Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiating modern and prebiotic Earth scenarios for TRAPPIST-1e: high-resolution transmission spectra and predictions for JWST

The TRAPPIST-1 system is a priority target for terrestrial exoplanet characterization. TRAPPIST-1e, residing in the habitable zone, will be observed during the JWST GTO Program. Here, we assess the prospects of differentiating between prebiotic and modern Earth scenarios for TRAPPIST-1e via transmission spectroscopy. Using updated TRAPPIST-1 stellar models from the Mega-MUSCLES survey, we compute self-consistent model atmospheres for a 1 bar prebiotic Earth scenario and two modern Earth scenarios (1 and 0.5 bar eroded atmosphere). Our modern and prebiotic high-resolution transmission spectra (0.4− 20μm at R ∼100,000) are made available online. We conduct a Bayesian atmospheric retrieval analysis to ascertain the molecular detectability, abundance measurements, and temperature constraints achievable for both scenarios with JWST. We demonstrate that JWST can differentiate between our prebiotic and modern Earth scenarios within 20 NIRSpec Prism transits via CH4 abundance measurements. However, JWST will struggle to detect O3 for our modern Earth scenario to > 2σ confidence within the nominal mission lifetime (∼ 80 transits over 5 years). The agnostic combination of N2O and/or O3 offers better prospects, with a predicted detection significance of 2. 7σ with 100 Prism transits. We show that combining MIRI LRS transits with Prism data provides little improvement to atmospheric constraints compared to observing additional Prism transits. Though biosignatures will be challenging to detect for TRAPPIST-1e with JWST, the abundances for several important molecules – CO2, CH4, and H2O – can be measured to a precision of . 0.7 dex (a factor of 5) within a 20 Prism transit JWST program.
Gaussian boson sampling with partial distinguishability

Junheng Shi 2 and Tim Byrnes 3, 1, 4, 5, ∗ State Key Laboratory of Precision Spectroscopy, School of Physical and Material Sciences, East China Normal University, Shanghai 200062, China New York University Shanghai, 1555 Century Ave, Pudong, Shanghai 200122, China NYU-ECNU Institute of Physics at NYU Shanghai, 3663 Zhongshan Road North, Shanghai 200062, China National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan Department of Physics, New York University, New York, New York 10003, USA (Dated: May 20,2021)
Using Machine Learning Techniques to Identify Key Risk Factors for Diabetes and Undiagnosed Diabetes

This paper reviews a wide selection of machine learning models built to predict both the presence of diabetes and the presence of undiagnosed diabetes using eight years of National Health and Nutrition Examination Survey (NHANES) data. Models are tuned and compared via their Brier Scores. The most relevant variables of the best performing models are then compared. A Support Vector Machine with a linear kernel performed best for predicting diabetes, returning a Brier score of 0.0654 and an AUROC of 0.9235 on the test set. An elastic net regression performed best for predicting undiagnosed diabetes with a Brier score of 0.0294 and an AUROC of 0.9439 on the test set. Similar features appear prominently in the models for both sets of models. Blood osmolality, family history, the prevalance of various compounds, and hypertension are key indicators for all diabetes risk. For undiagnosed diabetes in particular, there are ethnicity or genetic components which arise as strong correlates as well.
Facile assembly of nanosheet array-like CuMgAl-layered double hydroxide/rGO nanohybrids for highly efficient reduction of 4-nitrophenol

