24 papers · ranked by Valyu relevance
Tristan Pollner, Amin Saberi, Anders Wikum
We study two-stage bipartite matching, in which the edges of a bipartite graph on vertices (B 1 ∪ B2, I) are revealed in two batches. In stage one, a matching must be selected from among revealed edges E ⊆ B 1 × I. In stage two, edges E θ ⊆ B 2 × I are sampled from a known distribution, and a second matching must be…
Ruifen Dai, Xin Zheng, Fang Wang, Lei Guo
—The investigation of legal judgment prediction (LJP), such as sentencing prediction, has attracted broad attention for its potential to promote judicial fairness, making the accuracy and reliability of its computation result an increasingly critical concern. In view of this, we present a new sentencing model that…
Evgenii Yu. Zlokazov, Rostislav S. Starikov, Pavel A. Cheremkhin, Timur Z. Minikhanov + 1 more
High-speed realization of computer-generated holograms (CGHs) is a crucial problem in the field of modern 3D visualization and optical image processing system development. Binary CGHs can be realized using high-resolution, high-speed spatial light modulators such as ferroelectric liquid crystals on silicon devices or…
Iasonas Nikolaou, Miltiadis Stouras, Stratis Ioannidis, Evimaria Terzi
Given a collection of monotone submodular functions, the goal of Two-Stage Submodular Maximization (2SSM) [Balkanski et al., [2016]] is to restrict the ground set so an objective selected u.a.r. from the collection attains a high maximal value, on average, when optimized over the restricted ground set. We introduce the…
Tianying Feng, Li Cai
The expectation-maximization (EM) algorithm is widely used for parameter estimation in item response theory (IRT) modeling. However, when applied to datasets with large numbers of individuals and items, the standard EM algorithm can be slow to converge, with computationally expensive E-steps. We propose a modified EM…
Lingyu Zhao, Xiaorong Zhu, Jianhong Cai, Jingjing Wang
With the rapid expansion of data scale, compute-intensive tasks will become a core application of 6G networks. As Unmanned Aerial Vehicle (UAV) technology advances, UAVs can assist in task offloading for mobile edge computing by collaborating to overcome individual UAV limitations in battery life and computational…
Jumpei Kato, Akira Tanji, Hiroyuki Harada, Kaito Wada + 2 more
Estimating properties of unknown quantum states via quantum singular value transformation (QSVT) often requires high-degree polynomials to handle small eigenvalues of density matrices. Specifically, the existing approaches determine the polynomial degree by relying on overly conservative worst-case bounds based on the…
Hailin Sun, Xiaojun Chen
This paper introduces a class of two-stage stochastic minimax problems where the first-stage objective function is nonconvex-concave while the second-stage objective function is strongly convexconcave. We establish properties of the second-stage minimax value function and solution functions, and characterize the…
Hissah Albaqami, Mehdi Mrad, Anis Gharbi, Munevver Mine Subasi + 1 more
This paper presents a Monte Carlo simulation-based approach for solving stochastic two-stage bond portfolio optimization problems. The main objective is to optimize the cost of the bond portfolio while making decisions on bond purchases, holdings, and sales under random market conditions such as interest rate…
Riley, Benjamin P., Daoutidis, Prodromos + 2 more
Two-stage stochastic mixed-integer linear programs with mixed-integer recourse arise in many practical applications but are computationally challenging due to their large size and the presence of integer decisions in both stages. The integer L-shaped method with alternating cuts is a widely used decomposition algorithm…
Chen Chen, Min Ren, Min Zhang, Dabao Zhang
We propose a two-stage penalized least squares method to build large systems of structural equations based on the instrumental variables view of the classical two-stage least squares method. We show that, with large numbers of endogenous and exogenous variables, the system can be constructed via consistent estimation…
R. Kandasamy, S. Anbu Karuppusamy
Energy-efficient routing in Wireless Sensor Networks (WSNs) is a critical challenge due to uneven energy depletion and dynamic topology changes. The paper suggests a Lifetime-Aware Ant Colony Optimization-based Routing Algorithm (LTAWSN) which incorporates the residual energy, hop count and spatial proximity to…
Wei Liu, Roberto dos Reis, Chad A. Mirkin, Vinayak P. Dravid + 2 more
Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four-dimensional scanning transmission electron…
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…
Jiu Luo, Xing Liu, Jin Wang, Yi Heng
Enhancing the energy efficiency for energy-intensive seawater desalination technologies is imperative to sustainably mitigate water scarcity while reducing carbon footprints. This work presents a transformative advance in reverse osmosis desalination technology by fundamentally redefining the long-standing trade-off…
David W. Craig, Andrei S. Rodin
Spatial transcriptomics, multiplex imaging, and computational pathology now map tissue organization at cellular resolution, but the analyses applied to these data remain correlational. Clustering and co-occurrence statistics describe which features appear together; they cannot say which feature drives the others. We…
Andrea C. Graf, Jürgen Zanghellini
Multi-stage continuous bioprocessing can increase volumetric productivity, operational consistency, and process throughput, but its design is complicated by coupling among dilution rate, reactor volume, feed allocation, and cellular physiology. Here, we present ContiDesigner, available at…
Albert Jiménez-Blanco, Lorién López-Villellas, Juan Carlos Moure, Miquel Moreto + 1 more
Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long…
Hayato Maeda, Shuo Wang, Akihiro Funamizu
Animals and humans use multiple behavioral strategies to perform tasks. However, neural implementations of multiple strategies remain elusive, as some studies propose distinct pathways, while others observe overlapping brain regions associated with strategies. We propose a hybrid deep reinforcement learning (H-DRL)…
Mao Yasueda, Masakazu Taira, Thomas Akam, Mark E. Walton + 1 more
Reinforcement learning theory formulates distinct decision-making strategies, including reactive model-free and deliberative model-based strategies. This study investigates how mice adjust their reinforcement learning strategies while learning decision-making in dynamic environments. Unlike previous studies that…
Authors not listed
Synthetic spider silk fibers currently achieve tensile strengths up to approximately 1.2 gigapascals (GPa), limiting their deployment in advanced protective materials. This theoretical study presents a novel, four-stage bio-engineering protocol for recombinant spider silk (rSpidroin) fibers with predicted ultimate…
Authors not listed
Two kinetic schemes of the general modifier mechanism have been analysed in a quasi-steady state approximation, assuming that the reaction product concentration is negligible (a natural assumption for the initial rate method) and without additional simplifying assumptions. The characteristic equations have been…
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
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…