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Large-scale distributed storage systems, such as object stores, usually apply hashing-based placement and lookup methods to achieve scalability and resource efficiency. However, when object locations are determined by hash values, placement becomes inflexible, failing to optimize or satisfy application requirements such as load balance, failure tolerance, parallelism, and network/system performance. This work presents a novel solution to achieve the best of two worlds: flexibility while maintaining cost-effectiveness and scalability. The proposed method Smash is an object placement and lookup method that achieves full placement flexibility, balanced load, low resource cost, and short latency. Smash utilizes a recent space-efficient data structure and applies it to object-location lookups. We implement Smash as a prototype system and evaluate it in a public cloud. The analysis and experimental results show that Smash achieves full placement flexibility, fast storage operations, fast recovery from node dynamics, and lower DRAM cost (

The majority of our genome is composed of repeated DNA sequences that assemble into heterochromatin, a highly compacted structure that constrains their mutational potential. How heterochromatin forms during development and how its structure is maintained are not fully understood. Here, we show that mouse heterochromatin phase-separates after fertilization, during the earliest stages of mammalian embryogenesis. Using high-resolution quantitative imaging and molecular biology approaches, we show that pericentromeric heterochromatin displays properties consistent with a liquid-like state at the two-cell stage, which change at the four-cell stage, when chromocenters mature and heterochromatin becomes silent. Disrupting the condensates results in altered transcript levels of pericentromeric heterochromatin, suggesting a functional role for phase separation in heterochromatin function. Thus, our work shows that mouse heterochromatin forms membrane-less compartments with biophysical properties that change during development and provides new insights into the self-organization of chromatin domains during mammalian embryogenesis.

Micromeles has been the focus of discussion concerning its relationship with other genera of Sorbus s.l., Maleae, Rosaceae. With the aim to shed light on its circumscription, phylogenetic relationships were investigated using plastomes. The 14 newly sequenced Micromeles plastomes range in size from 159,753 to 160,512 bp, contain 113 unique genes arranged in the same order, including 79 protein-coding, 30 tRNA, and 4 rRNA genes. Phylogenetic analyses using ML/BI indicated that Micromeles and the other five genera traditionally segregated from Sorbus s.l., Aria, Chamaemespilus, Cormus, Sorbus s.s. and Torminalis, are monophyletic. However, Micromeles and the five genera recently delimited by Rushforth in Sorbus s.l., Alniaria, Dunnaria, Griffitharia, Thomsonaria and Wilsonaria, are paraphyletic. Micromeles is here supported to include all the Asian simple-leaved members of Sorbus s.l.

Federated learning (FL) has been widely adopted to train machine learning models over massive data in edge computing. However, machine learning faces critical challenges, \eg, data imbalance, edge dynamics, and resource constraints, in edge computing.The existing FL solutions cannot well cope with data imbalance or edge dynamics, and may cause high resource cost. In this paper, we propose an adaptive asynchronous federated learning (AAFL) mechanism. To deal with edge dynamics, the parameter server will aggregate local updated models only from a certain fraction $\alpha$ of all edge nodes in each epoch. Moreover, the system can intelligently vary the number of local updated models for global model aggregation in different epochs with network situations. We then propose experience-driven algorithms based on deep reinforcement learning (DRL) to adaptively determine the optimal value of $\alpha$ in each epoch for two cases of AAFL, single learning task and multiple learning tasks, so as to achieve less completion time of training under resource constraints. Extensive experiments on the classical models and datasets show high effectiveness of the proposed algorithms. Specifically, AAFL can reduce the completion time by about 55\% and improve the learning accuracy by 18\% under resource constraints, compared with the state-of-the-art solutions.

Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This paper presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

We investigate the dynamical transition processes of an Einstein-Maxwell-scalar gravitational system between two local ground states and an excited state in the anti-de Sitter spacetime. From the linear perturbation theory, only the excited state possesses a single unstable mode, indicating the dynamical instability. Such an instability is associated with the tachyonic instability due to the presence of an effective potential well near the event horizon. From the nonlinear dynamics simulation, through the scalar field accretion mechanism, the critical phenomena in the transition process of the gravitational system between the two local ground states are revealed. The threshold of the accretion strength indicates the existence of a dynamical barrier in this transition process, which depends on the coupling strength between the scalar and Maxwell fields. On the other hand, for the unstable excited state, there exists a special kind of critical dynamics with a zero threshold for the perturbation strength. The perturbations of different signs push the gravitational system to fall into different local ground states. Interestingly, in an extended parameter space, there exist specific parameters such that the perturbations of non-zero amplitude fail to trigger the single unstable mode of the excited state.

Blockchain-based supply chains provide a new solution to decentralized multi-party product management. However, existing methods, including ID-based and cryptographic-based solutions, cannot achieve both counterfeit resistance and decentralization in supply chain management. We argue that this dilemma comes from the disconnection and inconsistency of the data records and physical product entities. This paper proposes RF-Chain, a novel decentralized supply chain management solution that uniquely combines data record authentication and physical-layer RFID tag authentication to effectively achieve credibility and counterfeit resistance. The main contribution of this work is to integrate physical-layer authentication of cheap commodity RFID tags into a blockchain-based information management system, and RF-Chain is the first to do so. The proposed cross-layer authentication can effectively defend against counterfeit attacks without relying on a central key management service. Real-world experiments utilizing the Ethereum (ETH) platform and more than 100 RFID tags demonstrate that RF-Chain is secure, effective, time-efficient, and cost-efficient.

The accurate description of the structures of water and hydrated ions is important in electrochemical desalination, ion separation, and supercapacitors. In this work, we present an ab initio atomistic simulation-based study to explore the structure of water and hydrated monovalent ions (Li

Objective. Although the prognostic effect of statins on patients with prostate cancer (PCa) has been frequently evaluated, a consistent result is still lacking. We aimed to evaluate the association between statin use and mortality among patients with PCa after definite therapies. Methods. A systematic search of PubMed and other databases for cohort studies about the effect of statins on patients with PCa was performed until April 2022. Meta-analysis was performed using R software version 4.1.2. Results. 24 cohort studies involving 369, 206 participants were finally included. We found statin use significantly reduced the risk of prostate cancer-specific mortality (PCSM) with a pooled hazard ratio (pHR) = 0.76 (95% CI: 0.69–0.84, 18 studies), especially for postdiagnostic statin users: pHR = 0.81 (95% CI: 0.77–0.85) and patients who accepted androgen deprivation therapy (ADT): pHR = 0.69 (95% CI: 0.59–0.81). Statin use was also associated with a 24% reduction in the risk of all-cause mortality (ACM): pHR = 0.76 (95% CI: 0.68–0.85, 17 studies), especially for postdiagnostic statin users: pHR = 0.81 (95% CI: 0.78–0.85) and patients treated with ADT: pHR = 0.72 (95% CI: 0.63–0.82) or radiotherapy (RT): pHR = 0.68 (95% CI: 0.50–0.93). Conclusion. In conclusion, the use of statins could promote the prognosis of patients with PCa, especially for postdiagnostic users. For patients who received either ADT or radical prostatectomy (RP), statin use could decrease the PCSM. As for those who received either ADT or RT, statin use could decrease the ACM.

Cadmium (Cd) contamination in soil is a global problem. Recently, phytoremediation with plants, possessing high biomass and moderate Cd enrichment ability, has received excessive attention as a cost-effective method for Cd remediation from the soil. In this study, the plant growth, physiological responses, Cd concentration, accumulation, and distribution of the C4 grass hybrid Pennisetum (HP) were studied in different levels of Cd-contaminated soil in a pot experiment. Furthermore, a field trial was also conducted to accurately assess its practical phytoremediation potential in natural Cd-contaminated fallow filed. The results showed that HP possessed effective antioxidant enzymes to scavenge ROS and strong physiological coordination in response to Cd stress. The HP had a considerable Cd enrichment ability, and the maximal Cd uptake of 1.08 mg plant

