Miracle-dz/UniFAR-FTUs huggingface dataset:
NONLINEARITIES IN THE QUANTUM MULTIVERSE

It has been recently proposed that the multiverse of eternal inflation, string landscape and the many-worlds interpretation of quantum mechanics can be identified, yielding a new view on the measure and measurement problems. In the present note, we argue that a nonlinear evolution of observables in the quantum multiverse would be an obstacle for such a description and that these nonlinearities are expected from quite general arguments.
Fidelity approach to the Hubbard model

We use the fidelity approach to quantum critical points to study the zero-temperature phase diagram of the one-dimensional Hubbard model. Using a variety of analytical and numerical techniques, we analyze the fidelity metric in various regions of the phase diagram with particular care to the critical points. Specifically we show that close to the Mott transition, taking place at on-site repulsion $U=0$ and electron density $n=1$, the fidelity metric satisfies an hyperscaling form which we calculate. This implies that in general, as one approaches the critical point $U=0$, $n=1$, the fidelity metric tends to a limit which depends on the path of approach. At half-filling, the fidelity metric is expected to diverge as ${U}^{\ensuremath{-}4}$ when $U$ is sent to zero.
Accuracy of load and generation forecasts for the operational planning of power distribution systems

Operational planning in future power distribution systems will depend on load and generation forecasts as decisions and analyses will be performed in an increasingly coordinated manner at lead times. Forecasts on distribution level are significantly less accurate than on transmission level. The evaluation of forecast accuracy is therefore necessary to ensure system security. In this paper, forecast errors of wind power, photovoltaic power and load are evaluated for a medium-voltage grid region. Various influencing variables on forecast accuracy are examined. The evaluations show that forecasts in particular become worse for low aggregation levels and higher forecast horizons. In addition, the forecast error depends on characteristics such as the time of day or the magnitude of the forecast. For a secure system operation, forecast errors as well as their dependencies should be taken into account. Operational decisions should be made within the shortest possible lead time.
Understanding Banding—Perceptual Modeling and Machine Learning Approaches for Banding Detection

Banding is an annoying visual artifact that frequently appears at various stages along the chain of video acquisition, production, distribution, and display. With the thriving popularity of ultrahigh definition (UHD), high-dynamic range (HDR), wide-color-gamut (WCG) content, and the increasing user expectations that follow, the banding effect has been attracting increased attention for its strong negative impact on viewer experience in visual content that would otherwise have nearly perfect quality. Here, we present two different types of frameworks to detect the banding artifact. The first is knowledge-driven and is built upon computational models that account for the characteristics of the human visual system (HVS), the content acquisition, production, distribution, and display processes, and the interplay between them. The second is data-driven and is based on machine learning methods, by training deep neural networks (DNNs) with large-scale datasets.
Minimum tomography of two entangled qutrits using local measurements of one-qutrit SIC-POVM

W. M. Pimenta, ∗ B. Marques, T. O. Maciel, R. O. Vianna, A. Delgado, 3 C. Saavedra, 3 and S. Pádua Departamento de F́ısica, Universidade Federal de Minas Gerais, Caixa Postal 702, Belo Horizonte, MG 30123-970, Brazil. Center for Optics and Photonics, Universidad de Concepción, Casilla 4016, Concepción, Chile. Departamento de F́ısica, Universidad de Concepción, Casilla 160-C, Concepción, Chile. (Dated: December 5, 2013)
Etude multi-échelle de modèles probabilistes pour les systèmes excitables avec composante spatiale

