Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial functional data modeling of plant reflectances

Plant reflectance spectra - the profile of light reflected by leaves across different wavelengths - supply the spectral signature for a species at a spatial location to enable estimation of functional and taxonomic diversity for plants. We consider leaf spectra as"responses"to be explained spatially. These spectra/reflectances are functions over a wavelength band that respond to the environment. Our motivating dataset leads us to develop rich novel spatial models that can explain spectra for genera within families. Wavelength responses for an individual leaf are viewed as a function of wavelength, leading to functional data modeling. Local environmental features become covariates. We introduce wavelength - covariate interaction since the response to environmental regressors may vary with wavelength, so may variance. Formal spatial modeling enables prediction of reflectances for genera at unobserved locations with known environmental features. We incorporate spatial dependence, wavelength dependence, and space-wavelength interaction (in the spirit of space-time interaction). Our data are gathered for several families from the Cape Floristic Region (CFR) in South Africa. We implement out-of-sample validation to select a best model, discovering that the model features listed above are all informative for the functional data analysis. We then supply interpretation of the results under the selected model.
VVV survey near-infrared colour catalogue of known variable stars

Context. The Vista Variables in the Via Lactea (VVV) near-infrared variability survey explores some of the most complex regions of the Milky Way bulge and disk in terms of high extinction and high crowding. Aims. We add a new wavelength dimension to the optical information available at the American Association of Variable Star Observers International Variable Star Index (VSX-AAVSO) catalogue to test the VVV survey near-infrared photometry to better characterise these objects. Methods. We cross-matched the VVV and the VSX-AAVSO catalogues along with Gaia Data Release 2 photometry and parallax. Results. We present a catalogue that includes accurate individual coordinates, near-infrared magnitudes (ZYJHKs), extinctions AKs, and distances based on Gaia parallaxes. We also show the near-infrared CMDs and spatial distributions for the different VSX types of variable stars, including important distance indicators, such as RR Lyrae, Cepheids, and Miras. By analysing the photometric flags in our catalogue, we found that about 20% of the stars with measured and verified variability are flagged as non-stellar sources, even when they are outside of the saturation and/or noise regimes. Additionally, we pair-matched our sample with the VIVA catalogue and found that more than half of our sources are missing from the VVV variability list, mostly due to observations with low signal-to-noise ratio or photometric problems with a low percentage due to failures in the selection process. Conclusions. Our results suggest that the current knowledge of the variability in the Galaxy is biased to nearby stars with low extinction. The present catalogue also provides the groundwork for characterising the results of future large variability surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time in the highly crowded and reddened regions of the Galactic plane, as well as follow-up campaigns for characterising specific types of variables. The analysis of the incorrectly flagged stars can be used to improve the photometric classification of the VVV data, allowing us to expand the amount of data considered useful for science purposes. In addition, we provide a list of stars that are missed by the VIVA procedures for which the observations are good and which were missed due to some failure in the VIVA selection process.
Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.
A Variational Information Bottleneck Approach to Multi-Omics Data Integration

Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, such integrative analyses on multiple omics, which we refer to as views, involve learning from incomplete observations with various view-missing patterns. This is challenging because i) complex interactions within and across observed views need to be properly addressed for optimal predictive power and ii) observations with various view-missing patterns need to be flexibly integrated. To address such challenges, we propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Most importantly, by modeling the joint representations as a product of marginal representations, we can efficiently learn from observed views with various view-missing patterns. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
Incorporating Kinematic Wave Theory Into a Deep Learning Method for High-Resolution Traffic Speed Estimation

