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Guterres said there needed to be'a recognition, especially by superpowers, that their power isn't so super. Guterres said there needed to be'a recognition, especially by superpowers, that their power isn't so super. Superpowers cannot solve world's problems, UN chief says in final general assembly address The world's fault lines have turned from cracks into canyons as superpowers show that they alone do not have the military, economic and technological power to guarantee security or solve the world's problems, the outgoing secretary general of the United Nations has said in his final address to the general assembly. In a speech laced with despair but also glimpses of defiant hope, António Guterres said that over the last decade, largely spanning his 10-year period in office, "wars erupted with devastating consequences and dragged on with despicable cruelty. He continued: "Geopolitical divides have deepened.

Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Chef Nelson German doesn't believe in the idea of the "self-taught" culinarian. "How can you really teach?" asks the Dominican American executive chef and restaurateur.

To keep scammers at bay, dating apps are embracing biometric scanning tools and “verified human” badges. These moves are sparking concerns about surveillance and privacy.

What happens when AI runs out of pictures? A hospital may only ever collect a few dozen scans of a rare condition - for example, an unusual tumour. The radiology department wants software to flag this on a scan - not to replace the specialist, but so a hospital without one still gets their scan checked the same way. The clinicians know what they're looking for. Over a decade, the hospital might gather 40 confirmed cases.

Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID).

Multimodal Large Language Models (MLLMs) have achieved considerable accuracy in Optical Character Recognition (OCR) from static images. However, their efficacy in video OCR is significantly diminished due to factors such as motion blur, temporal variations, and visual effects inherent in video content. To provide clearer guidance for training practical MLLMs, we introduce MMEVideoOCR benchmark, which encompasses a comprehensive range of video OCR application scenarios.

Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Product Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive Patent-Product Image Retrieval Dataset (PPIRD) including a test set with 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set of 3,799,695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter to select discriminative patent images based on visual features via analogical reasoning. Extensive experiments on PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7.58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR.

WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI). Its ability to operate continuously and non-intrusively while preserving user privacy makes it particularly suitable for health monitoring. However, existing WiFi sensing systems struggle to generalize in real-world settings, largely due to datasets collected in controlled environments with homogeneous hardware and fragmented, session-based recordings that fail to reflect continuous daily activity. We present CSI-Bench, a large-scale, in-the-wild benchmark dataset collected using commercial WiFi edge devices across 26 diverse indoor environments with 35 real users.

Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Additionally, these signals exhibit strong non-stationarity, with sharp peaks and abrupt changes that evolve continuously, making them difficult to represent using traditional time-domain or filtering methods. To address these issues, a novel waveletbased approach for physiological signal analysis is presented, aiming to capture multi-scale time-frequency features in various physiological signals. Leveraging this technique, two large-scale pretrained models specific to EMG and ECG are introduced for the first time, achieving superior performance and setting new baselines in downstream tasks. Additionally, a unified multi-modal framework is constructed by integrating pretrained EEG model, where each modality is guided through its dedicated branch and fused via learnable weighted fusion. This design effectively addresses challenges such as low signal-to-noise ratio, high inter-subject variability, and device mismatch, outperforming existing methods on multi-modal tasks. The proposed wavelet-based architecture lays a solid foundation for analysis of diverse physiological signals, while the multi-modal design points to nextgeneration physiological signal processing with potential impact on wearable health monitoring, clinical diagnostics, and broader biomedical applications.