Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching for Spectroscopic Signatures of Ongoing Quenching in SDSS Galaxies

In this paper, we estimate the “star formation change parameter,” SFR79, which characterizes the current SFR relative to the average during the last 800 Myr, for ∼300,000 galaxies selected from the Sloan Digital Sky Survey (SDSS). The goals are to examine, in a much larger and independent sample, the trends previously reported in a sample of star-forming (SF) MaNGA galaxies and also to search for spectroscopic signatures of ongoing quenching in the so-called “Green Valley,” which is generally believed to contain galaxies that are migrating from the SF population to the quenched population of galaxies. Measuring SFR79 for our large sample of SDSS galaxies, we first confirm the basic results of SF galaxies published by Wang & Lilly. We then discuss in detail the calibration and meaning of SFR79 for galaxies that are well below the SFMS and establish the expected statistical signature of systematic ongoing quenching from modeling the z ∼ 0 quenching rate of the SF population. We conclude that it is not possible at present to establish unambiguous observational evidence for systematic ongoing quenching processes, due to limitations both in the noise of the observational data, in particular in the measurements of Hδ absorption, and in the calibration of SFR79, as well as biases introduced by the necessity of selecting objects with significant Hα emission. We do however see plausible indications of ongoing quenching, which are quantitatively consistent with expectations from “growth+quenching” models of galaxy evolution and a typical e-folding timescale for quenching of ∼500 Myr.
Secondary Protein Structure Prediction Using Neural Networks

In this paper we experiment with using neural network structures to predict a protein's secondary structure ({\alpha} helix positions) from only its primary structure (amino acid sequence). We implement a fully connected neural network (FCNN) and preform three experiments using that FCNN. Firstly, we do a cross-species comparison of models trained and tested on mouse and human datasets. Secondly, we test the impact of varying the length of protein sequence we input into the model. Thirdly, we compare custom error functions designed to focus on the center of the input window. At the end of paper we propose a alternative, recurrent neural network model which can be applied to the problem.
Resolving Matrix Spencer Conjecture Up to Poly-logarithmic Rank

We give a simple proof of the matrix Spencer conjecture up to poly-logarithmic rank: given symmetric d × d matrices A1,…,An each with ||Ai||op ≤ 1 and rank at most n/log3 n, one can efficiently find ± 1 signs x1,…,xn such that their signed sum has spectral norm ||∑i=1n xi Ai||op = O(√n). This result also implies a logn − Ω( loglogn) qubit lower bound for quantum random access codes encoding n classical bits with advantage ≫ 1/√n. Our proof uses the recent refinement of the non-commutative Khintchine inequality in [Bandeira, Boedihardjo, van Handel, 2021] for random matrices with correlated Gaussian entries.
Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction

Concomitant administration of drugs can cause drug-drug interactions (DDIs). Some drug combinations are beneficial, but other ones may cause negative effects which are previously unrecorded. Previous works on DDI prediction usually rely on hand-engineered domain knowledge, which is laborious to obtain. In this work, we propose a novel model, Molecular Substructure-Aware Network (MSAN), to effectively predict potential DDIs from molecular structures of drug pairs. We adopt a Transformer-like substructure extraction module to acquire a fixed number of representative vectors that are associated with various substructure patterns of the drug molecule. Then, interaction strength between the two drugs' substructures will be captured by a similarity-based interaction module. We also perform a substructure dropping augmentation before graph encoding to alleviate overfitting. Experimental results from a real-world dataset reveal that our proposed model achieves the state-of-the-art performance. We also show that the predictions of our model are highly interpretable through a case study.
Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries

Adversarial examples are inputs intentionally generated for fooling a deep neural network. Recent studies have proposed unrestricted adversarial attacks that are not norm-constrained. However, the previous unrestricted attack methods still have limitations to fool real-world applications in a black-box setting. In this paper, we present a novel method for generating unrestricted adversarial examples using GAN where an attacker can only access the top-1 final decision of a classification model. Our method, Latent-HSJA, efficiently leverages the advantages of a decision-based attack in the latent space and successfully manipulates the latent vectors for fooling the classification model. With extensive experiments, we demonstrate that our proposed method is efficient in evaluating the robustness of classification models with limited queries in a black-box setting. First, we demonstrate that our targeted attack method is query-efficient to produce unrestricted adversarial examples for a facial identity recognition model that contains 307 identities. Then, we demonstrate that the proposed method can also successfully attack a real-world celebrity recognition service.
Theory of melting of glasses

