Miracle-dz/UniFAR-FTUs huggingface dataset:
Process convergence of fluctuations of linear eigenvalue statistics of random circulant matrices

We discuss the process convergence of the time dependent fluctuations of linear eigenvalue statistics of random circulant matrices with independent Brownian motion entries, as the dimension of the matrix tends to [Formula: see text]. Our derivation is based on the trace formula of circulant matrix, method of moments and some combinatorial techniques.
Elemental Abundances in M31: A Comparative Analysis of Alpha and Iron Element Abundances in the the Outer Disk, Giant Stellar Stream, and Inner Halo of M31

We measured [Fe/H] and [α/Fe] using spectral synthesis of low-resolution stellar spectroscopy for 70 individual red-giant-branch stars across four fields spanning the outer disk, Giant Stellar Stream (GSS), and inner halo of M31. Fields at M31-centric projected distances of 23 kpc in the halo, 12 kpc in the halo, 22 kpc in the GSS, and 26 kpc in the outer disk are α-enhanced, with [α/Fe] = 0.43, 0.50, 0.41, and 0.58, respectively. The 23 and 12 kpc halo fields are relatively metal-poor, with [Fe/H] = −1.54 and −1.30, whereas the 22 kpc GSS and 26 kpc outer disk fields are relatively metal-rich with [Fe/H] = −0.84 and −0.92, respectively. For fields with substructure, we separated the stellar populations into kinematically hot stellar halo components and kinematically cold components. We did not find any evidence of a radial [α/Fe] gradient along the high surface brightness core of the GSS between ∼17 and 22 kpc. However, we found tentative suggestions of a negative radial [α/Fe] gradient in the stellar halo, which may indicate that different progenitor(s) or formation mechanisms contributed to the build up of the inner versus outer halo. Additionally, the [α/Fe] distribution of the metal-rich ([Fe/H] > −1.5), smooth inner stellar halo (rproj ≲ 26 kpc) is inconsistent with having formed from the disruption of a progenitor(s) similar to present-day M31 satellite galaxies. The 26 kpc outer disk is most likely associated with the extended disk of M31, where its high α-enhancement provides support for an episode of rapid star formation in M31's disk possibly induced by a major merger.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

We focus on the cross-domain context-dependent text-to-SQL generation task. Based on the observation that adjacent natural language questions are often linguistically dependent and their corresponding SQL queries tend to overlap, we utilize the interaction history by editing the previous predicted query to improve the generation quality. Our editing mechanism views SQL as sequences and reuses generation results at the token level in a simple manner. It is flexible to change individual tokens and robust to error propagation. Furthermore, to deal with complex table structures in different domains, we employ an utterance-table encoder and a table-aware decoder to incorporate the context of the user utterance and the table schema. We evaluate our approach on the SParC dataset and demonstrate the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch. Our code is available at https://github.com/ryanzhumich/sparc_atis_pytorch.
Asymmetric butterfly velocities in 2-local Hamiltonians

The speed of information propagation is finite in quantum systems with local interactions. In many such systems, local operators spread ballistically in time and can be characterized by a ``butterfly velocity", which can be measured via out-of-time-ordered correlation functions. In general, the butterfly velocity can depend asymmetrically on the direction of information propagation. In this work, we construct a family of simple 2-local Hamiltonians for understanding the asymmetric hydrodynamics of operator spreading. Our models live on a one dimensional lattice and exhibit asymmetric butterfly velocities between the left and right spatial directions. This asymmetry is transparently understood in a free (non-interacting) limit of our model Hamiltonians, where the butterfly speed can be understood in terms of quasiparticle velocities.
AtLoc: Attention Guided Camera Localization

Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers. To some extent, this has been tackled by sequential (multi-images) or geometry constraint approaches, which can learn to reject dynamic objects and illumination conditions to achieve better performance. In this work, we show that attention can be used to force the network to focus on more geometrically robust objects and features, achieving state-of-the-art performance in common benchmark, even if using only a single image as input. Extensive experimental evidence is provided through public indoor and outdoor datasets. Through visualization of the saliency maps, we demonstrate how the network learns to reject dynamic objects, yielding superior global camera pose regression performance. The source code is avaliable at https://github.com/BingCS/AtLoc.
Schwarzschild modeling of barred galaxies

