Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipophilic extractives in different tree fractions and forestry assortments of Pinus sylvestris due for thinning or final cutting

ABSTRACT The objectives were to determine the content and composition of lipophilic extractives, particularly fatty and resin acids, in three Scots pine trees from each of two stands in northern Sweden, one that was due for thinning and one due for final cutting. Extractives content of branches, needles, barks, stem discs from branch whorls, and sapwood and heartwood samples from various heights was measured. Fractions had very different concentrations and distributions of extractives due to differences in the composition of tissues. The needles had the highest total extractive concentration (around 150 kg/ton d.w.), followed by the branches, bark, timber from the final cut stand, and then the pulpwood and timber from the thinning stand. The final cut stand generally provided higher yields of extractives in assortments per hectare than the thinning stand; the difference was five-fold for timber, three-fold for whole trees, two-fold for branches, needles and bark and similar for pulpwood. This information on the content and composition of extractives in different tissues, and the dependence of these quantities on tree age could be used together with information on conventional stand data such as tree numbers and dimensions to guide the selection and management of feedstocks for biorefineries.
Tidally excited oscillations in hot white dwarfs

We study the flux variation in helium white dwarfs (WDs) induced by dynamical tides for a variety of WD models with effective temperatures ranging from $T=10\, {\rm kK}$ to $T=26\, {\rm kK}$. At linear order, we find the dynamical tide can significantly perturb the observed flux in hot WDs. If the temperature $T\gtrsim 14\, {\rm kK}$, then the dynamical tide may induce a fractional change in the flux by $\gt 1{{\ \rm per\ cent}}$ when the orbital period is $P_{\rm orb}\simeq 20{\! -\! }60\, {\rm min}$. The ratio between the flux modulation due to the dynamical tide and that due to the equilibrium tide (i.e. ellipsoidal variability) increases as the WD’s radius decreases, and it could exceed $\mathcal {O}(10)$ if the WD has a radius R ≲ 0.03 R⊙. Unlike the ellipsoidal variability which is in phase with the orbital motion, the pulsation caused by the dynamical tide may have a substantial phase shift. A cold WD with $T\simeq 10\, {\rm kK}$, on the other hand, is unlikely to show observable pulsations due to the dynamical tide. At shorter orbital periods, the dynamical tide may break and become highly non-linear. We approximate this regime by treating the waves as one-way travelling waves and find the flux variation is typically reduced to 0.1–1 per cent and the excess phase is ∼90° (though with large uncertainty). Even in the travelling-wave limit, the flux perturbation due to dynamical tide could still exceed the ellipsoidal variability for compact WDs with R ≲ 0.02 R⊙. We further estimate the non-linear flux perturbations oscillating at four times the orbital frequency dominated by a self-coupled parent g-mode driving low-order daughter p modes. The non-linear flux variation could be nearly $50{{\ \rm per\ cent}}$ of the linear variation for very hot WD models with $T\gtrsim 26\, {\rm kK}$ and $1{{\ \rm per\ cent}}$ linear flux variation. We thus predict that both the linear and non-linear flux variations due to dynamical tides are likely to have significant observational signatures.
Resonant Interaction of Modulation-correlated Quantum Electron Wavepackets with Bound Electron States.

Free-Electron Bound-Electron Resonant Interaction (FEBERI) is the resonant inelastic interaction of periodically density-bunched free electrons with a quantum two level system. We present a comprehensive relativistic quantum mechanical theory for this interaction in a model in which the electrons are represented as quantum electron wavepackets (QEW). The analysis reveals the wave-particle duality nature of the QEW, delineating the point-particle-like and wave-like interaction regimes, and manifesting the physical reality of the wavefunction dimensions and its density modulation characteristics in interaction with matter. The analysis comprehends the case of laser-beam-modulated multiple QEWs that are modulation-phase correlated. Based on the Born interpretation of the electron wavefunction we predict quantum transitions enhancement proportional to the number of electrons squared, analogous to superradiance.
Multipath Wave Particle Duality with a Spooky Path Detector

According to Bohr's principle of complementarity, a quanton can behave either as a wave or a particle, depending on the choice of the experimental setup. Some recent two-path interference experiments have devised methods where one can have a quantum superposition of the two choices, thus indicating that a quanton may be in a superposition of wave and particle nature. These experiments have been of interest from the point of view of Wheeler's delayed-choice experiment. However, it has also been claimed that this experiment can violate complementarity. Here we theoretically analyze a multipath interference experiment that has a which-path detector in a quantum superposition of being present and absent. We show that a tight multipath wave-particle duality relation is respected in all such situations, and complementarity holds good. The apparent violation of complementarity may be due to incorrect evaluation of path distinguishability in such scenarios.
Gaussian Process Bandit Optimization of theThermodynamic Variational Objective

Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann integral approximation, requires choosing a "schedule" of sorted discretization points. This paper introduces a bespoke Gaussian process bandit optimization method for automatically choosing these points. Our approach not only automates their one-time selection, but also dynamically adapts their positions over the course of optimization, leading to improved model learning and inference. We provide theoretical guarantees that our bandit optimization converges to the regret-minimizing choice of integration points. Empirical validation of our algorithm is provided in terms of improved learning and inference in Variational Autoencoders and Sigmoid Belief Networks.
QUBIC II: Spectral polarimetry with bolometric interferometry

Bolometric interferometry is a novel technique that has the ability to perform spectral imaging. A bolometric interferometer observes the sky in a wide frequency band and can reconstruct sky maps in several sub-bands within the physical band in post-processing of the data. This provides a powerful spectral method to discriminate between the cosmic microwave background (CMB) and astrophysical foregrounds. In this paper, the methodology is illustrated with examples based on the Q & U Bolometric Interferometer for Cosmology (QUBIC) which is a ground-based instrument designed to measure the B-mode polarization of the sky at millimeter wavelengths. We consider the specific cases of point source reconstruction and Galactic dust mapping and we characterize the point spread function as a function of frequency. We study the noise properties of spectral imaging, especially the correlations between sub-bands, using end-to-end simulations together with a fast noise simulator. We conclude showing that spectral imaging performance are nearly optimal up to five sub-bands in the case of QUBIC.
An environmental dependence of the physical and structural properties in the Hydra cluster galaxies

The nearby Hydra Cluster ($\sim$50 Mpc) is an ideal laboratory to understand, in detail, the influence of the environment on the morphology and quenching of galaxies in dense environments. We study the Hydra cluster galaxies in the inner regions ($1R_{200}$) of the cluster using data from the Southern Photometric Local Universe Survey (S-PLUS), which uses 12 narrow and broad band filters in the visible region of the spectrum. We analyse structural (Sersic index, effective radius) and physical (colours, stellar masses and star formation rates) properties. Based on this analysis, we find that $\sim$88 percent of the Hydra cluster galaxies are quenched. Using the Dressler-Schectman test approach, we also find that the cluster shows possible substructures. Our analysis of the phase-space diagram together with DBSCAN algorithm indicates that Hydra shows an additional substructure that appears to be in front of the cluster centre, which is still falling into it. Our results, thus, suggest that the Hydra Cluster might not be relaxed. We analyse the median Sersic index as a function of wavelength and find that for red ($(u-r)\geq$2.3) and early-type galaxies it displays a slight increase towards redder filters (13 and 18 percent, for red and early-type respectively) whereas for blue+green ($(u-r)$<2.3) galaxies it remains constant. Late-type galaxies show a small decrease of the median Sersic index toward redder filters. Also, the Sersic index of galaxies, and thus their structural properties, do not significantly vary as a function of clustercentric distance and density within the cluster; and this is the case regardless of the filter.
Recurrent Neural Networks for video object detection

There is lots of scientific work about object detection in images. For many applications like for example autonomous driving the actual data on which classification has to be done are videos. This work compares different methods, especially those which use Recurrent Neural Networks to detect objects in videos. We differ between feature-based methods, which feed feature maps of different frames into the recurrent units, box-level methods, which feed bounding boxes with class probabilities into the recurrent units and methods which use flow networks. This study indicates common outcomes of the compared methods like the benefit of including the temporal context into object detection and states conclusions and guidelines for video object detection networks.
On $BT_1$ group schemes and Fermat Jacobians

Let $p$ be a prime number and let $k$ be an algebraically closed field of characteristic $p$. A $BT_1$ group scheme over $k$ is a finite commutative group scheme which arises as the kernel of $p$ on a $p$-divisible (Barsotti--Tate) group. Our main result is that every $BT_1$ scheme group over $k$ occurs as a direct factor of the $p$-torsion group scheme of the Jacobian of an explicit curve defined over $\mathbb{F}_p$. To prove this, we give a careful account of three classifications of $BT_1$ group schemes, due in large part to Kraft, Ekedahl, and Oort, and we apply these classifications to study the $p$-torsion group schemes of Jacobians of Fermat curves.
Voluminous Silica Precipitated from Martian Waters during Late-stage Aqueous Alteration

