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Journal articles on the topic 'Stochastic rounding'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Winkler, Julian, and Sabah Badri-Hoeher. "Implementation of a Low-Cost Navigation System Using Data Fusion of a Micro-Electro-Mechanical System Inertial Sensor and an Ultra Short Baseline on a Microcontroller." Sensors 25, no. 10 (2025): 3125. https://doi.org/10.3390/s25103125.

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In this work, a low-cost low-power navigation solution for autonomous underwater vehicles is introduced utilizing a Micro-Electro-Mechanical System (MEMS) inertial sensor and an ultra short baseline (USBL) system. The complete signal processing is implemented on a cheap 16-bit fixed-point arithmetic microcontroller. For data fusion and calibration, an error state Kalman filter in square root form is used, which preserves stability in case of rounding errors. To further reduce the influence of rounding errors, a stochastic rounding scheme is applied. The USBL measurements are integrated using t

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2

Paxton, E. Adam, Matthew Chantry, Milan Klöwer, Leo Saffin, and Tim Palmer. "Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding." Journal of Climate 35, no. 4 (2022): 1215–29. http://dx.doi.org/10.1175/jcli-d-21-0343.1.

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Abstract Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across opera

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van Stee, Rob. "SIGACT News Online Algorithms Column 42." ACM SIGACT News 55, no. 3 (2024): 52–82. http://dx.doi.org/10.1145/3695887.3695894.

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For this issue, I am pleased to present a tutorial by Will Ma on using randomized rounding to design online policies. Randomized rounding has of course been used extensively in the design of approximation algorithms and also to approximately solve sequential stochastic optimization problems. Will describes several examples that show how randomized rounding can be used in this context. As always, I would like to invite more contributions to this column, be it surveys, conference reports, or technical articles related to online algorithms and competitive analysis. If you are considering becoming

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Connolly, Michael P., Nicholas J. Higham, and Theo Mary. "Stochastic Rounding and Its Probabilistic Backward Error Analysis." SIAM Journal on Scientific Computing 43, no. 1 (2021): A566—A585. http://dx.doi.org/10.1137/20m1334796.

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5

Gupta, Anupam, R. Ravi, and Amitabh Sinha. "LP Rounding Approximation Algorithms for Stochastic Network Design." Mathematics of Operations Research 32, no. 2 (2007): 345–64. http://dx.doi.org/10.1287/moor.1060.0237.

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Dexter, Gregory, Christos Boutsikas, Linkai Ma, Ilse C. F. Ipsen, and Petros Drineas. "Stochastic Rounding Implicitly Regularizes Tall-and-Thin Matrices." SIAM Journal on Matrix Analysis and Applications 46, no. 1 (2025): 341–69. https://doi.org/10.1137/24m1647679.

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7

Arciniega, Armando, and Edward Allen. "Rounding Error in Numerical Solution of Stochastic Differential Equations." Stochastic Analysis and Applications 21, no. 2 (2003): 281–300. http://dx.doi.org/10.1081/sap-120019286.

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Arar, El-Mehdi El, Devan Sohier, Pablo de Oliveira Castro, and Eric Petit. "Stochastic Rounding Variance and Probabilistic Bounds: A New Approach." SIAM Journal on Scientific Computing 45, no. 5 (2023): C255—C275. http://dx.doi.org/10.1137/22m1510819.

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9

McCarl, Bruce A. "Generalized Stochastic Dominance: An Empirical Examination." Journal of Agricultural and Applied Economics 22, no. 2 (1990): 49–55. http://dx.doi.org/10.1017/s1074070800001796.

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Abstract Use of generalized stochastic dominance (GSD) requires one to place lower and upper bounds on the risk aversion coefficient. This study showed that breakeven risk aversion coefficients found assuming the exponential utility function delineate the places where GSD preferences switch between prospects. However, between these break points, multiple, overlapping GSD intervals can be found. Consequently, when one does not have risk aversion coefficient information, discovery of breakeven coefficients instead of GSD use is recommended. The investigation also showed GSD results are insensiti

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El Arar, E.-M., D. Sohier, P. de Oliveira Castro, and E. Petit. "Bounds on Nonlinear Errors for Variance Computation with Stochastic Rounding." SIAM Journal on Scientific Computing 46, no. 5 (2024): B579—B599. http://dx.doi.org/10.1137/23m1563001.

