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Journal articles on the topic 'Dot product kernels'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Menegatto, V. A., C. P. Oliveira, and A. P. Peron. "Conditionally positive definite dot product kernels." Journal of Mathematical Analysis and Applications 321, no. 1 (2006): 223–41. http://dx.doi.org/10.1016/j.jmaa.2005.08.024.

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Menegatto, V. A., C. P. Oliveira, and Ana P. Peron. "On conditionally positive definite dot product kernels." Acta Mathematica Sinica, English Series 24, no. 7 (2008): 1127–38. http://dx.doi.org/10.1007/s10114-007-6227-4.

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Lu, Fangyan, and Hongwei Sun. "Positive definite dot product kernels in learning theory." Advances in Computational Mathematics 22, no. 2 (2005): 181–98. http://dx.doi.org/10.1007/s10444-004-3140-6.

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Griffiths, Matthew P., Denys Grombacher, Mason A. Kass, Mathias Ø. Vang, Lichao Liu, and Jakob Juul Larsen. "A surface NMR forward in a dot product." Geophysical Journal International 234, no. 3 (2023): 2284–90. http://dx.doi.org/10.1093/gji/ggad203.

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SUMMARY The computation required to simulate surface nuclear magnetic resonance (SNMR) data increases proportionally with the number of sequences and number of pulses in each sequence. This poses a particular challenge to modelling steady-state SNMR, where suites of sequences are acquired, each of which require modelling 10–100 s of pulses. To model such data efficiently, we have developed a reformulation of surface NMR forward model, where the geometry of transmit and receive fields are encapsulated into a vector (or set of vectors), which we call B1-volume-receive (BVR) curves. Projecting BV

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Donini, Michele, and Fabio Aiolli. "Learning deep kernels in the space of dot product polynomials." Machine Learning 106, no. 9-10 (2016): 1245–69. http://dx.doi.org/10.1007/s10994-016-5590-8.

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Filippas, Dionysios, Chrysostomos Nicopoulos, and Giorgos Dimitrakopoulos. "Templatized Fused Vector Floating-Point Dot Product for High-Level Synthesis." Journal of Low Power Electronics and Applications 12, no. 4 (2022): 56. http://dx.doi.org/10.3390/jlpea12040056.

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Machine-learning accelerators rely on floating-point matrix and vector multiplication kernels. To reduce their cost, customized many-term fused architectures are preferred, which improve the latency, power, and area of the designs. In this work, we design a parameterized fused many-term floating-point dot product architecture that is ready for high-level synthesis. In this way, we can exploit the efficiency offered by a well-structured fused dot-product architecture and the freedom offered by high-level synthesis in tuning the design’s pipeline to the selected floating-point format and archite

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Bishwas, Arit Kumar, Ashish Mani, and Vasile Palade. "Gaussian kernel in quantum learning." International Journal of Quantum Information 18, no. 03 (2020): 2050006. http://dx.doi.org/10.1142/s0219749920500069.

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The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity

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Iakymchuk, Roman, Stef Graillat, David Defour, and Enrique S. Quintana-Ortí. "Hierarchical approach for deriving a reproducible unblocked LU factorization." International Journal of High Performance Computing Applications 33, no. 5 (2019): 791–803. http://dx.doi.org/10.1177/1094342019832968.

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We propose a reproducible variant of the unblocked LU factorization for graphics processor units (GPUs). For this purpose, we build upon Level-1/2 BLAS kernels that deliver correctly-rounded and reproducible results for the dot (inner) product, vector scaling, and the matrix-vector product. In addition, we draw a strategy to enhance the accuracy of the triangular solve via iterative refinement. Following a bottom-up approach, we finally construct a reproducible unblocked implementation of the LU factorization for GPUs, which accommodates partial pivoting for stability and can be eventually int

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Xiao, Lechao, Hong Hu, Theodor Misiakiewicz, Yue M. Lu, and Jeffrey Pennington. "Precise learning curves and higher-order scaling limits for dot-product kernel regression ^*." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 11 (2023): 114005. http://dx.doi.org/10.1088/1742-5468/ad01b7.

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Abstract As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improvements under different model and data scaling regimes. Currently, theoretical understanding of the learning curves (LCs) that characterize how the prediction error depends on the number of samples is restricted to either large-sample asymptotics ( m → ∞ ) or, for certain simple data distributions, to the high-dimensional asymptotics in which the number of samples scales linearly with the dimension ( m ∝ d ).

