We introduce a novel regularization scheme for autoencoders based on matricial free energy. Our approach defines a differentiable loss function in terms of the singular values of the code matrix (code dimension x batch size). From the standpoint of free probability an d random matrix theory, this loss achieves its minimum when the singular value distribution of the code matrix coincides with that of an appropriately sculpted random metric with i.i.d. Gaussian entries. Empirical simulations demonstrate that minimizing the negative matricial free energy through standard stochastic gradient-based training yields Gaussian-like codes that generalize across training and test sets. Building on this foundation, we propose a matricidal free energy maximizing autoencoder that reliably produces Gaussian codes and show its application to underdetermined inverse problems.

Multimodal AI research has overwhelmingly focused on high-resource languages, hindering the democratization of advancements in the field. To address this, we present AfriCaption, a comprehensive framework for multilingual image captioning in 20 African languages and our contributions are threefold: (i) a curated dataset built on Flickr8k, featuring semantically aligned captions generated via a context-aware selection and translation process; (ii) a dynamic, context-preserving pipeline that ensures ongoing quality through model ensembling and adaptive substitution; and (iii) the AfriCaption model, a 0.5B parameter vision-to-text architecture that integrates SigLIP and NLLB200 for caption generation across under-represented languages. This unified framework ensures ongoing data quality and establishes the first scalable image-captioning resource for under-represented African languages, laying the groundwork for truly inclusive multimodal AI.

Abstract--This paper presents a novel framework for speech transcription and synthesis, leveraging edge-cloud parallelism to enhance processing speed and accessibility for Kinyarwanda and Swahili speakers. It addresses the scarcity of powerful language processing tools for these widely spoken languages in East African countries with limited technological infrastructure. The framework utilizes the Whisper and SpeechT5 pre-trained models to enable speech-to-text (STT) and text-to-speech (TTS) translation. The architecture uses a cascading mechanism that distributes the model inference workload between the edge device and the cloud, thereby reducing latency and resource usage, benefiting both ends. On the edge device, our approach achieves a memory usage compression of 9.5% for the SpeechT5 model and 14% for the Whisper model, with a maximum memory usage of 149 MB. Experimental results indicate that on a 1.7 GHz CPU edge device with a 1 MB/s network bandwidth, the system can process a 270-character text in less than a minute for both speech-to-text and text-to-speech transcription. Using real-world survey data from Kenya, it is shown that the cascaded edge-cloud architecture proposed could easily serve as an excellent platform for STT and TTS transcription with good accuracy and response time. I. INTRODUCTION In today's digital age, the need for accurate and efficient speech transcription and synthesis models has been increasing rapidly. These models play an important role in a variety of applications, such as learning new language(s), accessibility tools for people with difficulties in reading and hearing, as well as automated voice assistants [1]. Kinyarwanda and Swahili are two of the local languages spoken in East Africa. While Swahili is the most widely spoken language in Eastern Africa, the speakers range from 60 million to over 150 million [2].

Lung cancer, a malignancy originating in lung tissues, is commonly diagnosed and classified using medical imaging techniques, particularly computed tomography (CT). Despite the integration of machine learning and deep learning methods, the predictive efficacy of automated systems for lung cancer classification from CT images remains below the desired threshold for clinical adoption. Existing research predominantly focuses on binary classification, distinguishing between malignant and benign lung nodules. In this study, a novel deep learning-based approach is introduced, aimed at an improved multi-class classification, discerning various subtypes of lung cancer from CT images. Leveraging a pre-trained ResNet model, lung tissue images were classified into three distinct classes, two of which denote malignancy and one benign. Employing a dataset comprising 15,000 lung CT images sourced from the LC25000 histopathological images, the ResNet50 model was trained on 10,200 images, validated on 2,550 images, and tested on the remaining 2,250 images. Through the incorporation of custom layers atop the ResNet architecture and meticulous hyperparameter fine-tuning, a remarkable test accuracy of 98.8% was recorded. This represents a notable enhancement over the performance of prior models on the same dataset.

Despite recent advances in AI, multimodal culturally diverse datasets are still limited, particularly for regions in the Middle East and Africa. In this paper, we introduce EgMM-Corpus, a multimodal dataset dedicated to Egyptian culture. By designing and running a new data collection pipeline, we collected over 3,000 images, covering 313 concepts across landmarks, food, and folklore. Each entry in the dataset is manually validated for cultural authenticity and multimodal coherence. EgMM-Corpus aims to provide a reliable resource for evaluating and training vision-language models in an Egyptian cultural context. We further evaluate the zero-shot performance of Contrastive Language-Image Pre-training CLIP on EgMM-Corpus, on which it achieves 21.2% Top-1 accuracy and 36.4% Top-5 accuracy in classification. These results underscore the existing cultural bias in large-scale vision-language models and demonstrate the importance of EgMM-Corpus as a benchmark for developing culturally aware models.

