Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Multidecadal Changes in Vegetation Dynamics and Association with Intra-Annual Climate Variability in the Columbia River Basin

Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC).
Periodic Freight Demand Forecasting for Large-scale Tactical Planning

Crucial to freight carriers is the tactical planning of the service network. The aim is to obtain a cyclic plan over a given tactical planning horizon that satisfies predicted demand at a minimum cost. A central input to the planning process is the periodic demand, that is, the demand expected to repeat in every period in the planning horizon. We focus on large-scale tactical planning problems that require deterministic models for computational tractability. The problem of estimating periodic demand in this setting broadly present in practice has hitherto been overlooked in the literature. We address this gap by formally introducing the periodic demand estimation problem and propose a two-step methodology: Based on time series forecasts obtained in the first step, we propose, in the second step, to solve a multilevel mathematical programming formulation whose solution is a periodic demand estimate that minimizes fixed costs, and variable costs incurred by adapting the tactical plan at an operational level. We report results in an extensive empirical study of a real large-scale application from the Canadian National Railway Company. We compare our periodic demand estimates to the approach commonly used in practice which simply consists in using the mean of the time series forecasts. The results clearly show the importance of the periodic demand estimation problem. Indeed, the planning costs exhibit an important variation over different periodic demand estimates and using an estimate different from the mean forecast can lead to substantial cost reductions. For example, the costs associated with the period demand estimates based on forecasts were comparable to, or even better than those obtained using the mean of actual demand.
Convergence rates for the Vlasov-Fokker-Planck equation and uniform in time propagation of chaos in non convex cases

We prove the existence of a contraction rate for Vlasov-Fokker-Planck equation in Wasserstein distance, provided the interaction potential is (locally) Lipschitz continuous and the confining potential is both Lipschitz continuous and greater than a quadratic function, thus requiring no convexity conditions. Our strategy relies on coupling methods suggested by A. Eberle adapted to the kinetic setting enabling also to obtain uniform in time propagation of chaos in a non convex setting.
Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data

The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this letter, we address the problem of moving object segmentation from 3D LiDAR scans. We propose a novel approach that pushes the current state of the art in LiDAR-only moving object segmentation forward to provide relevant information for autonomous robots and other vehicles. Instead of segmenting the point cloud semantically, i.e., predicting the semantic classes such as vehicles, pedestrians, roads, etc., our approach accurately segments the scene into moving and static objects, i.e., also distinguishing between moving cars vs. parked cars. Our proposed approach exploits sequential range images from a rotating 3D LiDAR sensor as an intermediate representation combined with a convolutional neural network and runs faster than the frame rate of the sensor. We compare our approach to several other state-of-the-art methods showing superior segmentation quality in urban environments. Additionally, we created a new benchmark for LiDAR-based moving object segmentation based on SemanticKITTI. We published it to allow other researchers to compare their approaches transparently and we furthermore published our code.
Confronting Structural Inequities in AI for Education

Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artiﬁcial intelligence technologies—in education and beyond—have led to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems’ disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems to demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models’ performance. We close with alternative visions for a more equitable future for educational AI research.
Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation

Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many tasks. In this paper we present a transformer-based model for multi-turn dialog response generation. Our solution is based on a hybrid approach which augments a transformer-based generative model with a novel retrieval mechanism, which leverages the memorized information in the training data via k-Nearest Neighbor search. Our system is evaluated on two datasets made by customer/assistant dialogs: the Taskmaster-1, released by Google and holding high quality, goal-oriented conversational data and a proprietary dataset collected from a real customer service call center. Both achieve better BLEU scores over strong baselines.
Incentivized Bandit Learning with Self-Reinforcing User Preferences