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Azevedo, D., and V. A. Menegatto. "Sharp estimates for eigenvalues of integral operators generated by dot product kernels on the sphere." Journal of Approximation Theory 177 (January 2014): 57–68. http://dx.doi.org/10.1016/j.jat.2013.10.002.

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Micchelli, Charles A., and Massimiliano Pontil. "On Learning Vector-Valued Functions." Neural Computation 17, no. 1 (2005): 177–204. http://dx.doi.org/10.1162/0899766052530802.

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In this letter, we provide a study of learning in a Hilbert space of vector-valued functions. We motivate the need for extending learning theory of scalar-valued functions by practical considerations and establish some basic results for learning vector-valued functions that should prove useful in applications. Specifically, we allow an output space Y to be a Hilbert space, and we consider a reproducing kernel Hilbert space of functions whose values lie in Y. In this setting, we derive the form of the minimal norm interpolant to a finite set of data and apply it to study some regularization fun

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Chen, Honghuan, and Keming Wang. "Fusing DCN and BBAV for Remote Sensing Image Object Detection." International Journal of Cognitive Informatics and Natural Intelligence 17, no. 1 (2024): 1–16. http://dx.doi.org/10.4018/ijcini.335496.

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At the oriented object detection in aerial remote sensing images, the perceptual field boundaries of ordinary convolutional kernels are often not parallel to the boundaries of the objects to be detected, affecting the model precision. Therefore, an object detection model (DCN-BBAV) that fuses deformable convolution networks (DCNs) and box boundary-aware vectors (BBAVs) is proposed. Firstly, a BBAV is used as the baseline, replacing the normal convolution kernels in the backbone network with deformable convolution kernels. Then, the spatial attention module (SAM) and channel attention mechanism

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Soliman, Mostafa I., and Elsayed A. Elsayed. "Simultaneous Multithreaded Matrix Processor." Journal of Circuits, Systems and Computers 24, no. 08 (2015): 1550114. http://dx.doi.org/10.1142/s0218126615501145.

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This paper proposes a simultaneous multithreaded matrix processor (SMMP) to improve the performance of data-parallel applications by exploiting instruction-level parallelism (ILP) data-level parallelism (DLP) and thread-level parallelism (TLP). In SMMP, the well-known five-stage pipeline (baseline scalar processor) is extended to execute multi-scalar/vector/matrix instructions on unified parallel execution datapaths. SMMP can issue four scalar instructions from two threads each cycle or four vector/matrix operations from one thread, where the execution of vector/matrix instructions in threads

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Anil, Gautham Govind, Pascal Esser, and Debarghya Ghoshdastidar. "When Can We Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?" Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15399–407. https://doi.org/10.1609/aaai.v39i15.33690.

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Contrastive learning is a paradigm for learning representations from unlabelled data and several recent works have claimed that such models effectively learn spectral embeddings and show relations between (wide) contrastive models and kernel principal component analysis (PCA). However, it is not known if trained contrastive models indeed correspond to kernel methods or PCA. In this work, we analyze the training dynamics of two-layer contrastive models, with non-linear activation, and answer when these models are close to PCA or kernel methods. It is well known in the supervised setting that ne

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Suárez-Cuenca, Jorge Juan, Wei Guo, and Qiang Li. "INTEGRATION OF MULTIPLE CLASSIFIERS FOR COMPUTERIZED DETECTION OF LUNG NODULES IN CT." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (2015): 1550040. http://dx.doi.org/10.4015/s1016237215500404.

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The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a computer-aided diagnosis (CAD) system for pulmonary nodule detection in computed tomography (CT). We employed 85 CT scans with 110 nodules in the publicly available Lung Image Database Consortium (LIDC) dataset. We first applied our CAD scheme trained previously to the LIDC cases for identifying initial nodule candidates, and extracting 18 features for each nodule candidate. We used eight individual classifiers for false positives (FPs) reduction, including l

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R R, Lim, Ng S K, Mathews J, Chew T D, and Azizah M J. "Effect of Acephate in Oil Palm Fruits and Palm Products After Trunk Injection." Planter 100, no. 1180 (2024): 409–18. http://dx.doi.org/10.56333/tp.2024.007.