Crushing the demolished waste concrete as aggregate to prepare fresh concrete may cause aggregate internal damage leading to strength reduction. To counter this issue, in the current study, the recycled aggregate is mixed with nano-calcium oxide in a vacuum reactor, and then a high concentration of carbon dioxide is introduced to fill the internal damages of the recycled aggregate. A layer of hydrophilic alkaline substance is formed on the surface of recycled aggregate to absorb carbon dioxide, which improves the strength of recycled aggregate particles and the bonding performance of aggregate-cement interface area. The test results show that the mechanical properties of recycled aggregate particles are improved after vacuum carbon strengthening, and the vacuum carbon strengthening effect of recycled aggregate with large particle size is more obvious. Compression strength of concrete and mortar prepared by vacuum carbon-reinforced aggregate is observed to be 13.6% and 30% higher than ordinary recycled concrete, respectively. Also, the proposed recycled aggregate improvement method manifests a 21.1% reduction in carbon emissions. These findings offer crucial parameters for the engineering use of recycled concrete, promote new ideas to reduce carbon emissions caused by fresh concrete production, and provide an important reference for the sustainable recycling of waste concrete.

Methylation reaction is a fundamental chemical reaction that plays an important role in the modification of drug molecules, DNA, as well as proteins. This work focuses on seeking potential novel methylation reagents through a systematic investigation of the thermodynamics and reactivity of methyl-substituted organic hydride radical cations (XH

Understanding urban functions help with government planning and resource allocation to promote economic development. Few studies have reflected the influence of population movement and migration on urban functions overtime on a large scale. This study adopted the time-series social media data and points of interest to analyse urban functions’ distribution at the county scale. By employing dynamic time warping distance and K-Medoids to cluster cities, the result of China’s hourly population distribution over different periods achieves a high accuracy (Pearson’s R = 0.821, R2 = 0.668). From the clustering results, time-series population data effectively reflect cities’ socioeconomic characteristics and identify the spatial distribution of China’s urban functional patterns. We select four representative urban agglomerations to deeply analyse their urban function patterns. Furthermore, urban functions can also influence changes in time-series populations. This study explores the correlations between time-series population mobility and urban functions, which could help analyse urban functions and urban socioeconomic conditions.

Distributed in-memory computing frameworks usually have lots of parameters (e.g., the buffer size of shuffle) to form a configuration for each execution. A well-tuned configuration can bring large improvements of performance. However, to improve resource utilization, jobs are often share the same cluster, which causes dynamic cluster load conditions. According to our observation, the variation of cluster load reduces effectiveness of configuration tuning. Besides, as a common problem of cluster computing jobs, overestimation of resources also occurs during configuration tuning. It is challenging to efficiently find the optimal configuration in a shared cluster with the consideration of memory-sparing. In this paper, we introduce MespaConfig, a job-level configuration optimizer for distributed in-memory computing jobs. Advancements of MespaConfig over previous work are features including memory-sparing and load-sensitive. We evaluate MespaConfig by 6 typical Spark programs under different load conditions. The evaluation results show that MespaConfig improves the performance of six typical programs by up to 12 compared with default configurations. MespaConfig also achieves at most 41% reduction of configuration memory usage and reduces the optimization time overhead by 10.8 compared with the state-of-the-art approach.

Taking the construction project of the National Cybersecurity Talents and Innovation Base as an example, this paper studies the construction technology of freeform surface spatial bending-torsion steel structure based on structural design model transformation, including the parametric modeling of deepening design model, computer-aided bending and torsion frame production, flexible docking in the bending-torsion combination unit, and welding stress deformation, in hope to provide reference for similar projects in the future.

