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The Five-hundred-meter Aperture Spherical Radio Telescope (FAST) Core Array is a proposed extension of FAST, integrating 24 secondary 40-m antennas implanted within 5 km of the FAST site. This original array design will combine the unprecedented sensitivity of FAST with a high angular resolution (4.3" at a frequency of 1.4 GHz), thereby exceeding the capabilities at similar frequencies of next-generation arrays such as the Square Kilometre Array Phase 1 or the next-generation Very Large Array. This article presents the technical specifications of the FAST Core Array, evaluates its potential relatively to existing radio telescope arrays, and describes its expected scientific prospects. The proposed array will be equipped with technologically advanced backend devices, such as real-time signal processing systems. A phased array feed receiver will be mounted on FAST to improve the survey efficiency of the FAST Core Array, whose broad frequency coverage and large field of view (FOV) will be essential to study transient cosmic phenomena such as fast radio bursts and gravitational wave events, to conduct surveys and resolve structures in neutral hydrogen galaxies, to monitor or detect pulsars, and to investigate exoplanetary systems. Finally, the FAST Core Array can strengthen China's major role in the global radio astronomy community, owing to a wide range of potential scientific applications from cosmology to exoplanet science., Comment: 11 pages, 7 figures

Relevance modeling is a critical component for enhancing user experience in search engines, with the primary objective of identifying items that align with users' queries. Traditional models only rely on the semantic congruence between queries and items to ascertain relevance. However, this approach represents merely one aspect of the relevance judgement, and is insufficient in isolation. Even powerful Large Language Models (LLMs) still cannot accurately judge the relevance of a query and an item from a semantic perspective. To augment LLMs-driven relevance modeling, this study proposes leveraging user interactions recorded in search logs to yield insights into users' implicit search intentions. The challenge lies in the effective prompting of LLMs to capture dynamic search intentions, which poses several obstacles in real-world relevance scenarios, i.e., the absence of domain-specific knowledge, the inadequacy of an isolated prompt, and the prohibitive costs associated with deploying LLMs. In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively. Specifically, we perform the user-driven behavior neighbors retrieval from the daily search logs to obtain domain-specific knowledge in time, retrieving candidates that users consider to meet their expectations. Then, we guide LLMs for relevance modeling by employing advanced prompting techniques that progressively improve the outputs of the LLMs, followed by a progressive aggregation with comprehensive consideration of diverse aspects. For online serving, we have developed an industrial application framework tailored for the deployment of LLMs in relevance modeling. Experiments on real-world industry data and online A/B testing demonstrate our proposal achieves promising performance.

We report a milestone in achieving large-scale, ultrathin (~5 nm) superconducting NbN thin films on 300 mm Si wafers using a high-volume manufacturing (HVM) industrial physical vapor deposition (PVD) system. The NbN thin films possess remarkable structural uniformity and consistently high superconducting quality across the entire 300 mm Si wafer, by incorporating an AlN buffer layer. High-resolution X-ray diffraction and transmission electron microscopy analyses unveiled enhanced crystallinity of (111)-oriented {\delta}-phase NbN with the AlN buffer layer. Notably, NbN films deposited on AlN-buffered Si substrates exhibited a significantly elevated superconducting critical temperature (~2 K higher for the 10 nm NbN) and a higher upper critical magnetic field or Hc2 (34.06 T boost in Hc2 for the 50 nm NbN) in comparison with those without AlN. These findings present a promising pathway for the integration of quantum-grade superconducting NbN films with the existing 300 mm CMOS Si platform for quantum information applications.

Given two (hereditary) complete cotorsion pairs $(\mathcal{X}_1,\mathcal{Y}_1)$ and $(\mathcal{X}_2,\mathcal{Y}_2)$ in an exact category with $\mathcal{X}_1\subseteq \mathcal{Y}_2$, we prove that $\left({\rm Smd}\langle \mathcal{X}_1,\mathcal{X}_2 \rangle,\mathcal{Y}_1\cap \mathcal{Y}_2\right)$ is also a (hereditary) complete cotorsion pair, where ${\rm Smd}\langle \mathcal{X}_1,\mathcal{X}_2 \rangle$ is the class of direct summands of extension of $\mathcal{X}_1$ and $\mathcal{X}_2$. As an application, we construct complete cotorsion pairs, such as $(^\perp\mathcal{GI}^{\leqslant n},\mathcal{GI}^{\leqslant n})$, where $\mathcal{GI}^{\leqslant n}$ is the class of modules of Gorenstein injective dimension at most $n$. And we also characterize the left orthogonal class of exact complexes of injective modules and the classes of modules with finite Gorenstein projective, Gorenstein flat, and PGF dimensions.

