Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications

Deep neural networks offer numerous potential applications across geoscience, for example, one could argue that they are the state-of-the-art method for predicting faults in seismic datasets. In quantitative reservoir characterization workflows, it is common to incorporate the uncertainty of predictions thus such subsurface models should provide calibrated probabilities and the associated uncertainties in their predictions. It has been shown that popular Deep Learning-based models are often miscalibrated, and due to their deterministic nature, provide no means to interpret the uncertainty of their predictions. We compare three different approaches to obtaining probabilistic models based on convolutional neural networks in a Bayesian formalism, namely Deep Ensembles, Concrete Dropout, and Stochastic Weight Averaging-Gaussian (SWAG). These methods are consistently applied to fault detection case studies where Deep Ensembles use independently trained models to provide fault probabilities, Concrete Dropout represents an extension to the popular Dropout technique to approximate Bayesian neural networks, and finally, we apply SWAG, a recent method that is based on the Bayesian inference equivalence of mini-batch Stochastic Gradient Descent. We provide quantitative results in terms of model calibration and uncertainty representation, as well as qualitative results on synthetic and real seismic datasets. Our results show that the approximate Bayesian methods, Concrete Dropout and SWAG, both provide well-calibrated predictions and uncertainty attributes at a lower computational cost when compared to the baseline Deep Ensemble approach. The resulting uncertainties also offer a possibility to further improve the model performance as well as enhancing the interpretability of the models.
Immersive virtual reality methods in cognitive neuroscience and neuropsychology: Meeting the criteria of the National Academy of Neuropsychology and American Academy of Clinical Neuropsychology

Clinical tools involving immersive virtual reality (VR) may bring several advantages to cognitive neuroscience and neuropsychology. However, there are some technical and methodological pitfalls. The American Academy of Clinical Neuropsychology (AACN) and the National Academy of Neuropsychology (NAN) raised 8 key issues pertaining to Computerized Neuropsychological Assessment Devices. These issues pertain to: (1) the safety and effectivity; (2) the identity of the end-user; (3) the technical hardware and software features; (4) privacy and data security; (5) the psychometric properties; (6) examinee issues; (7) the use of reporting services; and (8) the reliability of the responses and results. The VR Everyday Assessment Lab (VR-EAL) is the first immersive VR neuropsychological battery with enhanced ecological validity for the assessment of everyday cognitive functions by offering a pleasant testing experience without inducing cybersickness. The VR-EAL meets the criteria of the NAN and AACN, addresses the methodological pitfalls, and brings advantages for neuropsychological testing. However, there are still shortcomings of the VR-EAL, which should be addressed. Future iterations should strive to improve the embodiment illusion in VR-EAL and the creation of an open access VR software library should be attempted. The discussed studies demonstrate the utility of VR methods in cognitive neuroscience and neuropsychology.
Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are required to detect the small plants present at the early stages. This study explores the impact of image ground sampling distance (GSD) on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm. Data collected at high resolution (GSD ≈ 0.3 cm) over six contrasted sites were used for model training. Two additional sites with images acquired both at high and low (GSD ≈ 0.6 cm) resolutions were used to evaluate the model performances. Results show that Faster-RCNN achieved very good plant detection and counting (rRMSE = 0.08) performances when native high-resolution images are used both for training and validation. Similarly, good performances were observed (rRMSE = 0.11) when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images. Conversely, poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution. Training on a mix of high- and low-resolution images allows to get very good performances on the native high-resolution (rRMSE = 0.06) and synthetic low-resolution (rRMSE = 0.10) images. However, very low performances are still observed over the native low-resolution images (rRMSE = 0.48), mainly due to the poor quality of the native low-resolution images. Finally, an advanced super resolution method based on GAN (generative adversarial network) that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images. Results show some significant improvement (rRMSE = 0.22) compared to bicubic upsampling approach, while still far below the performances achieved over the native high-resolution images.
BASS: Boosting Abstractive Summarization with Unified Semantic Graph

Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text. In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. Further, a graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process by leveraging the graph structure. Specifically, several graph augmentation methods are designed to encode both the explicit and implicit relations in the text while the graph-propagation attention mechanism is developed in the decoder to select salient content into the summary. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.
Efficient generation of turbulent collisionless shocks in laser-ablated counter-streaming plasmas

