Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive spirals and shock influenced star formation in the merging cluster A3376

We present a detailed analysis of star formation properties of galaxies in a nearby (z ∼ 0.046) young (∼0.6 Gyr) post-merger cluster system A3376, with a moderate merger shock front (vs ∼1630 km s−1; $\mathcal {M}$ ∼ 2) observed as symmetric radio relics. Exploiting the spectroscopic data from the wide-field OmegaWINGS survey and the associated photometric information, our investigations reveal the plausible effects of the dynamic post-merger environment differing from the high-density cluster environment experienced prior to the merging activity. The remnants of the pre-merger relaxed cluster environment are realized through the existence of passive spiral galaxies located in the central regions of the cluster between the two brightest cluster galaxies. We discover A3376 to contain a population of massive (log (M*/M⊙) > 10) blue regular star-forming spirals in regions of maximum merger shock influence but exhibiting star formation rates similar to those in relaxed clusters at similar epoch. We further discover low-mass (log (M*/M⊙) ≤ 10) late-type blue post-starburst galaxies which could either be formed as a result of rapid quenching of low-mass spirals following the shock-induced star formation or due to the intense surge in the intracluster medium pressures at the beginning of the merger. With the possibility of the merger shock affecting high- and low-mass spirals differently, our results bridge the seemingly contradictory results observed in known merging cluster systems so far and establish that different environmental effects are at play right from pre- to post-merger stages.
A Bayes-Optimal View on Adversarial Examples

The ability to fool modern CNN classifiers with tiny perturbations of the input has lead to the development of a large number of candidate defenses and often conflicting explanations. In this paper, we argue for examining adversarial examples from the perspective of Bayes-Optimal classification. We construct realistic image datasets for which the Bayes-Optimal classifier can be efficiently computed and derive analytic conditions on the distributions so that the optimal classifier is either robust or vulnerable. By training different classifiers on these datasets (for which the "gold standard" optimal classifiers are known), we can disentangle the possible sources of vulnerability and avoid the accuracy-robustness tradeoff that may occur in commonly used datasets. Our results show that even when the optimal classifier is robust, standard CNN training consistently learns a vulnerable classifier. At the same time, for exactly the same training data, RBF SVMs consistently learn a robust classifier. The same trend is observed in experiments with real images.
A Structured Prediction Approach for Conditional Meta-Learning

Optimization-based meta-learning algorithms are a powerful class of methods for learning-to-learn applications such as few-shot learning. They tackle the limited availability of training data by leveraging the experience gained from previously observed tasks. However, when the complexity of the tasks distribution cannot be captured by a single set of shared meta-parameters, existing methods may fail to fully adapt to a target task. We address this issue with a novel perspective on conditional meta-learning based on structured prediction. We propose task-adaptive structured meta-learning (TASML), a principled estimator that weighs meta-training data conditioned on the target task to design tailored meta-learning objectives. In addition, we introduce algorithmic improvements to tackle key computational limitations of existing methods. Experimentally, we show that TASML outperforms state-of-the-art methods on benchmark datasets both in terms of accuracy and efficiency. An ablation study quantifies the individual contribution of model components and suggests useful practices for meta-learning.
The ALMA Spectroscopic Survey in the HUDF: The Cosmic Dust and Gas Mass Densities in Galaxies up to z ∼ 3

Using the deepest 1.2 mm continuum map to date in the Hubble Ultra Deep Field, which was obtained as part of the ALMA Spectroscopic Survey (ASPECS) large program, we measure the cosmic density of dust and implied gas (H2+H i) mass in galaxies as a function of look-back time. We do so by stacking the contribution from all H-band selected galaxies above a given stellar mass in distinct redshift bins, and . At all redshifts, and grow rapidly as M decreases down to 1010 M⊙, but this growth slows down toward lower stellar masses. This flattening implies that at our stellar mass-completeness limits (108 M⊙ and 108.9 M⊙ at z ∼ 0.4 and z ∼ 3), both quantities converge toward the total cosmic dust and gas mass densities in galaxies. The cosmic dust and gas mass densities increase at early cosmic time, peak around z ∼ 2, and decrease by a factor ∼4 and 7, when compared to the density of dust and molecular gas in the local universe, respectively. The contribution of quiescent galaxies (i.e., with little on-going star formation) to the cosmic dust and gas mass densities is minor (≲10%). The redshift evolution of the cosmic gas mass density resembles that of the SFR density, as previously found by CO-based measurements. This confirms that galaxies have relatively constant star formation efficiencies (within a factor ∼2) across cosmic time. Our results also imply that by z ∼ 0, a large fraction (∼90%) of dust formed in galaxies across cosmic time has either been destroyed or ejected to the intergalactic medium.
Contextual Lensing of Universal Sentence Representations