The rice panicle traits significantly influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and difficult to capture the rice panicle traits under natural growth conditions. Here, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field using smartphone. The proposed method combined the large model Segment Anything Model (SAM) and the small model You Only Look Once version 8 (YOLOv8) to achieve high-precision segmentation of rice panicle images. The NeRF technique was then employed for 3D reconstruction using the images with 2D segmentation. Finally, the resulting point clouds are processed to successfully extract panicle traits. The results show that PanicleNeRF effectively addressed the 2D image segmentation task, achieving a mean F1 Score of 86.9% and a mean Intersection over Union (IoU) of 79.8%, with nearly double the boundary overlap (BO) performance compared to YOLOv8. As for point cloud quality, PanicleNeRF significantly outperformed traditional SfM-MVS (structure-from-motion and multi-view stereo) methods, such as COLMAP and Metashape. The panicle length was then accurately extracted with the rRMSE of 2.94% for indica and 1.75% for japonica rice. The panicle volume estimated from 3D point clouds strongly correlated with the grain number (R2 = 0.85 for indica and 0.82 for japonica) and grain mass (0.80 for indica and 0.76 for japonica). This method provides a low-cost solution for high-throughput in-field phenotyping of rice panicles, accelerating the efficiency of rice breeding.

Some companies(e.g., Microsoft Research and Google DeepMind) have discovered some of the limitations of GPTs autoregressive paradigm next-word prediction, manifested in the model lack of planning, working memory, backtracking, and reasoning skills. GPTs rely on a local and greedy process of generating the next word, without a global understanding of the task or the output.We have confirmed the above limitations through specialized empirical studies of code comprehension. Although GPT4 is good at producing fluent and coherent text, it cannot handle complex logic and generate new code that haven not been seen, and it relies too much on the formatting of the prompt to generate the correct code.We propose a new paradigm for code understanding that goes beyond the next-word prediction paradigm, inspired by the successful application of diffusion techniques to image generation(Dalle2, Sora) and protein structure generation(AlphaFold3), which have no autoregressive constraints.Instead of encoding the code in a form that mimics natural language, we encode the code as a heterogeneous image paradigm with a memory of global information that mimics both images and protein structures.We then refer to Sora's CLIP upstream text-to-image encoder model to design a text-to-code encoder model that can be applied to various downstream code understanding tasks.The model learns the global understanding of code under the new paradigm heterogeneous image, connects the encoding space of text and code, and encodes the input of text into the vector of code most similar to it.Using self-supervised comparative learning on 456,360 text-code pairs, the model achieved a zero-shot prediction of new data. This work is the basis for future work on code generation using diffusion techniques under a new paradigm to avoid autoregressive limitations., Comment: 10 page, 14 figures

Type-II multiferroicity, where electric polarization is induced by specific spin patterns, is crucial in fundamental physics and advanced spintronics. However, the spin model and magnetoelectric coupling mechanisms in prototypical type-II multiferroic CuFeO$_2$ and Al-doped CuFeO$_2$ remain unclear. Here, by considering both spin and alloy degrees of freedom, we develop a magnetic cluster expansion method, which considers all symmetry allowed interactions. Applying such method, we not only obtain realistic spin model that can correctly reproduce observations for both CuFeO$_2$ and CuFe$_{1-x}$Al$_x$O$_2$, but also revisit well-known theories of the original spin-current (SC) model and $p$-$d$ hybridization model. Specifically, we find that (i) a previously overlooked biquadratic interaction is critical to reproduce the $\uparrow\uparrow\downarrow\downarrow$ ground state and excited states of CuFeO$_2$; (ii) the combination of absent biquadratic interaction and increased magnetic frustration around Al dopants stabilizes the proper screw state; and (iii) it is the generalized spin-current (GSC) model that can correctly characterize the multiferroicity of CuFeO$_2$. These findings have broader implications for understanding novel magnetoelectric couplings in, e.g., monolayer multiferroic NiI$_2$.

In order to understand how nonlocal diffusion and pulse intervention affect dynamics of species, we focus on an age-structured nonlocal diffusion model in moving and heterogeneous environment, where nonlocal diffusion describes the long range dispersal of species itself and time-periodic harvesting pulse exacting on the adult reflects human intervention. A generalized principal eigenvalue involving harvesting rate used to identify the spreading and vanishing outcomes is firstly defined and the existence of the principal eigenvalue is given under some conditions. Subsequently, properties of the generalized principal eigenvalue and the principal eigenvalue related to harvesting rate and length of habitat interval are analyzed, respectively. The criteria to governing spreading or vanishing of the species are finally investigated, with sufficient conditions for spreading-vanishing established. Our results indicate that complexities can be induced by the internal long rang dispersal and expanding capacities of species, as well as external harvesting intervention of human. Specifically, appropriate harvesting rate and expanding capacities can even change the reciprocal outcomes of species from co-existence to co-extinction., Comment: 31 pages, 1 figure

China is the leading crop producer and has successfully implemented sustainable development programs related to agriculture. Sustainable agriculture has been promoted to achieve national food security targets such as food self-sufficiency through the well-facilitated farmland construction (WFFC) approach. The WFFC is introduced in Chinas current national 10-year plan to consolidate farmlands into large and simplified production areas to maximise automation, and improve soil fertility and productivity. However, research suggests that diversified and smaller farms faciliate ecosystem services, can improve yield resilience, defuse human health threats, and increase farm profitability. Currently, WFFC has not considered ecological farmland improvements and it may miss long-term environmental benefits including ecosystem service preservation conducive to yields. Moreover, the nutritional status in China has changed in recent decades with undernutrition being dramatically reduced, but the prevalence of overweight, obesity, and chronic diseases being increased. While a strategic choice and management of crop and livestock species can improve nutrition, the environmental and production benefits of agricultural diversification are currently not well interlinked with Chinas food and nutrition security discussions. Lastly, the role of agricultural technology for socioeconomic benefits and the link with diversified agricultural production may provide vast benefits for food security. Here, we focus on the opportunities and co-benefits of agricultural diversification and technology innovations to advance food and nutrition security in China through ecosystem service and yield benefits. Our applied five-point research agenda can provide evidence-based opportunities to support China in reaching its ambitious food security targets through agricultural diversification with global ramifications.