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The study investigates the residual effect of acephate and its metabolite methamidophos in various components of oil palm bunches following trunk injection. Fifty palms were treated with acephate 97% a.i.) at 8.63 g of a.i. per palm and monitored alongside twenty untreated control palms. Residue analysis was performed on fruitlets, kernels, and crude palm oil (CPO) at intervals of 5, 10, 15, and 20 days after treatment (DAT). Laboratory analysis revealed non-detectable levels of acephate in all components, while methamidophos was transiently present only in the fruitlets, peaking at 0.007 mg p

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Ünal, Ali Burak, Mete Akgün, and Nico Pfeifer. "ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9988–96. http://dx.doi.org/10.1609/aaai.v35i11.17199.

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Training sophisticated machine learning models usually requires many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in epsilon-differential privacy) or omitting certain values (e.g., for k-anonymity). Other measures based on cryptographic methods can lead to very time-consu

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Yang, Kai, Xingpeng Dong, and Jianfeng Zhang. "Polarity-reversal correction for vector-based elastic reverse time migration." GEOPHYSICS 86, no. 1 (2021): S45—S58. http://dx.doi.org/10.1190/geo2020-0033.1.

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Polarity reversal is a well-known problem in elastic reverse time migration, and it is closely related to the imaging conditions. The dot product of source and receiver wavefields is a stable and efficient way to construct scalar imaging conditions for decomposed elastic vector wavefields. However, for PP images, the dot product introduces an angle-dependent factor that will change the polarity of image amplitudes at large opening angles, and it is also contaminated by low-wavenumber artifacts when sharp contrasts exist in the velocity model. Those two problems can be suppressed by muting the

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Yang, Yanqi, and Shuangping Tao. "Singular integrals with variable kernel and fractional differentiation in homogeneous Morrey-Herz-type Hardy spaces with variable exponents." Open Mathematics 16, no. 1 (2018): 326–45. http://dx.doi.org/10.1515/math-2018-0036.

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AbstractLet T be the singular integral operator with variable kernel defined by $$\begin{array}{} \displaystyle Tf(x)= p.v. \int\limits_{\mathbb{R}^{n}}\frac{{\it\Omega}(x,x-y)}{|x-y|^{n}}f(y)\text{d}y \end{array} $$and Dγ(0 ≤ γ ≤ 1) be the fractional differentiation operator. Let T∗ and T♯ be the adjoint of T and the pseudo-adjoint of T, respectively. The aim of this paper is to establish some boundedness for TDγ − DγT and (T∗ − T♯)Dγ on the homogeneous Morrey-Herz-type Hardy spaces with variable exponents $\begin{array}{} HM\dot{K}^{\alpha(\cdot),q}_{p(\cdot),\lambda} \end{array} $ via the c

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Atarashi, Kyohei, Subhransu Maji, and Satoshi Oyama. "Random Feature Maps for the Itemset Kernel." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3199–206. http://dx.doi.org/10.1609/aaai.v33i01.33013199.

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Although kernel methods efficiently use feature combinations without computing them directly, they do not scale well with the size of the training dataset. Factorization machines (FMs) and related models, on the other hand, enable feature combinations efficiently, but their optimization generally requires solving a non-convex problem. We present random feature maps for the itemset kernel, which uses feature combinations, and includes the ANOVA kernel, the all-subsets kernel, and the standard dot product. Linear models using one of our proposed maps can be used as an alternative to kernel metho

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Fitra, Guguh Makbul Rahmadani, and Sunardi Sunardi. "Design and Development of a IoT-Based Moisture Detection Device for Corn Seeds." Buletin Ilmiah Sarjana Teknik Elektro 5, no. 3 (2023): 359–66. https://doi.org/10.12928/biste.v5i3.8345.

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Lately the planting of corn has increased and increased in several areas. The increasing popularity of corn is due to its high economic value. Corn that has been harvested cannot be sold immediately because it must meet certain moisture content requirements. Farmers must know the maximum value of the moisture content in the corn kernels resulting from the harvest to meet one of the standards set by the industry. The water content contained in corn kernels can have a big influence on determining its quality or selling value. This study aims to design and implement a device for detecting the moi

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Amoah, Barbara A., and Rizana M. Mahroof. "Ozone as a Potential Fumigant Alternative for the Management of Sitophilus oryzae (Coleoptera: Curculionidae) in Wheat." Journal of Economic Entomology 112, no. 4 (2019): 1953–63. http://dx.doi.org/10.1093/jee/toz071.

