Offloading large language models (LLMs) states to host memory during inference promises to reduce operational costs by supporting larger models, longer prompts, and larger batch sizes. However, the design of existing memory offloading mechanisms does not take latency service-level objectives (SLOs) into consideration. As a result, they either lead to frequent SLO violations or underutilize host memory, thereby incurring economic loss and thus defeating the purpose of memory offloading.

Traditional methods for raising awareness of privacy protection often fail to engage users or provide hands-on insights into how privacy vulnerabilities are exploited.To address this, we incorporate an adversarial mechanic in the design of the dialogue-based serious game Cracking Aegis.Leveraging LLMs to simulate natural interactions, the game challenges players to impersonate characters and

Traditional methods for evaluating the robustness of large language models (LLMs) often rely on standardized benchmarks, which can escalate costs and limit evaluations across varied domains. This paper introduces a novel framework designed to autonomously evaluate the robustness of LLMs by incorporating refined adversarial prompts and domain-constrained knowledge guidelines in the form of knowledge graphs. Our method systematically generates descriptive sentences from domain-constrained knowledge graph triplets to formulate adversarial prompts, enhancing the relevance and challenge of the evaluation. These prompts, generated by the LLM itself and tailored to evaluate its own robustness, undergo a rigorous filtering and refinement process, ensuring that only those with high textual fluency and semantic fidelity are used. This self-evaluation mechanism allows the LLM to evaluate its robustness without the need for external benchmarks. We assess the effectiveness of our framework through extensive testing on both proprietary models like ChatGPT and open-source models such as Llama-3.1, Phi-3, and Mistral. Results confirm that our approach not only reduces dependency on conventional data but also provides a targeted and efficient means of evaluating LLM robustness in constrained domains.

Hybrid stepping motors (HSMs) have been widely used in the robotics field due to their high torque and resolution advantages over other types of stepping motors. Unfortunately, in some applications where space is limited, the motor is often expected to have higher torque density to meet design requirements. Therefore, this paper presents an enhanced stator permanent magnet HSM (ESPMHSM), which possesses PM bars in the slots of stator poles. The implementation of PM bars changes the air gap magnetic field, which affects the other main electromagnetic characteristics of the motor. Consequently, it is important to qualitatively and quantitatively analyze the position and dimensions of PM bars on the electromagnetic characteristics. Firstly, the structure of ESPMHSM is introduced and the different positions of PM bars in slots are descried. Secondly, the motor is designed and the structural parameters are determined. Thirdly, the electromagnetic characteristics including the distribution of magnetic flux and magnetic density, air-gap flux density, back electromotive force, detent torque and so on are analyzed based on the finite element method. The results show that the closer the PM bars are the air gap, the better the electromagnetic characteristics of the motor.

High-speed permanent magnet synchronous motors (PMSMs) are widely used in the robotics industry due to their advantages such as high power density, high efficiency, and compact size. However, PMSMs applied in robotic joints generate significant heat at high speeds, making effective cooling techniques essential to control temperature rise, ensure motor stability and service life, and maintain the robot's operational capability in specific applications. This study focuses on the wet rotor cooling technique for high-speed PMSMs, investigating the temperature rise calculation method based on the static rotor equivalent dynamic rotor model, as well as the effect of coolant injection on rotor wall shear friction and power loss. Simulation results confirm the rationality of the proposed calculation model and verify the effectiveness of the cooling strategy.

In this paper, a deep deterministic policy gradient (DDPG) based parameter identification method for permanent magnet synchronous motor (PMSM) is proposed for the simultaneous estimation of stator resistance, inductance, and magnetic chain. The method's efficacy is demonstrated by its ability to accurately identify motor parameters while exhibiting good robustness and convergence. This is achieved by designing a continuous action space and a multi-objective reward function in conjunction with an Actor-Critic network structure.

The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.

With the proliferation of deep learning, there exists a strong need to efficiently operate GPU clusters for deep learning production in giant AI companies, as well as for research and development (R&D) in small-sized research institutes and universities. Existing works have performed thorough trace analysis on large-scale production-level clusters in giant companies, which discloses the characteristics of deep learning production jobs and motivates the design of scheduling frameworks. However, R&D clusters significantly differ from production-level clusters in both job properties and user behaviors, calling for a different scheduling mechanism. In this paper, we present a detailed workload characterization of an R&D cluster, CloudBrain-I, in a research institute, Peng Cheng Laboratory. After analyzing the fine-grained resource utilization, we discover a severe problem for R&D clusters, resource underutilization, which is especially important in R&D clusters while not characterised by existing works. We further investigate two specific underutilization phenomena and conclude several implications and lessons on R&D cluster scheduling. The traces will be open-sourced to motivate further studies in the community.

Deep autoencoder (AE) has demonstrated promising performances in visual anomaly detection (VAD). Learning normal patterns on normal data, deep AE is expected to yield larger reconstruction errors for anomalous samples, which is utilized as the criterion for detecting anomalies. However, this hypothesis cannot be always tenable since the deep AE usually captures the low-level shared features between normal and abnormal data, which leads to similar reconstruction errors for them. To tackle this problem, we propose a self-supervised representation-augmented deep AE for unsupervised VAD, which can enlarge the gap of anomaly scores between normal and abnormal samples by introducing autoencoding transformation (AT). Essentially, AT is introduced to facilitate AE to learn the high-level visual semantic features of normal images by introducing a self-supervision task (transformation reconstruction). In particular, our model inputs the original and transformed images into the encoder for obtaining latent representations; afterward, they are fed to the decoder for reconstructing both the original image and applied transformation. In this way, our model can utilize both image and transformation reconstruction errors to detect anomaly. Extensive experiments indicate that the proposed method outperforms other state-of-the-art methods, which demonstrates the validity and advancement of our model.