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a"self-reinforcing"effect in the sense that they will attract more users of similar arm preferences. Besides addressing the tradeoff of exploration and exploitation, another key feature of this new MAB model is to balance reward and incentivizing payment. The goal of the agent is to maximize the total reward over a fixed time horizon $T$ with a low total payment. Our contributions in this paper are two-fold: (i) We propose a new MAB model with random arm selection that considers the relationship of users' self-reinforcing preferences and incentives; and (ii) We leverage the properties of a multi-color Polya urn with nonlinear feedback model to propose two MAB policies termed"At-Least-$n$ Explore-Then-Commit"and"UCB-List". We prove that both policies achieve $O(log T)$ expected regret with $O(log T)$ expected payment over a time horizon $T$. We conduct numerical simulations to demonstrate and verify the performances of these two policies and study their robustness under various settings.
Unsupervised Learning of Adaptive Codebooks for Deep Feedback Encoding in FDD Systems

In this work, we propose a joint adaptive codebook construction and feedback generation scheme in frequency divi-sion duplex (FDD) systems. Both unsupervised and supervised deep learning techniques are used for this purpose. Based on a recently discovered equivalence of uplink (UL) and downlink (DL) channel state information (CSI) in terms of neural network learning, the codebook and associated deep encoder for feedback signaling is based on UL data only. Subsequently, the feedback encoder can be offloaded to the mobile terminals (MTs) to generate channel feedback there as efficiently as possible, without any training effort at the terminals or corresponding transfer of training and codebook data. Numerical simulations demonstrate the promising performance of the proposed method.
The Global Form of Flavor Symmetries and 2-Group Symmetries in 5d SCFTs

2-group symmetries arise when 1-form symmetries and 0-form symmetries of a theory mix with each other under group multiplication. We discover the existence of 2-group symmetries in 5d \mathcal{N}=1𝒩=1 abelian gauge theories arising on the (non-extended) Coulomb branch of 5d superconformal field theories (SCFTs), leading us to argue that the UV 5d SCFT itself admits a 2-group symmetry. Furthermore, our analysis determines the global forms of the 0-form flavor symmetry groups of 5d SCFTs, irrespective of whether or not the 5d SCFT admits a 1-form symmetry. As a concrete application of our method, we analyze 2-group symmetries of all 5d SCFTs, which reduce in the IR, after performing mass deformations, to 5d \mathcal{N}=1𝒩=1 non-abelian gauge theories with simple, simply connected gauge groups. For rank-1 Seiberg theories, we check that our predictions for the flavor symmetry groups match with the superconformal and ray indices available in the literature. We also comment on the mixed `t Hooft anomaly between 1-form and 0-form symmetries arising in 5d \mathcal{N}=1𝒩=1 non-abelian gauge theories and its relation to the 2-groupS.
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning

We curated WikiPII, an automatically labeled dataset composed of Wikipedia biography pages, annotated for personal information extraction. Although automatic annotation can lead to a high degree of label noise, it is an inexpensive process and can generate large volumes of annotated documents. We trained a BERT-based NER model with WikiPII and showed that with an adequately large training dataset, the model can significantly decrease the cost of manual information extraction, despite the high level of label noise. In a similar approach, organizations can leverage text mining techniques to create customized annotated datasets from their historical data without sharing the raw data for human annotation. Also, we explore collaborative training of NER models through federated learning when the annotation is noisy. Our results suggest that depending on the level of trust to the ML operator and the volume of the available data, distributed training can be an effective way of training a personal information identifier in a privacy-preserved manner. Research material is available at https://github.com/ratmcu/wikipiifed.
Teukolsky master equation and Painlevé transcendents: Numerics and extremal limit

We conduct an analysis of the quasi-normal modes for generic spin perturbations of the Kerr black hole using the isomonodromic method. The strategy consists of solving the Riemann-Hilbert map relating the accessory parameters of the differential equations involved to monodromy properties of the solutions, using the $\tau$-function for the Painlev\'e V transcendent. We show good accordance of the method with the literature for generic rotation parameter $a<M$. In the extremal limit, we determined the dependence of the modes with the black hole temperature and establish that the extremal values of the modes are obtainable from the Painlev\'e V and III transcendents.
A Stellar Constraint on Eddington-inspired Born–Infeld Gravity from Cataclysmic Variable Binaries