Laser-ablated high-energy-density (HED) plasmas offer a promising route to study astrophysically relevant processes underlying collisionless shock formation, magnetic field amplification, and particle acceleration in the laboratory. Using large-scale, multi-dimensional particle-in-cell simulations, we explore the interpenetration of laser-ablated counter-streaming plasmas for realistic experimental flow profiles. We find that the shock formation and its structure are substantially different from those of more idealized and commonly considered uniform flows: shock formation can be up to 10 times faster due to the transition from small-angle scattering to magnetic reflection and the shock front develops strong corrugations at the ion gyroradius scale. These findings have important consequences for current experimental programs and open exciting prospects for studying the microphysics of turbulent collisionless shocks with currently available high-energy laser systems.
SGD with Coordinate Sampling: Theory and Practice

While classical forms of stochastic gradient descent algorithm treat the different coordinates in the same way, a framework allowing for adaptive (non uniform) coordinate sampling is developed to leverage structure in data. In a non-convex setting and including zeroth order gradient estimate, almost sure convergence as well as non-asymptotic bounds are established. Within the proposed framework, we develop an algorithm, MUSKETEER, based on a reinforcement strategy: after collecting information on the noisy gradients, it samples the most promising coordinate (all for one); then it moves along the one direction yielding an important decrease of the objective (one for all). Numerical experiments on both synthetic and real data examples confirm the effectiveness of MUSKETEER in large scale problems.
Towards Understanding the Condensation of Two-layer Neural Networks at Initial Training

Studying the implicit regularization effect of the nonlinear training dynamics of neural networks (NNs) is important for understanding why over-parameterized neural networks often generalize well on real dataset. Empirically, for two-layer NN, existing works have shown that input weights of hidden neurons (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) condense on isolated orientations with a small initialization. The condensation dynamics implies that NNs can learn features from the training data with a network conﬁguration effectively equivalent to a much smaller network during the training. In this work, we show that the multiple roots of activation function at origin (referred as “multiplicity”) is a key factor for understanding the condensation at the initial stage of training. Our experiments of multilayer networks suggest that the maximal number of condensed orientations is twice the multiplicity of the activation function used. Our theoretical analysis of two-layer networks conﬁrms experiments for two cases, one is for the activation function of multiplicity one, which contains many common activation functions, and the other is for the one-dimensional input. This work makes a step towards understanding how small initialization implicitly leads NNs to condensation at initial training stage, which lays a foundation for the future study of the nonlinear dynamics of NNs and its implicit regularization effect at a later stage of training.
Fluid-structure interaction of a flexible cantilever cylinder at low Reynolds numbers

We present a numerical study to investigate the fluid-structure interaction of a flexible circular cantilever cylinder in a uniform cross-flow. We employ a fully-coupled fluid-structure solver based on the three-dimensional Navier-Stokes equations and the Euler-Bernoulli beam theory. We examine the dynamics of the cylinder for a wide range of reduced velocities (U∗), mass ratios (m∗), and Reynolds numbers (Re). Of particular interest is to explore the possibility of flow-induced vibrations in a slender cantilever cylinder of aspect ratio AR = 100 at laminar subcritical Re regime (i.e., no periodic vortex shedding). We assess the extent to which such a flexible cylindrical beam can sustain flow-induced vibrations and characterize the contribution of the beam’s flexibility to the stability of the wake at low Re. We show that when certain conditions are satisfied, the flexible cantilever cylinder undergoes sustained large-amplitude vibrations. The frequency of the oscillations is found to match the frequency of the periodic fluid forces for a particular range of system parameters. In this range, the frequency of the transverse vibrations is shown to match the first-mode natural frequency of the cylinder, indicating the existence of the lock-in phenomenon. The range of the lock-in regime is shown to have a strong dependence on Re and m∗. We discover that unlike the steady wake behind a stationary rigid cylinder, the wake of a low mass ratio flexible cantilever cylinder could lose its stability in the lock-in regime at Reynolds numbers as low as Re = 22. A combined VIV-galloping type instability is shown to be the possible cause of the wake instability at this Re regime. These findings attempt to generalize our understanding of the flow-induced vibrations in flexible cantilever structures and can have a profound impact on the development of novel flow-measurement sensors.
Modelling a multi-spacecraft coronal mass ejection encounter with EUHFORIA