The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the genuine emotional state of each utterance based on the multi-modal information in the conversation, which is crucial for conversation understanding. Existing methods focus on using graph neural networks (GNN) to model conversational relationships and capture contextual latent semantic relationships. However, due to the complexity of GNN, existing methods cannot efficiently capture the potential dependencies between long-distance utterances, which limits the performance of MERC. In this paper, we propose an Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN) for multi-modal emotion recognition in conversations. Specifically, we first use pre-extracted text, video and audio features as input to Bi-LSTM to capture contextual semantic information and obtain low-level utterance features. Then, we use low-level utterance features to construct a conversational emotion interaction graph. To efficiently capture the potential dependencies between long-distance utterances, we use the dilated generalized forward push algorithm to precompute the emotional propagation between global utterances and design an emotional relation-aware operator to capture the potential semantic associations between different utterances. Furthermore, we combine early fusion and adaptive late fusion mechanisms to fuse latent dependency information between speaker relationship information and context. Finally, we obtain high-level discourse features and feed them into MLP for emotion prediction. Extensive experimental results show that ELR-GNN achieves state-of-the-art performance on the benchmark datasets IEMOCAP and MELD, with running times reduced by 52\% and 35\%, respectively., Comment: 11 pages, 3 tables

CeRh$_6$Ge$_4$ stands out as the first stoichiometric metallic compound with a ferromagnetic quantum critical point, thereby garnering significant attention. Ferromagnetic Kondo lattice compounds ReRh$_6$Ge$_4$ (Re=Ce, Ho, Er, Tm) have been systematically investigated with density functional theory incorporating Coulomb interaction U and spin-orbital coupling. We determined the magnetic easy axis of CeRh$_6$Ge$_4$ is within the ab plane, which is in agreement with previous magnetization measurements conducted under external magnetic field and muSR experiments. We also predicted the magnetic easy axes for the other three compounds. For TmRh$_6$Ge$_4$, the magnetic easy axis aligns along the c axis, thus preserving the $C_3$ rotational symmetry of the c axis. Especially, there are triply degenerate nodal points along the $\Gamma-A$ direction in the band structure including spin-orbital coupling. A possible localized to itinerant crossover is revealed as $4f$ electrons increase from CeRh$_6$Ge$_4$ to TmRh$_6$Ge$_4$. Specifically, the $4f$ electrons of TmRh$_6$Ge$_4$ contribute to the formation of a large Fermi surface, indicating their participation in the conduction process. Conversely, the $4f$ electrons in HoRh$_6$Ge$_4$, ErRh$_6$Ge$_4$ and CeRh$_6$Ge$_4$ remain localized, which result in smaller Fermi surfaces for these compounds. These theoretical investigations on electronic structure and magnetic properties shed deep insight into the unique nature of $4f$ electrons, providing critical predictions for subsequent experimental studies.

Detecting anomalies in business processes is crucial for ensuring operational success. While many existing methods rely on statistical frequency to detect anomalies, it's important to note that infrequent behavior doesn't necessarily imply undesirability. To address this challenge, detecting anomalies from a semantic viewpoint proves to be a more effective approach. However, current semantic anomaly detection methods treat a trace (i.e., process instance) as multiple event pairs, disrupting long-distance dependencies. In this paper, we introduce DABL, a novel approach for detecting semantic anomalies in business processes using large language models (LLMs). We collect 143,137 real-world process models from various domains. By generating normal traces through the playout of these process models and simulating both ordering and exclusion anomalies, we fine-tune Llama 2 using the resulting log. Through extensive experiments, we demonstrate that DABL surpasses existing state-of-the-art semantic anomaly detection methods in terms of both generalization ability and learning of given processes. Users can directly apply DABL to detect semantic anomalies in their own datasets without the need for additional training. Furthermore, DABL offers the capability to interpret the causes of anomalies in natural language, providing valuable insights into the detected anomalies.

Change detection in remote sensing images is essential for tracking environmental changes on the Earth's surface. Despite the success of vision transformers (ViTs) as backbones in numerous computer vision applications, they remain underutilized in change detection, where convolutional neural networks (CNNs) continue to dominate due to their powerful feature extraction capabilities. In this paper, our study uncovers ViTs' unique advantage in discerning large-scale changes, a capability where CNNs fall short. Capitalizing on this insight, we introduce ChangeViT, a framework that adopts a plain ViT backbone to enhance the performance of large-scale changes. This framework is supplemented by a detail-capture module that generates detailed spatial features and a feature injector that efficiently integrates fine-grained spatial information into high-level semantic learning. The feature integration ensures that ChangeViT excels in both detecting large-scale changes and capturing fine-grained details, providing comprehensive change detection across diverse scales. Without bells and whistles, ChangeViT achieves state-of-the-art performance on three popular high-resolution datasets (i.e., LEVIR-CD, WHU-CD, and CLCD) and one low-resolution dataset (i.e., OSCD), which underscores the unleashed potential of plain ViTs for change detection. Furthermore, thorough quantitative and qualitative analyses validate the efficacy of the introduced modules, solidifying the effectiveness of our approach. The source code is available at https://github.com/zhuduowang/ChangeViT.

