Miracle-dz/UniFAR-FTUs huggingface dataset:
Denoising Atmospheric Temperature Measurements Taken by the Mars Science Laboratory on the Martian Surface

In this article, we analyze data from two temperature sensors of the Mars Science Laboratory, which has been active in Mars since August 2012. Temperature measurements received from the rover are noisy and must be processed and validated before being delivered to the scientific community. Currently, a simple moving average (MA) filter is used to perform signal denoising. The application of this basic method relies on the assumption that the noise is stationary and statistically independent of the underlying structure of the signal, an arguable assumption in this kind of harsh environment. In this article, we analyze the application of two alternative methods to process the temperature sensor measurements: the discrete wavelet transform (DWT) and the Hilbert–Huang transform (HHT). We consider two different data sets: one belonging to the current Martian measurement campaigns, and the other to the thermal vacuum tests. The processing of these data sets allows to separate the random noise from the interference created by other systems. The experiments show that the MA filter may provide useful results under given circumstances. However, the proposed methods allow a better fitting for all the realistic scenarios while providing the possibility to identify and analyze other interesting signal features and artifacts that could be later studied and classified. The large amount of data to be processed makes computational efficiency an important requirement in this mission. Considering the computational cost and the filtering performance, we propose the method based on DWT as more suitable for this application.
FasterRCNN Monitoring of Road Damages: Competition and Deployment

Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, computer vision has made impressive strides in recent years, mainly due to successful applications of deep learning models. These novel progresses are allowing the automation of vision tasks, which were previously impossible to automate, offering promising possibilities to assist administrators in optimizing their infrastructure maintenance operations. In this context, the IEEE 2020 global Road Damage Detection (RDD) Challenge is giving an opportunity for deep learning and computer vision researchers to get involved and help accurately track pavement damages on road networks. This paper proposes two contributions to that topic: In a first part, we detail our solution to the RDD Challenge. In a second part, we present our efforts in deploying our model on a local road network, explaining the proposed methodology and encountered challenges.
Oscillations of 2D ESTER models

Recent numerical and theoretical considerations have shown that low-degree acoustic modes in rapidly rotating stars follow an asymptotic formula and recent observations of pulsations in rapidly rotating delta Scuti stars seem to match these expectations. However, a key question is whether strong gradients or discontinuities can adversely affect this pattern to the point of hindering its identification. Other important questions are how rotational splittings are affected by the 2D rotation profiles expected from baroclinic effects and whether it is possible to probe the rotation profile using these splittings. Accordingly, we numerically calculate pulsation modes in continuous and discontinuous rapidly rotating models produced by the 2D ESTER (Evolution STEllaire en Rotation) code. This spectral multi-domain code self-consistently calculates the rotation profile based on baroclinic effects and allows us to introduce discontinuities without loss of numerical accuracy. Pulsations are calculated using an adiabatic version of the Two-dimensional Oscillation Program (TOP) code. The variational principle is used to confirm the high accuracy of the pulsation frequencies and to derive an integral formula that closely matches the generalised rotational splittings, except when modes are involved in avoided crossings. This potentially allows us to probe the the rotation profile using inverse theory. Acoustic glitch theory, applied along the island mode orbit deduced from ray dynamics, can correctly predict the periodicity of the glitch frequency pattern produced by a discontinuity or the Gamma1 dip related to the He II ionisation zone in some of the models. The asymptotic frequency pattern remains sufficiently well preserved to potentially allow its detection in observed stars.
How Do Fair Decisions Fare in Long-term Qualification?

Although many fairness criteria have been proposed for decision making, their long-term impact on the well-being of a population remains unclear. In this work, we study the dynamics of population qualification and algorithmic decisions under a partially observed Markov decision problem setting. By characterizing the equilibrium of such dynamics, we analyze the long-term impact of static fairness constraints on the equality and improvement of group well-being. Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions. We also consider possible interventions that can effectively improve group qualification or promote equality of group qualification. Our theoretical results and experiments on static real-world datasets with simulated dynamics show that our framework can be used to facilitate social science studies.
Lattice Diversities.

