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Journal articles on the topic 'Mixed precision computation'

Author: Grafiati

Published: 25 January 2025

Last updated: 31 July 2025

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1

Van Zee, Field G., Devangi N. Parikh, and Robert A. Van De Geijn. "Supporting Mixed-domain Mixed-precision Matrix Multiplication within the BLIS Framework." ACM Transactions on Mathematical Software 47, no. 2 (2021): 1–26. http://dx.doi.org/10.1145/3402225.

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We approach the problem of implementing mixed-datatype support within the general matrix multiplication ( gemm ) operation of the BLAS-like Library Instantiation Software framework, whereby each matrix operand A , B , and C may be stored as single- or double-precision real or complex values. Another factor of complexity, whereby the matrix product and accumulation are allowed to take place in a precision different from the storage precisions of either A or B , is also discussed. We first break the problem into orthogonal dimensions, considering the mixing of domains separately from mixing prec

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Al-Marakeby, A. "PRECISION ON DEMAND: A NOVEL LOSSLES MIXED-PRECISION COMPUTATION TECHNIQUE." Journal of Al-Azhar University Engineering Sector 15, no. 57 (2020): 1046–56. http://dx.doi.org/10.21608/auej.2020.120378.

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Wang, Shengquan, Chao Wang, Yong Cai, and Guangyao Li. "A novel parallel finite element procedure for nonlinear dynamic problems using GPU and mixed-precision algorithm." Engineering Computations 37, no. 6 (2020): 2193–211. http://dx.doi.org/10.1108/ec-07-2019-0328.

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Purpose The purpose of this paper is to improve the computational speed of solving nonlinear dynamics by using parallel methods and mixed-precision algorithm on graphic processing units (GPUs). The computational efficiency of traditional central processing units (CPUs)-based computer aided engineering software has been difficult to satisfy the needs of scientific research and practical engineering, especially for nonlinear dynamic problems. Besides, when calculations are performed on GPUs, double-precision operations are slower than single-precision operations. So this paper implemented mixed

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Liu, Xingchao, Mao Ye, Dengyong Zhou, and Qiang Liu. "Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8697–705. http://dx.doi.org/10.1609/aaai.v35i10.17054.

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We consider the post-training quantization problem, which discretizes the weights of pre-trained deep neural networks without re-training the model. We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers; this is in contrast to typical quantization methods that approximate each weight using a single low precision number. Computationally, we construct the multipoint quantization with an efficient greedy selection procedure, and adaptively decides the number of low precision poin

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Zhang, Jianfei, and Lei Zhang. "Efficient CUDA Polynomial Preconditioned Conjugate Gradient Solver for Finite Element Computation of Elasticity Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/398438.

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Graphics processing unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using compute unified device architecture (CUDA). Sliced block ELLPACK (SBELL) format is introduced to store sparse matrix arising from finite element discretization of elasticity with fewer padding zeros than traditional ELLPACK-based formats. Polynomial preconditioning

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C., T. Kelly. "Newton's Method in Mixed-Precision." SIAM Review 64, no. 1 (2022): 191–211. https://doi.org/10.5281/zenodo.6835437.

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<strong>Abstract: </strong> We investigate the use of reduced precision arithmetic to solve the linear equation for the Newton step. If one neglects the backward error in the linear solve, then well-known convergence theory implies that using single precision in the linear solve has very little negative effect on the nonlinear convergence rate. However, if one considers the effects of backward error, then the usual textbook estimates are very pessimistic and even the state-of-the-art estimates using probabilistic rounding analysis do not fully conform to experiments. We report on experime

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Tian, Luo, and Peng Wang. "An Effective Mixed-Precision Quantization Method for Joint Image Deblurring and Edge Detection." Electronics 14, no. 9 (2025): 1767. https://doi.org/10.3390/electronics14091767.