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11

Hamdan, Abdellatef, Khalil-Hani Mohamed, Shaikh-Husin Nasir, and Omid Ayat Sayed. "Stochastic Computing Correlation Utilization in Convolutional Neural Network Basic Functions." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 6 (2018): 2835–43. https://doi.org/10.12928/TELKOMNIKA.v16i6.8955.

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In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stoch

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Hopkins, Michael, Mantas Mikaitis, Dave R. Lester, and Steve Furber. "Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (2020): 20190052. http://dx.doi.org/10.1098/rsta.2019.0052.

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Although double-precision floating-point arithmetic currently dominates high-performance computing, there is increasing interest in smaller and simpler arithmetic types. The main reasons are potential improvements in energy efficiency and memory footprint and bandwidth. However, simply switching to lower-precision types typically results in increased numerical errors. We investigate approaches to improving the accuracy of reduced-precision fixed-point arithmetic types, using examples in an important domain for numerical computation in neuroscience: the solution of ordinary differential equatio

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Ji, Sai, Dachuan Xu, Donglei Du, and Yijing Wang. "LP-rounding approximation algorithms for two-stage stochastic fault-tolerant facility location problem." Applied Mathematical Modelling 58 (June 2018): 76–85. http://dx.doi.org/10.1016/j.apm.2017.12.009.

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14

Tovissodé, Chénangnon Frédéric, Sèwanou Hermann Honfo, Jonas Têlé Doumatè, and Romain Glèlè Kakaï. "On the Discretization of Continuous Probability Distributions Using a Probabilistic Rounding Mechanism." Mathematics 9, no. 5 (2021): 555. http://dx.doi.org/10.3390/math9050555.

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Most existing flexible count distributions allow only approximate inference when used in a regression context. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under-, and over-dispersion). The new method, referred to as “balanced discretization”, consists of discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation (pro

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Чубич, Владимир Михайлович, and Светлана Олеговна Кулабухова. "Square-root algorithms for robust modifications of the continuous-discrete cubature Kalman filter." Вычислительные технологии, no. 3 (July 15, 2020): 88–98. http://dx.doi.org/10.25743/ict.2020.25.3.010.

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Предложены две устойчивые к ошибкам машинного округления и к аномальным данным квадратно-корневые модификации непрерывно-дискретного кубатурного фильтра Калмана, основанные на вариационном байесовском и коррентропийном подходах. Апробация разработанных алгоритмов на модельной задаче со случайным характером расположения аномальных наблюдений показала их работоспособность при сопоставимом качестве фильтрации. Подтверждена алгебраическая эквивалентность представленных квадратно-корневых и стандартных версий Rounding errors due to the finite length of machine word can significantly affect the qual

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Noeiaghdam, Samad, Aliona Dreglea, Jihuan He, et al. "Error Estimation of the Homotopy Perturbation Method to Solve Second Kind Volterra Integral Equations with Piecewise Smooth Kernels: Application of the CADNA Library." Symmetry 12, no. 10 (2020): 1730. http://dx.doi.org/10.3390/sym12101730.

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This paper studies the second kind linear Volterra integral equations (IEs) with a discontinuous kernel obtained from the load leveling and energy system problems. For solving this problem, we propose the homotopy perturbation method (HPM). We then discuss the convergence theorem and the error analysis of the formulation to validate the accuracy of the obtained solutions. In this study, the Controle et Estimation Stochastique des Arrondis de Calculs method (CESTAC) and the Control of Accuracy and Debugging for Numerical Applications (CADNA) library are used to control the rounding error estima

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Korzilius, Stan, and Berry Schoenmakers. "Divisions and Square Roots with Tight Error Analysis from Newton–Raphson Iteration in Secure Fixed-Point Arithmetic." Cryptography 7, no. 3 (2023): 43. http://dx.doi.org/10.3390/cryptography7030043.

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In this paper, we present new variants of Newton–Raphson-based protocols for the secure computation of the reciprocal and the (reciprocal) square root. The protocols rely on secure fixed-point arithmetic with arbitrary precision parameterized by the total bit length of the fixed-point numbers and the bit length of the fractional part. We perform a rigorous error analysis aiming for tight accuracy claims while minimizing the overall cost of the protocols. Due to the nature of secure fixed-point arithmetic, we perform the analysis in terms of absolute errors. Whenever possible, we allow for stoc

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Carling, Paul A. "Coevolving edge rounding and shape of glacial erratics: the case of Shap granite, UK." Earth Surface Dynamics 12, no. 1 (2024): 381–97. http://dx.doi.org/10.5194/esurf-12-381-2024.

