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Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation can be realized using oblivious transfer as a primitive function. In this paper, we present an experimental implementation of a quantum-secure quantum oblivious transfer (QOT) protocol using an adapted quantum key distribution system combined with a bit commitment scheme, surpassing previous approaches only secure in the noisy storage model. We demonstrate the first practical application of the QOT protocol by solving the private set intersection, a prime example of secure multiparty computation, where two parties aim to find common elements in their datasets without revealing any other information. In our experiments, two banks can identify common suspicious accounts without disclosing any other data. This not only proves the experimental functionality of QOT, but also showcases its real-world commercial applications.

In recent years, numerous Transformer-based models have been applied to long-term time-series forecasting (LTSF) tasks. However, recent studies with linear models have questioned their effectiveness, demonstrating that simple linear layers can outperform sophisticated Transformer-based models. In this work, we review and categorize existing Transformer-based models into two main types: (1) modifications to the model structure and (2) modifications to the input data. The former offers scalability but falls short in capturing inter-sequential information, while the latter preprocesses time-series data but is challenging to use as a scalable module. We propose $\textbf{sTransformer}$, which introduces the Sequence and Temporal Convolutional Network (STCN) to fully capture both sequential and temporal information. Additionally, we introduce a Sequence-guided Mask Attention mechanism to capture global feature information. Our approach ensures the capture of inter-sequential information while maintaining module scalability. We compare our model with linear models and existing forecasting models on long-term time-series forecasting, achieving new state-of-the-art results. We also conducted experiments on other time-series tasks, achieving strong performance. These demonstrate that Transformer-based structures remain effective and our model can serve as a viable baseline for time-series tasks.

In this paper, we introduce and study the class of $\phi$-$w$-P-flat modules, which can be seen as generalizations of both $\phi$-P-flat modules and $w$-P-flat modules. In particular, we obtain that the class of $\phi$-$w$-P-flat modules is covering. We also utilize the class of $\phi$-$w$-P-flat modules to characterize $\phi$-von Neumann regular rings, strong $\phi$-rings and $\phi$-PvMRs.

Existing operations research (OR) models and tools play indispensable roles in smart-city operations, yet their practical implementation is limited by the complexity of modeling and deficiencies in optimization proficiency. To generate more relevant and accurate solutions to users' requirements, we propose a large language model (LLM)-based agent ("City-LEO") that enhances the efficiency and transparency of city management through conversational interactions. Specifically, to accommodate diverse users' requirements and enhance computational tractability, City-LEO leverages LLM's logical reasoning capabilities on prior knowledge to scope down large-scale optimization problems efficiently. In the human-like decision process, City-LEO also incorporates End-to-end (E2E) model to synergize the prediction and optimization. The E2E framework be conducive to coping with environmental uncertainties and involving more query-relevant features, and then facilitates transparent and interpretable decision-making process. In case study, we employ City-LEO in the operations management of e-bike sharing (EBS) system. The numerical results demonstrate that City-LEO has superior performance when benchmarks against the full-scale optimization problem. With less computational time, City-LEO generates more satisfactory and relevant solutions to the users' requirements, and achieves lower global suboptimality without significantly compromising accuracy. In a broader sense, our proposed agent offers promise to develop LLM-embedded OR tools for smart-city operations management., Comment: 26 pages, 8 figures, 5 tables

Let $R$ be a commutative ring. An $R$-module $M$ is said to be almost projective if ${\rm Ext}^1_R(M, N) = 0$ for any $R_{\mathfrak{m}}$-module $N$ and any maximal ideal $\mathfrak{m}$ of $R$. In this paper, we investigate rings $R$ over which every $R$-module has an almost projective envelope. We also characterize rings $R$ over which every $R$-module has an almost projective envelope with some special properties.

Real-world scenarios are usually accompanied by continuously appearing classes with scare labeled samples, which require the machine learning model to incrementally learn new classes and maintain the knowledge of base classes. In this Few-Shot Class-Incremental Learning (FSCIL) scenario, existing methods either introduce extra learnable components or rely on a frozen feature extractor to mitigate catastrophic forgetting and overfitting problems. However, we find a tendency for existing methods to misclassify the samples of new classes into base classes, which leads to the poor performance of new classes. In other words, the strong discriminability of base classes distracts the classification of new classes. To figure out this intriguing phenomenon, we observe that although the feature extractor is only trained on base classes, it can surprisingly represent the semantic similarity between the base and unseen new classes. Building upon these analyses, we propose a simple yet effective Training-frEE calibratioN (TEEN) strategy to enhance the discriminability of new classes by fusing the new prototypes (i.e., mean features of a class) with weighted base prototypes. In addition to standard benchmarks in FSCIL, TEEN demonstrates remarkable performance and consistent improvements over baseline methods in the few-shot learning scenario. Code is available at: https://github.com/wangkiw/TEEN, Comment: Accepted to NeurIPS 2023. Code is available at: https://github.com/wangkiw/TEEN

