In this paper we prove a quantiative local limit theorem for the distribution of the number of triangles in the Erdős-Renyi random graph $G(n,p)$, for a fixed $p\in (0,1)$. This proof is an extension of the previous work of Gilmer and Kopparty, who proved that the local limit theorem held asymptotically for triangles. Our work gives bounds on the $\ell^1$ and $\ell^\infty$ distance of the triangle distribution from a suitable discrete normal.

With the rapid and transformative advances of large language models (LLMs) in natural language processing, the capabilities of these models in knowledge integration and reasoning have opened new technological pathways for intelligent industrial applications. This review systematically surveys key adaptation techniques, representative application scenarios, and future development trends of LLMs in industrial scenarios. It also provides an integrated overview of their application paradigms and technical characteristics across core industrial processes. Key adaptation techniques for industrial scenarios are first analyzed, including prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning, together with a summary of commonly used evaluation metrics and LLM-based assessment approaches. Representative practices of LLMs are then systematically reviewed across the product lifecycle, covering product design, process planning, production and manufacturing, as well as operation and maintenance. The effectiveness of LLMs in addressing practical industrial problems, facilitating technological innovation, and improving application performance is examined. Finally, major challenges currently encountered in industrial applications of LLMs are identified, including the scarcity of high-quality datasets, limited multimodal fusion capability, insufficient domain specificity, reliability concerns, constrained interpretability, and the lack of standardized evaluation frameworks. Corresponding future research directions are outlined, such as the development of data augmentation and secure sharing mechanisms, the exploration of novel model architectures, and the establishment of intelligent evaluation systems. Overall, this review provides a comprehensive reference for systematic investigations of LLM applications across the entire industrial process and offers theoretical foundations and methodological guidance for both academic research and engineering practice.

Intelligent omni-surfaces (IOSs) with 360° electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a “DISCO” IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a “DISCO ball” that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results1based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.

Reconfigurable intelligent surfaces (RISs) have recently attracted significant attention, while simultaneously raising new concerns regarding multiple-input multiple-output (MIMO) jamming-resistant communications. In this work, we investigate a potential threat to MIMO jamming-resistant communications posed by a disco RIS (DRIS), which is characterized by random and time-varying reflection coefficients and exhibits behavior similar to that of "disco ball". The time-varying DRIS induces active channel aging (ACA), which results in imperfect channel state information (CSI) and undermines MIMO jamming-resistant communications. We model a MIMO uplink jamming-resistant communication system in the presence of a DRIS, and adopt the achievable rates obtained from the widely-used zero-forcing (ZF) and minimum mean-square error (MMSE) receiver filters to quantify the rate degradation induced by the DRIS. Furthermore, we theoretically analyze the impact of the time-varying DRIS on achievable uplink rates. Numerical results are provided to verify the derived theoretical analysis and to assess the impact of the DRIS.

MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lack interpretability. To address these limitations, we propose miCGR, an end-to-end deep learning framework for predicting functional miRNA targets. MiCGR employs 2D convolutional neural networks alongside an enhanced Chaos Game Representation (CGR) of both miRNA sequences and their candidate target site (CTS) on mRNA. This advanced CGR transforms genetic sequences into informative 2D graphical representations based on sequence composition and subsequence frequencies, and explicitly incorporates important prior knowledge of seed regions and subsequence positions. Unlike one-dimensional methods based solely on sequence characters, this approach identifies functional motifs within sequences, even if they are distant in the original sequences. Our model outperforms existing methods in predicting functional targets at both the site and gene levels. To enhance interpretability, we incorporate Shapley value analysis for each subsequence within both miRNA sequences and their target sites, allowing miCGR to achieve improved accuracy, particularly with more lenient CTS selection criteria. Finally, two case studies demonstrate the practical applicability of miCGR, highlighting its potential to provide insights for optimizing artificial miRNA analogs that surpass endogenous counterparts.

