Tinglesbar incorporates ASMR into elementary school and doctor's visits simulations, offering a social haven for introverts. "It's time for your lice check," a woman who goes by "Ms. K" whispers directly into my ear as she starts running her fingers into my scalp and through each strand of hair. I'm in a dark room, Eastern flute music playing in the background as I sit across from my partner who's also having his hair caressed by a stranger. We close our eyes so we don't burst out laughing.

Classical methods for AMR depend on heuristics or expensive error estimators, hindering their use for complex simulations.

Federated Learning (FL) allows the training of deep neural networks in a distributed and privacy-preserving manner. However, this concept suffers from malfunctioning updates sent by the attending clients that cause global model performance degradation. Reasons for this malfunctioning might be technical issues, disadvantageous training data, or malicious attacks. Most of the current defense mechanisms are meant to require impractical prerequisites like knowledge about the number of malfunctioning updates, which makes them unsuitable for real-world applications. To counteract these problems, we introduce a novel method called Angular Support for Malfunctioning Client Resilience (ASMR), that dynamically excludes malfunctioning clients based on their angular distance. Our novel method does not require any hyperparameters or knowledge about the number of malfunctioning clients.

We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimization, ASMR leverages Centaur -- a recently proposed foundation model of human cognition -- to identify gaps in an interpretable cognitive model. These gaps are then addressed through automated revisions generated by a language-based reasoning model. We demonstrate the utility of this approach in a multi-attribute decision-making task, showing that ASMR discovers cognitive models that predict human behavior at noise ceiling while retaining interpretability. Taken together, our results highlight the potential of ASMR to automate core components of the cognitive modeling pipeline.

On March 18, the official White House account on X posted two photographs of Virginia Basora-Gonzalez, a woman who was arrested earlier this month by U.S. Immigration and Customs Enforcement. The post described her as a "previously deported alien felon convicted of fentanyl trafficking," and celebrated her capture as a win for the administration. In one photograph, Basora-Gonzalez is shown handcuffed and weeping in a public parking lot. The White House account posted about Basora-Gonzalez again yesterday--this time, rendering her capture in the animated style of the beloved Japanese filmmaker Hayao Miyazaki, who co-founded the animation company Studio Ghibli. Presumably, whoever runs the account had used ChatGPT, which has been going viral this week for an update to its advanced "4o" model that enables it to transform photographs in the style of popular art, among other things.

Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element Method (FEM) are widely used. However, the FEM becomes computationally expensive as problem complexity and accuracy demands increase. Adaptive Mesh Refinement (AMR) improves the FEM by dynamically allocating mesh elements on the domain, balancing computational speed and accuracy. Classical AMR depends on heuristics or expensive error estimators, limiting its use in complex simulations. While learning-based AMR methods are promising, they currently only scale to simple problems. In this work, we formulate AMR as a system of collaborating, homogeneous agents that iteratively split into multiple new agents. This agent-wise perspective enables a spatial reward formulation focused on reducing the maximum mesh element error. Our approach, Adaptive Swarm Mesh Refinement (ASMR), offers efficient, stable optimization and generates highly adaptive meshes at user-defined resolution during inference. Extensive experiments, including volumetric meshes and Neumann boundary conditions, demonstrate that ASMR exceeds heuristic approaches and learned baselines, matching the performance of expensive error-based oracle AMR strategies. ASMR additionally generalizes to different domains during inference, and produces meshes that simulate up to 2 orders of magnitude faster than uniform refinements in more demanding settings.

Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor rendering MR inaccessible is lengthy scan times. An MR scanner collects measurements associated with the underlying anatomy in the Fourier space, also known as the k-space. Creating a high-fidelity image requires collecting large quantities of such measurements, increasing the scan time. Traditionally to accelerate an MR scan, image reconstruction from under-sampled k-space data is the method of choice. However, recent works show the feasibility of bypassing image reconstruction and directly learning to detect disease directly from a sparser learned subset of the k-space measurements. In this work, we propose Adaptive Sampling for MR (ASMR), a sampling method that learns an adaptive policy to sequentially select k-space samples to optimize for target disease detection. On 6 out of 8 pathology classification tasks spanning the Knee, Brain, and Prostate MR scans, ASMR reaches within 2% of the performance of a fully sampled classifier while using only 8% of the k-space, as well as outperforming prior state-of-the-art work in k-space sampling such as EMRT, LOUPE, and DPS.

Department of Computer Science, University of Toronto jasonlcl@connect.hku.hk Coordinate network or implicit neural representation (INR) is a fast-emerging method for encoding natural signals (such as images and videos) with the benefits of a compact neural representation. While numerous methods have been proposed to increase the encoding capabilities of an INR, an often overlooked aspect is the inference efficiency, usually measured in multiply-accumulate (MAC) count. This is particularly critical in use cases where inference throughput is greatly limited by hardware constraints. To this end, we propose the Activation-Sharing Multi-Resolution (ASMR) coordinate network that combines multi-resolution coordinate decomposition with hierarchical modulations. Specifically, an ASMR model enables the sharing of activations across grids of the data. This largely decouples its inference cost from its depth which is directly correlated to its reconstruction capability, and renders a near O(1) inference complexity irrespective of the number of layers. Experiments show that ASMR can reduce the MAC of a vanilla SIREN model by up to 500 while achieving an even higher reconstruction quality than its SIREN baseline. Neural networks have been proven to be very effective at learning representations of various modalities of data such as images, videos, 3D shapes, neural fields, and many more. In particular, Sitzmann et al. (2020); Mildenhall et al. (2021); Park et al. (2019); Li et al. (2024) have demonstrated that simple coordinate networks, taking in a coordinate system and outputting the modality-specific data, exhibit state-of-the-art (SOTA) expressivity as an implicit neural representation (INR). However, while numerous methods have been proposed to improve the encoding capabilities of an INR, an aspect that is often overlooked is the network's cost of inference Currently, hybrid INRs that make use of explicit representations such as Plenoxels (Fridovich-Keil et al., 2022) and Instant-NGP (Müller et al., 2022) are the best at low-cost inference as they remove the need to learn a complex neural network.

Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy. Classical methods for AMR depend on heuristics or expensive error estimators, hindering their use for complex simulations. Recent learning-based AMR methods tackle these issues, but so far scale only to simple toy examples. We formulate AMR as a novel Adaptive Swarm Markov Decision Process in which a mesh is modeled as a system of simple collaborating agents that may split into multiple new agents. This framework allows for a spatial reward formulation that simplifies the credit assignment problem, which we combine with Message Passing Networks to propagate information between neighboring mesh elements. We experimentally validate our approach, Adaptive Swarm Mesh Refinement (ASMR), on challenging refinement tasks. Our approach learns reliable and efficient refinement strategies that can robustly generalize to different domains during inference. Additionally, it achieves a speedup of up to $2$ orders of magnitude compared to uniform refinements in more demanding simulations. We outperform learned baselines and heuristics, achieving a refinement quality that is on par with costly error-based oracle AMR strategies.