The Status and Prospects of Community Education Workers in China

Professionalization, career development prospects, and social value are the three basic components of the status and prospects of community education workers, which influence their choice to continue their careers or not. In China, these problems are complex and lacking in systematic research, and the current situation does not meet the needs of community education. This study interviewed 24 community workers regarding their salaries, working conditions, and training and career advancement opportunities to evaluate this situation in Ningbo City. The findings highlight challenges in the evaluation processes and work motivations of community education workers, including teams without professional knowledge, lack of training opportunities, unsupportive policies, and low salaries. These findings can be used by governments and community workers to find collaborative ways to facilitate community education processes, including the provision of adult education for community educators. New legal policies to raise the status of community educators are also suggested

Contraction: A Unified Perspective of Correlation Decay and Zero-Freeness of 2-Spin Systems

We study complex zeros of the partition function of 2-spin systems, viewed as a multivariate polynomial in terms of the edge interaction parameters and the uniform external field. We obtain new zero-free regions in which all these parameters are complex-valued. Crucially based on the zero-freeness, we show the existence of correlation decay in these regions. As a consequence, we obtain an FPTAS for computing the partition function of 2-spin systems on graphs of bounded degree for these parameter settings. We introduce the contraction property as a unified sufficient condition to devise FPTAS via either Weitz's algorithm or Barvinok's algorithm. Our main technical contribution is a very simple but general approach to extend any real parameter of which the 2-spin system exhibits correlation decay to its complex neighborhood where the partition function is zero-free and correlation decay still exists. This result formally establishes the inherent connection between two distinct notions of phase transition for 2-spin systems: the existence of correlation decay and the zero-freeness of the partition function via a unified perspective, contraction.Comment: 21 pages, 3 figures. Update: two correlation decay sets were added; a discussion with an independent work by Liu with similar results was give

Research on Typical Cases of Integration of Jiangsu Logistics Industry and Manufacturing Industry

This project aims to comprehensively investigate the current status of the modern logistics industry and advanced manufacturing development in Jiangsu, China. Its primary goal is to gain a precise understanding of the challenges and shortcomings in the integration of the logistics and manufacturing sectors in Jiangsu, as well as to delve deeply into the factors constraining this integration. Additionally, through empirical research on representative cases and study areas, it intends to propose policy recommendations for the development of the integration between the logistics and manufacturing industries. These recommendations aim to promote high-quality development of manufacturing in the context of the new development pattern, strengthen the foundational capabilities for logistics-manufacturing integration, and foster innovative models and formats for the integration of logistics and manufacturing

UK equity market microstructure in the age of machine

Financial markets perform two major functions. The first is the provision of liquidity in order to facilitate direct investment, hedging and diversification; the second is to ensure the efficient price discovery required in order to direct resources to where they can be best utilised within an economy. How well financial markets perform these functions is critical to the financial welfare of every individual in modern economies. As an example, retirement savings across the world are mostly invested in capital markets. Hence, the functioning of financial markets is linked to the standard of living of individuals. Technological advancements and new market regulations have in recent times significantly impacted how financial markets function, with no period in history having witnessed a more rapid pace of change than the last decade. Financial markets have become very complex, with most of the order execution now done by computer algorithms. New high-tech trading venues, such as dark pools, also now play outsized roles in financial markets. A lot of the impacts of these developments are poorly understood. In the EU particularly, the introduction of the Markets in Financial Instruments Directive (MiFID) and advancements in technology have combined to unleash a dramatic transformation of European capital markets. In order to better understand the role of high-tech trading venues in the modern financial markets’ trading environment generally and in the UK in particular, I conduct three studies investigating questions linked to the three major developments in financial markets over the past decade; these are algorithmic/high-frequency trading, market fragmentation and dark trading. In the first study, I examine the changing relationship between the price impact of block trades and informed trading, by considering this phenomenon within a high-frequency trading environment on intraday and inter-day bases. I find that the price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery. The second study exploits the regulatory differences between the US and the EU to examine the impact of market fragmentation on dimensions of market quality. Unlike the US’s Regulation National Market System, the EU’s MiFID does not impose a formal exchange trading linkage or guarantee a best execution price. This has raised concerns about consolidated market quality in increasingly fragmented European markets. The second study therefore investigates the impact of visible trading fragmentation on the quality of the London equity market and find a quadratic relationship between fragmentation and adverse selection costs. At low levels of fragmentation, order flow competition reduces adverse selection costs, improves market transparency and enhances market efficiency by reducing arbitrage opportunities. However, high levels of fragmentation increase adverse selection costs. The final study compares the impact of lit and dark venues’ liquidity on market liquidity. I find that compared with lit venues, dark venues proportionally contribute more liquidity to the aggregate market. This is because dark pools facilitate trades that otherwise might not easily have occurred in lit venues when the spread widens and the limit order queue builds up. I also find that informed and algorithmic trading hinder liquidity creation in lit and dark venues, while evidence also suggests that stocks exhibiting low levels of informed trading across the aggregate market drive dark venues’ liquidity contribution

SQ Lower Bounds for Learning Mixtures of Linear Classifiers

We study the problem of learning mixtures of linear classifiers under Gaussian covariates. Given sample access to a mixture of $r$ distributions on $\mathbb{R}^n$ of the form $(\mathbf{x},y_{\ell})$, $\ell\in [r]$, where $\mathbf{x}\sim\mathcal{N}(0,\mathbf{I}_n)$ and $y_\ell=\mathrm{sign}(\langle\mathbf{v}_\ell,\mathbf{x}\rangle)$ for an unknown unit vector $\mathbf{v}_\ell$, the goal is to learn the underlying distribution in total variation distance. Our main result is a Statistical Query (SQ) lower bound suggesting that known algorithms for this problem are essentially best possible, even for the special case of uniform mixtures. In particular, we show that the complexity of any SQ algorithm for the problem is $n^{\mathrm{poly}(1/\Delta) \log(r)}$, where $\Delta$ is a lower bound on the pairwise $\ell_2$-separation between the $\mathbf{v}_\ell$'s. The key technical ingredient underlying our result is a new construction of spherical designs that may be of independent interest.Comment: To appear in NeurIPS 202

StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation

We present new insights and a novel paradigm (StEik) for learning implicit neural representations (INR) of shapes. In particular, we shed light on the popular eikonal loss used for imposing a signed distance function constraint in INR. We show analytically that as the representation power of the network increases, the optimization approaches a partial differential equation (PDE) in the continuum limit that is unstable. We show that this instability can manifest in existing network optimization, leading to irregularities in the reconstructed surface and/or convergence to sub-optimal local minima, and thus fails to capture fine geometric and topological structure. We show analytically how other terms added to the loss, currently used in the literature for other purposes, can actually eliminate these instabilities. However, such terms can over-regularize the surface, preventing the representation of fine shape detail. Based on a similar PDE theory for the continuum limit, we introduce a new regularization term that still counteracts the eikonal instability but without over-regularizing. Furthermore, since stability is now guaranteed in the continuum limit, this stabilization also allows for considering new network structures that are able to represent finer shape detail. We introduce such a structure based on quadratic layers. Experiments on multiple benchmark data sets show that our new regularization and network are able to capture more precise shape details and more accurate topology than existing state-of-the-art