The Analysis of the Difficult Points on Developing E-Commerce of the Western Region in China

From 1999, China started to the project of “Development of Western Region of China” and many preferential policies were issued by the central government. However, after almost 5 years, compared with eastern region, the development of infrastructure is still relatively lower. As to the development of E-commerce, the most typical phenomenon is unbalance which means that the eastern region is much faster than the western because of territorial and economic factors. So it is necessary to get a whole picture and get a clear understanding of problems of current situation of E-commerce in west part of China in order to accelerate it. In this article, the difficult points of E-commerce development in west region are discussed, such as the law issue, infrastructure, information service providers and talents people and some strategies will be given finally based on the current situation of E-commerce in west part of China

SAGDA: Achieving O(ϵ−2)\mathcal{O}(\epsilon^{-2}) Communication Complexity in Federated Min-Max Learning

To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as in conventional federated learning. However, due to the more complicated inter-outer problem structure in federated min-max learning, theoretical understandings of communication complexity for federated min-max learning with infrequent communications remain very limited in the literature. This is particularly true for settings with non-i.i.d. datasets and partial client participation. To address this challenge, in this paper, we propose a new algorithmic framework called stochastic sampling averaging gradient descent ascent (SAGDA), which i) assembles stochastic gradient estimators from randomly sampled clients as control variates and ii) leverages two learning rates on both server and client sides. We show that SAGDA achieves a linear speedup in terms of both the number of clients and local update steps, which yields an $\mathcal{O}(\epsilon^{-2})$ communication complexity that is orders of magnitude lower than the state of the art. Interestingly, by noting that the standard federated stochastic gradient descent ascent (FSGDA) is in fact a control-variate-free special version of SAGDA, we immediately arrive at an $\mathcal{O}(\epsilon^{-2})$ communication complexity result for FSGDA. Therefore, through the lens of SAGDA, we also advance the current understanding on communication complexity of the standard FSGDA method for federated min-max learning.Comment: Published as a conference paper at NeurIPS 202

Explaining E-Tailers’ Source of Competitiveness: An Integrative Framework

E-tailers refer to small and medium size enterprises or individual entrepreneurs primarily conducting businesses on online shopping platforms. Although many works on e-marketplaces have been done, theory-driven studies that explain e-tailers’ source of competitiveness are relatively scarce. The current work developed an integrative theoretical model in which online social capital, structural assurance, and online word-of-month are proposed to affect e-tailers’ business performance. The current study offers implications on: 1) what are the unique sources of competitiveness for businesses operating in pure online environment; 2) how can the resource-scare e-tailers survive in their rivalry with large offline retailers

Federated Multi-Objective Learning

In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed nature and data privacy needs of such multi-agent multi-task learning applications. This motivates us to propose a new federated multi-objective learning (FMOL) framework with multiple clients distributively and collaboratively solving an MOO problem while keeping their training data private. Notably, our FMOL framework allows a different set of objective functions across different clients to support a wide range of applications, which advances and generalizes the MOO formulation to the federated learning paradigm for the first time. For this FMOL framework, we propose two new federated multi-objective optimization (FMOO) algorithms called federated multi-gradient descent averaging (FMGDA) and federated stochastic multi-gradient descent averaging (FSMGDA). Both algorithms allow local updates to significantly reduce communication costs, while achieving the {\em same} convergence rates as those of their algorithmic counterparts in the single-objective federated learning. Our extensive experiments also corroborate the efficacy of our proposed FMOO algorithms.Comment: Accepted in NeurIPS 202

Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation

Source-free unsupervised domain adaptation (SFUDA) aims to learn a target domain model using unlabeled target data and the knowledge of a well-trained source domain model. Most previous SFUDA works focus on inferring semantics of target data based on the source knowledge. Without measuring the transferability of the source knowledge, these methods insufficiently exploit the source knowledge, and fail to identify the reliability of the inferred target semantics. However, existing transferability measurements require either source data or target labels, which are infeasible in SFUDA. To this end, firstly, we propose a novel Uncertainty-induced Transferability Representation (UTR), which leverages uncertainty as the tool to analyse the channel-wise transferability of the source encoder in the absence of the source data and target labels. The domain-level UTR unravels how transferable the encoder channels are to the target domain and the instance-level UTR characterizes the reliability of the inferred target semantics. Secondly, based on the UTR, we propose a novel Calibrated Adaption Framework (CAF) for SFUDA, including i)the source knowledge calibration module that guides the target model to learn the transferable source knowledge and discard the non-transferable one, and ii)the target semantics calibration module that calibrates the unreliable semantics. With the help of the calibrated source knowledge and the target semantics, the model adapts to the target domain safely and ultimately better. We verified the effectiveness of our method using experimental results and demonstrated that the proposed method achieves state-of-the-art performances on the three SFUDA benchmarks. Code is available at https://github.com/SPIresearch/UTR

