Miracle-dz/UniFAR-FTUs huggingface dataset:
WASP-127b: a misaligned planet with a partly cloudy atmosphere and tenuous sodium signature seen by ESPRESSO

Context. The study of exoplanet atmospheres is essential for understanding the formation, evolution, and composition of exoplanets. The transmission spectroscopy technique is playing a significant role in this domain. In particular, the combination of state-of-the-art spectrographs at low- and high-spectral resolution is key to our understanding of atmospheric structure and composition. Aims. We observed two transits of the close-in sub-Saturn-mass planet, WASP-127b, with ESPRESSO in the frame of the Guaranteed Time Observations Consortium. We aim to use these transit observations to study the system architecture and the exoplanet atmosphere simultaneously. Methods. We used the Reloaded Rossiter-McLaughlin technique to measure the projected obliquity λ and the projected rotational velocity veq ⋅sin(i*). We extracted the high-resolution transmission spectrum of the planet to study atomic lines. We also proposed a new cross-correlation framework to search for molecular species and we applied it to water vapor. Results. The planet is orbiting its slowly rotating host star (veq ⋅sin(i*) = 0.53−0.05+0.07 km s−1) on a retrograde misaligned orbit (λ = −128.41−5.46+5.60 °). We detected the sodium line core at the 9-σ confidence level with an excess absorption of 0.34 ± 0.04%, a blueshift of 2.74 ± 0.79 km s−1, and a full width at half maximum of 15.18 ± 1.75 km s−1. However, we did not detect the presence of other atomic species but set upper limits of only a few scale heights. Finally, we put a 3-σ upper limit on the average depth of the 1600 strongest water lines at equilibrium temperature in the visible band of 38 ppm. This constrains the cloud-deck pressure between 0.3 and 0.5 mbar by combining our data with low-resolution data in the near-infrared and models computed for this planet. Conclusions. WASP-127b, with an age of about 10 Gyr, is an unexpected exoplanet by its orbital architecture but also by the small extension of its sodium atmosphere (~7 scale heights). ESPRESSO allows us to take a step forward in the detection of weak signals, thus bringing strong constraints on the presence of clouds in exoplanet atmospheres. The framework proposed in this work can be applied to search for molecular species and study cloud-decks in other exoplanets.
May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance

We investigate using Named Entity Recognition on a new type of user-generated text: a call center conversation. These conversations combine problems from spontaneous speech with problems novel to conversational Automated Speech Recognition, including incorrect recognition, alongside other common problems from noisy user-generated text. Using our own corpus with new annotations, training custom contextual string embeddings, and applying a BiLSTM-CRF, we match state-of- the-art results on our novel task.
Naive Analytics Equilibrium ( Draft )

We study interactions with uncertainty about demand sensitivity. In our solution concept (1) firms choose seemingly-optimal strategies given the level of sophistication of their data analytics, and (2) the levels of sophistication form best responses to one another. Under the ensuing equilibrium firms underestimate price elasticities and overestimate advertising effectiveness, as observed empirically. The misestimates cause firms to set prices too high and to over-advertise. In games with strategic complements (substitutes), profits Pareto dominate (are dominated by) those of the Nash equilibrium. Applying the model to team production games explains the prevalence of overconfidence among entrepreneurs and salespeople.
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions

Recently, invariant risk minimization (IRM) (Arjovsky et al.) was proposed as a promising solution to address out-of-distribution (OOD) generalization. In Ahuja et al., it was shown that solving for the Nash equilibria of a new class of "ensemble-games" is equivalent to solving IRM. In this work, we extend the framework in Ahuja et al. for linear regressions by projecting the ensemble-game on an $\ell_{\infty}$ ball. We show that such projections help achieve non-trivial OOD guarantees despite not achieving perfect invariance. For linear models with confounders, we prove that Nash equilibria of these games are closer to the ideal OOD solutions than the standard empirical risk minimization (ERM) and we also provide learning algorithms that provably converge to these Nash Equilibria. Empirical comparisons of the proposed approach with the state-of-the-art show consistent gains in achieving OOD solutions in several settings involving anti-causal variables and confounders.
Galvin's Question on non-$\sigma$-Well Ordered Linear Orders

Assume $\mathcal{C}$ is the class of all linear orders $L$ such that $L$ is not a countable union of well ordered sets, and every uncountable subset of $L$ contains a copy of $\omega_1$. We show it is consistent that $\mathcal{C}$ has minimal elements. This answers an old question due to Galvin.
$d_p$ convergence and $\epsilon$-regularity theorems for entropy and scalar curvature lower bounds.

