A Japan Ground Self-Defense Force Type-88 surface-to-ship missile is fired during the Balikatan exercises at Culili Point Sand Dunes in Paoay, Ilocos Norte province, Philippines, on May 6. | REUTERS Singapore - Defense Minister Shinjiro Koizumi and his Philippine counterpart, Gilberto Teodoro, affirmed Sunday that talks will be launched on the export of surface-to-ship missiles from Japan to the Southeast Asian nation. Koizumi revealed this in talks with reporters after holding a meeting with the Philippine defense chief in Singapore earlier in the day. Type-88 surface-to-ship guided missiles of Japan's Ground Self-Defense Force are expected to be up for consideration. The Philippine side is believed to have shown an interest in procuring the missiles as the Self-Defense Forces used them in the Balikatan multilateral exercises conducted in Manila between April and May. The SDF, which had taken part in the annual exercises organized by the United States and the Philippines as an observer since 2012, joined the drills on a full scale for the first time this year following the entry into force of the Japan-Philippine reciprocal access agreement in September 2025. The possible procurement of Type-88 missiles is expected to help reinforce the deterrent and response capabilities of the Philippines, which is in a territorial dispute with China in the South China Sea.

Defense Minister Shinjiro Koizumi speaks to reporters as New Zealand defense chief Chris Penk (left) and Australian Defense Minister Richard Marles listen on the sidelines of the Shangri-La Dialogue security summit in Singapore on Saturday. Held on the margins of the Shangri-La Dialogue security forum in Singapore on Saturday, the meeting came as Wellington actively evaluates purchasing the same class of advanced warships that Japan recently sold to Australia, in order to maintain interoperability with its sole formal defense ally. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories. However, before writing your first comment, please create a display name in the Profile section of your subscriber account page.

Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain. For example, high-performing models for out-of-hospital cardiac arrest (OHCA) rely on detailed prehospital measurements routinely collected in high-resource settings but unavailable in many international registries. Existing methods either discard missing covariates, sacrificing predictive information, or rely on untestable assumptions about their target distribution. We propose DRUM (\underline{D}istributionally \underline{R}obust \underline{U}nsupervised transfer learning with structurally \underline{M}issing covariates), a framework that transfers prediction models to target populations where certain covariates are structurally absent and outcome labels are unavailable. DRUM partitions covariates into shared components ($X$), observed across all settings, and missing components ($A$), observed only in the source. Rather than imputing missing covariates, DRUM optimizes worst-case predictive performance over the unknown target distribution of $A \mid X$ using a neural network generator, with a robustness parameter controlling allowable deviation from the source conditional. We further develop a bias correction procedure that reduces sensitivity to nuisance estimation error. Simulations show substantial improvements in both mean and worst-case prediction error under distribution shift. Applied to cross-national OHCA prediction, transferring models from a US registry to multiple Asian registries where prehospital variables are unrecorded, DRUM yields better-calibrated predictions and improved clinical classification performance across sites.

The 10th International Workshop on Health Intelligence (W3PHIAI-26) celebrated a decade of bringing AI and health research together, building on a lineage that began with the AAAI-W3PHI workshops focused on population health (2014-2016), the AAAI-HIAI workshops focused on personalized health (2013-2016), and the subsequent joint W3PHIAI workshops held annually from 2017 through 2025. Over this decade, the series has produced hundreds of talks and high-impact publications that have collectively received thousands of citations, shaping the research agenda in both population health intelligence and personalized healthcare AI. This year's special theme, "Foundation Models and AI Agents," reflected the field's rapidly evolving frontier: the emergence of autonomous and semi-autonomous AI systems reshaping clinical workflows, patient management, health system operations, and public health surveillance. Day 1 of the workshop focused on medical imaging and the translation of AI for clinical ...

From small publishers to US federal agencies, websites are reporting unusual spikes in automated traffic linked to IP addresses in Lanzhou, China. For a brief moment in October, Alejandro Quintero thought he had made it big in China . The Bogotá-based data analyst owns and manages a website that publishes articles about paranormal activities, like ghosts and aliens. The content is written in "Spanglish," he says, and was never intended for an Asian audience. But last fall, Quintero's site suddenly began receiving a large volume of visits from China and Singapore.

Table 1: Finetuning pruned models with more training steps. Note that the only difference lies in the position of the summation. It is easy to observe that our model achieves convergence rapidly. The dataset size of LSUN Bedroom is 44.48GB, which is We conducted further investigations to explore the effectiveness of knowledge distillation in enhancing pruning techniques. Table 3 profiles the pre-trained and the pruned models on a single A5000, with a batch size of 1.