L'objet de cette these est l'etude mathematique de modeles probabilistes pour la generation et la propagation d'un potentiel d'action dans les neurones et plus generalement de modeles aleatoires pour les systemes excitables. En effet, nous souhaitons etudier l'influence du bruit sur certains systemes excitables multi-echelles possedant une composante spatiale, que ce soit le bruit contenu intrinsequement dans le systeme ou le bruit provenant du milieu. Pour etudier le bruit intrinseque, nous considerons des processus de Markov deterministes par morceaux a valeurs dans des espaces de Hilbert ("Hilbert-valued PDMP"). Nous nous sommes interesses a l'aspect multi-echelles de ces processus et a leur comportement en temps long. Dans un premier temps, nous etudions le cas ou la composante rapide est une composante discrete du PDMP. Nous demontrons un theoreme limite lorsque la composante rapide est infiniment acceleree. Nous etudions ensuite les fluctuations du modele multi-echelles autour du modele moyenne en montrant que celles-ci sont gaussiennes a travers la preuve d'un theoreme de type central limit. Dans un deuxieme temps, nous abordons le cas ou la composante rapide est elle-meme un PDMP. Cela requiert de connaitre la mesure invariante d'un PDMP a valeurs dans un espace de Hilbert. Nous montrons qu'il existe une unique mesure invariante et la convergence exponentielle du processus vers cette mesure. Pour etudier le bruit externe, nous considerons des systemes d'equations aux derivees partielles stochastiques (EDPS) conduites par des bruits colores. Nous menons l'analyse numerique d'un schema de type differences finies en temps et elements finis en espace.
Oil Spill Detection and Mapping Using Sentinel-1 and Sentinel-2 in the Arabian Gulf Coastal Waters

Oil spills can cause long-term damage to the marine ecosystem and force desalination plants to shut down. Thus, this study aims to detect the oil spill in Arabian Gulf which produces a quarter of the world's oil. Sentinel-1 SAR and Sentinel-2 spectral imageries are utilized to detect oil spill. Sentinel-1 SAR is used to perform semantic segmentation with deep convolutional neural networks (DCNNs) using a U-Net structure and Sentinel-2 band ratio for mapping oil spill. Based on the segmentation results, the intersection-over-union (IoU) shows low values of <0.1 for the oil spill incidents along the Gulf coasts, but higher values for the offshore oil spill incidents (>0.65). Coastal land has adversely affected the segmentation process. As for the band ratio approach, it succeeded in distinguishing the oil spill, however prior knowledge or an expert interpretation is needed to verify the presence of the spill.
Class-Aware Regularized Self-Distillation Learning Method for Land Cover Classification

The rapid development of remote sensing provides abundant data for land cover classification. Nevertheless, densely labeling newly-acquired data is an expensive job. With labeled data from different imaging locations, obtaining good performance on unlabeled test data has two challenges: (1) Due to the geographic shift, deep models trained on labeled data lack generalization to other geographic locations. (2) The samples of different classes in remote sensing images with diverse scenes are extremely imbalanced. To conquer these obstacles, we propose a class-aware regularized self-distillation learning method. For the former, the model trained on labeled data is multi-round self-distilled on unlabeled data with the supervision of pseudo-labels, and each class is set with specific distillation rounds. For the latter, we assign a tailored weight to each class during the self-distillation learning. In the end, our proposal achieves a mIoU of 49.83%, ranking third place in Track SLM of 2022 IEEE GRSS Data fusion contest.
Interfacing energy management with supervisory control for hybrid power plants

Variability and uncertainties of wind and solar bring significant challenges into power system operation and control. Hybrid power plants (HPPs), which incorporates the complementary nature of wind and solar together with other technologies, such as energy storage, is a solution to cope with these challenges. To maximize the revenue and enable the operation of HPPs, the energy management system and the supervisory controller are both needed, namely the HPP EMS and the HPPC. The HPP EMS provides optimal dispatch strategies in order to maximize the revenue through market bids. Meanwhile, the HPPC executes dispatch plans from the HPP EMS in a real-time fashion. The paper highlights a new design of HPP EMS and HPPC with a focus on interface design between the two. The variables exchanged between the HPP EMS and the HPPC are presented, and practical issues such as time coordination and robustness over communication failure are discussed in detail.
A Morphological-Long Short Term Memory Network Applied to Crop Classification

