Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Learning for Koopman Spectral Analysis with Short Time-series

Koopman spectral analysis has attracted attention for nonlinear dynamical systems since we can analyze nonlinear dynamics with a linear regime by embedding data into a Koopman space by a nonlinear function. For the analysis, we need to find appropriate embedding functions. Although several neural network-based methods have been proposed for learning embedding functions, existing methods require long time-series for training neural networks. This limitation prohibits performing Koopman spectral analysis in applications where only short time-series are available. In this paper, we propose a meta-learning method for estimating embedding functions from unseen short time-series by exploiting knowledge learned from related but different time-series. With the proposed method, a representation of a given short time-series is obtained by a bidirectional LSTM for extracting its properties. The embedding function of the short time-series is modeled by a neural network that depends on the time-series representation. By sharing the LSTM and neural networks across multiple time-series, we can learn common knowledge from different time-series while modeling time-series-specific embedding functions with the time-series representation. Our model is trained such that the expected test prediction error is minimized with the episodic training framework. We experimentally demonstrate that the proposed method achieves better performance in terms of eigenvalue estimation and future prediction than existing methods.
Coadded Spectroscopic Stellar Parameters and Abundances from the LAMOST Low Resolution Survey

I combine duplicate spectroscopic stellar parameter estimates in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 6 Low Resolution Spectral Survey A, F, G, and K Type stellar parameter catalog. Combining repeat measurements results in a factor of two improvement in the precision of the spectroscopic stellar parameter estimates. Moreover, this trivializes the process of performing coordinate-based cross-matching with other catalogs. Similarly, I combine duplicate stellar abundance estimates for the Xiang et al. catalog which was produced using LAMOST Data Release 5 Low Resolution Spectral Survey data. These data have numerous applications in stellar, galactic, and exoplanet astronomy. The catalogs I produce are available as machine-readable tables at https://doi.org/10.7281/T1/QISGRU.
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network

Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority. Semantic segmentation is one of the essential components of road scene perception that provides semantic information of the surrounding environment. Recently, several methods have been introduced for 3D LiDAR semantic segmentation. While they can lead to improved performance, they are either afflicted by high computational complexity, therefore are inefficient, or they lack fine details of smaller object instances. To alleviate these problems, we propose (AF)2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation. We present a novel multibranch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder. Our (AF)2-S3Net fuses the voxel-based learning and point-based learning methods into a unified framework to effectively process the potentially large 3D scene. Our experimental results show that the proposed method outperforms the state-of-the-art approaches on the large-scale nuScenes-lidarseg and SemanticKITTI benchmark, ranking 1st on both competitive public leaderboard competitions upon publication.
On an Inflated Unit-Lindley Distribution

Modeling fractional data in various real-life scenarios is a challenging task. This paper considers situations where fractional data are observed in the interval [0,1]. The unit- Lindley distribution has been discussed in the literature where its support lies between 0 and 1. In this paper, we focus on an inflated variant of the unit-Lindley distribution, where the inflation occurs at both 0 and 1. Various properties of the inflated unit-Lindley distribution are discussed and examined, including point estimation based on the maximum likelihood method and interval estimation. Finally, extensive Monte Carlo simulation and real-data analyses are carried out to compare the fit of our proposed distribution along with some of the existing ones such as the inflated beta and the inflated Kumaraswamy distributions. Journal of Statistical Research 2021, Vol. 55, No. 2, pp. 299-311
Cellular objects in isotropic motivic categories

Our main purpose is to describe the category of isotropic cellular spectra over flexible fields. Guided by [6], we show that it is equivalent, as a stable $\infty$-category equipped with a $t$-structure, to the derived category of left comodules over the dual of the classical topological Steenrod algebra. In order to obtain this result, the category of isotropic cellular modules over the motivic Brown-Peterson spectrum is also studied, and isotropic Adams and Adams-Novikov spectral sequences are developed. As a consequence, we also compute hom sets in the category of isotropic Tate motives between motives of isotropic cellular spectra.
A Generic Strategy Iteration Method for Simple Stochastic Games