Diversities are a generalization of metric spaces, where instead of the non-negative function being defined on pairs of points, it is defined on arbitrary finite sets of points. Diversities have a well-developed theory. This includes the concept of a diversity tight span that extends the metric tight span in a natural way. Here we explore the generalization of diversities to lattices. Instead of defining diversities on finite subsets of a set we consider diversities defined on members of an arbitrary lattice (with a 0). We show that many of the basic properties of diversities continue to hold. However, the natural map from a lattice diversity to its tight span is not a lattice homomorphism, preventing the development of a complete tight span theory as in the metric and diversity cases.
First record of "Dysschema sacrifica" (Hübner, [1831]) on Soybean ("Glycine max" (L.) Merr) (Lepidoptera: Erebidae, Arctiinae)

Fil: Gonzalez, Ezequiel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto Multidisciplinario de Biologia Vegetal. Universidad Nacional de Cordoba. Facultad de Ciencias Exactas Fisicas y Naturales. Instituto Multidisciplinario de Biologia Vegetal; Argentina. Universidad Nacional de Cordoba. Facultad de Ciencias Exactas, Fisicas y Naturales. Centro de Investigaciones Entomologicas de Cordoba; Argentina
Marked colimits and higher cofinality

Given a marked $\infty$-category $\mathcal{D}^{\dagger}$ (i.e. an $\infty$-category equipped with a specified collection of morphisms) and a functor $F: \mathcal{D} \to \mathbb{B}$ with values in an $\infty$-bicategory, we define $\operatorname{colim}^{\dagger} F$, the marked colimit of $F$. We provide a definition of weighted colimits in $\infty$-bicategories when the indexing diagram is an $\infty$-category and show that they can be computed in terms of marked colimits. In the maximally marked case $\mathcal{D}^{\sharp}$, our construction retrieves the $\infty$-categorical colimit of $F$ in the underlying $\infty$-category $\mathcal{B} \subseteq \mathbb{B}$. In the specific case when $\mathbb{B}=\mathfrak{Cat}_{\infty}$, the $\infty$-bicategory of $\infty$-categories and $\mathcal{D}^{\flat}$ is minimally marked, we recover the definition of lax colimit of Gepner-Haugseng-Nikolaus. We show that a suitable $\infty$-localization of the associated coCartesian fibration $\operatorname{Un}_{\mathcal{D}}(F)$ computes $\operatorname{colim}^{\dagger} F$. Our main theorem is a characterization of those functors of marked $\infty$-categories $f:\mathcal{C}^{\dagger} \to \mathcal{D}^{\dagger}$ which are marked cofinal. More precisely, we provide sufficient and necessary criteria for the restriction of diagrams along $f$ to preserve marked colimits.
Response of rumen bacterial diversity and fermentation parameters in beef cattle to diets containing supplemental daidzein

Abstract An experiment was conducted to determine the effects of soy isoflavone daidzein on ruminal fermentation, microbial protein synthesis and rumen bacterial community in beef cattle. Six rumen-cannulated adult beef cattle were assigned to three blocks according to similar body weight in a randomised block design, with two cattle each block. Each block randomly received one of the two dietary treatments: control (basal concentrate) and daidzein (supplementation with 500 mg daidzein/kg basal concentrate). High-throughput sequencing data showed that supplemental daidzein increased the relative abundance of bacteria belonging to phylum Bacteroidetes (p = .050), but reduced and tended to reduce the relative abundance of phylum Spirochaetae (p = .030) and Firmicutes (p = .080). At the genus level, the relative abundance of Prevotella (p = .036), RC9_gut_group (p = .019), Succinivibrio (p = .093) and Ruminobacter (p = .085) were increased or tended to be increased by daidzein supplementation. However, supplemental daidzein reduced or tended to reduce some uncultured and unclassified bacteria genus belonging to Ruminococcaceae (p = .028), Prevotellaceae (p = .088) and Lachnospiraceae (p = .068). The ruminal pH (p = .023) and ammonia-N (p = .031) concentration were lower in the daidzein group than in the control group, but the daidzein group tended to have greater ruminal total VFA (p = .063) concentration. Supplemental daidzein increased urinary excretion of allantoin (p = .005) and total purine derivatives (p = .007). Current results suggest that supplemental daidzein can affect ruminal fermentation by changing ruminal microbial community, increase the production of VFA and enhance microbial protein synthesis in the rumen, showing the potential of daidzein for improving ruminal fermentation in beef cattle.
Fast computation of half-integral weight modular forms

For many studies, it is essential to have a large number of Fourier coefficients of modular forms at one's disposal, for example Sato-Tate like problems where one needs many Fourier coefficients to obtain significant statistical results. In this article, we exhibit bases for the space of modular forms of any half-integral weight and level 4, which have the property that many coefficients can be computed (relatively) quickly on a computer.
Multi-dimensional Style Transfer for Partially Annotated Data using Language Models as Discriminators

Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus. A common aspect among the existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under consideration. Availability of such dataset across a combination of styles is a limiting factor in extending state-of-the art style transfer setups to multiple style dimensions. While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other. In our work, we attempt to relax this restriction on requirement of jointly annotated data across multiple styles being inspected and make use of independently acquired data across different style dimensions without any additional annotations. We initialize an encoder-decoder setup with large transformer-based language models pre-trained on a generic corpus and enhance its re-writing capability to multiple styles by employing multiple language models as discriminators. Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Model identification and local linear convergence of coordinate descent

