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We present a language $L_n$ which is recognizable by a probabilistic finite automaton (PFA) with probability $1 - \epsilon$ for all $\epsilon > 0$ with $O(log^2n)$ states, with a deterministic finite automaton (DFA) with O(n) states, but a…

Quantum Physics · Physics 2007-05-23 Gatis Midrijanis

Let $t=t_1t_2\cdots$ be an element of the full shift with shift map $\tau$ on a finite set of characters $\mathcal{A}$ and let $ \Sigma=\text{ closure} \{\tau^i(t):\;i\in\N\cup\{0\}\}$. Let $f_t=f_{t_1,\,\infty}=\cdots\circ f_{t_2}\circ…

Dynamical Systems · Mathematics 2022-06-22 Dawoud Ahmadi Dastjerdi , Mahdi Aghaee

We introduce a new Forward-Flux Sampling in Time (FFST) algorithm to efficiently measure transition times in rare-event processes in non-equilibrium systems, and apply it to study the first-order (discontinuous) kinetic transition in the…

Statistical Mechanics · Physics 2015-05-19 David A. Adams , Robert M. Ziff , Leonard M. Sander

Weighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, chunking,…

Computation and Language · Computer Science 2017-01-18 Arturo Argueta , David Chiang

This paper proposes a new method that combines check-pointing methods with error-controlled lossy compression for large-scale high-performance Full-Waveform Inversion (FWI), an inverse problem commonly used in geophysical exploration. This…

The Quantum Alternating Operator Ansatz (QAOA+) framework has recently gained attention due to its ability to solve discrete optimization problems on noisy intermediate-scale quantum (NISQ) devices in a manner that is amenable to derivation…

Quantum Physics · Physics 2020-11-25 Sagnik Chatterjee , Debajyoti Bera

Nondeterministic weighted automata are finite automata with numerical weights on transitions. They define quantitative languages L that assign to each word w a real number L(w). The value of an infinite word w is computed as the maximal…

Logic in Computer Science · Computer Science 2009-09-10 Krishnendu Chatterjee , Laurent Doyen , Thomas A. Henzinger

Let $G$ be a directed graph on $n$ vertices. Given an integer $k<=n$, the SIMPLE $k$-PATH problem asks whether there exists a simple $k$-path in $G$. In case $G$ is weighted, the MIN-WT SIMPLE $k$-PATH problem asks for a simple $k$-path in…

Data Structures and Algorithms · Computer Science 2014-01-28 Ran Ben-Basat , Ariel Gabizon

A prominent problem in scheduling theory is the weighted flow time problem on one machine. We are given a machine and a set of jobs, each of them characterized by a processing time, a release time, and a weight. The goal is to find a…

Data Structures and Algorithms · Computer Science 2023-08-14 Alexander Armbruster , Lars Rohwedder , Andreas Wiese

Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental…

Physics and Society · Physics 2016-02-18 Yu-Qin Song , Jin-Long Liu , Zu-Guo Yu , Bao-Gen Li

Weighted automata are nondeterministic automata with numerical weights on transitions. They can define quantitative languages~$L$ that assign to each word~$w$ a real number~$L(w)$. In the case of infinite words, the value of a run is…

Logic in Computer Science · Computer Science 2015-07-01 Krishnendu Chatterjee , Laurent Doyen , Thomas A Henzinger

The total complexity (measured as the total number of gradient computations) of a stochastic first-order optimization algorithm that finds a first-order stationary point of a finite-sum smooth nonconvex objective function $F(w)=\frac{1}{n}…

Optimization and Control · Mathematics 2019-04-24 Lam M. Nguyen , Marten van Dijk , Dzung T. Phan , Phuong Ha Nguyen , Tsui-Wei Weng , Jayant R. Kalagnanam

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a…

Machine Learning · Computer Science 2019-11-27 Zhenmao Li , Yichao Wu , Ken Chen , Yudong Wu , Shunfeng Zhou , Jiaheng Liu , Junjie Yan

In this paper, the online variants of the classical Frank-Wolfe algorithm are considered. We consider minimizing the regret with a stochastic cost. The online algorithms only require simple iterative updates and a non-adaptive step size…

Machine Learning · Statistics 2016-08-16 Jean Lafond , Hoi-To Wai , Eric Moulines

Downlink beamforming is an essential technology for wireless cellular networks; however, the design of beamforming vectors that maximize the weighted sum rate (WSR) is an NP-hard problem and iterative algorithms are typically applied to…

Signal Processing · Electrical Eng. & Systems 2022-06-28 Jingyuan Xia , Gunduz Deniz

Continual learning in natural language processing plays a crucial role in adapting to evolving data and preventing catastrophic forgetting. Despite significant progress, existing methods still face challenges, such as inefficient parameter…

Machine Learning · Computer Science 2026-02-04 Min Zeng , Xi Chen , Haiqin Yang , Yike Guo

Preparing a quantum circuit that implements a given sparse state is an important building block that is necessary for many different quantum algorithms. In the context of fault-tolerant quantum computing, the so-called non-Clifford gates…

Quantum Physics · Physics 2025-08-08 Renaud Vilmart , Sunheang Ty , Chetra Mang

Let G=(V,E) be a graph with f:V\to Z_+ a function assigning degree bounds to vertices. We present the first efficient algebraic algorithm to find an f-factor. The time is \tilde{O}(f(V)^{\omega}). More generally for graphs with integral…

Data Structures and Algorithms · Computer Science 2013-04-26 Harold N. Gabow , Piotr Sankowski

Optimizing LLM-based workflows is typically formulated as a global search, where candidate workflows are evaluated based on a scalar metric. This paradigm, however, suffers from a critical flaw: information collapse. By reducing rich,…

Artificial Intelligence · Computer Science 2025-10-14 Jusheng Zhang , Kaitong Cai , Qinglin Zeng , Ningyuan Liu , Stephen Fan , Ziliang Chen , Keze Wang

Frank-Wolfe (FW) algorithms have been often proposed over the last few years as efficient solvers for a variety of optimization problems arising in the field of Machine Learning. The ability to work with cheap projection-free iterations and…

Machine Learning · Statistics 2015-10-27 Emanuele Frandi , Ricardo Nanculef , Stefano Lodi , Claudio Sartori , Johan A. K. Suykens