Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularization of multiplicative SDEs through additive noise

We investigate the regularizing effect of certain additive continuous perturbations on SDEs with multiplicative fractional Brownian motion (fBm). Traditionally, a Lipschitz requirement on the drift and diffusion coefficients is imposed to ensure existence and uniqueness of the SDE. We show that suitable perturbations restore existence, uniqueness and regularity of the flow for the resulting equation, even when both the drift and the diffusion coefficients are distributional, thus extending the program of regularization by noise to the case of multiplicative SDEs. Our method relies on a combination of the non-linear Young formalism developed by Catellier and Gubinelli, and stochastic averaging estimates recently obtained by Hairer and Li.
The Turbulent Gas Structure in the Centers of NGC 253 and the Milky Way

We compare molecular gas properties in the starbursting center of NGC 253 and the Milky Way Galactic center (GC) on scales of ∼1–100 pc using dendrograms and resolution-, area-, and noise-matched data sets in CO (1–0) and CO (3–2). We find that the size–line width relations in NGC 253 and the GC have similar slope, but NGC 253 has larger line widths by factors of ∼2–3. The σ2/R dependency on column density shows that, in the GC, on scales of 10–100 pc the kinematics of gas over N > 3 × 1021 cm−2 are compatible with gravitationally bound structures. In NGC 253 this is only the case for column densities N > 3 × 1022 cm−2. The increased line widths in NGC 253 originate in the lower column density gas. This high velocity dispersion, not gravitationally self-bound gas, is likely in transient structures created by the combination of high average densities and feedback in the starburst. The high densities turn the gas molecular throughout the volume of the starburst, and the injection of energy and momentum by feedback significantly increases the velocity dispersion at a given spatial scale over what is observed in the GC.
On the Accuracy of CRNNs for Line-Based OCR: A Multi-Parameter Evaluation

We investigate how to train a high quality optical character recognition (OCR) model for difficult historical typefaces on degraded paper. Through extensive grid searches, we obtain a neural network architecture and a set of optimal data augmentation settings. We discuss the influence of factors such as binarization, input line height, network width, network depth, and other network training parameters such as dropout. Implementing these findings into a practical model, we are able to obtain a 0.44% character error rate (CER) model from only 10,000 lines of training data, outperforming currently available pretrained models that were trained on more than 20 times the amount of data. We show ablations for all components of our training pipeline, which relies on the open source framework Calamari.
Enabling ab initio configurational sampling of multicomponent solids with long-range interactions using neural network potentials and active learning.

We propose a scheme for ab initio configurational sampling in multicomponent crystalline solids using Behler-Parinello type neural network potentials (NNPs) in an unconventional way: the NNPs are trained to predict the energies of relaxed structures from the perfect lattice with configurational disorder instead of the usual way of training to predict energies as functions of continuous atom coordinates. Training set bias is avoided through an active learning scheme. This idea is demonstrated on the calculation of the temperature dependence of the degree of A/B site inversion in MgAl$_2$O$_4$, which is a multivalent system requiring careful handling of long-range interactions. The present scheme may serve as an alternative to cluster expansion for `difficult' systems, e.g., complex bulk or interface systems with many components and sublattices that are relevant to many technological applications today.
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets

Ideally Open-Domain Question Answering models should exhibit a number of competencies, ranging from simply memorizing questions seen at training time, to answering novel question formulations with answers seen during training, to generalizing to completely novel questions with novel answers. However, single aggregated test set scores do not show the full picture of what capabilities models truly have. In this work, we perform a detailed study of the test sets of three popular open-domain benchmark datasets with respect to these competencies. We find that 30% of test-set questions have a near-duplicate paraphrase in their corresponding train sets. In addition, we find that 60-70% of answers in the test sets are also present in the train sets. Using these findings, we evaluate a variety of popular open-domain models to obtain greater insight into what extent they can generalize, and what drives their overall performance. We find that all models perform substantially worse on questions that cannot be memorized from train sets, with a mean absolute performance difference of 61% between repeated and non-repeated data. Finally we show that simple nearest-neighbor models outperform a BART closed-book QA model, further highlighting the role that train set memorization plays in these benchmarks
Moduli of spherical tori with one conical point

