Miracle-dz/UniFAR-FTUs huggingface dataset:
From starburst to quiescence: post-starburst galaxies and their large-scale clustering over cosmic time

We present the first study of the large-scale clustering of post-starburst (PSB) galaxies in the high-redshift Universe (0.5 < z < 3.0). We select ∼4000 PSB galaxies photometrically, the largest high-redshift sample of this kind, from two deep large-scale near-infrared surveys: the UKIDSS Ultra Deep Survey Data Release 11 and the Cosmic Evolution Survey. Using angular cross-correlation techniques, we estimate the halo masses for this large sample of PSB galaxies and compare them with quiescent and star-forming galaxies selected in the same fields. We find that low-mass, low-redshift (0.5 < z < 1.0) PSB galaxies preferentially reside in very high mass dark matter haloes (Mhalo > 1014 M⊙), suggesting that they are likely to be infalling satellite galaxies in cluster-like environments. High-mass PSB galaxies are more weakly clustered at low redshifts, but they reside in higher mass haloes with increasing look-back time, suggesting strong redshift-dependent halo downsizing. These key results are consistent with previous results, suggesting that two main channels are responsible for the rapid quenching of galaxies. While high-redshift (z > 1) galaxies appear to be quenched by secular feedback mechanisms, processes associated with dense environments are likely to be the key driver of rapid quenching in the low-redshift Universe (z < 1). Finally, we show that the clustering of photometrically selected PSBs is consistent with them being direct descendants of highly dust-enshrouded submillimetre galaxies, providing tantalizing evidence for the oft-speculated evolutionary pathway from starburst to quiescence.
Poking Holes: Looking for Gaps in LIGO/Virgo’s Black Hole Population

Stellar evolution models predict the existence of a gap in the black hole mass spectrum from ∼55 M ⊙–120 M ⊙ due to pair-instability supernovae (PISNe). We investigate the possible existence of such an “upper” mass gap in the second gravitational-wave transient catalog (GWTC-2) by hierarchically modeling the astrophysical distribution of black hole masses. We extend the Truncated and Powerlaw+Peak mass distribution families to allow for an explicit gap in the mass distribution, and apply the extended models to GWTC-2. We find that with the Truncated model there is mild evidence favoring an upper mass gap with log Bayes Factor , inferring the lower and upper bounds at and respectively. When using the Powerlaw+Peak model, we find no preference for the gap. When imposing tighter priors on the gap bounds centered on the expected PISNe gap bounds, the log Bayes factors in favor of a gap mildly increase. These results are however contingent on the parameter inference for the most massive binary, GW190521, for which follow-up analyses showed the source may be an intermediate mass ratio merger that has component masses straddling the gap. Using the GW190521 posterior samples from the analysis in Nitz & Capano (2021), we find an increase in Bayes factors in favor of the gap. However, the overall conclusions are unchanged: there is no preference for a gap when using the Powerlaw+Peak model. This work paves the way for constraining the physics of pair-instability and pulsational pair-instability supernovae and high-mass black hole formation.
Measurement-induced purification in large-
N
 hybrid Brownian circuits

Gregory S. Bentsen, ∗ Subhayan Sahu, ∗ and Brian Swingle 2, 4 Martin A. Fisher School of Physics, Brandeis University, Waltham MA 02453, USA Condensed Matter Theory Center and Department of Physics, University of Maryland, College Park, MD 20742, USA Martin A. Fisher School of Physics, Brandeis University, Waltham MA, USA Maryland Center for Fundamental Physics and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA (Dated: March 29, 2022)
An Expressiveness Hierarchy of Behavior Trees and Related Architectures

In this letter, we provide a formal framework for comparing the expressive power of Behavior Trees (BTs) to other action selection architectures. Taking inspiration from the analogous comparisons of structural programming methodologies, we formalise the concept of ‘expressiveness’. This leads us to an expressiveness hierarchy of control architectures, which includes BTs, Decision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines (FSMs). By distinguishing between BTs with auxiliary variables and those without, we demonstrate the existence of a trade-off in BT design between readability and expressiveness. We discuss what this means for BTs in practice.
Flow in an hourglass: particle friction and stiffness matter

