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A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies using designed experiments have identified multiple category-selective regions; however, these approaches rely on preconceived hypotheses about categories. Subsequent data-driven discovery methods have sought to address this limitation but are often limited by simple, typically linear encoding models. We propose an in silico approach for data-driven discovery of novel category-selectivity hypotheses based on an encoder-decoder transformer model. The architecture incorporates a brain-region to image-feature cross-attention mechanism, enabling nonlinear mappings between high-dimensional deep network features and semantic patterns encoded in the brain activity. We further introduce a method to characterize the selectivity of individual parcels by leveraging diffusion-based image generative models and large-scale datasets to synthesize and select images that maximally activate each parcel. Our approach reveals regions with complex, compositional selectivity involving diverse semantic concepts, which we validate in silico both within and across subjects. Using a brain encoder as a "digital twin" offers a powerful, data-driven framework for generating and testing hypotheses about visual selectivity in the human brain--hypotheses that can guide future fMRI experiments.

A fine-grained account of functional selectivity in the cortex is essential for understanding how visual information is processed and represented in the brain. Classical studies using designed experiments have identified multiple category-selective regions; however, these approaches rely on preconceived hypotheses about categories. Subsequent data-driven discovery methods have sought to address this limitation but are often limited by simple, typically linear encoding models. We propose an in silico approach for data-driven discovery of novel category-selectivity hypotheses based on an encoder-decoder transformer model. The architecture incorporates a brain-region to image-feature cross-attention mechanism, enabling nonlinear mappings between high-dimensional deep network features and semantic patterns encoded in the brain activity. We further introduce a method to characterize the selectivity of individual parcels by leveraging diffusion-based image generative models and large-scale datasets to synthesize and select images that maximally activate each parcel. Our approach reveals regions with complex, compositional selectivity involving diverse semantic concepts, which we validate in silico both within and across subjects. Using a brain encoder as a "digital twin" offers a powerful, data-driven framework for generating and testing hypotheses about visual selectivity in the human brain--hypotheses that can guide future fMRI experiments.

'We had people come just to see it': Amazon delivers its first UK parcels by drone Amazon has become the first retailer in the UK to start a drone delivery service with a limited launch in Darlington, County Durham. Packages weighing less than 5lb (2.2kg) and containing everyday items such as beauty products, batteries and cables are now being delivered within a 7.5 mile (12km) radius of Amazon's fulfilment centre. The tech giant is convinced there is demand for ultra-fast deliveries and hopes to slowly expand the service. Rob Shield let Amazon use an Airbnb on his farm for its first test runs. Initially it was a novelty, so we were ordering everything under the sun, he says.

Middle-mile logistics describes the problem of routing parcels through a network of hubs, which are linked by a fixed set of trucks. The main challenge comes from the finite capacity of the trucks. The decision to allocate a parcel to a specific truck might block another parcel from using the same truck. It is thus necessary to solve for all parcel routes simultaneously. Exact solution methods scale poorly with the problem size and real-world instances are intractable.

Abstract--The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satellite imagery for detecting digestate application, a practice that enhances soil fertility but poses environmental risks like mi-croplastic contamination and nitrogen losses. In the first instance, Sentinel-2 satellite image time series (SITS) analysis of specific indices (EOMI, NDVI, EVI) was used to characterize EOM's spectral behavior after application on the soils of four different crop types in Thessaly, Greece. Furthermore, Machine Learning (ML) models (namely Random Forest, k-NN, Gradient Boosting and a Feed-Forward Neural Network), were used to investigate digestate presence detection, achieving F1-scores up to 0.85. Agricultural systems can benefit from the application of Exogenous Organic Matter (EOM), which not only enhances soil fertility but also supports waste recycling and promotes circular economies [1], [2].

Will AI mean the end of call centres? Ask ChatGPT whether AI will replace humans in the customer service industry, and it will offer a diplomatic answer, the summary of which is they will work side by side. Humans though, are not so optimistic. Last year, the chief executive of Indian technology firm Tata Consultancy Services, K Krithivasan, told the Financial Times that AI may soon mean that there is minimal need for call centres in Asia. Meanwhile, AI will autonomously resolve 80% of common customer service issues by 2029, predicts business and technology research firm Gartner.

We derive an energy-based continuum limit for $\varepsilon$-graphs endowed with a general connectivity functional. We prove that the discrete energy and its continuum counterpart differ by at most $O(\varepsilon)$; the prefactor involves only the $W^{1,1}$-norm of the connectivity density as $\varepsilon\to0$, so the error bound remains valid even when that density has strong local fluctuations. As an application, we introduce a neural-network procedure that reconstructs the connectivity density from edge-weight data and then embeds the resulting continuum model into a brain-dynamics framework. In this setting, the usual constant diffusion coefficient is replaced by the spatially varying coefficient produced by the learned density, yielding dynamics that differ significantly from those obtained with conventional constant-diffusion models.

However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques.

Encoding models predict brain responses to a set of given stimuli. Recently, deep neural networks have been used as encoding models to predict brain activity as recorded by functional MRI (fMRI) [1, 2, 3, 4, 5, 6]. These studies investigate whether representations in deep neural networks correspond to those in the human brain. This relationship is often assessed using linear models, with successful prediction taken as evidence of shared representational structure. Studies have investigated representations from both unimodal and multimodal deep neural networks, including large language models (LLMs) [2, 4, 7, 8], vision models [9, 10], audio models [1, 11], and video-language models (VLMs) [12], to predict brain activity. However, existing studies face challenges in generalizability and comparability. Differences in stimulus modality, quantity, and content, as well as in preprocessing and scoring, make cross-study comparisons difficult. The Algonauts 2025 Challenge [13] provides a framework to address these issues, offering an openly available, preprocessed dataset with a large amount of data per subject and aligned stimuli across modalities, including video, audio, and transcripts, along with a standardized evaluation procedure. The challenge places particular emphasis on generalizability, including both in-distribution and out-of-distribution test sets to rigorously evaluate how well models transfer to new stimuli. 1