Abstract--The modular composite representation (MCR) is a computing model that represents information with high-dimensional integer vectors using modular arithmetic. Ori gi-nally proposed as a generalization of the binary spatter cod e model, it aims to provide higher representational power whi le remaining a lighter alternative to models requiring high-p recision components. However, despite this potential, MCR has recei ved limited attention in the literature. Systematic analyses o f its trade-offs and comparisons with other models, such as binar y spatter codes, multiply-add-permute, and Fourier hologra phic reduced representation, are lacking, sustaining the perce ption that its added complexity outweighs the improved expressiv ity over simpler models. In this work, we revisit MCR by presenti ng its first extensive evaluation, demonstrating that it achie ves a unique balance of information capacity, classification acc uracy, and hardware efficiency. Experiments measuring informatio n capacity demonstrate that MCR outperforms binary and integ er vectors while approaching complex-valued representation s at a fraction of their memory footprint. Evaluation on a collect ion of 123 classification datasets confirms consistent accuracy gains and shows that MCR can match the performance of binary spatter codes using up to 4.0 less memory. We investigate the hardware realization of MCR by showing that it maps naturally to digital logic and by designing the first dedicat ed accelerator for it. Evaluations on basic operations and sev en selected datasets demonstrate a speedup of up to three order s-of-magnitude and significant energy reductions compared to a software implementation. Furthermore, when matched for accuracy against binary spatter codes, MCR achieves on aver age 3.08 faster execution and 2.68 lower energy consumption. The work of CJK was supported by the Center for the Co-Design o f Cognitive Systems (CoCoSys), one of seven centers in JUMP 2.0, a Se miconductor Research Corporation (SRC) program sponsored by DARP A, in a ddition to the NDSEG Fellowship, Fernström Fellowship, Swartz Founda tion, and NSF Grants 2147640 and 2313149. The work of AL and DK was supporte d by Knut and Alice Wallenberg Foundation under the Wallenber g Scholars program (Grant No. KA W2023.0327).

Power-logistics scheduling in modern seaports typically follow a predict-then-optimize pipeline. To enhance the decision quality of forecasts, decision-focused learning has been proposed, which aligns the training of forecasting models with downstream decision outcomes. However, this end-to-end design inherently restricts the value of forecasting models to only a specific task structure, and thus generalize poorly to evolving tasks induced by varying seaport vessel arrivals. We address this gap with a decision-focused continual learning framework that adapts online to a stream of scheduling tasks. Specifically, we introduce Fisher information based regularization to enhance cross-task generalization by preserving parameters critical to prior tasks. A differentiable convex surrogate is also developed to stabilize gradient backpropagation. The proposed approach enables learning a decision-aligned forecasting model across a varying tasks stream with a sustainable long-term computational burden. Experiments calibrated to the Jurong Port demonstrate superior decision performance and generalization over existing methods with reduced computational cost.

Addressing climate change effectively requires more than cataloguing the number of policies in place; it calls for tools that can reveal their thematic priorities and their tangible impacts on development outcomes. Existing assessments often rely on qualitative descriptions or composite indices, which can mask crucial differences between key domains such as mitigation, adaptation, disaster risk management, and loss and damage. To bridge this gap, we develop a quantitative indicator of climate policy orientation by applying a multilingual transformer-based language model to official national policy documents, achieving a classification accuracy of 0.90 (F1-score). Linking these indicators with World Bank development data in panel regressions reveals that mitigation policies are associated with higher GDP and GNI; disaster risk management correlates with greater GNI and debt but reduced foreign direct investment; adaptation and loss and damage show limited measurable effects. This integrated NLP-econometric framework enables comparable, theme-specific analysis of climate governance, offering a scalable method to monitor progress, evaluate trade-offs, and align policy emphasis with development goals.

