by Luo et al.~\cite{luo2021phase}, we present a phase diagram of initial condensation for two-layer neural networks. Condensation is a phenomenon wherein the weight vectors of neural networks concentrate on isolated orientations during the training process, and it is a feature in non-linear learning process that enables neural networks to possess better generalization abilities. Our phase diagram serves to provide a comprehensive understanding of the dynamical regimes of neural networks and their dependence on the choice of hyperparameters related to initialization. Furthermore, we demonstrate in detail the underlying mechanisms by which small initialization leads to condensation at the initial training stage.

Vision Transformers (ViTs) with outstanding performance becomes a popular backbone of deep learning models for the main-stream vision tasks including classification, object detection, and segmentation. Other than the performance, reliability is also a critical metric for the adoption of ViTs in safety-critical applications such as autonomous driving and robotics. With the observation that the major computing blocks in ViTs such as multi-head attention and feed forward are usually performed with general matrix multiplication (GEMM), we propose a classical algorithm-based fault tolerance (ABFT) strategy originally developed for GEMM to protect ViTs against soft errors in the underlying computing engines. Unlike classical ABFT that will invoke the expensive error recovery procedure whenever computing errors are detected, we leverage the inherent fault-tolerance of ViTs and propose an approximate ABFT, namely ApproxABFT, to invoke the error recovery procedure only when the computing errors are significant enough, which skips many useless error recovery procedures and simplifies the overall GEMM error recovery. Meanwhile, it also relaxes the error threshold in error recovery procedure and ignores minor computing errors, which reduces the error recovery complexity and improves the error recovery quality. In addition, we also apply a fine-grained blocking strategy to ApproxABFT and split GEMM with distinct sizes into smaller sub blocks such that it can smooth the error thresholds across ViTs and further improve the error recovery quality. According to our experiments, the ApproxABFT reduces the computing overhead by 25.92\% to 81.62\% and improves the model accuracy by 2.63\% to 72.56\% compared to the baseline ABFT while the blocking optimization further reduces the computing overhead by 6.56\% to 73.5\% with comparable accuracy.

Vision Transformers (ViTs) that leverage self-attention mechanism have shown superior performance on many classical vision tasks compared to convolutional neural networks (CNNs) and gain increasing popularity recently. Existing ViTs works mainly optimize performance and accuracy, but ViTs reliability issues induced by soft errors in large-scale VLSI designs have generally been overlooked. In this work, we mainly study the reliability of ViTs and investigate the vulnerability from different architecture granularities ranging from models, layers, modules, and patches for the first time. The investigation reveals that ViTs with the self-attention mechanism are generally more resilient on linear computing including general matrix-matrix multiplication (GEMM) and full connection (FC) and show a relatively even vulnerability distribution across the patches. ViTs involve more fragile non-linear computing such as softmax and GELU compared to typical CNNs. With the above observations, we propose a lightweight block-wise algorithm-based fault tolerance (LB-ABFT) approach to protect the linear computing implemented with distinct sizes of GEMM and apply a range-based protection scheme to mitigate soft errors in non-linear computing. According to our experiments, the proposed fault-tolerant approaches enhance ViTs accuracy significantly with minor computing overhead in presence of various soft errors.

In this paper, a semantic communication framework for image transmission is developed. In the investigated framework, a set of servers cooperatively transmit images to a set of users utilizing semantic communication techniques. To evaluate the performance of studied semantic communication system, a multimodal metric is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem aiming to minimize each server's transmission latency while reaching the ISS requirement. To solve this problem, a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) is proposed, which enables servers to coordinate for training and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL, the proposed RL improves the valuable action exploration of servers and the probability of finding a globally optimal RB allocation policy based on local observation. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% compared to traditional multi-agent RL., Comment: 29 pages, 10 figures

In this paper, a class of smoothing modulus-based iterative method was presented for solving implicit complementarity problems. The main idea was to transform the implicit complementarity problem into an equivalent implicit fixed-point equation, then introduces a smoothing function to obtain its approximation solutions. The convergence analysis of the algorithm was given, and the efficiency of the algorithms was verified by numerical experiment

Dropout is a widely utilized regularization technique in the training of neural networks, nevertheless, its underlying mechanism and its impact on achieving good generalization abilities remain poorly understood. In this work, we derive the stochastic modified equations for analyzing the dynamics of dropout, where its discrete iteration process is approximated by a class of stochastic differential equations. In order to investigate the underlying mechanism by which dropout facilitates the identification of flatter minima, we study the noise structure of the derived stochastic modified equation for dropout. By drawing upon the structural resemblance between the Hessian and covariance through several intuitive approximations, we empirically demonstrate the universal presence of the inverse variance-flatness relation and the Hessian-variance relation, throughout the training process of dropout. These theoretical and empirical findings make a substantial contribution to our understanding of the inherent tendency of dropout to locate flatter minima.

