Miracle-dz/UniFAR-FTUs huggingface dataset:
Instabilities in multiplanet circumbinary systems

The majority of the discovered transiting circumbinary planets are located very near the innermost stable orbits permitted, raising questions about the origins of planets in such perturbed environments. Most favoured formation scenarios invoke formation at larger distances and subsequent migration to their current locations. Disc-driven planet migration in multiplanet systems is likely to trap planets in mean motion resonances and drive planets inwards into regions of larger dynamical perturbations from the binary. We demonstrate how planet–planet resonances can interact with the binary through secular forcing and mean-motion resonances, driving chaos in the system. We show how this chaos will shape the architecture of circumbinary systems, with specific applications to Kepler 47 and the Pluto–Charon system, limiting maximum possible stable eccentricities and indicating what resonances are likely to exist. We are also able to constrain the minimum migration rates of resonant circumbinary planets.
Estimating Information Flow in Deep Neural Networks

We study the estimation of the mutual information I(X;T`) between the input X to a deep neural network (DNN) and the output vector T` of its `th hidden layer (an “internal representation”). Focusing on feedforward networks with fixed weights and noisy internal representations, we develop a rigorous framework for accurate estimation of I(X;T`). By relating I(X;T`) to information transmission over additive white Gaussian noise channels, we reveal that compression, i.e. reduction in I(X;T`) over the course of training, is driven by progressive geometric clustering of the representations of samples from the same class. Experimental results verify this connection. Finally, we shift focus to purely deterministic DNNs, where I(X;T`) is provably vacuous, and show that nevertheless, these models also cluster inputs belonging to the same class. The binningbased approximation of I(X;T`) employed in past works to measure compression is identified as a measure of clustering, thus clarifying that these experiments were in fact tracking the same clustering phenomenon. Leveraging the clustering perspective, we provide new evidence that compression and generalization may not be causally related and discuss potential future research ideas.
Observing Isotopologue Bands in Terrestrial Exoplanet Atmospheres with the James Webb Space Telescope: Implications for Identifying Past Atmospheric and Ocean Loss

Terrestrial planets orbiting M dwarfs may soon be observed with the James Webb Space Telescope (JWST) to characterize their atmospheric composition and search for signs of habitability or life. These planets may undergo significant atmospheric and ocean loss due to the superluminous pre-main-sequence phase of their host stars, which may leave behind abiotically generated oxygen, a false positive for the detection of life. Determining if ocean loss has occurred will help assess potential habitability and whether or not any O2 detected is biogenic. In the solar system, differences in isotopic abundances have been used to infer the history of ocean loss and atmospheric escape (e.g., Venus, Mars). We find that isotopologue measurements using transit transmission spectra of terrestrial planets around late-type M dwarfs like TRAPPIST-1 may be possible with JWST, if the escape mechanisms and resulting isotopic fractionation were similar to Venus. We present analyses of post-ocean-loss O2- and CO2-dominated atmospheres containing a range of trace gas abundances. Isotopologue bands are likely detectable throughout the near-infrared (1–8 μm), especially 3–4 μm, although not in CO2-dominated atmospheres. For Venus-like D/H ratios 100 times that of Earth, TRAPPIST-1b transit signals of up to 79 ppm are possible by observing HDO. Similarly, 18O/16O ratios 100 times that of Earth produce signals at up to 94 ppm. Detection at signal-to-noise ratio = 5 may be attained on these bands with as few as four to 11 transits, with optimal use of JWST’s NIRSpec Prism. Consequently, H2O and CO2 isotopologues could be considered as indicators of past ocean loss and atmospheric escape for JWST observations of terrestrial planets around M dwarfs.
Dark Energy Survey Year 1 Results: Wide-field mass maps via forward fitting in harmonic space

We present new wide-field weak lensing mass maps for the Year 1 Dark Energy Survey (DES) data, generated via a forward fitting approach. This method of producing maps does not impose any prior constraints on the mass distribution to be reconstructed. The technique is found to improve the map reconstruction on the edges of the field compared to the conventional Kaiser–Squires method, which applies a direct inversion on the data; our approach is in good agreement with the previous direct approach in the central regions of the footprint. The mapping technique is assessed and verified with tests on simulations; together with the Kaiser–Squires method, the technique is then applied to data from the DES Year 1 data and the differences between the two methods are compared. We also produce the first DES measurements of the convergence Minkowski functionals and compare them to those measured in simulations.
On Robustness of Principal Component Regression