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Abstract. The size distributions and the shapes of detrital rock clasts can shed light on the environmental history of the clast assemblages and the processes responsible for clast comminution. For example, mechanical fracture due to the stresses imposed on a basal rock surface by a body of flowing glacial ice releases initial “parent” shapes of large blocks of rock from an outcrop, which then are modified by the mechanics of abrasion and fracture during subglacial transport. The latter processes produce subsequent generations of shapes, possibly distinct in form from the parent blocks. A comp

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Borukaiev, Z. Kh, V. A. Evdokimov, and K. B. Ostapchenko. "Construction of the Multi-Agent Environment Architecture of the Pricing Process Simulation Model in the Electricity Market." Èlektronnoe modelirovanie 45, no. 6 (2023): 15–30. http://dx.doi.org/10.15407/emodel.45.06.015.

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The question of building the architecture of the multi-agent environment of the simulation model of the pricing process, as a space of heterogeneous interconnected organizational, infor-mational, technological and economic interactions of the simulated agents of the pricing pro-cess, is considered.Using the example of a complex organizational and technical system (COTS) of the electricity micro-market in local electric power systems, the set of agents sur-rounding them and ensuring the vital activity of the COTS of pricing is formalized, consisting of classified internal agents and environment

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20

Qi, Cheng, Junwei Xie, Haowei Zhang, Zihang Ding, and Xiao Yang. "Optimal Configuration of Array Elements for Hybrid Distributed PA-MIMO Radar System Based on Target Detection." Remote Sensing 14, no. 17 (2022): 4129. http://dx.doi.org/10.3390/rs14174129.

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This paper establishes a hybrid distributed phased array multiple-input multiple-output (PA-MIMO) radar system model to improve the target detection performance by combining coherent processing gain and spatial diversity gain. First, the radar system signal model and array space configuration model for the PA-MIMO radar are established. Then, a novel likelihood ratio test (LRT) detector is derived based on the Neyman–Pearson (NP) criterion in a fixed noise background. It can jointly optimize the coherent processing gain and spatial diversity gain of the system by implementing subarray level an

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21

Heavey, Jack, Jiaming Cui, Chen Chen, B. Aditya Prakash, and Anil Vullikanti. "Provable Sensor Sets for Epidemic Detection over Networks with Minimum Delay." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10202–9. http://dx.doi.org/10.1609/aaai.v36i9.21260.

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The efficient detection of outbreaks and other cascading phenomena is a fundamental problem in a number of domains, including disease spread, social networks, and infrastructure networks. In such settings, monitoring and testing a small group of pre-selected nodes from the susceptible population (i.e., a sensor set) is often the preferred testing regime. We study the problem of selecting a sensor set that minimizes the delay in detection---we refer to this as the MinDelSS problem. Prior methods for minimizing the detection time rely on greedy algorithms using submodularity. We show that this a

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Filippov, A. I., M. A. Zelenova, T. M. Levina, and R. G. Gubaidullin. "THE EVOLUTION OF A CLAY CRUST IN A BOREHOLE." Petroleum Engineering 22, no. 5 (2024): 83–92. http://dx.doi.org/10.17122/ngdelo-2024-5-83-92.

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Based on the previously proposed quasi-stationary model of clay crust formation when drilling wells in depth intervals with permeable rocks, software was created and computational experiments were performed, which made it possible to clarify the contribution of various physical parameters to the process of its growth.Based on the analysis of the results of computational experiments, the contribution of the physical parameters of the formation and clay crust to the dependence of its size on time has been clarified. The model assumes that the filtration pressure fields are described by stationar

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Cowan, Wesley, and Michael N. Katehakis. "MULTI-ARMED BANDITS UNDER GENERAL DEPRECIATION AND COMMITMENT." Probability in the Engineering and Informational Sciences 29, no. 1 (2014): 51–76. http://dx.doi.org/10.1017/s0269964814000217.

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Generally, the multi-armed has been studied under the setting that at each time step over an infinite horizon a controller chooses to activate a single process or bandit out of a finite collection of independent processes (statistical experiments, populations, etc.) for a single period, receiving a reward that is a function of the activated process, and in doing so advancing the chosen process. Classically, rewards are discounted by a constant factor β∈(0, 1) per round.In this paper, we present a solution to the problem, with potentially non-Markovian, uncountable state space reward processes,

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24

Xia, Lu, Stefano Massei, and Michiel E. Hochstenbach. "On the convergence of the gradient descent method with stochastic fixed-point rounding errors under the Polyak–Łojasiewicz inequality." Computational Optimization and Applications, February 11, 2025. https://doi.org/10.1007/s10589-025-00656-1.