In this work, we systematically investigate linear multi-step methods for differential equations with memory. In particular, we focus on the numerical stability for multi-step methods. According to this investigation, we give some sufficient conditions for the stability and convergence of some common multi-step methods, and accordingly, a notion of A-stability for differential equations with memory. Finally, we carry out the computational performance of our theory through numerical examples.

Brain-inspired spiking neural networks (SNNs) replace the multiply-accumulate operations of traditional neural networks by integrate-and-fire neurons, with the goal of achieving greater energy efficiency. Specialized hardware implementations of those neurons clearly have advantages over general-purpose devices in terms of power and performance, but exhibit poor scalability when it comes to accelerating large neural networks. DeepFire2 introduces a hardware architecture which can map large network layers efficiently across multiple super logic regions in a multi-die FPGA. That gives more control over resource allocation and parallelism, benefiting both throughput and energy consumption. Avoiding the use of lookup tables to implement the AND operations of an SNN, prevents the layer size to be limited by logic resources. A deep pipeline does not only lead to an increased clock speed of up to 600 MHz. We double the throughput and power efficiency compared to our previous version of DeepFire, which equates to an almost 10-fold improvement over other previous implementations. Importantly, we are able to deploy a large ImageNet model, while maintaining a throughput of over 1500 frames per second.

Despite the simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs). Among various attempts to improve SGD, weight averaging (WA), which averages the weights of multiple models, has recently received much attention in the literature. Broadly, WA falls into two categories: 1) online WA, which averages the weights of multiple models trained in parallel, is designed for reducing the gradient communication overhead of parallel mini-batch SGD, and 2) offline WA, which averages the weights of one model at different checkpoints, is typically used to improve the generalization ability of DNNs. Though online and offline WA are similar in form, they are seldom associated with each other. Besides, these methods typically perform either offline parameter averaging or online parameter averaging, but not both. In this work, we firstly attempt to incorporate online and offline WA into a general training framework termed Hierarchical Weight Averaging (HWA). By leveraging both the online and offline averaging manners, HWA is able to achieve both faster convergence speed and superior generalization performance without any fancy learning rate adjustment. Besides, we also analyze the issues faced by existing WA methods, and how our HWA address them, empirically. Finally, extensive experiments verify that HWA outperforms the state-of-the-art methods significantly., Comment: Accept by TNNLS

Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative captions. To address this issue, this work provides a Visual Question Answering (VQA) perspective to boost the performance of CIR. The resulting VQA4CIR is a post-processing approach and can be directly plugged into existing CIR methods. Given the top-C retrieved images by a CIR method, VQA4CIR aims to decrease the adverse effect of the failure retrieval results being inconsistent with the relative caption. To find the retrieved images inconsistent with the relative caption, we resort to the "QA generation to VQA" self-verification pipeline. For QA generation, we suggest fine-tuning LLM (e.g., LLaMA) to generate several pairs of questions and answers from each relative caption. We then fine-tune LVLM (e.g., LLaVA) to obtain the VQA model. By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair. Consequently, the CIR performance can be boosted by modifying the ranks of inconsistently retrieved images. Experimental results show that our proposed method outperforms state-of-the-art CIR methods on the CIRR and Fashion-IQ datasets.

Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due to the overwhelming computation and memory requirements. Existing methods for long sequence training offer limited speedup and memory reduction, and may compromise accuracy. This paper presents a novel and efficient distributed training method, the Long Short-Sequence Transformer (LSS Transformer), for training transformer with long sequences. It distributes a long sequence into segments among GPUs, with each GPU computing a partial self-attention for its segment. Then, it uses a fused communication and a novel double gradient averaging technique to avoid the need to aggregate partial self-attention and minimize communication overhead. We evaluated the performance between LSS Transformer and the state-of-the-art Nvidia sequence parallelism on a Wikipedia enwik8 dataset. Results show that our proposed method lead to 5.6x faster and 10.2x more memory-efficient implementation compared to state-of-the-art sequence parallelism on 144 Nvidia V100 GPUs. Moreover, our algorithm scales to an extreme sequence length of 50,112 at 3,456 GPUs, achieving 161% super-linear parallel efficiency and a throughput of 32 petaflops.