We present a generic strategy iteration algorithm (GSIA) to ﬁnd an optimal strategy of a simple stochastic game (SSG). We prove the correctness of GSIA, and derive a general complexity bound, which implies and improves on the results of several articles. First, we remove the assumption that the SSG is stopping, which is usually obtained by a polynomial blowup of the game. Second, we prove a tight bound on the denominator of the values associated to a strategy, and use it to prove that all strategy iteration algorithms are in fact ﬁxed parameter tractable in the number of random vertices. All known strategy iteration algorithms can be seen as instances of GSIA, which allows to analyze the complexity of converge from below by Condon [10] and to propose a class of algorithms generalising Gimbert and Horn’s algorithm [14, 15]. These algorithms require less than r ! iterations in general and less iterations than the current best deterministic algorithm for binary SSGs given by Ibsen-Jensen and Miltersen [17].
Minimal volume entropy and fiber growth

This article deals with topological assumptions under which the minimal volume entropy of a closed manifold~$M$, and more generally of a finite simplicial complex~$X$, vanishes or is positive. These topological conditions are expressed in terms of the growth of the fundamental group of the fibers of maps from a given finite simplicial complex~$X$ to lower dimensional simplicial complexes~$P$. We also give examples of finite simplicial complexes with zero simplicial volume and arbitrarily large minimal volume entropy.
Simulating dense granular flow using the μ (I)‐rheology within a space‐time framework

A space‐time framework is applied to simulate dense granular flow. Two different numerical experiments are performed: a column collapse and a dam break on an inclined plane. The experiments are modeled as two‐phase flows. The dense granular material is represented by a constitutive model, the μ (I)‐rheology, that is based on the Coulomb's friction law, such that the normal stress applied by the pressure is related to the tangential stress. The model represents a complex viscoplastic material behavior. The interface between the dense granular material and the surrounding light fluid is captured with a level set function. Due to discontinuities close to the the interface, the mesh requires a sufficient resolution. The space‐time approach allows unstructured meshes in time and, therefore a well‐refined mesh in the temporal direction around the interface. In this study, results and performance of a flat and a simplex space time discretization are verified and analyzed.
Multiband superconductivity and charge density waves in the topological semimetal

Ni-doped ZrTe2 L. E. Correa, ∗ P. P. Ferreira, † L. R. de Faria, T. T. Dorini, Z. Fisk, M. S. Torikachvili, L. T. F. Eleno, and A. J. S. Machado ‡ Universidade de São Paulo, Escola de Engenharia de Lorena, Departamento de Engenharia de Materiais, Lorena, São Paulo, Brazil Université de Lorraine, CNRS, IJL, Nancy, France University of California, Irvine, California 92697, USA Department of Physics, San Diego State University, San Diego, California 92182-1233, USA (Dated: February 10, 2021)
Unsupervised Abstractive Summarization of Bengali Text Documents

Abstractive summarization systems generally rely on large collections of document-summary pairs. However, the performance of abstractive systems remains a challenge due to the unavailability of the parallel data for low-resource languages like Bengali. To overcome this problem, we propose a graph-based unsupervised abstractive summarization system in the single-document setting for Bengali text documents, which requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts. We also provide a human-annotated dataset with document-summary pairs to evaluate our abstractive model and to support the comparison of future abstractive summarization systems of the Bengali Language. We conduct experiments on this dataset and compare our system with several well-established unsupervised extractive summarization systems. Our unsupervised abstractive summarization model outperforms the baselines without being exposed to any human-annotated reference summaries.
MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs

This paper addresses black-box optimization over multiple information sources whose both fidelity and query cost change over the search space, that is they are location dependent. The approach uses: (i) an Augmented Gaussian Process, recently proposed in multi-information source optimization as a single model of the objective function over search space and sources, and (ii) a Gaussian Process to model the location-dependent cost of each source. The former is used into a Confidence Bound based acquisition function to select the next source and location to query, while the latter is used to penalize the value of the acquisition depending on the expected query cost for any source-location pair. The proposed approach is evaluated on a set of Hyperparameters Optimization tasks, consisting of two Machine Learning classifiers and three datasets of different sizes.
Quantum machine learning with adaptive linear optics

We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine – either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.
Efficient electrochemical reduction of CO2 to CO by soft functional materials

