A Good Score Does not Lead to A Good Generative Model

Score-based Generative Models (SGMs) is one leading method in generative modeling, renowned for their ability to generate high-quality samples from complex, high-dimensional data distributions. The method enjoys empirical success and is supported by rigorous theoretical convergence properties. In particular, it has been shown that SGMs can generate samples from a distribution that is close to the ground-truth if the underlying score function is learned well, suggesting the success of SGM as a generative model. We provide a counter-example in this paper. Through the sample complexity argument, we provide one specific setting where the score function is learned well. Yet, SGMs in this setting can only output samples that are Gaussian blurring of training data points, mimicking the effects of kernel density estimation. The finding resonates a series of recent finding that reveal that SGMs can demonstrate strong memorization effect and fail to generate

Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow

While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These include both communication and sensing attacks, which potentially jeopardize not only individual vehicles but also overall traffic safety and efficiency. While much research has focused on communication attacks, sensing attacks, which are equally critical, have garnered less attention. To address this gap, this study offers a comprehensive review of potential sensing attacks and their impact on target vehicles, focusing on commonly deployed sensors in CAVs such as cameras, LiDAR, Radar, ultrasonic sensors, and GPS. Based on this review, we discuss the feasibility of integrating hardware-in-the-loop experiments with microscopic traffic simulations. We also design baseline scenarios to analyze the macro-level impact of sensing attacks on traffic flow. This study aims to bridge the research gap between individual vehicle sensing attacks and broader macroscopic impacts, thereby laying the foundation for future systemic understanding and mitigation

Sequencing-enabled Hierarchical Cooperative CAV On-ramp Merging Control with Enhanced Stability and Feasibility

This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging control framework. The proposed framework consists of a two-layer design: the upper level control sequences the vehicles to harmonize the traffic density across mainline and on-ramp segments while enhancing lower-level control efficiency through a mixed-integer linear programming formulation. Subsequently, the lower-level control employs a longitudinal distributed model predictive control (MPC) supplemented by a virtual car-following (CF) concept to ensure asymptotic local stability, l_2 norm string stability, and safety. Proofs of asymptotic local stability and l_2 norm string stability are mathematically derived. Compared to other prevalent asymptotic local-stable MPC controllers, the proposed distributed MPC controller greatly expands the initial feasible set. Additionally, an auxiliary lateral control is developed to maintain lane-keeping and merging smoothness while accommodating ramp geometric curvature. To validate the proposed framework, multiple numerical experiments are conducted. Results indicate a notable outperformance of our upper-level controller against a distance-based sequencing method. Furthermore, the lower-level control effectively ensures smooth acceleration, safe merging with adequate spacing, adherence to proven longitudinal local and string stability, and rapid regulation of lateral deviations

GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models

The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accelerators for various AI workloads remains both labor- and time-intensive. While existing design exploration and automation tools can partially alleviate the need for extensive human involvement, they still demand substantial hardware expertise, posing a barrier to non-experts and stifling AI accelerator development. Motivated by the astonishing potential of large language models (LLMs) for generating high-quality content in response to human language instructions, we embark on this work to examine the possibility of harnessing LLMs to automate AI accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework intended to democratize AI accelerator design by leveraging human natural languages instead of domain-specific languages. Specifically, we first perform an in-depth investigation into LLMs' limitations and capabilities for AI accelerator design, thus aiding our understanding of our current position and garnering insights into LLM-powered automated AI accelerator design. Furthermore, drawing inspiration from the above insights, we develop a framework called GPT4AIGChip, which features an automated demo-augmented prompt-generation pipeline utilizing in-context learning to guide LLMs towards creating high-quality AI accelerator design. To our knowledge, this work is the first to demonstrate an effective pipeline for LLM-powered automated AI accelerator generation. Accordingly, we anticipate that our insights and framework can serve as a catalyst for innovations in next-generation LLM-powered design automation tools.Comment: Accepted by ICCAD 202

Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction

Neural Radiance Field (NeRF) based 3D reconstruction is highly desirable for immersive Augmented and Virtual Reality (AR/VR) applications, but achieving instant (i.e., < 5 seconds) on-device NeRF training remains a challenge. In this work, we first identify the inefficiency bottleneck: the need to interpolate NeRF embeddings up to 200,000 times from a 3D embedding grid during each training iteration. To alleviate this, we propose Instant-3D, an algorithm-hardware co-design acceleration framework that achieves instant on-device NeRF training. Our algorithm decomposes the embedding grid representation in terms of color and density, enabling computational redundancy to be squeezed out by adopting different (1) grid sizes and (2) update frequencies for the color and density branches. Our hardware accelerator further reduces the dominant memory accesses for embedding grid interpolation by (1) mapping multiple nearby points' memory read requests into one during the feed-forward process, (2) merging embedding grid updates from the same sliding time window during back-propagation, and (3) fusing different computation cores to support the different grid sizes needed by the color and density branches of Instant-3D algorithm. Extensive experiments validate the effectiveness of Instant-3D, achieving a large training time reduction of 41x - 248x while maintaining the same reconstruction quality. Excitingly, Instant-3D has enabled instant 3D reconstruction for AR/VR, requiring a reconstruction time of only 1.6 seconds per scene and meeting the AR/VR power consumption constraint of 1.9 W.Comment: Accepted by ISCA'2

Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design

Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural Radiance Fields (NeRFs) have gained increasing popularity thanks to their cross-scene generalization capability. Despite their promise, the real-device deployment of generalizable NeRFs is bottlenecked by their prohibitive complexity due to the required massive memory accesses to acquire scene features, causing their ray marching process to be memory-bounded. To this end, we propose Gen-NeRF, an algorithm-hardware co-design framework dedicated to generalizable NeRF acceleration, which for the first time enables real-time generalizable NeRFs. On the algorithm side, Gen-NeRF integrates a coarse-then-focus sampling strategy, leveraging the fact that different regions of a 3D scene contribute differently to the rendered pixel, to enable sparse yet effective sampling. On the hardware side, Gen-NeRF highlights an accelerator micro-architecture to maximize the data reuse opportunities among different rays by making use of their epipolar geometric relationship. Furthermore, our Gen-NeRF accelerator features a customized dataflow to enhance data locality during point-to-hardware mapping and an optimized scene feature storage strategy to minimize memory bank conflicts. Extensive experiments validate the effectiveness of our proposed Gen-NeRF framework in enabling real-time and generalizable novel view synthesis.Comment: Accepted by ISCA 202

Identification of an eight-LncRNA signature as the prognostic LncRNA markers in hepatocellular carcinoma patients

Long non-coding RNA (lncRNA) signature has been reputable for the predetermination of cancer prognosis. In the present study, we constructed a lncRNA model to predict the survival outcomes for hepatocellular carcinoma (HCC) patients. Using the transcriptome data from TCGA HCC samples, we identified that NEAT1 and MALAT1 were highly expressed in HCC and other tumor subtypes compared to the adjacent normal tissues. Based on the LASSO Cox regression model, we identified an eight-lncRNA signature that significantly correlated with the overall survival and disease-free survival of the HCC training group. The prognostic value of this signature was validated using the test group. Further analysis suggested that this signature was associated with the clinicopathological parameters such as vascular tumor invasion, pathological stage, and tumor grade. Integrated functional analysis showed that these eight-lncRNAs were involved in the cell cycle, metabolic process, and immune response. In conclusion, we constructed an applicable eight-lncRNA signature that is robust and reliable for the prognosis of HCC. This signature may provide an efficient clinical prediction for HCC patients, and further study is required to uncover the function of the identified lncRNAs better

The Chinese version of the Health Professional Communication Skills Scale: Psychometric evaluation

ObjectiveThis study aims to translate the Health Professional Communication Skills Scale (HP-CSS) into Chinese and assess its psychometric properties.MethodsA total of 836 healthcare professionals were recruited. The demographic characteristics form and HP-CSS were used for data collection. The psychometric properties of HP-CSS were evaluated by examining item analysis, construct validity, known-group discriminant validity, internal consistency, and split-half reliability.ResultsIn terms of item analysis, the critical ratio (CR) of 18 items was both &gt;3 (CR ranging from 9.937 to 28.816), and the score of each item was positively correlated with the total score (r ranging from 0.357 to 0.778, P &lt; 0.001). The fit indices showed that the original correlated four-factor model of HP-CSS was adequate: χ2 =722.801; df = 126; χ2/df = 5.737; RMSEA = 0.075; CFI = 0.923; NNFI = 0.908; TLI = 0.906; IFI = 0.923. In terms of known-group discriminant validity, the HP-CSS total score was related to gender, occupation, work years, and communication skill training. Cronbach's α coefficient was 0.922, and the split-half reliability was 0.865 for the total scale.ConclusionThe Chinese version of the HP-CSS is a reliable and valid instrument to evaluate communication skills among healthcare professionals in China

Pathway analysis of the impact of health literacy, social support and self-efficacy on self-management behaviors in pregnant women with gestational diabetes mellitus

ObjectiveThe purpose of this study was to investigate the pathways by which health literacy (HL), social support, and self-efficacy influence self-management behaviors of pregnant women with Gestational diabetes mellitus (GDM) and the interrelationships between the variables.MethodsA total of 565 pregnant women with GDM was recruited. The Demographic Characteristics Form, Health Literacy Scale, Perceived Social Support Scale, General Self-efficacy Scale and GDM Self-management Behavior Scale were used for data collection. Descriptive statistics, zero-ordered correlation analysis, and multiple linear regression analysis were performed on the variables; Structural Equation Model (SEM) were constructed for pathway analysis.ResultsA positive correlation was found between health literacy, social support, self-efficacy, and self-management behaviors among pregnant women with GDM after adjusting for age, education level, income level, work status, parity, and family history of diabetes (r ranging from 0.203 to 0.533). A further multiple linear regression analysis showed that functional HL, communicative HL, critical HL, social support, and self-efficacy were all independent influences on self-management behaviors and accounted for 36.3% of the variance. Communicative HL and critical HL explained the strongest self-management behaviors (β = 0.316 and 0.255, respectively, p &lt; 0.001). The SEM model was suitable for χ2/DF = 2.860, RMSEA = 0.060, IFI = 0.953, TLI = 0.943, and CFI = 0.952. The results showed direct positive effects of health literacy on self-management behaviors and self-efficacy, direct positive effects of social support on health literacy and self-efficacy. Social support and self-efficacy have had no significant direct impact on self-management behaviors, but social support may indirectly influence self-management behaviors through the health literacy mediation role.ConclusionHealthcare providers should pay attention to the positive impacts of health literacy and social support on self-management behaviors of pregnant women with GDM. Improving the health literacy level of pregnant women with GDM should be the key point of intervention in practice, and the social support system should be fully mobilized to enhance emotional support and life support to promote the improvement of self-management behaviors