Consider a sequence of Riemannian manifolds $(M^n_i,g_i)$ with scalar curvatures and entropies bounded below by small constants $R_i,\mu_i \geq-\epsilon_i$. The goal of this paper is to understand notions of convergence and the structure of limits for such spaces. Even in the seemingly rigid case $\epsilon_i\to 0$, we construct examples showing that such a sequence may converge wildly in the Gromov-Hausdorff or Intrinsic Flat sense. On the other hand, we will see that these classical notions of convergence are the incorrect ones to consider. Indeed, even a metric space is the wrong underlying category to be working on. Instead, we introduce $d_p$ convergence, a weaker notion of convergence that is valid for a class of rectifiable Riemannian spaces. These rectifiable spaces have well-behaved topology, measure theory, and analysis, though potentially there will be no reasonably associated distance function. Under the $d_p$ notion of closeness, a space with almost nonnegative scalar curvature and small entropy bounds must in fact be close to Euclidean space; this will constitute our $\epsilon$-regularity theorem. More generally, we have a compactness theorem saying that sequences of Riemannian manifolds $(M^n_i,g_i)$ with small lower scalar curvature and entropy bounds $R_i,\mu_i \geq -\epsilon$ must $d_p$ converge to such a rectifiable Riemannian space $X$. Comparing to the first paragraph, the distance functions of $M_i$ may be degenerating, even though in a well-defined sense the analysis cannot be. Applications for manifolds with small scalar and entropy lower bounds include an $L^\infty$-Sobolev embedding and apriori $L^p$ scalar curvature bounds for $p<1$.
Black hole interior in unitary gauge construction

A quantum system with a black hole accommodates two widely different, though physically equivalent, descriptions. In one description, based on global spacetime of general relativity, the existence of the interior region is manifest, while understanding unitarity requires nonperturbative quantum gravity effects such as Euclidean wormholes. The other description adopts a manifestly unitary, or holographic, description, in which the interior emerges effectively as a collective phenomenon of fundamental degrees of freedom. In this paper we study the latter approach, which we refer to as the unitary gauge construction. In this picture, the formation of a black hole is signaled by the emergence of a surface (stretched horizon) possessing special dynamical properties: quantum chaos, fast scrambling, and low energy universality. These properties allow for constructing interior operators, as we do explicitly, without relying on details of microscopic physics. A key role is played by certain coarse modes in the zone region (hard modes), which determine the degrees of freedom relevant for the emergence of the interior. We study how the interior operators can or cannot be extended in the space of microstates and analyze irreducible errors associated with such extension. This reveals an intrinsic ambiguity of semiclassical theory formulated with a finite number of degrees of freedom. We provide an explicit prescription of calculating interior correlators in the effective theory, which describes only a finite region of spacetime. We study the issue of state dependence of interior operators in detail and discuss a connection of the resulting picture with the quantum error correction interpretation of holography.
How do Offline Measures for Exploration in Reinforcement Learning behave?

Sufficient exploration is paramount for the success of a reinforcement learning agent. Yet, exploration is rarely assessed in an algorithm-independent way. We compare the behavior of three data-based, offline exploration metrics described in the literature on intuitive simple distributions and highlight problems to be aware of when using them. We propose a fourth metric,uniform relative entropy, and implement it using either a k-nearest-neighbor or a nearest-neighbor-ratio estimator, highlighting that the implementation choices have a profound impact on these measures.
Accdoa: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization And Detection

Neural-network (NN)-based methods show high performance in sound event localization and detection (SELD). Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target. The two-branch representation with a single network has to decide how to balance the two objectives during optimization. Using two networks dedicated to each task increases system complexity and network size. To address these problems, we propose an activity-coupled Cartesian DOA (ACCDOA) representation, which assigns a sound event activity to the length of a corresponding Cartesian DOA vector. The ACCDOA representation enables us to solve a SELD task with a single target and has two advantages: avoiding the necessity of balancing the objectives and model size increase. In experimental evaluations with the DCASE 2020 Task 3 dataset, the ACCDOA representation outperformed the two-branch representation in SELD metrics with a smaller network size. The ACCDOA-based SELD system also performed better than state-of-the-art SELD systems in terms of localization and location-dependent detection.
Uncovering Latent Biases in Text: Method and Application to Peer Review

