24 papers · ranked by Valyu relevance
Yuetian Luo, Rina Foygel Barber
Computational Constraints Authors: ['Yuetian Luo' 'Rina Foygel Barber'] Algorithmic stability is a central notion in learning theory that quantifies the sensitivity of an algorithm to small changes in the training data. If a learning algorithm satisfies certain stability properties, this leads to many important…
Junali Jasmine Jena, Samarendra Chandan Bindu Dash, Suresh Chandra Satapathy
'Suresh Chandra Satapathy'] Swarm-based optimization algorithms have been popularly used these days for optimization of various real world problems but sometimes it becomes hard to estimate the associated characteristics due to their stochastic nature. To ensure a steady performance of these techniques, it is essential…
Rong Ma, Xi Li, Jingyuan Hu, Bin Yu
Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it remains challenging to extract smooth, low-dimensional representations from inherently noisy, highdimensional single-cell data. Neighbor…
Tommaso Bradde, S. Grivet‐Talocia, Quirin Aumann, Ion Victor Gosea
In recent years, the Adaptive Antoulas-Anderson (AAA) algorithm has established itself as the method of choice for solving rational approximation problems. Data-driven Model Order Reduction (MOR) of large-scale Linear Time-Invariant (LTI) systems represents one of the many applications in which this algorithm has…
Zhe Shen
— Before 2025, no open-source system existed that could learn Lyapunov stability certificates directly from noisy, real-world flight data. This work addresses that gap by proposing a data-driven approach that learns Lyapunov functions from trajectory data under realistic, noise-corrupted conditions. Unlike statistical…
Thomas Izgin, Stefan Kopecz, Andreas Meister
Since almost twenty years, modified Patankar–Runge–Kutta (MPRK) methods have proven to be efficient and robust numerical schemes that preserve positivity and conservativity of the production-destruction system irrespectively of the time step size chosen. Due to these advantageous properties they are used for a wide…
Shenyu Liu, Sonia Martı́nez, Jorge Cortés
—This paper studies network resilience against structured additive perturbations to its topology. We consider dynamic networks modeled as linear time-invariant systems subject to perturbations of bounded energy satisfying specific sparsity and entry-wise constraints. Given an energy level, the structured pseudospectral…
Behnam Yousefi, Benno Schwikowski
Clustering plays an important role in a multitude of bioinformatics applications, including protein function prediction, population genetics, and gene expression analysis. The results of most clustering algorithms are sensitive to variations of the input data, the clustering algorithm and its parameters, and individual…
Sumedh S Nagrale, Alik S Widge
The use of Deep Brain Stimulation (DBS) on the ventral capsule/ventral striatum (VCVS) has therapeutic potential for patients with refractory psychiatric disorders, but clinical success is impeded by the need for a time-consuming and trial-and-error process when setting the parameters, this process relying on…
Ziyi Zhang, Yorie Nakahira, Guannan Qu
We study the problem of stabilizing an unknown partially observable linear time-invariant (LTI) system. For fully observable systems, leveraging an unstable/stable subspace decomposition approach, state-of-art sample complexity is independent from system dimension n and only scales with respect to the dimension of the…
Veerupaksh Singla, Qiyuan Zhao, Brett Savoie
The absence of computational methods to predict stressor-specific degradation susceptibilities represents a significant and costly challenge to the introduction of new materials into applications. Here, a machine-learning framework is developed that predicts stressor-specific stability scores from computationally…
Tianqi Xia, Mingming Zhang, Shaohong Wang, Heming Jia + 2 more
'Laith Abualigah' 'Xuewen Xia'] Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is employed for flow field feature…
Mathieu Hemery, François Fages
The Turing completeness of continuous Chemical Reaction Networks (CRNs) states that any computable real function can be computed by a continuous CRN on a finite set of molecular species, possibly restricted to elementary reactions, i.e. with at most two reactants and mass action law kinetics. In this paper, we…
Cong Xie, Kun Wang, Ivanka Stamova
Uniform error estimates with power-type asymptotic constants of the finite element method for the unsteady Navier-Stokes equations are deduced in this paper. By introducing an iterative scheme and studying its convergence, we firstly derive that the solution of the Navier-Stokes equations is bounded by power-type…
Zhenghong Chen
Persistent homology, building its foundation on homology theory, has been successfully applied various fields containing computation of topology. Here, we derive and implement persistent homology theory to specefic topological spaces with G-invariant covering, whereby persistence over quotient space can be canonically…
Yuyong Tan, Jianfeng Wang, Bin Wang, Yongquan Zhou
The intelligent optimization algorithm has become a key tool in complex and intertwined engineering and science fields. However, with the increasing complexity of the problem and the rapid expansion of the data scale, the performance of the algorithm has been challenged unprecedentedly. The artificial lemming algorithm…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…
Zih-Yu Lin, Jiaonan Sun, Stephen Shiring, Letian Dou + 1 more
Organic-inorganic hybrid perovskite semiconductors are under investigation for many applications owing to their excellent optoelectronic properties and relatively simple processing. Two-dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due…
Lionel Zoubritzky, François-Xavier Coudert
We present here an open-source Julia library for the topological identification of crystalline materials, with algorithmic and computational improvements over the previously available software in the field, resulting in a speed increase of one order of magnitude. This new algorithm and implementation can therefore be…
Authors not listed
The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…
Doaa Sami Khafaga, Abdelhameed Ibrahim, El-Sayed M. El-Kenawy, Abdelaziz A. Abdelhamid + 9 more
'Abdelaziz A. Abdelhamid' 'Faten Khalid Karim' 'Seyedali Mirjalili' 'Nima Khodadadi' 'Wei Hong Lim' 'Marwa M. Eid' 'Mohamed E. Ghoneim' 'Sameer Antani' 'Azhar Zam' 'Esa Prakasa'] Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing…
Serdar Ekinci, Davut Izci, Mostafa Jabari, Mohit Bajaj + 2 more
Achieving precise and stable engine speed regulation in spark-ignition (SI) systems remains a challenging task because of the inherent nonlinearities, time-varying characteristics, and external disturbances of internal combustion engines (ICEs). Conventional proportional-integral-derivative (PID) controllers often fail…
Takafumi Shiraogawa, Jun-ya Hasegawa
Inverse molecular design allows optimization of molecules in chemical space and is promising for accelerating the development of functional molecules and materials. To design realistic molecules, it is necessary to consider geometric stability during optimization. In this work, we introduce an inverse design method…