Miracle-dz/UniFAR-FTUs huggingface dataset:
Mineral lick use by moose in Alaska.

Chemical analyses and observations of lick use indicated that natural licks are an important source of sodium for moose (Alces alces) in Alaska during early summer (June). Most lick use occurred in late evening, unlike the diel activity patterns of moose not at licks. Bulls used licks earlier in the summer than cows and there was a disproportionately high rate of lick use by bulls. Calves used licks less than dams and started using licks later in the season. Sex and age differences in lick use would bias population counts from lick observations. Natural licks appeared to influence movements and distribution of moose during spring and summer.
Ellipticity dependence of excitation and ionization of argon atoms by short-pulse infrared radiation

When atoms or molecules are exposed to strong short-pulse infrared radiation, ionization as well as "frustrated tunneling ionization"can occur, in which some of the nearly freed electrons recombine into the initial ground or an excited bound state. We analyze the ellipticity dependence of the relative signals that are predicted in a single-active-electron (SAE) approximation, the validity of which is checked against a parameter-free multielectron R-matrix (close-coupling) with time-dependence approach. We find good agreement between the results from both models, thereby providing confidence in the SAE model potential to treat the process of interest. Comparison of the relative excitation probabilities found in our numerical calculations with the predictions of Landsman et al. [A. S. Landsman, New J. Phys. 15, 013001 (2013)NJOPFM1367-263010.1088/1367-2630/15/1/013001] and Zhao et al. [Y. Zhao, Opt. Express 27, 21689 (2019)OPEXFF1094-408710.1364/OE.27. 021689] reveals good agreement with the former for short pulses. For longer pulses, the ellipticity dependence becomes wider than that obtained from the Landsman et al. formula, but we do not obtain the increase compared to linearly polarized radiation predicted by Zhao et al.
Karyological Studies in Hemerocallis

1. The gametic and zygotic chromosome numbers of nine species and a variety of Hemerrcallis are determined respectively as follows:In polle mother-cell In root-tip cellH. flava L. 11 (1n) 22 (2n) H. aurantiaca BAK. 11 (1n) 22 (2n) H. Middendorfii TRAUTV. et MEY. 11 (1n) 22 (2n) H. disticha DONN 11 (1n) 22 (2n) H. longituba MIQ. 11 (1n) 22 (2n) H. minor MILL. 11 (1n) 22 (2n) H. sp. (α) 11 (1n) 22 (2n) H. sp. (β) 11 (1n) 33 (3n) H. fulva L. 11-20 33 (3n) H. disticha var. Kwanso NAKAI 11-20 33 (3n)2. H. disticha var. Kwanso is probably an autotriploid species derived from H. disticha. H. fulva too seems to be an autotriploid species.3. The cause of sterility in H. fulva and H. disticha var. Kwanso may be ascribed to irregular meiosis, which is common in triploid plants and results in the formation of pollen grains or embryo-sacs in which the normal numerical relation of different chromosomes and consequently of genes are disturbed.In concluding, the writer wishes to express his hearty thanks to Emer. Prof. K. FUJII for his kind advice and valuable criticism throughout the course of the work.
Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-advancements. However, these advances have often come at the cost of both the scale and complexity of the underlying RL algorithms. Increases in complexity have in turn made it more difficult for researchers to reproduce published RL algorithms or rapidly prototype ideas. To address this, we introduce Acme, a tool to simplify the development of novel RL algorithms that is specifically designed to enable simple agent implementations that can be run at various scales of execution. Our aim is also to make the results of various RL algorithms developed in academia and industrial labs easier to reproduce and extend. To this end we are releasing baseline implementations of various algorithms, created using our framework. In this work we introduce the major design decisions behind Acme and show how these are used to construct these baselines. We also experiment with these agents at different scales of both complexity and computation-including distributed versions. Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.
Sequential change point detection in high dimensional time series

Change point detection in high dimensional data has found considerable interest interest in recent years. Most of the literature designs methodology for a retrospective analysis, where the whole sample is already available when the statistical inference begins. This paper develops monitoring schemes for the online scenario, where high dimensional data arrives steadily and changes shall be detected as fast as possible controlling at the same time the probability of a false alarm. We develop sequential procedures capable of detecting changes in the mean vector of a successively observed high dimensional time series with spatial and temporal dependence. In a high dimensional scenario it is shown that the new monitoring schemes have asymptotic level alpha under the null hypothesis of no change and are consistent under the alternative of a change in at least one component. The properties of the new methodology are illustrated by means of a simulation study and in the analysis of a data example. As a side result, we show that the range of a Brownian motion is in the domain of attraction of the Gumbel distribution.
Learning Distributed Controllers for V-Formation