The combination of Convolutional Neural Networks (CNN) with Long Short Term Memory (LSTM) networks in the form of CNN-LSTMs, is one of the currently widely used temporal data series processing approaches. It harnesses the CNN's feature extraction ability along with the LSTM's capacity to account for sequential dependencies. Mathematical morphology on the other hand is known for its spatial analysis potential. In this study, we explore the combination of morphological neural networks (MNNs) with LSTMs, in the form of MNN-LSTMs, and apply it to the problem of crop classification from multi-spectral/temporal remote sensing images. The explored method is tested with two real datasets, where it exhibits either superior or comparable performance to CNN-LSTMs and other state-of-the-art alternative approaches.
Intercomparison of Earth Observation Data and Methods for Forest Mapping in the Context of Forest Carbon Monitoring

ESA Forest Carbon Monitoring project (FCM) is developing Earth Observation based, user-centric approaches for forest carbon monitoring. Forest carbon accounting based on forest inventory requires precise and timely estimation of forest variables at various spatial levels accompanied by verifiable uncertainty information. In this paper, we present the algorithm trade-off and selection approach and preliminary results of the algorithm intercomparison exercise in the FCM project. The studies were performed over 7 European test sites located in Finland, Ireland, Romania, Spain and Switzerland, and one tropical forest site in Peru. EO datasets were represented by Sentinel-1, Sentinel-2, TanDEM-X and ALOS-2 PALSAR-2 imagery. Examined approaches include popular parametric and SAR/InSAR scattering physics based approaches, and nonparametric and machine learning approaches such as k-NN, random forests, support vector regression.
UAL: Unchanged Area Loss-Function for Change Detection Networks

A novel change detection approach is proposed in this paper, which is based on dual task learning model and unchanged area loss-function(UAL). Dual task model combines change detection(CD) and semantic segmentation(SS) based on Siamese neural network to improve the feature separability, and UAL aims to establish the semantic label correspondence within unchanged regions. Experiments demonstrate the effectiveness and advantages of the proposed approach. Our code and models are available at https://github.com/Chuan-shanjial/A-loss-function-for-change-detection.
Silicon Quantum Dots with Large Stokes Shift and Long Photoluminescence Lifetime for Energy and Biomedical Applications

Since the demonstration of visible photoluminescence (PL) emitted from porous silicon in 1990s [1], silicon quantum dots (SiQDs) have drawn much attention due to its plentiful earth abundance, exceptional biocompatibility, versatile surface chemistry and high PL quantum yield (PLQY). Compared to conventional II-VI or III-V QDs and organic dyes, the SiQDs are free of heavy metal toxicity and much less susceptible to photobleaching. In addition to the advantages described above, in this wok we focus on another two unique optical properties of SiQDs, namely large Stokes shift and long PL lifetime. In addition to the quasi-direct bandgap and bandgap enlargement due to the quantum confinement effect, the PL process of the SiQDs also involves radiative recombination between oxygen related surface defect states [2]. As a result, nearly all SiQDs reported in literature are characterized by a strong Fourier-transform infrared (FTIR) absorption peak (950–1250 cm-1) due to the Si-O-Si bond and relatively long PL lifetimes (typically 20–40 μs). On the other hand, the light absorption property of the SiQDs remains similar to the bulk silicon, with the primary absorption lies in the ultraviolet (UV) range and negligible absorption in the visible range.
Preliminary Snow Water Equivalent Retrieval of SnowEX20 Swesarr Data

This paper explores the retrieval of snow water equivalent (SWE) through the use of machine learning techniques and active radar data collected over the 2020 SnowEx campaign. The retrieval makes use of active radar measurements provided by NASA's SWESARR instrument for direct sensing of snowpack sensitivity to SWE. The example results show that an RMSE of 1.93 cm can be obtained through a combined use of SAR data with sufficient ancillary data. Such results may indicate successful SWE estimation by means of pairing spaceborne SAR measurements with sufficient auxiliary information.
SEN23E: A Cloudless Geo-Referenced Multi-Spectral Sentinel-2/Sentinel-3 Dataset for Data Fusion Analysis