We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We introduce two key approaches that allow us to incorporate kinematic wave theory principles to improve the robustness of existing learning-based estimation methods. First, we propose an anisotropic traffic kernel for the Deep CNN. The anisotropic kernel explicitly accounts for space-time correlations in macroscopic traffic and effectively reduces the number of trainable parameters in the Deep CNN model. Second, we propose to use simulated data for training the Deep CNN. Using a targeted simulated data for training provides an implicit way to impose desirable traffic physical features on the learning model. In the experiments, we highlight the benefits of using anisotropic kernels and evaluate the transferability of the trained model to real-world traffic using the Next Generation Simulation (NGSIM) and the German Highway Drone (HighD) datasets. The results demonstrate that anisotropic kernels significantly reduce model complexity and model over-fitting, and improve the physical correctness of the estimated speed fields. We find that model complexity scales linearly with problem size for anisotropic kernels compared to quadratic scaling for isotropic kernels. Furthermore, evaluation on real-world datasets shows acceptable performance, which establishes that simulation-based training is a viable surrogate to learning from real-world data. Finally, a comparison with standard estimation techniques shows the superior estimation accuracy of the proposed method.
1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed

Scalable training of large models (like BERT and GPT-3) requires careful optimization rooted in model design, architecture, and system capabilities. From a system standpoint, communication has become a major bottleneck, especially on commodity systems with standard TCP interconnects that offer limited network bandwidth. Communication compression is an important technique to reduce training time on such systems. One of the most effective methods is error-compensated compression, which offers robust convergence speed even under 1-bit compression. However, state-of-the-art error compensation techniques only work with basic optimizers like SGD and momentum SGD, which are linearly dependent on the gradients. They do not work with non-linear gradient-based optimizers like Adam, which offer state-of-the-art convergence efficiency and accuracy for models like BERT. In this paper, we propose 1-bit Adam that reduces the communication volume by up to $5\times$, offers much better scalability, and provides the same convergence speed as uncompressed Adam. Our key finding is that Adam's variance (non-linear term) becomes stable (after a warmup phase) and can be used as a fixed precondition for the rest of the training (compression phase). Experiments on up to 256 GPUs show that 1-bit Adam enables up to $3.3\times$ higher throughput for BERT-Large pre-training and up to $2.9\times$ higher throughput for SQuAD fine-tuning. In addition, we provide theoretical analysis for our proposed work.
Effects of quantum resources and noise on the statistical complexity of quantum circuits

We investigate how the addition of quantum resources changes the statistical complexity of quantum circuits by utilizing the framework of quantum resource theories. Measures of statistical complexity that we consider include the Rademacher complexity and the Gaussian complexity, which are well-known measures in computational learning theory that quantify the richness of classes of real-valued functions. We derive bounds for the statistical complexities of quantum circuits that have limited access to certain resources and apply our results to two special cases: (a) stabilizer circuits that are supplemented with a limited number of T gates and (b) instantaneous quantum polynomial-time Clifford circuits that are supplemented with a limited number of CCZ gates. We show that the increase in the statistical complexity of a quantum circuit when an additional quantum channel is added to it is upper bounded by the free robustness of the added channel. Moreover, as noise in quantum systems is a major obstacle to implementing many quantum algorithms on large quantum circuits, we also study the effects of noise on the Rademacher complexity of quantum circuits. Finally, we derive bounds for the generalization error associated with learning from training data arising from quantum circuits.
Estimating 2-Sinkhorn Divergence between Gaussian Processes from Finite-Dimensional Marginals

\emph{Optimal Transport} (OT) has emerged as an important computational tool in machine learning and computer vision, providing a geometrical framework for studying probability measures. OT unfortunately suffers from the curse of dimensionality and requires regularization for practical computations, of which the \emph{entropic regularization} is a popular choice, which can be 'unbiased', resulting in a \emph{Sinkhorn divergence}. In this work, we study the convergence of estimating the 2-Sinkhorn divergence between \emph{Gaussian processes} (GPs) using their finite-dimensional marginal distributions. We show almost sure convergence of the divergence when the marginals are sampled according to some base measure. Furthermore, we show that using $n$ marginals the estimation error of the divergence scales in a dimension-free way as $\mathcal{O}\left(\epsilon^ {-1}n^{-\frac{1}{2}}\right)$, where $\epsilon$ is the magnitude of entropic regularization.
Model Agnostic Answer Reranking System for Adversarial Question Answering

