22 papers · ranked by Valyu relevance
Tianhang Lu, Runtian Ren, Shengcai Liu, Ke Tang
This paper studies learning-augmented and randomized online aggregation with delays on a line metric. We consider advice given as online suggested service lengths, and evaluate the algorithms in terms of robustness and consistency. For each $λ\in (0,1]$, we first propose a deterministic learning-augmented…
Sizhe Li, Nicolas Christianson, Tongxin Li
Algorithms with predictions has emerged as a powerful framework to combine the robustness of traditional online algorithms with the data-driven performance benefits of machine-learned (ML) predictions. However, most existing approaches in this paradigm are overly conservative, as they do not leverage problem structure…
Allan Borodin, Christodoulos Karavasilis, David Zhang
Interest in the random order model (ROM) leads us to initiate a study of utilizing random-order arrivals to extract random bits with the goal of de-randomizing algorithms. Besides producing simple algorithms, simulating random bits through random arrivals enhances our understanding of the comparative strength of…
Mahmudur Rahman Hera, David Koslicki, Conrado Martínez
With the surge in sequencing data generated from an ever-expanding range of biological studies, designing scalable computational techniques has become essential. One effective strategy to enable large-scale computation is to split long DNA or protein sequences into k-mers, and summarize large k-mer sets into compact…
Alenrex Maity, Yen-Lin Chen, Wassim Alexan, Por Lip Yee + 1 more
This study proposes a fast image encryption method for color images, integrating an autoencoder to compress the image and a 6D hyperchaotic system to ensure enhanced security. Initially, a hash value is obtained from the original color image. The hash value, which serves as the secret key of the proposed encryption…
Mengqi Zhang, Guangqiang Teng, Xiaoyu Lei, Boris Ryabko
Lei proposed an algorithm Algorithm $A_{3}$ in 2023 to generate an exact discrete uniform distribution from an unknown biased Bernoulli source. The present paper does not claim a new extraction algorithm. Its contributions are analytical: first, we provide a Fourier-analytic proof of the uniformity mechanism based on…
Sander Borst, Danish Kashaev
We study the online load balancing problem on unrelated machines, with the objective of minimizing the square of the ℓ 2 norm of the loads on the machines. The greedy algorithm of Awerbuch et al. (STOC'95) is optimal for deterministic algorithms and achieves a competitive ratio of 3 + 2 √ 2 ≈ 5.828, and an improved…
Zeynep Haber, Harun Uguz, Huseyin Hakli, AbdElRahman Ahmed ElSaid
The Artificial Bee Colony (ABC) algorithm is a simple and effective population-based optimization method, but it may exhibit unstable convergence and weak exploitation capability in discrete and highly constrained problems. This study proposes an improved ABC framework that integrates a probabilistic Uniform crossover…
Zihao Cheng, Li Cao, Yang Qiu, Yinggao Yue + 1 more
Aiming at the problems of uneven population initialization distribution, easy trapping in local optima, unbalanced exploration and exploitation capabilities, insufficient optimization accuracy and convergence speed of the original Greater Cane Rat Algorithm (GCRA), this paper proposes a Chaos-Integrated…
Zahraa Elsayed Mohamed, Walid Dabour
This study proposes a hybrid variant of Elephant Herding Optimization that combines an adaptive clan-updating schedule, Lévy-flight exploration from Cuckoo Search, and elite opposition-based sampling. The design targets three known limitations-premature convergence, imbalance between exploration and exploitation, and…
Achmad Pratama Rifai, Wangi Pandan Sari
The multi-row layout problem (MRLP) involves arranging machines of varying sizes across multiple rows to minimize material handling costs. It is a significant design problem that frequently arises in practical situations. Some industrial settings require safety regulations to ensure a minimum distance between machines.…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
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…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Deepak Kumar Verma, Mamta Rani, Roli Dikshit, Himanshu Gupta + 1 more
This research delves into the concerns raised by numerous researchers regarding the security of Baptista’s chaotic cryptosystem. The paper scrutinizes two fundamental issues in the Baptista algorithm: the imperfect distribution of chaotic data and the constraints of a single attractor. To address these issues, we…
Authors not listed
Atomistic simulations provide essential mechanistic insights into chemical processes, yet many important phenomena in chemistry and materials science occur on timescales that are inaccessible to molecular dynamics. Existing computational approaches force a choice between atomic resolution on relatively short timescales…
Authors not listed
Exploring the potential energy surface to sample transition state regions is crucial to understand the atomic processes that govern chemical reactivity. Ideally, the exploration does not require any collective variables that are based on prior chemical domain knowledge. With this in mind, we adapt the stochastic saddle…
Amanda Forde, Gibran Hemani, John Ferguson
Mendelian randomization (MR) is a popular statistical technique that uses genetic variants to explore causal relationships in observational epidemiology. Summary-level MR, the most common form, relies on published GWAS summary statistics to estimate causal effects between exposures and outcomes. However, empirical…
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…
Rabea Turon, Lars C. Reining, Philipp A. Hummel, Lynn Schmittwilken + 5 more
Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants’ responses. However, in high-dimensional spaces, identifying…