In this paper we determine the topology of the moduli space $\mathcal{MS}_{1,1}(\vartheta)$ of surfaces of genus one with a Riemannian metric of constant curvature $1$ and one conical point of angle $2\pi\vartheta$. In particular, for $\vartheta\in (2m-1,2m+1)$ non-odd, $\mathcal{MS}_{1,1}(\vartheta)$ is connected, has orbifold Euler characteristic $-m^2/12$, and its topology depends on the integer $m>0$ only. For $\vartheta=2m+1$ odd, $\mathcal{MS}_{1,1}(2m+1)$ has $\lceil{m(m+1)/6}\rceil$ connected components. For $\vartheta=2m$ even, $\mathcal{MS}_{1,1}(2m)$ has a natural complex structure and it is biholomorphic to $\mathbb{H}^2/G_m$ for a certain subgroup $G_m$ of $\mathrm{SL}(2,\mathbb{Z})$ of index $m^2$, which is non-normal for $m>1$.
Giant Spin Current Rectification Due to the Interplay of Negative Differential Conductance and a Non-Uniform Magnetic Field

In XXZ chains with large enough interactions, spin transport can be significantly suppressed when the bias of the dissipative driving becomes large enough. This phenomenon of negative differential conductance is caused by the formation of two oppositely polarized ferromagnetic domains at the edges of the chain. Here, we show that this many-body effect, combined with a non-uniform magnetic field, can allow for a high degree of control of the spin current. In particular, by studying all of the possible shapes of local magnetic fields potentials, we find that a configuration in which the magnetic field points up for half of the chain and down for the other half, can result in giant spin-current rectification, for example, up to 108 for a system with only 8 spins. Our results show clear indications that the rectification can increase with the system size.
Terrestrial arthropod abundance and phenology in the Canadian Arctic: modelling resource availability for Arctic-nesting insectivorous birds

Abstract Arctic arthropods are essential prey for many vertebrates, including birds, but arthropod populations and phenology are susceptible to climate change. The objective of this research was to model the relationship between seasonal changes in arthropod abundance and weather variables using data from a collaborative pan-Canadian (Southampton, Herschel, Bylot, and Ellesmere Islands) study on terrestrial arthropods. Arthropods were captured with passive traps that provided a combined measure of abundance and activity (a proxy for arthropod availability to foraging birds). We found that 70% of the deviance in daily arthropod availability was explained by three temperature covariates: mean daily temperature, thaw degree-day, and thaw degree-day2. Models had an adjusted R2 of 0.29–0.95 with an average among sites and arthropod families of 0.67. This indicates a moderate to strong fit to the raw data. The models for arthropod families with synchronous emergence, such as Tipulidae (Diptera), had a better fit (average adjusted R2 of 0.80) than less synchronous taxa, such as Araneae (R2 = 0.60). Arthropod abundance was typically higher in wet than in mesic habitats. Our models will serve as tools for researchers who want to correlate insectivorous bird breeding data to arthropod availability in the Canadian Arctic. Résumé Dans la toundra arctique, les arthropodes constituent des proies essentielles pour de nombreux vertébrés dont les oiseaux. Cependant, les populations d'arthropodes et leur phénologie sont susceptibles de subir des modifications face aux changements climatiques. Notre étude utilise des données sur les arthropodes terrestres provenant d'une initiative pancanadienne (Îles Southampton, Herschel, Bylot et Ellesmere), afin de modéliser la relation entre les changements saisonniers d'abondance d'arthropodes et les variables environnementales. Des pièges fournissant une mesure combinée de l'abondance et de l'activité des arthropodes ont été utilisés afin d'obtenir un indice de la disponibilité des arthropodes pour les oiseaux. Dans nos modèles, trois covariables liées à la température (température, degrés-jours et degrés-jours2) expliquent 70% de la déviance. Selon les sites et les familles d'arthropodes modélisés, les R2 ajustés des modèles ont variés de 0.29–0.95 (moyenne de 0.67). Les modèles pour les familles d'arthropodes ayant une émergence synchronisée, comme les Tipulidae (Diptera), avaient de meilleurs R2 ajustés (0.80 en moyenne) comparativement aux groupes dont la disponibilité est plus répartie dans le temps, comme les araignées (0.60). L'abondance d'arthropodes était généralement plus grande dans les milieux humides que dans les milieux plus secs. Nos modèles pourront servir d'outil aux chercheurs qui désireraient corréler leurs données sur la reproduction des insectivores avec des données sur la disponibilité d'arthropodes dans l'Arctique Canadien.
Influence of seasonal variables associated with climate change on photochemical diurnal cycles of marine phytoplankton from Patagonia (Argentina)