Abstract Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued, that is, discrete and continuous, covariates is not understood and remains an important open challenge. As the main contribution of this work, we establish the robustness of PCR, without any change, in this respect and provide meaningful finite-sample analysis. To do so, we establish that PCR is equivalent to performing linear regression after preprocessing the covariate matrix via hard singular value thresholding (HSVT). As a result, in the context of counterfactual analysis using observational data, we show PCR is equivalent to the recently proposed robust variant of the synthetic control method, known as robust synthetic control (RSC). As an immediate consequence, we obtain finite-sample analysis of the RSC estimator that was previously absent. As an important contribution to the synthetic controls literature, we establish that an (approximate) linear synthetic control exists in the setting of a generalized factor model, or latent variable model; traditionally in the literature, the existence of a synthetic control needs to be assumed to exist as an axiom. We further discuss a surprising implication of the robustness property of PCR with respect to noise, that is, PCR can learn a good predictive model even if the covariates are tactfully transformed to preserve differential privacy. Finally, this work advances the state-of-the-art analysis for HSVT by establishing stronger guarantees with respect to the -norm rather than the Frobenius norm as is commonly done in the matrix estimation literature, which may be of interest in its own right.
You Can't See Me: Anonymizing Graphs Using the Szemerédi Regularity Lemma

Complex networks gathered from our online interactions provide a rich source of information that can be used to try to model and predict our behavior. While this has very tangible benefits that we have all grown accustomed to, there is a concrete privacy risk in sharing potentially sensitive data about ourselves and the people we interact with, especially when this data is publicly available online and unprotected from malicious attacks. k-anonymity is a technique aimed at reducing this risk by obfuscating the topological information of a graph that can be used to infer the nodes' identity. In this paper we propose a novel algorithm to enforce k-anonymity based on a well-known result in extremal graph theory, the Szemerédi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemerédi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave almost randomly. With this partition, we anonymize the graph by randomizing the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. We test the proposed approach on real-world networks extracted from Facebook. Our experimental results show that the proposed approach is able to anonymize a graph while retaining most of its structural information.
M-GWAP: An Online and Multimodal Game With A Purpose in WordPress for Mental States Annotation

M-GWAP is a multimodal game with a purpose of that leverages on the wisdom of crowds phenomenon for the annotation of multimedia data in terms of mental states. This game with a purpose is developed in WordPress to allow users implementing the game without programming skills. The game adopts motivational strategies for the player to remain engaged, such as a score system, text motivators while playing, a ranking system to foster competition and mechanics for identify building. The current version of the game was deployed after alpha and beta testing helped refining the game accordingly.
The EEG and MEG, Using a Model of Eccentric Spheres to Describe the Head

Two spherical models describing the head are used to compute EEG's and MEG's for current dipolar sources located in the visual cortex area. In one of the models, the spheres are concentric, in the other eccentric, fitting the compartment boundaries of the head best in the source area. In forward computations, a strong dependency on the volume conductor model is found for the EEG's, but not for the MEG's. The dependency of the EEG due to realistic values of the eccentricities of the spheres is in accordance with experiments described in the literature. All spheres are obtained by means of a least squares fitting algorithm in which the points on the surface boundaries, used to fit the sphere, may move within an interval determined by the measuring inaccuracy. This strategy used in the fitting algorithm is shown to give considerably better results than the one normally used.
Deep Learning for Stress Field Prediction Using Convolutional Neural Networks

The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies. As one step toward accelerating structural analysis, this work explores a deep learning-based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNNs). Two different architectures are implemented that take as input the structure geometry, external loads, and displacement boundary conditions, and output the predicted von Mises stress field. The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multichannel input network called StressNet. Accuracy analysis shows that StressNet results in significantly lower prediction errors than SCSNet on three loss functions, with a mean relative error of 2.04% for testing. These results suggest that deep learning models may offer a promising alternative to classical methods in structural design and topology optimization. Code and dataset are available.2
Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework

Set-Based Multi-Task Priority is a recent framework to handle inverse kinematics for redundant structures. Both equality tasks, i.e., control objectives to be driven to a desired value, and set-bases tasks, i.e., control objectives to be satisfied with a set/range of values can be addressed in a rigorous manner within a priority framework. In addition, optimization tasks, driven by the gradient of a proper function, may be considered as well, usually as lower priority tasks. In this paper the proper design of the tasks, their priority and the use of a Set-Based Multi-Task Priority framework is proposed in order to handle several constraints simultaneously in real-time. It is shown that safety related tasks such as, e.g., joint limits or kinematic singularity, may be properly handled by consider them both at an higher priority as set-based task and at a lower within a proper optimization functional. Experimental results on a 7DOF Jaco$^{2}$ arm with and without the proposed approach show the effectiveness of the proposed method.
The Rock Cook Wrasse Centrolabrus exoletus Aims to Clean

