Miracle-dz/UniFAR-FTUs huggingface dataset:
Implications of Increased Central Mass Surface Densities for the Quenching of Low-mass Galaxies

We use the Cosmic Assembly Deep Near-infrared Extragalactic Legacy Survey data to study the relationship between quenching and the stellar mass surface density within the central radius of 1 kpc (Σ1) of low-mass galaxies (stellar mass M * ≲ 109.5 M ⊙) at 0.5 ≤ z < 1.5. Our sample is mass complete down to ∼109 M ⊙ at 0.5 ≤ z < 1.0. We compare the mean Σ1 of star-forming galaxies (SFGs) and quenched galaxies (QGs) at the same redshift and M *. We find that low-mass QGs have a higher Σ1 than low-mass SFGs, similar to galaxies above 1010 M ⊙. The difference of Σ1 between QGs and SFGs increases slightly with M * at M * ≲ 1010 M ⊙ and decreases with M * at M * ≳ 1010 M ⊙. The turnover mass is consistent with the mass where quenching mechanisms transition from internal to environmental quenching. At 0.5 ≤ z < 1.0, we find that Σ1 of galaxies increases by about 0.25 dex in the green valley (i.e., the transition region from star forming to fully quenched), regardless of their M *. Using the observed specific star formation rate gradient in the literature as a constraint, we estimate that the quenching timescale (i.e., time spent in the transition) of low-mass galaxies is a few (∼4) Gyr at 0.5 ≤ z < 1.0. The mechanisms responsible for quenching need to gradually quench star formation in an outside-in way, i.e., preferentially ceasing star formation in outskirts of galaxies while maintaining their central star formation to increase Σ1. An interesting and intriguing result is the similarity of the growth of Σ1 in the green valley between low-mass and massive galaxies, which suggests that the role of internal processes in quenching low-mass galaxies is a question worthy of further investigation.
Selecting accreted populations: metallicity, elemental abundances, and ages of the Gaia-Sausage-Enceladus and Sequoia populations

Identifying stars found in the Milky Way as having formed in situ or accreted can be a complex and uncertain undertaking. We use Gaia kinematics and APOGEE elemental abundances to select stars belonging to the Gaia-Sausage-Enceladus (GSE) and Sequoia accretion events. These samples are used to characterize the GSE and Sequoia population metallicity distribution functions, elemental abundance patterns, age distributions, and progenitor masses. We find that the GSE population has a mean [Fe/H] ∼ −1.15 and a mean age of 10–12 Gyr. GSE has a single sequence in [Mg/Fe] versus [Fe/H] consistent with the onset of SN Ia Fe contributions and uniformly low [Al/Fe] of ∼−0.25 dex. The derived properties of the Sequoia population are strongly dependent on the kinematic selection. We argue the selection with the least contamination is Jϕ/Jtot < −0.6 and (Jz − JR)/Jtot < 0.1. This results in a mean [Fe/H] ∼ −1.3 and a mean age of 12–14 Gyr. The Sequoia population has a complex elemental abundance distribution with mainly high-[Mg/Fe] stars. We use the GSE [Al/Fe] versus [Mg/H] abundance distribution to inform a chemically based selection of accreted stars, which is used to remove possible contaminant stars from the GSE and Sequoia samples.
Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in the CV community. With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era. To this end, in this paper, we consider WSOL is a sub-task of WSOD and provide a comprehensive survey of the recent achievements of WSOD. Specifically, we firstly describe the formulation and setting of the WSOD, including the background, challenges, basic framework. Meanwhile, we summarize and analyze all advanced techniques and training tricks for improving detection performance. Then, we introduce the widely-used datasets and evaluation metrics of WSOD. Lastly, we discuss the future directions of WSOD. We believe that these summaries can help pave a way for future research on WSOD and WSOL.
Multi-Dimensional Screening: Buyer-Optimal Learning and Informational Robustness

What is the optimal mechanism that a monopolist should use to sell multiple goods to a single buyer? Despite being a classic economic problem, multi-dimensional screening is notoriously intractable. Even if the seller has just two goods and the buyer's values are additive, independent, and identically distributed, the optimal mechanism is hard to characterize generally. In this paper, we study a general version (with arbitrarily many goods and non-additive values) of this problem but with the novel feature of buyer learning. As it turns out, introducing this new feature makes the model tractable and in certain environments---including the one with independent and additive values---makes pure bundling an optimal mechanism.
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale

