In recent years, deep learning has transformed data processing, justifying its use in the current study. These models consist of multiple layers that calibrate weights to optimize accuracy. By mimicking neural processes, these networks facilitate high-performance predictive modeling. This study focused on the application of convolutional neural networks for forecasting Sargassum seaweed arrivals in Guadeloupe, using varied climatic data such as winds, surface currents, satellite images, and observations of seaweed arrivals on the Guadeloupe coast. We tested two different approaches: classification models for forecasting up to 14 days and zone regression models for forecasting up to 14 days, also indicating a probability of algae arrival. The results showed that the classification models achieved a mean accuracy of 94.75%, a mean specificity of 97.42%, and a mean sensitivity of 98.78%. Zone regression models achieved a mean accuracy of around 90%, a mean specificity of around 97%, and a mean sensitivity of around 85%, with a mean MAE of 0.13 and a mean RMSE of 0.18. These models perform better than the decision tree proposed in the state of the art for the same problem.

The cost of high-quality aerodynamics simulations for realistic automotive configurations makes comprehensive design studies unfeasible. Data-driven surrogates (learning from data) are an appealing alternative, and there is no shortage of approaches that target shape-to-aerodynamics predictions. However, there is a fundamental limitation (data insufficiency problem) in this context: owing to the proprietary nature of commercial automotive designs, training datasets are limited to a few freely-available geometries. In a previous work the authors, a strategy to construct datasets for training surrogates was introduced. It enables controlled generation of an arbitrary number of samples, by convex interpolation between a small number of basis geometries. In this work, we extend this strategy by introducing three features, namely size, density, and diversity that characterize more general datasets. These are important to assess how useful is a dataset for a specific prediction task (data for learning). A formal measure of diversity is developed and then, datasets of successively increasing diversity but constant size are constructed. We show that the dataset diversity has an impact on the predictive accuracy of machine learning surrogates. A power-law scaling, $ \varepsilon \hskip0.5em \propto \hskip0.5em {M}^{1/2}\hskip0.1em {m}^{-1/6} $, where $ \varepsilon $ is the prediction error, $ M $ is the diversity, and $ m $ the dataset size, collapses 23 controlled experiments onto a single curve, revealing that diversity dominates size in determining prediction error. The proposed framework allows for more rigorous a priori evaluation of models than is currently possible and can be applied readily to other shape optimization problems.

Research that assesses individual judges’ ability to shape decisions typically focuses on courts that publish separate votes and opinions. Yet, many courts issue per curiam judgments that do not permit public dissent. To overcome this limitation, we use a convolutional neural network (CNN) to model the variation in judges’ expressed preferences from language in aggregated judgments. Specifically, we construct a CNN to analyze the written judgments of judge-rapporteurs and opinions of advocates-general from the Court of Justice of the European Union. Along a pro-/anti-EU dimension, we estimate how judgments differ within (1) each case relative to the advocate-general’s opinion, and (2) each judge-rapporteur, which captures how judges alter their writing across cases. Our results provide novel empirical support for theoretical models of European judicial decision-making: more pro-EU opinions driven by the Court, not the advocate-general or the judge-rapporteur, are associated with larger chambers and stronger external signals of compliance.

Accurate short-term precipitation forecasts predominantly rely on dense weather-radar networks, limiting operational value in places most exposed to climate extremes. We present TUPANN (Transferable and Universal Physics-Aligned Nowcasting Network), a satellite-only model trained on GOES-16 RRQPE. Unlike most deep learning models for nowcasting, TUPANN decomposes the forecast into physically meaningful components: a variational encoder–decoder infers motion and intensity fields from recent imagery under optical-flow supervision, a lead-time-conditioned MaxViT evolves the latent state, and a differentiable advection operator reconstructs future frames. We evaluate TUPANN on both GOES-16 and IMERG data, in up to four distinct climates (Rio de Janeiro, Manaus, Miami, La Paz) at 10–180-min lead times using the CSI and HSS metrics over 4–64 mm/h thresholds. Comparisons against optical-flow, deep learning, and hybrid baselines show that TUPANN achieves the best or second-best skill in most settings, with pronounced gains at higher thresholds. Training in multiple cities further improves performance, while cross-city experiments show modest degradation and occasional gains for rare heavy-rain regimes. The model produces smooth, interpretable motion fields aligned with numerical optical flow and runs in near real time due to the low latency of GOES-16. These results indicate that physically aligned learning can provide nowcasts that are skillful, transferable, and global.