Abstract We review the Schwarzschild orbit-superposition approach and present a new implementation of this method, which can deal with a large class of systems, including rotating barred disk galaxies. We discuss two conceptual problems in this field: the intrinsic degeneracy of determining the potential from line-of-sight kinematics, and the non-uniqueness of deprojection and related biases in potential inference, especially acute for triaxial bars. When applied to mock datasets with known 3d shape, our method correctly recovers the pattern speed and other potential parameters. However, more work is needed to systematically address these two problems for real observational datasets.
On Learning Disentangled Representations for Gait Recognition

Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and viewing angle. To remedy this issue, we propose a novel AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature. Both of them are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency. We further compare our GaitNet with state-of-the-art face recognition to demonstrate the advantages of gait biometrics identification under certain scenarios, e.g., long-distance/lower resolutions, cross viewing angles. Source code is available at http://cvlab.cse. msu.edu/project-gaitnet.html.
Many Zeros of Many Characters of GL(n,q)

For $G=\textrm{GL}(n,q)$, the proportion $P_{n,q}$ of pairs $(\chi ,g)$ in $\textrm{Irr}(G)\times G$ with $\chi (g)\neq 0$ satisfies $P_{n,q}\to 0$ as $n\to \infty $.
The richness-to-mass relation of CAMIRA galaxy clusters from weak-lensing magnification in the Subaru Hyper Suprime-Cam survey

We present a statistical weak-lensing magnification analysis on an optically selected sample of 3029 CAMIRA (Cluster finding Algorithm based on Multiband Identification of Red-sequence gAlaxies) galaxy clusters with richness N > 15 at redshift 0.2 ≤ z < 1.1 in the Subaru Hyper Suprime-Cam survey. We use two distinct populations of colour-selected, flux-limited background galaxies, namely the low-z and high-z samples at mean redshifts of ≈1.1 and ≈1.4, respectively, from which to measure the weak-lensing magnification signal by accounting for cluster contamination as well as masking effects. Our magnification bias measurements are found to be uncontaminated according to validation tests against the ‘null-test’ samples for which the net magnification bias is expected to vanish. The magnification bias for the full CAMIRA sample is detected at a significance level of 9. 51σ, which is dominated by the high-z background. We forward-model the observed magnification data to constrain the normalization of the richness-to-mass (N–M) relation for the CAMIRA sample with informative priors on other parameters. The resulting scaling relation is N∝ (M500)0.92 ± 0.13(1 + z)−0.48 ± 0.69, with a characteristic richness of N = 17.72 ± 2.60 and intrinsic lognormal scatter of 0.15 ± 0.07 at M500 = 1014 h−1 M⊙. With the derived N–M relation, we provide magnification-calibrated mass estimates of individual CAMIRA clusters, with the typical uncertainty of ≈39 and ≈32 per cent at richness of ≈20 and ≈40, respectively. We further compare our magnification-inferred N–M relation with those from the shear-based results in the literature, finding good agreement.
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher ROUGE scores. We provide extensive comparisons with strong baseline methods, prior state of the art work as well as multiple variants of our approach including those using only transformers, only extractive techniques and combinations of the two. We examine these models using four different summarization tasks and datasets: arXiv papers, PubMed papers, the Newsroom and BigPatent datasets. We find that transformer based methods produce summaries with fewer n-gram copies, leading to n-gram copying statistics that are more similar to human generated abstracts. We include a human evaluation, finding that transformers are ranked highly for coherence and fluency, but purely extractive methods score higher for informativeness and relevance. We hope that these architectures and experiments may serve as strong points of comparison for future work. Note: The abstract above was collaboratively written by the authors and one of the models presented in this paper based on an earlier draft of this paper.
Relevance Matrix Factorization

Implicit feedback plays a critical role to construct recommender systems because this type of feedback is prevalent in the real-world. However, effectively utilizing implicit feedback is challenging because of positive-unlabeled or missing-not-at-random problems. To tackle these challenges, in this paper, we first show that existing approaches are biased toward the true metric. Subsequently, we provide a theoretically principled approach to handle the problems inspired by estimation methods in causal inference. In particular, we propose an unbiased estimator for the true metric of interest solving the above problems simultaneously. Experiments on two standard real-world datasets demonstrate the superiority of the proposed approach against state-of-the-art recommendation algorithms.
Value-centric dynamic partial order reduction