For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g., support identification for the Lasso) after a finite number of iterations, provided the objective function is regular enough. Results concerning coordinate descent are scarcer and model identification has only been shown for specific estimators, the support-vector machine for instance. In this work, we show that cyclic coordinate descent achieves model identification in finite time for a wide class of functions. In addition, we prove explicit local linear convergence rates for coordinate descent. Extensive experiments on various estimators and on real datasets demonstrate that these rates match well empirical results.
A mechanism for electric turbulence in cardiac tissue with optogenetic modification.

Interruptions in nonlinear wave propagation, commonly referred to as wave breaks, are typical of many complex excitable systems. In the heart they lead to fatal rhythm disorders, the so-called arrhythmias, which are one of the main causes of sudden death in the industrialized world. Progress in the treatment and therapy of cardiac arrhythmias requires a detailed understanding of the triggers and dynamics of these wave breaks. In particular, two very important questions are: 1) What determines the potential of a wave break to initiate re-entry? and 2) How do these breaks evolve such that the system is able to maintain spatiotemporally chaotic electrical activity? Here we approach these questions numerically using optogenetics in an in silico model of human atrial tissue that has undergone chronic atrial fibrillation (cAF) remodelling. In the lesser known sub-threshold illumination regime, we discover a new mechanism of wave break initiation in cardiac tissue that occurs for gentle slopes of the restitution characteristics. This mechanism involves "conditioning" or reshaping the wave profile from front to back, such that, removal of the external light source causes rapid recovery of cells at the waveback, leading to the creation of vulnerable windows for sustained re-entry in spatially extended systems.
In vitro acidification potential and fermentation pattern of cereal grains incubated with inoculum of animals given forage or concentrate-based diets

This work aimed to study the acidification and fermentation pattern of maize (M1, M2, M3), barley (B1, B2, B3) and sorghum (S1, S2, S3) varieties depending on the rumen environment (inoculum from forage or concentrate diets, FI or CI), in 10 h incubation series with a low buffered medium. With CI, gas volume from barley was similar to maize (P > 0.05) except at 4 and 6 h, when M2 and M1 were lower. Barley or maize varieties did not differ in gas production (P > 0.05). After 10 h, barley and maize produced on average 82.5 and 73.0 mL/g organic matter (OM), whereas S1, S2 and S3 rendered 68.4, 31.1 and 39.7 mL/g. With FI, differences between barley and maize were detected after 6 h (61.0, 35.3 and 14.1 mL/g OM at 10 h for barley, maize and sorghum). Among inocula, pH with CI was lower than with FI (P < 0.001). Incubation pH at 10 h was 5.51, 5.21 and 5.00 with CI, and 5.52, 5.85 and 5.91 with FI, for sorghum, maize and barley varieties. Gas production and total volatile fatty acid (VFA) concentration were higher (P < 0.001) with CI than FI (67.3 vs 36.8 mL/g and 31.0 vs 19.9 mmol/L at 10 and 8 h). Butyrate and valerate proportions were higher with CI (P < 0.001), whereas acetate and branched chain VFA were lower (P < 0.001). Fermentative activity against vitreous starch such as maize or sorghum was lower for forage than concentrate inoculum. Study of fermentation of starch-rich substrates in a low buffered medium gives a more realistic picture than conventionally buffered conditions.
STARFORGE: the effects of protostellar outflows on the IMF

The initial mass function (IMF) of stars is a key quantity affecting almost every field of astrophysics, yet it remains unclear what physical mechanisms determine it. We present the first runs of the STAR FORmation in Gaseous Environments project, using a new numerical framework to follow the formation of individual stars in giant molecular clouds (GMCs) using the gizmo code. Our suite includes runs with increasingly complex physics, starting with isothermal ideal magnetohydrodynamics (MHD) and then adding non-isothermal thermodynamics and protostellar outflows. We show that without protostellar outflows the resulting stellar masses are an order of magnitude too high, similar to the result in the base isothermal MHD run. Outflows disrupt the accretion flow around the protostar, allowing gas to fragment and additional stars to form, thereby lowering the mean stellar mass to a value similar to that observed. The effect of jets upon global cloud evolution is most pronounced for lower mass GMCs and dense clumps, so while jets can disrupt low-mass clouds, they are unable to regulate star formation in massive GMCs, as they would turn an order unity fraction of the mass into stars before unbinding the cloud. Jets are also unable to stop the runaway accretion of massive stars, which could ultimately lead to the formation of stars with masses ${\gt}500\, \mathrm{M}_{\rm \odot }$. Although we find that the mass scale set by jets is insensitive to most cloud parameters (i.e. surface density, virial parameter), it is strongly dependent on the momentum loading of the jets (which is poorly constrained by observations) as well as the temperature of the parent cloud, which predicts slightly larger IMF variations than observed. We conclude that protostellar jets play a vital role in setting the mass scale of stars, but additional physics are necessary to reproduce the observed IMF.
Filament Rotation in the California L1482 Cloud