With a background of carbon peak and neutrality, the economic and environmental requirements are increasing for microgrids. In view of the problem of energy wastage and conflicts of interest among multiple microgrid integrated energy systems, it is important to study the operation optimization of microgrid clusters while considering the sharing and trading of both carbon emissions and green certificates. In this study, a Stackelberg game mechanism is applied, in which the microgrid operator is the leader and its subscribers are the followers, forming a master–slave interaction model. Following this, breaking the trading barriers of energy and various policy markets, the joint carbon and green certificate trading mechanism is proposed. Moreover, a mutually beneficial shared trading model of multi-microgrids considering coupled energy and carbon and green certificate trading is proposed to avoid the problem of double counting of environmental attributes. In addition, a cooperative sharing center is assumed to propose a flexible multi-resource sharing price mechanism. It guides each microgrid operator to conduct internal multi-resource sharing trading, so as to reduce the daily operating costs of energy supplying entities in the cooperative system of multiple microgrids, effectively reduce carbon emissions, and improve the balance of network group mutual aid. According to the simulation results of an illustrative example, the proposed trading strategy can effectively unlock the potential of resource sharing and mutual aid within multi-microgrids and improve the economy and carbon reduction effects of the overall system.

In the present paper we shall investigate the Waring's problem for upper triangular matrix algebras. The main result is the following: Let $n\geq 2$ and $m\geq 1$ be integers. Let $p(x_1,\ldots,x_m)$ be a noncommutative polynomial with zero constant term over an infinite field $K$. Let $T_n(K)$ be the set of all $n\times n$ upper triangular matrices over $K$. Suppose $1, Comment: 22 pages. This version has corrected some minor errors in the first version. Comments are welcome. arXiv admin note: text overlap with arXiv:2305.11734

After three years of global rampage, the COVID-19 epidemic, the most serious infectious disease to occur worldwide since the 1918 influenza pandemic, is nearing its end. From the global experience, medical control and social control are the two main dimensions in the prevention and control of COVID-19. From the perspective of “two types of control”, namely medical control and social control, this paper finds that the political system, economic structure, and cultural values of the United States greatly limit the government’s ability to impose social control, forcing it to adopt medical control to fight the virus in a single dimension. In contrast, China’s political system, economic structure, and cultural values allow its government to adopt stringent, extensive, and frequent social control, as well as medical control to fight the virus. This approach departs from the traditional pathway of fighting the epidemic, i.e., “infection–treatment–immunization”, thereby outpacing the evolution of the virus and controlling its spread more rapidly. This finding helps explain why the Chinese government adopted a strict “zeroing” and “dynamic zeroing” policy during the first three years, at the cost of enormous economic, social, and even political legitimacy. It was not until late 2022, when the Omicron variant with the waning virulence became prevalent, that China chose to “coexist” with the virus, thus avoiding a massive epidemic-related death. While the United States adopted a pulsed-style strategy at the beginning of the epidemic, i.e., “relaxation–suppression–relaxation–suppression”, and began to “coexist” with the virus in just one year, resulting in a large number of excess deaths associated with the epidemic. The study contributes to explaining the difference in the interplay between public health priorities and COVID-19 response strategies in China and the United States, based on the specific public health context and the perspective of “medical control” and “social control”.

We investigated the critical dynamical scalarization and descalarization of black holes within the framework of the Einstein-Maxwell-scalar theory featuring higher-order coupling functions. Both the critical scalarization and descalarization displayed first-order phase transitions. When examining the nonlinear dynamics near the threshold, we always observed critical solutions that are linearly unstable static scalarized black holes. The critical dynamical scalarization and descalarization share certain similarities with the type I critical gravitational collapse. However, their initial configurations, critical solutions, and final outcomes differ significantly. To provide further insights into the dynamical results, we conducted a comparative analysis involving static solutions and perturbative analysis., 38 pages, 12 figures