Tremendous success of machine learning (ML) and the unabated growth in model complexity motivated many ML-specific designs in hardware architectures to speed up the model inference. While these architectures are diverse, highly optimized low-precision arithmetic is a component shared by most. Nevertheless, recommender systems important to Facebook’s personalization services are demanding and complex: They must serve billions of users per month responsively with low latency while maintaining high prediction accuracy. Do these low-precision architectures work well with our production recommendation systems? They do. But not without significant effort. In this article, we share our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the tool chain to maintain our models’ accuracy throughout their lifespan. We believe our lessons from the trenches can promote better codesign between hardware architecture and software engineering, and advance the state of the art of ML in industry.
Block dense weighted networks with augmented degree correction

Abstract Dense networks with weighted connections often exhibit a community-like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on each node’s community membership. We propose a new framework for generating and estimating dense weighted networks with potentially different connectivity patterns across different communities. The proposed model relies on a particular class of functions which map individual node characteristics to the edges connecting those nodes, allowing for flexibility while requiring a small number of parameters relative to the number of edges. By leveraging the estimation techniques, we also develop a bootstrap methodology for generating new networks on the same set of vertices, which may be useful in circumstances where multiple data sets cannot be collected. Performance of these methods is analyzed in theory, simulations, and real data.
LAGER Lyα Luminosity Function at z ∼ 7: Implications for Reionization

We present a new measurement of the Lyα luminosity function (LF) at redshift z = 6.9, finding moderate evolution from z = 5.7 that is consistent with a fully or largely ionized z ∼ 7 intergalactic medium. Our result is based on four fields of the LAGER (Lyman Alpha Galaxies in the Epoch of Reionization) project. Our survey volume of 6.1 × 106 Mpc3 is double that of the next largest z ∼ 7 survey. We combine two new LAGER fields (WIDE12 and GAMA15A) with two previously reported LAGER fields (COSMOS and CDFS). In the new fields, we identify N = 95 new z = 6.9 Lyα emitter (LAEs) candidates, characterize our survey’s completeness and reliability, and compute Lyα LFs. The best-fit Schechter LF parameters for all four LAGER fields are in good general agreement. Two fields (COSMOS and WIDE12) show evidence for a bright-end excess above the Schechter function fit. We find that the Lyα luminosity density declines at the same rate as the UV continuum LF from z = 5.7 to 6.9. This is consistent with an intergalactic medium that was fully ionized as early as redshift z ∼ 7 or with a volume-averaged neutral hydrogen fraction of x H I < 0.33 at 1σ.
Chemical abundances in seven metal-poor H ii regions and a determination of the primordial helium abundance

We conducted a long-slit spectrophotometry analysis to obtain the chemical abundances of seven metal-poor H II regions in three galaxies: UM 160, UM 420, and TOL 0513−393. The data have been taken with the Focal Reducer Low Dispersion Spectrograph 1 (FORS1) at the 8.2-m Very Large Telescope. We derived the physical conditions and the chemical abundances of N, O, Ne, S, Ar and Cl. We also performed a detailed analysis that involves abundance determinations using the t2 formalism. Based on He I recombination line intensity ratios, together with the Helio14 code, we derived the abundance of He. In addition, for a value ΔY/ΔZO = 3.3 ± 0.7, we have estimated that the primordial helium abundance by mass is YP = 0.2448± 0.0033. This value agrees with values derived from Standard Big Bang Nucleosynthesis and with other recent determinations of YP.
Intriguing Parameters of Structural Causal Models

In recent years there has been a lot of focus on adversarial attacks, especially on deep neural networks. Here, we argue that they are more general in nature and can easily affect a larger class of models, e.g., any differentiable perturbed optimizers. We further show that such attacks can be determined by the hidden confounders in a domain, thus drawing a novel connection between such attacks and causality. Establishing this causal perspective is characterized by the inﬂuence of the structural causal model’s data generating process on the subsequent optimization thereby exhibiting intriguing parameters of the former. We reveal the existence of such parameters for three combinatorial optimization problems, namely linear assignment, shortest path and a real world problem of energy systems. Our empirical examination also unveils worrisome consequences of these attacks on differentiable perturbed optimizers thereby highlighting the criticality of our ﬁndings.
TreeBERT: A Tree-Based Pre-Trained Model for Programming Language

Source code can be parsed into the abstract syntax tree (AST) based on defined syntax rules. However, in pre-training, little work has considered the incorporation of tree structure into the learning process. In this paper, we present TreeBERT, a tree-based pre-trained model for improving programming language-oriented generation tasks. To utilize tree structure, TreeBERT represents the AST corresponding to the code as a set of composition paths and introduces node position embedding. The model is trained by tree masked language modeling (TMLM) and node order prediction (NOP) with a hybrid objective. TMLM uses a novel masking strategy designed according to the tree's characteristics to help the model understand the AST and infer the missing semantics of the AST. With NOP, TreeBERT extracts the syntactical structure by learning the order constraints of nodes in AST. We pre-trained TreeBERT on datasets covering multiple programming languages. On code summarization and code documentation tasks, TreeBERT outperforms other pre-trained models and state-of-the-art models designed for these tasks. Furthermore, TreeBERT performs well when transferred to the pre-trained unseen programming language.
Dynamics of quantum Hall interfaces