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Abstract Gaseous ozone, an oxidizing agent used as a disinfectant in food processing and preservation, has potential for the control of stored product insects. In this study, we investigated ozone for the management of the rice weevil, Sitophilus oryzae (L.) (Coleoptera: Curculionidae), a serious stored product insect pest. We exposed eggs, immature stages within wheat kernels, and adults of the rice weevil to 200-ppm ozone for 12, 24, 36, 48, and 60 h. Insects were placed at 5, 15, or 25 cm depth within a wheat mass in PVC pipes (10 cm in diameter, 30 cm in height) and exposed to ozone. Egg e

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Shirakol, Shrikanth, and Kerur S S. "ARCHITECTURAL DESIGN AND OPTIMIZATION OF DISTRIBUTED ARITHMETIC BASED 2-D DISCRETE COSINE TRANSFORM." ICTACT Journal on Microelectronics 8, no. 1 (2022): 1275–82. http://dx.doi.org/10.21917/ijme.2022.0220.

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DCT is immensely used in Multimedia applications because it provides high energy compaction. The proposed architectural design of 1D-DCT employs an efficient computational technique, Distributed arithmetic and is synthesized using front end VLSI technique. The motive of utilising Distributed arithmetic is to have multiplier-less architecture that reduces the Area-delay product in comparison to the multiplier-based design by retaining the same structural regularities. The symmetric property of the DCT kernel matrix is applied to develop the proposed architecture which reduces the requirements o

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Miller, Kai J., Klaus-Robert Müller, Gabriela Ojeda Valencia, et al. "Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation." PLOS Computational Biology 19, no. 5 (2023): e1011105. http://dx.doi.org/10.1371/journal.pcbi.1011105.

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Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We descr

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Shamsolmoali, Pourya, Masoumeh Zareapoor, Eric Granger, and Michael Felsberg. "SeTformer Is What You Need for Vision and Language." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4713–21. http://dx.doi.org/10.1609/aaai.v38i5.28272.

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The dot product self-attention (DPSA) is a fundamental component of transformers. However, scaling them to long sequences, like documents or high-resolution images, becomes prohibitively expensive due to the quadratic time and memory complexities arising from the softmax operation. Kernel methods are employed to simplify computations by approximating softmax but often lead to performance drops compared to softmax attention. We propose SeTformer, a novel transformer where DPSA is purely replaced by Self-optimal Transport (SeT) for achieving better performance and computational efficiency. SeT i

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Fabec, R., G. Ólafsson, and A. N. Sengupta. "Holomorphic Fock spaces for positive linear transformations." MATHEMATICA SCANDINAVICA 98, no. 2 (2006): 262. http://dx.doi.org/10.7146/math.scand.a-14995.

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Suppose $A$ is a positive real linear transformation on a finite dimensional complex inner product space $V$. The reproducing kernel for the Fock space of square integrable holomorphic functions on $V$ relative to the Gaussian measure $d\mu_A(z)=\frac {\sqrt{\det A}} {\pi^n}e^{-\Re\langle Az,z\rangle}\,dz$ is described in terms of the linear and antilinear decomposition of the linear operator $A$. Moreover, if $A$ commutes with a conjugation on $V$, then a restriction mapping to the real vectors in $V$ is polarized to obtain a Segal-Bargmann transform, which we also study in the Gaussian-measu

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Suryadi, Usep Tatang, and Nindi Azis Andriyani. "Klasifikasi Status Calon Pendonor Darah Menggunakan Algoritma Support Vector Machine dan Kernel RBF." Jurnal ICT : Information Communication & Technology 19, no. 1 (2020): 27–33. http://dx.doi.org/10.36054/jict-ikmi.v19i1.113.