The charge equilibration of laser-accelerated carbon ion beams in 2 mg/cm3 foam target was investigated experimentally. The ions were generated through target normal sheath acceleration mechanism in laser-foil interaction scheme. This allows to get the equilibrium charge state in wide energy range near Bragg peak within a single shot. By using foam, the charge equilibration measurement in density regime between gas and solid state was firstly reached out experimentally. It was found that the theoretical predictions with tabulated cross section data for gas target greatly underestimated the charge states. The experimental data are in close agreement with both semi-empirical formula as well as rate equation predictions based on ion-solid interactions. The important role of target density effects that increase the ionization probability and decrease the electron capture probability through frequent multi-collisions in foam are demonstrated. The double electron processes are shown to have little influence on the average charge states. The findings are essential for high energy density physics research where the foams are widely used, and have impacts on a broad range of applications in medical, biological and material fields. The method also provides a new approach to investigate the interaction mechanism of swift heavy ions in matter by taking advantage of the laser-accelerated short-pulse wide-energy range ions.

A novel intense beam-driven scheme for high yield of the tri-alpha reaction 11B(p,{\alpha})2{\alpha} was investigated. We used a foam target made of cellulose triacetate (TAC, C_9H_{16}O_8) doped with boron. It was then heated volumetrically by soft X-ray radiation from a laser heated hohlraum and turned into a homogenous, and long living plasma. We employed a picosecond laser pulse to generate a high-intensity energetic proton beam via the well-known Target Normal Sheath Acceleration (TNSA) mechanism. We observed up to 10^{10}/sr {\alpha} particles per laser shot. This constitutes presently the highest yield value normalized to the laser energy on target. The measured fusion yield per proton exceeds the classical expectation of beam-target reactions by up to four orders of magnitude under high proton intensities. This enhancement is attributed to the strong electric fields and nonequilibrium thermonuclear fusion reactions as a result of the new method. Our approach shows opportunities to pursue ignition of aneutronic fusion.

The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning. Leveraging pre-trained models to extract knowledge and expedite learning in real-world tasks, known as "Model Reuse", has become crucial in various applications. Previous research focuses on reusing models within a certain aspect, including reusing model weights, structures, and hypothesis spaces. This paper introduces ZhiJian, a comprehensive and user-friendly toolbox for model reuse, utilizing the PyTorch backend. ZhiJian presents a novel paradigm that unifies diverse perspectives on model reuse, encompassing target architecture construction with PTM, tuning target model with PTM, and PTM-based inference. This empowers deep learning practitioners to explore downstream tasks and identify the complementary advantages among different methods. ZhiJian is readily accessible at https://github.com/zhangyikaii/lamda-zhijian facilitating seamless utilization of pre-trained models and streamlining the model reuse process for researchers and developers.

Nuclear isomers play a key role in the creation of the elements in the universe and have a number of fascinating potential applications related to the controlled release of nuclear energy on demand. Particularly, $^{93m}$Mo isomer is a good candidate for studying the depletion of nuclear isomer via nuclear excitation by electron capture. For such purposes, efficient approach for $^{93m}$Mo production needs to be explored. In the present work, we demonstrate experimentally an efficient production of $^{93m}$Mo through $^{93}$Nb(p, n) reaction induced by intense laser pulse. When a ps-duration, 100-J laser pulse is employed, the $^{93m}$Mo isomer at 2425 keV (21/2$^+$, $T_{1/2}$ = 6.85 h) are generated with a high yield of $1.8\times10^6$ particles/shot. The resulting peak efficiency is expected to be $10^{17}$ particles/s, which is at least five orders of magnitudes higher than using classical proton accelerator. The effects of production and destruction of $^{93m}$Mo on the controversial astrophysical p-isotope $^{92}$Mo are studied. It is found that the $^{93}$Nb(p, n)-$^{93m}$Mo reaction is an important production path for ^{93m}Mo seed nucleus, and the influence of ^{93m}Mo-^{92}Mo reaction flow on ^{92}Mo production cannot be ignored. In addition, we propose to directly measure the astrophysical rate of (p, n) reaction using laser-induced proton beam since the latter one fits the Maxwell-Boltzmann distribution well. We conclude that laser-induced proton beam opens a new path to produce nuclear isomers with high peak efficiency towards the understanding of p-nuclei nucleosythesis., Comment: 21 pages, 9 figure

We study the Aharonov-Bohm (AB) caging effect in rhombic exciton-polariton lattices, with the Rashba-Dresselhaus spin-orbit coupling (RDSOC) in acting a synthetic gauge field. The effective magnetic flux through each plaquette is controlled by the orientation of the RDSOC and geometry of the rhombic lattice. The results show that the interplay of lattice geometry and the RDSOC will dramatically influence the energy band structure, furthermore, determining the transportation properties of exciton-polariton condensates. Non-Hermitian effects, which arise from the polariton intrinsic loss mechanism, on the AB caging is also discussed in detail. Meanwhile, the effect of disorder on the dynamics of AB caging is investigated, and we find that the disorder will lead to the inverse Anderson localization. We propose that using the AB caging effect allows to trap and steer the propagation of polaritons in a given parameter regime. Considering the specific example of a photonic liquid crystal microcavity to achieve our theoretical predictions, the AB caging could be switched on and off by applying an external voltage.

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems.