arXiv subjects

Zhi Li

Publications and source records attributed to Zhi Li.

At least 163 records · Page 9Linked to original sources

arXiv2024

Optimal Dispatch Strategy for a Multi-microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism

To coordinate resources among multi-level stakeholders and enhance the integration of electric vehicles (EVs) into multi-microgrids, this study proposes an optimal dispatch strategy within a multi-microgrid cooperative alliance using a nuanced two-stage pricing mechanism. Initially, the strategy assesses electric energy interactions between microgrids and distribution networks to establish a foundation for collaborative scheduling. The two-stage pricing mechanism initiates with a leader-follower game, wherein the microgrid operator acts as the leader and users as followers. Subsequently, it adjusts EV tariffs based on the game's equilibrium, taking into account factors such as battery degradation and travel needs to optimize EVs' electricity consumption. Furthermore, a bi-level optimization model refines power interactions and pricing strategies across the network, significantly enhancing demand response capabilities and economic outcomes. Simulation results demonstrate that this strategy not only increases renewable energy consumption but also reduces energy costs, thereby improving the overall efficiency and sustainability of the system.

eess.SY↗

arXiv2024

Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation

Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains. While significant endeavors have been made, they primarily concentrated on developing advanced transfer modules and aligning user representations using self-supervised learning techniques. However, the problem of aligning item representations has received limited attention, and misaligned item representations can potentially lead to sub-optimal sequential modeling and user representation alignment. To this end, we propose a model-agnostic framework called \textbf{C}ross-domain item representation \textbf{A}lignment for \textbf{C}ross-\textbf{D}omain \textbf{S}equential \textbf{R}ecommendation (\textbf{CA-CDSR}), which achieves sequence-aware generation and adaptively partial alignment for item representations. Specifically, we first develop a sequence-aware feature augmentation strategy, which captures both collaborative and sequential item correlations, thus facilitating holistic item representation generation. Next, we conduct an empirical study to investigate the partial representation alignment problem from a spectrum perspective. It motivates us to devise an adaptive spectrum filter, achieving partial alignment adaptively. Furthermore, the aligned item representations can be fed into different sequential encoders to obtain user representations. The entire framework is optimized in a multi-task learning paradigm with an annealing strategy. Extensive experiments have demonstrated that CA-CDSR can surpass state-of-the-art baselines by a significant margin and can effectively align items in representation spaces to enhance performance.

cs.IR↗

arXiv2024

Comment on "ENN's roadmap for proton-boron fusion based on spherical torus" [Phys. Plasmas 31, 062507 (2024)]

This comment discusses the feasibility of hot ion mode ${{T}_{i}}/{{T}_{e}}=4$ for proton-boron fusion which is critical for the roadmap proposed in Liu, M et al [Phys. Plasmas 31, 062507 (2024)]. The hot ion mode ${{T}_{i}}/{{T}_{e}}=4$ has been calculated to be far from accessible (${{T}_{i}}/{{T}_{e}}<1.5$ for ${{T}_{i}}=150\text{keV}$) under the most optimal conditions if fusion provides the heating (Xie, H [Introduction to Fusion Ignition Principles: Zeroth Order Factors of Fusion Energy Research (in Chinese), USTC Press, Hefei, 2023]), i.e., that all fusion power serves to heat the ions and that electrons acquire energy only through interactions with ions. We also discuss if hot ion mode of ${{T}_{i}}/{{T}_{e}}=4$ could be achieved by an ideal heating method which is much more efficient than fusion itself (near 20 times fusion power for ${{T}_{i}}=150\text{keV}$) and only heats the ions, whether it makes sense economically.

physics.plasm-ph↗

arXiv2024

KnowPC: Knowledge-Driven Programmatic Reinforcement Learning for Zero-shot Coordination

Zero-shot coordination (ZSC) remains a major challenge in the cooperative AI field, which aims to learn an agent to cooperate with an unseen partner in training environments or even novel environments. In recent years, a popular ZSC solution paradigm has been deep reinforcement learning (DRL) combined with advanced self-play or population-based methods to enhance the neural policy's ability to handle unseen partners. Despite some success, these approaches usually rely on black-box neural networks as the policy function. However, neural networks typically lack interpretability and logic, making the learned policies difficult for partners (e.g., humans) to understand and limiting their generalization ability. These shortcomings hinder the application of reinforcement learning methods in diverse cooperative scenarios.We suggest to represent the agent's policy with an interpretable program. Unlike neural networks, programs contain stable logic, but they are non-differentiable and difficult to optimize.To automatically learn such programs, we introduce Knowledge-driven Programmatic reinforcement learning for zero-shot Coordination (KnowPC). We first define a foundational Domain-Specific Language (DSL), including program structures, conditional primitives, and action primitives. A significant challenge is the vast program search space, making it difficult to find high-performing programs efficiently. To address this, KnowPC integrates an extractor and an reasoner. The extractor discovers environmental transition knowledge from multi-agent interaction trajectories, while the reasoner deduces the preconditions of each action primitive based on the transition knowledge.

cs.AI↗

arXiv2024

Formation of WNL stars for the MW and LMC based on the k-omega model

We adopt a set of second-order differential equations ($k-ω$ model) to handle core convective overshooting in massive stars, simulate the evolution of WNL stars with different metallicities and initial masses, both rotating and non-rotating models, and compare the results with the classical overshooting model. The results indicate that under the same initial conditions, the $k-ω$ model generally produces larger convective cores and wider overshooting regions, thereby increasing the mass ranges and extending the lifetimes of WNL stars, as well as the likelihood of forming WNL stars. The masses and lifetimes of WNL stars both increase with higher metallicities and initial masses. Under higher-metallicity conditions, the two overshooting schemes significantly differ in their impacts on lifetimes of the WNL stars, but insignificant in the mass ranges of the WNL stars. Rotation may drive the formation of WNL stars in low-mass, metal-poor counterparts, with this effect being more pronounced in the OV model. The surface nitrogen of metal-rich WNL stars formed during the MS phase is likely primarily from the CN-cycle, while it may come from both the CN- and NO-cycles for relatively metal-poor counterparts. Our model can effectively explain the distribution of WNL stars in the Milky Way, but appears to have inadequacies in explaining the WNL stars in the LMC.

astro-ph.SR↗

arXiv2024

Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answering

Retrieving external knowledge and prompting large language models with relevant information is an effective paradigm to enhance the performance of question-answering tasks. Previous research typically handles paragraphs from external documents in isolation, resulting in a lack of context and ambiguous references, particularly in multi-document and complex tasks. To overcome these challenges, we propose a new retrieval framework IIER, that leverages Inter-chunk Interactions to Enhance Retrieval. This framework captures the internal connections between document chunks by considering three types of interactions: structural, keyword, and semantic. We then construct a unified Chunk-Interaction Graph to represent all external documents comprehensively. Additionally, we design a graph-based evidence chain retriever that utilizes previous paths and chunk interactions to guide the retrieval process. It identifies multiple seed nodes based on the target question and iteratively searches for relevant chunks to gather supporting evidence. This retrieval process refines the context and reasoning chain, aiding the large language model in reasoning and answer generation. Extensive experiments demonstrate that IIER outperforms strong baselines across four datasets, highlighting its effectiveness in improving retrieval and reasoning capabilities.

cs.CL↗

arXiv2024

Improving Faithfulness of Large Language Models in Summarization via Sliding Generation and Self-Consistency

Despite large language models (LLMs) have demonstrated impressive performance in various tasks, they are still suffering from the factual inconsistency problem called hallucinations. For instance, LLMs occasionally generate content that diverges from source article, and prefer to extract information that appears at the beginning and end of the context, especially in long document summarization. Inspired by these findings, we propose to improve the faithfulness of LLMs in summarization by impelling them to process the entire article more fairly and faithfully. We present a novel summary generation strategy, namely SliSum, which exploits the ideas of sliding windows and self-consistency. Specifically, SliSum divides the source article into overlapping windows, and utilizes LLM to generate local summaries for the content in the windows. Finally, SliSum aggregates all local summaries using clustering and majority voting algorithm to produce more faithful summary of entire article. Extensive experiments demonstrate that SliSum significantly improves the faithfulness of diverse LLMs including LLaMA-2, Claude-2 and GPT-3.5 in both short and long text summarization, while maintaining their fluency and informativeness and without additional fine-tuning and resources. We further conduct qualitative and quantitative studies to investigate why SliSum works and impacts of hyperparameters in SliSum on performance.

cs.CL↗

arXiv2024

A Brief Discussion on the Philosophical Principles and Development Directions of Data Circulation

The data circulation is a complex scenario involving a large number of participants and different types of requirements, which not only has to comply with the laws and regulations, but also faces multiple challenges in technical and business areas. In order to systematically and comprehensively address these issues, it is essential to have a comprehensive and profound understanding of 'data circulation'. The traditional analysis method tends to proceed based on the traditional circulation model of commodities, that is, tangible objects, which has some defects and shortcomings, and tends to be a formalized approach, which is faced numerous challenges in practice. This paper analyzes the circulation of data with a philosophical approach, obtains the new explication of data and executing entity, and provides a new definition of the concepts of data utilization and data key stakeholders (objects). At the same time, it puts forward the idea of ``data alienation'', and constructs a new interpretive framework of ``data circulation''. Based on the framework of this interpretation, it is clearly proposed that ``data alienation'' is the core of ``data circulation'', benefit distribution is the driving force, and legal compliance is the foundation, and further discussed the three modes of ``data circulation''. It further discusses the three modes of ``data circulation''. It is pointed out that ``data circulation'' is different from traditional ``commodity circulation''. To achieve ``data circulation'',a comprehensive information infrastructure needs to be established. from a theoretical point of view, it lays a solid foundation for the development of ``data circulation''.

cs.OH↗

arXiv2024

Emergent strength-dependent scale-free mobility edge in a non-reciprocal long-range Aubry-André-Harper model

We investigate the properties of mobility edge in an Aubry-André-Harper model with non-reciprocal long-range hopping. The results reveal that there can be a new type of mobility edge featuring both strength-dependent and scale-free properties. By calculating the fractal dimension, we find that the positions of mobility edges are robust to the strength of non-reciprocal long-range hopping. Furthermore, through scale analysis of the observables such as fractal dimension, eigenenergy and eigenstate, etc., we show that four different specific mobility edges can be observed in the system. This paper extends the family tree of mobility edges and hopefully it will shed more light on the related theory and experiment.

cond-mat.dis-nn↗

arXiv2024

Ring Structure in the Complex Plane: A Fingerprint of non-Hermitian Mobility Edge

By Avila's global theory, we analytically reveal that the non-Hermitian mobility edge will take on a ring structure in the complex plane, which we name as "mobility ring". The universality of mobility ring has been checked and supported by the Hermitian limit, $PT$-symmetry protection and without $PT$-symmetry cases. Further, we study the evolution of mobility ring versus quasiperiodic strength, and find that in the non-Hermitian system, there will appear multiple mobility ring structures. With cross-reference to the multiple mobility edges in Hermitian case, we give the expression of the maximum number of mobility rings. Finally, by comparing the results of Avila's global theorem and self-duality method, we show that self-duality relation has its own limitations in calculating the critical point in non-Hermitian systems. As we know, the general non-Hermitian system has a complex spectrum, which determines that the non-Hermitian mobility edge can but exhibit a ring structure in the complex plane.

cond-mat.dis-nn↗

arXiv2024

Homotopy, Symmetry, and Non-Hermitian Band Topology

Non-Hermitian matrices are ubiquitous in the description of nature ranging from classical dissipative systems, including optical, electrical, and mechanical metamaterials, to scattering of waves and open quantum many-body systems. Seminal line-gap and point-gap classifications of non-Hermitian systems using K-theory have deepened the understanding of many physical phenomena. However, ample systems remain beyond this description; reference points and lines do not in general distinguish whether multiple non-Hermitian bands exhibit intriguing exceptional points, spectral braids and crossings. To address this we consider two different notions: non-Hermitian band gaps and separation gaps that crucially encompass a broad class of multi-band scenarios, enabling the description of generic band structures with symmetries. With these concepts, we provide a unified and comprehensive classification of both gapped and nodal systems in the presence of physically relevant parity-time ($\mathcal{PT}$) and pseudo-Hermitian symmetries using homotopy theory. This uncovers new stable topology stemming from both eigenvalues and wave functions, and remarkably also implies distinct fragile topological phases. In particular, we reveal different Abelian and non-Abelian phases in $\mathcal{PT}$-symmetric systems, described by frame and braid topology. The corresponding invariants are robust to symmetry-preserving perturbations that do not induce (exceptional) degeneracy, and they also predict the deformation rules of nodal phases. We further demonstrate that spontaneous $\mathcal{PT}$ symmetry breaking is captured by Chern-Euler and Chern-Stiefel-Whitney descriptions, a fingerprint of unprecedented non-Hermitian topology previously overlooked. These results open the door for theoretical and experimental exploration of a rich variety of novel topological phenomena in a wide range of physical platforms.

cond-mat.mes-hall↗

arXiv2024

Sign Language Recognition Based On Facial Expression and Hand Skeleton

Sign language is a visual language used by the deaf and dumb community to communicate. However, for most recognition methods based on monocular cameras, the recognition accuracy is low and the robustness is poor. Even if the effect is good on some data, it may perform poorly in other data with different interference due to the inability to extract effective features. To solve these problems, we propose a sign language recognition network that integrates skeleton features of hands and facial expression. Among this, we propose a hand skeleton feature extraction based on coordinate transformation to describe the shape of the hand more accurately. Moreover, by incorporating facial expression information, the accuracy and robustness of sign language recognition are finally improved, which was verified on A Dataset for Argentinian Sign Language and SEU's Chinese Sign Language Recognition Database (SEUCSLRD).

cs.CV↗

arXiv2024

Using Geometrical information to Measure the Vibration of A Swaying Millimeter-wave Radar

This paper presents two new, simple yet effective approaches to measure the vibration of a swaying millimeter-wave radar (mmRadar) utilizing geometrical information. Specifically, for the planar vibrations, we firstly establish an equation based on the area difference between the swaying mmRadar and the reference objects at different moments, which enables the quantification of planar displacement. Secondly, volume differences are also utilized with the same idea, achieving the self-vibration measurement of a swaying mmRadar for spatial vibrations. Experimental results confirm the effectiveness of our methods, demonstrating its capability to estimate both the amplitude and a crude direction of the mmRadar's self-vibration.

eess.SP↗

arXiv2024

Generative Pretrained Hierarchical Transformer for Time Series Forecasting

Recent efforts have been dedicated to enhancing time series forecasting accuracy by introducing advanced network architectures and self-supervised pretraining strategies. Nevertheless, existing approaches still exhibit two critical drawbacks. Firstly, these methods often rely on a single dataset for training, limiting the model's generalizability due to the restricted scale of the training data. Secondly, the one-step generation schema is widely followed, which necessitates a customized forecasting head and overlooks the temporal dependencies in the output series, and also leads to increased training costs under different horizon length settings. To address these issues, we propose a novel generative pretrained hierarchical transformer architecture for forecasting, named \textbf{GPHT}. There are two aspects of key designs in GPHT. On the one hand, we advocate for constructing a mixed dataset under the channel-independent assumption for pretraining our model, comprising various datasets from diverse data scenarios. This approach significantly expands the scale of training data, allowing our model to uncover commonalities in time series data and facilitating improved transfer to specific datasets. On the other hand, GPHT employs an auto-regressive forecasting approach, effectively modeling temporal dependencies in the output series. Importantly, no customized forecasting head is required, enabling \textit{a single model to forecast at arbitrary horizon settings.} We conduct sufficient experiments on eight datasets with mainstream self-supervised pretraining models and supervised models. The results demonstrated that GPHT surpasses the baseline models across various fine-tuning and zero/few-shot learning settings in the traditional long-term forecasting task. We make our codes publicly available\footnote{https://github.com/icantnamemyself/GPHT}.

