Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Dwarf Irregular Galaxy NGC 6822. I. Young, Intermediate, and Old Stellar Populations

We present accurate and deep multiband (g, r, i) photometry of the Local Group dwarf irregular galaxy NGC 6822. The images were collected with wide-field cameras at 2 m/4 m (INT, CTIO, CFHT) and 8 m class telescopes (Subaru) covering a 2 deg2 field of view across the center of the galaxy. We performed point-spread function photometry of ≈7000 CCD images, and the final catalog includes more than 1 million objects. We developed a new approach to identify candidate field and galaxy stars and performed a new estimate of the galaxy center by using old stellar tracers, finding that it differs by 1. ′15 (R.A.) and 1. ′53 (decl.) from previous estimates. We also found that young (main sequence, red supergiants), intermediate (red clump, asymptotic giant branch (AGB)), and old (red giant branch) stars display different radial distributions. The old stellar population is spherically distributed and extends to radial distances larger than previously estimated (∼1°). The young population shows a well-defined bar and a disk-like distribution, as suggested by radio measurements, that is off-center compared with the old population. We discuss pros and cons of the different diagnostics adopted to identify AGB stars and develop new ones based on optical–near-IR–mid-IR color–color diagrams to characterize oxygen- and carbon-rich stars. We found a mean population ratio between carbon and M-type (C/M) stars of 0.67 ± 0.08 (optical/near-IR/mid-IR), and we used the observed C/M ratio with empirical C/M–metallicity relations to estimate a mean iron abundance of [Fe/H] ∼ −1.25 (σ = 0.04 dex), which agrees quite well with literature estimates.
Thyroid Stimulating Disease Immunoglobulin ( TS I ) in Graves '

IT HAS been 40 years since the discovery of long acting thyroid stimulator (LATS), now known as the thyroid stimulating antibody (TSAb) or thyroid stimulating immunoglobulin (TSI). An immunoglobulin G (IgG) with thyroid stimulating activity accepted as the etiological factor of hyperthyroidism in Graves' disease. On the other hand, IgG which inhibits TSH binding to and stimulation of the TSH receptor is called thyroid stimulation blocking antibody (TSBAb). These 2 types of IgG are considered to be TSH receptor antibodies (TRAb) and are currently measured by the TSH receptor assay and bioassay. In the TSH receptor assay, IgG which inhibits TSH binding to the receptor is called TSH binding inhibitory immunoglobulin (TBII). TSAb and TSBAb are determined by measuring CAMP (marker of thyroid stimulation) produced in thyroid cells exposed to IgG in the absence and presence of TSH, respectively [1, 2]. Since clarification of the structure of the TSH receptor the binding epitope for TRAb has been studied, but the nature of the epitope for TRAb is still controversial. We review the recent advance in knowledge concerning TSI and TRAb research and describe recent results obtained in our laboratory. LATS
Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks

Many complex systems in the real world can be characterized by attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been paid much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes meanwhile clustering nodes on representation vectors learnt from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to respectively learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions of two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP.
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph

Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning focus on static KGs, while temporal knowledge graphs (TKGs) have not been fully explored. Reasoning over TKGs has two challenges: 1. The query should answer entities or timestamps; 2. The operators should consider both set logic on entity set and temporal logic on timestamp set. To bridge this gap, we define the multi-hop logical reasoning problem on TKGs. With generated three datasets, we propose the first temporal CQE named Temporal Feature-Logic Embedding framework (TFLEX) to answer the temporal complex queries. We utilize vector logic to compute the logic part of Temporal Feature-Logic embeddings, thus naturally modeling all First-Order Logic (FOL) operations on entity set. In addition, our framework extends vector logic on timestamp set to cope with three extra temporal operators (After, Before and Between). Experiments on numerous query patterns demonstrate the effectiveness of our method.
Close Major-merger Pairs at z = 0: Bulge-to-total Ratio and Star Formation Enhancement