In the present paper, we define the sheath by a monotone stationary solution to the nonisentropic Euler-Poisson system under a condition known as the Bohm criterion and consider a situation in which charged particles accumulate on the boundary due to the flux from the inner region. Under this fluid-boundary interactive setting, we prove the large time asymptotic stability of the sheath provided that the initial perturbation is sufficiently small in some weighted Sobolev spaces. Moreover, the convergence rate of the solution toward the sheath is obtained. The proof is based on the weighted energy method., Comment: arXiv admin note: substantial text overlap with arXiv:2011.02111; text overlap with arXiv:2403.09730 by other authors

Lighting and display, relying on electric and optical down-conversion emission with sluggish power efficiency, account for >15% global electricity consumption1,2. In 2014, quantum-dot (QD) LEDs (QLEDs) with near-optimal external quantum efficiency emerged3 and promised a pathway to avoid the vast down-conversion energy loss4,5. Despite a decade of progress4-22, fabrication of energy-efficient QLEDs with application-relevant brightness remains elusive. Here, the main roadblock is identified as the oxidative species adsorbed in the nanocrystalline electron-injection layer of QLEDs, which is then addressed by a simple reductive treatment to simultaneously boosts electron conductivity and hole blockage of the electron-injection layer. The resulting sub-bandgap-driven QLEDs with optimal efficiency achieve ultra-high brightness across the entire visible spectrum at least 2.6-fold higher than existing benchmarks. The brightness fully satisfies the demands of various forms of lighting and display, which surges to a remarkable level sufficient for QD laser diodes with a moderate bias (~9 V). Optimized electron injection further enables new types of QD-blend LEDs for diffuse white-light sources surpassing the 2035 R&D targets set by the U.S. Department of Energy. Our findings open a door for understanding and optimizing carrier transport in nanocrystalline semiconductors shared by various types of solution-processed optoelectronic devices.

Electrocardiograms (ECG), which record the electrophysiological activity of the heart, have become a crucial tool for diagnosing these diseases. In recent years, the application of deep learning techniques has significantly improved the performance of ECG signal classification. Multi-resolution feature analysis, which captures and processes information at different time scales, can extract subtle changes and overall trends in ECG signals, showing unique advantages. However, common multi-resolution analysis methods based on simple feature addition or concatenation may lead to the neglect of low-resolution features, affecting model performance. To address this issue, this paper proposes the Multi-Resolution Mutual Learning Network (MRM-Net). MRM-Net includes a dual-resolution attention architecture and a feature complementary mechanism. The dual-resolution attention architecture processes high-resolution and low-resolution features in parallel. Through the attention mechanism, the high-resolution and low-resolution branches can focus on subtle waveform changes and overall rhythm patterns, enhancing the ability to capture critical features in ECG signals. Meanwhile, the feature complementary mechanism introduces mutual feature learning after each layer of the feature extractor. This allows features at different resolutions to reinforce each other, thereby reducing information loss and improving model performance and robustness. Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net significantly outperforms existing methods in multi-label ECG classification performance. The code for our framework will be publicly available at https://github.com/wxhdf/MRM.

The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.

Let $(\mathcal{T}',\mathcal{T},\mathcal{T}'')$ be a recollement of triangulated categories. Two complete ideal cotorsion pairs in $\mathcal{T}'$ and $\mathcal{T}''$ can be induced by a complete ideal cotorsion pair in $\mathcal{T}$. If $(\mathcal{I},\mathcal{I}^\perp )$ and $(\mathcal{J},\mathcal{J}^\perp)$ are two complete ideal cotorsion pair in triangulated category, then $(\mathcal{I}\cap\mathcal{J},\langle\mathcal{I}^\perp,\mathcal{J}^\perp\rangle)$ is also a complete ideal cotorsion pair. In this way, a series of ideal cotorsion pairs in $\mathcal{T}$ can be induced by two ideal cotorsion pairs in $\mathcal{T}'$ and $\mathcal{T}''$., Comment: arXiv admin note: text overlap with arXiv:1501.06810 by other authors

Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks.

Background: Drug development is often inefficient, costly and lengthy, yet it is essential for evaluating the safety and efficacy of new interventions. Compared with other disease areas, this is particularly true for Phase II / III cancer clinical trials where high attrition rates and reduced regulatory approvals are being seen. In response to these challenges, seamless clinical trials and master protocols have emerged to streamline the drug development process. Methods: Seamless clinical trials, characterized by their ability to transition seamlessly from one phase to another, can lead to accelerating the development of promising therapies while Master protocols provide a framework for investigating multiple treatment options and patient subgroups within a single trial. Results: We discuss the advantages of these methods through real trial examples and the principals that lead to their success while also acknowledging the associated regulatory considerations and challenges. Conclusion: Seamless designs and Master protocols have the potential to improve confirmatory clinical trials. In the disease area of cancer, this ultimately means that patients can receive life-saving treatments sooner., Comment: 12 manuscript pages, 2 figures and 2 tables

Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. How well LLMs accomplish event reasoning on various relations and reasoning paradigms remains unknown. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs. We introduce a novel benchmark EV2 for EValuation of EVent reasoning. EV2 consists of two levels of evaluation of schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV2. We find that LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. We also notice the imbalance of event reasoning abilities in LLMs. Besides, LLMs have event schema knowledge, however, they're not aligned with humans on how to utilize the knowledge. Based on these findings, we guide the LLMs in utilizing the event schema knowledge as memory leading to improvements on event reasoning.