Cleaning behaviour between teleost fish in the marine environment is known to be a classic example of mutualistic cooperation, in which cleaners and their so-called clients exchange benefits. These mutualisms occur globally. However, studies of cleaning interactions in temperate regions are scarce compared with studies in the tropics. Here we focused on the rock cook, Centrolabrus exoletus, a wrasse present in the North-East Atlantic, considered to be the main cleaner inhabiting the coast of Portugal, although little is known about its ecology and behaviour. We found that these cleaners attended clients in specific locations while others were roaming freely, leaving open the question of which strategy cleaners preferentially use. Interestingly, interactions were initiated more often by clients and terminated more often by cleaners, suggesting that the intake of parasites are the cleaner’s primary interest, which was confirmed by the analysis of their diet, mostly composed of gnathiidae parasites. Moreover, this honesty-based relationship between these cleaners and their clients, calls for a re-interpretation of the very meaning of client-jolts (abrupt movements in response to cleaner mouth touch) since interactions with client jolts lasted longer than interactions with no jolts. This study provides new and important evidence on the mutualistic relationship between C. exoletus and its clientele, thus contributing to a better understanding of the behavioural ecology of this cleaner fish system.
Enzymatic Activities of CYP3A4 Allelic Variants on Quinine 3-Hydroxylation In Vitro

Cytochrome P450 3A4 (CYP3A4) enzyme activity is known to show considerable ethnic heterogeneity and inter-individual differences, affecting the outcome of drug treatment. CYP3A4 genetic polymorphisms are believed to be one of the important causes, leading to inter-individual variability in drug metabolism. Quinine is an antipyretic drug with antimalarial properties that is metabolized primarily by CYP3A4. Quinine 3-hydroxylation has been proven as a biomarker reaction for evaluating CYP3A4 ability. Quinine has frequent adverse effects and there are distinct inter-individual differences in quinine sensitivity. The open reading frame for 30 CYP3A4 allelic variants were constructed from wild-type CYP3A4*1A by an overlap extension polymerase chain reaction. Recombinant CYP3A4 variants were expressed using baculovirus-insect cell expression system, and their catalytic activities towards quinine hydroxylation were determined and evaluated. Of the 30 CYP3A4 allelic variants, 23 variants exhibited significantly reduced intrinsic clearance towards quinine, 2 variants showed increased intrinsic clearance for quinine, 2 variants possessed no significant differences towards quinine, compared with CYP3A4*1A, and 3 variants had no detected expression and enzyme activity. Our assessment on the enzymatic activities of CYP3A4 variants towards quinine may contribute to laying an experimental foundation for further clinical studies so as to accelerate the process of determining the associations between genetic variations and clinical phenotypes.
Isgur-Wise function in Bc decays to charmonium with the Bethe-Salpeter method

The Isgur-Wise function vastly reduces the weak-decay form factors of hadrons containing one heavy quark. In this paper, we extract the Isgur-Wise functions from the instantaneous Bethe-Salpeter method, and give the numerical results for the $B_c$ decays to charmonium where the final states include $1S$, $1P$, $2S$ and $2P$. The overlapping integral of the wave functions for the initial and final states is the Isgur-Wise function, as the heavy quark effective theory does. In the case of accurate calculation, describing form factors need to introduce more relativistic corrections which are the overlapping integrals with the relative momentum between the quark and antiquark to Isgur-Wise function. The relativistic corrections to Isgur-Wise function provide greater contributions especially involving the excited state, and therefore are necessary to be adopted.
Pain and Use of Alcohol in Later Life

Objective: To determine whether (a) late-life pain predicts growth in older adults’ use of alcohol, and elevated risk of drinking problems; and (b) sociodemographic characteristics moderate these relationships. Method: Five times over an 8-year interval, N = 5,446 Health and Retirement Study (HRS) participants provided information about their pain and alcohol use. Two-part latent growth modeling and logistic regression were used to analyze these data. Results: Participants with more pain at baseline had lower initial levels and a faster rate of decline over the next 8 years in alcohol consumption, but they also were at elevated risk of having drinking problems. Income and African American background interacted with pain to predict 8-year change in alcohol consumption and presence of drinking problems. Discussion: Late-life pain does not predict growth in older adults’ alcohol consumption, but is nonetheless linked to elevated risk of drinking problems, especially among African Americans.
Multiband Atmospheric Correction Algorithm for Ocean Color Retrievals

