The advent of multi-omics technologies has revolutionized biomedical research, enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic layers [Wang et al., 2021a]. This integrative paradigm has yielded unprecedented insights into the molecular architecture of complex diseases, particularly neurodegenerative disorders such as Alzheimer's disease. However, multi-omics datasets are often characterized by high-dimensional variables and limited sample sizes--a configuration known as high-dimension low-sample size (HDLSS). Under such constraints, conventional statistical methods suffer from reduced power and unrealistic assumptions [Fan and Lv, 2008], while deep learning models may exhibit overfitting and lack interpretability [LeCun et al., 2015]. Recent advances in dementia biomarker discovery have embraced multi-omics integration. For example, Iturria-Medina [2018] fused neuroimaging and omics data to identify disease-relevant signatures. Zhang [2020] employed transcriptomic-proteomic fusion to uncover molecular markers, and Lee [2022] demonstrated the discriminative utility of metabolomic features in Alzheimer's pathology. These efforts build upon foundational work in integrative omics [Hasin, 2017, Karczewski and Snyder, 2018], yet challenges persist in elucidating latent gene networks and selecting statistically robust features amidst inter-feature dependencies.

Training is fully supervised, We generate abstractions of buildings, reflecting the essential based on a dataset of procedural buildings paired aspects of their geometry and structure, by learning with corresponding point cloud simulations. We develop to invert procedural models. We first build a dataset of various technical components tailored to the generation of abstract procedural building models paired with simulated abstractions. This includes the design of a programmatic point clouds and then learn the inverse mapping through a language to efficiently represent abstractions, its combination transformer. Given a point cloud, the trained transformer with a technique to guarantee transformer outputs consistent then infers the corresponding abstracted building in terms with the structure imposed by this language, and an of a programmatic language description.

Despite the technological advancements in the construction and surveying sector, the inspection of salient features like windows in an under-construction or existing building is predominantly a manual process. Moreover, the number of windows present in a building is directly related to the magnitude of deformation it suffers under earthquakes. In this research, a method to accurately detect and count the number of windows of a building by deploying an Unmanned Aerial Vehicle (UAV) based remote sensing system is proposed. The proposed two-stage method automates the identification and counting of windows by developing computer vision pipelines that utilize data from UAV's onboard camera and other sensors. Quantitative and Qualitative results show the effectiveness of our proposed approach in accurately detecting and counting the windows compared to the existing method.

Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Existing work has combined these in simple "retrieve-then-read" pipelines in which the RM retrieves passages that are inserted into the LM prompt. To begin to fully realize the potential of frozen LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware demonstrations, search for relevant passages, and generate grounded predictions, systematically breaking down problems into small transformations that the LM and RM can handle more reliably. We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings, establishing in early evaluations new state-of-the-art in-context learning results and delivering 37-120%, 8-39%, and 80-290% relative gains against the vanilla LM (GPT-3.5), a standard retrieve-then-read pipeline, and a contemporaneous self-ask pipeline, respectively. We release DSP at https://github.com/stanfordnlp/dsp

An agricultural technology (ag-tech) startup in San Francisco, Plenty, plants its crops vertically indoors, in a year-round operation employing AI and robots that uses 95% less water and 99% less land than conventional farming. Plenty's vertical farm approach can produce the same quantity of fruits and vegetables as a 720-acre flat farm, on only two acres. "Vertical farming exists because we want to grow the world's capacity for fresh fruits and vegetables, and we know it's necessary," stated Nate Storey, cofounder and chief science officer of the startup Plenty, in an account in Intelligent Living. The yield of 400x that of flat farms makes vertical farming "not just an incremental improvement," and the fraction of water use "is also critical in a time of increasing environmental stress and climate uncertainty," Storey stated. "All of these are truly game-changers."

We consider controlling the false discovery rate for many tests with unknown correlation structure. Given a large number of hypotheses, false and missing discoveries can plague an analysis. While many procedures have been proposed to control false discovery, they either assume independent hypotheses or lack statistical power. We propose a novel method for false discovery control using null bootstrapping. By bootstrapping from the correlated null, we achieve superior statistical power to existing methods and prove that the false discovery rate is controlled. Simulated examples illustrate the efficacy of our method over existing methods. We apply our proposed methodology to financial asset pricing, where the goal is to determine which "factors" lead to excess returns out of a large number of potential factors.

Transport for NSW has built a proof-of-concept using machine learning technology from Microsoft to identify potentially dangerous traffic intersections and fast-track remediation works. The'dangerous intersections' proof-of-concept, which took place last year, analysed telematic data collected from 50 vehicles travelling on Wollongong's roads over a 10-month period. The data – sent from the vehicles at a rate of 25 records a second – was used to pinpoint five previously unknown blackspots, with the two highest-risk now slated for upgrades later this financial year. TfNSW's data discovery program lead Julianna Bodzan came up with the idea while driving down the Mount Ousley descent on the Princes Highway – a notorious, four-and-a-half kilometre stretch of road leading into North Wollongong. She said the telematics data collected from the vehicles was compared with crash data from known blackspots to discern whether or not other intersections in the coastal city were potentially risky.

With 100 million Toyota vehicles on the planet emitting greenhouse gases at a rate roughly comparable to those of France, the Toyota Motor Corporation has set a goal of reducing all tailpipe emissions by 90 percent by 2050, according to Brian Storey, who directs the Toyota Research Institute (TRI) Accelerated Materials Design and Discovery program from its Kendall Square office in Cambridge, Massachusetts. He gave the keynote address at the MIT Materials Research Laboratory's Materials Day Symposium on Oct. 9. "A rapid shift from the traditional vehicle to electric vehicles has started," Storey says. "And we want to enable that to happen at a faster pace." "Our role at TRI is to develop tools for accelerating the development of emissions-free vehicles," Storey said. He added that machine learning is helping to speed up those innovations, but the challenges are very great, so his team has to be a little humble about what it can actually accomplish.

Getting an autonomous vehicle to drive safely under idealized road conditions has technically been possible for a while now, but for the real world, the cars are going to have to learn to drive a little bit more like us. That's where Comma.ai, a startup founded by notorious iPhone hacker George Hotz, comes in. Rather than teaching its computer systems what a tree or a stop sign looks like, Comma.ai's Openpilot technology analyzes the patterns of everyday drivers to train its self-driving models. The company is pulling in millions of miles of driving data from a dashcam app called Chffr and a plug-in module called Panda, then aggregating that data to create an autonomous system that mimics human drivers.

Researchers at Princeton and Stanford believe they have shown how to end the escalating blocker/anti-blocker battle as a result of that crucial point, and in favor of user choice. While a "war to win our eyeballs" sounds like the theme of a Guillermo del Toro film, it describes the interplay between advertisers (and ad-technology companies) and the visitors who reject the panoply of tracking techniques and page bloat that come with current online ads. Some sites go beyond just trying to route around blocking techniques used by Ghostery, AdBlock Plus, and others by showing a scolding message when they detect blocking action in use. A visitor often has to disable an ad blocker or add a rules exception to proceed to a site. But Princeton and Stanford's academics have determined it's possible to identify ads with an extremely high degree of reliability without using any of the current ad-blocking tricks of identifying underlying page elements, domains, and the like, and also block counter-defenses from sites and adtech companies.