Explosions have been heard at the Port of Sudan, a critical lifeline and entry point for aid, as attacks on the city continued for a fourth day in the latest confrontation between Sudanese Armed Forces (SAF) and the paramilitary Rapid Support Forces (RSF) in the country's brutal two-year civil war. The attacks have been blamed on the RSF by Sudan's army and by residents. On Wednesday morning, an army source told the AFP news agency on condition of anonymity that the explosion was due to a drone attack that was met with "anti-aircraft missiles". The Port of Sudan on the Red Sea coast had been a haven city hosting hundreds of thousands of displaced people since the war began and serves as an interim seat for Sudan's military-allied government, which has been at war with the RSF since 2023. The attacks on Port Sudan have increased fears of disruptions to desperately needed aid deliveries in the country suffering one of the world's most dire humanitarian crises, and where famine has been declared in some areas.

Dual Horn clauses mirror key properties of Horn clauses. This paper explores the "other side of the looking glass" to reveal some expected and unexpected symmetries and their practical uses. We revisit Dual Horn clauses as enablers of a form of constructive negation that supports goal-driven forward reasoning and is valid both intuitionistically and classically. In particular, we explore the ability to falsify a counterfactual hypothesis in the context of a background theory expressed as a Dual Horn clause program. With Dual Horn clause programs, by contrast to negation as failure, the variable bindings in their computed answers provide explanations for the reasons why a statement is successfully falsified. Moreover, in the propositional case, by contrast to negation as failure as implemented with stable models semantics in ASP systems, and similarly to Horn clause programs, Dual Horn clause programs have polynomial complexity. After specifying their execution model with a metainterpreter, we devise a compilation scheme from Dual Horn clause programs to Horn clause programs, ensuring their execution with no performance penalty and we design the embedded SymLP language to support combined Horn clause and Dual Horn clause programs. As a (motivating) application, we cast LLM reasoning chains into propositional Horn and Dual Horn clauses that work together to constructively prove and disprove goals and enhance Generative AI with explainability of reasoning chains. Keywords: Dual Horn clauses; constructive negation; counterfactual reasoning; theory falsification; LLM generated logic programs; metainterpretation and compilation to Prolog.

Israel's military campaign in Gaza has brought "relentless death and destruction" to Palestinians in the strip, United Nations Secretary-General Antonio Guterres has said. In a speech marking six months since Israel's war on Gaza began, the UN chief said that "nothing can justify the collective punishment of the Palestinian people." Respect for international humanitarian law is in tatters," he added. "During my visit to the Rafah crossing 10 days ago, I met veteran humanitarians who told me categorically that the crisis and suffering in Gaza is unlike any they have ever seen," Guterres said, adding that long lines of trucks with aid continued to face "obstacle after obstacle". "When the gates to aid are closed, the doors to starvation are opened," he said. "More than half the population – over a million people – are facing catastrophic hunger.

Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in new research. The simple idea behind this approach, as we explained in the journal Nature on March 16, 2022, is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms--which are fancy tools for pattern recognition--can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor. As the COVID-19 pandemic spread in early 2020, our research team helped Togo's Ministry of Digital Economy and GiveDirectly, a nonprofit that sends cash to people living in poverty, turn this insight into a new type of aid program. First, we collected recent, reliable and representative data.

The Research Brief is a short take about interesting academic work. Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in newly published research. The simple idea behind this approach is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms – which are fancy tools for pattern recognition – can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor.

The United States has issued fresh sanctions on alleged members of an illicit network financing Yemen's Houthi rebels, citing the group's involvement in the continuing war in Yemen and recent drone and missile attacks on Washington's Gulf allies. In a statement on Wednesday, the US Department of the Treasury said the network "has transferred tens of millions of dollars to Yemen via a complex international network of intermediaries in support of the Houthis' attacks". The new sanctions target alleged front companies and ships that the US says worked with a branch of Iran's Islamic Revolutionary Guard Corps to smuggle petroleum and other commodities around the Middle East, Asia and Africa to help fund the Houthis. "Despite pleas to negotiate an end to this devastating conflict, Houthi leaders continue to launch missile and unmanned aerial vehicle attacks against Yemen's neighbors, killing innocent civilians, while millions of Yemeni civilians remain displaced and hungry," Treasury Under-secretary Brian E Nelson said in the statement. The Houthi rebels have ramped up their missile and drone attacks against Saudi Arabia and started directly targeting the UAE in recent weeks, but the penalties appeared to fall short of the tougher measures that the Saudis and Emiratis, key strategic partners of the US, had sought from the Biden administration.

The third Tallinn Digital Summit was recently held in Tallinn with a focus on AI for public value and to mark the occasion we spoke to Dr Julien Cornebise, who leads AI for Good at Element AI. He is an awarded scientist who has worked with Amnesty International and he was an early employee at DeepMind. We talked about the hype around AI, but also the all the good it could be used for with the right incentives. We have a government team within Element AI and we've had some requests, but like every contact we get – whether it's from NGOs, agencies or governments – we make very sure to explain to the people who reach out to separate the hype from reality. In some cases, we've said that it's not feasible now, but maybe after a few more years of research. More generally, yes, we want to work with governments around AI for good, because the sustainable development goals are not just for NGOs.

DALLAS--(BUSINESS WIRE)--To solve humanity's most complex and demanding problems ranging from creating sustainable global food production and preventing infectious disease epidemics to ensuring the safety of our planet and natural resources is the HPC community's next grand challenge. HPC and AI are revolutionizing how we untangle and solve global threats and humanitarian crises. The SC18 plenary session will examine the potential for advanced computing to help mitigate human suffering and elevate our capacity to protect the most vulnerable. This plenary session will hear from innovators who are redefining how we predict and prevent humanitarian crises by leveraging advanced computing. The session is the kick-off event, which immediately precedes the Exhibitor Opening Gala.

Artificial intelligence, or AI, is undergoing a period of massive expansion. This is not because computers have achieved human-like consciousness, but because of advances in machine learning, where computers learn from huge databases how to classify new data. At the cutting edge are the neural networks that have learned to recognise human faces or play Go. Recognising patterns in data can also be used as a predictive tool. AI is being applied to echocardiograms to predict heart disease, to workplace data to predict if employees are going to leave, and to social media feeds to detect signs of incipient depression or suicidal tendencies.

The last few years have seen a growing range of technologies deployed to assist humanitarian efforts, whether it's peacekeeping drones, crowdsourcing, or image analytics. With the rise in AI, however, it's perhaps no surprise that artificial intelligence can be utilized -- and a recent paper describes how. The paper uses AI to predict the gender of pre-paid mobile phone users with a high degree of accuracy. Rescue teams already use mobile phone data to help track those in need of assistance, but this new approach aims to go even further by helping to identify their gender, and therefore identify vulnerable groups such as women and children. Whilst mobile phones are almost ubiquitous, in the developing world, many are pre-paid, meaning that data often lacks key demographic identifiers.