As a popular and easy-to-implement machine learning method for solving differential equations, the physics-informed neural network (PINN) sometimes may fail and find poor solutions which bias against the exact ones. In this paper, we establish a framework of modified equation to explain the failure phenomenon and characterize the implicit bias of a general residual minimization (RM) method. We provide a simple way to derive the modified equation which models the numerical solution obtained by RM methods. Next, we show the modified solution deviates from the original exact solution. The proof uses a by-product of this paper, that is, a necessary and sufficient condition on characterizing the singularity of the coefficients. This equivalent condition can be extended to other types of equations in the future. Finally, we prove, as a complete characterization of the implicit bias, that RM method implicitly biases the numerical solution against the exact solution and towards a modified solution. In this work, we focus on elliptic equations with discontinuous coefficients, but our approach can be extended to other types of equations and our understanding of the implicit bias may shed light on further development of deep learning based methods for solving equations.

Under mild assumptions, we investigate the structure of loss landscape of two-layer neural networks near global minima, determine the set of parameters which give perfect generalization, and fully characterize the gradient flows around it. With novel techniques, our work uncovers some simple aspects of the complicated loss landscape and reveals how model, target function, samples and initialization affect the training dynamics differently. Based on these results, we also explain why (overparametrized) neural networks could generalize well.

Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought by winograd are usually overlooked. In this work, we observe the great potential of winograd convolution in improving neural network (NN) fault tolerance. Based on the observation, we evaluate winograd convolution fault tolerance comprehensively from different granularities ranging from models, layers, and operation types for the first time. Then, we explore the use of inherent fault tolerance of winograd convolution for cost-effective NN protection against soft errors. Specifically, we mainly investigate how winograd convolution can be effectively incorporated with classical fault-tolerant design approaches including triple modular redundancy (TMR), fault-aware retraining, and constrained activation functions. According to our experiments, winograd convolution can reduce the fault-tolerant design overhead by 55.77\% on average without any accuracy loss compared to standard convolution, and further reduce the computing overhead by 17.24\% when the inherent fault tolerance of winograd convolution is considered. When it is applied on fault-tolerant neural networks enhanced with fault-aware retraining and constrained activation functions, the resulting model accuracy generally shows significant improvement in presence of various faults.

We propose an optimistic estimate to evaluate the best possible fitting performance of nonlinear models. It yields an optimistic sample size that quantifies the smallest possible sample size to fit/recover a target function using a nonlinear model. We estimate the optimistic sample sizes for matrix factorization models, deep models, and deep neural networks (DNNs) with fully-connected or convolutional architecture. For each nonlinear model, our estimates predict a specific subset of targets that can be fitted at overparameterization, which are confirmed by our experiments. Our optimistic estimate reveals two special properties of the DNN models -- free expressiveness in width and costly expressiveness in connection. These properties suggest the following architecture design principles of DNNs: (i) feel free to add neurons/kernels; (ii) restrain from connecting neurons. Overall, our optimistic estimate theoretically unveils the vast potential of nonlinear models in fitting at overparameterization. Based on this framework, we anticipate gaining a deeper understanding of how and why numerous nonlinear models such as DNNs can effectively realize their potential in practice in the near future.

BackgroundDownregulation of claudin-5 in the heart is associated with the end-stage heart failure. However, the underlying mechanism ofclaudin-5 is unclear. Here we investigated the molecular actions of claudin-5 in perspective of mitochondria in cardiomyocytes to better understand the role of claudin-5 in cardioprotection during ischemia.MethodsMyocardial ischemia/reperfusion (I/R; 30 min/24 h) and hypoxia/reoxygenation (H/R; 24 h/4 h) were used in this study. Confocal microscopy and transmission electron microscope (TEM) were used to observe mitochondrial morphology.ResultsClaudin-5 was detected in murine heart tissue and neonatal rat cardiomyocytes (NRCM). Its protein level was severely decreased after myocardial I/R or H/R. Confocal microscopy showedclaudin-5 presented in the mitochondria of NRCM. H/R-induced claudin-5 downregulation was accompanied by mitochondrial fragmentation. The mitofusin 2 (Mfn2) expressionwas dramatically decreased while the dynamin-related protein (Drp) 1 expression was significantly increased after H/R. The TEM indicatedH/R-induced mitochondrial swelling and fission. Adenoviral claudin-5 overexpression reversed these structural disintegration of mitochondria. The mitochondria-centered intrinsic pathway of apoptosis triggered by H/R and indicated by the cytochrome c and cleaved caspase 3 in the cytoplasm of NRCMs was also reduced by overexpressing claudin-5. Claudin-5 overexpression in mouse heart also significantly decreased cleaved caspase 3 and the infarct size in ischemic heart with improved systolic function.ConclusionWe demonstrated for the first time the presence of claudin-5 in the mitochondria in cardiomyocytes and provided the firm evidence for the cardioprotective role of claudin-5 in the preservation of mitochondrial dynamics and cell fate against hypoxia- or ischemia-induced stress.