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Abstract In the training of neural networks with low-precision computation and fixed-point arithmetic, rounding errors often cause stagnation or are detrimental to the convergence of the optimizers. This study provides insights into the choice of appropriate stochastic rounding strategies to mitigate the adverse impact of roundoff errors on the convergence of the gradient descent method, for problems satisfying the Polyak–Łojasiewicz inequality. Within this context, we show that a biased stochastic rounding strategy may be even beneficial in so far as it eliminates the vanishing gradient probl

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Weiß, Christian H., and Fukang Zhu. "Mean‐preserving rounding integer‐valued ARMA models." Journal of Time Series Analysis, September 10, 2024. http://dx.doi.org/10.1111/jtsa.12774.

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In the past four decades, research on count time series has made significant progress, but research on ‐valued time series is relatively rare. Existing ‐valued models are mainly of autoregressive structure, where the use of the rounding operator is very natural. Because of the discontinuity of the rounding operator, the formulation of the corresponding model identifiability conditions and the computation of parameter estimators need special attention. It is also difficult to derive closed‐form formulae for crucial stochastic properties. We rediscover a stochastic rounding operator, referred to

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Croci, Matteo, Massimiliano Fasi, Nicholas J. Higham, Theo Mary, and Mantas Mikaitis. "Stochastic rounding: implementation, error analysis and applications." Royal Society Open Science 9, no. 3 (2022). http://dx.doi.org/10.1098/rsos.211631.

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Stochastic rounding (SR) randomly maps a real number x to one of the two nearest values in a finite precision number system. The probability of choosing either of these two numbers is 1 minus their relative distance to x . This rounding mode was first proposed for use in computer arithmetic in the 1950s and it is currently experiencing a resurgence of interest. If used to compute the inner product of two vectors of length n in floating-point arithmetic, it yields an error bound with constant n u with high probability, where u is the unit round-off. This is not necessarily the case for round to

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Rukundo, Olivier, and Samuel Emil Schmidt. "Stochastic Rounding for Image Interpolation and Scan Conversion." International Journal of Advanced Computer Science and Applications 13, no. 3 (2022). http://dx.doi.org/10.14569/ijacsa.2022.0130303.

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Shah, Tapan. "Competence region estimation for black-box surrogate models." International FLAIRS Conference Proceedings 34, no. 1 (2021). http://dx.doi.org/10.32473/flairs.v34i1.128571.

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With advances in edge applications for industry andhealthcare, machine learning models are increasinglytrained on the edge. However, storage and memory in-frastructure at the edge are often primitive, due to costand real-estate constraints. A simple, effective methodis to learn machine learning models from quantized datastored with low arithmetic precision (1-8 bits). In thiswork, we introduce two stochastic quantization meth-ods, dithering and stochastic rounding. In dithering, ad-ditive noise from a uniform distribution is added tothe sample before quantization. In stochastic rounding,each s

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Choi, Ji In, Madeleine Georges, Jung Ah Shin, Olivia Wang, Tiffany Zhu, and Tapan Shah. "Learning from low precision samples." International FLAIRS Conference Proceedings 34, no. 1 (2021). http://dx.doi.org/10.32473/flairs.v34i1.128568.

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With advances in edge applications in industry and healthcare, machine learning models are increasingly trained on the edge. However, storage and memory infrastructure at the edge are often primitive, due to cost and real-estate constraints.A simple, effective method is to learn machine learning models from quantized data stored with low arithmetic precision (1-8 bits).In this work, we introduce two stochastic quantization methods, dithering and stochastic rounding. In dithering, additive noise from a uniform distribution is added to the sample before quantization. In stochastic rounding, each

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30

Suárez‐Marcote, Samuel, Laura Morán‐Fernández, and Verónica Bolón‐Canedo. "Optimising Resource Use Through Low‐Precision Feature Selection: A Performance Analysis of Logarithmic Division and Stochastic Rounding." Expert Systems 42, no. 3 (2025). https://doi.org/10.1111/exsy.70012.