While numerous methods have been proposed as defenses against adversarial examples in question answering (QA), these techniques are often model specific, require retraining of the model, and give only marginal improvements in performance over vanilla models. In this work, we present a simple model-agnostic approach to this problem that can be applied directly to any QA model without any retraining. Our method employs an explicit answer candidate reranking mechanism that scores candidate answers on the basis of their content overlap with the question before making the final prediction. Combined with a strong base QAmodel, our method outperforms state-of-the-art defense techniques, calling into question how well these techniques are actually doing and strong these adversarial testbeds are.
A new combined differential-discrete cellular automaton approach for biofilm modeling: application for growth in gel beads.

The theoretical basis and quantitative evaluation of a new approach for modeling biofilm growth are presented here. Soluble components (e.g., substrates) are represented in a continuous field, whereas discrete mapping is used for solid components (e.g., biomass). The spatial distribution of substrate is calculated by applying relaxation methods to the reaction-diffusion mass balance. A biomass density map is determined from direct integration in each grid cell of a substrate-limited growth equation. Spreading and distribution of biomass is modeled by a discrete cellular automaton algorithm. The ability of this model to represent diffusion-reaction-microbial growth systems was tested for a well-characterized system: immobilized cells growing in spherical gel beads. Good quantitative agreement with data for global oxygen consumption rate was found. The calculated concentration profiles of substrate and biomass in gel beads corresponded to those measured. Moreover, it was possible, using the discrete spreading algorithm, to predict the spatial two- and three-dimensional distribution of microorganisms in relation to, for example, substrate flux and inoculation density. The new technique looks promising for modeling diffusion-reaction-microbial growth processes in heterogeneous systems as they occur in biofilms.
Influence of NH 3 and ( NH 4 ) 2 SO 4 on heathland vegetation

10 6 in 1980(Asman <? /a/. 1987). Deposition of ammoniaandammonium(together NH V )is about half of the total nitrogen deposition in many countries (Malanchuk & Nilsson 1989). Background concentrationsofgaseous NH 3 and NH 4 + aerosol are estimated to be less than 1 and 1-3 pg m“ 3 respectively (Erisman et al. 1987), while annual means in regions with intensive livestock farming range from 10 to more than 25 pg m~ 3
Lithography Processable Ta2O5 Barrier-Layered Chitosan Electric Double Layer Synaptic Transistors

We proposed a synaptic transistor gated using a Ta2O5 barrier-layered organic chitosan electric double layer (EDL) applicable to a micro-neural architecture system. In most of the previous studies, a single layer of chitosan electrolyte was unable to perform lithography processes due to poor mechanical/chemical resistance. To overcome this limitation, we laminated a high-k Ta2O5 thin film on chitosan electrolyte to ensure high mechanical/chemical stability to perform a lithographic process for micropattern formation. Artificial synaptic behaviors were realized by protonic mobile ion polarization in chitosan electrolytes. In addition, neuroplasticity modulation in the amorphous In–Ga–Zn-oxide (a-IGZO) channel was implemented by presynaptic stimulation. We also demonstrated synaptic weight changes through proton polarization, excitatory postsynaptic current modulations, and paired-pulse facilitation. According to the presynaptic stimulations, the magnitude of mobile proton polarization and the amount of weight change were quantified. Subsequently, the stable conductance modulation through repetitive potential and depression pulse was confirmed. Finally, we consider that proposed synaptic transistor is suitable for advanced micro-neural architecture because it overcomes the instability caused when using a single organic chitosan layer.
Polyvinylidene Fluoride Membrane Via Vapour Induced Phase Separation for Oil/Water Emulsion Filtration

