Why Existing Machine Learning Methods Fails At Extracting the Information of Future Returns Out of Historical Sctock Prices : the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes

The financial time series analysis is important access to touch the complex laws of financial markets. Among many goals of the financial time series analysis, one is to construct a model that can extract the information of the future return out of the known historical stock data, such as stock price, financial news, and e.t.c. To design such a model, prior knowledge on how the future return is correlated with the historical stock prices is needed. In this work, we focus on the issue: in what mode the future return is correlated with the historical stock prices. We manually design several financial time series where the future return is correlated with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future return can be extracted from the curve shapes of the historical stock prices. By applying various kinds of existing algorithms on those pre-designed time series and real financial time series, we show that: (1) the major information of the future return is not contained in the curve-shape features of historical stock prices. That is, the future return is not mainly correlated with the historical stock prices in the CSF mode. (2) Various kinds of existing machine learning algorithms are good at extracting the curveshape features in the historical stock prices and thus are inappropriate for financial time series analysis although they are successful in the image recognition and natural language processing. That is, new models handling the NCSF series are needed in the financial time series analysis.

An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy

The fingerprint classification is an important and effective method to quicken the process and improve the accuracy in the fingerprint matching process. Conventional supervised methods need a large amount of pre-labeled data and thus consume immense human resources. In this paper, we propose a new and efficient unsupervised deep learning method that can extract fingerprint features and classify fingerprint patterns automatically. In this approach, a new model named constraint convolutional auto-encoder (CCAE) is used to extract fingerprint features and a hybrid clustering strategy is applied to obtain the final clusters. A set of experiments in the NIST-DB4 dataset shows that the proposed unsupervised method exhibits the efficient performance on fingerprint classification. For example, the CCAE achieves an accuracy of 97.3% on only 1000 unlabeled fingerprints in the NIST-DB4.

The Gutzwiller approximation approach to the SU(4) $t$-$J$ model

We develop the Gutzwiller approximation method to obtain the renormalized Hamiltonian of the SU(4) $t$-$J$ model, with the corresponding renormalization factors. Subsequently, a mean-field theory is employed on the renormalized Hamiltonian of the model on the honeycomb lattice under the scenario of a cooperative condensation of carriers moving in the resonating valence bond state of flavors. In particular, we find the extended $s$-wave superconductivity is much more favorable than the $d+id$ superconductivity in the doping range close to quarter filling. The pairing states of the SU(4) case reveal the property that the spin-singlet pairing and the spin-triplet pairing can exist simultaneously. Our results might provide new insights into the twisted bilayer graphene system.

Quantifying Success in Science: An Overview

Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions.

Quantifying the Impact of Scholarly Papers Based on Higher-Order Weighted Citations

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical.

Implicit Multi-feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous researches have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.

Hydrogen clustering in bcc metals: atomic origin and strong stress anisotropy

Hydrogen (H) induced damage in metals has been a long-standing woe for many industrial applications. One form of such damage is linked to H clustering, for which the atomic origin remains contended, particularly for non-hydride forming metals. In this work, we systematically studied H clustering behavior in bcc metals represented by W, Fe, Mo, and Cr, combining first-principles calculations, atomistic and Monte Carlo simulations. H clustering has been shown to be energetically favorable, and can be strongly facilitated by anisotropic stress field, dominated by the tensile component along one of the <001> crystalline directions. We showed that the stress effect can be well predicted by the continuum model based on H formation volume tensor, and that H clustering is thermodynamically possible at edge dislocations, evidenced by nanohydride formation at rather low levels of H concentration. Moreover, anisotropy in the stress effect is well reflected in nanohydride morphology around dislocations, with nanohydride growth occurring in the form of thin platelet structures that maximize one <001> tension. In particular, the <001> type edge dislocation, with the <001> tensile component maximized, has been shown to be highly effective in facilitating H aggregation, thus expected to play an important role in H clustering in bcc metals, in close agreement with recent experimental observations. This work explicitly and quantitatively clarifies the anisotropic nature of stress effect on H energetics and H clustering behaviors, offering mechanistic insights critical towards understanding H-induced damages in metals.

Accurate prediction of nanovoid structures and energetics in bcc metals

Knowledge on structures and energetics of nanovoids is fundamental to understand defect evolution in metals. Yet there remain no reliable methods able to determine essential structural details or to provide accurate assessment of energetics for general nanovoids. Here, we performed systematic first-principles investigations to examine stable structures and energetics of nanovoids in bcc metals, explicitly demonstrated the stable structures can be precisely determined by minimizing their Wigner-Seitz area, and revealed a linear relationship between formation energy and Wigner-Seitz area of nanovoids. We further developed a new physics-based model to accurately predict stable structures and energetics for arbitrary-sized nanovoids. This model was well validated by first-principles calculations and recent nanovoid annealing experiments, and showed distinct advantages over the widely used spherical approximation. The present work offers mechanistic insights that crucial for understanding nanovoid formation and evolution, being a critical step towards predictive control and prevention of nanovoid related damage processes in structural metals.

Improvements of computational ghost imaging by using Special-Hadamard patterns

We introduced a new kind of patterns named Special-Hadamard patterns, which could be used as structured illuminations of computational ghost imaging. Special-Hadamard patterns can get a better image quality than Hadamard patterns in a noisy environment. We can completely reconstruct the original object in a noiseless environment by using Special-Hadamard patterns, and the size of object also can be adjusted arbitrarily, these advantages cannot be achieved by other common patterns. We also performed simulations to compare the results of Special Hadamard patterns with the results of Hadamard patterns. We found Special Hadamard patterns can greatly improve the image quality of computational ghost imaging.

Predictive Model of Hydrogen Trapping and Bubbling in Nanovoids in BCC Metals

Interplay between hydrogen and nanovoids, despite long-recognized as a central aspect in hydrogen-induced damages in structural materials, remains poorly understood. Focusing on tungsten as a model BCC system, the present study, for the first time, explicitly demonstrated sequential adsorption of hydrogen adatoms on Wigner-Seitz squares of nanovoids with distinct energy levels. Interaction between hydrogen adatoms on the nanovoid surface is shown to be dominated by pairwise power law repulsion. A predictive model was established for quantitative prediction of configurations and energetics of hydrogen adatoms in nanovoids. This model, further combined with equation of states of hydrogen gas, enables prediction of hydrogen molecule formation in nanovoids. Multiscale simulations based on the predictive model were performed, showing excellent agreement with experiments. This work clarifies fundamental physics and provides full-scale predictive model for hydrogen trapping and bubbling in nanovoids, offering long-sought mechanistic insights crucial for understanding hydrogen-induced damages in structural materials.