AI platforms offering first-line mental health support have proliferated in Nigeria, where health services are sparse and underfunded. AI platforms offering first-line mental health support have proliferated in Nigeria, where health services are sparse and underfunded. 'At 2am, it feels like someone's there': why Nigerians are choosing chatbots to give them advice and therapy O n a quiet evening in her Abuja hotel, Joy Adeboye, 23, sits on her bed clutching her phone, her mind racing and chest tightening. On her screen is yet another abusive message from her stalker - a man she had met nine months earlier at her church. He had asked Adeboye out; when she declined, he began sending her intimidating, insulting and blackmailing messages on social media, as well as spreading false information about her online.

Are drones, AI making it harder to fight armed groups in the Sahel? The brazen attack on the international airport and nearby military airbase in Niamey, Niger's capital, came overnight between January 28 and 29. Balls of orange fire flew across the sky as the Nigerien army attempted to respond while residents ducked for cover and whispered prayers, as shown in videos on social media. ISIL (ISIS) in Sahel Province, or ISSP - a Niger-based outfit earlier known as the ISIL affiliate in the Greater Sahara or ISGS - has since claimed responsibility and says it killed several soldiers, although the Nigerien army disputes this. Many of its fighters had breached military drone hangars using RPGs and mortars, and managed to damage several aircraft and one civilian aeroplane, according to videos from the group.

Three West African juntas have turned to Russia. The US has declared a stark policy shift towards three West African countries which are battling Islamist insurgents and whose military governments have broken defence ties with France and turned towards Russia. The state department announced that Nick Checker, head of its Bureau of African Affairs, would visit Mali's capital Bamako to convey the United States' respect for Mali's sovereignty and chart a new course in relations, moving past policy missteps. It adds that the US also looks forward to co-operating with Mali's allies, neighbouring Burkina Faso and Niger, on shared security and economic interests. Absent from the agenda is the longstanding American concern for democracy and human rights.

'Genius' chimpanzee Ai dies in Japan at 49 Studies involving Ai, a genius chimpanzee who has died at the age of 49, are said to have revealed various aspects of the chimpanzee mind. Ai, a genius chimpanzee that could recognize more than 100 Chinese characters and the English alphabet, has died at the age of 49, Japanese researchers have said. Ai, whose name meant love in Japanese, took part in studies on perception, learning and memory that advanced our understanding of primate intelligence, the Center for the Evolutionary Origins of Human Behavior at Kyoto University said in a statement. She died Friday from multiple organ failure and ailments related to old age, the school said. Aside from mastering Chinese characters and the alphabet, Ai could also identify the Arabic numerals from zero to nine and 11 colors, primatologist Tetsuro Matsuzawa said in 2014.

Ai, a female chimpanzee famous for her cognitive skills has died at 49, according to the Japanese institute where she lived. The Kyoto University's Center for the Evolutionary Origins of Human Behavior said Ai died on 9 January of old age and organ failure and that she was surrounded by staff when she died. Ai was born in western Africa and arrived at the Japanese institute in 1977. There, she became the namesake of the Ai Project, a research programme into the chimpanzee mind. Among the institute's noteable findings were the fact that Ai was able to use numbers and identify colours.

Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remains challenging, especially when boundaries are derived from noisy, gridded environmental data. We present a unified framework that combines heteroskedastic Gaussian process (GP) regression with a scaled Maximum Absolute Difference (MAD) Global Envelope Test (GET) to estimate spatial boundary curves and assess whether they evolve over time. The heteroskedastic GP provides a flexible probabilistic reconstruction of boundary lines, capturing spatially varying mean structure and location specific variability, while the test offers a rigorous hypothesis testing tool for detecting departures from expected boundary behaviors. Simulation studies show that the proposed method achieves the correct size under the null and high power for detecting local boundary shifts. Applying our framework to the Sahel Sahara transition zone, using annual Koppen Trewartha climate classifications from 1960 to 1989, we find no statistically significant decade scale changes in the arid and semi arid or semi arid and non arid interfaces. However, the method successfully identifies localized boundary shifts during the extreme drought years of 1983 and 1984, consistent with climate studies documenting regional anomalies in these interfaces during that period.

