Abstract--V alidation of autonomous driving systems requires a trade-off between test fidelity, cost, and scalability. While miniaturized hardware-in-the-loop (HIL) platforms have emerged as a promising solution, a systematic framework supporting rigorous quantitative analysis is generally lacking, limiting their value as scientific evaluation tools. T o address this challenge, we propose MMRHP, a miniature mixed-reality HIL platform that elevates miniaturized testing from functional demonstration to rigorous, reproducible quantitative analysis. The core contributions are threefold. First, we propose a systematic three-phase testing process oriented toward the Safety of the Intended Functionality (SOTIF) standard, providing actionable guidance for identifying the performance limits and triggering conditions of otherwise correctly functioning systems. Second, we design and implement a HIL platform centered around a unified spatiotemporal measurement core to support this process, ensuring consistent and traceable quantification of physical motion and system timing. Finally, we demonstrate the effectiveness of this solution through comprehensive experiments. The platform itself was first validated, achieving a spatial accuracy of 10.27 mm RMSE and a stable closed-loop latency baseline of approximately 45 ms. Subsequently, an in-depth Autoware case study leveraged this validated platform to quantify its performance baseline and identify a critical performance cliff at an injected latency of 40 ms. This work shows that a structured process, combined with a platform offering a unified spatio-temporal benchmark, enables reproducible, interpretable, and quantitative closed-loop evaluation of autonomous driving systems. Index T erms--Autonomous Driving, Hardware-in-the-Loop (HIL), Mixed Reality, CARLA, SOTIF, V alidation and V erifi-cation (V&V). HE commercial deployment of autonomous vehicles (A Vs) faces a critical bottleneck that has shifted from achieving basic functionality to delivering statistically convincing safety in long-tail scenarios [1].

Algorithm Design (AD) is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising solutions. Over the past three years, the integration of LLMs into AD (LLM4AD) has seen substantial progress, with applications spanning optimization, machine learning, mathematical reasoning, and scientific discovery. Given the rapid advancements and expanding scope of this field, a systematic review is both timely and necessary. This paper provides a systematic review of LLM4AD. First, we offer an overview and summary of existing studies. Then, we introduce a taxonomy and review the literature across four dimensions: the roles of LLMs, search methods, prompt methods, and application domains with a discussion of potential and achievements of LLMs in AD. Finally, we identify current challenges and highlight several promising directions for future research.

This post contains a list of the AI-related seminars that are scheduled to take place between 9 January and 28 February 2023. All events detailed here are free and open for anyone to attend virtually. Machine learning beyond the data range: extreme quantile regression Speaker: Sebastian Engelke (University of Geneva) Organised by: University of Lisbon Register here. Title to be confirmed Speaker: Mauro Maggioni (Johns Hopkins University) Organised by: University of Minnesota Check the website nearer the time for the Zoom link to join. Title to be confirmed Speaker: Alhussein Fawzi (DeepMind) Organised by: University of Lisbon Register here.

This post contains a list of the AI-related seminars that are scheduled to take place between 12 December 2022 and 31 January 2023. All events detailed here are free and open for anyone to attend virtually. Title to be confirmed Speaker: Sarah Teichmann Organised by: EPFL Check the website nearer the time for instructions on how to join. Title to be confirmed Speaker: Sebastian Engelke (University of Geneva) Organised by: University of Lisbon Register here. Title to be confirmed Speaker: Mauro Maggioni Organised by: University of Minnesota Check the website nearer the time for instructions on how to join.

For regression, Neural legally protected by a company or individual from outside use Network is selected. A neural network is a network or circuit without consent. According to researchers around the world, of biological neurons, or, a in a modern sense, an artificial Intellectual Property incentives finance, creates jobs, neural network, composed of artificial neurons or nodes optimizes social utility, plays a significant role in the (Hopfield, 1982). Neural networks can be used in different contemporary economy, and the related industries are fields, in this platform, Neural Network is mainly used for developing rapidly with huge market.

Algorithmic Transparency in Action: How and Why Do Companies Disclose Information on Algorithms? Does the Accent of an Intelligent Conversational Agent Matter? What makes users reliant on the virtual assistant?

An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL), which affects people of all age groups, including children and adults. In this study, we propose an automated system to detect various-shaped ALL blast cells from microscopic blood smears images using Deep Neural Networks (DNN). The system can detect multiple subtypes of ALL cells with an accuracy of 98 percent. Moreover, we have developed a telediagnosis software to provide real-time support to diagnose ALL subtypes from microscopic blood smears images.

Scientists have curated an Artificial Intelligence which can identify and count distress signals from chickens. The tool can be used by farmers to improve conditions for chickens raised on crowded commercial farms. As per the Food and Agriculture Organization of the United Nations, there were over 33 billion chickens around the world as of 2020. Many of them were living in poor conditions. However, now, the researchers have claimed that the AI which quantifies distress calls made by chickens housed in huge indoor sheds, can correctly distinguish these calls from other barn noises.

A magnetic spray is capable to turning objects into moving robots, which could be used to navigate drugs throughout the body. Scientists at the City University in Hong Kong revealed the innovation made of polyvinyl alcohol, gluten and iron particles. Called'M-spray,' it is capable of sticking on the targeted object and when it activates, allows the object to walk, roll and crawl using a magnetic field. The team foresees their creation being applied to pills, which doctors to move to a targeted part of the body. A magnetic spray is capable to turning objects into moving robots, which could be used to navigate drugs throughout the body.

A glue-like magnetic spray can turn objects, such as pills, into mini robots that can be controlled by magnets and navigated through the body. The sprayed objects can be made to roll, flip and crawl using a magnetic field. Shen Yajing at City University of Hong Kong and his colleagues even used the spray to animate the wings of an origami crane. "Our spray can convert various tiny objects to mini robots directly," says Yajing. The object can be flat or three-dimensional, he says, and only a thin coating of spray is required.