Ray-Based Characterization of the AMPLE Model from 0.85 to 5 GHz

In this paper, we characterize the adaptive multiple path loss exponent (AMPLE) radio propagation model under urban macrocell (UMa) and urban microcell (UMi) scenarios from 0.85-5 GHz using Ranplan Professional. We first enhance the original AMPLE model by introducing an additional frequency coefficient to support path loss prediction across multiple carrier frequencies. By using measurement-validated Ranplan Professional simulator, we simulate four cities and validate the simulations for further path loss model characterization. Specifically, we extract the close-in (CI) model parameters from the simulations and compare them with parameters extracted from measurements in other works. Under the ray-based model characterization, we compare the AMPLE model with the 3rd Generation Partnership Project (3GPP) path loss model, the CI model, the alpha-beta-gamma (ABG) model, and those with simulation calibrations. In addition to standard performance metrics, we introduce the prediction-measurement difference error (PMDE) to assess overall prediction alignment with measurement, and mean simulation time per data point to evaluate model complexity. The results show that the AMPLE model outperforms existing models while maintaining similar model complexity.

Development and Experimental Evaluation of a Vibration-Based Adhesion System for Miniature Wall-Climbing Robots

In recent years, miniature wall-climbing robots have attracted widespread attention due to their significant potential in equipment inspection and in-situ repair applications. Traditional wall-climbing systems typically rely on electromagnetic, electrostatic, vacuum suction, or van der Waals forces for controllable adhesion. However, these conventional methods impose limitations when striving for both a compact design and high-speed mobility. This paper proposes a novel Vibration-Based Adhesion (VBA) technique, which utilizes a flexible disk vibrating near a surface to generate a strong and controllable attractive force without direct contact. By employing an electric motor as the vibration source, the constructed VBA system was experimentally evaluated, achieving an adhesion-to-weight ratio exceeding 51 times. The experimental results demonstrate that this adhesion mechanism not only provides a high normal force but also maintains minimal shear force, making it particularly suitable for high-speed movement and heavy load applications in miniature wall-climbing robots.

SMPR: A structure-enhanced multimodal drug-disease prediction model for drug repositioning and cold start

Repositioning drug-disease relationships has always been a hot field of research. However, actual cases of biologically validated drug relocation remain very limited, and existing models have not yet fully utilized the structural information of the drug. Furthermore, most repositioning models are only used to complete the relationship matrix, and their practicality is poor when dealing with drug cold start problems. This paper proposes a structure-enhanced multimodal relationship prediction model (SMRP). SMPR is based on the SMILE structure of the drug, using the Mol2VEC method to generate drug embedded representations, and learn disease embedded representations through heterogeneous network graph neural networks. Ultimately, a drug-disease relationship matrix is constructed. In addition, to reduce the difficulty of users' use, SMPR also provides a cold start interface based on structural similarity based on reposition results to simply and quickly predict drug-related diseases. The repositioning ability and cold start capability of the model are verified from multiple perspectives. While the AUC and ACUPR scores of repositioning reach 99% and 61% respectively, the AUC of cold start achieve 80%. In particular, the cold start Recall indicator can reach more than 70%, which means that SMPR is more sensitive to positive samples. Finally, case analysis is used to verify the practical value of the model and visual analysis directly demonstrates the improvement of the structure to the model. For quick use, we also provide local deployment of the model and package it into an executable program.

DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval

Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models (LM) to generate a ranked list of document identifiers (docid) for a given query. It simplifies the retrieval pipeline by replacing the large external index with model parameters. However, existing works merely learned the relationship between queries and document identifiers, which is unable to directly represent the relevance between queries and documents. To address the above problem, we propose a novel and general generative retrieval framework, namely Leveraging Document-Oriented Contrastive Learning in Generative Retrieval (DOGR), which leverages contrastive learning to improve generative retrieval tasks. It adopts a two-stage learning strategy that captures the relationship between queries and documents comprehensively through direct interactions. Furthermore, negative sampling methods and corresponding contrastive learning objectives are implemented to enhance the learning of semantic representations, thereby promoting a thorough comprehension of the relationship between queries and documents. Experimental results demonstrate that DOGR achieves state-of-the-art performance compared to existing generative retrieval methods on two public benchmark datasets. Further experiments have shown that our framework is generally effective for common identifier construction techniques.

Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting

Repetitive action counting (RAC) aims to estimate the number of class-agnostic action occurrences in a video without exemplars. Most current RAC methods rely on a raw frame-to-frame similarity representation for period prediction. However, this approach can be significantly disrupted by common noise such as action interruptions and inconsistencies, leading to sub-optimal counting performance in realistic scenarios. In this paper, we introduce a foreground localization optimization objective into similarity representation learning to obtain more robust and efficient video features. We propose a Localization-Aware Multi-Scale Representation Learning (LMRL) framework. Specifically, we apply a Multi-Scale Period-Aware Representation (MPR) with a scale-specific design to accommodate various action frequencies and learn more flexible temporal correlations. Furthermore, we introduce the Repetition Foreground Localization (RFL) method, which enhances the representation by coarsely identifying periodic actions and incorporating global semantic information. These two modules can be jointly optimized, resulting in a more discerning periodic action representation. Our approach significantly reduces the impact of noise, thereby improving counting accuracy. Additionally, the framework is designed to be scalable and adaptable to different types of video content. Experimental results on the RepCountA and UCFRep datasets demonstrate that our proposed method effectively handles repetitive action counting.