A series of novel hierarchical nanosheet array-like hybrids xCu-LDH/rGO (xCu-LDH: CuxMg3−xAl-layered double hydroxide (x = 0.5, 1.0, and 1.5), rGO: reduced graphene oxide) were assembled via a facile and green aqueous-phase coprecipitation method. Systematic characterization suggests that the hybrids were constructed by hexagonal LDH nanoplates (∼70 nm × 4.5 nm) interdigitated vertical to the surface of single-layer rGO. All the xCu-LDH/rGO hybrids exhibit a remarkable higher activity for catalytic reduction of 4-nitrophenol (4-NP) compared with pure Cu-LDH, commercial Pt/C and other recently reported Cu-related catalysts. These findings were carefully explained by deep study of the catalyst treated with NaBH4, and the xCu-LDH/rGO hybrids as a potential Cu2O reservoir were revealed for the first time. Typically for 1.0Cu-LDH/rGO, a part of Cu2+ ions on LDH layers were instantaneously in situ reduced to well-dispersed ultrafine Cu2O nanoparticles (∼6.8 nm) by NaBH4 in an aqueous reduction system and thus formed relatively strong interaction between Cu2O and LDH/rGO greatly favoring enhanced activity for the reduction of 4-NP, other nitroarenes and organic dyes at room temperature. The excellent activity of the xCu-LDH/rGO hybrids can be attributed to the possible Cu2O–Cu-LDH–rGO three-phase synergistic effect, increased adsorption capacity for reactants via π–π stacking, and unique nanoarray-like morphology of the hybrids. Moreover, the 1.0Cu-LDH/rGO can be cycled for 20 runs without significant loss of activity, giving the hybrid long-term stability.
Speaker disentanglement in video-to-speech conversion

The task of video-to-speech aims to translate silent video of lip movement to its corresponding audio signal. Previous approaches to this task are generally limited to the case of a single speaker, but a method that accounts for multiple speakers is desirable as it allows to (i) leverage datasets with multiple speakers or few samples per speaker; and (ii) control speaker identity at inference time. In this paper, we introduce a new video-to-speech architecture and explore ways of extending it to the multi-speaker scenario: we augment the network with an additional speaker-related input, through which we feed either a discrete identity or a speaker embedding. Interestingly, we observe that the visual encoder of the network is capable of learning the speaker identity from the lip region of the face alone. To better disentangle the two inputs-linguistic content and speaker identity-we add adversarial losses that dispel the identity from the video embeddings. To the best of our knowledge, the proposed method is the first to provide important functionalities such as (i) control of the target voice and (ii) speech synthesis for unseen identities over the state-of-the-art, while still maintaining the intelligibility of the spoken output.
The Challenge of Variable Effort Crowdsourcing and How Visible Gold Can Help

We consider a class of variable effort human annotation tasks in which the number of labels required per item can greatly vary (e.g., finding all faces in an image, named entities in a text, bird calls in an audio recording, etc.). In such tasks, some items require far more effort than others to annotate. Furthermore, the per-item annotation effort is not known until after each item is annotated since determining the number of labels required is an implicit part of the annotation task itself. On an image bounding-box task with crowdsourced annotators, we show that annotator accuracy and recall consistently drop as effort increases. We hypothesize reasons for this drop and investigate a set of approaches to counteract it. Firstly, we benchmark on this task a set of general best-practice methods for quality crowdsourcing. Notably, only one of these methods actually improves quality: the use of visible gold questions that provide periodic feedback to workers on their accuracy as they work. Given these promising results, we then investigate and evaluate variants of the visible gold approach, yielding further improvement. Final results show a 7% improvement in bounding-box accuracy over the baseline. We discuss the generality of the visible gold approach and promising directions for future research.
A Case Study on Pros and Cons of Regular Expression Detection and Dependency Parsing for Negation Extraction from German Medical Documents. Technical Report

We describe our work on information extraction in medical documents written in German, especially detecting negations using an architecture based on the UIMA pipeline. Based on our previous work on software modules to cover medical concepts like diagnoses, examinations, etc. we employ a version of the NegEx regular expression algorithm with a large set of triggers as a baseline. We show how a significantly smaller trigger set is sufficient to achieve similar results, in order to reduce adaptation times to new text types. We elaborate on the question whether dependency parsing (based on the Stanford CoreNLP model) is a good alternative and describe the potentials and shortcomings of both approaches.
The centres of M83 and the Milky Way: opposite extremes of a common star formation cycle