NASA’s current Atmospheric Correction (AC) algorithm for ocean color utilizes two bands and their ratio in the Near Infrared (NIR) to estimate aerosol reflectance and aerosol type. The algorithm then extrapolates the spectral dependence of aerosol reflectance to the visible wavelengths based on modeled spectral dependence of the identified aerosol type. Future advanced ocean color sensors, such as the Ocean Color Instrument (OCI) that will be carried on the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) satellite, will be capable of measuring the hyperspectral radiance from 340 to 890 nm at 5-nm spectral resolution and at 7 discrete Short-wave Infrared (SWIR) channels: 940, 1038, 1250, 1378, 1615, 2130, and 2260 nm. To optimally employ this unprecedented instrument capability, we propose an improved AC algorithm that utilizes all atmospheric-window channels in the NIR to SWIR spectral range to reduce the uncertainty in the AC process. A theoretical uncertainty analysis of this, namely Multi-Band AC (MBAC), indicates that the algorithm can reduce the uncertainty in remote sensing reflectance (Rrs) retrievals of the ocean caused by sensor random noise. Furthermore, in optically complex waters, where the NIR signal is affected by contributions from highly-reflective turbid waters, the MBAC algorithm can be adaptively weighted to the strongly-absorbing SWIR channels to enable improved ocean color retrievals in coastal waters. We provide here a description of the algorithm and demonstrate the improved performance in ocean color retrievals, relative to the current NASA standard AC algorithm, through comparison with field measurements and assessment of propagated uncertainties in applying the MBAC algorithm to MODIS and simulated PACE OCI data.
Planar Excitation of Dielectric Waveguide Antenna for Broadband and High-Gain Application

In this letter, a linearly tapered dielectric waveguide antenna with planar substrate integrated waveguide (SIW) feeding is presented for Ku-band (12–18 GHz) application. To make the feeding network planar, the SIW and central part of antenna are fabricated on a single substrate. Two metal strips are mounted on each top and bottom side of the central part of the antenna element for matching purpose. The antenna is designed and optimized using Ansoft HFSS. The optimized prototype is fabricated and measured. An endfire radiation pattern is achieved with low sidelobe (around 12 dB) and cross-polarization (around 20 dB) level in both E- and H-planes. The measured impedance bandwidth and maximum gain are 33.3% (11.5–16.1 GHz) and 12.4 dBi, respectively.
High-performance ternary alkali nitrides for renewable energy applications

Rapid decline in fossil fuel energy necessitates the immediate need for renewable energy resources. Here, we report a previously unexplored class of nitrides AMN$_2$ keeping renewable energy applications in mind. Using a detailed structure and stability analysis using first principles simulation, we discovered twelve such compounds (few of which are already synthesized before), which are chemically, mechanically and dynamically stable. These twelve compounds were then evaluated for their suitability for three renewable energy applications, (i) photovoltaics, (ii) water splitting, and (iii) thermoelectrics. Careful analysis of electronic structure reveals high optical transition strength resulting in sharp rise in absorption. This in turn yields high short circuit current and hence excellent solar efficiency for few compounds namely CsVN$_2$ and RbVN$_2$. Along with excellent absorption quality, some compounds show favorable band edge positions compared to water redox levels and hence are promising as photoelectrodes in photo(electro)chemical water splitting devices. Mixture of flat and dispersive bands in the band structure yields both high Seebeck and electrical conductivity, thus excellent power factor for seven compounds. Simulated lattice thermal conductivity shows moderate to ultralow values and thus the possibility of achieving high thermoelectric figure of merit (ZT), even at lower temperatures. From the experimental perspective, we discuss the possible challenges that may arise while utilizing these compounds for the desired applications, and suggest possible pathways to overcome them. We believe such theoretical prediction of promising materials are extremely useful for new materials discovery and anticipate rapid response from the experimental community.
Global Momentum Compression for Sparse Communication in Distributed SGD

With the rapid growth of data, distributed stochastic gradient descent~(DSGD) has been widely used for solving large-scale machine learning problems. Due to the latency and limited bandwidth of network, communication has become the bottleneck of DSGD when we need to train large scale models, like deep neural networks. Communication compression with sparsified gradient, abbreviated as \emph{sparse communication}, has been widely used for reducing communication cost in DSGD. Recently, there has appeared one method, called deep gradient compression~(DGC), to combine memory gradient and momentum SGD for sparse communication. DGC has achieved promising performance in practise. However, the theory about the convergence of DGC is lack. In this paper, we propose a novel method, called \emph{\underline{g}}lobal \emph{\underline{m}}omentum \emph{\underline{c}}ompression~(GMC), for sparse communication in DSGD. GMC also combines memory gradient and momentum SGD. But different from DGC which adopts local momentum, GMC adopts global momentum. We theoretically prove the convergence rate of GMC for both convex and non-convex problems. To the best of our knowledge, this is the first work that proves the convergence of distributed momentum SGD~(DMSGD) with sparse communication and memory gradient. Empirical results show that, compared with the DMSGD counterpart without sparse communication, GMC can reduce the communication cost by approximately 100 fold without loss of generalization accuracy. GMC can also achieve comparable~(sometimes better) performance compared with DGC, with extra theoretical guarantee.