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ABSTRACTThe growth in the number of wearable devices has increased the amount of data produced daily. Simultaneously, the limitations of such devices has also led to a growing interest in the implementation of machine learning algorithms with low‐precision computation. We propose green and efficient modifications of state‐of‐the‐art feature selection methods based on information theory and fixed‐point representation. We tested two potential improvements: stochastic rounding to prevent information loss, and logarithmic division to improve computational and energy efficiency. Experiments with se

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31

Kimpson, Tom, E. Adam Paxton, Matthew Chantry, and Tim Palmer. "Climate change modelling at reduced floating‐point precision with stochastic rounding." Quarterly Journal of the Royal Meteorological Society, February 17, 2023. http://dx.doi.org/10.1002/qj.4435.

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Akanuma, Takashi, Cong Chen, Tetsuo Sato, Roeland M. H. Merks, and Thomas N. Sato. "Memory of cell shape biases stochastic fate decision-making despite mitotic rounding." Nature Communications 7, no. 1 (2016). http://dx.doi.org/10.1038/ncomms11963.

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Gupta, Anupam, Amit Kumar, Viswanath Nagarajan, and Xiangkun Shen. "Stochastic Load Balancing on Unrelated Machines." Mathematics of Operations Research, August 24, 2020. http://dx.doi.org/10.1287/moor.2019.1049.

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We consider the problem of makespan minimization on unrelated machines when job sizes are stochastic. The goal is to find a fixed assignment of jobs to machines, to minimize the expected value of the maximum load over all the machines. For the identical-machines special case when the size of a job is the same across all machines, a constant-factor approximation algorithm has long been known. Our main result is the first constant-factor approximation algorithm for the general case of unrelated machines. This is achieved by (i) formulating a lower bound using an exponential-size linear program t

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Abdelhameed, Esam H., Samah Abdelraheem, Yehia Sayed Mohamed, and Ahmed A. Zaki Diab. "Effective hybrid search technique based constraint mixed-integer programming for smart home residential load scheduling." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-48717-x.

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AbstractIn this paper, the problem of scheduling smart homes (SHs) residential loads is considered aiming to minimize electricity bills and enhance the user comfort. The problem is addressed as a multi-objective constraint mixed-integer optimization problem (CP-MIP) to model the constrained load operation. As the CP-MIP optimization problem is non-convex, a novel hybrid search technique, that combines the Relaxation and Rounding (RnR) approach and metaheuristic algorithms to enhance the accuracy and relevance of decision variables, is proposed. This search technique is implemented through two

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MacRury, Calum, Will Ma, and Nathaniel Grammel. "On (Random-Order) Online Contention Resolution Schemes for the Matching Polytope of (Bipartite) Graphs." Operations Research, September 13, 2024. http://dx.doi.org/10.1287/opre.2023.0339.

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Online Contention Resolution Schemes for the Matching Polytope of Graphs Online contention resolution schemes (OCRSs) are used to select a subset of elements, subject to feasibility constraints. Originally developed as a randomized rounding tool for constrained submodular optimization, OCRSs have found numerous applications in online resource allocation and stochastic optimization. This includes problems such as prophet inequalities, stochastic probing, auction design, and matching in a gig economy. In “On (Random-Order) Online Contention Resolution Schemes for the Matching Polytope of (Bipart

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Michaelis, Carlo, Andrew B. Lehr, Winfried Oed, and Christian Tetzlaff. "Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian." Frontiers in Neuroinformatics 16 (November 9, 2022). http://dx.doi.org/10.3389/fninf.2022.1015624.

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Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond this, accessible and user-friendly prototyping is crucial to speed up the design pipeline. We developed an open source Loihi emulator based on the neural network simulator Brian that can easily be incorporated into existing simulation workflows. We demonstrate errorless Loihi emulation in software for a single neuron and for a recurrently connected spiking neural network. On-chip learning is also reviewed and implemente

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Andrieu, Cyril, Bren Hunyi Lee, and Anna Franz. "Cell deformations generated by stochastic actomyosin waves drive in vivo random-walk swimming migration." Journal of Cell Science, April 4, 2025. https://doi.org/10.1242/jcs.263787.

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Amoeboid cell migration drives many developmental and disease-related processes including immune responses and cancer metastasis. Swimming migration is a subtype of amoeboid migration observed in cells in suspension ex vivo. However, the mechanism underlying swimming migration in vivo is unknown. Using Drosophila fat body cells (FBCs) as a model, we show that FBCs actively swim to patrol the pupa by random-walk. Their migration is powered through actomyosin waves, that exert compressive forces as they travel to the cell rear causing cell deformations. Unlike in other types of amoeboid migratio

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Zheng, Yawen, and Song-Ping Zhu. "A generalized approach for pricing american options under a regime-switching model." IMA Journal of Management Mathematics, September 10, 2024. http://dx.doi.org/10.1093/imaman/dpae021.