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Deploying deep neural networks (DNNs) for joint image deblurring and edge detection often faces challenges due to large model size, which restricts practical applicability. Although quantization has emerged as an effective solution to this issue, conventional quantization methods frequently struggle to optimize for the unique characteristics of the targeted model. This paper introduces a mixed-precision quantization method that dynamically adjusts quantization precision based on the edge regions of the input image. High-precision quantization is applied to edge neighborhoods to preserve critic

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Molina, Roméo, Vincent Lafage, David Chamont, and Fabienne Jézéquel. "Investigating mixed-precision for AGATA pulse-shape analysis." EPJ Web of Conferences 295 (2024): 03020. http://dx.doi.org/10.1051/epjconf/202429503020.

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The AGATA project aims at building a 4π gamma-ray spectrometer consisting of 180 germanium crystals, each crystal being divided into 36 segments. Each gamma ray produces an electrical signal within several neighbouring segments, which is compared with a data base of reference signals, enabling to locate the interaction. This step is called Pulse-Shape Analysis (PSA). In the execution chain leading to the PSA, we observe successive data conversions: the original 14-bit integers given by the electronics are finally converted to 32-bit floats. This made us wonder about the real numerical accuracy

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Yang, Linjie, and Qing Jin. "FracBits: Mixed Precision Quantization via Fractional Bit-Widths." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 10612–20. http://dx.doi.org/10.1609/aaai.v35i12.17269.

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Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource

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Stupishin, Leonid U., and Konstantin E. Nikitin. "Mixed Finite Element of Geometrically Nonlinear Shallow Shells of Revolution." Applied Mechanics and Materials 501-504 (January 2014): 514–17. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.514.

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The computation method for shallow shell of revolution in mixed finite-element formulation is developed. Final equations are constructed by the Galerkin method. Results of solution of test task are represented. Precision and convergence of results is analyzed.

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Feng, Weilun, Haotong Qin, Chuanguang Yang, et al. "MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 16595–603. https://doi.org/10.1609/aaai.v39i16.33823.

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Diffusion models have received wide attention in generation tasks. However, the expensive computation cost prevents the application of diffusion models in resource-constrained scenarios. Quantization emerges as a practical solution that significantly saves storage and computation by reducing the bit-width of parameters. However, the existing quantization methods for diffusion models still cause severe degradation in performance, especially under extremely low bit-widths (2-4 bit). The primary decrease in performance comes from the significant discretization of activation values at low bit quan

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Stemmler, Fabian, Michael Schwarz, Julian Erhard, Sarah Tilscher, and Helmut Seidl. "Taking Out the Toxic Trash: Recovering Precision in Mixed Flow-Sensitive Static Analyses." Proceedings of the ACM on Programming Languages 9, PLDI (2025): 1167–93. https://doi.org/10.1145/3729297.

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Static analysis of real-world programs combines flow- and context-sensitive analyses of local program states with computation of flow- and context-insensitive invariants at globals, that, e.g., abstract data shared by multiple threads. The values of locals and globals may mutually depend on each other, with the analysis of local program states both making contributions to globals and querying their values. Usually, all contributions to globals are accumulated during fixpoint iteration, with widening applied to enforce termination. Such flow-insensitive information often becomes unnecessarily i

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Burkov, Andriy, and Brahim Chaib-draa. "An Approximate Subgame-Perfect Equilibrium Computation Technique for Repeated Games." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 729–36. http://dx.doi.org/10.1609/aaai.v24i1.7623.

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This paper presents a technique for approximating, up to any precision, the set of subgame-perfect equilibria (SPE) in repeated games with discounting. The process starts with a single hypercube approximation of the set of SPE payoff profiles. Then the initial hypercube is gradually partitioned on to a set of smaller adjacent hypercubes, while those hypercubes that cannot contain any SPE point are gradually withdrawn. Whether a given hypercube can contain an equilibrium point is verified by an appropriate mixed integer program. A special attention is paid to the question of extracting players'

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Lam, Michael O., and Jeffrey K. Hollingsworth. "Fine-grained floating-point precision analysis." International Journal of High Performance Computing Applications 32, no. 2 (2016): 231–45. http://dx.doi.org/10.1177/1094342016652462.