cs.LG↗

arXiv2024

ENN's Roadmap for Proton-Boron Fusion Based on Spherical Torus

ENN Science and Technology Development Co., Ltd. (ENN) is committed to generating fusion energy in an environmentally friendly and cost-effective manner, which requires abundant aneutronic fuel. Proton-boron ( p-$^{11}$B or p-B) fusion is considered an ideal choice for this purpose. Recent studies have suggested that p-B fusion, although challenging, is feasible based on new cross-section data, provided that a hot ion mode and high wall reflection can be achieved to reduce electron radiation loss. The high beta and good confinement of the spherical torus (ST) make it an ideal candidate for p-B fusion. By utilizing the new spherical torus energy confinement scaling law, a reactor with a major radius $R_0=4$ m, central magnetic field $B_0=6$ T, central temperature $T_{i0}=150$ keV, plasma current $I_p=30$ MA, and hot ion mode $T_i/T_e=4$ can yield p-B fusion with $Q>10$. A roadmap for p-B fusion has been developed, with the next-generation device named EHL-2. EHL stands for ENN He-Long, which literally means ``peaceful Chinese Loong". The main target parameters include $R_0\simeq1.05$ m, $A\simeq1.85$, $B_0\simeq3$ T, $T_{i0}\simeq30$ keV, $I_p\simeq3$ MA, and $T_i/T_e\geq2$. The existing ST device EXL-50 was simultaneously upgraded to provide experimental support for the new roadmap, involving the installation and upgrading of the central solenoid, vacuum chamber, and magnetic systems. The construction of the upgraded ST fusion device, EXL-50U, was completed at the end of 2023, and it achieved its first plasma in January 2024. The construction of EHL-2 is estimated to be completed by 2026.

physics.plasm-ph↗

arXiv2024

Phase space framework enables a variable-scale diffraction model for coherent imaging and display

The fast algorithms in Fourier optics have invigorated multifunctional device design and advanced imaging technologies. However, the necessity for fast computations has led to limitations in the widely used conventional Fourier methods, manifesting as fixed size image plane at a certain diffraction distance. These limitations pose challenges in intricate scaling transformations, 3D reconstructions and full-color displays. Currently, there is a lack of effective solutions, often resorting to pre-processing that compromise fidelity. In this paper, leveraging a higher-dimensional phase space method, we present a universal framework allowing for customized diffraction calculation methods. Within this framework, we establish a variable-scale diffraction computation model which allows the adjustment of the size of the image plane and can be operated by fast algorithms. We validate the model's robust variable-scale capabilities and its aberration automatic correction capability for full-color holography, achieving high fidelity. The large-magnification tomography experiment demonstrates that this model provides a superior solution for holographic 3D reconstruction. In addition, this model is applied to achieve full-color metasurface holography with near-zero crosstalk, showcasing its versatile applicability at nanoscale. Our model presents significant prospects for applications in the optics community, such as beam shaping, computer-generated holograms (CGHs), augmented reality (AR), metasurface optical elements (MOEs) and advanced holographic head-up display (HUD) systems.

physics.app-ph↗

arXiv2024

Possible Excitonic Insulating Phase in Quantum-Confined Sb Nanoflakes

In the 1960s, it was proposed that in small indirect band-gap materials, excitons can spontaneously form because the density of carriers is too low to screen the attractive Coulomb interaction between electrons and holes. The result is a novel strongly interacting insulating phase known as an excitonic insulator. Here we employ scanning tunnelling microscopy (STM) and spectroscopy (STS) to show that the enhanced Coulomb interaction in quantum-confined elemental Sb nanoflakes drives the system to the excitonic insulator state. The unique feature of the excitonic insulator, a charge density wave (CDW) without periodic lattice distortion, is directly observed. Furthermore, STS shows a gap induced by the CDW near the Fermi surface. Our observations suggest that the Sb(110) nanoflake is an excitonic insulator.

cond-mat.mtrl-sci↗

arXiv2024

Classification of Lagrangian translators and Lagrangian self-expanders in $\mathbb{C}^{2}$

In this paper, we obtain several classification results of $2$-dimensional complete Lagrangian translators and lagrangian self-expanders with constant squared norm $|\vec{H}|^{2}$ of the mean curvature vector in $\mathbb{C}^{2}$ by using a new Omori-Yau type maximum principle which was proved by Chen and Qiu \cite{CQ}. The same idea is also used to give a similar result of Lagrangian $ξ$-translators in $\mathbb{C}^{2}$.

math.DG↗