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram., Comment: 17 pages, 13 figures, accepted for publication by ApJ. arXiv admin note: text overlap with arXiv:1212.1011 by other authors. text overlap with arXiv:1212.1011 by other authors

Various pharmaceutical compounds often coexist in contaminated soils, yet little is known about how their interactions impact their mobility. We here show that two typical antibiotic and anti-inflammatory agents (nalidixic acid (NA) and niflumic acid (NFA)) commonly form dimers at several representative soil-and sediment-building minerals of contrasting composition and structure. Cobinding occurs in the form of a NFA-NA dimer stabilized by hydrogen bonding and van der Waals interactions. Using dynamic column experiments containing goethite-coated sand, we then demonstrated that presorbed NA effectively captured the otherwise weakly binding NFA from solution. Simultaneously injecting NA and NFA to presorbed NA enhanced even further both NA and NFA loadings, thereby altering their transport under flow-Through conditions. We also showed that environmental level amounts of natural organic matter can reduce the overall retention in column experiments, yet it does not suppress dimer formation. These environmentally relevant scenarios can be predicted using a new transport model that accounts for kinetics and cobinding reactions of NFA onto NA bound to goethite through metal-bonded, hydrogen-bonded, and outer-sphere complexes. These findings have important implications on assessing the fate of coexisting pharmaceutical compounds under dynamic flow conditions in contaminated soils.

Siyuan He,* Tao Luo,* Yuanlong Xie, Xiaobin Zhu, Jun Lei, Lin Cai, Zhouming Deng Department of Spine Surgery and Musculoskeletal Tumor, Zhongnan Hospital of Wuhan University, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhouming Deng, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuchang District, Wuhan, Hubei, People’s Republic of China, Tel +86 13971437341, Fax +86 02767813116, Email dengzhouming@foxmail.comAim: Skeletal involvement of Cryptococcus neoformans is rare and normally associated with disseminated cryptococcosis or potential predisposing factors. Here, we report an atypical case of osteoarticular cryptococcosis in an immunocompetent patient.Case Presentation: We report a case of cryptococcal osteomyelitis in a 45-year-old female who presented with swelling and pain in the left inner thigh. After a biopsy of the pubic bone and surrounding soft tissue, the pathological results and bacterial culture of the biopsy tissue confirmed Cryptococcus neoformans infection. After draining the pus by aspiration and administering oral fluconazole (400 mg/d) treatment, the patient’s symptoms disappeared.Conclusion: Cryptococcus neoformans is a rare etiology of infection of the entire pubis, and oral fluconazole and pus aspiration could benefit some cryptococcal osteomyelitis patients with soft-tissue cryptococcal infection.Keywords: cryptococcosis, pubis infection, antifungal therapy, immunocompetent

Evidence for CP violation in baryonic decays has not been seen in experiments. With large data events accumulated at $e^+e^-$ collider or Large Hadron Collider, the charmed baryon decays would provide a promising laboratory to test the CP symmetry. In this work, we formulate the $\Lambda_c^+\to\phi p$ and $ \Lambda_c^+\to\omega p$ decays for the measurements of their asymmetry parameters in weak decays at the BESIII or the LHCb experiments. The polarization transfer is analyzed in the two processes of $e^+ e^- \to\Lambda_c^+\bar\Lambda_c^-$ and $\Lambda_b^0\to \Lambda^+_c\pi^-$, and the formulas of the joint angular distribution for these processes are provided. The sensitivity of the measurement of asymmetry parameters for the above two decay processes is estimated for the future experiments.

Tao Luo,1 Yile Wang,1 Hailin Tang,1 Fei Zhou,2 Ying Chen,3 Bing Pei,4 Jinke Wang1 1State Key Laboratory of Bioelectronics, Southeast University, Nanjing, 210096, People’s Republic of China; 2School of Food Engineering and Biotechnology, Hanshan Normal University, Chaozhou, 521041, People’s Republic of China; 3School of Medical Technology, Xuzhou Medical University, Xuzhou, 221004, People’s Republic of China; 4Department of Clinical Laboratory, the Affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, Jiangsu, 223800, People’s Republic of ChinaCorrespondence: Jinke Wang, State Key Laboratory of Bioelectronics, Southeast University, Nanjing, 210096, People’s Republic of China, Email wangjinke@seu.edu.cnBackground: The inflammatory diseases pose a great threat to human health. Variant anti-inflammatory agents have been therefore developed. However, the current anti-inflammatory drugs are still challenged by low response and side effects. There remain great unmet treatments to inflammatory diseases.Methods: In this work, we fabricate a recombinant adeno-associated virus (rAAV), rAAV-DMP-miR533, by packaging a DNA molecule DMP-miR533 into AAV, in which DMP is a NF-κB-activatable promoter composed of a NF-κB decoy and a minimal promoter and miR533 codes an artificial microRNA targeting NF-κB RELA. We evaluate the in vitro and in vivo anti-inflammatory effect of the virus with inflammatory cells and the mice of three typical inflammatory diseases including the dextran sulphate sodium-induced acute colitis, imiquimod-induced psoriasis, and collagen-induced arthritis.Results: We found that rAAV-DMP-miR533 had marked anti-inflammatory effect in both cells and mice. In addition, rAAV-DMP-miR533 showed biosafety in mice.Conclusion: This study thus provides a promising gene therapy to variant inflammatory diseases by directly targeting NF-κB, an established hub regulator of inflammation.Keywords: NF-κB, inflammation, therapy, AAV, micr

Models with nonlinear architectures/parameterizations such as deep neural networks (DNNs) are well known for their mysteriously good generalization performance at overparameterization. In this work, we tackle this mystery from a novel perspective focusing on the transition of the target recovery/fitting accuracy as a function of the training data size. We propose a rank stratification for general nonlinear models to uncover a model rank as an "effective size of parameters" for each function in the function space of the corresponding model. Moreover, we establish a linear stability theory proving that a target function almost surely becomes linearly stable when the training data size equals its model rank. Supported by our experiments, we propose a linear stability hypothesis that linearly stable functions are preferred by nonlinear training. By these results, model rank of a target function predicts a minimal training data size for its successful recovery. Specifically for the matrix factorization model and DNNs of fully-connected or convolutional architectures, our rank stratification shows that the model rank for specific target functions can be much lower than the size of model parameters. This result predicts the target recovery capability even at heavy overparameterization for these nonlinear models as demonstrated quantitatively by our experiments. Overall, our work provides a unified framework with quantitative prediction power to understand the mysterious target recovery behavior at overparameterization for general nonlinear models.

Spiking neural networks (SNNs) are a viable alternative to conventional artificial neural networks when resource efficiency and computational complexity are of importance. A major advantage of SNNs is their binary information transfer through spike trains which eliminates multiplication operations. The training of SNNs has, however, been a challenge, since neuron models are non-differentiable and traditional gradient-based backpropagation algorithms cannot be applied directly. Furthermore, spike-timing-dependent plasticity (STDP), albeit being a spike-based learning rule, updates weights locally and does not optimize for the output error of the network. We present desire backpropagation, a method to derive the desired spike activity of all neurons, including the hidden ones, from the output error. By incorporating this desire value into the local STDP weight update, we can efficiently capture the neuron dynamics while minimizing the global error and attaining a high classification accuracy. That makes desire backpropagation a spike-based supervised learning rule. We trained three-layer networks to classify MNIST and Fashion-MNIST images and reached an accuracy of 98.41% and 87.56%, respectively. In addition, by eliminating a multiplication during the backward pass, we reduce computational complexity and balance arithmetic resources between forward and backward pass, making desire backpropagation a candidate for training on low-resource devices.