Qi Li, Ken K. W. Ma, ∗ Ruojun Wang, Zi-Xiang Hu, 4 Hao Wang, and Kun Yang GBA Branch of Aerospace Information Research Institute, Chinese Academy of Sciences, Guangzhou 510535, China National High Magnetic Field Laboratory and Department of Physics, Florida State University, Tallahassee, Florida 32306, USA Department of Physics, Chongqing University,Chongqing 401331, People’s Republic of China Chongqing Key Laboratory for Strongly Coupled Physics, Chongqing 401331, People’s Republic of China Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China (Dated: September 13, 2021)
Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method to plan overtaking maneuvers in autonomous racing. A Gaussian process is used to learn the behavior of the leading vehicle. Based on the outputs of the Gaussian process, a stochastic Model Predictive Control algorithm plans optimistic trajectories, such that the controlled autonomous race car is able to overtake the leading vehicle. The proposed method is tested in a simple simulation scenario.
Non-Hermitian Lindhard function and Friedel oscillations

Balázs Dóra, 2, ∗ Doru Sticlet, † and Cătălin Paşcu Moca 5, ‡ MTA-BME Lendület Topology and Correlation Research Group, Budapest University of Technology and Economics, 1521 Budapest, Hungary Department of Theoretical Physics, Budapest University of Technology and Economics, Budapest, Hungary National Institute for R&D of Isotopic and Molecular Technologies, 67-103 Donat, 400293 Cluj-Napoca, Romania MTA-BME Quantum Dynamics and Correlations Research Group, Institute of Physics, Budapest University of Technology and Economics, Budafoki ut 8. , H-1111 Budapest, Hungary Department of Physics, University of Oradea, 410087, Oradea, Romania (Dated: May 27, 2021)
Prosodic segmentation for parsing spoken dialogue

Parsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), but has assumed that the input to the parser is already segmented into sentence-like units (SUs), which isn’t true in existing speech applications. We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model). In experiments on the English Switchboard corpus, we find that when using transcripts alone, the turn-based model has trouble segmenting SUs, leading to worse parse performance than the SU-based model. However, prosody can effectively replace gold standard SU boundaries: with prosody, the turn-based model performs as well as the SU-based model (91.38 vs. 91.06 F1 score, respectively), despite performing two tasks (SU segmentation and parsing) rather than one (parsing alone). Analysis shows that pitch and intensity features are the most important for this corpus, since they allow the model to correctly distinguish an SU boundary from a speech disfluency – a distinction that the model otherwise struggles to make.
Adaptive Monte Carlo augmented with normalizing flows

Significance Monte Carlo methods, tools for sampling data from probability distributions, are widely used in the physical sciences, applied mathematics, and Bayesian statistics. Nevertheless, there are many situations in which it is computationally prohibitive to use Monte Carlo due to slow “mixing” between modes of a distribution unless hand-tuned algorithms are used to accelerate the scheme. Machine learning techniques based on generative models offer a compelling alternative to the challenge of designing efficient schemes for a specific system. Here, we formalize Monte Carlo augmented with normalizing flows and show that, with limited prior data and a physically inspired algorithm, we can substantially accelerate sampling with generative models.
Comparison of nonlinear solvers within continuation method for steady-state variably saturated groundwater flow modelling

Abstract Nonlinearity continuation method, applied to boundary value problems for steady-state Richards equation, gradually approaches the solution through a series of intermediate problems. Originally, the Newton method with simple line search algorithm was used to solve the intermediate problems. In the present paper, other solvers such as Picard and mixed Picard–Newton methods are considered, combined with slightly modified line search approach. Numerical experiments are performed with advanced finite volume discretizations for model and real-life problems.
Complete set of quasi-conserved quantities for spinning particles around Kerr

We revisit the conserved quantities of the Mathisson-Papapetrou-Tulczyjew equations describing the motion of spinning particles on a fixed background. Assuming Ricci-flatness and the existence of a Killing-Yano tensor, we demonstrate that besides the two non-trivial quasi-conserved quantities, i.e. conserved at linear order in the spin, found by Rüdiger, non-trivial quasi-conserved quantities are in one-to-one correspondence with non-trivial mixed-symmetry Killing tensors. We prove that no such stationary and axisymmetric mixed-symmetry Killing tensor exists on the Kerr geometry. We discuss the implications for the motion of spinning particles on Kerr spacetime where the quasi-constants of motion are shown not to be in complete involution.
Landau-Fermi liquids in disguise

