Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of intra-ring wood density by means of imaging VIS/NIR spectroscopy (hyperspectral imaging)

Abstract This paper reports a novel application of hyperspectral imaging (a spectroscopic technique) for measuring wood density profiles at the growth ring scale. The measurements were performed with a spatial resolution of 79 µm. In the present case, hyperspectral imaging was used to measure wood sample reflectance for light in the wavelength range between 380 and 1028 nm, with a resolution of approximately 0.6 nm. The work was performed with 34 samples collected from 34 trees of Pinus pinea. A total of 34,093 density points were used to create and validate a partial least-squares (PLS) regression that converts spectroscopic reflectance data into density values. The coefficient of determination value between the present method and X-ray microdensitometry is 0.810 with a root mean squared error of 6.54×10-2 g.cm-3.
Cocoa Quality and Processing

As in many sweet foods, flavour is an essential characteristic of cocoa and chocolate. The compounds responsible for the aroma are methylpyrazines, nitrogen heterocyclic substances formed during the roasting process. Among these compounds, 2-methylpyrazine and 2,3,5,6-tetramethylpyrazine are the most important compounds because they are both responsible for the respective sweet and strong cocoa aroma. They can represent a useful index for the assessment of product and process qualities. The aim of this work is to set-up and optimize the solid-phase microextraction (SPME) analytical method, in order to determine flavour compositions and, at the same time, perform a predictive analysis of the aroma content of different cocoa blends. Results show a statistical correlation between pyrazine content, geographical origin and sensory properties.
The crystal structure of comancheite, Hg<sup>2+</sup><sub>55</sub>N<sup>3ÂŒ</sup><sub>24</sub>(OH, NH<sub>2</sub>)<sub>4</sub>(Cl, Br)<sub>34</sub>, and crystal-chemical and spectroscopic discrimination of N<sup>3ÂŒ</sup> and O<sup>2</sup><sup>ÂŒ</sup> anions in Hg<sup>2+</sup> compounds

The crystal structure of comancheite, Hg 55N 3 24 (OH,NH2)4(Cl,Br)34, orthorhombic, space group Pnnm, a = 18.414(5), b = 21.328(6), c = 6.6976(19) Å, V = 2630(2) Å, Z = 1, was solved by direct methods and refined to an R1 index of 4.3% based on 4160 unique observed reflections. In the structure of comancheite, there are nine crystallographically distinct Hg cations, each of which is coordinated by two N anions to form near-linear N Hg N groups. Four other crystallographically distinct Hg cations are coordinated by a mixture of N , O , (OH) and (NH2) anions, and there is a small amount of [Hg Hg] dimer. In addition, there are eight crystallographically distinct halogen sites, three of which are completely occupied by Cl , and five of which are occupied by both Cl and Br . The principal anion, N , shows a strong preference for tetrahedral coordination by Hg, which results in a strongly bonded three-dimensional { Hg N } framework. This framework is both interrupted and contains large interstices that incorporate additional Hg cations, a very small amount of [Hg Hg] dimer and additional anion species, O , (OH) and (NH2) , that coordinate Hg. Comancheite was described originally as an Hg-oxide mineral. The major change in chemical composition indicated by the present work was approved by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association (Voting Proposal 13-B). The presence of N provides some analytical challenges, particularly in the presence of Hg. New bond-valence parameters were derived for Hg N bonds [Ro(N ) = 1.95] using well refined Hg structures, and this allows discrimination between Hg O and Hg N bonds based on the valence-sum rule. Comparison of the Raman spectra of several Hg-bearing minerals shows that peaks in the range 500 700 cm 1 are characteristic of Hg N stretching vibrations whereas peaks in the range 350 500 cm 1 are characteristic of Hg O stretching vibrations; Hg O and Hg N bonds may be discriminated on this basis.
A language in focus

The large and wide-ranging body of research in French as a second language has contributed significantly to the development of several branches of the broad discipline of applied linguistics. However, there have to date been few attempts to provide a comprehensive account of this literature as a distinct, language-specific body of knowledge. The present overview summarises a large number of studies published since the year 2000 under the twin broad categories of research in L2 French acquisition and research in L2 French teaching and learning. The overview of the studies, representing different epistemological approaches and targeting different research objectives, provides an up-to-date account of the main concerns of L2 French researchers working in different countries. The article also draws out salient themes that link this work (such as insights gained from analysis of the impact of different L1 backgrounds on L2 French acquisition) and highlights differences in L2 French research trends (such as different emphases and perspectives adopted in anglophone and francophone studies).
Effects of differential diffusion on thin and thick flames propagating in channels

