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Pseudoentropy characterizations provide a quantitatively precise demonstration of the close relationship between computational hardness and computational randomness. We prove a unified pseudoentropy characterization that generalizes and…

Computational Complexity · Computer Science 2025-09-05 Lunjia Hu , Salil Vadhan

We propose a unified approach to the formal long-wave reduction of several fluid models for thin-layer incompressible homogeneous flows driven by a constant external force like gravity. The procedure is based on a mathematical coherence…

Numerical Analysis · Mathematics 2013-06-17 François Bouchut , Sébastien Boyaval

At the core of the popular Transformer architecture is the self-attention mechanism, which dynamically assigns softmax weights to each input token so that the model can focus on the most salient information. However, the softmax structure…

Machine Learning · Computer Science 2025-05-27 Fanqi Yan , Huy Nguyen , Pedram Akbarian , Nhat Ho , Alessandro Rinaldo

Accurate load forecasting is critical for reliable and efficient planning and operation of electric power grids. In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature…

Machine Learning · Computer Science 2023-05-10 Jing Xiong , Yu Zhang

In the context of transition analysis, linear input-output analysis determines worst-case disturbances to a laminar base flow based on a generic right-hand-side volumetric/boundary forcing term. The worst-case forcing is not physically…

Fluid Dynamics · Physics 2023-02-15 Omar Kamal , Matthew T. Lakebrink , Tim Colonius

While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the `class-symmetric' nature of these…

Machine Learning · Computer Science 2026-05-28 Yasser Taha , Grégoire Montavon , Nils Körber

Transformers can perform in-context classification from a few labeled examples, yet the inference-time algorithm remains opaque. We study multi-class linear classification in the hard no-margin regime and make the computation identifiable…

Machine Learning · Computer Science 2026-04-20 Patrick Lutz , Themistoklis Haris , Arjun Chandra , Aditya Gangrade , Venkatesh Saligrama

Exact subgraph matching is a fundamental graph operator that supports many graph analytics tasks, yet it remains computationally challenging due to its NP-completeness. Recent learning-based approaches accelerate query processing via…

Databases · Computer Science 2026-04-22 Yutong Ye , Weilong Ren , Yang Liu , Mengyi Yan , Ruijie Wang , Li Sun , Jianxin Li , Philip S. Yu

With this contribution, we give a complete and comprehensive framework for modeling the dynamics of complex mechanical structures as port-Hamiltonian systems. This is motivated by research on the potential of lightweight construction using…

Computational Physics · Physics 2020-08-19 Alexander Warsewa , Michael Böhm , Oliver Sawodny , Cristina Tarín

Group-equivariant quantum models are designed to exploit symmetry and can improve trainability, but it remains unclear how symmetry constraints shape their adversarial robustness. We study this question through a feature-level analysis of…

Quantum Physics · Physics 2026-04-20 Maureen Krumtünger , Martin Sevior , Muhammad Usman

Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left…

Computation and Language · Computer Science 2016-09-29 Shihao Ji , Hyokun Yun , Pinar Yanardag , Shin Matsushima , S. V. N. Vishwanathan

Inference of the network structure (e.g., routing topology) and dynamics (e.g., link performance) is an essential component in many network design and management tasks. In this paper we propose a new, general framework for analyzing and…

Networking and Internet Architecture · Computer Science 2019-11-13 Jian Ni , Sekhar Tatikonda

The Multiplicative Weights Exponential Mechanism (MWEM) is a fundamental iterative framework for private data analysis, with broad applications such as answering $m$ linear queries, or privately solving systems of $m$ linear constraints.…

Machine Learning · Computer Science 2026-02-04 Themistoklis Haris , Steve Choi , Mutiraj Laksanawisit

Reachability analysis is a fundamental program analysis with a wide variety of applications. We present FlowCFL, a framework for type-based reachability analysis in the presence of mutable data. Interestingly, the underlying semantics of…

Programming Languages · Computer Science 2020-05-15 Ana Milanova

In real world applications, uncertain parameters are the rule rather than the exception. We present a reachability algorithm for linear systems with uncertain parameters and inputs using set propagation of polynomial zonotopes. In contrast…

Systems and Control · Electrical Eng. & Systems 2024-06-18 Yushen Huang , Ertai Luo , Stanley Bak , Yifan Sun

Often fairness assumptions need to be made in order to establish liveness properties of distributed systems, but in many situations these lead to false conclusions. This document presents a research agenda aiming at laying the foundations…

Logic in Computer Science · Computer Science 2017-11-15 Rob van Glabbeek

An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable-metric in that, in each iteration, a step is computed using a symmetric positive definite matrix whose value is updated as in a quasi-Newton…

Optimization and Control · Mathematics 2019-02-05 Frank E. Curtis , Daniel P. Robinson , Baoyu Zhou

The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior. This is especially important for interpretability and fairness…

Machine Learning · Computer Science 2023-07-17 Ouail Kitouni , Niklas Nolte , Michael Williams

Pair-based metric learning has been widely adopted to learn sentence embedding in many NLP tasks such as semantic text similarity due to its efficiency in computation. Most existing works employed a sequence encoder model and utilized…

Computation and Language · Computer Science 2020-05-26 Li Zhang , Han Wang , Lingxiao Li

Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a…

Materials Science · Physics 2024-03-12 Shengzhi Luan , Enze Chen , Joel John , Stavros Gaitanaros