Quantifying systematic disparities in numerical quantities such as employment rates and wages between population subgroups provides compelling evidence for the existence of societal biases. However, biases in the text written for members of different subgroups (such as in recommendation letters for male and non-male candidates), though widely reported anecdotally, remain challenging to quantify. In this work, we introduce a novel framework to quantify bias in text caused by the visibility of subgroup membership indicators. We develop a nonparametric estimation and inference procedure to estimate this bias. We then formalize an identification strategy to causally link the estimated bias to the visibility of subgroup membership indicators, provided observations from time periods both before and after an identity-hiding policy change. We identify an application wherein “ground truth” bias can be inferred to evaluate our framework, instead of relying on synthetic or secondary data. Specifically, we apply our framework to quantify biases in the text of peer reviews from a reputed machine-learning conference before and after the conference adopted a double-blind reviewing policy. We show evidence of biases in the review ratings that serves as “ground truth”, and show that our proposed framework accurately detects the presence (and absence) of these biases from the review text without having access to the review ratings.
Teaching a GAN What Not to Learn

Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution. Variants such as conditional GANs, auxiliary-classifier GANs (ACGANs) project GANs on to supervised and semi-supervised learning frameworks by providing labelled data and using multi-class discriminators. In this paper, we approach the supervised GAN problem from a different perspective, one that is motivated by the philosophy of the famous Persian poet Rumi who said, "The art of knowing is knowing what to ignore. " In the GAN framework, we not only provide the GAN positive data that it must learn to model, but also present it with so-called negative samples that it must learn to avoid - we call this "The Rumi Framework. " This formulation allows the discriminator to represent the underlying target distribution better by learning to penalize generated samples that are undesirable - we show that this capability accelerates the learning process of the generator. We present a reformulation of the standard GAN (SGAN) and least-squares GAN (LSGAN) within the Rumi setting. The advantage of the reformulation is demonstrated by means of experiments conducted on MNIST, Fashion MNIST, CelebA, and CIFAR-10 datasets. Finally, we consider an application of the proposed formulation to address the important problem of learning an under-represented class in an unbalanced dataset. The Rumi approach results in substantially lower FID scores than the standard GAN frameworks while possessing better generalization capability.
Machine Learning Regularized Solution of the Lippmann-Schwinger Equation

Solution of the discretized Lippmann-Schwinger equation in the spatial frequency domain involves the inversion of a linear operator specified by the scattering potential. To regularize this inevitably ill-conditioned problem, we propose a machine learning approach: a recurrent neural network with long short-term memory (LSTM) and with the null space projection of the Lippmann-Schwinger kernel on the recurrence path. The learning method is trained using examples of typical scattering potentials and their corresponding scattered fields. We test the proposed method in two cases: electromagnetic scattering by dielectric objects, and electron scattering by multiple screened Coulomb potentials. In both cases the solutions to test examples, disjoint from the training set, were obtained with fewer iterations and were accurate compared to linear solvers. We also observed surprising generalization ability: in the electromagnetic case, an LSTM trained with random arrangements of dielectric spheres was able to obtain the correct solutions for general topologically similar objects, such as polygons. This suggests that the LSTM successfully incorporates the physics of scattering into the inversion algorithm.
Isometric embeddings in trees and their use in the diameter problem

We prove that given a discrete space with $n$ points which is either embedded in a system of $k$ trees, or the Cartesian product of $k$ trees, we can compute all eccentricities in ${\cal O}(2^{{\cal O}(k\log{k})}(N+n)^{1+o(1)})$ time, where $N$ is the cumulative total order over all these $k$ trees. This is near optimal under the Strong Exponential-Time Hypothesis, even in the very special case of an $n$-vertex graph embedded in a system of $\omega(\log{n})$ spanning trees. However, given such an embedding in the strong product of $k$ trees, there is a much faster ${\cal O}(N + kn)$-time algorithm for this problem. All our positive results can be turned into approximation algorithms for the graphs and finite spaces with a quasi isometric embedding in trees, if such embedding is given as input, where the approximation factor (resp., the approximation constant) depends on the distortion of the embedding (resp., of its stretch). The existence of embeddings in the Cartesian product of finitely many trees has been thoroughly investigated for cube-free median graphs. We give the first-known quasi linear-time algorithm for computing the diameter within this graph class. It does not require an embedding in a product of trees to be given as part of the input. On our way, being given an $n$-node tree $T$, we propose a data structure with ${\cal O}(n\log{n})$ pre-processing time in order to compute in ${\cal O}(k\log^2{n})$ time the eccentricity of any subset of $k$ nodes. We combine the latter technical contribution, of independent interest, with a recent distance-labeling scheme that was designed for cube-free median graphs.
Oral Administration of Hot-water Extract of Tropical Brown Seaweed, Sargassum cristaefolium, to Enhance Immune Response, Stress Tolerance, and Resistance of White Shrimp, Litopenaeus vannamei, to Vibrio parahaemolyticus : ALGINATE SARGASSUM CRISTAEFOLIUM SHRIMP DISEASE