What makes a universal sentence encoder universal? The notion of a generic encoder of text appears to be at odds with the inherent contextualization and non-permanence of language use in a dynamic world. However, mapping sentences into generic fixed-length vectors for downstream similarity and retrieval tasks has been fruitful, particularly for multilingual applications. How do we manage this dilemma? In this work we propose Contextual Lensing, a methodology for inducing context-oriented universal sentence vectors. We break the construction of universal sentence vectors into a core, variable length, sentence matrix representation equipped with an adaptable `lens' from which fixed-length vectors can be induced as a function of the lens context. We show that it is possible to focus notions of language similarity into a small number of lens parameters given a core universal matrix representation. For example, we demonstrate the ability to encode translation similarity of sentences across several languages into a single weight matrix, even when the core encoder has not seen parallel data.
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

Reasoning over visual data is a desirable capability for robotics and vision-based applications. Such reasoning enables forecasting the next events or actions in videos. In recent years, various models have been developed based on convolution operations for prediction or forecasting, but they lack the ability to reason over spatiotemporal data and infer the relationships of different objects in the scene. In this letter, we present a framework based on graph convolution to uncover the spatiotemporal relationships in the scene for reasoning about pedestrian intent. A scene graph is built on top of segmented object instances within and across video frames. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is very crucial piece of information for autonomous vehicles to navigate safely and more smoothly. We approach the problem of intent prediction from two different perspectives and anticipate the intention-to-cross within both pedestrian-centric and location-centric scenarios. In addition, we introduce a new dataset designed specifically for autonomous-driving scenarios in areas with dense pedestrian populations: the Stanford-TRI Intent Prediction (STIP) dataset. Our experiments on STIP and another benchmark dataset show that our graph modeling framework is able to predict the intention-to-cross of the pedestrians with an accuracy of 79.10% on STIP and 79.28% on Joint Attention for Autonomous Driving (JAAD) dataset up to one second earlier than when the actual crossing happens. These results outperform baseline and previous work. Please refer to http://stip.stanford. edu/ for the dataset and code.
Computationally Tractable Riemannian Manifolds for Graph Embeddings

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases (e.g., hierarchical structures benefit from hyperbolic geometry). However, going beyond embedding spaces of constant sectional curvature, while potentially more representationally powerful, proves to be challenging as one can easily lose the appeal of computationally tractable tools such as geodesic distances or Riemannian gradients. Here, we explore two computationally efficient matrix manifolds, showcasing how to learn and optimize graph embeddings in these Riemannian spaces. Empirically, we demonstrate consistent improvements over Euclidean geometry while often outperforming hyperbolic and elliptical embeddings based on various metrics that capture different graph properties. Our results serve as new evidence for the benefits of non-Euclidean embeddings in machine learning pipelines.
Wall-bounded turbulence control: statistical characterisation of actions/states

The present paper reports the results of a Monte Carlo experiment using a turbulent channel flow. Different actions are proposed, varying the size, duration and sign of a localised volumetric force that acts near one wall of a turbulent channel flow, running at a small Reynolds (ReT = 165) in a small computational domain. The effect of each action is evaluated comparing the evolution of the flow with and without the action, gathering statistics over 1700 repetitions of the experiment for each action. The analysis of the results show that small/short forcings are equally likely to increase or decrease the skin friction drag, independently of the sign of the forcing (i.e., towards or away from the wall). When the size or the duration of the forcing increases, so does the probability of increasing the skin friction. Then, an a priori analysis is performed, evaluating the state of the flow just before the action, conditioned to actions that result in a decrease of the skin friction over a period of one eddy turn-over time. The resulting fields of velocity, wall shear stresses and wall pressure are consistent with an opposition control strategy, where the forcing is opposing the vertical motions near the wall. Finally, a preliminary analysis of the performance of actuation triggered by pressure or wall shear stresses sensors is evaluated (i.e., a posteriori analysis). Our results show that the actuation triggered by a wall shear sensor seems to be more effective than the actuation triggered by a wall pressure sensor, at least for the preliminary definitions of sensors and thresholds used here.
Caveats in Eliciting Mobile App Requirements