Conformal prediction is a general distribution-free approach for constructing prediction sets combined with any machine learning algorithm that achieve valid marginal or conditional coverage in finite samples. Ordinal classification is common in real applications where the target variable has natural ordering among the class labels. In this paper, we discuss constructing distribution-free prediction sets for such ordinal classification problems by leveraging the ideas of conformal prediction and multiple testing with FWER control. Newer conformal prediction methods are developed for constructing contiguous and non-contiguous prediction sets based on marginal and conditional (class-specific) conformal $p$-values, respectively. Theoretically, we prove that the proposed methods respectively achieve satisfactory levels of marginal and class-specific conditional coverages. Through simulation study and real data analysis, these proposed methods show promising performance compared to the existing conformal method., Comment: 13 pages, 4 figures; 3 supplementary pages

The discrimination of quantum operations has long been an intriguing challenge, with theoretical research significantly advancing our understanding of the quantum features in discriminating quantum objects. This challenge is closely related to the discrimination of quantum states, and proof-of-principle demonstrations of the latter have already been realized using optical photons. However, the experimental demonstration of discriminating general quantum operations, including both unitary and non-unitary operations, has remained elusive. In general quantum systems, especially those with high dimensions, the preparation of arbitrary quantum states and the implementation of arbitrary quantum operations and generalized measurements are non-trivial tasks. Here, for the first time, we experimentally demonstrate the optimal unambiguous discrimination of up to 6 displacement operators and the unambiguous discrimination of non-unitary quantum operations. Our results demonstrate powerful tools for experimental research in quantum information processing and are expected to stimulate a wide range of valuable applications in the field of quantum sensing., Comment: 11 pages, 4 figures, 2 tables

Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations and predict future events. The cutting-edge techniques for event reasoning play a crucial role in various natural language processing applications. Large language models (LLMs) have made significant advancements in event reasoning owing to their wealth of knowledge and reasoning capabilities. However, smaller instruction-tuned models currently in use do not consistently demonstrate exceptional proficiency in managing these tasks. This discrepancy arises from the absence of explicit modeling of events and the interconnections of them within their instruction data. Consequently, these models face challenges in comprehending event structures and semantics while struggling to bridge the gap between their interpretations and human understanding of events. Additionally, their limitations in grasping event relations lead to constrained event reasoning abilities to effectively deduce and incorporate pertinent event knowledge. In this paper, we propose Event-Oriented Instruction Tuning (EvIT) to train our LLM. Specifically, we first propose a novel structure named event quadruple which contains the structure and semantics of events and is complete in the event representation. We then design event-relation learning based on the structures. We encapsulate the learning into the instruction-tuning formulation to better stimulate the event reasoning capacity of our model. We design a heuristic unsupervised method to mine event quadruple from a large-scale corpus. At last, we finetune a Llama model on our Event-Oriented Instruction Tuning. We conduct extensive experiments on event reasoning tasks on several datasets. Automatic and human evaluations demonstrate EvIT achieves competitive performances on event reasoning.

Multi-modal Event Reasoning (MMER) endeavors to endow machines with the ability to comprehend intricate event relations across diverse data modalities. MMER is fundamental and underlies a wide broad of applications. Despite extensive instruction fine-tuning, current multi-modal large language models still fall short in such ability. The disparity stems from that existing models are insufficient to capture underlying principles governing event evolution in various scenarios. In this paper, we introduce Multi-Modal Event Evolution Learning (MEEL) to enable the model to grasp the event evolution mechanism, yielding advanced MMER ability. Specifically, we commence with the design of event diversification to gather seed events from a rich spectrum of scenarios. Subsequently, we employ ChatGPT to generate evolving graphs for these seed events. We propose an instruction encapsulation process that formulates the evolving graphs into instruction-tuning data, aligning the comprehension of event reasoning to humans. Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution. In such a case, we propose the guiding discrimination strategy, in which models are trained to discriminate the improper evolution direction. We collect and curate a benchmark M-EV2 for MMER. Extensive experiments on M-EV2 validate the effectiveness of our approach, showcasing competitive performance in open-source multi-modal LLMs.

Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities remain poorly understood. In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of "Concept Depth" to suggest that more complex concepts are typically acquired in deeper layers. Specifically, we categorize concepts based on their level of abstraction, defining them in the order of increasing complexity within factual, emotional, and inferential tasks. We conduct extensive probing experiments using layer-wise representations across various LLM families (Gemma, LLaMA, QWen) on various datasets spanning the three domains of tasks. Our findings reveal that models could efficiently conduct probing for simpler tasks in shallow layers, and more complex tasks typically necessitate deeper layers for accurate understanding. Additionally, we examine how external factors, such as adding noise to the input and quantizing the model weights, might affect layer-wise representations. Our findings suggest that these factors can impede the development of a conceptual understanding of LLMs until deeper layers are explored. We hope that our proposed concept and experimental insights will enhance the understanding of the mechanisms underlying LLMs. Our codes are available at https://github.com/Luckfort/CD., Comment: 12 pages

The generation of smooth and continuous images between domains has recently drawn much attention in image-to-image (I2I) translation. Linear relationship acts as the basic assumption in most existing approaches, while applied to different aspects including features, models or labels. However, the linear assumption is hard to conform with the element dimension increases and suffers from the limit that having to obtain both ends of the line. In this paper, we propose a novel rotation-oriented solution and model the continuous generation with an in-plane rotation over the style representation of an image, achieving a network named RoNet. A rotation module is implanted in the generation network to automatically learn the proper plane while disentangling the content and the style of an image. To encourage realistic texture, we also design a patch-based semantic style loss that learns the different styles of the similar object in different domains. We conduct experiments on forest scenes (where the complex texture makes the generation very challenging), faces, streetscapes and the iphone2dslr task. The results validate the superiority of our method in terms of visual quality and continuity., Comment: 14 pages

In this article, we investigate how the Witt basis serves as a link between real and complex variables in higher-dimensional spaces. Our focus is on the detailed construction of the Witt basis within the tensor product space combining Clifford algebra and multiple octonionic spaces. This construction effectively introduces complex coordinates. The technique is based on a specific subgroup of octonionic automorphisms, distinguished by binary codes. This method allows us to perform a Hermitian analysis of the complex structures within the tensor product space., Comment: 13pages

This work introduces a method for generating generalized structures of amorphous polymers using simulated polymerization and molecular dynamics equilibration, with a particular focus on amorphous polymers. The techniques and algorithms used in this method are described in the main text, and example input scripts are provided for the GMXPolymer code, which is based on the GROMACS molecular dynamics package. To demonstrate the efficacy of our method, we apply it to different glassy polymers exhibiting varying degrees of functionality, polarity, and rigidity. The reliability of the method is validated by comparing simulation results to experimental data for various structural and thermal properties, all of which show excellent agreement.

This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clustering. Secondly, multiple data sources, including Automatic Identification System (AIS), pilotage booking information, and meteorological data, are fused before latent feature extraction. Thirdly, a Temporal Convolutional Network (TCN) framework that incorporates a residual mechanism is constructed to learn the hidden arrival patterns of the vessels. Extensive tests on two real-world data sets from Singapore have been conducted and the following promising results have been obtained: 1) fusion of pilotage booking information and meteorological data improves the prediction accuracy, with pilotage booking information having a more significant impact; 2) using discrete embedding for the meteorological data performs better than using continuous embedding; 3) the TCN outperforms the state-of-the-art baseline methods in regression tasks, exhibiting Mean Absolute Error (MAE) ranging from 4.58 min to 4.86 min; and 4) approximately 89.41% to 90.61% of the absolute prediction residuals fall within a time frame of 10 min., Comment: The 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)

Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended towards Large Language Models (LLMs) which are often criticized for their lack of transparency. This extension calls for a significant transformation in XAI methodologies because of two reasons. First, many existing XAI methods cannot be directly applied to LLMs due to their complexity advanced capabilities. Second, as LLMs are increasingly deployed across diverse industry applications, the role of XAI shifts from merely opening the "black box" to actively enhancing the productivity and applicability of LLMs in real-world settings. Meanwhile, unlike traditional machine learning models that are passive recipients of XAI insights, the distinct abilities of LLMs can reciprocally enhance XAI. Therefore, in this paper, we introduce Usable XAI in the context of LLMs by analyzing (1) how XAI can benefit LLMs and AI systems, and (2) how LLMs can contribute to the advancement of XAI. We introduce 10 strategies, introducing the key techniques for each and discussing their associated challenges. We also provide case studies to demonstrate how to obtain and leverage explanations. The code used in this paper can be found at: https://github.com/JacksonWuxs/UsableXAI_LLM., Comment: 38 pages, 4 figures

The main concern of this paper is to study large-time behavior of the sheath to the full Euler-Poisson system. As is well known, the monotone stationary solution under the Bohm criterion can be referred to as the sheath which is formed by interactions of plasma with wall. So far, the existence and asymptotic stability of stationary solutions in one-dimensional half space to the full Euler-Poisson system have been proved in \cite{DYZ2021}. In the present paper, we extend the results in \cite{DYZ2021} to $N$-dimensional ($N$=1,2,3) half space. By assuming that the velocity of the positive ion satisfies the Bohm criterion at the far field, we establish the global unique existence and the large time asymptotic stability of the sheath in some weighted Sobolev spaces by weighted energy method. Moreover, the time-decay rates are also obtained. A key different point from \cite{DYZ2021} is to derive some boundary estimates on the derivative of the potential in the $x_1$-direction.

In this paper, we propose a two-phase magnetohydrodynamics (MHD) system based on a magnetic potential formulation in 3D case. This model ensures exactly divergence-free approximation of the magnetic field by introducing magnetic potential A and defining magnetic field B = curl A. Then, we give two completely decoupled, first-order, linear and energy stable schemes, and strictly derive the unconditional energy stability of both schemes under time discretization. Finally, several numerical examples demonstrate the stability and effectiveness for our schemes.

