Table of Contents

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize cumulative rewards through interactions with environments. The integration of RL with deep learning has recently resulted in impressive achievements in a wide range ...

Entity alignment refers to discovering identical entity pairs in 2 knowledge graphs, which is a significant task in knowledge fusion. Early automated entity alignment techniques are based mainly on similarity calculation and comparing symbolic features, ...

Inverse rendering estimates scene characteristics from image data. We derive an efficient framework for inverse rendering and specifically computed tomography (CT) of volumetric scattering objects. We focus on clouds, which have a key role in the climate ...

Brain–computer interfaces are enabling a range of new possibilities and routes for augmenting human capability. Here, we propose brain–computer interfaces as a route towards forms of computation, i.e., computational imaging, that blend the brain with ...

Synthesizing vivid images with descriptive texts is gradually emerging as a frontier cross-domain generation task. However, it is obviously inadequate to generate the high-quality image with one single sentence accurately due to the information asymmetry ...

Programmable illumination control is essential for many computational microscopy techniques. Conventional light source array is often arranged on a fixed grid of a planar surface for providing programmable sample illumination. Here, we report the ...

Motion understanding plays an important role in video-based cross-media analysis and multiple knowledge representation learning. This paper discusses physical motion recognition and prediction by deep neural networks (DNNs), such as convolutional neural ...

In the first half of the 20th century, 5 classic articles were written by 3 outstanding scholars, namely, Wiener (1894 to 1964), the father of cybernetics, Schrödinger (1887 to 1961), the father of quantum mechanics, and Turing (1912 to 1954), the father ...

Tomographic phase microscopy (TPM) in flow cytometry is one of the most promising computational imaging techniques for the quantitative 3-dimensional (3D) analysis of unstained single cells. Continuous cells’ flow, combined with the stain-free mode, can ...

Electronic noses, or e-noses, refer to systems powered by chemical gas sensors, signal processing, and machine learning algorithms for realizing artificial olfaction. They play a crucial role in various applications for decoding chemical environmental ...

Tiered distributed computing systems, where components run in Internet-of-things devices, in edge computers, and in the cloud, introduce unique difficulties in maintaining consistency of shared data while ensuring availability. A major source of ...

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in ...

Wide-field optical-resolution microscopy with structured illumination and single-pixel detection has been the topic of a number of research investigations. Its advantages over point scanning approaches are many and include a faster acquisition rate for ...

Solving challenges of enhanced imaging (resolution or speed) is a continuously changing frontier of research. Within this sphere, ghost imaging (and the closely related single-pixel imaging) has evolved as an alternative to focal plane detector arrays ...

A high-speed super-resolution computational imaging technique is introduced on the basis of classical and quantum correlation functions obtained from photon counts collected from quantum emitters illuminated by spatiotemporally structured illumination. ...

Measuring the 3-dimensional (3D) distance between 2 spots is a common task in microscopy, because it holds information on the degree of colocalization in a variety of biological systems. Often, the 2 spots are labeled with 2 different colors, as each spot ...

We present an intelligent programmable computational meta-imager that tailors its sequence of coherent scene illuminations not only to a specific information-extraction task (e.g., object recognition) but also adapts to different types and levels of ...

When light propagates through multimode optical fibres (MMFs), the spatial information it carries is scrambled. Wavefront shaping reverses this scrambling, typically one spatial mode at a time—enabling deployment of MMFs as ultrathin microendoscopes. Here,...

Graph analytics, which mainly includes graph processing, graph mining, and graph learning, has become increasingly important in several domains, including social network analysis, bioinformatics, and machine learning. However, graph analytics applications ...

Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining ...

Reconstruction of a stable and reliable solution from noisy and incomplete Fourier intensity data is a challenging problem for iterative phase retrieval algorithms. The typical methodology employed in the coherent X-ray imaging (CXI) literature involves ...

Stream data processing systems are becoming increasingly popular in the big data era. Systems such as Apache Flink typically provide a number (e.g., 30) of configuration parameters to flexibly specify the amount of resources (e.g., CPU cores and memory) ...

Imaging through scattering media is a longstanding issue in a wide range of applications, including biomedicine, security, and astronomy. Speckle-correlation imaging is promising for noninvasively seeing through scattering media by assuming shift ...

Colocating workloads are commonly used in datacenters to improve server utilization. However, the unpredictable application performance degradation caused by the contention for shared resources makes the problem difficult and limits the efficiency of this ...

Recently, transistor scaling is approaching its physical limit, hindering the further development of the computing capability. In the post-Moore era, emerging logic and storage devices have been the fundamental hardware for expanding the capability of ...

Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, ...

For enabling automatic deployment and management of cellular networks, the concept of self-organizing network (SON) was introduced. SON capabilities can enhance network performance, improve service quality, and reduce operational and capital expenditure (...

As machine learning (ML) becomes the prominent technology for many emerging problems, dedicated ML computers are being developed at a variety of scales, from clouds to edge devices. However, the heterogeneous, parallel, and multilayer characteristics of ...

Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the ...

Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource ...

Programming robot swarms is hard because system requirements are formulated at the swarm level (i.e., globally) while control rules need to be coded at the individual robot level (i.e., locally). Connecting global to local levels or vice versa through ...

In the past three years, we have witnessed the emergence of a new class of artificial intelligence systems–—so-called foundation models, which are characterised by very large machine learning models (with tens or hundreds of billions of parameters) ...