Eddington-inspired Born–Infeld gravity is an important modification of Einstein’s general relativity, which can give rise to nonsingular cosmologies at the classical level, and avoid the end-stage singularity in a gravitational collapse process. In the Newtonian limit, this theory gives rise to a modified Poisson’s equation, as a consequence of which stellar observables acquire model dependent corrections, compared to the ones computed in the low energy limit of general relativity. This can in turn be used to establish astrophysical constraints on the theory. Here, we obtain such a constraint using observational data from cataclysmic variable binaries. In particular, we consider the tidal disruption limit of the secondary star by a white dwarf primary. The Roche lobe filling condition of this secondary star is used to compute stellar observables in the modified gravity theory in a numerical scheme. These are then contrasted with the values obtained by using available data on these objects, via a Monte Carlo error progression method. This way, we are able to constrain the theory within the 5σ confidence level.
Heavy baryon spectrum with chiral multiplets of scalar and vector diquarks

and negative-parity states of ΛQ, ΣQ, Ξ (′) Q and ΩQ are obtained. The masses and interaction parameters of the effective theory are fixed partly from the lattice QCD data and also from fitting low-lying heavy baryon masses. We find that the negative parity excited states of ΞQ (flavor 3̄) are different from those of ΛQ, because of the inverse hierarchy of the pseudoscalar diquark.On the other hand, ΣQ,Ξ ′ Q and ΩQ (flavor 6) baryons have similar spectra. We compare our results of the heavy-quark–diquark model with experimental data as well as the quark model.
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech

Recently, we proposed a novel speaker diarization method called End-to-End-Neural-Diarization-vector clustering (EEND-vector clustering) that integrates clustering-based and end-to-end neural network-based diarization approaches into one framework. The proposed method combines advantages of both frameworks, i.e. high diarization performance and handling of overlapped speech based on EEND, and robust handling of long recordings with an arbitrary number of speakers based on clustering-based approaches. However, the method was only evaluated so far on simulated 2-speaker meeting-like data. This paper is to (1) report recent advances we made to this framework, including newly introduced robust constrained clustering algorithms, and (2) experimentally show that the method can now significantly outperform competitive diarization methods such as Encoder-Decoder Attractor (EDA)-EEND, on CALLHOME data which comprises real conversational speech data including overlapped speech and an arbitrary number of speakers. By further analyzing the experimental results, this paper also discusses pros and cons of the proposed method and reveals potential for further improvement. A set of the code to reproduce the results is available at https://github.com/nttcslab-sp/EEND-vector-clustering.
A Game-Theoretic Account of Responsibility Allocation

When designing or analyzing multi-agent systems, a fundamental problem is responsibility ascription: to specify which agents are responsible for the joint outcome of their behaviors and to which extent. We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. Forward responsibility identifies the responsibility of a group of agents for an outcome along all possible plays, whereas backward responsibility identifies the responsibility along a given play. We further distinguish between strategic and causal backward responsibility, where the former captures the epistemic knowledge of players along a play, while the latter formalizes which players – possibly unknowingly – caused the outcome. A formal connection between forward and backward notions is established in the case of perfect recall. We further ascribe quantitative responsibility through cooperative game theory. We show through a number of examples that our approach encompasses several prior formal accounts of responsibility attribution.
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D

This paper introduces a generative model equivariant to Euclidean symmetries: E( n ) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E( n ) graph neural networks and integrate them as a differential equation to obtain an invertible equivariant function: a continuous-time normalizing ﬂow. We demonstrate that E-NFs considerably outperform baselines and existing methods from the literature on particle systems such as DW4 and LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our knowledge, this is the ﬁrst likelihood-based deep generative model that generates molecules in 3D.
Magnetic structure and multiferroicity of Sc-substituted hexagonal 
YbFeO3

Y. S. Tang, S. M. Wang, L. Lin, V. Ovidiu Garlea, Tao Zou, S. H. Zheng, H. -M. Zhang, J. T. Zhou, Z. L. Luo, Z. B. Yan, S. Dong, T. Charlton, and J.-M. Liu Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, China Neutron Science Division, Oak Ridge National Laboratory, Tennessee 37831, USA Collaborative Innovation Center of Light Manipulations and Applications, Shangdong Normal University, Jinan 250358, China School of Physics, Southeast University, Nanjing 211189, China National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230026, China
Removable sets for Newtonian Sobolev spaces and a characterization of $p$-path almost open sets

