Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait

Phytoplankton in the ocean are extremely diverse. The abundance of various intracellular pigments are often used to study phytoplankton physiology and ecology, and identify and quantify different phytoplankton groups. In this study, phytoplankton absorption spectra ( a p h ( λ ) ) derived from underway flow-through AC-S measurements in the Fram Strait are combined with phytoplankton pigment measurements analyzed by high-performance liquid chromatography (HPLC) to evaluate the retrieval of various pigment concentrations at high spatial resolution. The performances of two approaches, Gaussian decomposition and the matrix inversion technique are investigated and compared. Our study is the first to apply the matrix inversion technique to underway spectrophotometry data. We find that Gaussian decomposition provides good estimates (median absolute percentage error, MPE 21–34%) of total chlorophyll-a (TChl-a), total chlorophyll-b (TChl-b), the combination of chlorophyll-c1 and -c2 (Chl-c1/2), photoprotective (PPC) and photosynthetic carotenoids (PSC). This method outperformed one of the matrix inversion algorithms, i.e., singular value decomposition combined with non-negative least squares (SVD-NNLS), in retrieving TChl-b, Chl-c1/2, PSC, and PPC. However, SVD-NNLS enables robust retrievals of specific carotenoids (MPE 37–65%), i.e., fucoxanthin, diadinoxanthin and 19 ′ -hexanoyloxyfucoxanthin, which is currently not accomplished by Gaussian decomposition. More robust predictions are obtained using the Gaussian decomposition method when the observed a p h ( λ ) is normalized by the package effect index at 675 nm. The latter is determined as a function of “packaged” a p h ( 675 ) and TChl-a concentration, which shows potential for improving pigment retrieval accuracy by the combined use of a p h ( λ ) and TChl-a concentration data. To generate robust estimation statistics for the matrix inversion technique, we combine leave-one-out cross-validation with data perturbations. We find that both approaches provide useful information on pigment distributions, and hence, phytoplankton community composition indicators, at a spatial resolution much finer than that can be achieved with discrete samples.
How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics

Most models for the statistical connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than halo mass, a phenomenon known as galaxy assembly bias. And yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data that are analysed using traditional halo occupation models. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxy–halo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function wp(rp), the galaxy–galaxy lensing signal ΔΣ(rp), the void probability function VPF(r), the distributions of counts-in-cylinders P(NCIC), and counts-in-annuli P(NCIA), and the distribution of the ratio of counts in cylinders of different sizes P(N2/N5). We find that despite the frequent use of the combination wp(rp) + ΔΣ(rp) in interpreting galaxy data, the count statistics, P(NCIC) and P(NCIA), are generally more efficient in constraining galaxy assembly bias when combined with wp(rp). Constraints based upon wp(rp) and ΔΣ(rp) share common degeneracy directions in the parameter space, while combinations of wp(rp) with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy–halo connection.
Inverse-chirp signals and spontaneous scalarisation with self-interacting potentials in stellar collapse

We study how the gravitational wave signal from stellar collapse in scalar-tensor gravity varies under the influence of scalar self-interaction. To this end, we extract the gravitational radiation from numerical simulations of stellar collapse for a range of potentials with higher-order terms in addition to the quadratic mass term. Our study includes collapse to neutron stars and black holes and we find the strong inverse-chirp signals obtained for the purely quadratic potential to be exceptionally robust under changes in the potential at higher orders; quartic and sextic terms in the potential lead to noticeable differences in the wave signal only if their contribution is amplified, implying a relative fine-tuning to within five or more orders of magnitude between the mass and self-interaction parameters.
Violation of lepton number in 3 units

The number of leptons may or may not be a conserved quantity. The Standard Model predicts that it is (in perturbative processes), but there is the well known possibility that new physics violates lepton number in one or two units. The first case (ΔL = 1) is associated to proton decay into mesons plus a lepton or an anti-lepton, while the second one (ΔL = 2) is usually associated to Majorana neutrino masses and neutrinoless double beta decay. It is also conceivable that leptons can only be created or destroyed in groups of three (ΔL = 3). Colliders and proton decay experiments can explore this possibility.
Speckled cross-spectral densities and their associated correlation singularities for a modern source of partially coherent x rays