The verification of concurrent programs remains an open challenge, as thread interaction has to be accounted for, which leads to state-space explosion. Stateless model checking battles this problem by exploring traces rather than states of the program. As there are exponentially many traces, dynamic partial-order reduction (DPOR) techniques are used to partition the trace space into equivalence classes, and explore a few representatives from each class. The standard equivalence that underlies most DPOR techniques is the happens-before equivalence, however recent works have spawned a vivid interest towards coarser equivalences. The efficiency of such approaches is a product of two parameters: (i) the size of the partitioning induced by the equivalence, and (ii) the time spent by the exploration algorithm in each class of the partitioning. In this work, we present a new equivalence, called value-happens-before and show that it has two appealing features. First, value-happens-before is always at least as coarse as the happens-before equivalence, and can be even exponentially coarser. Second, the value-happens-before partitioning is efficiently explorable when the number of threads is bounded. We present an algorithm called value-centric DPOR (VCDPOR), which explores the underlying partitioning using polynomial time per class. Finally, we perform an experimental evaluation of VCDPOR on various benchmarks, and compare it against other state-of-the-art approaches. Our results show that value-happens-before typically induces a significant reduction in the size of the underlying partitioning, which leads to a considerable reduction in the running time for exploring the whole partitioning.
Secure Transmission Strategy for Intelligent Reflecting Surface Enhanced Wireless System

In this paper, we investigate the design of secure transmission frameworks with an intelligent reflecting surface (IRS). Our design aims to minimize the system energy consumption in cases of rank-one and full-rank access point (AP)-IRS links. To facilitate the design, the problem is divided into two parts: design of beamforming vector at AP and phase shift at IRS. In the rank-one channel model, the beamforming vector design and phase shift design are independent. A closed-form expression of beamforming vector is derived. Meanwhile, some algorithms, including the semidefinite relaxation algorithm and projected gradient algorithm, are taken to solve the phase shift problem in the case of instantaneous channel, and in the statistical channel model, the impact of phase shift on the overall system is analyzed. However, since beamforming and phase shift depend on each other in the full-rank model, we refer to conventional wiretap model and utilize an eigenvalue-based algorithm to obtain beamforming vector, while the aforementioned two phase optimization schemes are also applied. Simulation results show that the IRS-enhanced system is envisioned to improve physical layer security.
Neural Language Model for Automated Classification of Electronic Medical Records at the Emergency Room. The Significant Benefit of Unsupervised Generative Pre-training

In order to build a national injury surveillance system based on emergency room (ER) visits we are developing a coding system to classify their causes from clinical notes in free-text. Supervised learning techniques have shown good results in this area but require large number of annotated dataset. New levels of performance have been recently achieved in neural language models (NLM) with models based on the Transformer architecture incorporating an unsupervised generative pre-training step. Our hypothesis is that methods involving a generative self-supervised pre-training step can significantly reduce the required number of annotated samples for supervised fine-tuning. In this case study, we assessed whether we could predict from free-text clinical notes whether a visit was the consequence of a traumatic or non-traumatic event. Using fully re-trained GPT-2 models (without OpenAI pre-trained weightings), we compared two scenarios: Scenario A (26 study cases of different training data sizes) consisted in training the GPT-2 on the trauma/non-trauma labeled (up to 161 930) clinical notes. In Scenario B (19 study cases), a first step of self-supervised pre-training phase with unlabeled (up to 151 930) notes and the second step of supervised fine-tuning with labeled (up to 10 000) notes. Results showed that, Scenario A needed to process >6 000 notes to achieve good performance (AUC>0.95), Scenario B needed only 600 notes, gain of a factor 10. At the end case of both scenarios, for 16 times more data (161 930 vs. 10 000), the gain from Scenario A compared to Scenario B is only an improvement of 0.89% in AUC and 2.12% in F1 score. To conclude, it is possible to adapt a multi-purpose NLM model such as the GPT-2 to create a powerful tool for classification of free-text notes with only very small number of labeled samples.
First [N ii]122 μm Line Detection in a QSO-SMG Pair BRI 1202−0725 at z = 4.69