Membrane-based technology is an attractive option for the treatment of oily wastewater because of its high oil removal efficiency, small footprint and operational simplicity. However, filtration performance is highly restricted by membrane fouling, especially when treating oil/water emulsion as a result of strong interaction between oil droplets and the hydrophobic property of the membrane. This study explores the fabrication of polyvinylidene fluoride (PVDF)-based membrane via the vapour induced phase separation (VIPS) method while incorporating polyvinyl pyrrolidone (PVP) as a hydrophilic additive to encounter membrane fouling issues and improve membrane filterability. The resulting membranes were characterized and tested for oil/water emulsion filtration to evaluate their hydraulic, rejection and anti-fouling properties. Results show that the changes in membrane morphology and structure from typical macrovoids with finger-like substructure to cellular structure and larger membrane pore size were observed by the prolonged exposure time from 0 to 30 min through the VIPS method. The enhanced clean water permeability is attributed to the addition of PVP–LiCl in the dope solution that enlarges the mean flow pore size from 0.210 ± 0.1 to 7.709 ± 3.5 µm. The best performing membrane was the VIPS membrane with an exposure time of 5 min (M-5), showing oil/water emulsion permeability of 187 Lm−2 h−1 bar−1 and oil rejection of 91.3% as well as an elevation of 84% of clean water permeability compared to pristine PVDF developed using a typical non-solvent induced phase separation (NIPS) method. Despite the relatively high total fouling, M-5 was able to maintain its high permeability by water flushing as a simple operation for membrane fouling control. The performance was achieved thanks to combination of the large mean flow pore size and hydrophilic property from residual PVP in the membarne matrix. Overall, the results demonstrate the potential of the optimum VIPS method in the presence of PVP and LiCl additives for oil/water emulsion treatment.
A Genetically Encoded Bioluminescence Intracellular Nanosensor for Androgen Receptor Activation Monitoring in 3D Cell Models

In recent years, there has been an increasing demand for predictive and sensitive in vitro tools for drug discovery. Split complementation assays have the potential to enlarge the arsenal of in vitro tools for compound screening, with most of them relying on well-established reporter gene assays. In particular, ligand-induced complementation of split luciferases is emerging as a suitable approach for monitoring protein–protein interactions. We hereby report an intracellular nanosensor for the screening of compounds with androgenic activity based on a split NanoLuc reporter. We also confirm the suitability of using 3D spheroids of Human Embryonic Kidney (HEK-293) cells for upgrading the 2D cell-based assay. A limit of detection of 4 pM and a half maximal effective concentration (EC50) of 1.7 ± 0.3 nM were obtained for testosterone with HEK293 spheroids. This genetically encoded nanosensor also represents a new tool for real time imaging of the activation state of the androgen receptor, thus being suitable for analysing molecules with androgenic activity, including new drugs or endocrine disrupting molecules.
An Adult Mouse Model of Dilated Cardiomyopathy Caused by Inducible Cardiac-Specific Bis Deletion

BCL-2 interacting cell death suppressor (BIS) is a multifunctional protein that has been implicated in cancer and myopathy. Various mutations of the BIS gene have been identified as causative of cardiac dysfunction in some dilated cardiomyopathy (DCM) patients. This was recently verified in cardiac-specific knock-out (KO) mice. In this study, we developed tamoxifen-inducible cardiomyocyte-specific BIS-KO (Bis-iCKO) mice to assess the role of BIS in the adult heart using the Cre-loxP strategy. The disruption of the Bis gene led to impaired ventricular function and subsequent heart failure due to DCM, characterized by reduced left ventricular contractility and dilatation that were observed using serial echocardiography and histology. The development of DCM was confirmed by alterations in Z-disk integrity and increased expression of several mRNAs associated with heart failure and remodeling. Furthermore, aggregation of desmin was correlated with loss of small heat shock protein in the Bis-iCKO mice, indicating that BIS plays an essential role in the quality control of cardiac proteins, as has been suggested in constitutive cardiac-specific KO mice. Our cardiac-specific BIS-KO mice may be a useful model for developing therapeutic interventions for DCM, especially late-onset DCM, based on the distinct phenotypes and rapid progressions.