We consider a realistic model for calculating the cross-spectral density of partially coherent beams from an x-ray undulator in a modern storage ring. This two-point coherence function is seen to have a speckled structure associated with the presence of x-ray coherence vortices and domain walls. Such cross-spectral density speckle is associated with a network of spatial pairs of points for which there is zero correlation. X-ray coherence vortices and domain walls are seen to emerge naturally as the number of coherent modes required increases. An understanding of the existence and nature of such correlation singularities enhances our ability to exploit partially coherent x-ray radiation from new or upgraded synchrotron sources, for both imaging and diffraction applications.
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

Adversarial examples are inputs to machine learning models designed by an adversary to cause an incorrect output. So far, adversarial examples have been studied most extensively in the image domain. In this domain, adversarial examples can be constructed by imperceptibly modifying images to cause misclassification, and are practical in the physical world. In contrast, current targeted adversarial examples applied to speech recognition systems have neither of these properties: humans can easily identify the adversarial perturbations, and they are not effective when played over-the-air. This paper makes advances on both of these fronts. First, we develop effectively imperceptible audio adversarial examples (verified through a human study) by leveraging the psychoacoustic principle of auditory masking, while retaining 100% targeted success rate on arbitrary full-sentence targets. Next, we make progress towards physical-world over-the-air audio adversarial examples by constructing perturbations which remain effective even after applying realistic simulated environmental distortions.
SARS-CoV-2 Community Transmission During Shelter-in-Place in San Francisco

ABSTRACT Background: We characterized SARS-CoV-2 infections in a densely-populated, majority Latinx San Francisco community six-weeks into the city's shelter-in-place order. Methods: We offered SARS-CoV-2 reverse transcription-PCR and antibody (Abbott ARCHITECT IgG) testing, regardless of symptoms, to all residents (>=4 years) and workers in a San Francisco census tract (population: 5,174) at outdoor, community-mobilized events over four days. We estimated SARS-CoV-2 point prevalence (PCR-positive) and cumulative incidence (antibody or PCR-positive) in the census tract and evaluated risk factors for recent (PCR-positive/antibody-negative) versus prior infection (antibody-positive/PCR-negative). SARS-CoV-2 genome recovery and phylogenetics were used to measure viral strain diversity, establish viral lineages present, and estimate number of introductions. Results: We tested 3,953 persons: 40% Latinx; 41% White; 9% Asian/Pacific Islander; and 2% Black. Overall, 2.1% (83/3,871) tested PCR-positive: 95% were Latinx and 52% asymptomatic when tested. 1.7% of residents and 6.0% of workers (non-census tract residents) were PCR-positive. Among 2,598 census tract residents, estimated point prevalence of PCR-positives was 2.3% (95%CI: 1.2-3.8%): 3.9% (95%CI: 2.0-6.4%) among Latinx vs. 0.2% (95%CI: 0.0-0.4%) among non-Latinx persons. Estimated cumulative incidence among residents was 6.1% (95%CI: 4.0-8.6%). Prior infections were 67% Latinx, 16% White, and 17% other ethnicities. Among recent infections, 96% were Latinx. Risk factors for recent infection were Latinx ethnicity, inability to shelter-in-place and maintain income, frontline service work, unemployment, and household income <$50,000/year. Five SARS-CoV-2 phylogenetic lineages were detected. Conclusion: SARS-CoV-2 infections from diverse lineages continued circulating among low-income, Latinx persons unable to work from home and maintain income during San Francisco's shelter-in-place ordinance.
Multi-Attribute Selectivity Estimation Using Deep Learning

Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases. Accurate estimation of query selectivity involving multiple correlated attributes is especially challenging. Poor cardinality estimates could result in the selection of bad plans by the query optimizer. We investigate the feasibility of using deep learning based approaches for both point and range queries and propose two complementary approaches. Our first approach considers selectivity as an unsupervised deep density estimation problem. We successfully introduce techniques from neural density estimation for this purpose. The key idea is to decompose the joint distribution into a set of tractable conditional probability distributions such that they satisfy the autoregressive property. Our second approach formulates selectivity estimation as a supervised deep learning problem that predicts the selectivity of a given query. We also introduce and address a number of practical challenges arising when adapting deep learning for relational data. These include query/data featurization, incorporating query workload information in a deep learning framework and the dynamic scenario where both data and workload queries could be updated. Our extensive experiments with a special emphasis on queries with a large number of predicates and/or small result sizes demonstrates that our proposed techniques provide fast and accurate selective estimates with minimal space overhead.
Automatic phenotyping of electronical health record: PheVis algorithm

