Miracle-dz/UniFAR-FTUs huggingface dataset:
AGC 226178 and NGVS 3543: Two Deceptive Dwarfs toward Virgo

The two sources AGC 226178 and NGVS 3543, an extremely faint, clumpy, blue stellar system and a low surface brightness dwarf spheroidal, are adjacent systems in the direction of the Virgo cluster. Both have been studied in detail previously, with it being suggested that they are unrelated normal dwarf galaxies or that NGVS 3543 recently lost its gas through ram pressure stripping and AGC 226178 formed from this stripped gas. However, with Hubble Space Telescope Advanced Camera for Surveys imaging, we demonstrate that the stellar population of NGVS 3543 is inconsistent with being at the distance of the Virgo cluster and that it is likely a foreground object at approximately 10 Mpc, whereas the stellar population of AGC 226178 is consistent with it being a very young (10–100 Myr) object in the Virgo cluster. Through a reanalysis of the original ALFALFA H i detection, we show that AGC 226178 likely formed from gas stripped from the nearby dwarf galaxy VCC 2034, a hypothesis strengthened by the high metallicity measured with MUSE VLT observations. However, it is unclear whether ram pressure or a tidal interaction is responsible for stripping the gas. Object AGC 226178 is one of at least five similar objects now known toward Virgo. These objects are all young and unlikely to remain visible for over ∼500 Myr, suggesting that they are continually produced in the cluster.
Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Background: Deep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated electroencephalogram (EEG) data. However, effectively using a large amount of raw EEG data remains a challenge. Objective: In this study, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task, and (2) provide better predictive performance than supervised models in scenarios involving fewer labels and noisy samples. Methods: We propose a self-supervised model, Contrast with the World Representation (ContraWR), for EEG signal representation learning. Unlike previous models that use a set of negative samples, our model uses global statistics (ie, the average representation) from the data set to distinguish signals associated with different sleep stages. The ContraWR model is evaluated on 3 real-world EEG data sets that include both settings: at-home and in-laboratory EEG recording. Results: ContraWR outperforms 4 recently reported self-supervised learning methods on the sleep staging task across 3 large EEG data sets. ContraWR also supersedes supervised learning when fewer training labels are available (eg, 4% accuracy improvement when less than 2% of data are labeled on the Sleep EDF data set). Moreover, the model provides informative, representative feature structures in 2D projection. Conclusions: We show that ContraWR is robust to noise and can provide high-quality EEG representations for downstream prediction tasks. The proposed model can be generalized to other unsupervised physiological signal learning tasks. Future directions include exploring task-specific data augmentations and combining self-supervised methods with supervised methods, building upon the initial success of self-supervised learning reported in this study.
Behavior of Spiral Wave Spectra with a Rank-Deficient Diffusion Matrix

Spiral waves emerge in numerous pattern forming systems and are commonly modeled with reactiondiffusion systems. Some systems used to model biological processes, such as ion-channel models, fall under the reaction-diffusion category and often have one or more non-diffusing species which results in a rankdeficient diffusion matrix. Previous theoretical research focused on spiral spectra for strictly positive diffusion matrices. In this paper, we use a general two-variable reaction-diffusion system to compare the essential and absolute spectra of spiral waves for strictly positive and rank-deficient diffusion matrices. We show that the essential spectrum is not continuous in the limit of vanishing diffusion in one component. Moreover, we predict locations for the absolute spectrum in the case of a non-diffusing slow variable. Predictions are confirmed numerically for the Barkley and Karma models.
Finite Horizon Q-learning: Stability, Convergence and Simulations

Q-learning is a popular reinforcement learning algorithm. This algorithm has however been studied and analysed mainly in the inﬁnite horizon setting. There are several important applications which can be modeled in the framework of ﬁnite horizon Markov decision processes. We develop a version of Q-learning algorithm for ﬁnite horizon Markov decision processes (MDP) and provide a full proof of its stability and convergence. Our analysis of stability and convergence of ﬁnite horizon Q-learning is based entirely on the ordinary differential equations (O.D.E) method. We also demonstrate the performance of our algorithm on a setting of random MDP.
Multi-ended Markovian triangulations and robust convergence to the UIPT

We classify completely the infinite, planar triangulations satisfying a weak spatial Markov property, without assuming one-endedness nor finiteness of vertex degrees. In particular, the Uniform Infinite Planar Triangulation (UIPT) is the only such triangulation with average degree 6. As a consequence, we prove that the convergence of uniform triangulations of the sphere to the UIPT is robust, in the sense that it is preserved under various perturbations of the uniform measure. As another application, we obtain large deviation estimates for the number of occurencies of a pattern in uniform triangulations.
Quantum oscillations in interaction-driven insulators