ABSTRACT Glassy matter like crystals resists change in shape. Therefore a theory for their continuous melting should show how the shear elastic constant μ goes to zero. Since viscosity is the long wave-length low frequency limit of shear correlations, the same theory should give phenomena like the Volger-Fulcher dependence of the viscosity on temperature near the transition. A continuum model interrupted randomly by asymmetric rigid defects with orientational degrees of freedom is considered. Such defects are orthogonal to the continuum excitations, and are required to be imprisoned by rotational motion of the nearby atoms of the continuum. The defects interact with an angle dependent potential. A renormalisation group for the elastic constants, and the fugacity of the defects in 3D is constructed. The principal results are that there is a scale-invariant reduction of μ as a function of length at any temperature , above which it is 0 macrosopically but has a finite correlation length which diverges as . Viscosity is shown to be proportional to and has the Vogel-Fulcher form. The specific heat is . As , the Kauzman temperature from above, the configuration entropy of the liquid is exhausted. The theory also gives the ‘fragility’ of glasses in terms of their .
Ad hoc test functions for homogenization of compressible viscous fluid with application to the obstacle problem in dimension two

In this paper we highlight a set of ad hoc test functions to study the homogenization of viscous compressible fluid in domains with tiny holes. This set of functions allows to improve previous results in dimensions two and three. As an application we show that the presence of a small obstacle does not influence the dynamics of a viscous compressible fluid in dimension two.
A high angular resolution view of the PAH emission in Seyfert galaxies using JWST/MRS data

Polycyclic aromatic hydrocarbons (PAHs) are carbon-based molecules that are ubiquitous in a variety of astrophysical objects and environments. In this work we use JWST / MIRI MRS spectroscopy of three Seyferts to compare their nuclear PAH emission with that of star-forming (SF) regions. This study represents the ﬁrst of its kind to use sub-arcsecond angular resolution data of local luminous Seyferts (L bol > 10 44 . 46 erg / s) with a wide wavelength coverage (4.9-28.1 µ m). We present an analysis of their nuclear PAH properties by comparing the observed ratios with PAH diagnostic model grids derived from theoretical spectra. Our results show that a suite of PAH features is present in the innermost parts of luminous Seyfert galaxies ( ∼ 0.45 (cid:48)(cid:48) at 12 µ m; in the inner ∼ 142-245pc). We ﬁnd that the nuclear regions of active galactic nuclei (AGN) lie at di ﬀ erent positions of the PAH diagnostic diagrams, whereas the SF regions are concentrated around the average values of SF galaxies. In particular, we ﬁnd that the nuclear PAH emission mainly originates in neutral PAHs. In contrast, PAH emission originating in the SF regions favours ionised PAH grains. The observed PAH ratios in the nuclear region of the AGN-dominated galaxy NGC6552 indicate the presence of larger PAH molecules compared with those of the SF regions. Therefore, our results provide evidence that the AGN have a signiﬁcant impact on the ionisation state (and probably the size) of the PAH grains on scales of ∼ 142-245pc.
Robustness to Unbounded Smoothness of Generalized SignSGD

Traditional analyses in non-convex optimization typically rely on the smoothness assumption, namely requiring the gradients to be Lipschitz. However, recent evidence shows that this smoothness condition does not capture the properties of some deep learning objective functions, including the ones involving Recurrent Neural Networks and LSTMs. Instead, they satisfy a much more relaxed condition, with potentially unbounded smoothness. Under this relaxed assumption, it has been theoretically and empirically shown that the gradient-clipped SGD has an advantage over the vanilla one. In this paper, we show that clipping is not indispensable for Adam-type algorithms in tackling such scenarios: we theoretically prove that a generalized SignSGD algorithm can obtain similar convergence rates as SGD with clipping but does not need explicit clipping at all. This family of algorithms on one end recovers SignSGD and on the other end closely resembles the popular Adam algorithm. Our analysis underlines the critical role that momentum plays in analyzing SignSGD-type and Adam-type algorithms: it not only reduces the effects of noise, thus removing the need for large mini-batch in previous analyses of SignSGD-type algorithms, but it also substantially reduces the effects of unbounded smoothness and gradient norms. We also compare these algorithms with popular optimizers on a set of deep learning tasks, observing that we can match the performance of Adam while beating the others.
An efficient and energy decaying discontinuous Galerkin method for Maxwell's equations for the Cole-Cole dispersive medium

