The tropical cyclone formation process is one of the most complex natural phenomena which is governed by various atmospheric, oceanographic, and geographic factors that varies with time and space. Despite several years of research, accurately predicting tropical cyclone formation remains a challenging task. While the existing numerical models have inherent limitations, the machine learning models fail to capture the spatial and temporal dimensions of the causal factors behind TC formation. In this study, a deep learning model has been proposed that can forecast the formation of a tropical cyclone with a lead time of up to 60 hours with high accuracy. The model uses the high-resolution reanalysis data ERA5 (ECMWF reanalysis 5th generation), and best track data IBTrACS (International Best Track Archive for Climate Stewardship) to forecast tropical cyclone formation in six ocean basins of the world. For 60 hours lead time the models achieve an accuracy in the range of 86.9% - 92.9% across the six ocean basins. The model takes about 5-15 minutes of training time depending on the ocean basin, and the amount of data used and can predict within seconds, thereby making it suitable for real-life usage.

Teenagers have always found ways around doing the hard work of actual learning. CliffsNotes date back to the 1950s, "No Fear Shakespeare" puts the playwright into modern English, YouTube offers literary analysis and historical explication from numerous amateurs and professionals, and so on. For as long as those shortcuts have existed, however, one big part of education has remained inescapable: writing. Barring outright plagiarism, students have always arrived at that moment when they're on their own with a blank page, staring down a blinking cursor, the essay waiting to be written. Now that might be about to change.

Supervisors in San Francisco voted Tuesday to give city police the ability to use potentially lethal, remote-controlled robots in emergency situations -- following an emotionally charged debate that reflected divisions on the politically liberal board over support for law enforcement. The vote was 8-3, with the majority agreeing to grant police the option despite strong objections from civil liberties and other police oversight groups. Opponents said the authority would lead to the further militarization of a police force already too aggressive with poor and minority communities. Supervisor Connie Chan, a member of the committee that forwarded the proposal to the full board, said she understood concerns over use of force but that "according to state law, we are required to approve the use of these equipments. So here we are, and it's definitely not a easy discussion."

Amid rising tensions in the South China Sea, the People's Liberation Army (PLA) has developed a low-cost fast deployable air defense system using radar reflector balloons to protect military facilities from aerial attacks. In the era of AI technology, the Chinese military is opting for a rather simplistic method to protect critical infrastructure. The PLA demonstrated its latest technique at a joint drill, called the Zhejiang Golden Shield-22, conducted by the Chinese military and local units in November. It involved the use of radar reflector balloons to safeguard military facilities in case of a long-range missile or drone attack, according to a report on The War Zone. It included the deployment of AD air balloons on UAVs avenues of approach&energy assets visual camouflage (Ukraine lesson learnt) pic.twitter.com/m21KrBIVnZ

Elon Musk's Neuralink is under federal investigation for animal-welfare violations amid staff complaints that its animal testing is being rushed, causing needless suffering and deaths, according to a Reuters review of documents and sources familiar with the investigation and company operations who spoke to Reuters. Neuralink, a medical devices company, is developing a brain implant it hopes will help paralyzed people walk again and cure other neurological ailments. The federal probe, which has not been previously reported, was opened in recent months by the U.S. Department of Agriculture's Inspector General at the request of a federal prosecutor, according to two sources with knowledge of the investigation. It comes amid growing employee dissent about Neuralink's animal testing, including complaints that pressure from CEO Musk to accelerate development has resulted in botched experiments, according to a Reuters review of dozens of Neuralink documents and interviews with more than 20 current and former employees. Elon Musk's Neuralink is facing a probe amid reports of botched animal testing according to Reuters In all, the company has killed about 1,500 animals, including more than 280 sheep, pigs and monkeys, following experiments since 2018, according to records reviewed by Reuters and sources with direct knowledge of the company's animal-testing operations.

Elon Musk's Neuralink is under federal investigation for animal-welfare violations amid staff complaints that its animal testing is being rushed, causing needless suffering and deaths, according to a Reuters review of documents and sources familiar with the investigation and company operations who spoke to Reuters. Neuralink, a medical devices company, is developing a brain implant it hopes will help paralyzed people walk again and cure other neurological ailments. The federal probe, which has not been previously reported, was opened in recent months by the U.S. Department of Agriculture's Inspector General at the request of a federal prosecutor, according to two sources with knowledge of the investigation. It comes amid growing employee dissent about Neuralink's animal testing, including complaints that pressure from CEO Musk to accelerate development has resulted in botched experiments, according to a Reuters review of dozens of Neuralink documents and interviews with more than 20 current and former employees. In all, the company has killed about 1,500 animals, including more than 280 sheep, pigs and monkeys, following experiments since 2018, according to records reviewed by Reuters and sources with direct knowledge of the company's animal-testing operations.

Elon Musk's Neuralink, a medical device company, is under federal investigation for potential animal-welfare violations amid internal staff complaints that its animal testing is being rushed, causing needless suffering and deaths, according to documents reviewed by Reuters and sources familiar with the investigation and company operations. Neuralink Corp is developing a brain implant it hopes will help paralyzed people walk again and cure other neurological ailments. The federal investigation, which has not been previously reported, was opened in recent months by the US Department of Agriculture's inspector general at the request of a federal prosecutor, according to two sources with knowledge of the investigation. The inquiry, one of the sources said, focuses on violations of the Animal Welfare Act, which governs how researchers treat and test some animals. The investigation has come at a time of growing employee dissent about Neuralink's animal testing, including complaints that pressure from Musk to accelerate development has resulted in botched experiments, according to a Reuters review of dozens of Neuralink documents and interviews with more than 20 current and former employees.

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP.

Determining changes in global temperature and precipitation that may indicate climate change is complicated by annual variations. One approach for finding potential climate change indicators is to train a model that predicts the year from annual means of global temperatures and precipitations. Such data is available from the CMIP6 ensemble of simulations. Here a two-hidden-layer neural network trained on this data successfully predicts the year. Differences among temperature and precipitation patterns for which the model predicts specific years reveal changes through time. To find these optimal patterns, a new way of interpreting what the neural network has learned is explored. Alopex, a stochastic correlative learning algorithm, is used to find optimal temperature and precipitation maps that best predict a given year. These maps are compared over multiple years to show how temperature and precipitations patterns indicative of each year change over time.

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world's population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.