Research on the Efficiency Assessment and Its Key Influencing Factors Analysis of the Investment in the Environment Governance of 15 Sub-Provincial Cities in China

Continued the environmental pollution restricts the city's sustainable and healthy development in the future. The public goods characteristics of urban environment need a lot of funds for environmental governance from local finance, so improving the efficiency of fiscal expenditure of environmental governance is an important way to solve the current lack of financial investment. This paper evaluates the fiscal expenditure efficiency of environmental governance in local cities in China and empirically examines the environmental variables that affect the efficiency of fiscal expenditure. Based on this, it puts forward some policy recommendations to further improve the efficiency of fiscal expenditure in environmental governance. This paper focuses on 15 sub-Provincial cities, which includes cities named Chengdu, Dalian, Guangzhou, Harbin, Hangzhou, Jinan, Nanjing, Ningbo, Qingdao, Xiamen, Shenzhen Shenyang Wuhan Xi'an and Changchun.The innovation of this paper systematically summarizes the investment efficiency of environmental governance, based on the principle of building evaluation index system, taking the total investment in environmental governance as input index and the performance of environmental governance investment in water, gas and solid control as output, establishing 15 sub-Provincial cities of China environmental governance investment efficiency evaluation index system, based on the DEA method of 15 sub-Provincial cities of China environmental governance investment efficiency evaluation model, and then the environmental governance investment efficiency as the mother factor, the evaluation indicators for environmental governance investment efficiency factors, the use of gray correlation Analysis method to construct the key influencing factor analysis model of 15 sub-Provincial cities of China environmental governance investment efficiency and collect the input-output data of 15 sub-Provincial cities of China, autonomous regions and municipalities through 15 sub-Provincial cities of China Environmental Statistical Yearbook 2017 and 15 sub-Provincial cities of China Statistical Yearbook 2017, Investment efficiency and its key influencing factors confirm that the key factor of control the total amount of environmental governance investment, can effectively improve the efficiency of environmental governance investment. Keywords: Governance Investment, Efficiency assessment, Data envelopment analysis, 15 sub-Provincial cities

An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market

Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models

Feedforward computational model for pattern recognition with spiking neurons

Humans and primates are remarkably good at pattern recognition and outperform the best machine vision systems with respect to almost any measure. Building a computational model that emulates the architecture and information processing in biological neural systems has always been an attractive target. To build a computational model that closely follows the information processing and architecture of the visual cortex, in this paper, we have improved the latency-phase encoding to express the external stimuli in a more abstract manner. Moreover, inspired by recent findings in the biological neural system, including architecture, encoding, and learning theories, we have proposed a feedforward computational model of spiking neurons that emulates object recognition of the visual cortex for pattern recognition. Simulation results showed that the proposed computational model can perform pattern recognition task well. In addition, the success of this computational model suggests a plausible proof for feedforward architecture of pattern recognition in the visual cortex

The Pontryagin Class for Pre-Courant Algebroids

In this paper, we show that the Jacobiator $J$ of a pre-Courant algebroid is closed naturally. The corresponding equivalence class $[J^\flat]$ is defined as the Pontryagin class, which is the obstruction of a pre-Courant algebroid to be deformed into a Courant algebroid. We construct a Leibniz 2-algebra and a Lie 2-algebra associated to a pre-Courant algebroid and prove that these algebraic structures are isomorphic under deformations. Finally, we introduce the twisted action of a Lie algebra on a manifold to give more examples of pre-Courant algebroids, which include the Cartan geometry.Comment: 26 page

DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention

Most of the existing multi-modal models, hindered by their incapacity to adeptly manage interleaved image-and-text inputs in multi-image, multi-round dialogues, face substantial constraints in resource allocation for training and data accessibility, impacting their adaptability and scalability across varied interaction realms. To address this, we present the DeepSpeed-VisualChat framework, designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities, with a focus on enhancing the proficiency of Large Vision and Language Models in handling interleaved inputs. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions in multi-round, multi-image conversations. Compared to existing frameworks, DeepSpeed-VisualChat shows superior scalability up to 70B parameter language model size, representing a significant advancement in multi-modal language models and setting a solid foundation for future explorations