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To donate blood, generally must qualify the requirements, including physically and mentally healthy, aged 17-65 years, minimum body weight of 45kg, Hb levels 12.5g% - 17.0g%, upper tension (systole) 100-170mmHg, under tension (diastole) 70-100mmHg, 36.6-37.5 degrees Celsius body temperature, never had hemophilia, pulse range 50-100 times/minute, and the timescales 3 months after a previous blood donors. The problem that arises is the small number of officers who often have difficulty recording donor data on the form sheets. Thus allowing unwanted error occurred when registering the identity or

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Han, Yoonsang, Inseo Kim, Jinsung Kim, and Gordon Euhyun Moon. "Tensor Core-Adapted Sparse Matrix Multiplication for Accelerating Sparse Deep Neural Networks." Electronics 13, no. 20 (2024): 3981. http://dx.doi.org/10.3390/electronics13203981.

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Sparse matrix–matrix multiplication (SpMM) is essential for deep learning models and scientific computing. Recently, Tensor Cores (TCs) on GPUs, originally designed for dense matrix multiplication with mixed precision, have gained prominence. However, utilizing TCs for SpMM is challenging due to irregular memory access patterns and a varying number of non-zero elements in a sparse matrix. To improve data locality, previous studies have proposed reordering sparse matrices before multiplication, but this adds computational overhead. In this paper, we propose Tensor Core-Adapted SpMM (TCA-SpMM),

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Chin, Yu-Hao, Chang-Hong Lin, Ernestasia Siahaan, and Jia-Ching Wang. "Music Emotion Detection Using Hierarchical Sparse Kernel Machines." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/270378.

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For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The

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Chukanov, S. N., I. S. Chukanov, and S. V. Leykhter. "Formation of machine learning features based on computational topology methods." Mathematical structures and modeling, no. 4 (2022): 89–99. http://dx.doi.org/10.24147/2222-8772.2022.4.89-99.

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The use of traditional methods of algebraic topology to obtain information about the shape of an object is associated with the problem of forming a small amount of information: Betti numbers and Euler characteristics. The central tool for topological data analysis is the persistent homology method, which summarizes the geometric and topological information in the data using persistent diagrams and barcodes. Based on persistent homology methods, analysis of topological data can be performed to obtain information about the shape of an object. The construction of persistent barcodes and persisten

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Makbal, Rachida, Myra O. Villareal, Chemseddoha Gadhi, Abdellatif Hafidi, and Hiroko Isoda. "Argania Spinosa Fruit Shell Extract-Induced Melanogenesis via cAMP Signaling Pathway Activation." International Journal of Molecular Sciences 21, no. 7 (2020): 2539. http://dx.doi.org/10.3390/ijms21072539.

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We have previously reported that argan oil and argan press-cake from the kernels of Argania spinosa have an anti-melanogenesis effect. Here, the effect of argan fruit shell ethanol extract (AFSEE) on melanogenesis in B16F10 cells was determined, and the mechanism underlying its effect was elucidated. The proliferation of AFSEE-treated B16F10 cells was evaluated using the 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide (MTT) assay, while the melanin content was quantified using a spectrophotometric method. The expression of melanogenesis-related proteins was determined by Western bl

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Donini, Michele, and Fabio Aiolli. "Learning deep kernels in the space of dot product polynomials." November 7, 2016. https://doi.org/10.1007/s10994-016-5590-8.

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Sutherland, Danica, Junier Oliva, Barnabás Póczos, and Jeff Schneider. "Linear-Time Learning on Distributions with Approximate Kernel Embeddings." Proceedings of the AAAI Conference on Artificial Intelligence 30, no. 1 (2016). http://dx.doi.org/10.1609/aaai.v30i1.10308.

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Many interesting machine learning problems are best posed by considering instances that are distributions, or sample sets drawn from distributions. Most previous work devoted to machine learning tasks with distributional inputs has done so through pairwise kernel evaluations between pdfs (or sample sets). While such an approach is fine for smaller datasets, the computation of an N × N Gram matrix is prohibitive in large datasets. Recent scalable estimators that work over pdfs have done so only with kernels that use Euclidean metrics, like the L2 distance. However, there are a myriad of other u

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BUI, HUY-QUI, THE ANH BUI, and XUAN THINH DUONG. "WEIGHTED BESOV AND TRIEBEL–LIZORKIN SPACES ASSOCIATED WITH OPERATORS AND APPLICATIONS." Forum of Mathematics, Sigma 8 (2020). http://dx.doi.org/10.1017/fms.2020.6.