In periodic systems of interacting electrons, Fermi and Luttinger surfaces refer to the locations within the Brillouin zone of poles and zeros, respectively, of the single-particle Green's function at zero energy and temperature. Such difference in analytic properties underlies the emergence of well-defined quasiparticles close to a Fermi surface, in contrast to their supposed non-existence close to a Luttinger surface, where the single-particle density-of-states vanishes at zero energy. We here show that, contrary to such common belief, coherent `quasiparticles` do exist also approaching a Luttinger surface in compressible interacting electron systems. Thermodynamic and dynamic properties of such `quasiparticles` are just those of conventional ones. For instance, they yield well defined quantum oscillations in Luttinger's surface and linear in temperature specific heat, which is striking given the vanishing density of states of physical electrons, but actually not uncommon in strongly correlated materials.
Detecting Biological Locomotion in Video: A Computational Approach

Animals locomote for various reasons: to search for food, to ﬁnd suitable habitat, to pursue prey, to escape from predators, or to seek a mate. The grand scale of biodiversity contributes to the great locomotory design and mode diversity. In this report, the locomotion of general biological species is referred to as biolocomotion . The goal of this report is to develop a computational approach to detect biolocomotion in any unprocessed video. The ways biological entities locomote through an environment are extremely diverse. Various creatures make use of legs, wings, ﬁns, and other means to move through the world. Signiﬁcantly, the motion exhibited by the body parts to navigate through an environment can be modelled by a combination of an overall positional advance with an overlaid asymmetric oscillatory pattern, a distinctive signature that tends to be absent in non-biological objects in locomotion. In this report, this key trait of positional advance with asymmetric oscillation along with diﬀerences in an object’s common motion (extrinsic motion) and localized motion of its parts (intrinsic motion) is exploited to detect biolocomotion. In particular, a computational algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion. An alternative algorithm, based on generic handcrafted features combined with learning is assembled out of components from allied areas of investigation, also is presented as a basis of comparison to the main proposed algorithm. A novel biolocomotion dataset encompassing a wide range of moving biological and non-biological objects in natural settings is provided. Also, biolocomotion annotations to an extant camouﬂage animals dataset is provided. Quantitative results indicate that the proposed algorithm considerably outperforms the alternative approach, supporting the hypothesis that biolocomotion can be detected reliably based on its distinct signature of positional advance with asymmetric oscillation and extrinsic/intrinsic motion dissimilarity. results to validate the proposed algorithm’s ability to detect biolocomotion and its superiority over an alternative approach that combines handcrafted features with learning. The results conﬁrmed that biolocomotion can be modelled by measuring spatial advance of an object (locomotion) in conjunction with asymmetric oscillatory patterns and extrinsic motion dissimilarity. Furthermore, the results indicate that eschewing appearance information and relying solely on motion information in detecting biolocomotion yields superior performance. These studies were enabled by the introduction of the ﬁrst dataset built for biolocomotion detection, BOLD.
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with $ L $ layers of minimal widths $ r_1^*, \ldots, r_{L-1}^* $ reaches a zero-loss minimum at $ r_1^*! \cdots r_{L-1}^*! $ isolated points that are permutations of one another, we show that adding one extra neuron to each layer is sufficient to connect all these previously discrete minima into a single manifold. For a two-layer overparameterized network of width $ r^*+ h =: m $ we explicitly describe the manifold of global minima: it consists of $ T(r^*, m) $ affine subspaces of dimension at least $ h $ that are connected to one another. For a network of width $m$, we identify the number $G(r,m)$ of affine subspaces containing only symmetry-induced critical points that are related to the critical points of a smaller network of width $r<r^*$. Via a combinatorial analysis, we derive closed-form formulas for $ T $ and $ G $ and show that the number of symmetry-induced critical subspaces dominates the number of affine subspaces forming the global minima manifold in the mildly overparameterized regime (small $ h $) and vice versa in the vastly overparameterized regime ($h \gg r^*$). Our results provide new insights into the minimization of the non-convex loss function of overparameterized neural networks.
FINNger - Applying artificial intelligence to ease math learning for children

Kids have an amazing capacity to use modern electronic devices such as tablets, smartphones, etc. This has been incredibly boosted by the ease of access of these devices given the expansion of such devices through the world, reaching even third world countries. Also, it is well known that children tend to have difficulty learning some subjects at pre-school. We as a society focus extensively on alphabetization, but in the end, children end up having differences in another essential area: Mathematics. With this work, we create the basis for an intuitive application that could join the fact that children have a lot of ease when using such technological applications, trying to shrink the gap between a fun and enjoyable activity with something that will improve the children knowledge and ability to understand concepts when in a low age, by using a novel convolutional neural network to achieve so, named FINNger.