Intraday solar irradiance forecasting using public cameras

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Current progress in artificial intelligence is centered around so-called large language models that consist of neural networks processing long sequences of high-dimensional vectors called tokens. Statistical physics provides powerful tools to study the fun ...

Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

In recent decades, the rapid growth of cloud computing has transformed the technological landscape and profoundly impacted our daily lives, driven by the demand for secure, flexible, and cost-effective computing solutions. To meet these demands, computing ...

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how mach ...

Data centers are large electricity consumers due to the high consumption needs of servers and their cooling systems. With the rapid growth of crypto-currency and artificial intelligence, their electricity consumption is expected to increase substantially. ...

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

The EPFL Graph Platform is an open-source data infrastructure designed for academic institutions. It organizes educational and institutional data into a semantically interconnected knowledge graph, making it accessible through a graph-based search engine a ...

Holography enables quantitative phase sensing through light wave interference, allowing interesting imaging modalities applied in computational microscopy where phase shift is used as a way to generate image contrast. Recovering both amplitude and phase di ...

Pose-invariant face recognition has become a challenging problem for modern AI-based face recognition systems. It aims at matching a profile face captured in the wild with a frontal face registered in a database. Existing methods perform face frontalizatio ...

Cross-view localization, the task of estimating a camera's 3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with satellite images, is crucial for large-scale outdoor applications like autonomous navigation and augmented reality. Existing m ...

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low sig ...

Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive tra ...

Decarbonizing China's energy system requires both greening the power supply and electrifying end-use sectors. However, concerns exist that electrification may increase emissions while coal power dominates. Using a global climate model, we explore electrifi ...

The alarming increase in atmospheric carbon dioxide levels, primarily attributed to human-induced fossil fuel combustion for power generation, underscores the urgency for sustainable solutions. The solid oxide fuel cell (SOFC) is a promising candidate for ...

Continued greenhouse gas emissions will lead to increasing global warming. Effective adaptation and mitigation policies depend on accurately estimating climate sensitivity, the Earth's surface temperature response to increasing CO2 emissions. Global Climat ...

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

Origami-inspired structures facilitate the design of compliant and compact robots. However, physical origami robots possess inherent material compliance and mechanical imperfections, presenting challenges in modeling and redundant actuation for accurate co ...

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

Deep convolutional neural networks (DCNNs) have significantly advanced the field of computer vision and are often considered as plausible models for the human visual system. However, they still have several vulnerabilities in terms of adversarial attacks, ...

Intraday solar irradiance forecasting using public cameras

Chat with Graph Search

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-Dimensional Tokens

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

FADE: Forecasting for anomaly detection on ECG

Multi-Objective Management for Many-Core Computing Servers

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect

Achieving dispatchability in data centers: carbon and cost-aware sizing of energy storage and local photovoltaic generation

Nonparametric network summaries

GraphOntology Dataset

Cross-Net: Joint In-Line Holographic Image Reconstruction and Refocusing

Pose-Invariant Face Recognition via Feature-Space Pose Frontalization

GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

Anomaly Anything: Promptable Unseen Visual Anomaly Generation

Multilevel emission impacts of electrification and coal pathways in China's net-zero transition

Efficient, affordable, carbon-neutral power: Advanced solid oxide fuel cell-electrolyzer system

Using dimensionality reduction and causal inference to constrain precipitation and climate

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Data-Driven Kinematic Modeling of Physical Origami Robots

Discovering meaningful units from text sequences

Towards Robust Vision Models