WASHINGTON, Feb 1 (Reuters) - U.S. investors including the investment arms of Intel Corp (INTC.O) and Qualcomm Inc (QCOM.O) accounted for nearly a fifth of investments in Chinese artificial intelligence companies from 2015 to 2021, a report showed on Wednesday. The document, released by CSET, a tech policy group at Georgetown University, comes amid growing scrutiny of U.S. investments in AI, Quantum and semiconductors, as the Biden administration prepares to unveil new restrictions on U.S. funding of Chinese tech companies. According to the report, 167 U.S. investors took part in 401 transactions, or roughly 17% of the investments into Chinese AI companies in the period. Those transactions represented a total $40.2 billion in investment, or 37% of the total raised by Chinese AI companies in the 6-year period. It was not clear from the report, which pulled information from data provider Crunchbase, what percentage of the funding came from the U.S. firms.

House Oversight Committee Chairman James Comer told reporters Tuesday he believes Hunter Biden was "in proximity" to the classified documents found in President Biden's garage. Hunter Biden's lawyers called on federal and state prosecutors across the country to open criminal investigations into his critics on Wednesday – and in doing so, acknowledged that the notorious laptop is indeed Hunter's. Biden's attorney, Abbe Lowell, wrote letters to the Justice Department and the Delaware attorney general calling for investigations into Rudy Giuliani, Steve Bannon and John Mac Isaac, who owns the computer repair shop where Biden is said to have left his laptop. Biden's lawyers also sent cease and desist letters to others who obtained and disseminated the laptop's contents. Lowell argued in the letters that Mac Isaac and the others had no right to inspect the contents of Biden's laptop, much less make copies of it to share with the media.

An Iranian military facility was hit with a drone strike Jan. 29, 2023. Iran on Thursday blamed Israel for a drone strike that hit a military factory near the city of Isfahan over the weekend and threatened revenge, saying it "reserves its legitimate and inherent right" to respond. Reports surfaced earlier this week citing a U.S. official who attributed the attack to Israel, but Tehran's accusation could prolong what appears to have become a covert war between the Middle Eastern nations. "Early investigations suggest that the Israeli regime was responsible for this attempted act of aggression," Iranian Ambassador Amir Saeid Iravani said in a letter to the United Nations, though he did not cite the evidence Tehran has to back its accusations. Eyewitness footage shows what is said to be the moment of an explosion at a military industry factory in Isfahan, Iran, Jan. 29, 2023, in this still image obtained from a video.

A new Pentagon project envisages automated, coordinated attacks by swarms of many types of drones that operate in the air, on the ground and in the water. The idea is raising concerns about whether human oversight of such a "swarm of swarms" would be possible. The Autonomous Multi-Domain Adaptive Swarms-of-Swarms (AMASS) is a project from US defence research agency DARPA.

Medical applications of AI are replete with promise, but stymied by opacity: with lives on the line, concerns over AI models' often-inscrutable reasoning – and as a result, possible biases embedded in those models – largely prevent scaled applications of AI for medical treatment, no matter how promising the underlying research. Recently, researchers from Mederrata Research (a nonprofit aiming to use data-driven techniques to preempt medical errors), Sound Prediction (a digital health informatics company aiming to create transparent AI models) and the NIH leveraged supercomputing at the Pittsburgh Supercomputing Center (PSC) to design a method for recreating the benefits of AI models in medicine with more explicability. The root of the team's approach is multilevel modeling (MLM, not to be confused with multilevel marketing). Through MLM, groups of similar cases are bundled and differential equations are used to identify a limited set of controlling factors for each case, allowing for easier – and more consistent – identification of the model's reasoning compared to post-hoc analyses of more opaque models. The researchers designed and applied the AI toward predicting – and explaining – readmission and death among Medicare patients following a hospital visit, training the model on three years of data (2009-2011) and testing it on a fourth (2012).

Iran has blamed Israel for last week's drone attack on a military factory near the central city of Isfahan, promising revenge for what appeared to be the latest episode in a long-running covert war. The Iranian claim, carried by the semi-official ISNA news agency on Thursday, corroborates remarks made by United States officials following the attack. The attack came amid tension between Iran and the West over Tehran's nuclear activity and its supply of arms – including long-range "suicide drones" – for Russia's war in Ukraine, as well as months of anti-government demonstrations at home. In a letter to the United Nations chief, Iran's UN envoy, Amir Saeid Iravani, said "primary investigation suggested Israel was responsible" for Saturday night's attack, which Tehran had said caused no casualties or serious damage. "Iran reserves its legitimate and inherent right to defend its national security and firmly respond to any threat or wrongdoing of the Zionist regime [Israel] wherever and whenever it deems necessary," Iravani said in the letter.

Artificial intelligence (AI) is revolutionizing the way we view cybersecurity. While there are many benefits to AI, it comes with a range of challenges that organizations must address. This article will discuss how AI is changing the way we approach cybersecurity. We'll also cover some of the ways in which artificial intelligence can improve our security posture, as well as some of its drawbacks. AI is changing the way we approach cybersecurity.

The rapid ascent in carbon dioxide emissions is a major cause of global warming and climate change, which pose a huge threat to human survival and impose far-reaching influence on the global ecosystem. Therefore, it is very necessary to effectively control carbon dioxide emissions by accurately predicting and analyzing the change trend timely, so as to provide a reference for carbon dioxide emissions mitigation measures. This paper is aiming to select a suitable model to predict the near-real-time daily emissions based on univariate daily time-series data from January 1st, 2020 to September 30st, 2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic Aviation, International Aviation) in China. We proposed six prediction models, which including three statistical models: Grey prediction (GM(1,1)), autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX); three machine learning models: artificial neural network (ANN), random forest (RF) and long short term memory (LSTM). To evaluate the performance of these models, five criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination () are imported and discussed in detail. In the results, three machine learning models perform better than that three statistical models, in which LSTM model performs the best on five criteria values for daily emissions prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value, 14.8291% MAPE value and 0.9844 value.

I measure adaptation to climate change by comparing elasticities from short-run and long-run changes in damaging weather. I propose a debiased machine learning approach to flexibly measure these elasticities in panel settings. In a simulation exercise, I show that debiased machine learning has considerable benefits relative to standard machine learning or ordinary least squares, particularly in high-dimensional settings. I then measure adaptation to damaging heat exposure in United States corn and soy production. Using rich sets of temperature and precipitation variation, I find evidence that short-run impacts from damaging heat are significantly offset in the long run. I show that this is because the impacts of long-run changes in heat exposure do not follow the same functional form as short-run shocks to heat exposure.

Fast and efficient transport protocols are the foundation of an increasingly distributed world. The burden of continuously delivering improved communication performance to support next-generation applications and services, combined with the increasing heterogeneity of systems and network technologies, has promoted the design of Congestion Control (CC) algorithms that perform well under specific environments. The challenge of designing a generic CC algorithm that can adapt to a broad range of scenarios is still an open research question. To tackle this challenge, we propose to apply a novel Reinforcement Learning (RL) approach. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return and models the learning process as an infinite-horizon task. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch that researchers have encountered when applying RL to CC. We evaluated our solution on the task of file transfer and compared it to TCP Cubic. While further research is required, results have shown that MARLIN can achieve comparable results to TCP with little hyperparameter tuning, in a task significantly different from its training setting. Therefore, we believe that our work represents a promising first step toward building CC algorithms based on the maximum entropy RL framework.