The availability of geo-referenced coupled data of dif-ferent platforms is essential to train remote sensing (RS) multi-modal classification and bio-phyiscal parameter esti-mation learning methods. To properly develop a general-izing model different scenes and topographies are required. For this purpose, different multi-modal datasets have been published for the last years. Nevertheless, to our knowl-edge there is not any dataset composed of Sentinel-2 (S2) and Sentinel-3 (S3) geo-referenced images. In this paper we present SEN23, a dataset composed of 100 complete multi-spectral S2 and S3 paired images of different locations along Europe from the 2021 summer. The coupled images were obtained with a time difference of three or less days, containing less than a 1 % of cloud coverage and have a resolution difference of × 15. SEN23E is expected to help with the development of new multi-spectral, multi-resolution and multi-modal models for complex tasks which need con-text and complete images. SEN23E will be available at https://github.com/ibanezdf/SEN23E.
Extended Shannon's inequality and the asymptotic capacity of T-user binary adder channel

An extension of Shannon's inequality for discrete probability distribution with an infinite number of elements is considered. As an application, the asymptotic capacity of the T-user binary adder channel is exactly determined. The previously known asymptotically good (but not very) T-user code of Chang and Weldon (1979) is shown to be far from asymptotically very good. It is thus concluded that the achievability problem of the asymptotic capacity remains open.
Attention-Based Deep Sequential Network for Polsar Image Classification

In this paper, we proposed an attention-based deep sequential network (ADSN) for PolSAR images classification increasing the spatial information between pixels by way of spatial sequence. Specifically, the long short-term memory (LSTM) network is introduced to convert the time sequence into spatial sequence to extract the spatial features. Then, a spatial enhanced strategy is carried out to enhance the relationship between pixel spatial information based on LSTM. Finally, to avoid feature selection procedures, the attention mechanism is introduced in LSTM network to select the important information and improve the classification performance. The experiments clearly demonstrate that compared with state-of-art methods, the proposed method can achieve a much better performance and overall Classification accuracy.
CENTRAL ENERGY METER FOR SMART BUILDINGS

This paper presents developed Central Energy Meter application and its pilot installation in the campus of Brno University of Technology. IEC 61850-9-2 Sampled Values are source data for Central Energy Meter application. Digital data can be used in parallel for several applications if the connectivity, interoperability and data quality are enough. Specifically, Central Energy Meter application uses IEC61850-9-2 SV data for parallel applications - statistical energy meter, grid analysis - anomaly detection. This paper discusses general benefits of Central Energy Meter for building complexes such as University campuses or hospitals based on results from the pilot installation.
POWER QUALITY MONITORING DATA MANAGEMENT AND ANALYSIS FOR DISTRIBUTION NETWORKS

New energy technologies bring challenges regarding the management of power quality, loading and losses in distribution networks. This paper discusses the challenges in power quality data management and analysis. Vast data amounts are available from several sources, e.g. distribution automation and smart kWh meters. However, the relevant information easily remains hidden in the data. The available data are big and diverse, and complex automated pre-processing, integration and analyses are needed. Several advancements are to be made for comprehensive and long-term power quality management that bases on measurement data. Thus, there is a need to develop methods and systems for the handling and analysis of these data. The issues addressed in this paper are reflections of literature, relevant power quality projects and views of connected industry experts. To examine potential solutions, test sites were established and are presented in this paper with proposals for important future studies.
Percolation in Two-Dimensional Grain Boundary Structures and Polycrystal Property Closures

