Fujitsu (China)

Voter turnout has been decreasing, and news organizations need to attract voters' interest in elections and encourage them to vote. However, election coverage is not only constrained by law and fairness but also suffers from a shortage of news producers and individualization. Additionally, it remains unclear how voters evaluate information from election coverage. In this paper, we propose a framework that addresses these issues and bridges the gap between election coverage and voters. We demonstrate that data-driven extraction of "unexpected" reasons for winning elections can address the problem of a shortage of news producers and individualization. Furthermore, we show that these reasons effectively engage voters' interest and influence candidate selection, thereby addressing the issue of how voters evaluate such information. By modeling the process by which voters evaluate this information and validating the model, we provide guidelines for news organizations to make decisions on data-driven information for election coverage. Specifically, to attract voters' interest and assist them in selecting candidates, it is important to consider the relationship between the alignment of voters' knowledge and unexpectedness and to explain the information.

Abstract Portfolio optimization is a cornerstone of financial decision-making, traditionally relying on classical algorithms to balance risk and return. Recent advances in quantum computing offer a promising alternative, leveraging quantum algorithms to efficiently explore complex solution spaces and potentially outperform classical methods in high-dimensional settings. However, conventional quantum approaches typically assume a one-to-one correspondence between qubits and variables (e.g. financial assets), which severely limits the applicability of gate-based quantum systems due to current hardware constraints. As a result, only quantum annealing-like methods have been used in realistic scenarios. In this work, we show how a gate-based variational quantum algorithm can be applied to a real-world portfolio optimization problem by assigning multiple variables per qubit, using the Pauli Correlation Encoding algorithm. Specifically, we address a problem involving over 250 variables, where the market graph representing a real stock market is iteratively partitioned into sub-portfolios of highly correlated assets. This approach enables improved scalability compared to traditional variational methods and opens new possibilities for quantum-enhanced financial applications.

Defenses of God’s permission of evil by appeal to free will are alleged to have a value problem. Laura Ekstrom argues that free will does not obviously have a value which would outweigh or justify the disvalue associated with moral evil and its consequences. I propose that a free will defense of moral evil does not need to conceive of free will as being more valuable than moral evil or its consequences. Rather, free will is a moral transformer in virtue of which created persons can deserve their moral character and those consequences which follow upon it. From this perspective, I show that the alleged ‘value problem’ rests upon controversial conceptions of distributive justice and that Thomas Aquinas gives us a way to argue plausibly that God’s decisions to allow serious consequences to result from free agency, i.e., hell, could be distributively just and compatible with God’s love for persons.

Human identification at a distance (HID) faces challenges due to the difficulty of acquiring traditional biometric modalities like face and fingerprints. Gait recognition offers a viable solution since it can be captured at a distance. To promote progress in gait recognition and provide a fair evaluation platform, the International Competition on Human Identification at a Distance (HID) has been organized annually since 2020. Since 2023, the competition has adopted the challenging SUSTech-Competition dataset, which includes significant variations in clothing, carried objects, and view angles. No training data is provided, requiring participants to train their models using external datasets. Each year, the competition applies a different random seed to generate distinct evaluation splits, reducing the risk of overfitting and ensuring fair evaluation of cross-domain generalization. Although the previous two competitions (HID 2023 and HID 2024) already utilized this dataset, HID 2025 aimed explicitly to explore whether algorithmic improvements could surpass the accuracy limits observed previously. Despite these heightened challenges, participants again demonstrated significant advancements, with the highest accuracy reaching 94.2%, setting a new benchmark for this dataset. We also analyze key technical trends and outline potential directions for future research on gait recognition.

In this paper, we design quantum circuits for the exponential of scaled <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex>-qubit Pauli-strings using single-qubit rotation gates, Hadamard gate, and CNOT gates. A key result we derive is that any two Pauli-string operators composed of identity and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> gates are permutation similar, and the corresponding permutation matrices are product of CNOT gates, with the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex>-th qubit serving as the control qubit. Consequently, we demonstrate that the proposed circuit model for exponential of any Paulistring is implementable on low-connected quantum hardware and scalable i.e. quantum circuits for (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n+1$</tex>)-qubit systems can be constructed from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex>-qubit circuits by adding additional quantum gates and the extra qubit. We then apply these circuit models to approximate unitary evolution for several classes of Hamiltonians using the Suzuki-Trotter approximation. These Hamiltonians include 2-sparse block-diagonal Hamiltonians, Ising Hamiltonians, and both time-independent and time-dependent Random Field Heisenberg Hamiltonians and Transverse Magnetic Random Quantum Ising Hamiltonians. Simulations for systems of up to 18 qubits show that the circuit approximation closely matches the exact evolution, with errors comparable to the numerical Trotterization error. Finally, we consider noise models in quantum circuit simulations to account for gate implementation errors in NISQ computers, and observe that the noisy numerical simulation closely resembles the noiseless one when gate and idle errors are on the order of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O\left(10^{-3}\right)$</tex> or smaller.