Electrochemical reduction of CO2 to CO is a promising strategy. However, achieving high Faradaic efficiency with high current density using ILs electrolyte remains a challenge. In this study, the IL N octyltrimethyl 1,2,4 triazole ammonium shows outstanding performance for electrochemical reduction of CO2 to CO on the commercial Ag electrode, and the current density can be up to 50.8 mA cm-2 with a Faradaic efficiency of 90.6%. The current density of CO is much higher than those reported in the ILs electrolyte. In addition, the density functional theory calculation further proved that IL interacts with CO2 to form IL CO2 complex which played a key role in reducing the activation energy of CO2. According to the molecular orbital theory, the electrons obtained from ILs was filled in the anti bonding orbit of the CO2, resulting in reducing the C=O bond energy. This work provides a new strategy to design novel ILs for high efficiency electrochemical reduction of CO2 to CO.
Potential origin of the state-dependent high-energy tail in the black hole microquasar Cygnus X-1 as seen with INTEGRAL

Context. 0.1–10 MeV observations of the black hole microquasar Cygnus X-1 have shown the presence of a spectral feature in the form of a power law in addition to the standard black body (0.1–10 keV) and Comptonization (10–200 keV) components observed by INTEGRAL in several black-hole X-ray binaries. This so-called “high-energy tail” was recently shown to be strong in the hard spectral state of Cygnus X-1, and, in this system, has been interpreted as the high-energy part of the emission from a compact jet. Aims. This result was nevertheless obtained from a data set largely dominated by hard state observations. In the soft state, only upper limits on the presence and hence the potential parameters of a high-energy tail could be derived. Using an extended data set, we aim to obtain better constraints on the properties of this spectral component in both states. Methods. We make use of data obtained from about 15 years of observations with the INTEGRAL satellite. The data set is separated into the different states and we analyze stacked state-resolved spectra obtained from the X-ray monitors, the gamma-ray imager, and the gamma-ray spectrometer (SPI) onboard. Results. A high-energy component is detected in both states, confirming its earlier detection in the hard state and its suspected presence in the soft state with INTEGRAL, as seen in a much smaller SPI data set. We first characterize the high-energy tail components in the two states through a model-independent, phenomenological analysis. We then apply physical models based on hybrid Comptonization (eqpair and belm). The spectra are well modeled in all cases, with a similar goodness of the fits. While in the semi-phenomenological approach the high-energy tail has similar indices in both states, the fits with the physical models seem to indicate slightly different properties. Based on this approach, we discuss the potential origins of the high-energy components in both the soft and hard states, and favor an interpretation where the high-energy component is due to a compact jet in the hard state and hybrid Comptonization in either a magnetized or nonmagnetized corona in the soft state.
How Unique Is a Face: An Investigative Study

Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector. Surprisingly, while widely accepted, we still lack the understanding of uniqueness or distinctiveness of faces as biometric modality. In this work, we study the impact of factors such as image resolution, feature representation, database size, age and gender on uniqueness denoted by the Kullback-Leibler divergence between genuine and impostor distributions. Towards understanding the impact, we present experimental results on the datasets AT&T, LFW, IMDb-Face, as well as ND-TWINS, with the feature extraction algorithms VGGFace, VGG16, ResNet50, InceptionV3, MobileNet and DenseNet121, that reveal the quantitative impact of the named factors. While these are early results, our findings indicate the need for a better understanding of the concept of biometric uniqueness and its implication on face recognition.
Novel, highly active binuclear 2,5-disubstituted Amino-p-benzoquinone-nickel(II) ethylene polymerization catalysts

Reaction of salts of the 2,5-disubstituted amino-p-benzoquinone bridging ligand (1a−e) with trans-bis(triphenylphosphane)phenylnickel(II) chloride results in the binuclear complexes 2a−e, which show high activities for ethylene polymerization without any cocatalysts. High-molecular-weight, moderately branched polyethylene of broad molecular-weight distribution was obtained.
Hot graphite dust in the inner regime of NGC 4151

