Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate Camouflaged Object Detection via Mixture Convolution and Interactive Fusion

Camouﬂaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research eﬀorts in the ﬁeld of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A signiﬁcantly large receptive ﬁeld, which provides rich context information, and (ii) An eﬀective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive ﬁeld and eﬀective feature fusion into a uniﬁed framework. Speciﬁcally, we ﬁrst extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive ﬁeld. Finally, we fuse the features using specially-designed multi-level interactive fusion modules, each of which employs an attention mechanism along with feature interaction for eﬀective feature fusion. Our method detects camouﬂaged objects with an eﬀective fusion strategy, which aggregates the rich context information from a large receptive ﬁeld. All of these designs meet the requirements of COD well, allowing the accurate detection of camouﬂaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouﬂaged objects and outperforms the state-of-the-art methods.
Vision‐based vehicle speed estimation: A survey

The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.
A Survey on Active Deep Learning: From Model Driven to Data Driven

Which samples should be labelled in a large dataset is one of the most important problems for the training of deep learning. So far, a variety of active sample selection strategies related to deep learning have been proposed in the literature. We defined them as Active Deep Learning (ADL) only if their predictor or selector is a deep model, where the basic learner is called the predictor and the labeling schemes are called the selector. In this survey, we categorize ADL into model-driven ADL and data-driven ADL by whether its selector is model driven or data driven. We also introduce the different characteristics of the two major types of ADL, respectively. We summarized three fundamental factors in the designation of a selector. We pointed out that, with the development of deep learning, the selector in ADL also is experiencing the stage from model driven to data driven. The advantages and disadvantages between data-driven ADL and model-driven ADL are thoroughly analyzed. Furthermore, different sub-classes of data-drive or model-driven ADL are also summarized and discussed emphatically. Finally, we survey the trend of ADL from model driven to data driven.
Charting By Machines

We test the efficient market hypothesis by using machine learning to forecast future stock returns from historical performance. These forecasts strongly predict the cross section of future stock returns. The predictive power holds in most subperiods, is strong among the largest 500 stocks, and is distinct from momentum and reversal. The forecasting function has important nonlinearities and interactions and is remarkably stable through time. Our research design ensures that our findings are not a result of data mining. These findings question the efficient market hypothesis and indicate that investment strategies based on technical analysis and charting may have merit.
Global Code of Ethics in Tourism

The World Tourism Organisation established the Global Code of Ethics for Tourism (GCET) in 1999. Two years later, the United Nations acknowledged them and encouraged UNWTO to promote the effective follow-up of its provisions. Essentially, the global code of ethics are a ‘comprehensive set of ten principles whose purpose is to guide stakeholders in tourism development’. These ten principles were never meant to be legally binding, but they serve as guiding principles to governments, local communities, tourism operators and tourists, concerning preservation of the environment. The Code features a voluntary implementation mechanism through its recognition of the role of the World Committee on Tourism Ethics (WCTE), to which stakeholders may refer matters concerning the application and interpretation of the document. GCET’s 10 principles amply cover the economic, social, cultural and environmental components of travel and tourism.
Value Relevance of Accounting Information and Stock Price Reaction: Empirical Evidence from China

<br><br>Research question: This study investigates whether the release of financial accounting information is related to the change in stock prices. <br><br>Motivation: The relationship between accounting information and stock price reaction is well documented in the United States and other major developed economies. However, what is the relationship between share prices and accounting information in emerging economies? According to the standards of Wall Street, London, and Tokyo, the emerging capital markets in the post-communist countries are very small, but they can be a major source of capital for economic growth and development. <br><br>Idea: For investors, investment decisions about stock should be made by analyzing factors that can influence the stock price. Investors need to have information to determine whether they can benefit from the shares they bought. The financial condition of companies is one of the major information that investors should know. <br><br>Data: We obtained data from the A-share market in Shanghai and Shenzhen Stock Exchange of 1,272 listed companies. Data from selected companies’ annual report in 2008- 2018 and closing share prices in 2009-2019 were collected. The sample data were from the China Stock Market and Accounting Research database. <br><br>Tools: This study used a stepwise regression model to select variables that can provide significant effects and analyzed the regression of added variables and stock price. <br><br>Findings: We find that value relevance of accounting number, profitability, liquidity, and operational efficiency are positively related to stock price reaction. Other accounting variables, such as earnings per share, current ratio, quick ratio, and debt to equity ratio, have a more significant influence on the market share price. A linear relationship is observed between stock prices and earning per share, current ratio, and quick ratio according to the stepwise regression method. <br><br>Contribution: This study provides new insights for academic research and provides additional meaningful and useful information regarding the relevance of companies’ accounting information for foreign and domestic investors, specifically domestic investors. This research has scientific and practical usefulness for investors who care about their returns on investments. <br>
Does Time-Space Compression Affect Analysts’ Forecast Performance?