In recent years it has become understood that quantum oscillations of the magnetization as a function of magnetic field, long recognized as phenomena intrinsic to metals, can also manifest in insulating systems. Theory has shown that in certain simple band insulators, quantum oscillations can appear with a frequency set by the area traced by the minimum gap in momentum space, and are suppressed for weak fields by an intrinsic ``Dingle damping'' factor reflecting the size of the bandgap. Here we examine quantum oscillations of the magnetization in excitonic and Kondo insulators, for which interactions play a crucial role. In models of these systems, self-consistent parameters themselves oscillate with changing magnetic field, generating additional contributions to quantum oscillations. In the low-temperature, weak-field regime, we find that the lowest harmonic of quantum oscillations of the magnetization are unaffected, so that the zero-field bandgap can still be extracted by measuring the Dingle damping factor of this harmonic. However, these contributions dominate quantum oscillations of magnetization at all higher harmonics, thereby providing a route to measure this interaction effect.
DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications

Closing the gap between high data rates and low delay in real-time streaming applications is a major challenge in advanced communication systems. While adaptive network coding schemes have the potential of balancing the rate and the delay in real-time, they often rely on a prediction of the channel behavior. In practice, such a prediction is based on delayed feedbacks, making it difficult to acquire causality, particularly when the channel model is unknown. In this work, we propose a deep learning-based noise prediction (DeepNP) algorithm, which augments the recently proposed adaptive and causal random linear network coding scheme with a neural network that learns to carry out noise prediction from data. This neural augmentation is utilized to maximize the throughput while minimizing in-order delivery delay of the coding scheme, and operate in a channel-model-agnostic manner. We numerically show that performance can dramatically increase by the learned prediction of the channel noise rate, demonstrating that DeepNP gains up to a factor of four in mean and maximum delay and a factor of two in throughput compared with statistic-based network coding approaches.
The Warm Gas in the Milky Way: The Kinematical Model of C iv and Its Connection to Si iv

We compose a 265-sight-line Milky Way C iv line-shape sample using the Hubble Space Telescope/Cosmic Origins Spectrograph archive, which is complementary to the existing Si iv samples. C iv has a higher ionization potential (47–64 eV) than Si iv (33–45 eV), so it also traces warm gas, which is roughly cospatial with Si iv. The spatial density distribution and kinematics of C iv are identical to those Si iv within ≈2σ. C iv is more sensitive to the warm gas density distribution at large radii with a higher element abundance. Applying the kinematical model to the C iv sample, we find two possible solutions of the density distribution, which are distinguished by the relative extension along the disk midplane and the normal-line direction. Both solutions can reproduce the existing sample and suggest a warm gas disk mass of logM(M⊙)≈8 and an upper limit of logM(M⊙)<9.3 within 250 kpc, which is consistent with Si iv. There is a decrease in the C iv/Si iv column density ratio from the Galactic center to the outskirts by 0.2–0.3 dex, which may suggest a phase transition or different ionization mechanisms for C iv and Si iv. Also, we find that the difference between C iv and Si iv is an excellent tracer of small-scale features, and we find a typical size of 5°–10° for possible turbulence within individual clouds (≈1 kpc).
Class-wise Thresholding for Detecting Out-of-Distribution Data

We consider the problem of detecting Out-ofDistribution (OoD) input data when using deep neural networks, and we propose a simple yet effective way to improve the robustness of several popular OoD detection methods against label shift. Our work is motivated by the observation that most existing OoD detection algorithms consider all training/test data as a whole, regardless of which class entry each input activates (inter-class differences). Through extensive experimentation, we have found that such practice leads to a detector whose performance is sensitive and vulnerable to label shift. To address this issue, we propose a class-wise thresholding scheme that can apply to most existing OoD detection algorithms and can maintain similar OoD detection performance even in the presence of label shift in the test
Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26–69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19–74.17%), or with data-origin (84.44–86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78–88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy.
Hybrid improper dipolar density wave in NaLaCoWO$_6$

Hybrid Improper Ferroelectricity (HIF) allows the generation of an electrical polarization in the AA’BB’O 6 double perovskite materials thanks to the combination of two non-polar octahedral distortions. Nevertheless, for selected combination of the A/A’ cations a non-polar incommensurate phase is observed with average symmetry C 2 /m . Thanks to a detailed crystallographic description of the incommensurate phase, based on electron, neutron and x-ray diﬀraction data, we show that the incommensurate modulation is related to an abrupt change of the out-of-phase tilting along the a- and c-axis whereas the tilting along the b-axis remain constant across the structure. By using group theory and symmetry analysis we show that we observe an incommensurate analog of HIF which induces a hybrid improper dipolar density wave in NaLaCoWO 6 . The dipolar ordering is due also in this case to a trilinear invariant involving the commensurate and incommensurate octahedra tilting’s.
Identifying Lensed Quasars and Measuring Their Time Delays from Unresolved Light Curves

Identifying multiply imaged quasars is challenging owing to their low density in the sky and the limited angular resolution of wide-field surveys. We show that multiply imaged quasars can be identified using unresolved light curves, without assuming a light-curve template or any prior information. After describing our method, we show, using simulations, that it can attain high precision and recall when we consider high-quality data with negligible noise well below the variability of the light curves. As the noise level increases to that of the Zwicky Transient Facility telescope, we find that precision can remain close to 100% while recall drops to ∼60%. We also consider some examples from Time Delay Challenge 1 and demonstrate that the time delays can be accurately recovered from the joint light-curve data in realistic observational scenarios. We further demonstrate our method by applying it to publicly available COSMOGRAIL data of the observed lensed quasar SDSS J1226−0006. We identify the system as a lensed quasar based on the unresolved light curve and estimate a time delay in good agreement with the one measured by COSMOGRAIL using the individual image light curves. The technique shows great potential to identify lensed quasars in wide-field imaging surveys, especially the soon-to-be-commissioned Vera Rubin Observatory.
A Survey of Self-Supervised and Few-Shot Object Detection