This study focuses on micro-certification of teacher training students' educational practice ability, sorting out relevant research results, firstly, using the Delphi method and validating factor analysis to construct a micro-competency index system for teacher training students' educational practice, and after conducting Delphi three times, the overall mean reached the very important to important level, and the indexes covered four first-level dimensions, nine second-level dimensions of teacher moral practice ability, teaching ability, nurturing ability, and development ability, totaling The validation factor analysis showed that the micro-competency index system had high reliability and validity and reasonable distribution of entries. Then, based on the 31 micro-competency dimensions, we constructed a standardized framework for teacher education practice micro-competency certification according to the micro-certification development framework of "Competency Center - Evidence Driven - Task Grasp", and designed a micro-certification operation model for teacher education practice competency. The practical results show that it can better meet the needs of teacher-training students' educational practice ability assessment and educational teaching ability improvement, and can support teacher-training students' professional learning and the cultivation of independent learning ability. Finally, the study concludes that: 1. the identified competency index system can comprehensively reflect the educational practice competency of practicing teacher educators; 2. the specification and operation model of teacher education practice competency certification based on micro-certification are scientific and feasible; 3. the micro-certification model of teacher education practice competency is simple and universal, and clearly defines the connotation and extension of teacher education practice competency. It allows teachers from different disciplinary and professional backgrounds to interpret the four dimensions to be developed, which can realize the sustainable development of teacher education practice competency.

Purpose: To determine the expression of circRTN4 in gastric cancer (GC) and its role in tumor progression. Methods: GEO dataset GSE93541 was analyzed using GEO2R. Starbase website was used to predict the combination of miRNA and circRTN4. The relationship between circRTN4 and prognosis was analyzed using Kaplan-Meier Plotter database, while expression levels of circRTN4, miR-424-5p, and LATS2 were assessed by quantitative real time-polymerase chain reaction (qRT-PCR). CCK8, EDU, Transwell, and Western blot were used to assess GC proliferation, migration, invasion, and stemness. Lastly, co-transfection of miR-424-5p or si-LAST2 was reversely used to demonstrate the regulatory effect of circRTN4 on the progression of gastric cancer cells. Significantly downregulated circRTN4 in GC was screened, and the combined miR-424-5p and downstream gene LATS2 were predicted by Starbase. Results: The average relative expression of circRTN4 mRNA in GC tissues was significantly lower than in adjacent tissues. MiR-424-5p was highly expressed in tumor tissues, while LATS2 decreased (p < 0.05). Low expression of circRTN4 was associated with a low survival rate in patients. pLCDH- circRTN4 significantly inhibited the proliferation of gastric cancer cells (p < 0.05). Overexpression of circRTN4 inhibited the migration and invasion of tumor cells, while pLCDH-circRTN4 reduced the ability of GC stem cells and expressions of MMP2 and OCT4. Conclusion: Expression of circRTN4 decreases in GC, and contributes to the development and progression of this disease by increasing the levels of LATS2 and binding with miR-424-5p. This suggests that circRTN4 may serve as a promising prognostic marker as well as a potential therapeutic target for gastric cancer

This thesis is dedicated to the study of open spin networks. We formulate quasi-local descriptions of loop quantum gravity. We investigate the coarse-graining procedure via tracing over bulk degrees of freedom, which encodes all that we can know about the quantum state of geometry from probing the boundary. We prove a boundary-to bulk universal reconstruction procedure, to be understood as a purification of the mixed boundary state into a pure bulk state. We then move to define multipartite entanglement in spin networks, and show the computation of entanglement excitation from holonomy operator, which also allows us to glimpse bulk curvature from entanglement. Moreover, by investigating another coarse-graining procedure - via gauge-fixing, which does not trace over any bulk degrees of freedom, we show a new interesting connection between bulk geometry and boundary observables via the dynamics of entanglement. Finally, we define the spin network entanglement between spin sub-networks, which correspond to spatial sub-regions. We then generalize the coarse-graining approach, and prove that the entanglement between spin sub-networks is preserved under the coarse-graining (via gauge-fixing)., This article has been removed by arXiv administrators due to a copyright claim by a third party

Meetings are increasingly important for collaborations. Action items in meeting transcripts are crucial for managing post-meeting to-do tasks, which usually are summarized laboriously. The Action Item Detection task aims to automatically detect meeting content associated with action items. However, datasets manually annotated with action item detection labels are scarce and in small scale. We construct and release the first Chinese meeting corpus with manual action item annotations. In addition, we propose a Context-Drop approach to utilize both local and global contexts by contrastive learning, and achieve better accuracy and robustness for action item detection. We also propose a Lightweight Model Ensemble method to exploit different pre-trained models. Experimental results on our Chinese meeting corpus and the English AMI corpus demonstrate the effectiveness of the proposed approaches., 5 pages, 2 figures. Paper accepted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), Rhodes, Greece

Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models. With knowledge distillation, these models may also benefit from the knowledge encoded by language models that are pre-trained on rich sources of texts. The distillation process, however, is challenging due to the modal disparity between textual and speech embedding spaces. This paper studies metric-based distillation to align the embedding space of text and speech with only a small amount of data without modifying the model structure. Since the semantic and granularity gap between text and speech has been omitted in literature, which impairs the distillation, we propose the Prior-informed Adaptive knowledge Distillation (PAD) that adaptively leverages text/speech units of variable granularity and prior distributions to achieve better global and local alignments between text and speech pre-trained models. We evaluate on three spoken language understanding benchmarks to show that PAD is more effective in transferring linguistic knowledge than other metric-based distillation approaches.

Water resources in arid and semi-arid inland regions are highly vulnerable, facing threats from global climate change and human activities. Ensuring water resource sustainability requires scientifically evaluating the vulnerability of water resources and its driving factors. Taking the Shule River Basin, an inland river in northwest China, as an example, this study established an assessment system considering 16 influencing factors based on three aspects, including natural vulnerability, anthropogenic vulnerability, and carrying capacity vulnerability. The mature-element fuzzy model based on comprehensive weight and the Delphi method were used to evaluate the water resource vulnerability of the basin from 2005 to 2021. The results were as follows: (1) The water resource vulnerability in the Shule River Basin was between severe and moderate from 2005 to 2021, with a trend towards severe vulnerability. (2) The barrier analysis at the index level shows that factors of natural vulnerability had a low impact on water resource vulnerability in the basin from 2005 to 2019 but exerted a greater impact in 2020–2021, an impact that was far higher than that caused by factors of man-made vulnerability and water resource vulnerability. The impact of factors of anthropogenic vulnerability on water resource vulnerability was relatively low in the entire study period, except in 2016, 2017, 2020, and 2021. In 2005–2010, the factors of bearing capacity vulnerability had a great impact on water resource vulnerability, but in 2011–2021, the impact was alleviated and was gradually reduced. (3) Among the 16 factors affecting water resource vulnerability, the obstacle degree was higher than 6.5% for the following factors: the ratio of irrigation water use on farmland, the annual precipitation, total water resources, the annual quantity of wastewater effluent, the urbanization rate, the surface water control rate, and the degree of groundwater resource amount, exploration, and utilization obstacle values.

Fluorescence lifetime imaging microscopy (FLIM) is a popular modality to create additional contrast in fluorescence images. By carefully analyzing pixel-based nanosecond lifetime patterns, FLIM allows studying complex molecular populations. At the single molecule or single particle level, however, image series often suffer from low signal intensities per pixel, rendering it difficult to quantitatively disentangle different lifetime species, such as during FRET analysis in the presence of a significant donor-only fraction. To address this problem, we combined particle localization with phasor-based FLIM analysis. Using simulations, we first showed that an average of ~300 photons, spread over the different pixels encompassing single fluorescing particles and without background, is enough to determine a correct phasor signature (standard deviation

For speech interaction, voice activity detection (VAD) is often used as a front-end. However, traditional VAD algorithms usually need to wait for a continuous tail silence to reach a preset maximum duration before segmentation, resulting in a large latency that affects user experience. In this paper, we propose a novel semantic VAD for low-latency segmentation. Different from existing methods, a frame-level punctuation prediction task is added to the semantic VAD, and the artificial endpoint is included in the classification category in addition to the often-used speech presence and absence. To enhance the semantic information of the model, we also incorporate an automatic speech recognition (ASR) related semantic loss. Evaluations on an internal dataset show that the proposed method can reduce the average latency by 53.3% without significant deterioration of character error rate in the back-end ASR compared to the traditional VAD approach., Accepted by Interspeech2023