Tootfinder

Object personification in #autism: This paper will be very sad if you don't read it - PubMed
https://pubmed.ncbi.nlm.nih.gov/30101594/
Object personification is the attrib…

Freer Arrows and Why You Need Them in Haskell
Grant VanDomelen, Gan Shen, Lindsey Kuper, Yao Li
https://arxiv.org/abs/2506.12212 https://

Freer Arrows and Why You Need Them in Haskell
Freer monads are a useful structure commonly used in various domains due to their expressiveness. However, a known issue with freer monads is that they are not amenable to static analysis. This paper explores freer arrows, a relatively expressive structure that is amenable to static analysis. We propose several variants of freer arrows. We conduct a case study on choreographic programming to demonstrate the usefulness of freer arrows in Haskell.

High-expressibility Quantum Neural Networks using only classical resources
Marco Maronese, Francesco Ferrari, Matteo Vandelli, Daniele Dragoni
https://arxiv.org/abs/2506.13605

High-expressibility Quantum Neural Networks using only classical resources
Quantum neural networks (QNNs), as currently formulated, are near-term quantum machine learning architectures that leverage parameterized quantum circuits with the aim of improving upon the performance of their classical counterparts. In this work, we show that some desired properties attributed to these models can be efficiently reproduced without necessarily resorting to quantum hardware. We indeed study the expressibility of parametrized quantum circuit commonly used in QNN applications and …

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Yupeng Zhou, Can Cui, Juntong Peng, Zichong Yang, Juanwu Lu, Jitesh H Panchal, Bin Yao, Ziran Wang
https://arxiv.org/abs/2506.14100

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text pairs. However, adapting these models from broad web-scale data to the safety-critical context of driving presents a significant challenge, commonly referred to as domain shift. Existing simulation-based and dataset-driven evaluation methods, although valuable, often fail to capture the full complexity of real-world …

A DC-Reformulation for Gradient-$L^0$-Constrained Problems in Function Spaces
Bastian Dittrich, Evelyn Herberg, Roland Herzog, Georg M\"uller
https://arxiv.org/abs/2506.11917

A DC-Reformulation for Gradient-$L^0$-Constrained Problems in Function Spaces
Cardinality constraints in optimization are commonly of $L^0$-type, and they lead to sparsely supported optimizers. An efficient way of dealing with these constraints algorithmically, when the objective functional is convex, is reformulating the constraint using the difference of suitable $L^1$- and largest-$K$-norms and subsequently solving a sequence of penalized subproblems in the difference-of-convex (DC) class. We extend this DC-reformulation approach to problems with $L^0$-type cardinalit…

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective
Sibo Zhang, Bruno Clerckx
https://arxiv.org/abs/2506.12646

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective
Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information, fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an infor…

The Limits of Tractable Marginalization
Oliver Broadrick, Sanyam Agarwal, Guy Van den Broeck, Markus Bl\"aser
https://arxiv.org/abs/2506.12020 https:/…

The Limits of Tractable Marginalization
Marginalization -- summing a function over all assignments to a subset of its inputs -- is a fundamental computational problem with applications from probabilistic inference to formal verification. Despite its computational hardness in general, there exist many classes of functions (e.g., probabilistic models) for which marginalization remains tractable, and they can be commonly expressed by polynomial size arithmetic circuits computing multilinear polynomials. This raises the question, can all…

Inference for microbe--metabolite association networks using a latent graph model
Jing Ma
https://arxiv.org/abs/2506.12275 https://ar…

Inference for microbe--metabolite association networks using a latent graph model
Correlation networks are commonly used to infer associations between microbes and metabolites. The resulting p-values are then corrected for multiple comparisons using existing methods such as the Benjamini and Hochberg procedure to control the false discovery rate (FDR). However, most existing methods for FDR control assume the p-values are weakly dependent. Consequently, they can have low power in recovering microbe-metabolite association networks that exhibit important topological features, …

The Multi-Wavelength Context of Delayed Radio Emission in TDEs: Evidence for Accretion-Driven Outflows
Kate D. Alexander, Raffaella Margutti, Sebastian Gomez, Michael Stroh, Ryan Chornock, Tanmoy Laskar, Y. Cendes, Edo Berger, Tarraneh Eftekhari, Noah Franz, Aprajita Hajela, B. D. Metzger, Giacomo Terreran, Michael Bietenholz, Collin Christy, Fabio de Colle, S. Komossa, Matt Nicholl, Enrico Ramirez-Ruiz, Richard Saxton, Genevieve Schroeder, Peter Williams, William Wu

The Multi-Wavelength Context of Delayed Radio Emission in TDEs: Evidence for Accretion-Driven Outflows
Recent observations presented in Cendes et al. (2024a) show that optically selected tidal disruption events (TDEs) commonly produce delayed radio emission that can peak years post-disruption. Here, we explore the multi-wavelength properties of a sample of radio-observed optically selected TDEs to shed light on the physical process(es) responsible for the late-rising radio emission. We combine new late-time X-ray observations with archival optical, UV, X-ray, and radio data to conclude that a di…

ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
Ryan Quach, Yidi Wang, Ali Jahanshahi, Daniel Wong, Hyoseung Kim
https://arxiv.org/abs/2506.12598

ECLIP: Energy-efficient and Practical Co-Location of ML Inference on Spatially Partitioned GPUs
As AI inference becomes mainstream, research has begun to focus on improving the energy consumption of inference servers. Inference kernels commonly underutilize a GPU's compute resources and waste power from idling components. To improve utilization and energy efficiency, multiple models can co-locate and share the GPU. However, typical GPU spatial partitioning techniques often experience significant overheads when reconfiguring spatial partitions, which can waste additional energy through rep…