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Floating-point computation is ubiquitous in high-performance scientific computing, but rounding error can compromise the results of extended calculations, especially at large scales. In this paper, we present new techniques that use binary instrumentation and modification to do fine-grained floating-point precision analysis, simulating any level of precision less than or equal to the precision of the original program. These techniques have an average of 40–70% lower overhead and provide more fine-grained insights into a program’s sensitivity than previous mixed-precision analyses. We also pres

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Isupov, Konstantin. "High-Performance Computation in Residue Number System Using Floating-Point Arithmetic." Computation 9, no. 2 (2021): 9. http://dx.doi.org/10.3390/computation9020009.

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Residue number system (RNS) is known for its parallel arithmetic and has been used in recent decades in various important applications, from digital signal processing and deep neural networks to cryptography and high-precision computation. However, comparison, sign identification, overflow detection, and division are still hard to implement in RNS. For such operations, most of the methods proposed in the literature only support small dynamic ranges (up to several tens of bits), so they are only suitable for low-precision applications. We recently proposed a method that supports arbitrary modul

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Ran, Yingqiang, Shikun Dai, Qingrui Chen, Ying Zhang, and Jiaxuan Ling. "Numerical Simulation of 2D Strong Magnetic Field in Space-Wavenumber Mixed Domain." Journal of Physics: Conference Series 2651, no. 1 (2023): 012083. http://dx.doi.org/10.1088/1742-6596/2651/1/012083.

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Abstract Strong magnetic body has self-demagnetization effect, which is not conducive to processing and interpretation of magnetic measurement data. A 2D numerical simulation method in the spatial-wavenumber mixed domain is proposed to calculate magnetic anomalies of 2D strong magnetic objects. In this method, the partial differential equation satisfying the magnetic potential of 2D strong magnetic body is transformed into independent ordinary differential equation between different wave numbers by 1D FFT, which greatly reduces the computation and storage requirements. The compact operator is

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Yang, Tao, Zhezhi He, Tengchuan Kou, et al. "BISWSRBS: A Winograd-based CNN Accelerator with a Fine-grained Regular Sparsity Pattern and Mixed Precision Quantization." ACM Transactions on Reconfigurable Technology and Systems 14, no. 4 (2021): 1–28. http://dx.doi.org/10.1145/3467476.

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Field-programmable Gate Array (FPGA) is a high-performance computing platform for Convolution Neural Networks (CNNs) inference. Winograd algorithm, weight pruning, and quantization are widely adopted to reduce the storage and arithmetic overhead of CNNs on FPGAs. Recent studies strive to prune the weights in the Winograd domain, however, resulting in irregular sparse patterns and leading to low parallelism and reduced utilization of resources. Besides, there are few works to discuss a suitable quantization scheme for Winograd. In this article, we propose a regular sparse pruning pattern in the

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Isupov, Konstantin, and Vladimir Knyazkov. "Interval Estimation of Relative Values in Residue Number System." Journal of Circuits, Systems and Computers 27, no. 01 (2017): 1850004. http://dx.doi.org/10.1142/s0218126618500044.

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Residue number system (RNS), due to its carry-free nature, is popular in many applications of high-speed computer arithmetic, especially in digital signal processing and cryptography. However, the main limiting factor of RNS is a high complexity of such operations as magnitude comparison, sign determination and overflow detection. These operations have, for many years, been a major obstacle to more widespread use of parallel residue arithmetic. This paper presents a new efficient method to perform these operations, which is based on computation and analysis of the interval estimation for the r

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Ye, Yuejin, Zhenya Song, Shengchang Zhou, et al. "swNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer." Geoscientific Model Development 15, no. 14 (2022): 5739–56. http://dx.doi.org/10.5194/gmd-15-5739-2022.