The decline of cognitive inhibition significantly impacts older adults' quality of life and well-being, making it a vital public health problem in today's aging society. Previous research has demonstrated that Virtual reality (VR) exergames have great potential to enhance cognitive inhibition among older adults. However, existing commercial VR exergames were unsuitable for older adults' long-term cognitive training due to the inappropriate cognitive activation paradigm, unnecessary complexity, and unbefitting difficulty levels. To bridge these gaps, we developed a customized VR cognitive training exergame (LightSword) based on Dual-task and Stroop paradigms for long-term cognitive inhibition training among healthy older adults. Subsequently, we conducted an eight-month longitudinal user study with 12 older adults aged 60 years and above to demonstrate the effectiveness of LightSword in improving cognitive inhibition. After the training, the cognitive inhibition abilities of older adults were significantly enhanced, with benefits persisting for 6 months. This result indicated that LightSword has both short-term and long-term effects in enhancing cognitive inhibition. Furthermore, qualitative feedback revealed that older adults exhibited a positive attitude toward long-term training with LightSword, which enhanced their motivation and compliance., Comment: 23 pages

Large-scale high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to an improvement in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labeled data is still a challenge, hindering the largescale application of land cover classification methods. In this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data. First, to capture long-range dependencies without missing the fine-grained details of objects, we propose a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention. Second, we propose an anti-noise loss calculation (ANLC) module based on optimal transport (OT). Anti-noise loss calculation identifies confident areas (CA) and vague areas (VA) based on the OT matrix, which relieves the impact of noises in outdated land cover products. By introducing a weakly supervised loss with weights and employing unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolution and the corresponding ground-truths of six states in the United States were employed to validate the performance of the proposed method. The experiments utilized outdated land cover products with 30 m resolution from 2013 as training labels, and produced land cover maps with 1 m resolution from 2017. The results show the superiority of the proposed method compared to state-of-the-art methods. The code is available at https://github.com/yu-ni1989/ANLC-Former.

Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opaque. This lack of transparency presents challenges such as hallucinations, toxicity, and misalignment with human values, hindering the safe and beneficial deployment of LLMs. This paper aims to uncover the mechanisms underlying LLM functionality through the lens of explainability. First, we review how knowledge is architecturally composed within LLMs and encoded in their internal parameters via mechanistic interpretability techniques. Then, we summarize how knowledge is embedded in LLM representations by leveraging probing techniques and representation engineering. Additionally, we investigate the training dynamics through a mechanistic perspective to explain phenomena such as grokking and memorization. Lastly, we explore how the insights gained from these explanations can enhance LLM performance through model editing, improve efficiency through pruning, and better align with human values., Comment: 8 pages, 2 figures

While large language models (LLMs) excel at understanding and generating plain text, they are not specifically tailored to handle hierarchical text structures. Extracting the task-desired property from their natural language responses typically necessitates additional processing steps. In fact, selectively comprehending the hierarchical structure of large-scale text is pivotal to understanding its substance. Aligning LLMs more closely with the classification or regression values of specific task through prompting also remains challenging. To this end, we propose a novel framework called Tree-Based Hard Attention with Self-Motivation for Large Language Models (TEAROOM). TEAROOM incorporates a tree-based hard attention mechanism for LLMs to process hierarchically structured text inputs. By leveraging prompting, it enables a frozen LLM to selectively focus on relevant leaves in relation to the root, generating a tailored symbolic representation of their relationship. Moreover, TEAROOM comprises a self-motivation strategy for another LLM equipped with a trainable adapter and a linear layer. The selected symbolic outcomes are integrated into another prompt, along with the predictive value of the task. We iteratively feed output values back into the prompt, enabling the trainable LLM to progressively approximate the golden truth. TEAROOM outperforms existing state-of-the-art methods in experimental evaluations across three benchmark datasets, showing its effectiveness in estimating task-specific properties. Through comprehensive experiments and analysis, we have validated the ability of TEAROOM to gradually approach the underlying golden truth through multiple inferences.

During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, aimed at fulfilling the testing needs of large models with enhanced capabilities. The cognitive science-inspired AGI tests encompass the full spectrum of intelligence facets, including crystallized intelligence, fluid intelligence, social intelligence, and embodied intelligence. To assess the multidimensional intelligence of large models, the AGI tests consist of a battery of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.

Maritime transport is a pivotal logistics mode for the long-distance and bulk transportation of goods. However, the intricate planning involved in this mode is often hindered by uncertainties, including weather conditions, cargo diversity, and port dynamics, leading to increased costs. Consequently, accurately estimating vessel total (stay) time at port and potential delays becomes imperative for effective planning and scheduling in port operations. This study aims to develop a port operation solution with competitive prediction and classification capabilities for estimating vessel Total and Delay times. This research addresses a significant gap in port analysis models for vessel Stay and Delay times, offering a valuable contribution to the field of maritime logistics. The proposed solution is designed to assist decision-making in port environments and predict service delays. This is demonstrated through a case study on Brazil ports. Additionally, feature analysis is used to understand the key factors impacting maritime logistics, enhancing the overall understanding of the complexities involved in port operations., Comment: 13 pages, 9 figures, 4 Tables. Submitted at IEEE IJCNN 2024

Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in medical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of `similarity' between historical and the new trials. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focusing on a commensurate predictive prior method that can incorporate historical data from multiple sources, we highlight a particular issue of nonmonotonicity and explain why this causes issues with interpretability of the discounting factors (hereafter referred to as `weights'). We propose a solution for this, from which an analytical sample size formula is derived. We then propose a linearization technique such that the sample size changes uniformly over the weights. Our approach leads to interpretable weights that represent the probability that historical data are (ir)relevant to the new trial, and could therefore facilitate easier elicitation of expert opinion on their values. Keywords: Bayesian sample size determination; Commensurate priors; Historical borrowing; Prior aggregation; Uniform shrinkage., Comment: A submitted paper with 33 pages (inclusive of references, 4 figures and 5 tables)

The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency. While previous studies have made progress in optimizing model performance for single-round medical Q&A tasks, there is a need to enhance the model's capability for multi-round conversations to avoid logical inconsistencies. To address this, we propose an approach called preference learning from process feedback~(PLPF), which integrates the doctor's diagnostic logic into LLMs. PLPF involves rule modeling, preference data generation, and preference alignment to train the model to adhere to the diagnostic process. Experimental results using Standardized Patient Testing show that PLPF enhances the diagnostic accuracy of the baseline model in medical conversations by 17.6%, outperforming traditional reinforcement learning from human feedback. Additionally, PLPF demonstrates effectiveness in both multi-round and single-round dialogue tasks, showcasing its potential for improving medical dialogue generation., Comment: Accepted by ACL2024 Findings

Chain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on this, we have conducted several empirical experiments to explore the relations. Specifically, we design experiments that expand and compress the rationale reasoning steps within CoT demonstrations while keeping all other factors constant. We have the following key findings. First, the results indicate that lengthening the reasoning steps in prompts, even without adding new information into the prompt, considerably enhances LLMs' reasoning abilities across multiple datasets. Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models. This finding highlights the importance of the number of steps in CoT prompts and provides practical guidance to make better use of LLMs' potential in complex problem-solving scenarios. Second, we also investigated the relationship between the performance of CoT and the rationales used in demonstrations. Surprisingly, the result shows that even incorrect rationales can yield favorable outcomes if they maintain the requisite length of inference. Third, we observed that the advantages of increasing reasoning steps are task-dependent: simpler tasks require fewer steps, whereas complex tasks gain significantly from longer inference sequences. The code is available at https://github.com/MingyuJ666/The-Impact-of-Reasoning-Step-Length-on-Large-Language-Models, Comment: Findings of ACL 2024

Existing Spatial Interaction Models (SIMs) are limited in capturing the complex and context-aware interactions between business clusters and trade areas. To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas. The model innovatively represents the integrated system of business clusters, trade areas, and transportation infrastructure within an urban region using a connected graph. Then, a graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the complexity and interdependencies of business clusters. We developed this model with data collected from the Miami metropolitan area in Florida. We then demonstrated its effectiveness in capturing varying attractiveness of business clusters to different residential neighborhoods and across scenarios with an eXplainable AI approach. We contribute a novel method supplementing conventional SIMs to predict and analyze the dynamics of inter-connected community business clusters. The analysis results can inform data-evidenced and place-specific planning strategies helping community business clusters better accommodate their customers across scenarios, and hence improve the resilience of community businesses.

The designing of efficient signal detectors is important and yet challenge for orthogonal time frequency space (OTFS) systems in high-mobility scenarios. In this letter, we develop an efficient message feedback interference cancellation aided unitary approximate message passing (denoted as UAMPMFIC) iterative detector, where the latest feedback messages from variable nodes are utilized for more reliable interference cancellation and performance improvement. A fast recursive scheme is leveraged in the proposed UAMP-MFIC detector to prevent complexity increasing. To further alleviate the error-propagation and improve the receiver performance, we also develop the bidirectional symbol detection structures, where Turbo UAMP-MFIC detector and iterative weight UAMP-MFIC detector are proposed to efficiently fuse the estimation results of forward and backward UAMP-MFIC detectors. The simulation results are finally provided to demonstrate performance improvement of our proposed detectors over existing detectors.

Learning robust multi-modal feature representations is critical for boosting tracking performance. To this end, we propose a novel X Modality Assisting Network (X-Net) to shed light on the impact of the fusion paradigm by decoupling the visual object tracking into three distinct levels, facilitating subsequent processing. Firstly, to tackle the feature learning hurdles stemming from significant differences between RGB and thermal modalities, a plug-and-play pixel-level generation module (PGM) is proposed based on self-knowledge distillation learning, which effectively generates X modality to bridge the gap between the dual patterns while reducing noise interference. Subsequently, to further achieve the optimal sample feature representation and facilitate cross-modal interactions, we propose a feature-level interaction module (FIM) that incorporates a mixed feature interaction transformer and a spatial-dimensional feature translation strategy. Ultimately, aiming at random drifting due to missing instance features, we propose a flexible online optimized strategy called the decision-level refinement module (DRM), which contains optical flow and refinement mechanisms. Experiments are conducted on three benchmarks to verify that the proposed X-Net outperforms state-of-the-art trackers.