Mars’ transition from an early “warm and wet” to a “cold and dry” environment left fingerprints on the geological record of fluvial activity on Mars. The morphological and mineralogical observations of aqueous activity provided varying constraints on the condition and duration of liquid water on the Martian surface. In this study, we surveyed the mineralogy of Martian alluvial fans and deltas and investigated the hydrated silica-bearing deposits associated with some of these landforms. Using CRISM data, we identified 35 locations across Mars with hydrated silica in proximity to fans/deltas, where the spectral characteristics are consistent with immature or dehydrated opal-A. In a few stepped fans/deltas, we find hydrated silica occurs within the bulk fan deposits and form sedimentary layers correlated with elevation. Meanwhile, in the older fans/deltas, silica mostly occurs at distal locations, and the relation to primary sedimentary deposits is more complex. We propose that the hydrated silica-bearing deposits in stepped fans/deltas likely formed authigenically from Martian surface waters, mainly during the Late Hesperian and Early Amazonian. These silica-bearing deposits could be a tracer for the temperature or duration of water involved in the formation of these deposits, given more precise and detailed observations of the sedimentary context, accessory minerals, the concentration of hydrated silica, and sediment-to-water ratio. Therefore, we consider that silica-bearing deposits should be among the most critical samples to investigate for future Mars missions, which are accessible in the landing sites of Mars 2020 and ExoMars 2022 missions.
Eccentricity evolution of compact binaries and applications to gravitational-wave physics

Searches for gravitational waves from compact binaries focus mostly on quasicircular motion with the rationale that wave emission circularizes the orbit. Here, we study the generality of this result, when astrophysical environments (e.g., accretion disks) or other fundamental interactions are taken into account. We are motivated by possible electromagnetic counterparts to binary black hole coalescences and orbits, but also by the possible use of eccentricity as a smoking gun for new physics. We find that (i) backreaction from radiative mechanisms, including scalars, vectors and gravitational waves, circularize the orbital motion. (ii) By contrast, environmental effects such as accretion and dynamical friction increase the eccentricity of binaries. Thus, it is the competition between radiative mechanisms and environmental effects that dictates the eccentricity evolution. We study this competition within an adiabatic approach, including gravitational radiation and dynamical friction forces. We show that there is a critical semimajor axis below which gravitational radiation dominates the motion and the eccentricity of the system decreases. However, the eccentricity inherited from the environment-dominated stage can be substantial, and in particular can affect LISA sources. We provide examples for GW190521-like sources.
Analyzing the tree-layer structure of Deep Forests

Random forests on the one hand, and neural networks on the other hand, have met great success in the machine learning community for their predictive performance. Combinations of both have been proposed in the literature, notably leading to the so-called deep forests (DF) [25]. In this paper, we investigate the mechanisms at work in DF and outline that DF architecture can generally be simplified into more simple and computationally efficient shallow forests networks. Despite some instability, the latter may outperform standard predictive tree-based methods. In order to precisely quantify the improvement achieved by these light network configurations over standard tree learners, we theoretically study the performance of a shallow tree network made of two layers, each one composed of a single centered tree. We provide tight theoretical lower and upper bounds on its excess risk. These theoretical results show the interest of tree-network architectures for well-structured data provided that the first layer, acting as a data encoder, is rich enough.
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets.
GWBENCH: a novel Fisher information package for gravitational-wave benchmarking

We present a new Python package, gwbench, implementing the well-established Fisher information formalism as a fast and straightforward tool for the purpose of gravitational-wave benchmarking, i.e. the estimation of signal-to-noise ratios and measurement errors of gravitational waves observed by a network of detectors. Such an infrastructure is necessary due to the high computational cost of Bayesian parameter estimation methods which renders them less effective for the scientific assessment of gravitational waveforms, detectors, and networks of detectors, especially when determining their effects on large populations of gravitational-wave sources spread throughout the Universe. gwbench further gives quick access to detector locations and sensitivities, while including the effects of Earth’s rotation on the latter, as well as waveform models and their derivatives, while giving access to the host of waveforms available in the LSC Algorithm Library. With the provided functionality, gwbench is relevant for a wide variety of applications in gravitational-wave astronomy such as waveform modeling, detector development, cosmology, and tests of general relativity.
Fast Minimal Presentations of Bi-graded Persistence Modules