Coronal mass ejections (CMEs) are a manifestation of the Sun's eruptive nature. They can have a great impact on Earth, but also on human activity in space and on the ground. Therefore, modelling their evolution as they propagate through interplanetary space is essential. EUropean Heliospheric FORecasting Information Asset (EUHFORIA) is a data-driven, physics-based model, tracing the evolution of CMEs through background solar wind conditions. It employs a spheromak flux rope, which provides it with the advantage of reconstructing the internal magnetic field configuration of CMEs. This is something that is not included in the simpler cone CME model used so far for space weather forecasting. This work aims at assessing the spheromak CME model included in EUHFORIA. We employed the spheromak CME model to reconstruct a well observed CME and compare model output to in situ observations. We focus on an eruption from 6 January 2013 encountered by two radially aligned spacecraft, Venus Express and STEREO-A. We first analysed the observed properties of the source of this CME eruption and we extracted the CME properties as it lifted off from the Sun. Using this information, we set up EUHFORIA runs to model the event. The model predicts arrival times from half to a full day ahead of the in situ observed ones, but within errors established from similar studies. In the modelling domain, the CME appears to be propagating primarily southward, which is in accordance with white-light images of the CME eruption close to the Sun. In order to get the observed magnetic field topology, we aimed at selecting a spheromak rotation angle for which the axis of symmetry of the spheromak is perpendicular to the direction of the polarity inversion line (PIL). The modelled magnetic field profiles, their amplitude, arrival times, and sheath region length are all affected by the choice of radius of the modelled spheromak.
Analysis of Photon Characteristics in Anticorrelation of a Hong-Ou-Mandel Dip for On-Demand Quantum Correlation Control

Over the last several decades, quantum entanglement has been intensively studied for potential applications in quantum information science. The Hong-Ou-Mandel (HOM) dip is the most important test tool for direct proof of entanglement between paired photons, whose coincidence detection results in anticorrelation due to photon bunching on a beam splitter. Although anticorrelation is due to destructive quantum interference between paired photons, a wavelength-sensitive interference fringe has never been observed in any HOM-type experiments. Here, a typical HOM dip is investigated for entangled photon pairs generated by parametric down conversion processes (SPDC) to understand fundamental physics of anticorrelation. In addition, a pure coherence optics-based Hong-Ou-Mandel scheme is proposed and analyzed for general understanding of anticorrelation in an interferometric system. This study sheds light on deterministic quantum correlation control and opens the door to potential applications of on-demand quantum information science.
Weak Damping of Propagating MHD Kink Waves in the Quiescent Corona

Propagating transverse waves are thought to be a key transporter of Poynting flux throughout the Sun’s atmosphere. Recent studies have shown that these transverse motions, interpreted as the magnetohydrodynamic kink mode, are prevalent throughout the corona. The associated energy estimates suggest the waves carry enough energy to meet the demands of coronal radiative losses in the quiescent Sun. However, it is still unclear how the waves deposit their energy into the coronal plasma. We present the results from a large-scale study of propagating kink waves in the quiescent corona using data from the Coronal Multi-channel Polarimeter (CoMP). The analysis reveals that the kink waves appear to be weakly damped, which would imply low rates of energy transfer from the large-scale transverse motions to smaller scales via either uniturbulence or resonant absorption. This raises questions about how the observed kink modes would deposit their energy into the coronal plasma. Moreover, these observations, combined with the results of Monte Carlo simulations, lead us to infer that the solar corona displays a spectrum of density ratios, with a smaller density ratio (relative to the ambient corona) in quiescent coronal loops and a higher density ratio in active-region coronal loops.
Positional Artefacts Propagate Through Masked Language Model Embeddings

In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa’s hidden state vectors that consistently bear the smallest or largest values in said vectors. In an attempt to investigate the source of this information, we introduce a neuron-level analysis method, which reveals that the outliers are closely related to information captured by positional embeddings. We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings. These outliers, we find, are the major cause of anisotropy of encoders’ raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance.
Cannabinoids from inflorescences fractions of Trema orientalis (L.) Blume (Cannabaceae) against human pathogenic bacteria