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding method

Soft errors in large VLSI circuits pose dramatic influence on computing- and memory-intensive neural network (NN) processing. Understanding the influence of soft errors on NNs is critical to protect against soft errors for reliable NN processing. Prior work mainly rely on fault simulation to analyze the influence of soft errors on NN processing. They are accurate but usually specific to limited configurations of errors and NN models due to the prohibitively slow simulation speed especially for large NN models and datasets. With the observation that the influence of soft errors propagates across a large number of neurons and accumulates as well, we propose to characterize the soft error induced data disturbance on each neuron with normal distribution model according to central limit theorem and develop a series of statistical models to analyze the behavior of NN models under soft errors in general. The statistical models reveal not only the correlation between soft errors and NN model accuracy, but also how NN parameters such as quantization and architecture affect the reliability of NNs. The proposed models are compared with fault simulation and verified comprehensively. In addition, we observe that the statistical models that characterize the soft error influence can also be utilized to predict fault simulation results in many cases and we explore the use of the proposed statistical models to accelerate fault simulations of NNs. According to our experiments, the accelerated fault simulation shows almost two orders of magnitude speedup with negligible simulation accuracy loss over the baseline fault simulations.

In this paper, a semantic communication framework is proposed for textual data transmission. In the studied model, a base station (BS) extracts the semantic information from textual data, and transmits it to each user. The semantic information is modeled by a knowledge graph (KG) that consists of a set of semantic triples. After receiving the semantic information, each user recovers the original text using a graph-to-text generation model. To measure the performance of the considered semantic communication framework, a metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed. Due to wireless resource limitations, the BS may not be able to transmit the entire semantic information to each user and satisfy the transmission delay constraint. Hence, the BS must select an appropriate resource block for each user as well as determine and transmit part of the semantic information to the users. As such, we formulate an optimization problem whose goal is to maximize the total MSS by jointly optimizing the resource allocation policy and determining the partial semantic information to be transmitted. To solve this problem, a proximal-policy-optimization-based reinforcement learning (RL) algorithm integrated with an attention network is proposed. The proposed algorithm can evaluate the importance of each triple in the semantic information using an attention network and then, build a relationship between the importance distribution of the triples in the semantic information and the total MSS. Compared to traditional RL algorithms, the proposed algorithm can dynamically adjust its learning rate thus ensuring convergence to a locally optimal solution.

Evidence for CP violation in baryonic decays has not been seen in experiments. With large data events accumulated at $e^+e^-$ collider or Large Hadron Collider, the charmed baryon decays would provide a promising laboratory to test the CP symmetry. In this work, we formulate the $\Lambda_c^+\to\phi p$ and $ \Lambda_c^+\to\omega p$ decays for the measurements of their asymmetry parameters in weak decays at the BESIII or the LHCb experiments. The polarization transfer is analyzed in the two processes of $e^+ e^- \to\Lambda_c^+\bar\Lambda_c^-$ and $\Lambda_b^0\to \Lambda^+_c\pi^-$, and the formulas of the joint angular distribution for these processes are provided. The sensitivity of the measurement of asymmetry parameters for the above two decay processes is estimated for the future experiments.

The enhanced X-ray Timing and Polarimetry mission (eXTP) is a flagship observatory for X-ray timing, spectroscopy and polarimetry developed by an International Consortium. Thanks to its very large collecting area, good spectral resolution and unprecedented polarimetry capabilities, eXTP will explore the properties of matter and the propagation of light in the most extreme conditions found in the Universe. eXTP will, in addition, be a powerful X-ray observatory. The mission will continuously monitor the X-ray sky, and will enable multiwavelength and multi-messenger studies. The mission is currently in phase B, which will be completed in the middle of 2022.

Urbanization has caused significant landscape changes in rural areas, leading to the emergence of urbanized landscapes (ULs), which have been generally criticized by authorities and professionals. However, perceptions of ULs among local residents have rarely been studied. In this study, we chose five typical categories of ULs from rapidly transforming villages in Fujian Province, China — hardened water bank (HWB), big pavilion (BPA), big memorial arch (BMA), big ornamental lawn (BOL), and big square (BSQ) to do the study. We identified how these ULs were rated and ranked by on-site surveys, as well as how related aesthetic and multifunctional landscape characters (LCs) played a role. The results (N=550) showed that 1) residents supported the construction of ULs, and the most preferred category was that with the most natural elements (BOL) that was simultaneously well maintained. 2) For the residents, the longer they had resided in the village and the fewer connections they had with the city, the more in favor they were of the ULs, and the more eager they were for landscape change. In addition, residents with higher education and Communist Party of China membership valued the naturalness related LCs more highly. 3) Two contradictory preference features, naturalness and livability, should be well coordinated and balanced to construct an improved favorable village for the residents, to realize a balanced and sustainable development path. This study makes great theoretical contributions to landscape research and provides new insights into rural planning and construction.