Point clouds are widely regarded as one of the best dataset types for urban mapping purposes. Hence, point cloud datasets are commonly investigated as benchmark types for various urban interpretation methods. Yet, few researchers have addressed the use of point cloud benchmarks for fa\c{c}ade segmentation. Robust fa\c{c}ade segmentation is becoming a key factor in various applications ranging from simulating autonomous driving functions to preserving cultural heritage. In this work, we present a method of enriching existing point cloud datasets with fa\c{c}ade-related classes that have been designed to facilitate fa\c{c}ade segmentation testing. We propose how to efficiently extend existing datasets and comprehensively assess their potential for fa\c{c}ade segmentation. We use the method to create the TUM-FA\c{C}ADE dataset, which extends the capabilities of TUM-MLS-2016. Not only can TUM-FA\c{C}ADE facilitate the development of point-cloud-based fa\c{c}ade segmentation tasks, but our procedure can also be applied to enrich further datasets.

Object detection has seen remarkable progress in recent years with the introduction of Convolutional Neural Networks (CNN). Object detection is a multi-task learning problem where both the position of the objects in the images as well as their classes needs to be correctly identified. The idea here is to maximize the overlap between the ground-truth bounding boxes and the predictions i.e. the Intersection over Union (IoU). In the scope of work seen currently in this domain, IoU is approximated by using the Huber loss as a proxy but this indirect method does not leverage the IoU information and treats the bounding box as four independent, unrelated terms of regression. This is not true for a bounding box where the four coordinates are highly correlated and hold a semantic meaning when taken together. The direct optimization of the IoU is not possible due to its non-convex and non-differentiable nature. In this paper, we have formulated a novel loss namely, the Smooth IoU, which directly optimizes the IoUs for the bounding boxes. This loss has been evaluated on the Oxford IIIT Pets, Udacity self-driving car, PASCAL VOC, and VWFS Car Damage datasets and has shown performance gains over the standard Huber loss.

Modelling functional time series, time series of random functions defined within a finite interval, has became one of the main frontiers of developments in time series models. Various functional linear and nonlinear time series models have been proposed and extensively studied in the past two decades (e.g., Bosq, 2000; Hörmann and Kokoszka, 2010; Horváth and Kokoszka, 2012; Hörmann, Horváth and Reeder, 2013; Li, Robinson and Shang, 2020). These models together with relevant methodologies have been applied to various fields such as biology, demography, economics, environmental science and finance. However, the model frameworks and methodologies developed in the aforementioned literature heavily rely on the stationarity assumption, which is often rejected when testing the functional time series data in practice. For example, Horváth, Kokoszka and Rice (2014) find evidence of nonstationarity for intraday price curves of some stocks collected in the US market; Aue, Rice and Sönmez (2018) reject the null hypothesis of stationarity for the temperature curves collected in Australia; and Li, Robinson and Shang (2023) reveal evidence of nonstationary feature for the functional time series constructed from the age-and sex-specific life-table death counts. It thus becomes imperative to test whether the collected functional time series are stationary. The primary interest of this paper is to test whether there exist structural breaks in the mean function over time and subsequently estimate locations of breaks if they do exist. There have been increasing interests on detecting and estimating structural breaks in functional time series. Broadly speaking, there are two types of detection techniques.

Earlier this year Microsoft revealed a new artificial intelligence (AI) system which could recreate a person's voice after listening to them speak for only three seconds. It was a sign of just how quickly AI could be used to convincingly replicate a key piece of someone's identity. Here is an example of someone's three-second voice prompt, which was fed into the system: And here is what the AI, known as VALL-E, generated when it was asked to recreate that person's voice while saying the following phrase: "The others resented postponement, but it was just his scruples that charmed me." Vice reporter Joseph Cox later used similar AI technology to reportedly gain access to a bank account with an AI-replicated version of his own voice. In March, Guardian Australia journalist Nick Evershed said he was able to use an AI version of his own voice to gain access to his Centrelink self-service account, which raised concerns for some security experts.

Warning: Some readers may find some of the scenes described in this article disturbing. Kyiv, Ukraine – Ivan Ukraintsev, a stern-faced insurance broker turned director of a wartime charity providing crucial aid to Ukraine's military forces, is on a mission: to help Ukraine win the drone war. He is a polite but no-nonsense character, and he is here to talk about drones. "If we [Ukraine] had enough drones, we could end this war in two months," he says firmly. Ivan, who heads up the charity Starlife, had recently returned from overseeing a drone delivery to Bakhmut, a city in eastern Ukraine that has become the focal point for months of bloody battles between Ukrainian and Russian forces. Trench warfare, pockmarked and corpse-ridden swathes of no man's land, and constant artillery bombardments have drawn comparisons to battlefield conditions during World War I.