We present a study of the bulge-to-total ratio (B/T) of a Ks-band-selected sample of 88 close major-merger pairs of galaxies (H-KPAIR), based on two-dimensional decompositions of SDSS r-band images with galfit. We investigate the dependence of the interaction-induced specific star formation rate enhancement (sSFRenh) on the B/T ratio, and the effects of this dependence on the differences between star-forming galaxies (SFGs) in spiral+spiral (S+S) and spiral+elliptical (S+E) pairs. Of all the 132 spiral galaxies in H-KPAIR, the 44 in S+E pairs show higher B/T than those in the 44 S+S pairs, with means of B/T = 0.35 ± 0.05 and B/T = 0.26 ± 0.03, respectively. There is a strong negative dependence of sSFRenh on the B/T ratio, and only paired SFGs with B/T < 0.3 show significant (>5σ) enhancement. Paired SFGs in S+S pairs show a similar trend, and many disky SFGs (B/T < 0.1) in S+S pairs have strong sSFR enhancements (sSFRenh > 0.7 dex). For SFGs in S+E pairs, the sSFR has no clear B/T dependence, nor any significant enhancement in any B/T bin. Disky SFGs in S+S pairs show significant (>4σ) enhancement in molecular gas content ( MH2/Mstar ), while SFGs in S+E pairs have no such enhancement in any B/T bin. No significant enhancement of the total gas content (M gas/M star) is found in any B/T bin for paired galaxies. The star formation efficiency of either the total gas (SFEgas = SFR/M gas) or molecular gas ( SFEH2=SFR/MH2 ) does not depend on the B/T ratio. The only significant (> 4σ) SFE enhancement found for paired SFGs is the SFEgas for disky SFGs in S+S pairs.
Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach

With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system. In particular, the abnormal signals may emulate the normal signals, which makes it very challenging for abnormal signal recognition. In this paper, we propose a new abnormal signal recognition scheme, which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals. Firstly, we emulate synthetic abnormal communication signals including seven jamming patterns. Then, we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver. To improve the performance, we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm. Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations, even under low signal-to-noise ratio (SNR) and low jamming-to-signal ratio (JSR) conditions.
3 1 M ay 2 02 2 Demon driven by geometrical phase

We theoretically study an entropy production and a work extracted from a system connected to two reservoirs by periodic modulations of chemical potentials of the reservoirs and one parameter in the system Hamiltonian under an isothermal condition. We ﬁnd that the modulation of parameters can drive a geometrical state, which is away from a nonequilibrium steady state. With the aid of this property, we construct a demon in which the relative entropy increases with time and we can extract the work, if we begin with the nonequilibrium steady state without modulations of parameters. We employ the Anderson model to demonstrate that the relative entropy can increase with time.
Deep Learning for Flood Extent Prediction

Flooding is among the most destructive natural disasters in the world. As a result of this, there is a great need to develop accurate flood prediction models to prevent flooding ahead of time. However, due to the many factors that are related to predicting flood extent, floods are highly complex to model. Machine learning techniques have shown promise in this space but the current state-of-the-art techniques fail to generalise to other flood events. This study shows that Long Short-Term Memory (LSTM) networks are effective at predicting flood extent by surpassing the current state-of-the-art and generalising to other types of flood events.
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection

This paper focuses on jointly inferring network and community structures from the dynamics of complex systems. Although many approaches have been designed to solve these two problems solely, none of them consider explicit shareable knowledge across these two tasks. Community detection (CD) from dynamics and network reconstruction (NR) from dynamics are natural synergistic tasks that motivate the proposed evolutionary multitasking NR and CD framework, called network collaborator (NC). In the process of NC, the NR task explicitly transfers several better network structures for the CD task, and the CD task explicitly transfers a better community structure to assist the NR task. Moreover, to transfer knowledge from the NR task to the CD task, NC models the study of CD from dynamics to find communities in the dynamic network and then considers whether to transfer knowledge across tasks. A test suite for multitasking NR and CD problems (MTNRCDPs) is designed to verify the performance of NC. The experimental results conducted on the designed MTNRCDPs have demonstrated that joint NR with CD has a synergistic effect, where the network structure used to inform the existence of communities is also inherently employed to improve the reconstruction accuracy, which, in turn, can better demonstrate the discovering of the community structure. The code is available at: https://github.com/xiaofangxd/EMTNRCD.
Provably Auditing Ordinary Least Squares in Low Dimensions