We report the first detection obtained with the Atacama Large Millimeter/submillimeter Array of the [N ii] 122 μm line emission from a galaxy group BRI 1202−0725 at z = 4.69 consisting of a quasi-stellar object (QSO) and a submillimeter-bright galaxy (SMG). Combining this with a detection of [N ii] 205 μm line in both galaxies, we constrain the electron densities of the ionized gas based on the line ratio of [N ii] 122/205. The derived electron densities are 26 − 11 + 12 and 134 − 39 + 50 cm−3 for the SMG and the QSO, respectively. The electron density of the SMG is similar to that of the Galactic Plane and to the average of the local spirals. However, higher electron densities (by up to a factor of three) could be possible for systematic uncertainties of the line flux estimates. The electron density of the QSO is comparable to high-z star-forming galaxies at z = 1.5–2.3, obtained using rest-frame optical lines and with the lower limits suggested from stacking analysis on lensed starbursts at z = 1–3.6 using the same tracer of [N ii]. Our results suggest a large scatter of electron densities in global scale at fixed star formation rates for extreme starbursts. The success of the [N ii] 122 μm and 205 μm detections at z = 4.69 demonstrates the power of future systematic surveys of extreme starbursts at z > 4 for probing the interstellar medium conditions and the effects on surrounding environments.
Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior.We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape -30,000 images and their captions of 38 Kenyan food types.We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya.We used the second dataset, KenyanFood13, to train a classifier Kenyan-FTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019.
Order-free Learning Alleviating Exposure Bias in Multi-label Classification

Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability.
AS-REGULARITY OF GEOMETRIC ALGEBRAS OF PLANE CUBIC CURVES

Abstract In noncommutative algebraic geometry an Artin–Schelter regular (AS-regular) algebra is one of the main interests, and every three-dimensional quadratic AS-regular algebra is a geometric algebra, introduced by Mori, whose point scheme is either $\mathbb {P}^{2}$ or a cubic curve in $\mathbb {P}^{2}$ by Artin et al. [‘Some algebras associated to automorphisms of elliptic curves’, in: The Grothendieck Festschrift, Vol. 1, Progress in Mathematics, 86 (Birkhäuser, Basel, 1990), 33–85]. In the preceding paper by the authors Itaba and Matsuno [‘Defining relations of 3-dimensional quadratic AS-regular algebras’, Math. J. Okayama Univ. 63 (2021), 61–86], we determined all possible defining relations for these geometric algebras. However, we did not check their AS-regularity. In this paper, by using twisted superpotentials and twists of superpotentials in the Mori–Smith sense, we check the AS-regularity of geometric algebras whose point schemes are not elliptic curves. For geometric algebras whose point schemes are elliptic curves, we give a simple condition for three-dimensional quadratic AS-regular algebras. As an application, we show that every three-dimensional quadratic AS-regular algebra is graded Morita equivalent to a Calabi–Yau AS-regular algebra.
Bright and Effectual Perovskite Light-Emitting Electrochemical Cells Leveraging Ionic Additives

Perovskite light-emitting diodes (PeLEDs) have drawn considerable attention for their favorable optoelectronic properties. Perovskite light-emitting electrochemical cells (PeLECs) _ devices that utilize mobile ions _ have recently been reported but have yet to reach the performance of the best PeLEDs. We leveraged a poly(ethylene oxide) electrolyte and lithium dopant in CsPbBr3 thin films to produce PeLECs of improved brightness and efficiency. In particular, we found that a single layer PeLEC from CsPbBr3:PEO:LiPF6 with 0.5% wt. LiPF6 produced highly efficient (22 cd/A) and bright (~15000 cd/m2) electroluminescence. To understand this improved performance among PeLECs, we characterized these perovskite thin films with photoluminescence (PL) spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). These studies revealed that this optimal LiPF6 concentration improves electrical double layer formation, reduces the occurrence of voids, charge traps, and pinholes, and increases grain size and packing density.