When OpenAI, an artificial intelligence startup, released its ChatGPT tool in November, it seemed like little more than a toy--an automated chat engine that could spit out intelligent-sounding responses on a wide range of topics for the amusement of you and your friends. In many ways, it didn't seem much more sophisticated than previous experiments with AI-powered chat software, such as the infamous Microsoft bot Tay--which was launched in 2016, and quickly morphed from a novelty act into a racism scandal before being shut down--or even Eliza, the first automated chat program, which was introduced way back in 1966. Since November, however, ChatGPT and an assortment of nascent counterparts have sparked a debate not only over the extent to which we should trust this kind of emerging technology, but how close we are to what experts call "Artificial General Intelligence," or AGI, which, they warn, could transform society in ways that we don't understand yet. Bill Gates, the billionaire cofounder of Microsoft, wrote recently that artificial intelligence is "as revolutionary as mobile phones and the Internet." The new wave of AI chatbots has already been blamed for a host of errors and hoaxes that have spread around the internet, as well as at least one death: La Libre, a Belgian newspaper, reported that a man died by suicide after talking with a chat program called Chai; based on statements from the man's widow and chat logs, the software appears to have encouraged the user to kill himself. When Pranav Dixit, a reporter at BuzzFeed, used FreedomGPT--another program based on an open source version of ChatGPT, which, according to its creator, has no guardrails around sensitive topics--that chatbot "praised Hitler, wrote an opinion piece advocating for unhoused people in San Francisco to be shot to solve the city's homeless crisis, [and] used the n-word."

Hey, have you heard the news? Generative AI is taking over the world and is here to stay! "According to Tractica, the market for AI software, hardware and services is expected to skyrocket from $644 million in 2016 to a whopping $37.3 billion by 2025." The possibilities are endless for AI, and as technology continues to evolve and take new shapes, it can be challenging for businesses to keep up. In this guide, we will break down one exciting application of Ai, Generative Ai, with use cases and future projections to help you determine whether to implement it in your business. So, whether you're a tech enthusiast or simply curious about the future of AI, we're here to keep you up to date on the latest trends and technologies to make your business successful in the digital age. Generative AI refers to a class of artificial intelligence algorithms designed to generate new content, whether images, text, music or some other type of media. These algorithms learn patterns and structures from existing data and then use that knowledge to generate new, previously unseen examples similar in style or content to the original data. These algorithms have been used for many applications, including creating realistic images, writing poetry and fiction, composing music, and generating code.

Spatial objects often come with textual information, such as Points of Interest (POIs) with their descriptions, which are referred to as geo-textual data. To retrieve such data, spatial keyword queries that take into account both spatial proximity and textual relevance have been extensively studied. Existing indexes designed for spatial keyword queries are mostly built based on the geo-textual data without considering the distribution of queries already received. However, previous studies have shown that utilizing the known query distribution can improve the index structure for future query processing. In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload. One key challenge is how to utilize both structured spatial attributes and unstructured textual information during learning the index. We first divide the data objects into partitions, aiming to minimize the processing costs of the given query workload. We prove the NP-hardness of the partitioning problem and propose a machine learning model to find the optimal partitions. Then, to achieve more pruning power, we build a hierarchical structure based on the generated partitions in a bottom-up manner with a reinforcement learning-based approach. We conduct extensive experiments on real-world datasets and query workloads with various distributions, and the results show that WISK outperforms all competitors, achieving up to 8x speedup in querying time with comparable storage overhead.

AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as to whether actually follow that advice: they have to "appropriately" rely on correct and turn down incorrect advice. However, current research on appropriate reliance still lacks a common definition as well as an operational measurement concept. Additionally, no in-depth behavioral experiments have been conducted that help understand the factors influencing this behavior. In this paper, we propose Appropriateness of Reliance (AoR) as an underlying, quantifiable two-dimensional measurement concept. We develop a research model that analyzes the effect of providing explanations for AI advice. In an experiment with 200 participants, we demonstrate how these explanations influence the AoR, and, thus, the effectiveness of AI advice. Our work contributes fundamental concepts for the analysis of reliance behavior and the purposeful design of AI advisors.

Google AlphaGos win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education). These advances have resulted in numerous new concepts and technologies, which are too many for people to catch up to and even make them confused, especially for newcomers to the ML area. This paper is aimed to present a clear picture of the state-of-the-art of the existing ML technologies with a comprehensive survey. We lay out this survey by viewing ML as a MLOps (ML Operations) process, where the key concepts and activities are collected and elaborated with representative works and surveys. We hope that this paper can serve as a quick reference manual (a survey of surveys) for newcomers (e.g., researchers, practitioners) of ML to get an overview of the MLOps process, as well as a good understanding of the key technologies used in each step of the ML process, and know where to find more details.