23 papers · ranked by Valyu relevance
Gabriele Carcassi, Christine A. Aidala, Andrei Khrennikov, Carlos Mejía-Monasterio
'Carlos Mejía-Monasterio'] Measure theory is used in physics, not just to capture classical probability, but also to quantify the number of states. In previous works, we found that state quantification plays a foundational role in classical mechanics, and, therefore, we set ourselves to construct the quantum equivalent…
Hasindu Kariyawasam, Ramith Hettiarachchi, Quansan Yang, Alex Matlock + 6 more
Optical processors, built with “optical neurons”, can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in micro-fabrication, such optical processors can now be 3D fabricated, but with a…
Jorn W. T. Peters, Marios Fournarakis, Markus Nagel, Mart van Baalen + 1 more
'Tijmen Blankevoort'] Quantizing neural networks is one of the most effective methods for achieving efficient inference on mobile and embedded devices. In particular, mixed precision quantized (MPQ) networks, whose layers can be quantized to different bitwidths, achieve better task performance for the same resource…
MohammadHossein AskariHemmat, Reyhane Askari Hemmat, Alexander F. Hoffman, Ivan Lazarevich + 4 more
'Alexander F. Hoffman' 'Ivan Lazarevich' 'Ehsan Saboori' 'Olivier Mastropietro' 'Yvon Savaria' 'Jean‐Pierre David'] In this paper we study the effects of quantization in DNN training. We hypothesize that weight quantization is a form of regularization and the amount of regularization is correlated with the quantization…
Xuefu Sui, Qunbo Lv, Changjun Ke, Mingshan Li + 4 more
'Haiyang Yu' 'Zheng Tan' 'Hai Dong'] In the field of edge computing, quantizing convolutional neural networks (CNNs) using extremely low bit widths can significantly alleviate the associated storage and computational burdens in embedded hardware, thereby improving computational efficiency. However, such quantization…
Xia Yu, Huaiyu Zhuang, Yani Cui, Jiaxian Deng + 2 more
'Haixia Long'] Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity…
Joonhyuk Yoo, Guenwoo Ban, Weichuan Zhang, Yanbing Li + 2 more
'Jin Lu'] With the rapid growth of sensor technology and computer vision, efficient deep learning models are essential for real-time image feature extraction in resource-constrained environments. However, most existing quantized deep neural networks (DNNs) are highly sensitive to outliers, leading to severe performance…
Ruochong Zheng, Yutian Liu, Yian Zhao, Zhiwei Nie + 5 more
Three-dimensional atomic arrangements of biomolecules are key to demystifying biological functions. The rapid expansion of accessible structural data, driven by advances in AI for science, highlights the critical challenge of efficiently modeling large-scale biomolecular structures, which are high-dimensional systems…
Paulo V. Itaborai, Eduardo Reck Miranda
By the time of writing, quantum audio still is a very young area of study, even within the quantum signal processing community. This chapter introduces the state of the art in quantum audio and discusses methods for the quantum representation of audio signals. Currently, no quantum representation strategy claims to be…
Martín Rivara-Espasandín, Lucía Balestrazzi, Guillermo Dufort y Álvarez, Idoia Ochoa + 4 more
We investigate the effect of quality score information loss on downstream analysis from nanopore sequencing FASTQ files. We polished denovo assemblies for a mock microbial community and a human genome, and we called variants on a human genome. We repeated these experiments using various pipelines, under various…
Qing Shao
We benchmark six numerical precision configurations for ESM-2 protein language models across throughput, memory footprint and predictive accuracy, on two workloads with sharply different characteristics: bulk embedding extraction and deep mutational scanning (DMS) variant-effect scoring. Accuracy is evaluated on the…
Lu, Weizhi, Chen, Mingrui + 2 more
In machine learning, quantization is widely used to simplify data representation and facilitate algorithm deployment on hardware. Given the fundamental role of classification in machine learning, it is crucial to investigate the impact of quantization on classification. Current research primarily focuses on…
Dawei Shen, Yao-zhong Zhang, Seiya Imoto
Whole Slide Images (WSIs) are gigapixel, high-resolution digital scans of microscope slides, providing detailed tissue profiles for pathological analysis. Due to their gigapixel size and lack of detailed annotations, Multiple Instance Learning (MIL) becomes the primary technique for WSI analysis. However, current MIL…
Bin Duan, Logan A Walker, Bin Xie, Wei Jie Lee + 3 more
Recent advances in microscopy have pushed imaging data generation to an unprecedented scale. While scientists benefit from higher spatiotemporal resolutions and larger imaging volumes, the increasing data size presents significant storage, visualization, sharing, and analysis challenges. Lossless compression typically…
Jiulu Gong, Qunlin Chen, Wei Zhu, Zepeng Wang + 1 more
Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements…
Pumiao Yan, Dante G. Muratore, E.J. Chichilnisky, Boris Murmann + 1 more
Scaling neural recording systems to thousands of channels creates extreme bandwidth demands, posing a challenge for resource-constrained, implantable devices. This work introduces an adaptive, multi-stage compression framework for high-bandwidth neural interfaces. The system combines a Wired-OR analog-to-digital…
David Thompson, Johan Gielis
Our understanding of quantum phenomena often begins with simple particle-in-a-box style problems, the solutions of which introduce the student to foundational quantum concepts such as degeneracy and quantization. Simple model geometries of confinement afford analytic solutions, which are readily derivable, easily…
David Ellerman
The purpose of this paper is to show that the mathematics of quantum mechanics (QM) is the mathematics of set partitions (which specify indefiniteness and definiteness) linearized to vector spaces, particularly in Hilbert spaces. That is, the math of QM is the Hilbert space version of the math to describe objective…
David Ellerman
This paper presents a new 'partitional' approach to understanding or interpreting standard quantum mechanics (QM). The thesis is that the mathematics (not the physics) of QM is the Hilbert space version of the math of partitions on a set and, conversely, the math of partitions is a skeletonized set level version of the…
Michael Taylor, Arkajit Mandal, Pengfei Huo
Recent progress in enabling new chemical reactivities by strongly coupling molecular systems to quantized radiation has stimulated theoretical developments in molecular quantum electrodynamics As this field of cavity quantum electrodynamics (cQED) is highly interdisciplinary drawing from both quantum optics and…
Authors not listed
One of the main applications for which quantum computers are hoped to find utility is in simulating ground state energies and other observables of molecular chemical systems. The recently proposed sample-based diagonalization method is a readily implementable method for this task on current-day hardware using short…
Authors not listed
Quantum state tomography has been widely used to reconstruct the quantum state of a system from a set of informationally-complete measurements. Obtaining enough information about, e.g., the wavefunction of a molecule allows its complete characterization. On the other hand, deep learning models for molecular property…
Authors not listed
This article is the second in a two-part tutorial review on electronic spin-dependent dynamics. In Part I, we presented the fundamental theory within the adiabatic Born– Huang framework that describes the interaction between nuclear motion and the elec- tronic (spin and spatial) degrees of freedom. In particular, we…