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Abstract. The current large-scale parallel barrier of ocean general circulation models (OGCMs) makes it difficult to meet the computing demand of high resolution. Fully considering both the computational characteristics of OGCMs and the heterogeneous many-core architecture of the new Sunway supercomputer, swNEMO_v4.0, based on NEMO4 (Nucleus for European Modelling of the Ocean version 4), is developed with ultrahigh scalability. Three innovations and breakthroughs are shown in our work: (1) a highly adaptive, efficient four-level parallelization framework for OGCMs is proposed to release a new

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Mohamed Ben Ali, Amina, Salah Bouziane, and Hamoudi Bouzerd. "Computation of mode I strain energy release rate of symmetrical and asymmetrical sandwich structures using mixed finite element." Frattura ed Integrità Strutturale 15, no. 56 (2021): 229–39. http://dx.doi.org/10.3221/igf-esis.56.19.

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The use of composite materials is on the rise in different engineering fields, the main advantage of these materials for the aerospace industry is their low weight for excellent mechanical qualities. The analysis of failure modes, such as delamination, of these materials has received great attention from researchers. This paper proposes a method to evaluate the mode I Strain Energy Release Rate (SERR) of sandwich structures. This method associated a two-dimensional mixed finite element with virtual crack extension technique for the analysis of interfacial delamination of sandwich beams. A symm

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Tang, Siyuan, Qiangqiang Bao, Qingyu Ji, et al. "Improvement of RT-DETR model for ground glass pulmonary nodule detection." PLOS ONE 20, no. 3 (2025): e0317114. https://doi.org/10.1371/journal.pone.0317114.

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Currently, pulmonary nodules detection work mostly focus on recognition and diagnosis of solid nodules. However, ground glass nodules have higher probability of malignancy, posing greater identification challenges and thus greater value for detection. To achieve rapid and accurate detection of ground glass nodules. This article proposed an algorithm based on RT-DETR model with the following enhancement: 1) optimize the backbone network with FCGE blocks to increase the detection accuracy of small-sized and blurred edge nodules; 2) replace the AIFI module with HiLo-AIFI module to reduce redundan

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Wang, Yiyang, and Jing Zhao. "A Unified and Resource-Aware Framework for Adaptive Inference Acceleration on Edge and Embedded Platforms." Electronics 14, no. 11 (2025): 2188. https://doi.org/10.3390/electronics14112188.

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Efficient and scalable inference is essential for deploying large-scale generative models across diverse hardware platforms, especially in real-time or resource-constrained scenarios. To address this, we propose a novel unified and resource-aware inference optimization framework that uniquely integrates three complementary techniques: sensitivity-aware mixed-precision quantization, heterogeneous sparse attention for reducing attention complexity, and capacity-aware dynamic expert routing for input-adaptive computation. This framework distinctively achieves fine-grained adaptivity by dynamicall

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Fasfous, Nael, Manoj Rohit Vemparala, Alexander Frickenstein, et al. "HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–25. http://dx.doi.org/10.1145/3476997.

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Model compression through quantization is commonly applied to convolutional neural networks (CNNs) deployed on compute and memory-constrained embedded platforms. Different layers of the CNN can have varying degrees of numerical precision for both weights and activations, resulting in a large search space. Together with the hardware (HW) design space, the challenge of finding the globally optimal HW-CNN combination for a given application becomes daunting. To this end, we propose HW-FlowQ, a systematic approach that enables the co-design of the target hardware platform and the compressed CNN mo

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Lv, Haifeng, Xiaoyu Ji, and Yong Ding. "A Mixed Intrusion Detection System utilizing K-means and Extreme Gradient Boosting." Journal of Physics: Conference Series 2517, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2517/1/012016.

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Abstract The intrusion detection system (IDS) plays an important part because it offers an efficient way to prevent and mitigate cyber attacks. Numerous deep learning methods for intrusion anomaly detection have been developed as a result of recent advances in artificial intelligence (AI) in order to strengthen internet security. The balance among the high detection rate (DR), the low false alarm rate (FAR) and disaster of dimensionality is the crucial apprehension while devising an effective IDS. For the binary classification of intrusion detection systems, we present in this study a mixed mo

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Fatima, El Barakaz, Boutkhoum Omar, and El Moutaouakkil Abdelmajid. "A hybrid naïve Bayes based on similarity measure to optimize the mixed-data classification." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (2021): pp. 155~162. https://doi.org/10.12928/TELKOMNIKA.v19i1.18024.