The effects of temperature and ultraviolet radiation (UVR; 280–400 nm) on seasonal succession in phytoplankton assemblages of Patagonia (Argentina) were studied in the context of global change. Samples collected during pre‐bloom, bloom onset, bloom, and spring were exposed to in situ and increased (+4°C) temperatures and solar radiation with and without UVR. Daily cycles of effective photochemical quantum yield exhibited a pattern of high values in the morning, decreasing towards noon, and increasing in the afternoon. The decrease in yields towards noon as the season progressed increased from 30% in the pre‐bloom to 80% in the spring; in the latter there were significant differences between radiation treatments under both temperature conditions. The highest inhibition rates were during the bloom, whereas the highest recovery rates were during the spring. Inhibition rates were generally higher in treatments exposed to UVR in comparison to photosynthetically active radiation‐only treatments and some stages of the succession exhibited an additional temperature effect. Increasing temperatures had little effect on pre‐bloom communities but helped to counteract the magnitude of the yield decrease during the bloom onset. However, during the bloom and in the spring, temperature and UVR acted synergistically, increasing the overall photochemical inhibition. Feedback mechanisms of increased temperatures causing a shallower mixing depth will expose phytoplankton to higher radiation, which will have a negative effect on the bloom and on spring assemblages. Due to the differential effects of solar UVR and increased temperature on phytoplankton, future studies should consider the repercussions on higher trophic levels.
Gibbs Sampling with People

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or trustworthiness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, MCMCP's binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments combine GSP with a state-of-the-art image synthesis network (StyleGAN) and a recent network interpretability technique (GANSpace), enabling GSP to efficiently explore high-dimensional perceptual spaces, and demonstrating how GSP can be a powerful tool for jointly characterizing semantic representations in humans and machines.
Learnable Graph Inception Network for Emotion Recognition

Analyzing emotion from verbal and non-verbal behavioral cues is critical for many intelligent human-centric systems. The emotional cues can be captured using audio, video, motion-capture (mocap) or other modalities. We propose a generalized graph approach to emotion recognition that can take any time-varying (dynamic) data modality as input. To alleviate the problem of optimal graph construction, we cast this as a joint graph learning and classification task. To this end, we present the \emph{Learnable Graph Inception Network} (L-GrIN) that jointly learns to recognize emotion and to identify the underlying graph structure in data. Our architecture comprises multiple novel components: a new graph convolution operation, a graph inception layer, learnable adjacency, and a learnable pooling function that yields a graph-level embedding. We evaluate the proposed architecture on four benchmark emotion recognition databases spanning three different modalities (video, audio, mocap), where each database captures one of the following emotional cues: facial expressions, speech and body gestures. We achieve state-of-the-art performance on all databases outperforming several competitive baselines and relevant existing methods.
Improving the assessment of medication adherence: Challenges and considerations with a focus on low-resource settings

Improving patient survival and quality of life in chronic diseases requires prolonged and often lifelong medication intake. Less than half of patients with chronic diseases globally are adherent to their prescribed medications which preclude the full benefit of treatment, worsens therapeutic outcomes, accelerates disease progression, and causes enormous economic losses. The accurate assessment of medication adherence is pivotal for both researchers and clinicians. Medication adherence can be assessed through both direct and indirect measures. Indirect measures include both subjective (self-report measures such as questionnaire and interview) and objective (pill count and secondary database analysis) measures and constitute the mainstay of assessing medication adherence. However, the lack of an inexpensive, ubiquitous, universal gold standard for assessment of medication adherence emphasizes the need to utilize a combination of measures to differentiate adherent and nonadherent patients. The global heterogeneity in health systems precludes the development of a universal guideline for evaluating medication adherence. Methods based on the secondary database analysis are mostly ineffectual in low-resource settings lacking electronic pharmacy and insurance databases and allowing refills without updated, valid prescriptions from private pharmacies. This significantly restricts the choices for assessing adherence until digitization of medical data takes root in much of the developing world. Nevertheless, there is ample scope for improving self-report measures of adherence. Effective interview techniques, especially accounting for suboptimal patient health literacy, validation of adherence questionnaires, and avoiding conceptual fallacies in reporting adherence can improve the assessment of medication adherence and promote understanding of its causal factors.
Identification and susceptibility of Klebsiella and Enterobacter spp. isolated from meat products

Identification and susceptibility to 8� selected antimicrobial agents of Klebsiella and Enterobacter bacterial species was studied in 15 marketed meat samples (chicken, turkey-hen, beef, sheep, pig, dromedary, ostrich, and fish). � The isolates were identified with the API 20E system, resulting in 7 clusters: Enterobacter aerogenes (2 isolates), Enterobacter cloacae (6), Enterobacter sakazakii (3), Enterobacter spp (14), Klebsiella oxytoca (5), Klebsiella pneumoniae (2) and Klebsiella ornithinolytica (12). The identities of isolates identified as Enterobacter and Klebsiella spp. were confirmed by Amplified Ribosomal DNA Analysis (ARDRA), using AluI, MspI, RsaI restriction enzymes. Identification of isolates by ARDRA and API 20E system gave similar results with 90,2 % (44/51) of the collection. Susceptibility to antibiotics was evaluated by the disk method according to French recommendations (CA-SFM). Resistance rates were as follows:� Ampicillin (62%), cefalotin (30%), tetracycline (14%), chloramphenicol 2%. aminoglycosides 0% and nalidixic acid 0%. Strains isolated from 'industrially reared animals' showed higher resistance rates than 'naturally reared' ones.
The northward shifting neophyte Tragopogon dubius is just as effective in forming mycorrhizal associations as the native T. pratensis