Granular flow out of a silo is studied experimentally and numerically. The time evolution of the discharge rate as well as the normal force (apparent weight) at the bottom of the container is monitored. We show that particle stiffness has a strong effect on the qualitative features of silo discharge. For deformable grains with a Young modulus of about Y m ≈ 40 kPa in a silo with basal pressure of the order of 4 kPa, lowering the friction coefficient leads to a gradual change in the discharge curve: the flow rate becomes filling height dependent, it decreases during the discharge process. For hard grains with a Young modulus of about Y m ≈ 500 MPa the flow rate is much less sensitive to the value of the friction coefficient. Using DEM data combined with a coarse-graining methodology allows us to compute all the relevant macroscopic fields, namely, linear momentum, density and stress tensors. The observed difference in the discharge in the low friction limit is connected to a strong difference in the pressure field: while for hard grains Janssen-screening is effective, leading to high vertical stress near the silo wall and small pressure above the orifice region, for deformable grains the pressure above the orifice is larger and gradually decreases during the discharge process. We have analyzed the momentum balance in the region of the orifice (near the location of the outlet) for the case of soft particles with low friction coefficient, and proposed a phenomenological formulation that predicts the linear decrease of the flow rate with decreasing filling height.
Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work on evaluating factual consistency in abstractive summarization, we propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue using automatic question generation and question answering. Our metric, denoted Q^2, compares answer spans using natural language inference (NLI), instead of token-based matching as done in previous work. To foster proper evaluation, we curate a novel dataset of dialogue system outputs for the Wizard-of-Wikipedia dataset, manually annotated for factual consistency. We perform a thorough meta-evaluation of Q^2 against other metrics using this dataset and two others, where it consistently shows higher correlation with human judgements.
Is Your Language Model Ready for Dense Representation Fine-tuning?

Pre-trained language models (LM) have become go-to text representation encoders. Prior research used deep LMs to encode text sequences such as sentences and passages into single dense vector representations. These dense representations have been used in efﬁ-cient text comparison and embedding-based retrieval. However, dense encoders suffer in low resource situations. Many techniques have been developed to solve this problem. Despite their success, not much is known about why this happens. This paper shows that one cause lies in the readiness of the LM to expose its knowledge through dense representation in ﬁne-tuning, which we term Optimization Readiness. To validate the theory, we present Condenser , a general pre-training architecture based on Transformer LMs, to improve dense optimization readiness. We show that ﬁne-tuning from Condenser signiﬁcantly improves performance for small and/or noisy training sets. 1
The maximum of branching Brownian motion in $\mathbb{R}^d$

(When the dimension d is clear from the context, we omit it from the notation, writing e.g. mt for mt(d), etc.) When d = 1, Bramson [5] proved the convergence in distribution of maxv∈NtX (v) t − mt(1), and the limit was identified by Lalley and Selke [10] to be the limit of a certain derivative martingale. It is not hard to deduce from their results and methods (see, e.g., [15, Thm. 1.1]) that, when d= 1,
The spatial dissemination of COVID-19 and associated socio-economic consequences

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.
Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization

In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication.
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders

Previous work has indicated that pretrained Masked Language Models (MLMs) are not effective as universal lexical and sentence encoders off-the-shelf, i.e., without further task-specific fine-tuning on NLI, sentence similarity, or paraphrasing tasks using annotated task data. In this work, we demonstrate that it is possible to turn MLMs into effective lexical and sentence encoders even without any additional data, relying simply on self-supervision. We propose an extremely simple, fast, and effective contrastive learning technique, termed Mirror-BERT, which converts MLMs (e.g., BERT and RoBERTa) into such encoders in 20-30 seconds with no access to additional external knowledge. Mirror-BERT relies on identical and slightly modified string pairs as positive (i.e., synonymous) fine-tuning examples, and aims to maximise their similarity during “identity fine-tuning”. We report huge gains over off-the-shelf MLMs with Mirror-BERT both in lexical-level and in sentence-level tasks, across different domains and different languages. Notably, in sentence similarity (STS) and question-answer entailment (QNLI) tasks, our self-supervised Mirror-BERT model even matches the performance of the Sentence-BERT models from prior work which rely on annotated task data. Finally, we delve deeper into the inner workings of MLMs, and suggest some evidence on why this simple Mirror-BERT fine-tuning approach can yield effective universal lexical and sentence encoders.
Rigid body equations on spaces of pseudo-differential operators with renormalized trace