Background Cannabinoids; tetrahydrocannabinol (THC), cannabidiol (CBD) and cannabinol (CBN), might show antibacterial activity. Trema orientalis is a species in the Cannabaceae that is closely related to Cannabis through plastome phylogenetic evidence. This species is widely distributed throughout tropical Asia and is used as traditional medicine, particularly for the treatment of infectious diseases. However, no studies on the antibacterial activity of cannabinoid-containing inflorescences extracts are available. Thus, the aim of this study was to determine cannabinoid content and antibacterial activity of inflorescences fractions from T. orientalis native to Thailand. Methods We hypothesized that inflorescences from T. orientalis might display cannabinoids similar to Cannabis because of their close taxonomic relationship. We extracted the mature inflorescences and infructescence of T. orientalis in three disparate populations from different Thailand floristic regions. Extractions were subsequently partitioned into hydrophilic and lipophilic fractions using distilled water and chloroform. The lipophilic extracts were further fractionated by the column chromatography with gradient elution and analyzed by gas chromatography-mass spectrometry (GC-MS). Characterized cannabinoids were used in bioassays with multidrug-resistance bacteria. Results Lipophilic extracts and fractions of inflorescences from all Thailand floristic regions consistently displayed cannabinoids (THC, CBD and CBN) in various quantities. These extracts exhibited inhibitory activity for Staphylococcus aureus, Pseudomonas aeruginosa, and Acinetobacter baumannii strains with minimum inhibitory concentration values varying from 31.25 to 125 µg/mL. Conclusion Our study is the first to report cannabinoid detection in extracts from inflorescences of T. orientalis, a species in the Cannabaceae. These extracts and their fractions containing cannabinoids showed pronounced antibacterial activity. The use of analytic methods also demonstrated reproducible cannabinoid extraction.
Association of plasma VEGF levels and the antidepressant effects of ketamine in patients with depression

Aims: Growing evidence suggests that vascular endothelial growth factor (VEGF) may be involved in the neuronal mechanisms underlying both depression aetiology and the response to ketamine treatments. The aim of this study was to examine whether changes in plasma VEGF levels are associated with the antidepressant effects of repeated ketamine infusions in patients with depression. Methods: Ninety-six patients with depression were enrolled and received six ketamine infusions during a 12-day period. Depressive symptom severity and plasma VEGF levels were measured by the Montgomery–Åsberg Depression Rating Scale (MADRS) and an enzyme-linked immunosorbent assay (ELISA) respectively, at baseline, 13 days and 26 days. Results: Despite a significant improvement in MADRS scores after patients received six ketamine infusions (p < 0.001), no changes in plasma VEGF levels were observed at 13 days when compared with baseline. Moreover, no significant difference in plasma VEGF levels at baseline and 13 days was found between ketamine responders and nonresponders. No association was found between the antidepressant effects of repeated ketamine treatments and plasma VEGF levels. Conclusion: This study indicated that VEGF may not be a potential predictor of antidepressant response to repeated intravenous administration of ketamine in patients with depression.
Distribution and Particle Size of Type A Botulinum Toxin in Body Fluids of Intravenously Injected Rabbits.∗

Summary In rabbits which were given type A botulinum toxin intravenously, and which died rapidly of acute poisoning the toxin was found in lymph and plasma with no tendency to diffuse into erythrocytes. Ratio of toxin in lymph to plasma ranged between 1:6 to 1:12. Sedimentation coefficients of the toxin in these body fluids were similar and had the dimensions of a protein. The particle size was decreased in relation to crystalline toxin, was similar to toxin in lymph from rats given toxin intraduodenally, but was not sufficiently smaller to warrant the assumption of complete dissociation of toxin from the hemagglutinin present in crystalline toxin. Toxin was absent from fetal blood, but appeared in small quantities in urine and bile. Cerebrospinal fluid was free of toxin except for a small amount at death. In the normal eye none or only a small quantity of toxin appears in the intraocular fluid. By experimental reduction of intraocular pressure toxin in intraocular fluid tends to approach concentrations present in plasma. Inflammation can lead to the occurrence of toxin in intraocular fluid. The findings are compatible with the hypothesis that the toxin in vivo has the dimensions of protein and passes tissue barriers by filtration mechanisms normally operative.