Flame propagation in channels and cracks is a problem of considerable interest with applications in many combustion devices and in fire hazard scenarios. In this paper, the propagation of premixed flames in two-dimensional channels of variable width with a prescribed Poiseuille flow is discussed. The main objective is to assess the effects of differential diffusion on the burning process. For both thick (narrow channels) and thin (wide channels) flames, explicit asymptotic results are obtained for the burning rate and flame shape. These are complemented with numerical calculations spanning the remaining range of moderate flame thicknesses. The results show that unlike thin flames, known to be affected by the effective Lewis number of the mixture, in narrow channels Lewis number effects are negligible. Furthermore, in wide channels, not only does the burning rate strongly depend on the Lewis number, but flame tipopening or dead-space near thewall may result in mixtures with a Lewis number sufficiently less than one.
A Review of the State of the Art, Limitations, and Perspectives of Infrared Spectroscopy for the Analysis of Wine Grapes, Must, and Grapevine Tissue

Abstract As a fast and easy-to-operate technique, infrared (IR) spectroscopy has gained wide industrial acceptance for routine wine analysis. Considering the continuing improvements in hardware and software design and the analytical requirements of real-time or multiparametric analysis by the modern grape and wine industry, it is anticipated that in the near future IR spectroscopy will progressively become a routine method for process monitoring and process control in different stages of grape and wine production. This review highlights recent developments and applications of IR spectroscopy (near- and mid-infrared) to measure compositional parameters in wine grapes, grape juice, and grapevine tissues (e.g., leaves, stems, grapevine wood). In addition, some critical aspects and limitations in instrument availability, type of application, and overall understanding of the technology, which can be barriers for adoption of IR technologies by the grape and wine industry, will be discussed.
On the importance of source population models for gravitational-wave cosmology

S. Mastrogiovanni, K. Leyde, C. Karathanasis, E. Chassande-Mottin, D. A. Steer, J. Gair, A. Ghosh, R. Gray, S. Mukherjee, 7, 8 and S. Rinaldi 10 Université de Paris, CNRS, AstroParticule et Cosmologie (APC), F-75013 Paris, France Institut de F́ısica d’Altes Energies (IFAE), Barcelona Institute of Science and Technology, Barcelona, Spain Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am Mühlenberg 1, Potsdam 14476, Germany Ghent University, Proeftuinstraat 86, 9000 Gent, Belgium SUPA, University of Glasgow, Glasgow G12 8QQ, United Kingdom Gravitation Astroparticle Physics Amsterdam (GRAPPA), Anton Pannekoek Institute for Astronomy and Institute for Physics, University of Amsterdam, Science Park 904, 1090 GL Amsterdam, The Netherlands Institute Lorentz, Leiden University, PO Box 9506, Leiden 2300 RA, The Netherlands Delta Institute for Theoretical Physics, Science Park 904, 1090 GL Amsterdam, The Netherlands Dipartimento di Fisica ”E. Fermi”, Università di Pisa, I-56127 Pisa, Italy INFN, Sezione di Pisa, I-56127 Pisa, Italy (Dated: March 30, 2021)
The NEWFIRM HETDEX Survey: Photometric Catalog and a Conservative Sample of Massive Quiescent Galaxies at z = 3–5 over 17.5 deg2 in the SHELA Field

We present the results of a deep K s -band (2.1 μm) imaging survey of the Spitzer/HETDEX Exploratory Large-Area (SHELA) field using the NEWFIRM near-infrared camera on the KPNO Mayall 4 m telescope. This NEWFIRM HETDEX Survey reaches a 5σ depth of 22.4 AB mag (2″-diameter apertures corrected to total), is ∼50% and 90% complete at K ∼ 22.65 and K ∼ 22.15, respectively, and covers 22 deg2 of the 24 deg2 SHELA Spitzer/IRAC footprint (within “Stripe 82” of the Sloan Digital Sky Survey). We present a K s -band-selected catalog that includes deep ugriz imaging from the Dark Energy Camera and 3.6 and 4.5 μm imaging from Spitzer/IRAC, with forced photometry of 1.7 million sources across 17.5 deg2. The large area and moderate depth of this catalog enable the study of the most massive galaxies at high redshift and minimize uncertainties associated with counting statistics and cosmic variance. As a demonstration, we derive stellar masses (M *) and star formation rates (SFRs) for candidate galaxies at 3 ≲ z ≲ 5 and select a conservative sample of nine candidate massive (M * > 1011 M ⊙) quiescent galaxies, which have measured SFRs significantly below the main sequence at this redshift. Five are ultramassive with M * > 1012, though uncertainties in IRAC blending, gravitational lensing, or active galactic nucleus emission could result in true masses that are lower. Simulations predict that these galaxies should be extremely rare; thus, we conclude by discussing what physical processes in models could be altered to allow the formation of such massive quiescent galaxies at such early times.
Whitening Sentence Representations for Better Semantics and Faster Retrieval

