24 papers · ranked by Valyu relevance
Hengjin Ke, Chang Cai, Lihua Yao, Dan Chen
1## Introduction Psychiatry stands at a pivotal turning point shaped by rapid technological advances and pressing clinical demands (1). Mental health disorders, defined by multifaceted etiologies and heterogeneous presentations, have traditionally relied on subjective assessments and qualitative interviews. However…
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel + 2 more
Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye. However, conventional optical microscopy faces challenges such as optical aberrations, diffraction-limited resolution, low signal-to-noise ratio…
Kishore Kumar Tarafdar
Deep learning models are widely used to process multidimensional signals such as time series, images, and volumetric medical images, but their learned representations often lack explicit signal structure and are difficult to inspect. This thesis develops model-based, signal-theoretic learning systems guided by data and…
Chloe A. Game, Nils Piechaud, Kerry L. Howell
Deep learning (DL) is a powerful tool to extract ecological information from large image datasets efficiently and consistently. However, applying these methods remains challenging, due in part to the complexity of DL workflows and the dynamic nature of available tools. To address this, we created a practical guide and…
Behnam Karami, Tarana Nigam, Sandrin S. Plewe, Caspar M. Schwiedrzik
The primate visual system is a hierarchical network of brain areas that transform retinal inputs into rich percepts. According to efficient neural coding principles, basic visual features such as orientation, which are already represented in early visual areas, should not be redundantly encoded in higher areas like V4…
S. Deepika, V. Arunachalam
Exploiting unstructured sparsity in the hardware accelerator of a Convolutional Neural Networks (CNNs) based inference can improve energy efficiency. However, it needs a complex controller for indexing and load-balancing. A controller for managing unstructured sparsity in Fully Connected (FC) layers is designed. In a…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Osvaldo Simeone
—The rapid growth of artificial intelligence (AI) has brought novel data processing and generative capabilities but also escalating energy requirements. This challenge motivates renewed interest in neuromorphic computing principles, which promise brain-like efficiency through discrete and sparse activations, recurrent…
Wendi Ma, Aryaman Sharma, Wei Dai, Shekhar S. Chandra
Psychovisual models suggest human vision decouples low-level feature extraction from higher cognition by first forming intermediate abstractions. In contrast, deep learning-based vision models routinely extract and aggregate features using homogeneous stacks of spatial layers, rendering their decision-making processes…
Authors not listed
High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
Wendy Flores-Fuentes, Oleg Sergiyenko, Julio C. Rodríguez-Quiñonez, Jesús E. Miranda-Vega
Information theory is the foundation for computer vision and image processing across diverse applications. It constitutes the application of sophisticated mathematical principles to electrical engineering development for advanced research in vision systems and machine learning. Within this field of study, 3D visual…
Authors not listed
Modeling of chemical reactions is essential for understanding kinetic mechanisms and predicting possible outcomes of reacting systems. Quantum mechanical calculations are accurate but often prohibitively expensive. Deep learning has emerged as a faster alternative, but progress is slowed by a fragmented software…
Sheng Zhou, Chang Gao, Tobi Delbruck, Marian Verhelst + 1 more
Artificial intelligence (AI) has made significant strides towards efficient online processing of sensory signals at the edge through the use of deep neural networks with ever-expanding size. However, this trend has brought with it escalating computational costs and energy consumption, which have become major obstacles…
Ari Peden-Asarch, Meredith Weinstock, Kevin R. Coffey, John F. Neumaier
Miniscope calcium imaging provides a unique window into the activity of neurons during behavior while enabling spatial localization of individual cells across time. Despite its potential to revolutionize in vivo imaging alongside the rise of optogenetic tools, miniscopes remain underutilized. This gap may stem from the…
Drew E. Winters
Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been a central focus for understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, providing a foundation for developing…
Manjunath, Varun, Ramesh, Pranav + 2 more
NeuroFlex is a column-level accelerator that co-executes artificial and spiking neural networks to minimize energy–delay product on sparse edge workloads with competitive accuracy. The design extends integer-exact QCFS ANN-SNN conversion from layers to independent columns. It unifies INT8 storage with on-the-fly spike…
Zhijie Qu, Jinquan Zhang, Yuewei Zhou, Lina Ni + 4 more
The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly…
Aonan He, Xi Wang, Jiangwei Yu, Xiaojia Wang + 6 more
Electroencephalography (EEG) serves as a fundamental tool in modern neurology, cognitive neuroscience, and brain-computer interfaces, but its practical application is often compromised by artifacts. Physiological artifacts are particularly intractable due to overlapping spectral features with neural signals, hindering…
Sara Varetti, Sebastian Goldt, Eugenio Piasini
In vision neuroscience, the temporal dynamics of the sensory stream and of its neural representations are thought to be deeply linked to the function of the hierarchy of cortical areas that deal with object recognition, known as the visual ventral stream. Neural representations that are invariant under…
Drew E. Winters
Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been a central focus for understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, providing a foundation for developing…
Authors not listed
Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
Han Yue, Xiang Shuiying, Tao Zou, Huang Zhiquan + 5 more
With the development of hardware-optimized deployment of spiking neural networks (SNNs), SNN processors based on field-programmable gate arrays (FPGAs) have become a research hotspot due to their efficiency and flexibility. However, existing methods rely on multi-timestep training and reconfigurable computing…
Mattias Westerink, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez + 1 more
Event-driven neuromorphic inference exploits activation sparsity by updating neuron state only on spikes. However, weight sparsity introduces irregular gather-style updates that undermine lockstep Single Instruction Multiple Data (SIMD) execution. We call the resulting overheads in control, metadata, and memory…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…