In this work, we investigate the propagation of electromagnetic waves in the Cole-Cole dispersive medium by using the discontinuous Galerkin (DG) method to solve the coupled time-domain Maxwell's equations and polarization equation. We define a new and sharpened total energy function for the Cole-Cole model, which better describes the behaviors of the energy than what is available in the current literature. A major theme in the time-domain numerical modeling of this problem has been tackling the difficulty of handling the nonlocal term involved in the time-domain polarization equation. Based on the diffusive representation and the quadrature formula, we derive an approximate system, where the convolution kernel is replaced by a finite number of auxiliary variables that satisfy local-in-time ordinary differential equations. To ensure the resulted approximate system is stable, a nonlinear constrained optimization numerical scheme is established to determine the quadrature coefficients. By a special choice of the numerical fluxes and projections, we obtain {for the constant coefficient case } an optimal-order convergence result for the semi-discrete DG scheme. The temporal discretization is achieved by the standard two-step backward difference formula and a fast algorithm with linear complexity is constructed. Numerical examples are provided for demonstrating the efficiency of the proposed algorithm, validating the theoretical results and illustrating the behaviors of the energy.
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting

Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE.
Online multiple hypothesis testing for reproducible research

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p -values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.
The amplification of cosmological magnetic fields in extended f(T,B) teleparallel gravity

Observations indicate that intergalactic magnetic fields have amplitudes of the order of ∼ 10-6 G and are uniform on scales of ∼ 10 kpc. Despite their wide presence in the Universe, their origin remains an open issue. Even by invoking a dynamo mechanism or a compression effect for magnetic field amplification, the existence of seed fields before galaxy formation is still problematic. General Relativity predicts an adiabatic decrease of the magnetic field evolving as |B| ∝ 1/a 2, where a is the scale factor of the Universe. It results in very small primordial fields, unless the conformal symmetry of the electromagnetic sector is broken. In this paper, we study the possibility that a natural mechanism for the amplification of primordial magnetic field can be related to extended teleparallel gravity f(T,B) models, where T is the torsion scalar, and B the boundary term. In particular, we consider a non-minimal coupling with gravity in view to break conformal symmetry in a teleparallel background, investigating, in particular, the role of boundary term B, which can be consider as a further scalar field. We find that, after solving exactly the f(T,B) field equations both in inflation and reheating eras, a non-adiabatic behavior of the magnetic field is always possible, and a strong amplification appears in the reheating epoch. We also compute the ratio r = ρB /ργ between the magnetic energy density and the cosmic microwave energy density during inflation, in order to explain the present value r ≃ 1, showing that, in the slow-roll approximation, power-law teleparallel theories with Bn have effects indistinguishable from metric theories Rn where R is the Ricci curvature scalar.
On a Built-in Conflict between Deep Learning and Systematic Generalization

In this paper, we hypothesize that internal function sharing is one of the reasons to weaken o.o. d. or systematic generalization in deep learning for classification tasks. Under equivalent prediction, a model partitions an input space into multiple parts separated by boundaries. The function sharing prefers to reuse boundaries, leading to fewer parts for new outputs, which conflicts with systematic generalization. We show such phenomena in standard deep learning models, such as fully connected, convolutional, residual networks, LSTMs, and (Vision) Transformers. We hope this study provides novel insights into systematic generalization and forms a basis for new research directions.
Implementação de um provador de teoremas por resolução para lógicas modais normais

In this work we present an implementation of a theorem prover for multi-agent normal modal logic systems, based on the modal resolution technique published by Nalon and Dixon [2007]. Differently from other approaches for resolution proof systems or methods in modal logic, where inference rules are developed for use in a particular system e.g. K, S4 or S5, the resolution method used as a basis for the implementation presented here focuses on the restrictions imposed on the accessibility relations between worlds for each modal system. Thus, we have specific inference rules for serial, reflexive, symmetric, transitive and Euclidean systems, as well as a set of inference rules for every normal modal system. Therefore, the implementation becomes comprehensive, since it can deal with several modal systems by combination of the inference rules mentioned above; and extensible, since we only need to implement new inference rules which capture another restriction in the accessibility relations to increase the range of systems included in the calculus, without compromising the soundness and completeness of the results for modal systems already covered before. The presented program, coded in the C++ language, automatically produces proofs for the fifteen distinct normal modal systems defined by the different combinations of the axioms Di, Ti, Bi, 4i and 5i, including systems like K(n), D(n), T(n), B(n), S4(n) and S5(n), where the (n) index indicates a multi-agent version of a system.
On Asymptotic Normality of Limiting Density Functions with Bayesian Implications