Through the difference-in-differences (DID) methodology, we find that the connection of China’s high-speed railway (HSR) as an exogenous shock could improve analysts’ forecast performance, leading to more accurate forecasts, decrease the dispersion between analysts, stimulate more forecast revisions with less revision volatility, evidenced by analyst site visits to firms. The results are robust with a battery of robustness checks such as 2SLS regression and so on. And the counterfactual relations caused by the lightning accident in 2011 also confirm our previous assumption. Furthermore, when the local economic development is weak, the trips by HSR is more convenient, or the information environment of firms are weak, the correlations will be stronger. Moreover, the connection of HSR also improves the information availability of stock recommendations issued by analysts. Overall, our study contributes to the relevant study of sell-side analysts’ performance and has an important impact on studying the role of geographic proximity on information efficiency.
Search with Learning in the Retail Gasoline Market

This article estimates a model of optimal search where consumers learn the distribution of gasoline prices during their driving trips. Our estimation incorporates traffic information and leverages the ordered search environment to recover parameters of the search and learning process using only station-level price and market share data. We find learning to be a crucial component of search in this market. Consumers' prior beliefs regularly deviate from the true price distribution but are updated quickly following each new price observation. Counterfactuals reveal that these learning dynamics can help explain commonly observed patterns of asymmetric cost pass-through.
Public Private-Partnership: Clustering of Chinese Small, Medium and Big Cities Utilities

This research considers in detail and subject to an analysis the discovery of essential features or logical consequences of clustering. It is focused on the main promoters and constrains to the most effective procedures and seeks to identify the innovation for utility’s models. It is likewise focused on the problems and challenges facing Chinese government possibly, Public Private-Partnership. The scientific explanation of this research explored the correlational statistics to clustering and identifying samples of aggregated structures that could potentially be analyzed. The scientific results proved that clustering model is a perfect framework. Positive impacts if all the three neighboring cities are connected with government cooperation was observed and the results indicated strong positive impact, greater-opportunities and economic success.
Education and Health for Inclusiveness

We discuss existing shortfalls and inequalities in the accumulation of human capital—knowledge, skills, and health. We analyze their immediate and systemic causes, and assess the scope for public intervention. The broad policy goals should be to improve: the quality, and not just the quantity, of education and health care; outcomes for disadvantaged groups; and lifelong outcomes. The means to achieve these goals, while maximizing value for money, include: focusing on results rather than just inputs; moving from piecemeal interventions to systemic reform; and adopting a “whole-of-society” approach. Reforms must be underpinned by a robust evidence base.
Comparative Studies on Development Strategies of Korea, Vietnam and China in the Economic Take-Off Period

This article conducts a series of comparison of development scenarios and development strategies among Korea, Vietnam and the mainland China. The research finds that although the take-off periods differ for the three countries there are some similarities in the strategies and successes they achieved on their way to economic growth and development. As to development strategies, China depends less on foreign capital than Korea and Vietnam. The contribution of exports and trade is great in the three countries, which is spurred by devaluation of local currency and stable exchange rates. A typical feature of China’s development strategies which does not apply to Korea and Vietnam is the pursuit of current account surplus.
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching

Order dispatch is one of the central problems to ride-sharing platforms. Recently, value-based reinforcement learning algorithms have shown promising performance on this problem. However, in real-world applications, the non-stationarity of the demand-supply system poses challenges to re-utilizing data generated in different time periods to learn the value function. In this work, motivated by the fact that the relative relationship between the values of some states is largely stable across various environments, we propose a pattern transfer learning framework for value-based reinforcement learning in the order dispatch problem. Our method efficiently captures the value patterns by incorporating a concordance penalty. The superior performance of the proposed method is supported by experiments.
Analyzing time series activity of Twitter political spambots