This study presents a real-time, portable brain-computer interface (BCI) system designed to support hand rehabilitation for stroke patients. The system combines a low cost 3D-printed robotic exoskeleton with an embedded controller that converts brain signals into physical hand movements. EEG signals are recorded using a 14-channel Emotiv EPOC+ headset and processed through a supervised convolutional autoencoder (CAE) to extract meaningful latent features from single-trial data. The model is trained on publicly available EEG data from healthy individuals (WAY-EEG-GAL dataset), with electrode mapping adapted to match the Emotiv headset layout. Among several tested classifiers, Ada Boost achieved the highest accuracy (89.3%) and F1-score (0.89) in offline evaluations. The system was also tested in real time on five healthy subjects, achieving classification accuracies between 60% and 86%. The complete pipeline - EEG acquisition, signal processing, classification, and robotic control - is deployed on an NVIDIA Jetson Nano platform with a real-time graphical interface. These results demonstrate the system's potential as a low-cost, standalone solution for home-based neurorehabilitation.

Survivors of sex abuse by members of the Catholic clergy have expressed hope after meeting Pope Leo at the Vatican for the first time. Gemma Hickey, board president of Ending Clergy Abuse (ECA Global), told the BBC it spoke volumes he had met them so soon in his papacy. The group is pushing for a global zero-tolerance policy, already adopted in the US, of permanently removing a priest who admits or is proven to have sexually abused a child. The Pope acknowledged there was resistance in some parts of the world to this, Hickey said. The new Pope, who assumed the role in May, has inherited the issue, which has haunted the Catholic Church for decades and the Vatican has struggled to root out.

Last December, flames engulfed Ohio's Overfield Tavern Museum. Now archaeologists get to dig beneath the floorboards. A 50 cent coin from 1817 (left) and an 1846 Liberty head one-cent coin (right) are just two of the numerous objects uncovered in a dig in Troy, Ohio. Breakthroughs, discoveries, and DIY tips sent every weekday. Out of the ashes of a devastating fire, archeologists are uncovering exciting insights into Ohio history.

Government investigators are gathering data to confirm what exactly cracked the windshield of a 737 Max aircraft at above 30,000 feet. An United Airlines Boeing 737 MAX 9 airplane, similar to the one that was struck, approaches San Diego International Airport for a landing.Photograph: Kevin Carter; Getty Images Save this storyThe National Transportation Safety Board confirmed Sunday that it is investigating an airliner that was struck by an object in its windscreen, mid-flight, over Utah. "NTSB gathering radar, weather, flight recorder data," the federal agency said on the social media site X. "Windscreen being sent to NTSB laboratories for examination." The strike occurred Thursday, during a United Airlines flight from Denver to Los Angeles. Images shared on social media showed that one of the two large windows at the front of a 737 MAX aircraft was significantly cracked. Related images also reveal a pilot's arm that has been cut multiple times by what appear to be small shards of glass.

Marjorie Taylor Greene's revenge mission: Ex-GOP strategist reveals why firebrand has turned on Trump administration Accountant arrested after'opening fire on MAGA supporter' in row over Trump sign... as victim recalls bullets whizzing past his head'Half the internet' goes down after Amazon cloud outage leaving millions unable to use Ring, Alexa, and banking apps - as experts say we'can't rule out a cyberattack' This is exactly how I look this good at 68 - and you can too: World-famous make-up guru BOBBI BROWN reveals her 10 beauty tricks to hide wrinkles, tighten skin... and the one thing that's almost as good as a facelift Shocking behavior that led to little girl's horror plunge from Disney cruise ship is revealed for first time Keri Russell, 49, SLAMS plastic surgery trends in Hollywood and admits it feels'strange' to look natural Doctors expose the truth about melatonin... as terrifying side-effects soar Trump drops expletive as he issues blunt response to millions of'No Kings' protesters Shock new twist in death of ex-NFL star Doug Martin, 36, as it emerges he died'after brief struggle with police' How I lost 4st fast WITHOUT weight-loss jabs. Virginia Giuffre's memoir appears in book shops a day early: Prince Andrew accuser details multiple encounters with the disgraced royal and reveals how her traumatic childhood made her'perfect victim' for Epstein Greedy waitress chases down customer and calls cops because he didn't tip her: 'Who said he's obligated to tip?' Bailey Zimmerman debuts new cosmetic procedure after revealing he's been'insecure' about it since childhood Are you anxious, tired and have difficulty concentrating? You could be suffering from this common but widely misunderstood condition - and here's how to help yourself: DR MAX PEMBERTON Biden's former mouthpiece Karine Jean-Pierre reveals how ex-president left her'enraged and heartbroken' as she turns on Democrats Sarah Ferguson could turn on Andrew to'save her own skin' if cash runs low - as he faces potential police probe into'dirt digging' after losing his titles Sharon Stone played her mother who tied her to a bed to go party now the real daughter tells the story'Casino' didn't dare show Beware the eye in the sky! READ MORE: How AI cops are already patrolling Britain's streets Whether it's sending a quick text or casting an eye over your emails, those tempted to look at their phone while driving are finally being caught out. UK trials of an AI'traffic cop' have successfully detected thousands of drivers using their phone behind the wheel.