Recent interpretability methods have proposed to translate LLM internal representations into natural language descriptions using a second verbalizer LLM. This is intended to illuminate how the target model represents and operates on inputs. But do such activation verbalization approaches actually provide privileged knowledge about the internal workings of the target model, or do they merely convey information about its inputs? We critically evaluate popular verbalization methods across datasets used in prior work and find that they can succeed at benchmarks without any access to target model internals, suggesting that these datasets may not be ideal for evaluating verbalization methods. We then run controlled experiments which reveal that verbalizations often reflect the parametric knowledge of the verbalizer LLM which generated them, rather than the knowledge of the target LLM whose activations are decoded. Taken together, our results indicate a need for targeted benchmarks and experimental controls to rigorously assess whether verbalization methods provide meaningful insights into the operations of LLMs.

Preparticipation cardiovascular examination (PPCE) aims to prevent sudden cardiac death (SCD) by identifying athletes with structural or electrical cardiac abnormalities. Anthropometric measurements, such as waist circumference, limb lengths, and torso proportions to detect Marfan syndrome, can indicate elevated cardiovascular risk. Traditional manual methods are labor-intensive, operator-dependent, and challenging to scale. We present a fully automated deep-learning approach to estimate five key anthropometric measurements from 2D synthetic human body images. Using a dataset of 100,000 images derived from 3D body meshes, we trained and evaluated VGG19, ResNet50, and DenseNet121 with fully connected layers for regression. All models achieved sub-centimeter accuracy, with ResNet50 performing best, achieving a mean MAE of 0.668 cm across all measurements. Our results demonstrate that deep learning can deliver accurate anthropometric data at scale, offering a practical tool to complement athlete screening protocols. Future work will validate the models on real-world images to extend applicability.

Cybercrime has emerged as one of the most pervasive and economically destructive consequences of global digitalization. Contemporary online fraud and deception-based crimes now account for unprecedented financial losses worldwide, exceeding trillions of United States dollars (USD) annually (Morgan, 2016), while also inflicting severe psychological, social, and reputational harm on victims. Unlike classical cyberattacks targeting systems and networks, modern cybercrime increasingly exploits human vulnerabilities rather than purely technical weaknesses, relying on deception, persuasion, impersonation, emotional coercion, and trust manipulation as primary attack vectors (Holt, 2019; Yao, Zheng, Wu, Wu, Gao, Wang and Yang, 2025; Sarkar and Shukla, 2023; Sarkar, Singh, Kumar and Shukla, 2023). Existing cybersecurity frameworks, such as the Cyber Kill Chain and the MITRE ATT&CK framework, provide powerful abstractions for understanding technically sophisticated cyberattacks targeting enterprise systems and critical infrastructure (MITRE Corporation, 2025b,a). However, these models are fundamentally system-centric: they describe how adversaries compromise digital infrastructure, escalate privileges, and exfiltrate data. In contrast, cybercrime, particularly scams, fraud, impersonation, and extortion, primarily targets individual decision-making processes (Louderback and Antonaccio, 2017), often without exploiting any software vulnerability at all. Consequently, the investigative needs of cybercrime differ substantially from those of traditional cyberattacks.

Large Language Models increasingly possess capabilities that carry dual-use risks. While data filtering has emerged as a pretraining-time mitigation, it faces significant challenges: labeling whether data is harmful is expensive at scale, and given improving sample efficiency with larger models, even small amounts of mislabeled content could give rise to dangerous capabilities. To address risks associated with mislabeled harmful content, prior work proposed Gradient Routing (Cloud et al., 2024) -- a technique that localizes target knowledge into a dedicated subset of model parameters so they can later be removed. We explore an improved variant of Gradient Routing, which we call Selective GradienT Masking (SGTM), with particular focus on evaluating its robustness to label noise. SGTM zero-masks selected gradients such that target domain examples only update their dedicated parameters. We test SGTM's effectiveness in two applications: removing knowledge of one language from a model trained on a bilingual synthetic dataset, and removing biology knowledge from a model trained on English Wikipedia. In both cases SGTM provides better retain/forget trade-off in the presence of labeling errors compared to both data filtering and a previously proposed instantiation of Gradient Routing. Unlike shallow unlearning approaches that can be quickly undone through fine-tuning, SGTM exhibits strong robustness to adversarial fine-tuning, requiring seven times more fine-tuning steps to reach baseline performance on the forget set compared to a finetuning-based unlearning method (RMU). Our results suggest SGTM provides a promising pretraining-time complement to existing safety mitigations, particularly in settings where label noise is unavoidable.