We study removable sets for Newtonian Sobolev functions in metric measure spaces satisfying the usual (local) assumptions of a doubling measure and a Poincar\'e inequality. In particular, when restricted to Euclidean spaces, a closed set $E\subset \mathbf{R}^n$ with zero Lebesgue measure is shown to be removable for $W^{1,p}(\mathbf{R}^n \setminus E)$ if and only if $\mathbf{R}^n \setminus E$ supports a $p$-Poincar\'e inequality as a metric space. When $p>1$, this recovers Koskela's result (Ark. Mat. 37 (1999), 291--304), but for $p=1$, as well as for metric spaces, it seems to be new. We also obtain the corresponding characterization for the Dirichlet spaces $L^{1,p}$. To be able to include $p=1$, we first study extensions of Newtonian Sobolev functions in the case $p=1$ from a noncomplete space $X$ to its completion $\widehat{X}$. In these results, $p$-path almost open sets play an important role, and we provide a characterization of them by means of $p$-path open, $p$-quasiopen and $p$-finely open sets. We also show that there are nonmeasurable $p$-path almost open subsets of $\mathbf{R}^n$, $n \geq 2$, provided that the continuum hypothesis is assumed to be true. Furthermore, we extend earlier results about measurability of functions with $L^p$-integrable upper gradients, about $p$-quasiopen, $p$-path and $p$-finely open sets, and about Lebesgue points for $N^{1,1}$-functions, to spaces that only satisfy local assumptions.
Modelling and Simulations of Electrical Propagation in TransmuralSlabs of Scarred Left Ventricular Tissue

We report three-dimensional and time-dependent numerical simulations of the propagation of electrical action potentials in a model of rabbit ventricular tissue. The simulations are performed using a finite-element method for the solution of the monodomain equations of cardiac electrical excitation. The parameters of a detailed ionic ventricular cell model are re-fitted to available experimental data and the model is then used for the description of the transmembrane current and calcium dynamics. A region with reduced conductivity is introduced to model a myocardial infarction scar. Electrical activation times and density maps of the transmembrane voltage are computed and compared with experimental measurements in rabbit preparations with myocardial infarction obtained by a panoramic optical mapping method.
Flexible Specification Testing in Semi-Parametric Quantile Regression Models

We propose several novel consistent specification tests for quantile regression models which generalize former tests by important characteristics. First, we allow the covariate effects to be quantile-dependent and nonlinear, bypassing estimation difficulties such as multicollinearity. Second, we allow for parameterizing the conditional quantile functions by appropriate basis functions, rather than parametrically. In the framework of splines, we can thus test for the order and number of knots. We are hence able to test for functional forms beyond linearity, while retaining the linear effects as special cases. In both cases, the induced class of conditional distribution functions is tested with a Cramér-von Mises type test statistic for which we derive the theoretical limit distribution and propose a practical bootstrap method. To increase the power of the first test, we further suggest a modified test statistic using the B-spline approach from the second test. A detailed Monte Carlo experiment shows that the tests result in reasonable sized testing procedures with large power. Our first application to conditional income disparities between East and West Germany over the period 2001–2010 indicates that there are not only still significant differences between East and West but also across the quantiles of the conditional income distributions, when conditioning on age and year. The second application to data from the Australian national electricity market reveals the importance of using interaction effects for modelling the highly skewed and heavy-tailed distributions of energy prices conditional on day, time of day and demand.
Equivariant cohomology and the super reciprocal plane of a hyperplane arrangement

In this paper, we investigate certain graded-commutative rings which are related to the reciprocal plane compactification of the coordinate ring of a complement of a hyperplane arrangement. We give a presentation of these rings by generators and defining relations. This presentation was used by Holler and I. Kriz to calculate the $\mathbb{Z}$-graded coefficients of localizations of ordinary $RO((\mathbb{Z}/p)^n)$-graded equivariant cohomology at a given set of representation spheres, and also more recently by the author in a generalization to the case of an arbitrary finite group. We also give an interpretation of these rings in terms of superschemes, which can be used to further illuminate their structure.