The James Webb Space Telescope (JWST) hosts a non-redundant Aperture Masking Interferometer (AMI) in its Near Infrared Imager and Slitless Spectrograph (NIRISS) instrument, providing the only dedicated interferometric facility aboard – magnitudes more precise than any interferometric experiment previously flown. However, the performance of AMI (and other high resolution approaches such as kernel phase) in recovery of structure at high contrasts has not met design expectations. A major contributing factor has been the presence of uncorrected detector systematics, notably charge migration effects in the H2RG sensor, and insufficiently accurate mask metrology. Here we present Amigo, a data-driven calibration framework and analysis pipeline that forward-models the full JWST AMI system – including its optics, detector physics, and readout electronics – using an end-to-end differentiable architecture implemented in the Jax framework and in particular exploiting the $\partial$Lux optical modelling package. Amigo directly models the generation of up-the-ramp detector reads, using an embedded neural sub-module to capture non-linear charge redistribution effects, enabling the optimal extraction of robust observables, for example kernel amplitudes and phases, while mitigating systematics such as the brighter-fatter effect. We demonstrate Amigo’s capabilities by recovering the AB Dor AC binary from commissioning data with high-precision astrometry, and detecting both HD 206893 B and the inner substellar companion HD 206893 c: a benchmark requiring contrasts approaching 10 mag at separations of only 100 mas. These results exceed outcomes from all published pipelines and re-establish AMI as a viable competitor for imaging at high contrast at the diffraction limit. Amigo is publicly available as open-source software community resource .

Annual bluegrass (Poa annua L.) is an extremely problematic weed in turfgrass, posing a significant challenge for turfgrass management. Injudicious use of herbicides for controlling this weed has led to resistance issues and environmental concerns. Site-specific weed control offers an opportunity to achieve effective weed control with less herbicide use, but it requires the development of a pipeline for weed detection and localization, and a path planning algorithm. To achieve this, unmanned aerial system (UAS) based RGB imagery of P. annua plants in bermudagrass turf was collected at different weed growth stages at two locations in Texas: Deer Park and College Station. A CNN (YOLO11) and a transfer (RTDETRD) model were evaluated for weed detection. The results showed that the YOLO11n model achieved the highest F1-score (0.64) and mAP@0.50 (0.68), while the RTDETRD-x model achieved the lowest F1-score (0.52) and mAP@0.50 (0.51). The geo-transformation function transforms image coordinates into a world coordinate system with centimeter-level accuracy (mean error =1.5 cm). However, the precision of the transformation depends on the quality of the orthophoto georeferencing. Additionally, the path planning algorithm showed a significant reduction (37.7%) in travel distance compared to the original weed-model-derived distance. The research highlighted the potential of UAS-based imagery for weed detection and localization in turfgrass. Further improvements are needed to enhance model performance by modifying the model architecture (e.g., input image size, hyperparameters) and evaluating its robustness across different weed growth stages and turfgrass species.

Pater's (2019) target article proposes that neural networks will provide theories of learning that generative grammar lacks. We argue that his enthusiasm is premature since the biases of neural networks are largely unknown, and he disregards decades of work on machine learning and learnability. Learning biases form a two-way street: all learners have biases, and those biases constrain the space of learnable grammars in mathematically measurable ways. Analytical methods from the related fields of computational learning theory and grammatical inference allow one to study language learning, neural networks, and linguistics at an appropriate level of abstraction. The only way to satisfy our hunger and to make progress on the science of language learning is to confront these core issues directly.