Walker (1969) presented a straightforward approach to asymptotic normality of posterior distributions based on independent and identically distributed (i.i.d.) observations. Dawid (1970) extended this work by providing a weaker set of conditions to cover some distributions whose ranges depend on the parameters of interest. More recently, Heyde and Johnstone (1979) simplified Walker's conditions for stochastic processes. In this paper, we introduce three basic normality conditions for limiting density functions. In the Bayesian context, our approach is data-determined and is nonprobabilistic in nature. Nevertheless, the results thus obtained can be easily converted to probabilistic ones when compared with those given by other investigators. It is shown that our conditions as a whole have a greater generality and flexibility than those previously assumed either by Dawid or Heyde and Johnstone. In particular, for the i.i. d. case, the commonly assumed Wald-type uniform-boundedness condition for tail behaviours is shown to be unnecessary for asymptotic normality (Section 3.3). The advantage of our approach is further exemplified by a simple proof of the asymptotic normality of distributions conjugate to the exponential family.
Hearing impairment among adult foreign-born and Swedish-born individuals: A national Swedish study

Objectives To analyze the risk of hearing impairment in adult first-generation immigrants, i.e., foreign-born individuals as compared to Swedish-born individuals. Study design A register-based study follow-up study. Methods A nationwide study of individuals 25 years of age and older (N = 5 464 245; 2 627 364 men and 2 836 881 women) in Sweden. Hearing impairment was defined as at least one registered diagnosis in the National Patient Register between January 1st, 1998 and December 31st, 2015. Cox regression analysis was used to estimate the relative risk (hazard ratios (HR) with 95% confidence intervals (CI)) of incident hearing impairment in foreign-born compared to Swedish-born individuals. Cox regression models were stratified by sex and adjusted for age, comorbidities, and socioeconomic status. Results A total of 244 171 cases (124 349 men and 119 822 women) of hearing impairment were registered. Hearing impairment risk expressed as fully adjusted HRs (99% CI) was somewhat lower among immigrant men 0.95 (0.92–97) but not among immigrant women 0.97 (0.95–1.00), with significantly higher fully adjusted HRs among men and women from Asia, and Eastern Europe, and women from Africa. Conclusions We observed a somewhat lower risk of hearing impairment among foreign-born men, but there was a higher risk among men and women from some regions.
CXCR3 isoform A promotes head and neck cancer progression by enhancing stem-like property and chemoresistance.

The chemokine network orchestrates the cancer stem-like property and consequently participates in cancer progression. CXCR3 contributes cancer progressive property and immunomodulation in the tumor microenvironment. The two major isoforms of CXCR3 are scrutinized and the divergence is showed that CXCR3A promotes cancer cell growth and motility while CXCR3B functions contrarily in many studies. However, rare studies illustrate the role of CXCR3 isoforms in cancer stem-like property and chemoresistance, especially in head and neck cancer (HNC). First, we used immunohistochemistry staining to evaluate expression levels of CXCR3, CXCR3B, and Sox2 cancer stem cell (CSC) marker and association with tumor progression in HNC tissues. Results showed that high levels of CXCR3 were significantly associated with advanced stage (p <0.01), regional lymph node metastasis (p <0.05), and poor differentiation (p <0.005) and further correlated with worse survival rate in oral cancer patients (p = 0.036). Higher levels of CXCR3B were found in regional lymphatic invasion of HNC and progressive stage of squamous cell carcinoma. Elevated Sox2 expression was significantly associated with the advanced stage of HNC in the oral cavity, and demonstrated a co-expression pattern with CXCR3B. Furthermore, lentivirus-mediated overexpression of CXCR3A and CXCR3B in SAS human oral cancer cells promoted cell mobility. CXCR3A overexpression enhanced sphere-forming ability and chemoresistance of CSCs by upregulating stemness-related genes. This study first provides a novel insight of CXCR3 isoform A in HNC cancer progression via regulating cancer stem-like properties and chemoresistance, suggesting that CXCR3A may be a prognostic marker and novel target for HNC therapy.
A gut‐centric view of aging: Do intestinal epithelial cells contribute to age‐associated microbiota changes, inflammaging, and immunosenescence?

Intestinal epithelial cells (IECs) serve as both a physical and an antimicrobial barrier against the microbiota, as well as a conduit for signaling between the microbiota and systemic host immunity. As individuals age, the balance between these systems undergoes a myriad of changes due to age‐associated changes to the microbiota, IECs themselves, immunosenescence, and inflammaging. In this review, we discuss emerging data related to age‐associated loss of intestinal barrier integrity and posit that IEC dysfunction may play a central role in propagating age‐associated alterations in microbiota composition and immune homeostasis.