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Abstract Regime-switching models gained their popularity over the past decade because of their distinctive advantage of modelling different financial market statuses in a discrete manner rather than a continuous manner as in stochastic volatility models. When they are used in option pricing, the clear advantage is that they enable a larger parameter space for models to be calibrated for a specific market dynamics and thus allow a better quantitative risk management in terms of utilizing financial derivatives. However, when they are used to price American-style financial derivatives, a large nu

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Zhao, Junyun, Siyuan Huang, Osama Yousuf, Yutong Gao, Brian D. Hoskins, and Gina C. Adam. "Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices." Frontiers in Neuroscience 15 (November 22, 2021). http://dx.doi.org/10.3389/fnins.2021.749811.

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While promising for high-capacity machine learning accelerators, memristor devices have non-idealities that prevent software-equivalent accuracies when used for online training. This work uses a combination of Mini-Batch Gradient Descent (MBGD) to average gradients, stochastic rounding to avoid vanishing weight updates, and decomposition methods to keep the memory overhead low during mini-batch training. Since the weight update has to be transferred to the memristor matrices efficiently, we also investigate the impact of reconstructing the gradient matrixes both internally (rank-seq) and exter

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Ghenaiet, Adel. "Study of Sand Particle Trajectories and Erosion Into the First Compression Stage of a Turbofan." Journal of Turbomachinery 134, no. 5 (2012). http://dx.doi.org/10.1115/1.4004750.

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Aero-engines operating in dusty environments are subject to ingestion of erodent particles leading to erosion damage of blades and a permanent drop in performance. This work concerns the study of particle dynamics and erosion of the front compression stage of a commercial turbofan. Particle trajectories simulations used a stochastic Lagrangian tracking code that solves the equations of motion separately from the airflow in a stepwise manner, while the tracking of particles in different cells is based on the finite element method. As the locations of impacts and rates of erosion were predicted,

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Kolvoort, Ivar, Zachary J. Davis, Bob Rehder, and Leendert van Maanen. "Models of Variability in Probabilistic Causal Judgments." Computational Brain & Behavior, October 8, 2024. http://dx.doi.org/10.1007/s42113-024-00223-7.

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AbstractMost theories of causal reasoning aim to explain the central tendency of causal judgments. However, experimental studies show that causal judgments are quite variable. In this article, we report the results of an experiment using a novel repeated measures design that demonstrate the existence of meaningful (i.e., not noise-related) within-participant variability. Next, we introduce and assess multiple computational cognitive models that serve as potential accounts of the sources of variability and fit those models to the new empirical data. We find that the Bayesian Mutation Sampler ha

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Fei, Xinyu, Lucas T. Brady, Jeffrey Larson, Sven Leyffer, and Siqian Shen. "Binary Quantum Control Optimization with Uncertain Hamiltonians." INFORMS Journal on Computing, November 7, 2024. http://dx.doi.org/10.1287/ijoc.2024.0560.

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Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a discretized binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with t

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Klöwer, Milan, Peter V. Coveney, E. Adam Paxton, and Tim N. Palmer. "Periodic orbits in chaotic systems simulated at low precision." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-37004-4.

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AbstractNon-periodic solutions are an essential property of chaotic dynamical systems. Simulations with deterministic finite-precision numbers, however, always yield orbits that are eventually periodic. With 64-bit double-precision floating-point numbers such periodic orbits are typically negligible due to very long periods. The emerging trend to accelerate simulations with low-precision numbers, such as 16-bit half-precision floats, raises questions on the fidelity of such simulations of chaotic systems. Here, we revisit the 1-variable logistic map and the generalised Bernoulli map with vario

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Sarkar, Apurba, Aniket Jana, Atharva Agashe, et al. "Confinement in fibrous environments positions and orients mitotic spindles." PNAS Nexus, June 16, 2025. https://doi.org/10.1093/pnasnexus/pgaf201.

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Abstract Accurate positioning of the mitotic spindle within the rounded cell body is critical to physiological maintenance. Mitotic cells encounter confinement from neighboring cells or the extracellular matrix (ECM), which can cause rotation of mitotic spindles and tilting of the metaphase plate (MP). To understand the effect of confinement on mitosis by fibers (ECM confinement), we use flexible ECM-mimicking nanofibers that allow natural rounding of the cell body while confining it to differing levels. Rounded mitotic bodies are anchored in place by actin retraction fibers (RFs) originating

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