Electronic Health Records (EHRs) often lack reliable annotation of patient medical conditions. Yu et al. recently proposed PheNorm, an automated unsupervised algorithm to identify patient medical conditions from EHR data. PheVis extends PheNorm at the visit resolution. PheVis combines diagnosis codes together with medical concepts extracted from medical notes, incorporating past history in a machine learning approach to provide an interpretable white box predictor of the occurrence probability for a given medical condition at each visit. PheVis is applied to two real-world use-cases using the datawarehouse of the University Hospital of Bordeaux: i) rheumatoid arthritis, a chronic condition; ii) tuberculosis, an acute condition (cross-validated AUROC were respectively 0.948 [0.945 ; 0.950] and 0.987 [0.983 ; 0.990]). PheVis performs well for chronic conditions, though absence of exclusion of past medical history by natural language processing tools limits its performance in French for acute conditions.
Recurrent Back-Projection Network for Video Super-Resolution

We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super-resolution path for the target frame. In contrast to most prior work where frames are pooled together by stacking or warping, our model, the Recurrent Back-Projection Network (RBPN) treats each context frame as a separate source of information. These sources are combined in an iterative refinement framework inspired by the idea of back-projection in multiple-image super-resolution. This is aided by explicitly representing estimated inter-frame motion with respect to the target, rather than explicitly aligning frames. We propose a new video super-resolution benchmark, allowing evaluation at a larger scale and considering videos in different motion regimes. Experimental results demonstrate that our RBPN is superior to existing methods on several datasets.
Constraining mixing in massive stars in the Small Magellanic Cloud

Context. The evolution of massive stars is strongly influenced by internal mixing processes such as semiconvection, convective core overshooting, and rotationally induced mixing. None of these processes are currently well constrained. Aims. We investigate models for massive stars in the Small Magellanic Cloud (SMC), for which stellar-wind mass loss is less important than for their metal-rich counterparts. We aim to constrain the various mixing efficiencies by comparing model results to observations. Methods. For this purpose, we use the stellar-evolution code MESA to compute more than 60 grids of detailed evolutionary models for stars with initial masses of 9…100 M⊙, assuming different combinations of mixing efficiencies of the various processes in each grid. Our models evolve through core hydrogen and helium burning, such that they can be compared with the massive main sequence and supergiant population of the SMC. Results. We find that for most of the combinations of the mixing efficiencies, models in a wide mass range spend core-helium burning either only as blue supergiants, or only as red supergiants. The latter case corresponds to models that maintain a shallow slope of the hydrogen/helium (H/He) gradient separating the core and the envelope of the models. Only a small part of the mixing parameter space leads to models that produce a significant number of blue and red supergiants, which are both in abundance in the SMC. Some of our grids also predict a cut-off in the number of red supergiants above log L/L⊙ = 5…5.5. Interestingly, these models contain steep H/He gradients, as is required to understand the hot, hydrogen-rich Wolf-Rayet stars in the SMC. We find that unless it is very fast, rotation has a limited effect on the H/He profiles in our models. Conclusions. While we use specific implementations of the considered mixing processes, they comprehensively probe the two first-order structural parameters, the core mass and the H/He gradient in the core-envelope interface. Our results imply that in massive stars, mixing during the main-sequence evolution leads to a moderate increase in the helium core masses, and also that the H/He gradients above the helium cores become very steep. Our model grids can be used to further refine the various mixing efficiencies with the help of future observational surveys of the massive stars in the SMC, and thereby help to considerably reduce the uncertainties in models of massive star evolution.
Gradient Estimates for Divergence Form Elliptic Systems Arising from Composite Material

In this paper, we show that $W^{1,p}$ $(1\leq p<\infty)$ weak solutions to divergence form elliptic systems are Lipschitz and piecewise $C^{1}$ provided that the leading coefficients and data are of piecewise Dini mean oscillation, the lower order coefficients are bounded, and interfacial boundaries are $C^{1,\text{Dini}}$. This extends a result of Li and Nirenberg (\textit{Comm. Pure Appl. Math.} \textbf{56} (2003), 892-925). Moreover, under a stronger assumption on the piecewise $L^{1}$-mean oscillation of the leading coefficients, we derive a global weak type-(1,1) estimate with respect to $A_{1}$ Muckenhoupt weights for the elliptic systems without lower order terms.
F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning

When labeled training data is scarce, a promising data augmentation approach is to generate visual features of unknown classes using their attributes. To learn the class conditional distribution of CNN features, these models rely on pairs of image features and class attributes. Hence, they can not make use of the abundance of unlabeled data samples. In this paper, we tackle any-shot learning problems i.e. zero-shot and few-shot, in a unified feature generating framework that operates in both inductive and transductive learning settings. We develop a conditional generative model that combines the strength of VAE and GANs and in addition, via an unconditional discriminator, learns the marginal feature distribution of unlabeled images. We empirically show that our model learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e. inductive and transductive (generalized) zero- and few-shot learning settings. We also demonstrate that our learned features are interpretable: we visualize them by inverting them back to the pixel space and we explain them by generating textual arguments of why they are associated with a certain label.
Dielectric Characterization of In Vivo Abdominal and Thoracic Tissues in the 0.5–26.5 GHz Frequency Band for Wireless Body Area Networks

The dielectric properties of biological tissues are of utmost importance in the development of wireless body area networks (WBANs), especially for implanted devices. The early design stages of medical devices like capsule endoscopy, pacemakers, or physiological sensors rely on precise knowledge of the dielectric properties of the tissues present in their surrounding medium. Many of these applications make use of electromagnetic phantoms, which are software or physical models that imitate the shape and the electromagnetic properties of the tissues. They are used for designing devices in software simulations and for testing them in laboratory trials, aiding in both the development of WBAN antennas or in communication link evaluations. The existing reports about dielectric in vivo properties are limited and have drawbacks like: low variety of characterized tissues, lacking some relevant ones, and limitations and inhomogeneity in the measured frequency range. This paper aims at filling that gap by providing a new database of dielectric properties of biological tissues measured in vivo. In particular, it is focused on the tissues of the thoracic and the abdominal regions, measured at the same wide frequency band, on the same animal specimen, and under the same conditions. The properties have been obtained by measuring porcine tissues in the 0.5–26.5 GHz band with the open-ended coaxial technique. In this paper, we focus on those tissues that have been scarcely characterized so far in the literature, like heart, esophagus, stomach, and pancreas. The Cole–Cole fitting parameters of the measured tissues and their uncertainties are provided.
New Rapid and Robust Color Image Watermarking Technique in Spatial Domain

In this paper, a novel spatial domain color image watermarking technique is proposed to rapidly and effectively protect the copyright of the color image. First, the direct current (DC) coefficient of 2D-DFT obtained in the spatial domain is discussed, and the relationship between the change of each pixel in the spatial domain and the change of the DC coefficient in the Fourier transform is proved. Then, the DC coefficient is used to embed and extract watermark in the spatial domain by the proposed quantization technique. The novelties of this paper include three points: 1) the DC coefficient of 2D-DFT is obtained in the spatial domain without of the true 2D-DFT; 2) the relationship between the change of each pixel in the image block and the change of the DC coefficient of 2D-DFT is found, and; 3) the proposed method has the short running time and strong robustness. The experimental results on two publicly available image databases (CVG-UGR and USC-SIPI) have shown that the proposed method not only has satisfied the needs of invisibility but also has better performance in terms of robustness and real-time feature, which show the proposed method has both advantages of spatial domain and frequency domain.
Consider the Human Work Experience When Integrating Robotics in the Workplace

Worldwide, manufacturers are reimagining the future of their workforce and its connection to technology. Rather than replacing humans, Industry 5.0 explores how humans and robots can best complement one another's unique strengths. However, realizing this vision requires an in-depth understanding of how workers view the positive and negative attributes of their jobs, and the place of robots within it. In this paper, we explore the relationship between work attributes and automation goals by engaging in field research at a manufacturing plant. We conducted 50 face-to-face interviews with assembly-line workers $(\mathrm{n}=50)$, which we analyzed using discourse analysis and social constructivist methods. We found that the work attributes deemed most positive by participants include social interaction, movement and exercise, (human) autonomy, problem solving, task variety, and building with their hands. The main negative work attributes included health and safety issues, feeling rushed, and repetitive work. We identified several ways robots could help reduce negative work attributes and enhance positive ones, such as reducing work interruptions and cultivating physical and psychological well-being. Based on our findings, we created a set of integration considerations for organizations planning to deploy robotics technology, and discuss how the manufacturing and HRI communities can explore these ideas in the future.