The efficacy of hot‐water extract of tropical brown seaweed, Sargassum cristaefolium (SCE), supplemented in diets on immune response, stress tolerance, and disease resistance of Litopenaeus vannamei to Vibrio parahaemolyticus was evaluated. Shrimp were fed diets containing graded levels of SCE (0, 250, 500, 750, and 1000 mg/kg). The results showed that shrimp fed all diets containing SCE had significantly higher (P < 0.05) immune response in total hemocyte count (THC), differential hemocyte count (granular and hyaline cells), and phagocytic activity than those of shrimp fed the control diet. Similarly, in low dissolved oxygen stress tolerance test and the challenge test with V. parahaemolyticus, survival rates of shrimp fed all diets containing SCE were significantly higher (P < 0.05) (83–93% in stress test and 27–47% in challenge test) than those of shrimp fed the control diet (77 and 3.3%, respectively). These results suggest that oral administration of SCE at 500 and 750 mg/kg can be effectively used to enhance immune response, stress tolerance, and resistance of white shrimp, L. vannamei, against V. parahaemolyticus infection. These findings also confirm that using dietary SCE as immunostimulant is effective at increasing the nonspecific immune system in penaeid shrimp, L. vannamei.
Raw Audio for Depression Detection Can Be More Robust Against Gender Imbalance than Mel-Spectrogram Features

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in terms of the detection of depression. Datasets such as the Distress Analysis Interview Corpus - Wizard of Oz have been created to aid research in this area. However, on top of the challenges inherent in accurately detecting depression, biases in datasets may result in skewed classification performance. In this paper we examine gender bias in the DAIC-WOZ dataset using audio-based deep neural networks. We show that gender biases in DAIC-WOZ can lead to an overreporting of performance, which has been overlooked in the past due to the same gender biases being present in the test set. By using raw audio and different concepts from Fair Machine Learning, such as data re-distribution, we can mitigate against the harmful effects of bias.
Ontogenetic changes in spot configuration (numbers, circularity, size and asymmetry) and lateral line in Neurergus microspilotus (Caudata: Salamandridae)

Coloration in three of four species of the genus Neurergus including N. microspilotus is characterized by the presence of yellow spots on a dark skin, but there is no available information about changes in spot configuration, speed of development and degree of association between melanophore-free region and the lateral line. In this study, spot numbers, spot circularity, spot size and spot asymmetry were studied during larval to adult growth in N. microspilotus during July 2012 to June 2015. The mean numbers of spots increased during the late larval stage till postmetamorphic period from 13.33 ± 3.77 to 22.53 ± 4.09 and reached 42.62 ± 4.06 in adults. At the same time, the extent of spots gradually decreased in size from 5.80 ± 1.00 to 3.57 ± 0.97 mm2 and reached 3.55 ± 1.42 mm2 in adults, but the spot circularity increased from 0.48 ± 0.23 to 0.78 ± 0.49 and reached 0.80 ± 0.15 in adults. In adults, the numbers, circularity, size and asymmetry of spots remain stable with little but non-significant changes during the study period. Histological study shows that formation of a melanophore-free region correlates with the development of the lateral line receptors. This study demonstrates that the effects of lateral line on chromatophores persist through middle larval stages but diminish as metamorphosis completes.
The Histochemical Localization of β-Glucuronidase in the Digestive Gland of the Roman Snail (Helix pomatia)

A modification of the histochemical technique for the localization of β-glucuronidase originally suggested by Friedenwald and Becker (1948) has been applied to the digestive gland of the gastropod Helix pomatia . In the original technique the ferric 8-hydroxyquinoline formed by the enzymic hydrolysis of quinolyl-8-glucuronide, in a saturated solutionof ferric 8-hydroxyquinoline, was converted to Prussian blue. The Prussian blue conversion is omitted in the technique described in this paper as it appears to introduce errors in localization. The ferric 8-hydroxyquinoline crystals are sufficiently characteristic to be used as the end-point of the technique. The results obtained suggest that β-glucurcnidase is confined to the digestive cells in the digestive gland of the snail, and is associated with secretory granules in them.