Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy

Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are $l_2$ distance or Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels, such as correntropy, are proposed. However, the current correntropy-based NMF only targets on the low-level features without considering the intrinsic geometrical distribution of data. In this paper, we propose a new NMF algorithm that preserves local invariance by adding graph regularization into the process of max-correntropy-based matrix factorization. Meanwhile, each feature can learn corresponding kernel from the data. The experiment results of Caltech101 and Caltech256 show the benefits of such combination against other NMF algorithms for the unsupervised image clustering

Interpreting Horizontal Well Flow Profiles and Optimizing Well Performance by Downhole Temperature and Pressure Data

Horizontal well temperature and pressure distributions can be measured by production logging or downhole permanent sensors, such as fiber optic distributed temperature sensors (DTS). Correct interpretation of temperature and pressure data can be used to obtain downhole flow conditions, which is key information to control and optimize horizontal well production. However, the fluid flow in the reservoir is often multiphase and complex, which makes temperature and pressure interpretation very difficult. In addition, the continuous measurement provides transient temperature behavior which increases the complexity of the problem. To interpret these measured data correctly, a comprehensive model is required. In this study, an interpretation model is developed to predict flow profile of a horizontal well from downhole temperature and pressure measurement. The model consists of a wellbore model and a reservoir model. The reservoir model can handle transient, multiphase flow and it includes a flow model and a thermal model. The calculation of the reservoir flow model is based on the streamline simulation and the calculation of reservoir thermal model is based on the finite difference method. The reservoir thermal model includes thermal expansion and viscous dissipation heating which can reflect small temperature changes caused by pressure difference. We combine the reservoir model with a horizontal well flow and temperature model as the forward model. Based on this forward model, by making the forward calculated temperature and pressure match the observed data, we can inverse temperature and pressure data to downhole flow rate profiles. Two commonly used inversion methods, Levenberg- Marquardt method and Marcov chain Monte Carlo method, are discussed in the study. Field applications illustrate the feasibility of using this model to interpret the field measured data and assist production optimization. The reservoir model also reveals the relationship between temperature behavior and reservoir permeability characteristic. The measured temperature information can help us to characterize a reservoir when the reservoir modeling is done only with limited information. The transient temperature information can be used in horizontal well optimization by controlling the flow rate until favorite temperature distribution is achieved. With temperature feedback and inflow control valves (ICVs), we developed a procedure of using DTS data to optimize horizontal well performance. The synthetic examples show that this method is useful at a certain level of temperature resolution and data noise

The Relationship Between CO2 Emission Reductions and Firm Value: An Empirical Analysis of Chinese Listed High Polluting and Low Polluting Companies

Although the growth rate of global carbon emissions has slowed down, the overall carbon emissions will further increase. As the world's largest CO2 emitter, the Chinese government promises to reduce over 65% in 2030 from the 2005 level. Past research has focused on the impact of carbon disclosure on firm value which mainly focuses on information disclosure, however, research on whether CO2 emission reductions affect firm values are limited and the results are inconclusive. This paper contributes by examining the relationship between CO2 emission reductions and firm value in the context of China. We collect data from the Shanghai stock exchange and Shenzhen stock exchange from 2019-2021 and 870 observations are examined. Companies are divided into high polluting and low polluting to examine the relationship more specifically. The relationship is examined by OLS regression. The results obtained show that CO2 emission reductions have an impact on firm value. For high polluting companies with large CO2 emissions, reduction increase firm value. However, for high polluting companies with comparatively less CO2 emissions which indicate those companies has already taken action in previous years, reduction in CO2 decrease firm value. As regards the impact of CO2 emission reductions on low polluting companies’ value, our results show there is no relationship between CO2 emission reductions and firm value

The Relationship Between CO2 Emission Reductions and Firm Value: An Empirical Analysis of Chinese Listed High Polluting and Low Polluting Companies

Although the growth rate of global carbon emissions has slowed down, the overall carbon emissions will further increase. As the world's largest CO2 emitter, the Chinese government promises to reduce over 65% in 2030 from the 2005 level. Past research has focused on the impact of carbon disclosure on firm value which mainly focuses on information disclosure, however, research on whether CO2 emission reductions affect firm values are limited and the results are inconclusive. This paper contributes by examining the relationship between CO2 emission reductions and firm value in the context of China. We collect data from the Shanghai stock exchange and Shenzhen stock exchange from 2019-2021 and 870 observations are examined. Companies are divided into high polluting and low polluting to examine the relationship more specifically. The relationship is examined by OLS regression. The results obtained show that CO2 emission reductions have an impact on firm value. For high polluting companies with large CO2 emissions, reduction increase firm value. However, for high polluting companies with comparatively less CO2 emissions which indicate those companies has already taken action in previous years, reduction in CO2 decrease firm value. As regards the impact of CO2 emission reductions on low polluting companies’ value, our results show there is no relationship between CO2 emission reductions and firm value

Reconstructing Visual Stimulus Images from EEG Signals Based on Deep Visual Representation Model

