The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups.

Russia launched a large-scale drone-and-missile assault on Ukraine, killing one person in Kyiv and two in the southern port city of Odesa. At least 13 people were injured. A Ukrainian drone attack on a petrol station in the Russian city of Belgorod killed one person and injured four others, the region's governor, Vyacheslav Gladkov, said. Ukrainian President Volodymyr Zelenskyy said Russia's attack on Kyiv was "one of the biggest" in the three-year-old war. It caused several fires and damaged buildings, including St Sophia Cathedral, a UNESCO World Heritage landmark.

Understanding the semantics of individual regions or patches of unconstrained images, such as open-world object detection, remains a critical yet challenging task in computer vision. Building on the success of powerful image-level vision-language (ViL) foundation models like CLIP, recent efforts have sought to harness their capabilities by either training a contrastive model from scratch with an extensive collection of region-label pairs or aligning the outputs of a detection model with image-level representations of region proposals. Despite notable progress, these approaches are plagued by computationally intensive training requirements, susceptibility to data noise, and deficiency in contextual information. To address these limitations, we explore the synergistic potential of off-the-shelf foundation models, leveraging their respective strengths in localization and semantics. We introduce a novel, generic, and efficient architecture, named RegionSpot, designed to integrate position-aware localization knowledge from a localization foundation model (e.g., SAM) with semantic information from a ViL model (e.g., CLIP).

Recently, Cella and Martin proved how, under an assumption called consonance, a credal set (i.e. a closed and convex set of probabilities) can be derived from the conformal transducer associated with transductive conformal prediction. We show that the Imprecise Highest Density Region (IHDR) associated with such a credal set corresponds to the classical Conformal Prediction Region. In proving this result, we relate the set of probability density/mass functions (pdf/pmf's) associated with the elements of the credal set to the imprecise probabilistic concept of a cloud. As a result, we establish new relationships between Conformal Prediction and Imprecise Probability (IP) theories. A byproduct of our presentation is the discovery that consonant plausibility functions are monoid homomorphisms, a new algebraic property of an IP tool.

Herding-- one of humanity's most foundational ways of life--is a pillar of survival in West Africa's Sahel. Migratory herders usher cattle between seasonal pastures, since they rarely own land. However, these traditional ways of doing things are becoming increasingly impossible, thanks to a complex mix of climate change, politics and war. In more recent years, various Western players touting tech trends like artificial intelligence and predictive analysis have swooped in with promises to solve the region's myriad problems. But some think there could be a much simpler solution, that puts real data directly into the herders' hands. Recent advances in data collection--both from geosatellites and from herders themselves--have generated an abundance of information on ground cover quantity and quality, water availability, rain forecasts, livestock concentrations, and more.

In more recent years, various Western players touting tech trends like artificial intelligence and predictive analysis have swooped in with promises to solve the region's myriad problems. But Garbal--named after the word for a livestock market in the language of the Fulani, an ethnic group that makes up the majority of the Sahel's herders--aims to do things differently. Building on an approach pioneered by a 37-year-old American data scientist named Alex Orenstein, Garbal is focused on how humbler technologies might effectively support the 80% of Nigeriens who live off livestock and the land. "There's still this idea of'How can we use new tech?' But the tech is already there--we just need to be more intentional in applying it," Orenstein says, arguing that donor enthusiasm for shiny, complex solutions is often misplaced.

Asia is becoming the latest hunting ground for global investors in data centers, as companies from KKR & Co. to Bain Capital bet on the region's growing computing and data storage needs following an artificial intelligence boom. Like in the U.S., Asia is seeing a surge in demand for data centers as giants like Amazon and Alphabet's Google boost cloud services, the recent generative AI wave fuels data and capacity requirements, and the region's growing population spurs storage needs. Demand in Southeast Asia and North Asia is expected to expand about 25% a year through 2028, according to Cushman & Wakefield data. That compares with 14% a year in the U.S.

Market Statistics: The report provides market sizing and forecast across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2020 are considered as historical years, 2021 as the base year, 2022 as the estimated year, and years from 2023 to 2027 are considered as the forecast period. Market Segmentation & Coverage: This research report categorizes the Artificial Intelligence in Oil & Gas to forecast the revenues and analyze the trends in each of the following sub-markets: Based on Function, the market was studied across Field Services, Material Movement, Predictive Maintenance & Machine Inspection, Production Planning, Quality Control, and Reclamation. Based on Component, the market was studied across Hardware, Services, and Software.

Deep learning based 3d object detection based autonomous driving scenario. The Multiview 3D network(MV 3D) aims in taking RBG real world images as input and predict 3D oriented bounding box.The network contains 2 subnetworks The MV 3D network usually present the object detection in bird's eye view representation. The network would design a deep fusion schema to combine region wise feature from multiple view and enable interaction between intermediate layers of different path. The modern cars would feed with LIDAR sensor that play a important role in visual perception system for autonomous driving car. The camera would preserve the accurate depth information for detail semantic information.

Documents are a primary tool for record keeping, communication, collaboration, and transactions across many industries, including financial, medical, legal, and real estate. The millions of mortgage applications and hundreds of millions of W2 tax forms processed each year are just a few examples of such documents. Critical business data remains unlocked in unstructured documents such as scanned images and PDFs, and trying to get humans to read this data or even legacy OCR is tedious, expensive, and error prone. This is why we launched Amazon Textract in 2019 to help you automate your tedious document processing workflows powered by AI. Amazon Textract automatically extracts printed text, handwriting, and data from any document.