In the centres of the Milky Way and M83, the global environmental properties thought to control star formation are very similar. However, M83’s nuclear star formation rate (SFR), as estimated by synchrotron and H α emission, is an order of magnitude higher than the Milky Way’s. To understand the origin of this difference we use ALMA observations of HCN (1 − 0) and HCO+ (1 − 0) to trace the dense gas at the size scale of individual molecular clouds (0.54 arcsec, 12 pc) in the inner ∼500 pc of M83, and compare this to gas clouds at similar resolution and galactocentric radius in the Milky Way. We find that both the overall gas distribution and the properties of individual clouds are very similar in the two galaxies, and that a common mechanism may be responsible for instigating star formation in both circumnuclear rings. Given the considerable similarity in gas properties, the most likely explanation for the order of magnitude difference in SFR is time variability, with the Central Molecular Zone (CMZ) currently being at a more quiescent phase of its star formation cycle. We show M83’s SFR must have been an order of magnitude higher 5–7 Myr ago. M83’s ‘starburst’ phase was highly localized, both spatially and temporally, greatly increasing the feedback efficiency and ability to drive galactic-scale outflows. This highly dynamic nature of star formation and feedback cycles in galaxy centres means (i) modelling and interpreting observations must avoid averaging over large spatial areas or time-scales, and (ii) understanding the multiscale processes controlling these cycles requires comparing snapshots of a statistical sample of galaxies in different evolutionary stages.
Polarized image of equatorial emission in the Kerr geometry

We develop a simple toy model for polarized images of synchrotron emission from an equatorial source around a Kerr black hole by using a semi-analytic solution of the null geodesic equation and conservation of the Penrose-Walker constant. Our model is an extension of Narayan et al. (2021), which presented results for a Schwarzschild black hole, including a fully analytic approximation. Our model includes an arbitrary observer inclination, black hole spin, local boost, and local magnetic field configuration. We study the geometric effects of black hole spin on photon parallel transport and isolate these effects from the complicated combination of relativistic, gravitational, and electromagnetic processes in the emission region. We find an analytic approximation, consistent with previous work, for the subleading geometric effect of spin on observed face-on polarization rotation in the direct image: $\Delta {\rm EVPA} \sim -2a/r_{\rm s}^2$, where $a$ is the black hole spin and $r_{\rm s}$ is the emission radius. We further show that spin introduces an order unity effect on face-on subimages: $\Delta {\rm EVPA} \sim \pm a/\sqrt{27}$. We also use our toy model to analyze polarization"loops"observed during flares of orbiting hotspots. Our model provides insight into polarimetric simulations and observations of black holes such as those made by the EHT and GRAVITY.
Spin and Accretion Rate Dependence of Black Hole X-Ray Spectra

We present a survey of how the spectral features of black hole X-ray binary systems depend on spin, accretion rate, viewing angle, and Fe abundance when predicted on the basis of first-principles physical calculations. The power-law component hardens with increasing spin. The thermal component strengthens with increasing accretion rate. The Compton bump is enhanced by higher accretion rate and lower spin. The Fe Kα equivalent width grows sublinearly with Fe abundance. Strikingly, the Kα profile is more sensitive to accretion rate than to spin because its radial surface brightness profile is relatively flat, and higher accretion rate extends the production region to smaller radii. The overall radiative efficiency is at least 30%–100% greater than as predicted by the Novikov–Thorne model.
Orbital Decay of Short-period Exoplanets via Tidal Resonance Locking

A large fraction of known exoplanets have short orbital periods where tidal excitation of gravity waves within the host star causes the planets’ orbits to decay. We study the effects of tidal resonance locking, in which the planet locks into resonance with a tidally excited stellar gravity mode. Because a star’s gravity mode frequencies typically increase as the star evolves, the planet’s orbital frequency increases in lockstep, potentially causing much faster orbital decay than predicted by other tidal theories. Due to nonlinear mode damping, resonance locking in Sun-like stars likely only operates for low-mass planets (M ≲ 0.1 M Jup), but in stars with convective cores it can likely operate for all planetary masses. The orbital decay timescale with resonance locking is typically comparable to the star’s main-sequence lifetime, corresponding to a wide range in effective stellar quality factor (103 ≲ Q′ ≲ 109), depending on the planet’s mass and orbital period. We make predictions for several individual systems and examine the orbital evolution resulting from both resonance locking and nonlinear wave dissipation. Our models demonstrate how short-period massive planets can be quickly destroyed by nonlinear mode damping, while short-period low-mass planets can survive, even though they undergo substantial inward tidal migration via resonance locking.
Phase collapse in reversed-phase liquid chromatography