Reconstructing visual stimulus images is a significant task in neural decoding, and up to now, most studies consider the functional magnetic resonance imaging (fMRI) as the signal source. However, the fMRI-based image reconstruction methods are difficult to widely applied because of the complexity and high cost of the acquisition equipments. Considering the advantages of low cost and easy portability of the electroencephalogram (EEG) acquisition equipments, we propose a novel image reconstruction method based on EEG signals in this paper. Firstly, to satisfy the high recognizability of visual stimulus images in fast switching manner, we build a visual stimuli image dataset, and obtain the EEG dataset by a corresponding EEG signals collection experiment. Secondly, the deep visual representation model(DVRM) consisting of a primary encoder and a subordinate decoder is proposed to reconstruct visual stimuli. The encoder is designed based on the residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images, while the decoder is designed based on the deep neural network to reconstruct the visual stimulus image from the learned deep visual representation. The DVRM can fit the deep and multiview visual features of human natural state and make the reconstructed images more precise. Finally, we evaluate the DVRM in the quality of the generated images on our EEG dataset. The results show that the DVRM have good performance in the task of learning deep visual representation from EEG signals and generating reconstructed images that are realistic and highly resemble the original images

A METHOD FOR MITIGATING UNDERFITTING ISSUE IN TIME SERIES MODEL USING REGRESSORS

The present disclosure focuses on mitigating underfitting issue in time series model using regressors. The present disclosure replaces the large parameter with a new structure. The new structure may include a few layers of smaller models. Specifically, the “smaller” model may be defined as the number of parameters for the model may be smaller but can handle all the features. Thus, each smaller model is trained with all the data points. The smaller models may generate intermediate predictions which may be fed as the input to the next smaller model present in the next layer. As a result, the DPP value of each model in the structure is substantially higher and hence the underfitting issue may be efficiently resolved

An Automated Vulnerability Detection Framework for Smart Contracts

With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities therefore is an essential research problem. Existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities. However, this often causes many vulnerabilities to be ignored, and they are inefficient in detecting new vulnerabilities. In this study, to overcome such challenges, we propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract since the source code of smart contracts are rarely available in public. Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result. We conduct comprehensive experiments on large-scale benchmarks, and the quantitative results demonstrate the effectiveness and efficiency of our approach

CogVLM: Visual Expert for Pretrained Language Models

We introduce CogVLM, a powerful open-source visual language foundation model. Different from the popular shallow alignment method which maps image features into the input space of language model, CogVLM bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers. As a result, CogVLM enables deep fusion of vision language features without sacrificing any performance on NLP tasks. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and ranks the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., surpassing or matching PaLI-X 55B. Codes and checkpoints are available at https://github.com/THUDM/CogVLM

Manipulation of Magnetization Switching by Ultrafast Spin-Polarized Hot-Electron Transport in Synthetic Antiferromagnet

Uncovering the physical mechanisms that govern ultrafast charge and spin dynamics is becoming indispensable both at the fundamental level and to develop future spin-based electronics. Recently it has been shown that femtosecond pulsed-laser excitation of magnetic thin films produces intense and ultrafast spin-polarized hot electrons, thus attracting a lot of attention. While spin-polarized hot electrons are known to play a pivotal role in the ultrafast laser-induced demagnetization, their effect on magnetization switching remains an open issue. This study uncovers the effect of spin-polarized hot electrons generated by laser excitation on magnetization switching in a Co/Pt based perpendicular magnetic anisotropy-based synthetic antiferromagnet (p-SAF) using the time-resolved magneto-optical Kerr effect. It has been found that, at low pump fluence, the equivalent magnetic field generated by the hot-electron spin current plays a dominant role in assisting the magnetization switching of the lower layer in the antiferromagnetic configuration, while the strong thermal stability of the Ruderman Kittel Kasuya Yosida exchange interaction inhibits the further weakening of the switching field at high pump fluence. This study provides a viable way to control the magnetization switching of the antiferromagnetically exchange-coupled systems for spintronic applications with ultrafast control of the information operation

Efficient spin–orbit torque switching in perpendicularly magnetized CoFeB facilitated by Fe2O3 underlayer

Spin–orbit torque (SOT) is recognized as an effective way to manipulate magnetization in spintronic devices. For the low-power consumption and high-endurance requirements of future computer architectures, reducing the critical SOT switching current density and improving SOT efficiency are crucial, especially in the perpendicularly magnetized structures. Here, we have conducted a comprehensive study on improving the SOT efficiency of the Ta/CoFeB structure with a perpendicular magnetic anisotropy by inserting an oxide insulating layer Fe2O3 as the bottom layer. We found that only a 1–5 nm thickness of Fe2O3 significantly reduces the SOT critical switching current by 70% and enhances the spin Hall angle of Ta. The spin Hall angle increases from 0.078 for pure Ta/CoFeB to 0.13 for Fe2O3/Ta/CoFeB, and both types of spin–orbit torques, damping-like and field-like torques, are significantly enhanced. It is suggested that the atomic diffusion of O from the Fe2O3 underlayer leads to the partial oxidization of the Ta layer as well as the Ta/CoFeB interfaces, accounting for the observed enhanced SOT efficiency. Our results provide a reliable method to improve the SOT performance in perpendicularly magnetized structures by inserting the oxide underlayer using magnetron sputtering, in favor of its potential real-world application in spintronic devices