Factors such as app stores or platform choices heavily affect functional and non-functional mobile app requirements. We surveyed 45 companies and interviewed ten experts to explore how factors that impact mobile app requirements are understood by requirements engineers in the mobile app industry. We observed the lack of knowledge in several areas. For instance, we observed that all practitioners were aware of data privacy concerns, however, they did not know that certain third-party libraries, usage aggregators, or advertising libraries also occasionally leak sensitive user data. Similarly, certain functional requirements may not be implementable in the absence of a third-party library that is either banned from an app store for policy violations or lacks features, for instance, missing desired features in ARKit library for iOS made practitioners turn to Android. We conclude that requirements engineers should have adequate technical experience with mobile app development as well as sufficient knowledge in areas such as privacy, security and law, in order to make informed decisions during requirements elicitation.
ADM mass and the capacity-volume deficit at infinity

Based on the isoperimetric inequality, G. Huisken proposed a definition of total mass in general relativity that is equivalent to the ADM mass for (smooth) asymptotically flat 3-manifolds of nonnegative scalar curvature, but that is well-defined in greater generality. In a similar vein, we use the isocapacitary inequality (bounding capacity from below in terms of volume) to suggest a new definition of total mass. We prove an inequality between it and the ADM mass, and prove the reverse inequality with harmonically flat asymptotics, or, with general asymptotics, for exhaustions by balls (as opposed to arbitrary compact sets). This approach to mass may have applications to problems involving low regularity metrics and convergence in general relativity, and may have some advantages relative to the isoperimetric mass. Some conjectures, analogs of known results for CMC surfaces and isoperimetric regions, are proposed.
Propose, Test, Release: Differentially private estimation with high probability

We derive concentration inequalities for differentially private median and mean estimators building on the "Propose, Test, Release" (PTR) mechanism introduced by Dwork and Lei (2009). We introduce a new general version of the PTR mechanism that allows us to derive high probability error bounds for differentially private estimators. Our algorithms provide the first statistical guarantees for differentially private estimation of the median and mean without any boundedness assumptions on the data, and without assuming that the target population parameter lies in some known bounded interval. Our procedures do not rely on any truncation of the data and provide the first sub-Gaussian high probability bounds for differentially private median and mean estimation, for possibly heavy tailed random variables.
The Benefits of Pairwise Discriminators for Adversarial Training

Adversarial training methods typically align distributions by solving two-player games. However, in most current formulations, even if the generator aligns perfectly with data, a sub-optimal discriminator can still drive the two apart. Absent additional regularization, the instability can manifest itself as a never-ending game. In this paper, we introduce a family of objectives by leveraging pairwise discriminators, and show that only the generator needs to converge. The alignment, if achieved, would be preserved with any discriminator. We provide sufficient conditions for local convergence; characterize the capacity balance that should guide the discriminator and generator choices; and construct examples of minimally sufficient discriminators. Empirically, we illustrate the theory and the effectiveness of our approach on synthetic examples. Moreover, we show that practical methods derived from our approach can better generate higher-resolution images.
A Vision of C-V2X: Technologies, Field Testing, and Challenges With Chinese Development

Cellular vehicle-to-everything (C-V2X) is an important enabling technology for autonomous driving and intelligent transportation systems. It evolves from long-term evolution (LTE)-V2X to new radio (NR)-V2X, which will coexist and be complementary with each other to provide low-latency, high-reliability, and high-throughput communications for various C-V2X applications. In this article, a vision of C-V2X is presented. The requirements of the basic road safety and advanced applications, the architecture, the key technologies, and the standards of C-V2X are introduced, highlighting the technical evolution path from LTE-V2X to NR-V2X. Especially, based on the continual and active promotion of C-V2X research, field testing, and development in China, the related works and progresses are also presented. Finally, the trends of C-V2X applications with technical challenges are envisioned.
Adaptive Graph Auto-Encoder for General Data Clustering

Graph-based clustering plays an important role in the clustering area. Recent studies about graph neural networks (GNN) have achieved impressive success on graph-type data. However, in general clustering tasks, the graph structure of data does not exist such that GNN can not be applied to clustering directly and the strategy to construct a graph is crucial for performance. Therefore, how to extend GNN into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, AdaGAE, which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-euclidean structure sufficiently. Importantly, we find that the simple update of the graph will result in severe degeneration, which can be concluded as better reconstruction means worse update. We provide rigorous analysis theoretically and empirically. Then we further design a novel mechanism to avoid the collapse. Via extending the generative graph models to general type data, a graph auto-encoder with a novel decoder is devised and the weighted graphs can be also applied to GNN. AdaGAE performs well and stably in different scale and type datasets. Besides, it is insensitive to the initialization of parameters and requires no pretraining.
Space Efficient Deterministic Approximation of String Measures