The presence and complexity of political Twitter bots has increased in recent years, making it a very difficult task to recognize these accounts from real, human users. We intended to provide an answer to the following question: are temporal patterns of activity qualitatively different in fake and human accounts? We collected a large sample of tweets during the post-electoral conflict in the US in 2020 and performed supervised and non-supervised statistical learning technique sto quantify the predictive power of time-series features for human-bot recognition. Our results show that there are no substantial differences, suggesting that political bots are nowadays very capable of mimicking human behaviour. This finding reveals the need for novel, more sophisticated bot-detection techniques.
Towards a Better Understanding of Linear Models for Recommendation

Recently, linear regression models have shown to often produce rather competitive results against more sophisticated deep learning models. Meanwhile, the (weighted) matrix factorization approaches have been popular choices for recommendation in the past and widely adopted in the industry. In this work, we aim to theoretically understand the relationship between these two approaches, which are the cornerstones of model-based recommendations. Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix. We further introduce a new learning algorithm in searching (hyper)parameters for the closed-form solution and utilize it to discover the nearby models of the existing solutions. The experimental results demonstrate that the basic models and their closed-form solutions are indeed quite competitive against the state-of-the-art models, thus, confirming the validity of studying the basic models. The effectiveness of exploring the nearby models are also experimentally validated.
Hybrid Encoding for Generating Large Scale Game Level Patterns With Local Variations

Generative adversarial networks (GANs) are a powerful indirect genotype-to-phenotype mapping for evolutionary search. Much previous work applying GANs to level generation focuses on fixed-size segments combined into a whole level, but individual segments may not fit together cohesively. In contrast, segments in human designed levels are often repeated, directly or with variation, and organized into patterns (the symmetric eagle in Level 1 of The Legend of Zelda, or repeated pipe motifs in Super Mario Bros.). Such patterns can be produced with compositional pattern producing networks (CPPNs). CPPNs define latent vector GAN inputs as a function of geometry, organizing segments output by a GAN into complete levels. However, collections of latent vectors can also be evolved directly, producing more chaotic levels. We propose a hybrid approach that evolves CPPNs first, but allows latent vectors to evolve later, combining the benefits of both approaches. These approaches are evaluated in Super Mario Bros. and The Legend of Zelda. We previously demonstrated via a quality-diversity algorithm that CPPNs better cover the space of possible levels than directly evolved levels. Here, we show that the hybrid approach first, covers areas that neither of the other methods can, and second, achieves comparable or superior quality diversity (QD) scores.
MAGI-X: Manifold-Constrained Gaussian Process Inference for Unknown System Dynamics

Ordinary differential equations (ODEs), commonly used to characterize the dynamic systems, are difficult to propose in closed-form for many complicated scientific applications, even with the help of domain expert. We propose a fast and accurate data-driven method, MAGI-X, to learn the unknown dynamic from the observation data in a non-parametric fashion, without the need of any domain knowledge. Unlike the existing methods that mainly rely on the costly numerical integration, MAGI-X utilizes the powerful functional approximator of neural network to learn the unknown nonlinear dynamic within the MAnifold-constrained Gaussian process Inference (MAGI) framework that completely circumvents the numerical integration. Comparing against the state-of-the-art methods on three realistic examples, MAGI-X achieves competitive accuracy in both fitting and forecasting while only taking a fraction of computational time. Moreover, MAGI-X provides practical solution for the inference of partial observed systems, which no previous method is able to handle.
Security and Privacy in the Emerging Cyber-Physical World: A Survey

With the emergence of low-cost smart and connected IoT devices, the area of cyber-physical security is becoming increasingly important. Past research has demonstrated new threat vectors targeting the transition process between the cyber and physical domains, where the attacker exploits the sensing system as an attack surface for signal injection or extraction of private information. Recently, there have been attempts to characterize an abstracted model for signal injection, but they primarily focus on the path of signal processing. This paper aims to systematize the existing research on security and privacy problems arising from the interaction of cyber world and physical world, with the context of broad CPS applications. The primary goals of the systematization are to <xref rid="deqn1" ref-type="disp-formula">(1)</xref> reveal the attack patterns and extract a general attack model of existing work; <xref rid="deqn2" ref-type="disp-formula">(2)</xref> understand possible new attacks; and <xref rid="deqn3" ref-type="disp-formula">(3)</xref> motivate development of defenses against the emerging cyber-physical threats.
Preparation, characterization and properties of core-shell cobalt ferrite/polycaprolactone nanomagnetic biomaterials