Multi-parameter persistent homology is a recent branch of topological data analysis. In this area, data sets are investigated through the lens of homology with respect to two or more scale parameters. The high computational cost of many algorithms calls for a preprocessing step to reduce the input size. In general, a minimal presentation is the smallest possible representation of a persistence module. Lesnick and Wright proposed recently an algorithm (the LW-algorithm) for computing minimal presentations based on matrix reduction. In this work, we propose, implement and benchmark several improvements over the LW-algorithm. Most notably, we propose the use of priority queues to avoid extensive scanning of the matrix columns, which constitutes the computational bottleneck in the LW-algorithm, and we combine their algorithm with ideas from the multi-parameter chunk algorithm by Fugacci and Kerber. Our extensive experiments show that our algorithm outperforms the LW-algorithm and computes the minimal presentation for data sets with millions of simplices within a few seconds. Our software is publicly available.
Probabilistic Interval Predictor Based on Dissimilarity Functions

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided.
Measuring the Mass of Missing Baryons in the Halo of Andromeda Galaxy with Gamma-Ray Observations

It has been suggested that a huge amount of baryon resides in the circumgalactic medium (CGM) extending out to the virial radii of galaxies. In this work we attempt to measure the baryon mass in the CGM with a novel method based on the gamma-ray observations of the extended halo of the Andromeda galaxy Messier 31 (M31). Since cosmic-ray particles generated inside the galaxy will eventually escape to the CGM, they will produce gamma-ray emission via the proton–proton collision with the CGM and produce gamma-rays. Different from some traditional measurements that are sensitive only to certain metallic ions in specific temperature range, the hadronic gamma-ray flux is sensitive to baryonic gases in all phases and does not rely on the metallicity in the halo, hence the measured gamma-ray flux from the galaxy’s halo can be used to constrain the mass of the CGM. By dealing with the cosmic-ray transport in the halo and calculating the hadronic gamma-ray intensity, we find that the total baryon mass contained within the virial radius is less than (1.4–5) × 1010 M ⊙ according to the gamma-ray intensity obtained with a model-dependent analysis. It implies that the CGM of Andromeda galaxy may not account for more than 30% of the missing baryons, but the result is subject to uncertainties from the gamma-ray intensity upper limit, diffusion coefficient of the cosmic-rays in the halo, as well as the stellar mass and dark matter halo mass of the galaxy. This method will become more constraining provided better understandings on these issues and more sensitive gamma-ray telescopes in the future.
Tidal Love numbers of Kerr black holes

The open question of whether a Kerr black hole can become tidally deformed or not has profound implications for fundamental physics and gravitational-wave astronomy. We consider a Kerr black hole embedded in a weak and slowly varying, but otherwise arbitrary, multipolar tidal environment. By solving the static Teukolsky equation for the gauge-invariant Weyl scalar $\psi_0$, and by reconstructing the corresponding metric perturbation in an ingoing radiation gauge, for a general harmonic index $\ell$, we compute the linear response of a Kerr black hole to the tidal field. This linear response vanishes identically for a Schwarzschild black hole and for an axisymmetric perturbation of a spinning black hole. For a nonaxisymmetric perturbation of a spinning black hole, however, the linear response does not vanish, and it contributes to the Geroch-Hansen multipole moments of the perturbed Kerr geometry. As an application, we compute explicitly the rotational black hole tidal Love numbers that couple the induced quadrupole moments to the quadrupolar tidal fields, to linear order in the black hole spin, and we introduce the corresponding notion of tidal Love tensor. Finally, we show that those induced quadrupole moments are closely related to the well-known physical phenomenon of tidal torquing of a spinning body interacting with a tidal gravitational environment.
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks

Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing framework that expresses multiple parameters using a single code, and has recently achieved state-of-the-art network compression on a range of core vision and natural language processing tasks. Key to the success of vector quantization is deciding which parameter groups should be compressed together. Previous work has relied on heuristics that group the spatial dimension of individual convolutional filters, but a general solution remains unaddressed. This is desirable for pointwise convolutions (which dominate modern architectures), linear layers (which have no notion of spatial dimension), and convolutions (when more than one filter is compressed to the same codeword). In this paper we make the observation that the weights of two adjacent layers can be permuted while expressing the same function. We then establish a connection to rate-distortion theory and search for permutations that result in networks that are easier to compress. Finally, we rely on an annealed quantization algorithm to better compress the network and achieve higher final accuracy. We show results on image classification, object detection, and segmentation, reducing the gap with the uncompressed model by 40 to 70% w.r. t. the current state of the art. All our experiments can be reproduced using the code at https://github.com/uber-research/permute-quantize-finetune.