Measuring the stability of conclusions derived from Ordinary Least Squares linear regression is critically important, but most metrics either only measure local stability (i.e. against infinitesimal changes in the data), or are only interpretable under statistical assumptions. Recent work proposes a simple, global, finite-sample stability metric: the minimum number of samples that need to be removed so that rerunning the analysis overturns the conclusion, specifically meaning that the sign of a particular coefficient of the estimated regressor changes. However, besides the trivial exponential-time algorithm, the only approach for computing this metric is a greedy heuristic that lacks provable guarantees under reasonable, verifiable assumptions; the heuristic provides a loose upper bound on the stability and also cannot certify lower bounds on it. We show that in the low-dimensional regime where the number of covariates is a constant but the number of samples is large, there are efficient algorithms for provably estimating (a fractional version of) this metric. Applying our algorithms to the Boston Housing dataset, we exhibit regression analyses where we can estimate the stability up to a factor of $3$ better than the greedy heuristic, and analyses where we can certify stability to dropping even a majority of the samples.
Ensemble methods for survival function estimation with time-varying covariates

Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of a survival function. However, the traditional survival forests—conditional inference forest, relative risk forest and random survival forest—have accommodated only time-invariant covariates. We generalize the conditional inference and relative risk forests to allow time-varying covariates. We also propose a general framework for estimation of a survival function in the presence of time-varying covariates. We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L 2 difference between the true and estimated survival functions. In general, the performance of the two proposed forests substantially improves over the Kaplan-Meier estimate. Taking into account all other factors, under the proportional hazard setting, the best method is always one of the two proposed forests, while under the non-proportional hazard setting, it is the adapted transformation forest. K -fold cross-validation is used as an effective tool to choose between the methods in practice.
STN: Scalable Tensorizing Networks via Structure-Aware Training and Adaptive Compression

Deep neural networks (DNNs) have delivered a remarkable performance in many tasks of computer vision. How-ever, over-parameter- ized representations of popular architectures dramatically increase their computational complexity and storage costs, and hinder their availability in edge devices with constrained resources. Regardless of many tensor decomposition (TD) methods that have been well-studied for compressing DNNs to learn compact representations, they suffer from non-negligible performance degradation in practice. In this paper, we propose Scalable Tensorizing Networks (STN), which dynamically and adaptively adjust the model size and decomposition structure without retraining. First, we account for compression during training by adding a low-rank regularizer to guarantee networks’ desired low-rank characteristics in full tensor format. Then, considering network layers exhibit various low-rank structures, STN is obtained by a data-driven adaptive TD approach, for which the topological structure of decomposition per layer is learned from the pre-trained model, and the ranks are selected appropriately under speciﬁed storage constraints. As a result, STN is com-patible with arbitrary network architectures and achieves higher compression performance and ﬂexibility over other tensorizing versions. Comprehensive experiments on several popular architectures and benchmarks substantiate the superiority of our model towards improving parameter efﬁciency.
List-Decodable Sparse Mean Estimation

Robust mean estimation is one of the most important problems in statistics: given a set of samples in $\mathbb{R}^d$ where an $\alpha$ fraction are drawn from some distribution $D$ and the rest are adversarially corrupted, we aim to estimate the mean of $D$. A surge of recent research interest has been focusing on the list-decodable setting where $\alpha \in (0, \frac12]$, and the goal is to output a finite number of estimates among which at least one approximates the target mean. In this paper, we consider that the underlying distribution $D$ is Gaussian with $k$-sparse mean. Our main contribution is the first polynomial-time algorithm that enjoys sample complexity $O\big(\mathrm{poly}(k, \log d)\big)$, i.e. poly-logarithmic in the dimension. One of our core algorithmic ingredients is using low-degree sparse polynomials to filter outliers, which may find more applications.
Snoopy: A Webpage Fingerprinting Framework With Finite Query Model for Mass-Surveillance

