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Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. Therefore, understanding how LLMs reason and make decisions is crucial for their safe deployment. This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt. Leveraging the insight that LLMs learn to infer latent concepts during pretraining, we propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty. We show that the uncertainty decreases as the prompt's informativeness increases, similar to epistemic uncertainty. Our detailed experimental results on real datasets validate our proposed model., Comment: 27 pages, 11 figures

In our research, we have developed a novel mechanism that allows for a significant reduction in the smallest sampling unit of digital image sensors (DIS) to as small as 1/16th of a pixel, through measuring the intra-pixel quantum efficiency for the first time and recomputing the image. Employing our method, the physical sampling resolution of DIS can be enhanced by 16 times. The method has undergone rigorous testing in real-world imaging scenarios.

Polyetheretherketone (PEEK), as a semi-crystalline high-performance engineering plastic, has demonstrated good application prospects since its introduction. The ability of PEEK to be fabricated in complex architecture is a major limitation due to the inherent shortcomings of material extrusion 3D printing technology in terms of low resolution, low surface quality, and interlayer bonding. We propose a novel PEEK ink processing process based on digital light processing (DLP) 3D printing, which is based on high solid content PEEK ink to achieve green bodies with high accuracy, and one-step sintering to enhance the crystallinity of PEEK. We have investigated the processing mechanism of this process and constructed perfect process parameters in terms of mouldability, printing accuracy, material thermal properties, and PEEK crystallinity. Furthermore, the material and architecture performance of the proposed process was evaluated in terms of comprehensive thermal performance (including heat resistance of the substrate, thermal stability, surface energy after heat treatment, and coefficient of static friction and coefficient of kinetic friction), mechanical performance, and corrosion resistance (20 wt% hydrochloric acid, 20 wt% sodium hydroxide, 99 wt% acetone, and 99.5 wt% chloroform). The process is a bold extension of PEEK processing methods to utilize the properties of PEEK in more flexible and efficient applications.

We study single-photon scattering spectra of a giant atom chirally coupled to a one-dimensional waveguide at multiple connection points, and examine chirality induced effects in the scattering spectra. We show that the transmission spectra typically possess an anti-Lorentzian lineshape with a nonzero minimum, but by engineering the chirality of the multi-point coupling, the transmission spectrum of an incident photon can undergo a transition from complete transmission to total reflection at multiple frequency ``windows'', where the width of the anti-Lorentzian lineshape for each of the window can be flexibly tuned at a fixed frequency detuning. Moreover, we show that a perfect nonreciprocal photon scattering can be achieved due to the interplay between internal atomic spontaneous emission and the chirally external decay to the waveguide, in contrast to that induced by the non-Markovian retardation effect. We also consider the non-Markovian retardation effect on the scattering spectra, which allows for a photonic band gap even with only two chiral coupling points. The giant-atom-waveguide system with chiral coupling is a promising candidate for realizing single-photon routers with multiple channels., Comment: 15 pages, 8 figures

Shape memory structures are playing an important role in many cutting-edge intelligent fields. However, the existing technologies can only realize 4D printing of a single polymer or metal, which limits practical applications. Here, we report a construction strategy for TSMP/M heterointerface, which uses Pd2+-containing shape memory polymer (AP-SMR) to induce electroless plating reaction and relies on molecular dynamics, which has both shape memory properties and metal activity and information processing power. Through multi-material DLP 3D printing technology, the interface can be 3D selectively programmed on functional substrate parts of arbitrary shapes to become 4D electronic smart devices (Robotics). Microscopically, this type of interface appears as a composite structure with a nanometer-micrometer interface height, which is composed of a pure substrate layer (smart materials), an intermediate layer (a composite structure in which metal particles are embedded in a polymer cross-linked network) and a pure metal layer. The structure programmed by TSMP/M heterointerface exhibits both SMA characteristics and metal properties, thus having more intelligent functions (electroactive, electrothermal deformation, electronically controlled denaturation) and higher performance (selectivity of shape memory structures can be realized control, remote control, inline control and low voltage control). This is expected to provide a more flexible manufacturing process as platform technology for designing, manufacturing and applying smart devices with new concepts, and promote the development of cutting-edge industries such as smart robots and smart electronics., Comment: 37 Pages, 11 Figures

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security., Comment: 13 pages, 13 figures

Quantum information processing and its subfield, quantum image processing, are rapidly growing fields as a result of advancements in the practicality of quantum mechanics. In this paper, we propose a quantum algorithm for processing information, such as one-dimensional time series and two-dimensional images, in the frequency domain. The information of interest is encoded into the magnitude of probability amplitude or the coefficient of each basis state. The oracle for filtering operates based on postselection results, and its explicit circuit design is presented. This oracle is versatile enough to perform all basic filtering, including high pass, low pass, band pass, band stop, and many other processing techniques. Finally, we present two novel schemes for transposing matrices in this paper. They use similar encoding rules but with deliberate choices in terms of selecting basis states. These schemes could potentially be useful for other quantum information processing tasks, such as edge detection. The proposed techniques are implemented on the IBM Qiskit quantum simulator. Some results are compared with traditional information processing results to verify their correctness and are presented in this paper., Comment: 12 pages, 53 figures

Reinforcement Learning with Human Feedback (RLHF) has been demonstrated to significantly enhance the performance of large language models (LLMs) by aligning their outputs with desired human values through instruction tuning. However, RLHF is constrained by the expertise and productivity limitations of human evaluators. A response to this downside is to fall back to supervised fine-tuning (SFT) with additional carefully selected expert demonstrations. However, while this method has been proven to be effective, it invariably also leads to increased human-in-the-loop overhead. In this study, we propose another alternative approach: Reinforcement Learning with Generative Adversarial Feedback (RLGAF) to RLHF and SFT, which uses a generative adversarial training style to enable the LLMs to learn useful human expert demonstrations without being directly exposed to the training examples, thus enabling good generalization capabilities while preserving sample efficiency. Our preliminary findings indicate that RLGAF can help align LLMs outputs with competitive performance against RLHF and SFT, while not suffering from their respective inherent restrictions, suggesting promising avenues for further research on automating AI alignment., Comment: 22 pages, 9 figures, 12 tables

Adversarial attack serves as a major challenge for neural network models in NLP, which precludes the model's deployment in safety-critical applications. A recent line of work, detection-based defense, aims to distinguish adversarial sentences from benign ones. However, {the core limitation of previous detection methods is being incapable of giving correct predictions on adversarial sentences unlike defense methods from other paradigms.} To solve this issue, this paper proposes TextShield: (1) we discover a link between text attack and saliency information, and then we propose a saliency-based detector, which can effectively detect whether an input sentence is adversarial or not. (2) We design a saliency-based corrector, which converts the detected adversary sentences to benign ones. By combining the saliency-based detector and corrector, TextShield extends the detection-only paradigm to a detection-correction paradigm, thus filling the gap in the existing detection-based defense. Comprehensive experiments show that (a) TextShield consistently achieves higher or comparable performance than state-of-the-art defense methods across various attacks on different benchmarks. (b) our saliency-based detector outperforms existing detectors for detecting adversarial sentences.

The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects. A novel multi-to-one neural network is proposed and the concept of "batch frame" is introduced to improve the serial imaging method. The neural network extracts more correlation information from a small number of samples, thus reducing the sampling ratio of the ghost imaging technique. We combine the correlation characteristics between images to propose a frame merging algorithm, which eliminates the dynamic blur of high-speed moving objects and further improves the reconstruction quality of moving object images at a low sampling ratio. The experimental results are consistent with the simulation results., Comment: 12 pages, 7 figures