Background: As a consequence of climate warming, many organisms are shifting their range towards higher latitudes and altitudes. As not all do so at the same speed, this may disrupt biotic interaction. Release from natural enemies through range expansion can result in invasiveness, whereas loss of mutualists can reduce plant vigour and fitness. One of the most important groups of plant symbiotic mutualists is the arbuscular mycorrhizal fungi (AMF). Aims: We used greenhouse experiments to test whether Tragopogon dubius, a species that has recently expanded its range northward and colonised the Netherlands, is able to associate with the same AMF as the native congener T. pratensis. Methods: In soils collected from four locations in the new range of T. dubius we compared the density of infective AMF propagules associating with both plant species, as well as AMF colonisation of the roots. The AMF community structure in the roots of these species was also analysed using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Results: Tragopogon dubius and T. pratensis did not differ in any of these characteristics. Conclusions: We therefore conclude that the range-shifting T. dubius is as effective in the formation of mycorrhiza as the native congener.
Can I Pour Into It? Robot Imagining Open Containability Affordance of Previously Unseen Objects via Physical Simulations

Open containers, i.e., containers without covers, are an important and ubiquitous class of objects in human life. In this letter, we propose a novel method for robots to “imagine” the open containability affordance of a previously unseen object via physical simulations. The robot autonomously scans the object with an RGB-D camera. The scanned 3D model is used for open containability imagination which quantifies the open containability affordance by physically simulating dropping particles onto the object and counting how many particles are retained in it. This quantification is used for open-container vs. non-open-container binary classification (hereafter referred to as open container classification). If the object is classified as an open container, the robot further imagines pouring into the object, again using physical simulations, to obtain the pouring position and orientation for real robot autonomous pouring. We evaluate our method on open container classification and autonomous pouring of granular material on a dataset containing 130 previously unseen objects with 57 object categories. Although our proposed method uses only 11 objects for simulation calibration, its open container classification aligns well with human judgements. In addition, our method endows the robot with the capability to autonomously pour into the 55 containers in the dataset with a very high success rate. We also compare to a deep learning method. Results show that our method achieves the same performance as the deep learning method on open container classification and outperforms it on autonomous pouring. Moreover, our method is fully explainable.
Single-hemisphere Dynamos in M-dwarf Stars

M-dwarf stars below a certain mass are convective from their cores to their photospheres. These fully convective objects are extremely numerous, very magnetically active, and the likely hosts of many exoplanets. Here we study, for the first time, dynamo action in simulations of stratified, rotating, fully convective M-dwarf stars. Importantly, we use new techniques to capture the correct full ball geometry down to the center of the star. We find surprising dynamo states in these systems, with the global-scale mean fields confined strongly to a single hemisphere, in contrast to prior stellar dynamo solutions. These hemispheric-dynamo stars are likely to have profoundly different interactions with their surroundings, with important implications for exoplanet habitability and stellar spindown.
A quantitative explanation of the type-B QPOs in GX 339–4

Context. Type-B quasi periodic oscillations (QPOs) in black-hole X-ray binaries are a class of low-frequency QPOs that are observed in the soft intermediate state in the rising and the declining phases of an outburst. They are suspected to result from the precession of the jet that is ejected from the source. Aims. The objective of the present work is to investigate in detail the emissivity of the jet in hard X-rays and to see whether the type-B QPOs from GX 339−4, which is the best studied black-hole transient, can be explained quantitatively with a precessing jet. Methods. We used our simple jet model, which invokes Comptonization in the jet, and examined the angular dependence of the upscattered photons that emerge from the jet and their energy distribution, which is a power law. Results. Due to the elongation of the jet, assisted by the bulk motion of the electrons, the angular distribution of the emerging hard X-ray photons from the jet is not isotropic. More importantly, the photon-number spectral index, Γ, is an increasing function of the polar angle, θ, with respect to the axis of the jet. If the jet is fixed, then an observer at infinity sees the photon index, Γ, which corresponds to this specific observational direction. However, if the jet is precessing, then the observer sees a periodic variation of Γ with the precession period. Such a periodic variation of Γ has been observed in GX 339−4 and in this work, we reproduce it quantitatively, using our model. Conclusions. Our jet model nicely explains through quantitative means the type-B QPOs seen in GX 339−4 as originating from a precessing jet. The given model has previously explained several observed correlations thus far.