Internet users are vulnerable to privacy attacks despite the use of encryption. Webpage fingerprinting, an attack that analyzes encrypted traffic, can identify the webpages visited by a user. The key challenges in performing mass-scale webpage fingerprinting arise from (i) the sheer number of combinations of user behavior and preferences to account for, and; (ii) the bound on the number of website queries imposed by the defense mechanisms (e.g., DDoS defense) deployed at the website. These constraints preclude the use of conventional data-intensive ML-based techniques. In this work, we propose Snoopy, a first-of-its-kind framework, that performs webpage fingerprinting for a large number of users visiting a website. Snoopy caters to the generalization requirements of mass-surveillance while complying with a bound on the number of website accesses (finite query model) for traffic sample collection. We show that Snoopy achieves <inline-formula><tex-math notation="LaTeX">$\approx 90\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>≈</mml:mo><mml:mn>90</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="mitra-ieq1-3222462.gif"/></alternatives></inline-formula> accuracy when evaluated on most websites, across various browsing contexts. A simple ensemble of Snoopy and an ML-based technique achieves <inline-formula><tex-math notation="LaTeX">$\approx 97\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>≈</mml:mo><mml:mn>97</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="mitra-ieq2-3222462.gif"/></alternatives></inline-formula> accuracy while adhering to the finite query model, in cases when Snoopy alone does not perform well.
Testing for Geometric Invariance and Equivariance

Invariant and equivariant models incorporate the symmetry of an object to be estimated (here non-parametric regression functions $f : \mathcal{X} \rightarrow \mathbb{R}$). These models perform better (with respect to $L^2$ loss) and are increasingly being used in practice, but encounter problems when the symmetry is falsely assumed. In this paper we present a framework for testing for $G$-equivariance for any semi-group $G$. This will give confidence to the use of such models when the symmetry is not known a priori. These tests are independent of the model and are computationally quick, so can be easily used before model fitting to test their validity.
Rapid Regression Detection in Software Deployments through Sequential Testing

The practice of continuous deployment has enabled companies to reduce time-to-market by increasing the rate at which software can be deployed. However, deploying more frequently bears the risk that occasionally defective changes are released. For Internet companies, this has the potential to degrade the user experience and increase user abandonment. Therefore, quality control gates are an important component of the software delivery process. These are used to build confidence in the reliability of a release or change. Towards this end, a common approach is to perform a canary test to evaluate new software under production workloads. Detecting defects as early as possible is necessary to reduce exposure and to provide immediate feedback to the developer. We present a statistical framework for rapidly detecting regressions in software deployments. Our approach is based on sequential tests of stochastic order and of equality in distribution. This enables canary tests to be continuously monitored, permitting regressions to be rapidly detected while strictly controlling the false detection probability throughout. The utility of this approach is demonstrated based on two case studies at Netflix.
Non-convex online learning via algorithmic equivalence

We study an algorithmic equivalence technique between non-convex gradient descent and convex mirror descent. We start by looking at a harder problem of regret minimization in online non-convex optimization. We show that under certain geometric and smoothness conditions, online gradient descent applied to non-convex functions is an approximation of online mirror descent applied to convex functions under reparameterization. In continuous time, the gradient flow with this reparameterization was shown to be exactly equivalent to continuous-time mirror descent by Amid and Warmuth 2020, but theory for the analogous discrete time algorithms is left as an open problem. We prove an $O(T^{\frac{2}{3}})$ regret bound for non-convex online gradient descent in this setting, answering this open problem. Our analysis is based on a new and simple algorithmic equivalence method.
Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors

Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain data and the predictor parameters, thus addressing the data privacy and portability issues of standard domain adaptation. Existing DABP approaches mostly rely on model distillation from the black-box predictor, \emph{i.e.}, training the model with its noisy target-domain predictions, which however inevitably introduces the confirmation bias accumulated from the prediction noises. To mitigate such bias, we propose a new method, named BETA, to incorporate knowledge distillation and noisy label learning into one coherent framework. This is enabled by a new divide-to-adapt strategy. BETA divides the target domain into an easy-to-adapt subdomain with less noise and a hard-to-adapt subdomain. Then it deploys mutually-teaching twin networks to filter the predictor errors for each other and improve them progressively, from the easy to hard subdomains. As such, BETA effectively purifies the noisy labels and reduces error accumulation. We theoretically show that the target error of BETA is minimized by decreasing the noise ratio of the subdomains. Extensive experiments demonstrate BETA outperforms existing methods on all DABP benchmarks, and is even comparable with the standard domain adaptation methods that use the source-domain data.