This month's Column Watch examines the topic of phase collapse in reversed-phase liquid chromatography. Phase collapse is best described as a dewetting phenomenon. It can cause a total loss of retention and chromatographic problems such as peak tailing, nonreproducible retention times, and gradient regeneration delays. The authors offer suggestions for avoiding phase collapses and regenerating collapsed phases.
Data-Driven Unknown-Input Observers and State Estimation

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs. In this letter, we provide a novel data-driven UIO based on behavioral system theory and the result known as Fundamental Lemma proposed by Jan Willems and coworkers. We give necessary and sufficient conditions on the data collected from the system for the existence of a UIO providing asymptotically converging state estimates, and propose a purely data-driven algorithm for their computation. Even though we focus on UIOs, our results also apply to the standard case of completely known inputs. As an example, we apply the proposed method to distributed state estimation in DC microgrids.
Hidden scale invariance in Navier–Stokes intermittency

We expose a hidden scaling symmetry of the Navier–Stokes equations in the limit of vanishing viscosity, which stems from dynamical space–time rescaling around suitably defined Lagrangian scaling centres. At a dynamical level, the hidden symmetry projects solutions which differ up to Galilean invariance and global temporal scaling onto the same representative flow. At a statistical level, this projection repairs the scale invariance, which is broken by intermittency in the original formulation. Following previous work by the first author, we here postulate and substantiate with numerics that hidden symmetry statistically holds in the inertial interval of fully developed turbulence. We show that this symmetry accounts for the scale-invariance of a certain class of observables, in particular, the Kolmogorov multipliers. This article is part of the theme issue ‘Scaling the turbulence edifice (part 1)’.
A comprehensive comparative evaluation and analysis of Distributional Semantic Models

Distributional semantics has deeply changed in the last decades. First, predict models stole the thunder from traditional count ones, and more recently both of them were replaced in many NLP applications by contextualized vectors produced by Transformer neural language models. Although an extensive body of research has been devoted to Distributional Semantic Model (DSM) evaluation, we still lack a thorough comparison with respect to tested models, semantic tasks, and benchmark datasets. Moreover, previous work has mostly focused on task-driven evaluation, instead of exploring the differences between the way models represent the lexical semantic space. In this paper, we perform a comprehensive evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. First of all, we investigate the performance of embeddings in several semantic tasks, carrying out an in-depth statistical analysis to identify the major factors influencing the behavior of DSMs. The results show that i. ) the alleged superiority of predict based models is more apparent than real, and surely not ubiquitous and ii. ) static DSMs surpass contextualized representations in most out-of-context semantic tasks and datasets. Furthermore, we borrow from cognitive neuroscience the methodology of Representational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models. RSA reveals important differences related to the frequency and part-of-speech of lexical items.
Content-Augmented Feature Pyramid Network with Light Linear Transformers

: Recently, plenty of work has tried to introduce transformers into computer vision tasks, with good results. Unlike classic convolution networks, which extract features within a local receptive field, transformers can adaptively aggregate similar features from a global view using self-attention mechanism. For object detection, Feature Pyramid Network (FPN) proposes feature interaction across layers and proves its extremely importance. However, its interaction is still in a local manner, which leaves a lot of room for improvement. Since transformer was originally designed for NLP tasks, adapting processing subject directly from text to image will cause unaffordable computation and space overhead. In this paper, we utilize a linearized attention function to overcome above problems and build a novel architecture, named Content-Augmented Feature Pyramid Network (CA-FPN), which proposes a global content extraction module and deeply combines with FPN through light linear transformers. What’s more, light transformers can further make the application of multi-head attention mechanism easier. Most importantly, our CA-FPN can be readily plugged into existing FPN-based models. Extensive experiments on the challenging COCO object detection dataset demonstrated that our CA-FPN significantly outperforms competitive baselines without bells and whistles. Code will be made publicly available.
Limiting Spectral Distribution for Large Sample Covariance Matrices with Graph-Dependent Elements