We show how a high-performing, fully distributed and symmetric neural V-formation controller can be synthesized from a Centralized MPC (Model Predictive Control) controller using Deep Learning. This result is significant as we also establish that under very reasonable conditions, it is impossible to achieve V-formation using a deterministic, distributed, and symmetric controller. The learning process we use for the neural V-formation controller is significantly enhanced by CEGkR, a CounterexampleGuided k-fold Retraining technique we introduce, which extends prior work in this direction in important ways. Our experimental results show that our neural V-formation controller generalizes to a significantly larger number of agents than for which it was trained (from 7 to 15), and exhibits substantial speedup over the MPC-based controller. We use a form of statistical model checking to compute confidence intervals for our neural Vformation controller’s convergence rate and time to convergence.
Predicting Engagement in Video Lectures

The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners. We focus on building models to find the characteristics and features involved in context-agnostic engagement (i.e. population-based), a seldom researched topic compared to other contextualised and personalised approaches that focus more on individual learner engagement. Learner engagement, is arguably a more reliable measure than popularity/number of views, is more abundant than user ratings and has also been shown to be a crucial component in achieving learning outcomes. In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task. We further test different strategies for quantifying learner engagement signals. We demonstrate the use of our approach in the case of data scarcity. Additionally, we perform a sensitivity analysis of the best performing model, which shows promising performance and can be easily integrated into an educational recommender system for OERs.
Thermal Object Detection using Domain Adaptation through Style Consistency

A recent fatal accident of an autonomous vehicle opens a debate about the use of infrared technology in the sensor suite for autonomous driving to increase visibility for robust object detection. Thermal imaging has an advantage over lidar, radar, and camera because it can detect the heat difference emitted by objects in the infrared spectrum. In contrast, lidar and camera capture in the visible spectrum, and adverse weather conditions can impact their accuracy. The limitations of object detection in images from conventional imaging sensors can be catered to by thermal images. This paper presents a domain adaptation method for object detection in thermal images. We explore multiple ideas of domain adaption. First, a generative adversarial network is used to transfer the low-level features from the visible spectrum to the infrared spectrum domain through style consistency. Second, a cross-domain model with style consistency is used for object detection in the infrared spectrum by transferring the trained visible spectrum model. The proposed strategies are evaluated on publicly available thermal image datasets (FLIR ADAS and KAIST Multi-Spectral). We find that adapting the low-level features from the source domain to the target domain through domain adaptation increases in mean average precision by approximately 10%.
The impact of stellar and AGN feedback on halo-scale baryonic and dark matter accretion in the eagle simulations

We use the eagle suite of hydrodynamical simulations to analyse accretion rates (and the breakdown of their constituent channels) on to haloes over cosmic time, comparing the behaviour of baryons and dark matter (DM). We also investigate the influence of sub-grid baryon physics on halo-scale inflow, specifically the consequences of modelling radiative cooling, as well as feedback from stars and active galactic nuclei (AGNs). We find that variations in halo baryon fractions at fixed mass (particularly their circumgalactic medium gas content) are very well correlated with variations in the baryon fraction of accreting matter, which we show to be heavily suppressed by stellar feedback in low-mass haloes, Mhalo ≲ 1011.5 M⊙. Breaking down accretion rates into first infall, recycled, transfer, and merger components, we show that baryons are much more likely to be smoothly accreted than to have originated from mergers when compared to DM, finding (averaged across halo mass) a merger contribution of $\approx 6{{\ \rm per\ cent}}$ for baryons, and $\approx 15{{\ \rm per\ cent}}$ for DM at z ≈ 0. We also show that the breakdown of inflow into different channels is strongly dependent on sub-grid physics, particularly the contribution of recycled accretion (accreting matter that has been previously ejected from progenitor haloes). Our findings highlight the dual role that baryonic feedback plays in regulating the evolution of galaxies and haloes: by (i) directly removing gas from haloes, and (ii) suppressing gas inflow to haloes.
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations

Automated ways to extract stance (denying vs. supporting opinions) from conversations on social media are essential to advance opinion mining research. Recently, there is a renewed excitement in the field as we see new models attempting to improve the state-of-the-art. However, for training and evaluating the models, the datasets used are often small. Additionally, these small datasets have uneven class distributions, i.e., only a tiny fraction of the examples in the dataset have favoring or denying stances, and most other examples have no clear stance. Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e.g., replying vs. quoting on Twitter). Because of this, models trained on one event do not generalize to other events. In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues. To the best of our knowledge, this is currently the largest human-labeled stance dataset for Twitter conversations with over 5200 stance labels. More importantly, we designed a tweet collection methodology that favors the selection of denial-type responses. This class is expected to be more useful in the identification of rumors and determining antagonistic relationships between users. Moreover, we include many baseline models for learning the stance in conversations and compare the performance of various models. We show that combining data from replies and quotes decreases the accuracy of models indicating that the two modalities behave differently when it comes to stance learning.
An Exploratory Characterization of Bugs in COVID-19 Software Projects

Context: The dire consequences of the COVID-19 pandemic has influenced development of COVID-19 software i.e., software used for analysis and mitigation of COVID-19. Bugs in COVID-19 software can be consequential, as COVID-19 software projects can impact public health policy and user data privacy. Objective: The goal of this paper is to help practitioners and researchers improve the quality of COVID-19 software through an empirical study of open source software projects related to COVID-19. Methodology: We use 129 open source COVID-19 software projects hosted on GitHub to conduct our empirical study. Next, we apply qualitative analysis on 550 bug reports from the collected projects to identify bug categories. Findings: We identify 8 bug categories, which include data bugs i.e., bugs that occur during mining and storage of COVID-19 data. The identified bug categories appear for 7 categories of software projects including (i) projects that use statistical modeling to perform predictions related to COVID-19, and (ii) medical equipment software that are used to design and implement medical equipment, such as ventilators. Conclusion: Based on our findings, we advocate for robust statistical model construction through better synergies between data science practitioners and public health experts. Existence of security bugs in user tracking software necessitates development of tools that will detect data privacy violations and security weaknesses.
Periodicity in the cumulative hierarchy

We investigate the structure of rank-into-rank elementary embeddings incompatible with the Axiom of Choice. Assuming ZF set theory and the existence of a (non-trivial) elementary embedding \[ j:V_{\alpha+1}\to V_{\alpha+1}\] of a rank initial segment of the universe $V$ into itself, we show that the structure of $V_\alpha$ is fundamentally different to that of $V_{\alpha+1}$. (Here $\alpha$ may be either a limit or a successor ordinal.) We show that $j$ is definable from parameters over $V_{\alpha+1}$ iff $\alpha+1$ is an odd ordinal. Moreover, if $\alpha+1$ is odd then $j$ is definable over $V_{\alpha+1}$ from its restriction $j\upharpoonright V_{\alpha}$, and uniformly so. This parameter is optimal in that $j$ is not definable from any parameter in $V_\alpha$. Further, we also show that $\Sigma_1$-elementary embeddings $j:V_\lambda\to V_\lambda$ are non-definable for all limit $\lambda$. It is moreover known that if there is a Reinhardt cardinal, then for all sufficiently large ordinals $\alpha$, there is an elementary $j:V_\alpha\to V_\alpha$, and therefore the cumulative hierarchy is eventually \emph{periodic} (with period 2).
Submodular Bandit Problem Under Multiple Constraints

The linear submodular bandit problem was proposed to simultaneously address diversified retrieval and online learning in a recommender system. If there is no uncertainty, this problem is equivalent to a submodular maximization problem under a cardinality constraint. However, in some situations, recommendation lists should satisfy additional constraints such as budget constraints, other than a cardinality constraint. Thus, motivated by diversified retrieval considering budget constraints, we introduce a submodular bandit problem under the intersection of $l$ knapsacks and a $k$-system constraint. Here $k$-system constraints form a very general class of constraints including cardinality constraints and the intersection of $k$ matroid constraints. To solve this problem, we propose a non-greedy algorithm that adaptively focuses on a standard or modified upper-confidence bound. We provide a high-probability upper bound of an approximation regret, where the approximation ratio matches that of a fast offline algorithm. Moreover, we perform experiments under various combinations of constraints using a synthetic and two real-world datasets and demonstrate that our proposed methods outperform the existing baselines.
Non-staining fungi associated with the bark beetle Dendroctonus brevicomis (Coleoptera: Scolytidae) on Pinus ponderosa