DreamFit: Garment-Centric Human Generation via a Lightweight Anything-Dressing Encoder

Diffusion models for garment-centric human generation from text or image prompts have garnered emerging attention for their great application potential. However, existing methods often face a dilemma: lightweight approaches, such as adapters, are prone to generate inconsistent textures; while finetune-based methods involve high training costs and struggle to maintain the generalization capabilities of pretrained diffusion models, limiting their performance across diverse scenarios. To address these challenges, we propose DreamFit, which incorporates a lightweight Anything-Dressing Encoder specifically tailored for the garment-centric human generation. DreamFit has three key advantages: (1) \textbf{Lightweight training}: with the proposed adaptive attention and LoRA modules, DreamFit significantly minimizes the model complexity to 83.4M trainable parameters. (2)\textbf{Anything-Dressing}: Our model generalizes surprisingly well to a wide range of (non-)garments, creative styles, and prompt instructions, consistently delivering high-quality results across diverse scenarios. (3) \textbf{Plug-and-play}: DreamFit is engineered for smooth integration with any community control plugins for diffusion models, ensuring easy compatibility and minimizing adoption barriers. To further enhance generation quality, DreamFit leverages pretrained large multi-modal models (LMMs) to enrich the prompt with fine-grained garment descriptions, thereby reducing the prompt gap between training and inference. We conduct comprehensive experiments on both $768 \times 512$ high-resolution benchmarks and in-the-wild images. DreamFit surpasses all existing methods, highlighting its state-of-the-art capabilities of garment-centric human generation.

AgentPS: Agentic Process Supervision for Multi-modal Content Quality Assurance through Multi-round QA

The advanced processing and reasoning capabilities of multimodal large language models (MLLMs) have driven substantial progress in vision-language (VL) understanding tasks. However, while effective for tasks governed by straightforward logic, MLLMs often encounter challenges when reasoning over complex, interdependent logic structures. To address this limitation, we introduce \textit{AgentPS}, a novel framework that integrates Agentic Process Supervision into MLLMs via multi-round question answering during fine-tuning. \textit{AgentPS} demonstrates significant performance improvements over baseline MLLMs on proprietary TikTok datasets, due to its integration of process supervision and structured sequential reasoning. Furthermore, we show that replacing human-annotated labels with LLM-generated labels retains much of the performance gain, highlighting the framework's practical scalability in industrial applications. These results position \textit{AgentPS} as a highly effective and efficient architecture for multimodal classification tasks. Its adaptability and scalability, especially when enhanced by automated annotation generation, make it a powerful tool for handling large-scale, real-world challenges.

COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework

Recently, with the emergence of recent Multimodal Large Language Model (MLLM) technology, it has become possible to exploit its video understanding capability on different classification tasks. In practice, we face the difficulty of huge requirements for GPU resource if we need to deploy MLLMs online. In this paper, we propose COEF-VQ, a novel cascaded MLLM framework for better video quality understanding on TikTok. To this end, we first propose a MLLM fusing all visual, textual and audio signals, and then develop a cascade framework with a lightweight model as pre-filtering stage and MLLM as fine-consideration stage, significantly reducing the need for GPU resource, while retaining the performance demonstrated solely by MLLM. To demonstrate the effectiveness of COEF-VQ, we deployed this new framework onto the video management platform (VMP) at TikTok, and performed a series of detailed experiments on two in-house tasks related to video quality understanding. We show that COEF-VQ leads to substantial performance gains with limit resource consumption in these two tasks.

Hymba: A Hybrid-head Architecture for Small Language Models

We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, while SSM heads enable efficient context summarization. Additionally, we introduce learnable meta tokens that are prepended to prompts, storing critical information and alleviating the "forced-to-attend" burden associated with attention mechanisms. This model is further optimized by incorporating cross-layer key-value (KV) sharing and partial sliding window attention, resulting in a compact cache size. During development, we conducted a controlled study comparing various architectures under identical settings and observed significant advantages of our proposed architecture. Notably, Hymba achieves state-of-the-art results for small LMs: Our Hymba-1.5B-Base model surpasses all sub-2B public models in performance and even outperforms Llama-3.2-3B with 1.32% higher average accuracy, an 11.67x cache size reduction, and 3.49x throughput.

Design, manufacturing, and inverse dynamic modeling of soft parallel robots actuated by dielectric elastomer actuators

Soft parallel robots with their manipulation safety and low commercial cost show a promising future for delicate operations and safe human-robot interactions. However, promoting the use of electroactive polymers (EAPs) is still challenging due to the under-improving quality of the product and the dynamic modelling of the collaborations between multiple actuators. This article presents the design, fabrication, modelling and control of a parallel kinematics Delta robot actuated by dielectric elastomer actuators (DEAs). The trade-off between the actuation force and stroke is retaken by an angular stroke amplification mechanism, and the weight of the robot frame is reduced by utilizing 3D puzzling strip structures. A generic way of constructing a high-stability conductive paint on a silicon-based film has been achieved by laser scanning the DE-film and then sandwiching a conductive particle-based electrode with a paint which is mixed by the particles and photosensitive resin. Compared to the wildly used carbon grease, the fabricated electrode shows a higher consistency in its dynamic behaviour before and after the on-stand test. Finally, to predict the output force and inverse motion of the robot end effector, we constructed the inverse dynamic model by introducing an expanded Bergstrom-Boyce model to the constitutive behavior of the dielectric film. The experimental results show a prediction of robot output force with RSME of 12.4% when the end effector remains stationary, and a well-followed trajectory with less than RSME 2.5%.