PERCOLATION IN TWO-DIMENSIONAL GRAIN BOUNDARY STRUCTURES, AND POLYCRYSTAL PROPERTY CLOSURES David T. Fullwood Mechanical Engineering Department Master of Science The thesis addresses two topics in the study of material properties as determined by the microstructure. The first topic involves percolation as a tool in relating the grain boundary structure to global properties such as fracture and corrosion resistance. The second investigates optimization techniques in order to find the space of values that properties of a material can take, from consideration of the microstructure. In part I, the applicability of standard lattice percolation models to a random 2-D grain structure is explored. A random network based on the triangle lattice is proposed as a more appropriate model, and results in a higher percolation threshold (0.711 compared with 0.653 for the standard hexagonal lattice). The triple junction constraint inherent in grain boundary structures is subsequently applied to the new network. This results in a lowering of the percolation threshold to 0.686, which turns out to be very close to the value obtained from the hexagonal lattice under the same constraint. In Part II, an efficient method for finding the closure of a bi-objective optimization problem involving two material properties is formulated. The method is based upon two algorithms developed to find the Pareto front in multi-objective problems – the weighted sum, and the normal boundary intersection methods. The resultant procedure uses quadratic programming (QP) to find as many points on the closure as possible, changing to the less efficient sequential quadratic programming (SQP) only where necessary to find points on concave, or almost concave, regions. Improvements on the method are demonstrated using extra linear constraints in the generalized weighted sum (GWS) algorithm, and multiple GWS trials at each stage. Optimization using only the linear part of the problem is shown to give excellent results for the particular example used, and may help to achieve an approximate closure more quickly for larger problems. An adaptation of a common Pareto front method from genetic algorithms – the maxmin algorithm – is also demonstrated. The efficiency of the method is found to be reasonable for finding closures in the test case. Other general optimization techniques for the form of the problem in-hand are explored for completeness of the study. These include a survey of unconstrained techniques that might be useful in large-scale problems, and an in-depth application of QP for the constrained case.
Marker-Free Registration of Terrestrial Laser Scanning Data Under Forest with Stem Position Triplet Matching Based on Kd-Tree

Registration of Terrestrial Laser Scanning Data has been an effective tool for obtaining a complete view of a given forest area. In order to improve the efficiency of the registration, an improved method based on the existing marker-free registration method is proposed in this paper. In our method, only the stem positions are needed. By using the edge lengths and eigenvalues that calculated based on each set of tie point triplets as feature information for triplet matching and KD-Tree to speed up the triplets matching process to find the best transformation parameters. Through the comparison experiments, the results showed that the improved algorithm can achieve the purpose of registration.
A Grand Collaboration in Support of Digital Twin Studies: Geoscience, Sensing, and Healthcloud

In recent years, there has been a burst of discovery regarding the health benefits people gain from having contact with green landscapes [1]. These discoveries describe the salutary benefits of green urban and rural landscapes [2], small scale [3] and regional scale green landscapes [4]. Investigators have explored the impacts of varying levels of vegetation in landscapes on health outcomes such as mood [5], exercise [6], neural activities [7], COVID-19 [8], capacity to pay attention [9], [10], safety [11], and general health [12].
A Dual Sparsity Constrained Approach for Hyperspectral Target Detection

The problem of target detection in hyperspectral images is an unsupervised binary classification problem with extremely uneven samples. To highlight the target and suppress the background as much as possible, this paper proposes a target detection algorithm based on dual sparse constraints. Specifically, the original image can be decomposed into a background image and a target image. Combined with sparse representation, the target detection problem can be transformed into a problem of optimizing the target and background coefficient matrices. This problem can be solved by the alternating direction method of multipliers. Considering that both the target dictionary and background dictionary are unknown, this paper also proposes a dictionary construction algorithm based on spectral similarity and clustering to obtain relatively complete and pure target and background dictionaries. The experimental results demonstrate that the proposed method outperforms other competing methods in both quantitative performance and visual effect. Code and datasets are available at https://github.com/shendb2022/DSC.
A thin film broadband absorber based on multi-sized nanoantennas

We experimentally demonstrate an infrared broadband absorber based on an array of nanostrip antennas of several different sizes. The broadband property is due to the collective effect of magnetic responses excited by these nanoantennas at distinct wavelengths. By manipulating the differences of the nanostrip widths, the measured spectra clearly validate our design for the purpose of broadening the absorption band.