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In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the

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Mintourakis, I., G. Panou, and D. Paradissis. "Evaluation of ocean circulation models in the computation of the mean dynamic topography for geodetic applications. Case study in the Greek seas." Journal of Geodetic Science 9, no. 1 (2019): 154–73. http://dx.doi.org/10.1515/jogs-2019-0015.

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Abstract Precise knowledge of the oceanic Mean Dynamic Topography (MDT) is crucial for a number of geodetic applications, such as vertical datum unification and marine geoid modelling. The lack of gravity surveys over many regions of the Greek seas and the incapacity of the space borne gradiometry/gravity missions to resolve the small and medium wavelengths of the geoid led to the investigation of the oceanographic approach for computing the MDT. We compute two new regional MDT surfaces after averaging, for given epochs, the periodic gridded solutions of the Dynamic Ocean Topography (DOT) prov

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Wang, Yang, Jie Liu, Xiaoxiong Zhu, Qingyang Zhang, Shengguo Li, and Qinglin Wang. "Improving Structured Grid-Based Sparse Matrix-Vector Multiplication and Gauss–Seidel Iteration on GPDSP." Applied Sciences 13, no. 15 (2023): 8952. http://dx.doi.org/10.3390/app13158952.

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Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced into high-performance computing. In this paper, we designed various optimization methods, which included a blocking method to improve data locality and increase memory access efficiency, a multicolor reordering method to develop Gauss–Seidel fine-grai

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Lian, Feng, Liming Hou, Jing Liu та Chongzhao Han. "Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a δ-GLMB Filter". Sensors 18, № 7 (2018): 2308. http://dx.doi.org/10.3390/s18072308.

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The existing multi-sensor control algorithms for multi-target tracking (MTT) within the random finite set (RFS) framework are all based on the distributed processing architecture, so the rule of generalized covariance intersection (GCI) has to be used to obtain the multi-sensor posterior density. However, there has still been no reliable basis for setting the normalized fusion weight of each sensor in GCI until now. Therefore, to avoid the GCI rule, the paper proposes a new constrained multi-sensor control algorithm based on the centralized processing architecture. A multi-target mean-square e

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White, Alexander J., Lee A. Collins, Katarina Nichols, and S. X. Hu. "Mixed stochastic-deterministic time-dependent density functional theory: application to stopping power of warm dense carbon." Journal of Physics: Condensed Matter 34, no. 17 (2022): 174001. http://dx.doi.org/10.1088/1361-648x/ac4f1a.

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Abstract Warm dense matter (WDM) describes an intermediate phase, between condensed matter and classical plasmas, found in natural and man-made systems. In a laboratory setting, WDM is often created dynamically. It is typically laser or pulse-power generated and can be difficult to characterize experimentally. Measuring the energy loss of high energy ions, caused by a WDM target, is both a promising diagnostic and of fundamental importance to inertial confinement fusion research. However, electron coupling, degeneracy, and quantum effects limit the accuracy of easily calculable kinetic models

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Kavya, K. Guru Sai, G. Nagasowmya, G. Ankitha, K. Bharathi, K. Reshma, and M. Sharmila. "Analysis of Two Stage CMOS Operational Amplifier in 90nm CMOS Technology." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 444–49. http://dx.doi.org/10.22214/ijraset.2024.58338.

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Abstract: Operational amplifier circuits are used in computation, instrumentation and other applications. Precision op-amps previously used in instrumentation are now-a-days being used in industrial and automotive applications. Hence, there always exists a need for better precision op-amps. It should operate under a wide temperature range. Nowadays, due to the industry trend of applying standard process technologies to implement both analog circuits and digital circuits on the same chip, Complementary Metal-Oxide Semiconductor (CMOS) technology has become dominant over bipolar technology for a

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Kubacki, Jan, and Alina Jędrzejczak. "Small area estimation of income under spatial SAR model." Statistics in Transition new series 17, no. 3 (2016): 365–90. http://dx.doi.org/10.59170/stattrans-2016-022.

