Leaked recording reveals Campbell's exec's sickening remarks about iconic soup's ingredients How Lauren Sanchez would REALLY look if she'd never had rumored plastic surgery Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal These women have lost as much as nine stone WITHOUT jabs: Now they reveal secret to their stunning success, the extraordinary event that brought them together and how it's changed their lives... Judge throws out Comey and James cases as Trump's beauty queen prosecutor is humiliated Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Kate Gosselin's ex Jon is seen at his splashy wedding for the first time as son Collin weighs in on his siblings not attending Fugitive'Slender Man' stabber Morgan Geyser snapped'just Google me' when asked for ID by cops who found her with MUCH older lover It all seems to be falling apart now! Pete Hegseth drops hammer on Democrat senator in'sedition' storm as court martial looms after Trump's execution threat Sabrina Carpenter looks unrecognisable in throwback snap from seven years ago as fans call her rebranding'wild' Neuralink's'Patient 4' feared missing months after getting revolutionary brain chip... now his wife tells the REAL heartbreaking story NFL's first transgender cheerleader makes explosive allegation against Carolina Panthers Slash your cholesterol by a third in just a month... hundreds of thousands are on a new diet that's transforming lives. California was shaken early Monday as a series of earthquakes struck in quick succession, raising concern in the seismically active region. At least seven tremors have been reported, ranging in magnitude from 1.1 to 4.1, with the epicenter near The Geysers.

Previous models for learning the semantic vectors of items and their groups, such as words, sentences, nodes, and graphs, using distributed representation have been based on the assumption that the basic sense of an item corresponds to one vector composed of dimensions corresponding to hidden contexts in the target real world, from which multiple senses of the item are obtained by conforming to lexical databases or adapting to the context. However, there may be multiple senses of an item, which are hardly assimilated and change or evolve dynamically following the contextual shift even within a document or a restricted period. This is a process similar to the evolution or adaptation of a living entity with/to environmental shifts. Setting the scope of disambiguation of items for sensemaking, the author presents a method in which a word or item in the data embraces multiple semantic vectors that evolve via interaction with others, similar to a cell embracing chromosomes crossing over with each other. We obtained two preliminary results: (1) the role of a word that evolves to acquire the largest or lower-middle variance of semantic vectors tends to be explainable by the author of the text; (2) the epicenters of earthquakes that acquire larger variance via crossover, corresponding to the interaction with diverse areas of land crust, are likely to correspond to the epicenters of forthcoming large earthquakes. Keywords: evolutionary computing, diambiguity, items, words, earthquakes 1 Introduction Semantic vectors were invented in the 1960s, and have been applied to natural language analysis and large language models [Camacho-Collados and Pilevar 2018].

Teal's workers in Salt Lake City assemble their drones by hand, sitting at several long tables in an open workshop. There is no need for conveyor belts or automated production at their current scale. They do have one robot arm in the back, which is used to calibrate each drone's navigation systems. After calibration, they take the drones out to a grassy patch out front to run them through test flights, with the snow-capped Wasatch Mountains in the distance.

The National Center for High-Performance Computing (NCHC) and Academia Sinica have developed an artificial intelligence (AI) model that could help researchers predict earthquakes one day in advance. The model could predict earthquakes based on precursors to tectonic activity, researchers said. The research team, led by Academia Sinica researcher Lee Lou-chuang (李羅權) and NCHC associate researcher Tsai Tsung-che (蔡宗哲), developed an AI model using total electron content (TEC) data and the Taiwania 2 supercomputer. The model could predict a magnitude 6 or higher earthquake one day in advance by analyzing data from the previous 30 days, they said. Past studies also found that atmospheric TEC within a 50km radius of the epicenter of an earthquake show signs of change prior to a large earthquake, the Central Weather Bureau's Seismological Center said, adding that TEC above Taiwan proper was low just before the 1999 Jiji earthquake.

This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such coefficient of change represents the damage numerically an urban environment had to quantify the overall damage in the built environment. Such an index can give the government an estimate of the number of affected households and perhaps the extent of housing damage.

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction of components may be accessible or observable after a disruptive event. Past work has looked into inferring failed components given point probes, i.e. with a direct sample of failed components. In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain. We formulate this novel problem using the Minimum Description Length (MDL) principle, and then present a greedy algorithm that minimizes MDL cost effectively. We evaluate our algorithm on domain-expert simulations of real networks in the aftermath of an earthquake. Our algorithm successfully identify failed components, especially the critical ones affecting the overall system performance.

Qiang Yang, a professor at the Hong Kong University of Science and Technology, was looking forward to AAAI, one of the big artificial intelligence conferences, which takes place in New York this week. Yang was due to present an award-winning paper describing a way for an AI algorithm to perform image recognition by drawing from different datasets without ever revealing their contents. He decided to cancel his trip due to the global health emergency triggered by the coronavirus in China. Yang estimates that around 800 attendees from mainland China, about a fifth of the 4,000 registered for the conference, will miss the event due to a travel ban imposed by the US on Monday. "It's a big pity," Yang says via WeChat from his home in Hong Kong.

Kulldorff's (1997) seminal paper on spatial scan statistics (SSS) has led to many methods considering different regions of interest, different statistical models, and different approximations while also having numerous applications in epidemiology, environmental monitoring, and homeland security. SSS provides a way to rigorously test for the existence of an anomaly and provide statistical guarantees as to how "anomalous" that anomaly is. However, these methods rely on defining specific regions where the spatial information a point contributes is limited to binary 0 or 1, of either inside or outside the region, while in reality anomalies will tend to follow smooth distributions with decaying density further from an epicenter. In this work, we propose a method that addresses this shortcoming through a continuous scan statistic that generalizes SSS by allowing the point contribution to be defined by a kernel. We provide extensive experimental and theoretical results that shows our methods can be computed efficiently while providing high statistical power for detecting anomalous regions.

Is workplace surveillance about improving productivity or simply a way to control staff and weed out poor performers? Courtney Hagen Ford, 34, left her job working as a bank teller because she found the surveillance she was under was "dehumanising". Her employer logged her keystrokes and used software to monitor how many of the customers she helped went on to take out loans and fee-paying accounts. "The sales pressure was relentless," she recalls. She decided selling fast food would be better, but ironically, left the bank to do a doctorate in surveillance technology.

Global e-commerce activity is expanding fast with developing economies gaining prominence. E-Commerce: Payments through mobile, internet and cards – This has vastly transformed the way of doing business in the modern day. We all have come across or most likely heard of artificial intelligence in some or the other form unless some of us been burrowed deep underground for the last couple of years. Customer segmentation became essential and first priority and super easy with AI to identify systematic groups of customers to make marketing more precise. In our todays online shopping how much is artificial intelligence.