We equip the regular Fréchet Lie group of invertible, odd-class, classical pseudodifferential operators Cl odd (M,E) —in which M is a compact smooth manifold and E a (complex) vector bundle over M— with pseudoRiemannian metrics, and we use these metrics to introduce a class of rigid body equations. We prove the existence of a metric connection, we show that our rigid body equations determine geodesics on Cl odd (M,E), and we present rigorous formulas for the corresponding curvature and sectional curvature. Our main tool is the theory of renormalized traces of pseudodifferential operators on compact smooth manifolds.
PHANGS–ALMA: Arcsecond CO(2–1) Imaging of Nearby Star-forming Galaxies

We present PHANGS–ALMA, the first survey to map CO J = 2 → 1 line emission at ∼1″ ∼100 pc spatial resolution from a representative sample of 90 nearby (d ≲ 20 Mpc) galaxies that lie on or near the z = 0 “main sequence” of star-forming galaxies. CO line emission traces the bulk distribution of molecular gas, which is the cold, star-forming phase of the interstellar medium. At the resolution achieved by PHANGS–ALMA, each beam reaches the size of a typical individual giant molecular cloud, so that these data can be used to measure the demographics, life cycle, and physical state of molecular clouds across the population of galaxies where the majority of stars form at z = 0. This paper describes the scientific motivation and background for the survey, sample selection, global properties of the targets, Atacama Large Millimeter/submillimeter Array (ALMA) observations, and characteristics of the delivered data and derived data products. As the ALMA sample serves as the parent sample for parallel surveys with MUSE on the Very Large Telescope, the Hubble Space Telescope, AstroSat, the Very Large Array, and other facilities, we include a detailed discussion of the sample selection. We detail the estimation of galaxy mass, size, star formation rate, CO luminosity, and other properties, compare estimates using different systems and provide best-estimate integrated measurements for each target. We also report the design and execution of the ALMA observations, which combine a Cycle 5 Large Program, a series of smaller programs, and archival observations. Finally, we present the first 1″ resolution atlas of CO emission from nearby galaxies and describe the properties and contents of the first PHANGS–ALMA public data release.
Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

The modeling of Earth observation (EO) data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data are scarce or in extrapolation regimes. In this article, we argue that hybrid learning schemes that combine both approaches can address all these issues efficiently. We introduce Gaussian process (GP) convolution models for hybrid modeling in EO problems. We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modeling, analysis, and understanding. LFMs are hybrid models that incorporate physical knowledge encoded in differential equations into a multioutput GP model. LFMs can transfer information across time series, cope with missing observations, infer explicit latent functions forcing the system, and learn parameterizations which are very helpful for system analysis and interpretability. We illustrate the performance in two case studies. First, we consider time series of soil moisture (SM) from active Advanced Scatterometer (ASCAT) and passive [SM and ocean salinity (SMOS), advanced microwave scanning radiometer-2 (AMSR2)] microwave satellites. We show how assuming a first-order differential equation as governing equation, the model automatically estimates the e-folding time or decay rate related to SM persistence and discovers latent forces related to precipitation. In the second case study, we show how the model can fill in gaps of leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from moderate resolution imaging spectroradiometer (MODIS) optical time series by exploiting their relations across different spatial and temporal domains. The proposed hybrid methodology reconciles the two main approaches in remote-sensing parameter estimation by blending statistical learning and mechanistic modeling.
Heterogeneous Tensor Mixture Models in High Dimensions

We consider the problem of jointly modeling and clustering populations of tensors by introducing a ﬂexible high-dimensional tensor mixture model with heterogeneous covariances. The proposed mixture model exploits the intrinsic structures of tensor data, and is assumed to have means that are low-rank and internally sparse as well as heterogeneous covariances that are separable and conditionally sparse. We develop an eﬃcient high-dimensional expectation-conditional-maximization ( HECM ) algorithm that breaks the challenging optimization in the M-step into several simpler conditional optimization problems, each of which is convex, admits regularization and has closed-form updating formulas. We show that the proposed HECM algorithm, with an appropriate initialization, converges geometrically to a neighborhood that is within statistical precision of the true parameter. Such a theoretical analysis is highly nontrivial due to the dual non-convexity arising from both the EM-type estimation and the non-convex objective function in the M-step. The eﬃcacy of our proposed method is demonstrated through simulation studies and an application to an autism spectrum disorder study, where our analysis identiﬁes important brain regions for diagnosis.
Contrastive Learning With Stronger Augmentations

Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods are benefited from various data augmentations that are carefully designated to maintain their identities so that the images transformed from the same instance can still be retrieved. However, those carefully designed transformations limited us to further explore the novel patterns exposed by other transformations. Meanwhile, as shown in our experiments, direct contrastive learning for stronger augmented images can not learn representations effectively. Thus, we propose a general framework called Contrastive Learning with Stronger Augmentations (CLSA) to complement current contrastive learning approaches. Here, the distribution divergence between the weakly and strongly augmented images over the representation bank is adopted to supervise the retrieval of strongly augmented queries from a pool of instances. Experiments on the ImageNet dataset and downstream datasets showed the information from the strongly augmented images can significantly boost the performance. For example, CLSA achieves top-1 accuracy of 76.2% on ImageNet with a standard ResNet-50 architecture with a single-layer classifier fine-tuned, which is almost the same level as 76.5% of supervised results.
Iron-mediated coupling of microbial oxidation and abiotic reduction of organic contaminants in anoxic environments

In anoxic environments, oxidation of organic compounds such as BTEX fuel components by dissimilatory Fe(III) reduction can generate reactive, mineralbound Fe(II) species, which in turn are able to reduce other classes of organic and inorganic groundwater contaminants. In this study, we designed and evaluated an anaerobic batch reactor mimicking iron-reducing conditions in order to investigate the factors that favor the coupling of microbial toluene oxidation and abiotic reduction of nitroaromatic contaminants. We studied the influence of different Fe(III) bearing minerals and combinations thereof on the coupling of these two processes. Results from laboratory model systems show that complete oxidation of toluene to CO2 by Geobacter metallireducens in the presence of Fe(III) bearing minerals leads to the formation of mineral-bound Fe(II) species capable of reducing 4-acetylnitrobenzene. Whereas significant microbial toluene oxidation was only observed in the presence of amorphous Fe(III) phases, reduction of nitroaromatic compounds only proceeded with Fe(II) species bound to crystalline Fe(III) oxides. Our results suggest that in anoxic soils and sediments containing amorphous and crystalline iron phases simultaneously, coupling of microbial oxidation and abiotic reduction of organic compounds may allow for concurrent natural attenuation of different contaminant classes.
Symmetry Reduction to Optimize a Graph-based Polynomial From Queueing Theory

For given integers $n$ and $d$, both at least 2, we consider a homogeneous multivariate polynomial $f_d$ of degree $d$ in variables indexed by the edges of the complete graph on $n$ vertices and coefficients depending on cardinalities of certain unions of edges. Cardinaels, Borst and Van Leeuwaarden (arXiv:2111.05777, 2021) asked whether $f_d$, which arises in a model of job-occupancy in redundancy scheduling, attains its minimum over the standard simplex at the uniform probability vector. Brosch, Laurent and Steenkamp [SIAM J. Optim. 31 (2021), 2227--2254] proved that $f_d$ is convex over the standard simplex if $d=2$ and $d=3$, implying the desired result for these $d$. We give a symmetry reduction to show that for fixed $d$, the polynomial is convex over the standard simplex (for all $n\geq 2$) if a constant number of constant matrices (with size and coefficients independent of $n$) are positive semidefinite. This result is then used in combination with a computer-assisted verification to show that the polynomial $f_d$ is convex for $d\leq 9$.
LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation

This paper presents a set of experiments to evaluate and compare between the performance of using CBOW Word2Vec and Lemma2Vec models for Arabic Word-in-Context (WiC) disambiguation without using sense inventories or sense embeddings. As part of the SemEval-2021 Shared Task 2 on WiC disambiguation, we used the dev.ar-ar dataset (2k sentence pairs) to decide whether two words in a given sentence pair carry the same meaning. We used two Word2Vec models: Wiki-CBOW, a pre-trained model on Arabic Wikipedia, and another model we trained on large Arabic corpora of about 3 billion tokens. Two Lemma2Vec models was also constructed based on the two Word2Vec models. Each of the four models was then used in the WiC disambiguation task, and then evaluated on the SemEval-2021 test.ar-ar dataset. At the end, we reported the performance of different models and compared between using lemma-based and word-based models.