We model the near infrared SED of NGC 4151 with a 3-D radiative transfer SKIRT code, using which torus only (TO) and Ring And Torus (RAT) scenarios are studied. In the RAT models, a graphite ring-like structure (clumpy or smooth), is incorporated between the torus and the accretion disk. We vary the inclination angle (i), inner radius (of the torus and the ring, Rin,t and Rin,r respectively), torus half-opening angle (σ), optical depth (τ9.7,t of the torus and τ9.7,r of the ring ) and the dust clump size (Rclump). We perform a statistical analysis of the parameter space and find that all the models are able to explain the flat NIR SED of NGC 4151 with minor differences in the derived parameters. For the TO model, we get, Rin,t = 0.1 pc, σ = 30 ◦, i = 53◦, τ9. 7,t = 10 and the clumpsize, Rclump =0.4 pc. For the smooth RAT model, Rin,r = 0.04 pc, τ9. 7,total = 11 and for the clumpy RAT model, Rin,r = 0.04 pc/0.06 pc and τ9. 7,total = 20. The Rin,t from the TO model does not agree with the NIR observations (∼ 0.04 pc). Hence, the most likely scenario is that a hot graphite ring is located at a distance 0.04 pc from the centre, composed of a smooth distribution of dust followed by a dusty torus at 0.1 pc with ISM type of grains.
Semantic Borrowing for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is one of the most realistic problems, but also one of the most challenging problems due to the partiality of the classifier to supervised classes. Instance-borrowing methods and synthesizing methods solve this problem to some extent with the help of testing semantics, but therefore neither can be used under the class-inductive instance-inductive (CIII) training setting where testing data are not available, and the latter require the training process of a classifier after generating examples. In contrast, a novel method called Semantic Borrowing for improving GZSL methods with compatibility metric learning under CIII is proposed in this paper. It borrows similar semantics in the training set, so that the classifier can model the relationship between the semantics of zero-shot and supervised classes more accurately during training. In practice, the information of semantics of unseen or unknown classes would not be available for training while this approach does NOT need any information of semantics of unseen or unknown classes. The experimental results on representative GZSL benchmark datasets show that it can reduce the partiality of the classifier to supervised classes and improve the performance of generalized zero-shot classification.
Telling the What while Pointing the Where: Fine-grained Mouse Trace and Language Supervision for Improved Image Retrieval

Existing image retrieval systems use text queries to provide a natural and practical way for users to express what they are looking for. However, ﬁne-grained image retrieval often requires the ability to also express the where in the image the content they are looking for is. The textual modality can only cumbersomely express such localization preferences, whereas pointing would be a natural ﬁt. In this paper, we describe an image retrieval setup where the user simultaneously describes an image using both spoken natu-ral language (the “what”) and mouse traces over an empty canvas (the “where”) to express the characteristics of the desired target image. To this end, we learn an image retrieval model using the Localized Narratives dataset, which is capable of performing early fusion between text descriptions and synchronized mouse traces. Qualitative and quantitative experiments show that our model is capable of taking this spatial guidance into account, and provides more accurate retrieval results compared to text-only equivalent systems.
Ultrafast dynamics of gallium vacancy charge states in 
β−Ga2O3

Arjan Singh ,1,* Okan Koksal ,1 Nicholas Tanen,2 Jonathan McCandless ,1 Debdeep Jena,1,2 Huili (Grace) Xing,1,2 Hartwin Peelaers,3 and Farhan Rana1 1School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14853, USA 2Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, USA 3Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas 66045, USA
Altered Local Uniformization Of Rigid-Analytic Spaces

We prove a version of Temkin’s local altered uniformization theorem. We show that for any rig-smooth, quasi-compact and quasi-separated admissible formal OK -model X, there is a finite extension K/K such that XO K locally admits a rig-étale morphism g : X → XO K and a rig-isomorphism h : X → X with X being a successive semi-stable curve fibration over OK′ and X being a poly-stable formal OK′ -scheme. Moreover, X ′ admits an action of a finite group G such that g : X → XO K is G-invariant, and the adic generic fiber X′K′ becomes a G-torsor over its quasicompact open image U = gK′(X ′ K′). Also, we study properties of the quotient map X /G → XO K and show that it can be obtained as a composition of open immersions and rig-isomorphisms.
Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networks

Intent detection and slot filling are recognized as two very important tasks in a spoken language understanding (SLU) system. In order to model these two tasks at the same time, many joint models based on deep neural networks have been proposed recently and archived excellent results. In addition, graph neural network has made good achievements in the field of vision. Therefore, we combine these two advantages and propose a new joint model with a wheel-graph attention network (Wheel-GAT), which is able to model interrelated connections directly for single intent detection and slot filling. To construct a graph structure for utterances, we create intent nodes, slot nodes, and directed edges. Intent nodes can provide utterance-level semantic information for slot filling, while slot nodes can also provide local keyword information for intent detection. The two tasks promote each other and carry out end-to-end training at the same time. Experiments show that our proposed approach is superior to multiple baselines on ATIS and SNIPS datasets. Besides, we also demonstrate that using bi-directional encoder representation from transformer (BERT) model further boosts the performance of the SLU task.