Amazon plans to begin delivering some packages by drone to homes in a few Northern California communities this year, the company said Monday. Residents of San Joaquin County farming towns Lockeford and Acampo, as well as parts of Lodi, will be able to order "thousands of everyday items" online and can expect a drone to drop them in their backyards in less than an hour, said Av Zammit, an Amazon spokesperson. The Amazon Prime Air drones can carry packages that weigh 5 pounds or less -- such as beauty and cosmetic items, office and tech supplies, batteries and household items -- and will typically be the size of a large shoebox, Zammit said. The company is building a facility in Lockeford from which the drones will launch. Though Amazon Prime Air received certification to commercially fly cargo in 2020, it is still seeking approval from the Federal Aviation Administration and county officials for its plans in San Joaquin County.

BRUSSELS – Alphabet Inc. unit Google, Facebook Inc., Twitter Inc. and other tech companies will have to take measures to counter deepfakes and fake accounts on their platforms or risk hefty fines under an updated European Union code of practice, according to an EU document seen by Reuters. The European Commission is expected to publish the updated code of practice on disinformation on Thursday as part of its crackdown against fake news. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites. If this does not resolve the issue or you are unable to add the domains to your allowlist, please see out this support page.

Artificial intelligence has transformed industrial research and development in recent decades during what scientists call "the AI revolution." The technology enables detailed simulations and high-speed modeling that can streamline the journey from drawing board to production line by speeding up or cutting out costly, time-consuming steps to a practical working prototype. But those opportunities bring a new challenge: The simplest simulation package may require hours, days and sometimes weeks of training and configuration – even for users familiar with the software's details and requirements, which often vary from one computing platform to another. The process can cause not just headaches but wasted time and effort for busy engineers and others scrambling to meet tight deadlines. Simulations performed on the Summit supercomputer at Oak Ridge National Laboratory, or ORNL, could help eliminate that problem.

Our biggest undeclared war right now doesn't involve nuclear programs or any of the other technologies that usually take up headlines when it comes to this topic. In fact, our biggest war right now takes place on a completely different battlefield-Cyberspace. Cyberspace operations can be used to achieve strategic information warfare goals; an offensive cyberattack, for example, may be used to create psychological effects in a target population. There is a war on in cyberspace. Cyberspace is entirely human-made and has been designed, created, maintained, owned, and operated both by public and private stakeholders across nations. It is continually changing in response to technology transformation.

Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.

We examine how wealth inequality and diversity in the perception of risk of a collective disaster impact cooperation levels in the context of a public goods game with uncertain and non-linear returns. In this game, individuals face a collective-risk dilemma where they may contribute or not to a common pool to reduce their chances of future losses. We draw our conclusions based on social simulations with populations of independent reinforcement learners with diverse levels of risk and wealth. We find that both wealth inequality and diversity in risk assessment can hinder cooperation and augment collective losses. Additionally, wealth inequality further exacerbates long term inequality, causing rich agents to become richer and poor agents to become poorer. On the other hand, diversity in risk only amplifies inequality when combined with bias in group assortment--i.e., high probability that agents from the same risk class play together. Our results also suggest that taking wealth inequality into account can help to design effective policies aiming at leveraging cooperation in large group sizes, a configuration where collective action is harder to achieve. Finally, we characterize the circumstances under which risk perception alignment is crucial and those under which reducing wealth inequality constitutes a deciding factor for collective welfare.

Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company's performance. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs). Whereas there has been a great progress in pre-trained language models (LMs) that learn from tremendously large corpora of textual data, they still struggle in terms of effective representations for long documents. Our work fills this critical need, namely how to develop better models to extract useful information from long textual documents and learn effective features that can leverage the soft financial and risk information for text regression (prediction) tasks. In this paper, we propose and implement a deep learning framework that splits long documents into chunks and utilizes pre-trained LMs to process and aggregate the chunks into vector representations, followed by self-attention to extract valuable document-level features. We evaluate our model on a collection of 10-K public disclosure reports from US banks, and another dataset of reports submitted by US companies. Overall, our framework outperforms strong baseline methods for textual modeling as well as a baseline regression model using only numerical data. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs in representing long documents can improve the quality of representation of textual data, and therefore, help in improving predictive analyses.

Across the world, video cameras have become an accepted feature of urban life. Many cities in China now have dense networks of them, and London and New Delhi aren't far behind. Now France is playing catch-up. Since 2015, the year of the Bataclan terrorist attacks, the number of cameras in Paris has increased fourfold. The police have used such cameras to enforce pandemic lockdown measures and monitor protests.

A stunning drone video has emerged showing a Ukrainian warship narrowly escaping a massive Russian artillery fire, some of which lands as close as 200 feet. The footage, allegedly captured by a shooting spotter drone, shows the Ukrainian vessel Yuri Olefirenko, a Polnochny-class landing ship, coming under Russian attack as it sails along the Bugsko-Dneprovsko-Limansky Canal near the port of Ochakov in Mykolaiv region. According to defense analysts, the incident happened on June 3. The warship appears to be heading to Odessa when invaders rain down missiles on it. The artillery attack covers almost the entire area around the ship, some weapons falling dangerously close to the vessel.

"The study of thinking machines teaches us more about the brain than we can learn by introspective methods. Western man is externalizing himself in the form of gadgets." We are experiencing one of the biggest refugee crises since World War II. Within weeks of the beginning of the Russian invasion of Ukraine, more than 4 million people fled the country, according to the UN Refugee Agency (UNHCR). Although international media attention is focused on Eastern Europe, this is on top of an already-desperate situation in the Global South: It is estimated that countries in that region of the world have absorbed two-thirds of an estimated 82.4 million global refugees.