We obtain the limiting spectral distribution for large sample covariance matrices associated with random vectors having graph-dependent entries under the assumption that the interdependence among the entries grows with the sample size n. Our results are tight. In particular, they give necessary and sufficient conditions for the Marchenko-Pastur theorem for sample covariance matrices with m-dependent orthonormal elements when m = o(n).
Logarithmic landscape and power-law escape rate of SGD

Stochastic gradient descent (SGD) undergoes complicated multiplicative noise for the mean-square loss. We use this property of the SGD noise to derive a stochastic diﬀerential equation (SDE) with simpler additive noise by performing a non-uniform transformation of the time variable. In the SDE, the gradient of the loss is replaced by that of the logarithmized loss. Consequently, we show that, near a local or global minimum, the stationary distribution P ss ( θ ) of the network parameters θ follows a power-law with respect to the loss function L ( θ ), i.e. P ss ( θ ) ∝ L ( θ ) − φ with the exponent φ speciﬁed by the mini-batch size, the learning rate, and the Hessian at the minimum. We obtain the escape rate formula from a local minimum, which is determined not by the loss barrier height ∆ L = L ( θ s ) − L ( θ ∗ ) between a minimum θ ∗ and a saddle θ s but by the logarithmized loss barrier height ∆ log L = log[ L ( θ s ) /L ( θ ∗ )]. Our escape-rate formula explains an empirical fact that SGD prefers ﬂat minima with low eﬀective dimensions.
Comparrative Assay of Citalopram in Different Media

Citalopram is one of a class of antidepressants known as selective serotonin reuptake inhibitors. Citalopram blocks the reuptake of serotonin at the serotonin reuptake pump of the neuronal membrane, enhancing the actions of serotonin on 5HT1A autoreceptors. SSRIs bind with significantly less affinity to acetylcholine, histamine and norepinephrine receptors than tricyclic antidepressant drugs Using UV spectrophotometer a simple, economical least time consuming and accurate method for comparison of different brand of citalopram, has been developed. The assay is based on the UV absorbance maxima at about wavelength of 244 nm using distilled water as solvent. Four different brand of citalopram was dissolved in buffer of pH 1, pH 4 and water and then various dilutions are prepared (200 ppm, 100 ppm, 50 ppm and 25 ppm). The absorbance of these drugs was measured at 244 nm against the solvent blank and the assay was calculated by using the absorbance of active. We obtain linear relationship of four brands of citalopram when diluted to form 200 ppm, 100 ppm, 50 ppm, 25 ppm and absorbance of all brand are maximum in pH 4 . Squared correlation coefficient value of all the brands of citalopram are well within the limit.
On the Optimization of Behavioral Logic Locking for High-Level Synthesis

—The globalization of the electronics supply chain requires effective methods to thwart reverse engineering and IP theft. Logic locking is a promising solution, but there are still several open concerns. First, even when applied at a higher level of abstraction, locking has signiﬁcant overhead without improving the security metric. Second, optimizing a security metric is application-dependent and designers must evaluate and compare alternative solutions. We propose a framework to optimize the use of behavioral locking during the high-level synthesis (HLS) of IP cores. Our method operates on chip’s speciﬁcation (before HLS) and it is compatible with all HLS tools, complementing industrial EDA ﬂows. The framework supports different meta-heuristics to explore the design space and to select points to lock automatically. Our method optimizes a given security metric better than topological locking: 1) we always identify a valid solution that optimizes the security metric; 2) we minimize the number of bits used for locking; and 3) we make a better use of hardware resources.