The coexistence of ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services in 5G-based industrial wireless networks (IWNs) poses significant resource slicing challenges due to their inherent performance requirement conflicts. To address this challenge, this paper proposes a puncturing method that uses a model-aided deep reinforcement learning (DRL) algorithm for URLLC over eMBB services in uplink 5G networks. First, a puncturing-based optimization problem is formulated to maximize the eMBB accumulated rate under strict URLLC latency and reliability constraints. Next, we design a random repetition coding-based contention (RRCC) scheme for sporadic URLLC traffic and derive its analytical reliability model. To jointly optimize the scheduling parameters of URLLC and eMBB, a DRL solution based on the reliability model is developed, which is capable of dynamically adapting to changing environments. The accelerated convergence of the model-aided DRL algorithm is demonstrated using simulations, and the superiority in resource efficiency of the proposed method over existing approaches is validated.

Diffusion planning has been recognized as an effective decision-making paradigm in various domains. The capability of generating high-quality long-horizon trajectories makes it a promising research direction. However, existing diffusion planning methods suffer from low decision-making frequencies due to the expensive iterative sampling cost. To alleviate this, we introduce DiffuserLite, a super fast and lightweight diffusion planning framework, which employs a planning refinement process (PRP) to generate coarse-to-fine-grained trajectories, significantly reducing the modeling of redundant information and leading to notable increases in decision-making frequency. Our experimental results demonstrate that DiffuserLite achieves a decision-making frequency of $122.2$Hz ($112.7$x faster than predominant frameworks) and reaches state-of-the-art performance on D4RL, Robomimic, and FinRL benchmarks. In addition, DiffuserLite can also serve as a flexible plugin to increase the decision-making frequency of other diffusion planning algorithms, providing a structural design reference for future works. More details and visualizations are available at https://diffuserlite.github.io/.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.

Due to the divergence of accuracy caused by inertial measurement unit (IMU) cumulative error, it is difficult for a single IMU equipment to realize vehicle positioning. Therefore, this paper proposes an IMU pose state estimation algorithm based on modulation long short-term memory-unscented Kalman filter (ML-UKF) algorithm. First, the algorithm improves the memory mode of LSTM network by using Modulation LSTM neural network and establishes IMU state model and observation model. Then, in order to adapt to the application of deep learning algorithm in UKF, an equal spacing sigma sampling method is proposed. Finally, the effect of IMU pose state estimation is verified by experiments. Results show that the root mean square error of the ML-UKF algorithm is decreases by 65.43% relative to the state of the art, further verifying the effectiveness of the proposed algorithm.

Existing intelligent driving technology often has difficulty balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment and is prone to instability during emergencies. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on an Attention-long short-term memory (Attention LSTM) deep learning model with the idea of humanoid driving. First, we designed the autonomous obstacle avoidance control rules to guarantee the safety of unmanned vehicles. Second, we improved the autonomous obstacle avoidance control strategy combined with the stability analysis of special vehicles. Third, we constructed a deep learning obstacle avoidance control based on the Attention-LSTM network model through experiments, and the average relative error of this system was 14.95%. Finally, the stability and accuracy of this control strategy were verified numerically and experimentally. The method proposed in this study can ensure that the unmanned vehicle can successfully avoid obstacles while driving smoothly.

Facing the dual challenges of massive access and time-sensitive traffics, grant-free non-orthogonal multiple access (GF-NOMA) emerges as a promising technology for implementing massive ultra-reliable and low-latency communications (mURLLC). In this paper, we propose a differentiated power level access (DPLA) policy that exploits the correlations among power levels of GF-NOMA, and implement DPLA by a dynamically distributed GF-NOMA framework. Further, a closed-form expression to the reliability of DPLA is analytically derived and the optimal framework parameters to maximize reliability are obtained. Finally, considering the traffic variation over time, we propose a dynamically-distributed differentiated-layered transmission $(\mathrm{D}^{3}$ LT) algorithm to improve the reliability online. Simulation results show that the proposed scheme in this work outweighs existing schemes in transmission reliability.