We analyze the gas mass distribution, the gas kinematics, and the young stellar objects of the California Molecular Cloud L1482 filament. The mean Gaia DR2 YSO distance is pc. In terms of the gas, the line-mass (M/L) profiles are symmetric scale-free power laws consistent with cylindrical geometry. We calculate the gravitational potential and field profiles based on these. Our IRAM 30 m multi-tracer position–velocity diagrams highlight twisting and turning structures. We measure the C18O velocity profile perpendicular to the southern filament ridgeline. The profile is regular, confined (projected r ≲ 0.4 pc), antisymmetric, and, to first order, linear, with a break at r ∼ 0.25 pc. We use a simple solid-body rotation toy model to interpret it. We show that the centripetal force, compared to gravity, increases toward the break; when the ratio of forces approaches unity, the profile turns over, just before the implied filament breakup. The timescales of the inner (outer) gradients are ∼0.7 (6.0) Myr. The timescales and relative roles of gravity to rotation indicate that the structure is stable, long lived (∼a few times 6 Myr), and undergoing outside-in evolution. This filament has practically no star formation, a perpendicular Planck plane-of-the-sky magnetic field morphology, and 2D “zig-zag” morphology, which together with the rotation profile lead to the suggestion that the 3D shape is a “corkscrew” filament. These results, together with results in other regions, suggest evolution toward higher densities as rotating filaments shed angular momentum. Thus, magnetic fields may be an essential feature of high-mass (M ∼ 105 M ⊙) cloud filament evolution toward cluster formation.
Restoring Negative Information in Few-Shot Object Detection

Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of labeled data, it asks for the generalization of DNNs on new classes with few annotated samples. Recent advances in few-shot learning mainly focus on image classification while in this paper we focus on object detection. The initial explorations in few-shot object detection tend to simulate a classification scenario by using the positive proposals in images with respect to certain object class while discarding the negative proposals of that class. Negatives, especially hard negatives, however, are essential to the embedding space learning in few-shot object detection. In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives. We build our work on a recent few-shot pipeline RepMet with several new modules to encode negative information for both training and testing. Extensive experiments on ImageNet-LOC and PASCAL VOC show our method substantially improves the state-of-the-art few-shot object detection solutions. Our code is available at this https URL.
Accelerometer-Based Automated Counting of Ten Exercises without Exercise-Specific Training or Tuning

Measuring physical activity using wearable sensors is essential for quantifying adherence to exercise regiments in clinical research and motivating individuals to continue exercising. An important aspect of wearable activity tracking is counting particular movements. One limitation of many previous models is the need to design the counting for a specific exercise. However, during physical therapy, some movements are unique to the patient and also valuable to track. To address this, we create an automatic repetition counting system that is flexible enough to measure multiple distinct and repeating movements during physical therapy without being trained on the specific motion. Accelerometers, using smartphones, were attached to the body or held by participants to track repetitive motions during different exercises. 18 participants completed a series of 10 exercises for 30 seconds, including arm circles, bicep curls, bridges, sit-ups, elbow extensions, leg lifts, lunges, push-ups, squats, and upper trunk rotations. To count the repetitions of each exercise, we apply three analysis techniques: (a) threshold crossing, (b) threshold crossing with a low-pass filter, and (c) Fourier transform. The results demonstrate that arm circles and push-ups can be tracked well, while less periodic and irregular motions such as upper trunk rotations are more difficult. Overall, threshold crossing with low-pass filtering achieves the best performance among these methods. We conclude that the proposed automatic counting system is capable of tracking exercise repetition without prior training and development for that activity.
Planning with Submodular Objective Functions

We study planning with submodular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a submodular function. Our framework subsumes standard planning and submodular maximization with cardinality constraints as special cases, and thus many practical applications can be naturally formulated within our framework. Based on the notion of multilinear extension, we propose a novel and theoretically principled algorithmic framework for planning with submodular objective functions, which recovers classical algorithms when applied to the two special cases mentioned above. Empirically, our approach significantly outperforms baseline algorithms on synthetic environments and navigation tasks.