Localization of BEN1-LIKE protein and nuclear degradation during development of metaphloem sieve elements in Triticum aestivum L.

Metaphloem sieve elements (MSEs) in the developing caryopsis of Triticum aestivum L. undergo a unique type of programmed cell death (PCD); cell organelles gradually degrade with the MSE differentiation while mature sieve elements keep active. This study focuses on locating BEN1-LIKE protein and nuclear degradation in differentiating MSEs of wheat. Transmission electron microscopy (TEM) showed that nuclei degraded in MSE development. First, the degradation started at 2–3 days after flowering (DAF). The degraded fragments were then swallowed by phagocytic vacuoles at 4 DAF. Finally, nuclei almost completely degraded at 5 DAF. We measured the BEN1-LIKE protein expression in differentiating MSEs. In situ hybridization showed that BEN1-LIKE mRNA was a more obvious hybridization signal at 3–4 DAF at the microscopic level. Immuno-electron microscopy further revealed that BEN1-LIKE protein was mainly localized in MSE nuclei. Furthermore, MSE differentiation was tested using a TSQ Zn2+ fluorescence probe which showed that the dynamic change of Zn2+ accumulation was similar to BEN1-LIKE protein expression. These results suggest that nucleus degradation in wheat MSEs is associated with BEN1-LIKE protein and that the expression of this protein may be regulated by Zn2+ accumulation variation

Cloning, mapping and molecular characterization of porcine progesterone receptor membrane component 2 (PGRMC2) gene.

Progesterone plays an important role in sow reproduction by stimulating classic genomic pathways via nuclear receptors and non-genomic pathways via membrane receptors such a progesterone receptor membrane component 2 (PGRMC2). In this work, we used radiation hybrid mapping to assign PGRMC2 to pig chromosome 8 and observed that this receptor has two transcripts in pigs. The full-length cDNA of the large transcript is 1858 bp long and contains a 669-bp open reading frame (ORF) encoding a protein of 223 amino acids. The shorter transcript encodes a protein of 170 amino acids. The porcine PGRMC2 gene consists of three exons 446 bp, 156 bp and 1259 bp in length. The promoter sequence is GC-rich and lacks a typical TATA box. Several putative cis-regulatory DNA motifs were identified in the 208-bp upstream genomic region. Five single nucleotide polymorphisms (SNPs) were detected in introns* and the 3' UTR. RT-PCR indicated that the PGRMC2 gene is expressed ubiquitously in all pig tissues examined

Observation of a dissipative time crystal in a strongly interacting Rydberg gas

The notion of spontaneous symmetry breaking has been well established to characterize classical and quantum phase transitions of matters, such as in condensation, crystallization, and quantum magnetism, etc. Generalizations of this paradigm to the time dimension can further lead to an exotic dynamical phase, the time crystal, which spontaneously breaks the time translation symmetry of the system [1]. While the existence of a continuous time crystal at equilibrium has been challenged by the no-go theorems [2, 3], the difficulty can be circumvented by the dissipation in an open system. Here, we report the experimental observation of such a dissipative time crystalline order in a room-temperature atomic gas, where ground-state atoms are continuously driven to Rydberg states via electromagnetically induced transparency (EIT). The emergent time crystal is revealed by persistent oscillations of the probe-field transmission, with ultralong lifetime and no observable damping during the measurement. We show that the observed limit cycles arise from the coexistence and competition between distinct Rydberg components, in agreement with a mean-field analysis derived from the microscopic model. The random phase distribution of the oscillation for repeated realizations, together with the robustness against temporal noises further supports our realization of a dissipative time crystal.Comment: 6 pages, 4 figure