Abstract We present the first combinatorial algorithm for efficiently computing the Reeb space in all dimensions. The Reeb space is a higher‐dimensional generalization of the Reeb graph, which is standard practice in the analysis of scalar fields, along with other computational topology tools such as persistent homology and the Morse‐Smale complex. One significant limitation of topological tools for scalar fields is that data often involves multiple variables, where joint analysis is more insightful. Generalizing topological data structures to multivariate data has proven challenging and the Reeb space is one of the few available options. However, none of the existing algorithms can efficiently compute the Reeb space in arbitrary dimensions and there are no available implementations which are robust with respect to numerical errors. We propose a new algorithm for computing the Reeb space of a generic piecewise linear map over a simplicial mesh of any dimension called arrange and traverse. We implement a robust specialization of our algorithm for tetrahedral meshes and evaluate it on real‐life data.

FMCW radar-based human keypoints detection has been increasingly used in health monitoring, human-computer interaction, safety detection and other fields due to its characteristics of contact-free, privacy-preserving and less environmentdependence. Recently, most of studies have adopted deep learning method to detect human keypoints for its powerful feature extraction ability. However, its excellent performance relies on large-scale labeled datasets participating in model training, while the data labeling process is difficult and time-consuming, making it unsuitable for promotion and application. To address this issue, we propose a two-branch semi-supervised method to improve the accuracy and generalization of keypoints detection through using both labeled and unlabeled radar data. Models of two branches deal with different radar point clouds generated by same human activity, and promote the consistency and complementarity in the learning features of two branches through introducing a consistency loss function. Optimal performance in human keypoints detection can be achieved through adjusting weights between consistency loss and supervised loss. In addition, we develop a grouping processing technique of radar point clouds to obtain two different point cloud clusters with minimal feature overlap. We evaluate our model on a real-world radar dataset and compare its performance with a supervised method that only uses 20 labeled subjects. The experimental results demonstrate that the proposed method achieves a 10.7% improvement in accuracy compared to traditional supervised learning method, highlighting the significant advantages of our method in human keypoints detection and its substantial potential for broad application and promotion.

Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DCILP. In the divide phase, the Markov blanket MB(Xi) of each variable Xi is identified, and causal learning subproblems associated with each MB(Xi) are independently addressed in parallel. This approach benefits from a more favorable ratio between the number of data samples and the number of variables considered. In counterpart, it can be adversely affected by the presence of hidden confounders, as variables external to MB(Xi) might influence those within it. The reconciliation of the local causal graphs generated during the divide phase is a challenging combinatorial optimization problem, especially in large-scale applications. The main novelty of DCILP is an original formulation of this reconciliation as an integer linear programming (ILP) problem, which can be delegated and efficiently handled by an ILP solver. Through experiments on medium to large scale graphs, and comparisons with state-of-the-art methods, DCILP demonstrates significant improvements in terms of computational complexity, while preserving the learning accuracy on real-world problem and suffering at most a slight loss of accuracy on synthetic problems.

ABSTRACT The Russo-Ukrainian war has posed a complex geopolitical challenge for China, requiring it to balance a strategic partnership with Russia and economic ties with the West. This study introduces the concept of ‘discursive strategic neutrality’, defined by narrative management and adaptive pragmatism, as the best framework for understanding China’s stance. Using NLP techniques to analyze 712 MFA statements, the research identifies three core themes in China’s official discourse: promoting China’s mediation initiatives, advocating multilateral cooperation, and criticizing Western responses. Sentiment analysis reveals a generally neutral stance, with pro-Russia sentiment peaking during geopolitical tensions. These findings suggest that China’s ‘discursive strategic neutrality’ is a dynamic position that is highly responsive to external geopolitical pressures in order to safeguard its interests and promote China’s broader global agenda.