-- Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the V ehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-A ware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world V ehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles. Safe autonomous driving and Advanced Driver Assistance Systems (ADAS) require an accurate estimation for the ego vehicle state.

We present OuroMamba, the first data-free post-training quantization (DFQ) method for vision Mamba-based models (VMMs). We identify two key challenges in enabling DFQ for VMMs, (1) VMM's recurrent state transitions restricts capturing of long-range interactions and leads to semantically weak synthetic data, (2) VMM activations exhibit dynamic outlier variations across time-steps, rendering existing static PTQ techniques ineffective. To address these challenges, OuroMamba presents a two-stage framework: (1) OuroMamba-Gen to generate semantically rich and meaningful synthetic data. It applies contrastive learning on patch level VMM features generated through neighborhood interactions in the latent state space, (2) OuroMamba-Quant to employ mixed-precision quantization with lightweight dynamic outlier detection during inference. In specific, we present a thresholding based outlier channel selection strategy for activations that gets updated every time-step. Extensive experiments across vision and generative tasks show that our data-free OuroMamba surpasses existing data-driven PTQ techniques, achieving state-of-the-art performance across diverse quantization settings. Additionally, we implement efficient GPU kernels to achieve practical latency speedup of up to 2.36x. Code will be released soon.

Memelements have emerged as a promising class of devices, demonstrating remarkable performance, particularly when deployed in crossbar architectures [1-3]. Their integration into these structures significantly enhances the efficiency of vector-matrix multiplication (VMM) by enabling the parallel execution of product and summation operations through the devices. This capability is particularly beneficial in the domain of convolutional neural networks (CNNs), where extensive matrix operations are fundamental to both training and inference processes. The combination of in-memory computing (IMC) architectures with the adjustable analog memductance of memelements further contributes to power-efficient VMM and training, enabling the development of highly integrated memory architectures. Consequently, a wide array of CNN hardware designs utilizing memelements-based VMM accelerators [3-6] has been proposed, with their effectiveness consistently demonstrated in various studies. Neuromorphic computing, modeled after brain-like processes and grounded in artificial neural networks, presents effective solutions for a wide range of computationally demanding tasks. Originally conceptualized in the 1980s [7-8], this field has seen substantial progress with the advent of memristive devices [9] and the introduction of convolutional layers in deep neural networks [10-11]. These innovations have facilitated the development of various resistive neuromorphic systems that employ materials such as oxides [12-14], phase-change memory [15], spintronic devices [16-17], and ferroelectric components, including ferroelectric tunnel junctions [18-19] and ferroelectric field-effect transistors (FeFETs) [20-21].

These methods analysis by performing integration and clustering are notorious for finding structure where no structure exists simultaneously. We adapt the VampPrior (Tomczak (Chari & Pachter, 2023). When the embedding function & Welling, 2018) into a Dirichlet process does not account for systematic shifts in expression profiling Gaussian mixture model, resulting in the Vamp-between datasets and/or batches that use different scRNAseq Prior Mixture Model (VMM), a novel prior for technologies, misleading structure can arise, confounding DLVMs. We propose an inference procedure that standard analysis pipelines. Accordingly, Lähnemann alternates between variational inference and Empirical et al. (2020) identify atlas-level integration as one of the Bayes to cleanly distinguish variational grand challenges of single-cell data science.

Server virtualization in the form of virtual machines (VMs) with the use of a hypervisor or a Virtual Machine Monitor (VMM) is an essential part of cloud computing technology to provide infrastructure-as-a-service (IaaS). A fault or an anomaly in the VMM can propagate to the VMs hosted on it and ultimately affect the availability and reliability of the applications running on those VMs. Therefore, identifying and eventually resolving it quickly is highly important. However, anomalous VMM detection is a challenge in the cloud environment since the user does not have access to the VMM. This paper addresses this challenge of anomalous VMM detection in the cloud-based environment without having any knowledge or data from VMM by introducing a novel machine learning-based algorithm called IAD: Indirect Anomalous VMMs Detection. This algorithm solely uses the VM's resources utilization data hosted on those VMMs for the anomalous VMMs detection. The developed algorithm's accuracy was tested on four datasets comprising the synthetic and real and compared against four other popular algorithms, which can also be used to the described problem. It was found that the proposed IAD algorithm has an average F1-score of 83.7% averaged across four datasets, and also outperforms other algorithms by an average F1-score of 11\%.