Combination of magnetic and biocompatible materials to form core-shell nanomaterials has been widely used in medical fields. These core-shell magnetic biomaterials have a great potential for magnetic fluid hyperthermia (MFH) treatment to remedy cancer. The aims of this study were to investigate the production of core-shell cobalt ferrite/polycaprolactone (CoFe2O4/PCL) nanomaterials with different ratios of cobalt ferrite to caprolactone, to study the effects of using polymer in reducing the agglomerations between particles and to determine the structure, morphology, thermal and magnetic properties of these core-shell nanomaterials. The core-shell nanomaterials were produced by in situ polymerization method. The formation of the CoFe2O4/PCL was investigated by means of Fourier transform infrared spectroscopy (FTIR), x-ray diffractometer (XRD) and transmission electron microscopy (TEM). Its thermal properties were determined by using thermogravimetric analyzer (TGA). The vibrating sample magnetometer (VSM) was used to reveal the magnetic properties. The results for the XRD and FTIR spectra demonstrated the formation of cobalt ferrite and polycaprolactone in core-shell nanomaterials. From the TEM results, it was seen that the core-shell CoFe2O4/PCL nanomaterials were best formed at a ratio of CoFe2O4 to monomer caprolactone mixtures of 1:4.
Optimization Induced Equilibrium Networks

Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently. In this paper, we investigate an emerging question: can an implicit equilibrium model's equilibrium point be regarded as the solution of an optimization problem? To this end, we first decompose DNNs into a new class of unit layer that is the proximal operator of an implicit convex function while keeping its output unchanged. Then, the equilibrium model of the unit layer can be derived, named Optimization Induced Equilibrium Networks (OptEq), which can be easily extended to deep layers. The equilibrium point of OptEq can be theoretically connected to the solution of its corresponding convex optimization problem with explicit objectives. Based on this, we can flexibly introduce prior properties to the equilibrium points: 1) modifying the underlying convex problems explicitly so as to change the architectures of OptEq; and 2) merging the information into the fixed point iteration, which guarantees to choose the desired equilibrium point when the fixed point set is non-singleton. We show that deep OptEq outperforms previous implicit models even with fewer parameters. This work establishes the first step towards the optimization-guided design of deep models.
The explosion of 9-29 Msun stars as Type II supernovae: Results from radiative-transfer modeling at one year after explosion

We present a set of nonlocal thermodynamic equilibrium steady-state calculations of radiative transfer for one-year-old Type II super- novae (SNe) starting from state-of-the-art explosion models computed with detailed nucleosynthesis. This grid covers single-star progenitors with initial masses between 9 and 29 M (cid:12) , all evolved with the code KEPLER at solar metallicity and ignoring rotation. The [O I ] λλ 6300 , 6364 line ﬂux generally grows with progenitor mass, and H α exhibits an equally strong and opposite trend. The [Ca II ] λλ 7291 , 7323 strength increases at low 56 Ni mass, at low explosion energy, or with clumping. This Ca II doublet, which forms primarily in the explosively produced Si/S zones, depends little on the progenitor mass but may strengthen if Ca + dominates in the H-rich emitting zones or if Ca is abundant in the O-rich zones. Indeed, Si–O shell merging prior to core collapse may boost the Ca II doublet at the expense of the O I doublet, and may thus mimic the metal line strengths of a lower-mass progenitor. We ﬁnd that the 56 Ni bubble effect has a weak impact, probably because it is too weak to induce much of an ionization shift in the various emitting zones. Our simulations compare favorably to observed SNe II, including SN2008bk (e.g., the 9 M (cid:12) model), SN2012aw (12 M (cid:12) model), SN1987A (15 M (cid:12) model), or SN2015bs (25 M (cid:12) model with no Si–O shell merging). SNe II with narrow lines and a low 56 Ni mass are well matched by the weak explosion of 9–11 M (cid:12) progenitors. The nebular-phase spectra of standard SNe II can be explained with pro- genitors in the mass range 12–15 M (cid:12) , with one notable exception for SN2015bs. In the intermediate mass range, these mass estimates may increase by a few M (cid:12) , with allowance for clumping of the O-rich material or CO molecular cooling.