Pre-training models such as BERT have achieved great success in many natural language processing tasks. However, how to obtain better sentence representation through these pre-training models is still worthy to exploit. Previous work has shown that the anisotropy problem is an critical bottleneck for BERT-based sentence representation which hinders the model to fully utilize the underlying semantic features. Therefore, some attempts of boosting the isotropy of sentence distribution, such as flow-based model, have been applied to sentence representations and achieved some improvement. In this paper, we find that the whitening operation in traditional machine learning can similarly enhance the isotropy of sentence representations and achieve competitive results. Furthermore, the whitening technique is also capable of reducing the dimensionality of the sentence representation. Our experimental results show that it can not only achieve promising performance but also significantly reduce the storage cost and accelerate the model retrieval speed.
Control of Agreement and Disagreement Cascades with Distributed Inputs

For a group of autonomous communicating agents to carry out coordinated objectives, it is paramount that they can distinguish meaningful input from disturbance, and come rapidly and reliably to agreement or disagreement in response to that input. We study how opinion formation cascades through a group of networked decision makers in response to a distributed input signal. Using a nonlinear opinion dynamics model with dynamic feedback modulation of an attention parameter, we prove how the triggering of an opinion cascade and the collective decision itself depend on both the distributed input and node agreement and disagreement centrality indices, determined by the spectral properties of the network graph. Moreover, we show how the attention dynamics introduce an implicit threshold that distinguishes between distributed inputs that trigger cascades and ones that are rejected as disturbance.
Entropy methods for the confidence assessment of probabilistic classification models

Many classification models produce a probability distribution as the outcome of a prediction. This information is generally compressed down to the single class with the highest associated probability. In this paper we argue that part of the information that is discarded in this process can be in fact used to further evaluate the goodness of models, and in particular the confidence with which each prediction is made. As an application of the ideas presented in this paper, we provide a theoretical explanation of a confidence degradation phenomenon observed in the complement approach to the (Bernoulli) Näıve Bayes generative model.
Active RIS vs. Passive RIS: Which Will Prevail in 6G?

As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the “multiplicative fading” effect, the existing passive RISs only achieve limited capacity gains in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we develop and verify the signal model of active RISs through the experimental measurements based on a fabricated active RIS element. Based on the verified signal model, we further analyze the asymptotic performance of active RISs to reveal the substantial capacity gain they provide for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit beamforming and reflect precoding scheme is proposed to solve this problem. Simulation results show that, in a typical wireless system, passive RISs can realize only a limited sum-rate gain of 22%, while active RISs can achieve a significant sum-rate gain of 130%, thus overcoming the “multiplicative fading” effect.
Equivariant Imaging: Learning Beyond the Range Space

In various imaging problems, we only have access to compressed measurements of the underlying signals, hindering most learning-based strategies which usually require pairs of signals and associated measurements for training Learning only from compressed measurements is impossible in general, as the compressed observations do not contain information outside the range of the forward sensing operator. We propose a new end-to-end self-supervised framework that overcomes this limitation by exploiting the equivariances present in natural signals. Our proposed learning strategy performs as well as fully supervised methods. Experiments demonstrate the potential of this frame- work on inverse problems including sparse-view X-ray computed tomography on real clinical data and image inpainting on natural images. Code has been made available at: https://github.com/edongdongchen/EI.
High-efficiency Euclidean-based Models for Low-dimensional Knowledge Graph Embeddings

Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hyperbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclidean-based models, called RotL and Rot2L . The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the state-of-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time. The source code of our work is available on GitHub.
In Situ Dielectric Al2O3/β‐Ga2O3 Interfaces Grown Using Metal–Organic Chemical Vapor Deposition