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The paper presents the method of hierarchical Bayes (HB) estimation under small area models with spatially correlated random effects and a spatial structure implied by the Simultaneous Autoregressive (SAR) process. The idea was to improve the spatial EBLUP by incorporating the HB approach into the estimation algorithm. The computation procedure applied in the paper uses the concept of sampling from a posterior distribution under generalized linear mixed models implemented in WinBUGS software and adapts the idea of parameter estimation for small areas by means of the HB method in the case of kn

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Tan, Zhaoxiang, Shaofeng Lu, Kai Bao, et al. "Adaptive Partial Train Speed Trajectory Optimization." Energies 11, no. 12 (2018): 3302. http://dx.doi.org/10.3390/en11123302.

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Train speed trajectory optimization has been proposed as an efficient and feasible method for energy-efficient train operation without many further requirements to upgrade the current railway system. This paper focuses on an adaptive partial train speed trajectory optimization problem between two arbitrary speed points with a given traveling time and distance, in comparison with full speed trajectory with zero initial and end speeds between two stations. This optimization problem is of interest in dynamic applications where scenarios keep changing due to signaling and multi-train interactions.

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Chen, Xiangren, Bohan Yang, Wenping Zhu, et al. "A High-performance NTT/MSM Accelerator for Zero-knowledge Proof Using Load-balanced Fully-pipelined Montgomery Multiplier." IACR Transactions on Cryptographic Hardware and Embedded Systems 2025, no. 1 (2024): 275–313. https://doi.org/10.46586/tches.v2025.i1.275-313.

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Zero-knowledge proof (ZKP) is an attractive cryptographic paradigm that allows a party to prove the correctness of a given statement without revealing any additional information. It offers both computation integrity and privacy, witnessing many celebrated deployments, such as computation outsourcing and cryptocurrencies. Recent general-purpose ZKP schemes, e.g., zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK), suffer from time-consuming proof generation, which is mainly bottlenecked by the large-scale number theoretic transformation (NTT) and multi-scalar point multipl

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Ge, Xiaohui, Lu Shen, Chaoming Zheng, Peng Li, and Xiaobo Dou. "A Decoupling Rolling Multi-Period Power and Voltage Optimization Strategy in Active Distribution Networks." Energies 13, no. 21 (2020): 5789. http://dx.doi.org/10.3390/en13215789.

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With the increasing penetration of distributed photovoltaics (PVs) in active distribution networks (ADNs), the risk of voltage violations caused by PV uncertainties is significantly exacerbated. Since the conventional voltage regulation strategy is limited by its discrete devices and delay, ADN operators allow PVs to participate in voltage optimization by controlling their power outputs and cooperating with traditional regulation devices. This paper proposes a decoupling rolling multi-period reactive power and voltage optimization strategy considering the strong time coupling between different

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Kong, Yi-Fan, Shi-Zhu Li, Kai-Wen Wang, et al. "An Efficient Bayesian Method for Estimating the Degree of the Skewness of X Chromosome Inactivation Based on the Mixture of General Pedigrees and Unrelated Females." Biomolecules 13, no. 3 (2023): 543. http://dx.doi.org/10.3390/biom13030543.

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Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases. Several methods have been proposed to estimate the degree of XCI-S (denoted as γ) for quantitative and qualitative traits based on unrelated females. However, there is no method available for estimating γ based on general pedigrees. Therefore, in this paper, we propose a Bayesian method to obtain the point estimate and the credible interval of γ based on the mixture of general pedigrees and unrelated females (called mixed data for brevity), which is also suitable for only general pedigrees.

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Zha, Chengyuan, Lei Li, Fangting Zhu, and Yanzhe Zhao. "The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis." Sensors 24, no. 9 (2024): 2818. http://dx.doi.org/10.3390/s24092818.

