Energy-time entanglement preservation in plasmon-assisted light transmission

We report on experimental evidences of the preservation of energy-time entanglement for extraordinary plasmonic light transmission through sub-wavelength metallic hole arrays, and for long range surface plasmon polaritons. Plasmons are shown to coherently exist at two different times separated by much more than the plasmons lifetime. This kind of entanglement involving light and matter is expected to be useful for future processing and storing of quantum information.Comment: 4 pages, 4 figure

Imbalance-aware Presence-only Loss Function for Species Distribution Modeling

In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations.Comment: Tackling Climate Change with Machine Learning at ICLR 202

Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks

Learning feature representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work mostly embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features -- these embeddings assume a rectangular data domain even on global data, which can lead to artifacts, especially at the poles. At the same time, relatively little attention has been paid to the exact design of the neural network architectures these functional embeddings are combined with. This work proposes a novel location encoder for globally distributed geographic data that combines spherical harmonic basis functions, natively defined on spherical surfaces, with sinusoidal representation networks (SirenNets) that can be interpreted as learned Double Fourier Sphere embedding. We systematically evaluate the cross-product of positional embeddings and neural network architectures across various classification and regression benchmarks and synthetic evaluation datasets. In contrast to previous approaches that require the combination of both positional encoding and neural networks to learn meaningful representations, we show that both spherical harmonics and sinusoidal representation networks are competitive on their own but set state-of-the-art performances across tasks when combined. We provide source code at www.github.com/marccoru/locationencode

Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action

Although diets influence health and the environment, measuring and changing nutrition is challenging. Traditional measurement methods face challenges, and designing and conducting behavior-changing interventions is conceptually and logistically complicated. Situated local communities such as university campuses offer unique opportunities to shape the nutritional environment and promote health and sustainability. The present study investigates how passively sensed food purchase logs typically collected as part of regular business operations can be used to monitor and measure on-campus food consumption and understand food choice determinants. First, based on 38 million sales logs collected on a large university campus over eight years, we perform statistical analyses to quantify spatio-temporal determinants of food choice and characterize harmful patterns in dietary behaviors, in a case study of food purchasing at EPFL campus. We identify spatial proximity, food item pairing, and academic schedules (yearly and daily) as important determinants driving the on-campus food choice. The case studies demonstrate the potential of food sales logs for measuring nutrition and highlight the breadth and depth of future possibilities to study individual food-choice determinants. We describe how these insights provide an opportunity for stakeholders, such as campus offices responsible for managing food services, to shape the nutritional environment and improve health and sustainability by designing policies and behavioral interventions. Finally, based on the insights derived through the case study of food purchases at EPFL campus, we identify five future opportunities and offer a call to action for the nutrition research community to contribute to ensuring the health and sustainability of on-campus populations—the very communities to which many researchers belong

A User Study of Perceived Carbon Footprint

We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we design a system to probe people's perception from simple pairwise comparisons of the relative carbon footprint of their actions. The formulation of the model enables us to take an active-learning approach to selecting the pairs of actions that are maximally informative about the model parameters. We define a set of 18 actions and collect a dataset of 2183 comparisons from 176 users on a university campus. The early results reveal promising directions to improve climate communication and enhance climate mitigation

Application Concepts for PEF in Food and Biotechnology

While the Food and Biotechnology industries often use unit operations that have been known for some time, sometimes these processes are not efficient or sustainable. The need to develop more efficient processing lines to obtain higher quality products is of utmost importance. Over the last years, pulsed electric fields (PEF) processing has attracted the interest of numerous researchers and companies due to its ability to reduce processing time, preserve thermolabile compounds, which are responsible for the aroma, nutritional and bioactive properties of food products. Therefore, in this article, some of the most important studies regarding the application of PEF technology in food and biotechnology processing is discussed

Vascular remodeling after ischemic stroke: Mechanisms and therapeutic potentials

The brain vasculature has been increasingly recognized as a key player that directs brain development, regulates homeostasis, and contributes to pathological processes. Following ischemic stroke, the reduction of blood flow elicits a cascade of changes and leads to vascular remodeling. However, the temporal profile of vascular changes after stroke is not well understood. Growing evidence suggests that the early phase of cerebral blood volume (CBV) increase is likely due to the improvement in collateral flow, also known as arteriogenesis, whereas the late phase of CBV increase is attributed to the surge of angiogenesis. Arteriogenesis is triggered by shear fluid stress followed by activation of endothelium and inflammatory processes, while angiogenesis induces a number of pro-angiogenic factors and circulating endothelial progenitor cells (EPCs). The status of collaterals in acute stroke has been shown to have several prognostic implications, while the causal relationship between angiogenesis and improved functional recovery has yet to be established in patients. A number of interventions aimed at enhancing cerebral blood flow including increasing collateral recruitment are under clinical investigation. Transplantation of EPCs to improve angiogenesis is also underway. Knowledge in the underlying physiological mechanisms for improved arteriogenesis and angiogenesis shall lead to more effective therapies for ischemic stroke