Tor is a widely used anonymity network that conceals user identities by routing traffic through encrypted relays, yet it remains vulnerable to traffic correlation attacks that deanonymize users by matching patterns in ingress and egress traffic. However, existing correlation methods suffer from two major limitations: limited robustness to noise and partial observations, and poor scalability due to computationally expensive pairwise matching. To address these challenges, we propose RECTor, a machine learning-based framework for traffic correlation under realistic conditions. RECTor employs attention-based Multiple Instance Learning (MIL) and GRU-based temporal encoding to extract robust flow representations, even when traffic data is incomplete or obfuscated. These embeddings are mapped into a shared space via a Siamese network and efficiently matched using approximate nearest neighbor (aNN) search. Empirical evaluations show that RECTor outperforms state-of-the-art baselines such as DeepCorr, DeepCOFFEA, and FlowTracker, achieving up to 60% higher true positive rates under high-noise conditions and reducing training and inference time by over 50%. Moreover, RECTor demonstrates strong scalability: inference cost grows near-linearly as the number of flows increases. These findings reveal critical vulnerabilities in Tor's anonymity model and highlight the need for advanced model-aware defenses.

Abstract--General Visual Inspection is a manual inspection process regularly used to detect and localise obvious damage on the exterior of commercial aircraft. There has been increasing demand to perform this process at the boarding gate to minimise the downtime of the aircraft and automating this process is desired to reduce the reliance on human labour . Automating this typically requires estimating a camera's pose with respect to the aircraft for initialisation but most existing localisation methods require infrastructure, which is very challenging in uncontrolled outdoor environments and within the limited turnover time (approximately 2 hours) on an airport tarmac. Additionally, many airlines and airports do not allow contact with the aircraft's surface or using UA Vs for inspection between flights, and restrict access to commercial aircraft. Hence, this paper proposes an on-site method that is infrastructure-free and easy to deploy for estimating a pan-tilt-zoom camera's pose and localising scan images. This method initialises using the same pan-tilt-zoom camera used for the inspection task by utilising a Deep Convolutional Neural Network fine-tuned on only synthetic images to predict its own pose. We apply domain randomisation to generate the dataset for fine-tuning the network and modify its loss function by leveraging aircraft geometry to improve accuracy. We also propose a workflow for initialisation, scan path planning, and precise localisation of images captured from a pan-tilt-zoom camera. We evaluate and demonstrate our approach through experiments with real aircraft, achieving root-mean-square camera pose estimation errors of less than 0.24 m and 2 for all real scenes. General Visual Inspection (GVI) is a widely used technique as part of regular inspections of aircraft such as during pre-flight inspections on an airport tarmac or during maintenance usually performed in a hanger. This process involves visual examinations of the aircraft's exterior for noticeable damage or irregularities and provides a means for early detection of typical air-frame defects [2].

We present MERaLiON-SER, a robust speech emotion recognition model designed for English and Southeast Asian languages. The model is trained using a hybrid objective combining weighted categorical cross-entropy and Concordance Correlation Coefficient (CCC) losses for joint discrete and dimensional emotion modelling. This dual approach enables the model to capture both the distinct categories of emotion (like happy or angry) and the fine-grained, such as arousal (intensity), valence (positivity/negativity), and dominance (sense of control), leading to a more comprehensive and robust representation of human affect. Extensive evaluations across multilingual Singaporean languages (English, Chinese, Malay, and Tamil ) and other public benchmarks show that MERaLiON-SER consistently surpasses both open-source speech encoders and large Audio-LLMs. These results underscore the importance of specialised speech-only models for accurate paralinguistic understanding and cross-lingual generalisation. Furthermore, the proposed framework provides a foundation for integrating emotion-aware perception into future agentic audio systems, enabling more empathetic and contextually adaptive multimodal reasoning.

As financial applications of large language models (LLMs) gain attention, accurate Information Retrieval (IR) remains crucial for reliable AI services. However, existing benchmarks fail to capture the complex and domain-specific information needs of real-world banking scenarios. Building domain-specific IR benchmarks is costly and constrained by legal restrictions on using real customer data. To address these challenges, we propose a systematic methodology for constructing domain-specific IR benchmarks through LLM-based query generation. As a concrete implementation of this methodology, our pipeline combines single and multi-document query generation with an enhanced and reasoning-augmented answerability assessment method, achieving stronger alignment with human judgments than prior approaches. Using this methodology, we construct KoBankIR, comprising 815 queries derived from 204 official banking documents. Our experiments show that existing retrieval models struggle with the complex multi-document queries in KoBankIR, demonstrating the value of our systematic approach for domain-specific benchmark construction and underscoring the need for improved retrieval techniques in financial domains.