High quality dielectric‐semiconductor interfaces are critical for reliable high‐performance transistors. This paper reports the in situ metal–organic chemical vapor deposition of Al2O3 on β‐Ga2O3 as a potentially better alternative to the most commonly used atomic layer deposition (ALD). The growth of Al2O3 is performed in the same reactor as Ga2O3 using trimethylaluminum and O2 as precursors without breaking the vacuum at a growth temperature of 600 °C. The fast and slow near interface traps at the Al2O3/β‐Ga2O3 interface are identified and quantified using stressed capacitance–voltage (CV) measurements on metal oxide semiconductor capacitor (MOSCAP) structures. The density of shallow and deep level initially filled traps (Dit) are measured using ultraviolet‐assisted CV technique. The average Dit for the MOSCAP is determined to be 6.4 × 1011 cm−2eV−1. The conduction band offset of the Al2O3/ Ga2O3 interface is also determined from CV measurements and found out to be 1.7 eV which is in close agreement with the existing literature reports of ALD Al2O3/Ga2O3 interface. The current–voltage characteristics are also analyzed and the average breakdown field is extracted to be approximately 5.8 MV cm−1. This in situ Al2O3 dielectric on β‐Ga2O3 with improved dielectric properties can enable Ga2O3‐based high‐performance devices.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35Hz.
Self-supervised Graph Neural Networks without explicit negative sampling

Real world data is mostly unlabeled or only few instances are labeled. Manually labeling data is a very expensive and daunting task. This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-supervised/supervised techniques. Contrastive self-supervised learning has emerged as a powerful direction, in some cases outperforming supervised techniques. In this study, we propose, SelfGNN, a novel contrastive self-supervised graph neural network (GNN) without relying on explicit contrastive terms. We leverage Batch Normalization, which introduces implicit contrastive terms, without sacrificing performance. Furthermore, as data augmentation is key in contrastive learning, we introduce four feature augmentation (FA) techniques for graphs. Though graph topological augmentation (TA) is commonly used, our empirical findings show that FA perform as good as TA. Moreover, FA incurs no computational overhead, unlike TA, which often has O(N^3) time complexity, N-number of nodes. Our empirical evaluation on seven publicly available real-world data shows that, SelfGNN is powerful and leads to a performance comparable with SOTA supervised GNNs and always better than SOTA semi-supervised and unsupervised GNNs. The source code is available at https://github.com/zekarias-tilahun/SelfGNN.
Boosting the Speed of Entity Alignment 10 ×: Dual Attention Matching Network with Normalized Hard Sample Mining

Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as entity alignment (EA). However, most existing EA methods are inefficient and poor in scalability. A recent summary points out that some of them even require several days to deal with a dataset containing 200,000 nodes (DWY100K). We believe over-complex graph encoder and inefficient negative sampling strategy are the two main reasons. In this paper, we propose a novel KG encoder — Dual Attention Matching Network (Dual-AMN), which not only models both intra-graph and cross-graph information smartly, but also greatly reduces computational complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to smoothly select hard negative samples with reduced loss shift. The experimental results on widely used public datasets indicate that our method achieves both high accuracy and high efficiency. On DWY100K, the whole running process of our method could be finished in 1,100 seconds, at least 10 × faster than previous work. The performances of our method also outperform previous works across all datasets, where Hits@1 and MRR have been improved from 6% to 13%.
Microencapsulation of amoxicillin in poly(L-lactic acid) by supercritical antisolvent precipitation

Microparticles of poly(l-lactic acid) (PLLA), loaded with the antibiotic amoxicillin, have been produced with the SEDS (solution enhanced dispersion by supercritical fluid) process. Encapsulation of amoxicillin in PLLA was initially attempted from a suspension of amoxicillin microparticles in a solution of PLLA in dichloromethane (DCM). In addition, several mixtures of dichloromethane and dimethyl sulfoxide (DMSO), in which amoxicillin and PLLA were dissolved, were employed in an attempt to encapsulate the drug in the biodegradable matrix. The effect of process parameters such as pressure, temperature, concentration of solutes, and DCM/DMSO ratio on the encapsulation efficiency was investigated. In our study, high pressures and a coaxial nozzle for the introduction of the organic solution and the supercritical antisolvent were employed, resulting in the increased mixing of the two flows. An attempt was made to explain the different encapsulation percentages obtained at different operating conditions.