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Let $X$ be a space of homogeneous type and $L$ be a nonnegative self-adjoint operator on $L^{2}(X)$ satisfying Gaussian upper bounds on its heat kernels. In this paper, we develop the theory of weighted Besov spaces ${\dot{B}}_{p,q,w}^{\unicode[STIX]{x1D6FC},L}(X)$ and weighted Triebel–Lizorkin spaces ${\dot{F}}_{p,q,w}^{\unicode[STIX]{x1D6FC},L}(X)$ associated with the operator $L$ for the full range $0<p,q\leqslant \infty$ , $\unicode[STIX]{x1D6FC}\in \mathbb{R}$ and $w$ being in the Muckenhoupt weight class $A_{\infty }$ . Under rather weak assumptions on $L$ as stated above, we prove th

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Hwang, Soojin, Daehyeon Baek, Jongse Park, and Jaehyuk Huh. "Cerberus: Triple Mode Acceleration of Sparse Matrix and Vector Multiplication." ACM Transactions on Architecture and Code Optimization, March 17, 2024. http://dx.doi.org/10.1145/3653020.

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The multiplication of sparse matrix and vector (SpMV) is one of the most widely used kernels in high-performance computing as well as machine learning acceleration for sparse neural networks. The design space of SpMV accelerators has two axes: algorithm and matrix representation. There have been two widely used algorithms and data representations. Two algorithms, scalar multiplication and dot product, can be combined with two sparse data representations, compressed sparse and bitmap formats for the matrix and vector. Although the prior accelerators adopted one of the possible designs, it is ye

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Cao, Leilei, Yibo Guo, Ye Yuan, and Qiangguo Jin. "Prototype as query for few shot semantic segmentation." Complex & Intelligent Systems, July 10, 2024. http://dx.doi.org/10.1007/s40747-024-01539-4.

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AbstractFew-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referring to only a few annotated examples named support images. One of the characteristics of FSS is spatial inconsistency between query and support targets, e.g., texture or appearance. This greatly challenges the generalization ability of methods for FSS, which requires to effectively exploit the dependency of the query image and the support examples. Most existing methods abstracted support features into prototype vectors and implemented the interaction with query features using cosine sim

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Simon Fong, Robert, Boyu Li, and Peter Tiňo. "Linear simple cycle reservoirs at the edge of stability perform Fourier decomposition of the input driving signals." Chaos: An Interdisciplinary Journal of Nonlinear Science 35, no. 4 (2025). https://doi.org/10.1063/5.0251154.

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This paper explores the representational structure of linear Simple Cycle Reservoirs (SCRs) operating at the edge of stability. We view SCR as providing in their state-space feature representations of the input-driving time series. By endowing the state space with the canonical dot-product, we “reverse engineer” the corresponding kernel (inner product) operating in the original time series space. The action of this time series kernel is fully characterized by the eigenspace of the corresponding metric tensor. We demonstrate that when linear SCRs are constructed at the edge of stability, the ei

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Zhao, Yang, Faquir Jain, and Lei Wang. "An In-Memory-Computing Structure with Quantum-Dot Transistor Toward Neural Network Applications: From Analog Circuits to Memory Arrays." International Journal of High Speed Electronics and Systems, July 13, 2024. http://dx.doi.org/10.1142/s0129156424400597.

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The rapid advancements in artificial intelligence (AI) have demonstrated great success in various applications, such as cloud computing, deep learning, and neural networks, among others. However, the majority of these applications rely on fast computation and large storage, which poses significant challenges to the hardware platform. Thus, there is a growing interest in exploring new computation architectures to address these challenges. Compute-in-memory (CIM) has emerged as a promising solution to overcome the challenges posed by traditional computer architecture in terms of data transfer fr

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Zhao, Y., F. Qian, F. Jain, and L. Wang. "A Multi-Bit Non-Volatile Compute-in-Memory Architecture with Quantum-Dot Transistor Based Unit." International Journal of High Speed Electronics and Systems 31, no. 01n04 (2022). http://dx.doi.org/10.1142/s0129156422400183.