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page: https://gabrielhuang.github. io/fsod-survey/.
Estimating means of bounded random variables by betting

This paper derives confidence intervals (CI) and time-uniform confidence sequences (CS) for the classical problem of estimating an unknown mean from bounded observations. We present a general approach for deriving concentration bounds, that can be seen as a generalization and improvement of the celebrated Chernoff method. At its heart, it is based on a class of composite nonnegative martingales, with strong connections to testing by betting and the method of mixtures. We show how to extend these ideas to sampling without replacement, another heavily studied problem. In all cases, our bounds are adaptive to the unknown variance, and empirically vastly outperform existing approaches based on Hoeffding or empirical Bernstein inequalities and their recent supermartingale generalizations by Howard et al. [2021]. In short, we establish a new state-of-the-art for four fundamental problems: CSs and CIs for bounded means, when sampling with and without replacement.
GRAPHITE: A Practical Framework for Generating Automatic Physical Adversarial Machine Learning Attacks

—This paper investigates an adversary’s ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness , i.e., robustness of a attack to environmental physical variations such as viewpoint and lighting changes, and 3) supporting attacks in both white-box and black-box hard-label scenarios, so that the adversary can attack proprietary models. In particular, the art of automatically picking which areas to perturb remains largely unexplored — an efﬁcient solution would remove the need to search over possible locations, shapes, and sizes as in current patch attacks. In this work, we propose GRAPHITE, an efﬁcient and general framework for generating attacks that satisfy the above three key requirements. GRAPHITE takes advantage of transform-robustness, a metric based on expectation over transforms (EoT), to automatically generate small masks and optimize with gradient-free optimization. GRAPHITE is also ﬂexible as it can easily trade-off transform-robustness, perturbation size, and query count in black-box settings. On a GTSRB model in a hard-label black-box setting, we are able to ﬁnd attacks on all possible 1,806 victim-target class pairs with averages of 77.8% transform-robustness, perturbation size of 16.63% of the victim images, and 126K queries per pair. For digital-only attacks where achieving transform-robustness is not a requirement, GRAPHITE is able to ﬁnd successful small-patch attacks with an average of only 566 queries for 92.2% of victim-target pairs. GRAPHITE is also able to ﬁnd successful attacks using perturbations that modify small areas of the input image against PatchGuard, a recently proposed defense against patch-based attacks.
Student-Teacher Feature Pyramid Matching for Anomaly Detection

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones.
Driving determines the dynamics in transitional pipe flows

The transition to turbulence in a long, straight pipe is one of the outstanding unresolved problems in classical Physics. It is well-established by experiments that a ﬁnite amplitude disturbance is required to trigger the transition to turbulence from laminar ﬂow. Details of the processes involved in the transition are mysterious and a full understanding of them remains aloof. Here we take the novel approach of initiating the ﬂow in a turbulent state and then reducing the ﬂow rate suddenly, a so-called quench, so that the ﬂow decays from turbulence. We use two distinct methods of driving the ﬂow and ﬁnd that the dynamical processes involved in the decay, as well as the fraction of the ﬂow that remains turbulent, are qualitatively distinct for each driving protocol.
Preventing posterior collapse in variational autoencoders for text generation via decoder regularization

Variational autoencoders trained to minimize the reconstruction error are sensitive to the posterior collapse problem, that is the proposal posterior distribution is always equal to the prior. We propose a novel regularization method based on fraternal dropout to prevent posterior collapse. We evaluate our approach using several metrics and observe improvements in all the tested configurations.
Learning Continuous Face Representation with Explicit Functions

How to represent a human face pattern? While it is presented in a continuous way in human visual system, computers often store and process it in a discrete manner with 2D arrays of pixels. The authors attempt to learn a continuous surface representation for face image with explicit function. First, an explicit model (EmFace) for human face representation is proposed in the form of a finite sum of mathematical terms, where each term is an analytic function element. Further, to estimate the unknown parameters of EmFace, a novel neural network, EmNet, is designed with an encoder‐decoder structure and trained from massive face images, where the encoder is defined by a deep convolutional neural network and the decoder is an explicit mathematical expression of EmFace. The authors demonstrate that our EmFace represents face image more accurate than the comparison method, with an average mean square error of 0.000888, 0.000936, 0.000953 on LFW, IARPA Janus Benchmark‐B, and IJB‐C datasets. Visualisation results show that, EmFace has a higher representation performance on faces with various expressions, postures, and other factors. Furthermore, EmFace achieves reasonable performance on several face image processing tasks, including face image restoration, denoising, and transformation.