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The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient’s exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers’ attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this

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Xie, Wei, Wendi Zhu, Xiaozhong Tong, and Huiying Ma. "A Legendre Spectral-Element Method to Incorporate Topography for 2.5D Direct-Current-Resistivity Forward Modeling." Mathematics 12, no. 12 (2024): 1864. http://dx.doi.org/10.3390/math12121864.

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An effective and accurate solver for the direct-current-resistivity forward-modeling problem has become a cutting-edge research topic. However, computational limitations arise due to the substantial amount of data involved, hindering the widespread use of three-dimensional forward modeling, which is otherwise considered the most effective approach for identifying geo-electrical anomalies. An efficient compromise, or potentially an alternative, is found in two-and-a-half-dimensional (2.5D) modeling, which employs a three-dimensional current source within a two-dimensional subsurface medium. Con

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Limas Sierra, Robert, Juan David Guerrero Balaguera, Josie E. Rodriguez Condia, and Matteo Sonza Reorda. "A Structured Method to Generate Self-Test Libraries for Tensor Cores." Electronics 14, no. 11 (2025): 2148. https://doi.org/10.3390/electronics14112148.

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Modern computing systems increasingly rely on specialized hardware accelerators, such as Graphics Processing Units (GPUs), to meet growing computational demands. GPUs are essential for accelerating a wide range of applications, from machine learning and scientific computing to safety-critical domains like autonomous systems and aerospace. To enhance performance, modern GPUs integrate dedicated in-chip units, such as Tensor Cores(TCs), which are designed for efficient mixed-precision matrix operations. However, as semiconductor technologies scale down, reliability challenges emerge. Permanent h

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Yun, Bensheng, Xiaohan Xu, Jie Zeng, Zhenyu Lin, Jing He, and Qiaoling Dai. "An Improved Unmanned Aerial Vehicle Forest Fire Detection Model Based on YOLOv8." Fire 8, no. 4 (2025): 138. https://doi.org/10.3390/fire8040138.

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Forest fires have a great destructive impact on the Earth’s ecosystem; therefore, the top priority of current research is how to accurately and quickly monitor forest fires. Taking into account efficiency and cost-effectiveness, deep-learning-driven UAV remote sensing fire detection algorithms have emerged as a favored research trend and have seen extensive application. However, in the process of drone monitoring, fires often appear very small and are easily obstructed by trees, which greatly limits the amount of effective information that algorithms can extract. Meanwhile, considering the lim

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Poojitha, D. Guru. "Low Power Two Stage CMOS Operational Amplifier." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2088–91. https://doi.org/10.22214/ijraset.2025.68650.

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Abstract: Operational amplifier (Op-Amp) circuits play a critical role in computation, instrumentation, and a variety of industrial applications. With the growing demand for precision Op-Amps in automotive and industrial environments, there is an increasing need for designs that offer enhanced accuracy and robust performance across wide temperature ranges. The shift towards integrating both analog and digital circuits on a single chip has made Complementary Metal-Oxide Semiconductor (CMOS) technology the preferred choice over traditional bipolar technologies for analog circuit design in mixed-

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Harth-Kitzerow, Christopher, Ajith Suresh, and Georg Carle. "SoK: Truncation Untangled: Scaling Fixed-Point Arithmetic for Privacy-Preserving Machine Learning to Large Models and Datasets." Proceedings on Privacy Enhancing Technologies 2025, no. 4 (2025): 369–91. https://doi.org/10.56553/popets-2025-0135.

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Fixed Point Arithmetic (FPA) is widely used in Privacy-Preserving Machine Learning (PPML) to efficiently handle decimal values. However, repeated multiplications in FPA can lead to overflow, as the fractional part doubles in size with each multiplication. To address this, truncation is applied post-multiplication to maintain precision. Various truncation schemes based on Secure Multiparty Computation (MPC) exist, but trade-offs between accuracy and efficiency in PPML models and datasets remain underexplored. In this work, we analyze and consolidate different truncation approaches from the MPC

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Baboulin, Marc, Alfredo Buttari, Jack Dongarra, et al. "Accelerating scientific computations with mixed precision algorithms." Computer Physics Communications 180, no. 12 (2009): 2526–33. http://dx.doi.org/10.1016/j.cpc.2008.11.005.