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The recent advance of artificial intelligence (AI) has shown remarkable success for numerous tasks, such as cloud computing, deep-learning, neural network and so on. Most of those applications rely on fast computation and large storage, which brings various challenges to the hardware platform. The hardware performance is the bottle neck to break through and therefore, there is a lot of interest in exploring new solutions for computation architecture in recent years. Compute-in-memory (CIM) has drawn attention to the researchers and it is considered as one of the most promising candidates to so

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Ramírez, Cristian, Adrián Castelló, and Enrique S. Quintana-Ortí. "A BLIS-like matrix multiplication for machine learning in the RISC-V ISA-based GAP8 processor." Journal of Supercomputing, May 28, 2022. http://dx.doi.org/10.1007/s11227-022-04581-6.

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AbstractWe address the efficient realization of matrix multiplication (gemm), with application in the convolution operator for machine learning, for the RISC-V core present in the GreenWaves GAP8 processor. Our approach leverages BLIS (Basic Linear Algebra Instantiation Software) to develop an implementation that (1) re-organizes the gemm algorithm adapting its micro-kernel to exploit the hardware-supported dot product kernel in the GAP8; (2) explicitly orchestrates the data transfers across the hierarchy of scratchpad memories via DMA (direct memory access); and (3) operates with integer arit

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Kathuria, Kunal, Aakrosh Ratan, Michael McConnell, and Stefan Bekiranov. "Implementation of a Hamming distance–like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne." Quantum Machine Intelligence 2, no. 1 (2020). http://dx.doi.org/10.1007/s42484-020-00017-7.

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Abstract Motivated by the problem of classifying individuals with a disease versus controls using a functional genomic attribute as input, we present relatively efficient general purpose inner product–based kernel classifiers to classify the test as a normal or disease sample. We encode each training sample as a string of 1 s (presence) and 0 s (absence) representing the attribute’s existence across ordered physical blocks of the subdivided genome. Having binary-valued features allows for highly efficient data encoding in the computational basis for classifiers relying on binary operations. Gi

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Wang, Kaili, Xinwei Sun, Huijie He, Fenhua Bai, and Tao Shen. "Approximate‐Guided Representation Learning in Vision Transformer." CAAI Transactions on Intelligence Technology, July 15, 2025. https://doi.org/10.1049/cit2.70041.

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ABSTRACTIn recent years, the transformer model has demonstrated excellent performance in computer vision (CV) applications. The key lies in its guided representation attention mechanism, which uses dot‐product to depict complex feature relationships, and comprehensively understands the context semantics to obtain feature weights. Then feature enhancement is implemented by guiding the target matrix through feature weights. However, the uncertainty and inconsistency of features are widespread that prone to confusion in the description of relationships within dot‐product attention mechanisms. To

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Gan, Yao, Yanyun Fu, Deyong Wang, and Yongming Li. "A novel approach to attention mechanism using kernel functions: Kerformer." Frontiers in Neurorobotics 17 (August 24, 2023). http://dx.doi.org/10.3389/fnbot.2023.1214203.

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Artificial Intelligence (AI) is driving advancements across various fields by simulating and enhancing human intelligence. In Natural Language Processing (NLP), transformer models like the Kerformer, a linear transformer based on a kernel approach, have garnered success. However, traditional attention mechanisms in these models have quadratic calculation costs linked to input sequence lengths, hampering efficiency in tasks with extended orders. To tackle this, Kerformer introduces a nonlinear reweighting mechanism, transforming maximum attention into feature-based dot product attention. By exp

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Li, Futao, Zhongbin Wang, Lei Si, Dong Wei, Chao Tan, and Honglin Wu. "A novel recognition method of shearer cutting status based on SDP image and MCK-DCNN." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, June 29, 2023. http://dx.doi.org/10.1177/09544062231185486.

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To solve the problem of large interference of shearer rocker arm vibration signal and difficulty in feature selection, and recognize accurately the cutting status of shearer, a novel pattern identification method based on Symmetrized Dot Pattern (SDP), Local Mean Decomposition (LMD) and Multi-Scale Convolution Kernel Deep Convolutional Neural Network (MCK-DCNN) is presented. Firstly, the vibration signal of shearer rocker arm is decomposed by LMD to get multiple product functions (PFs). The previous three PFs are transformed into SDP images with different features by SDP method, which are inpu

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Fang, Jinwei, Lanying Huang, Ying Shi, Hanming Chen, and Bo Wang. "Three-dimensional elastic reverse-time migration using a high-order temporal and spatial staggered-grid finite-difference scheme." Frontiers in Earth Science 11 (January 26, 2023). http://dx.doi.org/10.3389/feart.2023.1069506.