Automated defect detection from UAV imagery of transmission lines is a challenging task due to the small size, ambiguity, and complex backgrounds of defects. This paper proposes TinyDef-DETR, a DETR-based framework designed to achieve accurate and efficient detection of transmission line defects from UAV-acquired images. The model integrates four major components: an edge-enhanced ResNet backbone to strengthen boundary-sensitive representations, a stride-free space-to-depth module to enable detail-preserving downsampling, a cross-stage dual-domain multi-scale attention mechanism to jointly model global context and local cues, and a Focaler-Wise-SIoU regression loss to improve the localization of small and difficult objects. Together, these designs effectively mitigate the limitations of conventional detectors. Extensive experiments on both public and real-world datasets demonstrate that TinyDef-DETR achieves superior detection performance and strong generalization capability, while maintaining modest computational overhead. The accuracy and efficiency of TinyDef-DETR make it a suitable method for UAV-based transmission line defect detection, particularly in scenarios involving small and ambiguous objects.

Physics-informed neural networks (PINNs) offer a powerful approach to solving partial differential equations (PDEs), which are ubiquitous in the quantitative sciences. Applied to both forward and inverse problems across various scientific domains, PINNs have recently emerged as a valuable tool in the field of scientific machine learning. A key aspect of their training is that the data -- spatio-temporal points sampled from the PDE's input domain -- are readily available. Influence functions, a tool from the field of explainable AI (XAI), approximate the effect of individual training points on the model, enhancing interpretability. In the present work, we explore the application of influence function-based sampling approaches for the training data. Our results indicate that such targeted resampling based on data attribution methods has the potential to enhance prediction accuracy in physics-informed neural networks, demonstrating a practical application of an XAI method in PINN training.

Continuous-time stochastic processes underlie many natural and engineered systems. In healthcare, autonomous driving, and industrial control, direct interaction with the environment is often unsafe or impractical, motivating offline reinforcement learning from historical data. However, there is limited statistical understanding of the approximation errors inherent in learning policies from offline datasets. We address this by linking reinforcement learning to the Hamilton-Jacobi-Bellman equation and proposing an operator-theoretic algorithm based on a simple dynamic programming recursion. Specifically, we represent our world model in terms of the infinitesimal generator of controlled diffusion processes learned in a reproducing kernel Hilbert space. By integrating statistical learning methods and operator theory, we establish global convergence of the value function and derive finite-sample guarantees with bounds tied to system properties such as smoothness and stability. Our theoretical and numerical results indicate that operator-based approaches may hold promise in solving offline reinforcement learning using continuous-time optimal control.

They gathered together on a sunny July evening, between the churchyard's trees and leaning tombstones, to give thanks for the heat pump. Facing the newly installed system, in its large green metal box, they sang hymns and said prayers. "To thank God, really, for being able to work His wonders in mysterious ways," says Karen Crowhurst, who is part of a committee that helps to run St. The previous month, a flatbed truck carrying a hefty new heat pump system had eased itself onto the church grounds. By late July, the device was fully installed, and soon followed an outdoor thanksgiving service .

Google is still aiming for its "moonshot" 2030 energy goals The company's electricity demand has doubled since 2020, making its end-of-decade target more of a challenge. Last week, we hosted EmTech MIT, MIT Technology Review's annual flagship conference in Cambridge, Massachusetts. Over the course of three days of main-stage sessions, I learned about innovations in AI, biotech, and robotics. But as you might imagine, some of this climate reporter's favorite moments came in the climate sessions. I was listening especially closely to my colleague James Temple's discussion with Lucia Tian, head of advanced energy technologies at Google. They spoke about the tech giant's growing energy demand and what sort of technologies the company is looking to to help meet it.

Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting method to enhance the performance of physics-informed neural networks (PINNs). The adaptive sampling component identifies training points in regions where the solution exhibits rapid variation, while the adaptive weighting component balances the convergence rate across training points. Numerical experiments show that applying only adaptive sampling or only adaptive weighting is insufficient to consistently achieve accurate predictions, particularly when training points are scarce. Since each method emphasizes different aspects of the solution, their effectiveness is problem dependent. By combining both strategies, the proposed framework consistently improves prediction accuracy and training efficiency, offering a more robust approach for solving PDEs with PINNs.