We study approximation algorithms for the following three string measures that are widely used in practice: edit distance (ED), longest common subsequence (LCS), and longest increasing sequence (LIS). All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly $\Theta(n)$ space, where $n$ is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space. Towards this, we design several algorithms that achieve $1+\epsilon$ or $1-\epsilon$ approximation for all three problems, where $\epsilon>0$ can be any constant and even slightly sub constant. Our algorithms are flexible and can be adjusted to achieve the following two regimes of parameters: 1) space $n^\delta$ for any constant $\delta>0$ with running time essentially the same as or slightly more than the standard algorithms; and 2) space $\mathsf{polylog}(n)$ with (a larger) polynomial running time, which puts the approximation versions of the three problems in Steve's class (\textbf{SC}). Our algorithms significantly improve previous results in terms of space complexity, where all known results need to use space at least $\Omega(\sqrt{n})$. Some of our algorithms can also be adapted to work in the asymmetric streaming model [SS13], and output the corresponding sequence. Furthermore, our results can be used to improve a recent result by Farhadi et. al. [FHRS20] about approximating ED in the asymmetric streaming model, reducing the running time from being exponential in [FHRS20] to a polynomial. Our algorithms are based on the idea of using recursion as in Savitch's theorem [Sav70], and a careful adaption of previous techniques to make the recursion work. Along the way we also give a new logspace reduction from longest common subsequence to longest increasing sequence, which may be of independent interest.
The CGM at Cosmic Noon with KCWI: Outflows from a Star-forming Galaxy at z = 2.071

We present the first results from our “CGM at Cosmic Noon with KCWI” program to study gas flows in the circumgalactic medium (CGM) at z = 2–3. Combining the power of a high-resolution VLT/UVES quasar spectrum, a Hubble Space Telescope/Advanced Camera for Surveys image, and integral field spectroscopy with Keck/KCWI (Keck Cosmic Web Imager), we detected Lyα emission from a 1.7L* galaxy at zgal = 2.0711 associated with a Lyman limit system with weak Mg ii (Wr(2796) = 0.24 Å) in quasar field J143040+014939. The galaxy is star-forming (SFRFUV = 37.8 M⊙ yr−1) and clumpy: either an edge-on disk (i = 85°) or, less likely, a major merger. The background quasar probes the galaxy at an impact parameter of D = 66 kpc along the projected galaxy minor axis (Φ = 89°). From photoionization modeling of the absorption system, we infer a total line-of-sight CGM metallicity of . The absorption system is roughly kinematically symmetric about zgal, with a full Mg ii velocity spread of ∼210 km s−1. Given the galaxy–quasar orientation, CGM metallicity, and gas kinematics, we interpret this gas as an outflow that has likely swept up additional material. By modeling the absorption as a polar outflow cone, we find the gas is decelerating with average radial velocity Vout = 109–588 km s−1 for half-opening angles of θ0 = 14°–75°. Assuming a constant Vout, it would take on average tout ∼ 111–597 Myr for the gas to reach 66 kpc. The outflow is energetic, with a mass outflow rate of M⊙ yr−1 and mass loading factor of η < 1.4 ± 1.0. We aim to build a sample of ∼50 Mg ii absorber–galaxy pairs at this epoch to better understand gas flows when they are most actively building galaxies.
Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation

A learning approach for optimal feedback gains for nonlinear continuous time control systems is proposed and analysed. The goal is to establish a rigorous framework for computing approximating optimal feedback gains using neural networks. The approach rests on two main ingredients. First, an optimal control formulation involving an ensemble of trajectories with ‘control’ variables given by the feedback gain functions. Second, an approximation to the feedback functions via realizations of neural networks. Based on universal approximation properties we prove the existence and convergence of optimal stabilizing neural network feedback controllers.
More on quantum chiral higher spin gravity

Chiral Higher Spin Gravity is unique in being the smallest higher spin extension of gravity and in having a simple local action both in flat and (anti)-de Sitter spaces. It must be a closed subsector of any other higher spin theory in four dimensions, which makes it an important building block and benchmark. Using the flat space version for simplicity, we perform a thorough study of quantum corrections in Chiral Theory, which strengthen our earlier results arXiv:1805.00048. Even though the interactions are naively non-renormalizable, we show that there are no UV-divergences in two-, three- and four-point amplitudes at one loop thanks to the higher spin symmetry. We also give arguments that the AdS Chiral Theory should exhibit similar properties. It is shown that Chiral Theory admits Yang-Mills gaugings with $U(N)$, $SO(N)$ and $USp(N)$ groups, which is reminiscent of the Chan-Paton symmetry in string theory.