Three fungi—Ceratocystis nigrocarpa Davidson, an unidentified hyphomycete, and an unidentified basidiomycete—were isolated from extensive non-stained areas of sapwood of ponderosa pine infested with western pine beetle, Dendroctonus brevicomis Lec. , in California. The two unidentified fungi were also present in the thoracic mycangium of the female beetle while C. nigrocarpa was found only externally on the beetle. The mycangium and its contents are illustrated. Ceratocystis minor (Hedgc.) Hunt, which was also found externally on the beetle but not in the mycangium, was isolated only from scattered patches of blue-stained sapwood. It is suggested that non-staining fungi play a role in causing the death of ponderosa pine trees attacked by D. brevicomis.
Learning Irreducible Representations of Noncommutative Lie Groups

Recent work has made exciting theoretical and practical progress towards neural networks that are equivariant to symmetries such as rotations. However, current techniques require explicit group representations, which generally limits us to those groups with analytically derived matrix formulas. In this work, we present a numerical technique for finding irreducible representations of noncommutative Lie groups. We demonstrate that the structure of the Lie algebras associated with these groups can be used to learn explicit representation matrices to high precision. This provides an automated program to build neural networks that are equivariant to a much wider class of transformations, including previously intractable cases such as the Poincare group (rotations, translations, and boosts).
Fourth Painlevé and Ermakov equations: quantum invariants and new exactly-solvable time-dependent Hamiltonians

In this work, we introduce a new realization of exactly-solvable time-dependent Hamiltonians based on the solutions of the fourth Painlevé and the Ermakov equations. The latter is achieved by introducing a shape-invariant condition between an unknown quantum invariant and a set of third-order intertwining operators with time-dependent coefficients. New quantum invariants are constructed after adding a deformation term to the well-known quantum invariant of the parametric oscillator. Such a deformation depends explicitly on time through solutions of the Ermakov equation, a property that simultaneously ensures the regularity of the new time-dependent potentials at each time. The fourth Painlevé equation appears after introducing an appropriate reparametrization of the spatial coordinate and the time parameter, where the parameters of the fourth Painlevé equation dictate the spectral information of the quantum invariant. In this form, the eigenfunctions of the third-order ladder operators lead to several sequences of solutions to the Schrödinger equation, which are determined in terms of the solutions of the Riccati equation, Okamoto polynomials, and nonlinear bound states of the derivative nonlinear Schrödinger equation. Remarkably, it is noticed that the solutions in terms of the nonlinear bound states lead to a quantum invariant with equidistant eigenvalues, which contains both an finite-dimensional and an infinite-dimensional sequences of eigenfunctions. The resulting family of time-dependent Hamiltonians is such that, to the authors’ knowledge, have been unnoticed in the literature of stationary and nonstationary systems.
Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning

One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging speed. We compared various convolutional neural network (CNN) architectures, and selected a Fully Dense U-net (FD U-net) model that produced the best results. To mimic various undersampling conditions in practice, we artificially downsampled fully-sampled PAM images of mouse brain vasculature at different ratios. This allowed us to not only definitively establish the ground truth, but also train and test our deep learning model at various imaging conditions. Our results and numerical analysis have collectively demonstrated the robust performance of our model to reconstruct PAM images with as few as 2% of the original pixels, which can effectively shorten the imaging time without substantially sacrificing the image quality.
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English

We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.
Abundance matching tested on small scales with galaxy dynamics

We present a comprehensive test of the relation between stellar and total mass in galaxies as predicted by popular models based on abundance matching (AM) techniques. We use the ``Spectroscopy and H-band Imaging of Virgo cluster galaxies'' (SHIVir) survey with photometric and dynamical profiles for 190 Virgo cluster galaxies to establish a relation between the stellar and dynamical masses measured within the isophotal radius $r_{23.5}$. Various dark matter and galaxy scaling relations are combined with results from the NIHAO suite of hydrodynamical simulations to recast AM predictions in terms of these observed quantities. Our results are quite insensitive to the exact choice of dark matter profile and halo response to baryon collapse. We find that theoretical models reproduce the slope and normalization of the observed stellar-to-halo mass relation (SHMR) over more than three orders of magnitude in stellar mass $(10^8 < M_*/M_{\odot} < 2 \times 10^{11})$. However, the scatter of the observed SHMR exceeds that of AM predictions by a factor $\sim$5. For systems with stellar masses exceeding $5 \times 10^{10}~M_{\odot}$, AM overpredicts the observed stellar masses for a given dynamical mass. The latter offset may support previous indications of a different stellar initial mass function in these massive galaxies. Overall our results support the validity of AM predictions on a wide dynamical range.