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Three-dimensional (3D) elastic reverse-time migration (ERTM) can image the subsurface 3D seismic structures, and it is an important tool for the Earth’s interior imaging. A common simulation kernel used in 3D ERTM is the current staggered-grid finite-difference (SGFD) method of the first-order elastic wave equation. However, the mere second-order accuracy in time of the current SGFD method can bring non-negligible time dispersion, which reduces the simulation accuracy and further leads to the distortion of the imaging results. This paper proposes a vector-based 3D ERTM using the high-order acc

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Chukanov, S. N. "Formation of features based on computational topology methods." Computer Optics 47, no. 3 (2023). http://dx.doi.org/10.18287/2412-6179-co-1190.

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The use of traditional methods of algebraic topology to obtain information about the shape of an object is associated with the problem of forming a small amount of information, namely, Betti numbers and Euler characteristics. The central tool for topological data analysis is the persistent homology method, which summarizes the geometric and topological information in the data using persistent diagrams and barcodes. Based on persistent homology methods, topological data can be analyzed to obtain information about the shape of an object. The construction of persistent barcodes and persistent dia

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Manzhos, Sergei, Johann Lüder, and Manabu Ihara. "Machine learning of kinetic energy densities with target and feature smoothing: Better results with fewer training data." Journal of Chemical Physics 159, no. 23 (2023). http://dx.doi.org/10.1063/5.0175689.

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Machine learning (ML) of kinetic energy functionals (KEFs), in particular kinetic energy density (KED) functionals, is a promising way to construct KEFs for orbital-free density functional theory (DFT). Neural networks and kernel methods including Gaussian process regression (GPR) have been used to learn Kohn–Sham (KS) KED from density-based descriptors derived from KS DFT calculations. The descriptors are typically expressed as functions of different powers and derivatives of the electron density. This can generate large and extremely unevenly distributed datasets, which complicates effective

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Abed, Qutaiba K., and Waleed A. Mahmoud Al-Jawher. "ANEW ARCHITECTURE OF KEY GENERATION USING DWT FOR IMAGE ENCRYPTION WITH THREE LEVELS ARNOLD TRANSFORM PERMUTATION." Journal Port Science Research 5, no. 3 (2023). http://dx.doi.org/10.36371/port.2022.3.6.

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The security of image transmission is an important issue in digital communication. As well as it is necessary to preserve and protect important information for several applications like military, medical and other services related to high confidentiality over the Internet or other unprotected networks. In this paper a proposed encryption scheme was introduced that using Lorenz system with a circular convolution and discreet cosine transform (DCT). The diffusion process was achieved using three levels of Arnold transform permutation: - namely, block level, inside each block and pixel level. The

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Zhang, Gezhi, Yong Long, Yuting Lin, Ronald C. Chen, and Hao Gao. "A treatment plan optimization method with direct minimization of number of energy jumps for proton arc therapy." Physics in Medicine & Biology, March 15, 2023. http://dx.doi.org/10.1088/1361-6560/acc4a7.

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Abstract Objective: The optimization of energy layer distributions is crucial to proton ARC therapy: on one hand, a sufficient number of energy layers is needed to ensure the plan quality; on the other hand, an excess number of energy jumps can substantially slow down the treatment delivery. This work will develop a new treatment plan optimization method with direct minimization of number of energy jumps (NEJ), which will be shown to outperform state-of-the-art methods in both plan quality and delivery efficiency.&#xD;Approach: The proposed method jointly optimizes the plan quality and min

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Li, Dejian, Changhong Jiang, and TieLin Liang. "REDef-DETR: Real-time and Efficient DETR for industrial surface defect detection." Measurement Science and Technology, July 9, 2024. http://dx.doi.org/10.1088/1361-6501/ad60ea.

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Abstract Industrial surface defect detection is an important part of industrial production, which aims to identify and detecting various defects on the surface of product to ensure quality and meet customer requirements. With the development of deep learning and image processing technologies, the surface defect detection methods based on computer vision has become the mainstream method. However, the prevalent convolutional neural network(CNN)-based de-fect detection methods also have many problems. For example, these methods rely on post-processing of Non-Maximum Suppression (NMS) and have poo

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