The target article (Pater 2019) proposes to use neural networks to model learning within existing grammatical frameworks. This is easier said than done. There is a fundamental gap to be bridged that does not receive attention in the article: how can we use neural networks to examine whether it is possible to learn some linguistic representation (a tree, for example) when, after learning is finished, we cannot even tell if this is the type of representation that has been learned (all we see is a sequence of numbers)? Drawing a correspondence between an abstract linguistic representational system and an opaque parameter vector that can (or perhaps cannot) be seen as an instance of such a representation is an implementational mapping problem. Rather than relying on existing frameworks that propose partial solutions to this problem, such as harmonic grammar, I suggest that fusional research of the kind proposed needs to directly address how to ‘find’ linguistic representations in neural network representations.

Joe Pater's (2019) target article calls for greater interaction between neural network research and linguistics. I expand on this call and show how such interaction can benefit both fields. Linguists can contribute to research on neural networks for language technologies by clearly delineating the linguistic capabilities that can be expected of such systems, and by constructing controlled experimental paradigms that can determine whether those desiderata have been met. In the other direction, neural networks can benefit the scientific study of language by providing infrastructure for modeling human sentence processing and for evaluating the necessity of particular innate constraints on language acquisition.

From my perspective, Pater's (2019) target article does a great service both to researchers who work in generative linguistics and to researchers who utilize neural networks—and especially to researchers who might find themselves wanting to do both by harnessing the insights of each tradition. The fusion of theories of linguistic representation and probabilistic learning techniques has certainly led to many interesting and valuable insights about the nature of both linguistic representation and the language acquisition process. However, I feel that the most exciting aspect of Pater's article is the increasing interpretability of neural network models, especially when combined with insights from the theoretical framework of generative linguistics. This allows for the possibility that neural networks could be used to actually generate new theories of representation. I describe how I think this theory-generation process might work with interpretable neural networks.

The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This article traces the development of these two approaches to cognitive science, from their largely autonomous early development in the first thirty years, through their collision in the 1980s around the past-tense debate (Rumelhart & McClelland 1986, Pinker & Prince 1988) and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines of research is likely impeding progress in both: on learning in generative linguistics, and on the representation of language in neural modeling. The article concludes with a brief argument that generative linguistics is unlikely to fulfill its promise of accounting for language learning if it continues to maintain its distance from neural and statistical approaches to learning.

Breast cancer is the second leading cause of cancer-related deaths among women globally and the most prevalent cancer in women. Artificial intelligence (AI)-based frameworks have shown great promise in correctly classifying breast carcinomas, particularly those that may have been difficult to discern through routine microscopy. Additionally, mitotic number quantification utilizing AI technology is more accurate than manual counting. With its many advantages, such as improved accuracy, efficiency and consistency as shown in this literature review, AI has promise for significantly enhancing breast cancer diagnosis in the clinical world despite the paramount obstacles that must be addressed. Ongoing research and innovation are essential for overcoming these challenges and effectively harnessing AI’s transformative potential in breast cancer detection and assessment.

In deep learning (DL), the instability phenomenon is widespread and well documented, and the most commonly used measure of stability is the Lipschitz constant. While a small Lipchitz constant is traditionally viewed as guarantying stability, it does not capture the instability phenomenon in DL for classification well. The reason is that a classification function – which is the target function to be approximated – is necessarily discontinuous, thus having an ‘infinite’ Lipchitz constant. As a result, the classical approach will deem every classification function unstable, yet basic classification functions a la ‘is there a cat in the image?’ will typically be locally very ‘flat’ – and thus locally stable – except at the decision boundary. The lack of an appropriate measure of stability hinders a rigorous theory for stability in DL, and consequently, there are no proper approximation theoretic results that can guarantee the existence of stable networks for classification functions. In this paper, we introduce a novel stability measure $\mathcal{S}(f)$, for any classification function $f$, appropriate to study the stability of classification functions and their approximations. We further prove two approximation theorems: First, for any $\epsilon \gt 0$ and any classification function $f$ on a compact set, there is a neural network (NN) $\psi$, such that $\psi - f \neq 0$ only on a set of measure $\lt \epsilon$; moreover, $\mathcal{S}(\psi ) \geq \mathcal{S}(f) - \epsilon$ (as accurate and stable as $f$ up to $\epsilon$). Second, for any classification function $f$ and $\epsilon \gt 0$, there exists a NN $\psi$ such that $\psi = f$ on the set of points that are at least $\epsilon$ away from the decision boundary.