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Yahyaoui, Zahra, Mansour Hajji, Majdi Mansouri, Kamaleldin Abodayeh, Kais Bouzrara, and Hazem Nounou. "Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM." Energies 15, no. 17 (2022): 6127. http://dx.doi.org/10.3390/en15176127.

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The current work presents an effective fault detection and diagnosis (FDD) technique in wind energy converter (WEC) systems. The proposed FDD framework merges the benefits of kernel principal component analysis (KPCA) model and the bidirectional long short-term memory (BiLSTM) classifier. In the developed FDD approach, the KPCA model is applied to extract and select the most effective features, while the BiLSTM is utilized for classification purposes. The developed KPCA-based BiLSTM approach involves two main steps: feature extraction and selection, and fault classification. The KPCA model is

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Manoj, Sharma, and Gupta Akshay. "Public Key Encryption and Keyword Search Mapping." Applied Science and Biotechnology Journal for Advanced Research 3, no. 1 (2024): 12–15. https://doi.org/10.5281/zenodo.10656146.

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This work addresses a difficult method for searching for crucial keywords for mixed cloud statistics (MRSE), which is a first step towards enabling secure cloud data processing. Among the different meanings of multiple terms, we choose the relevant concept of "relational coherence". Businesses can now more easily and affordably outsource a greater variety of products and services to community clouds thanks to cloud computing. To guarantee that sensitive personal data is kept enclosed before being supplied for their work. Given the enormous number of users of data and records in a search engine

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Moret, Bernard M. E., and Henry D. D. Shapiro. "Algorithms and Experiments: The New (and Old) Methodology." JUCS - Journal of Universal Computer Science 7, no. (5) (2001): 434–46. https://doi.org/10.3217/jucs-007-05-0434.

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The last twenty years have seen enormous progress in the design of algorithms, but little of it has been put into practice. Because many recently developed algorithms are hard to characterize theoretically and have large running_time coefficients, the gap between theory and practice has widened over these years. Experimentation is indispensable in the assessment of heuristics for hard problems, in the characterization of asymptotic behavior of complex algorithms, and in the comparison of competing designs for tractable problems. Implementation, although perhaps not rigorous experimentation, wa

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Hoo, Choon Lih, Sallehuddin Mohamed Haris, and Nik Abdullah Nik Mohamed. "A floating point conversion algorithm for mixed precision computations." Journal of Zhejiang University SCIENCE C 13, no. 9 (2012): 711–18. http://dx.doi.org/10.1631/jzus.c1200043.

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Kong, Linlin, and Min Huang. "Group Stable Matching Problem in Freight Pooling Service of Vehicle–Cargo Matching Platform." Systems 13, no. 6 (2025): 485. https://doi.org/10.3390/systems13060485.

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With the continuous advancement of the Internet and information technologies, the capacity for development and integration of vehicle and cargo resources has been significantly enhanced, driving the rapid emergence of vehicle–cargo matching platforms. Serving as critical intermediaries between vehicle owners and cargo owners, vehicle–cargo matching platforms effectively address key challenges in traditional logistics, such as low matching efficiency and information asymmetry. As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at th

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Kelley, C. T. "Newton's Method in Mixed Precision." SIAM Review 64, no. 1 (2022): 191–211. http://dx.doi.org/10.1137/20m1342902.

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Li, Yi, Woyu Zhang, Xiaoxin Xu, et al. "Mixed‐Precision Continual Learning Based on Computational Resistance Random Access Memory." Advanced Intelligent Systems 4, no. 8 (2022): 2270036. http://dx.doi.org/10.1002/aisy.202270036.

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Yue, Xiaoqiang, Zhiyong Wang, and Shu-Lin Wu. "Convergence Analysis of a Mixed Precision Parareal Algorithm." SIAM Journal on Scientific Computing 45, no. 5 (2023): A2483—A2510. http://dx.doi.org/10.1137/22m1510169.

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