Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit

Sorted l1 regularization has been incorporated into many methods for solving high-dimensional statistical estimation problems, including the SLOPE estimator in linear regression. In this paper, we study how this relatively new regularization technique improves variable selection by characterizing the optimal SLOPE trade-off between the false discovery proportion (FDP) and true positive proportion (TPP) or, equivalently, between measures of type I error and power. Assuming a regime of linear sparsity and working under Gaussian random designs, we obtain an upper bound on the optimal trade-off for SLOPE, showing its capability of breaking the Donoho-Tanner power limit. To put it into perspective, this limit is the highest possible power that the Lasso, which is perhaps the most popular l1-based method, can achieve even with arbitrarily strong effect sizes. Next, we derive a tight lower bound that delineates the fundamental limit of sorted l1 regularization in optimally trading the FDP off for the TPP. Finally, we show that on any problem instance, SLOPE with a certain regularization sequence outperforms the Lasso, in the sense of having a smaller FDP, larger TPP and smaller l2 estimation risk simultaneously. Our proofs are based on a novel technique that reduces a calculus of variations problem to a class of infinite-dimensional convex optimization problems and a very recent result from approximate message passing theory.
TexRel: a Green Family of Datasets for Emergent Communications on Relations

We propose a new dataset TexRel as a playground for the study of emergent communications, in particular for relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications. By comparison with using symbolic inputs, TexRel provides a more realistic alternative whilst remaining efficient and fast to learn. We compare the performance of TexRel with a related relations dataset Shapeworld. We provide baseline performance results on TexRel for sender architectures, receiver architectures and end-to-end architectures. We examine the effect of multitask learning in the context of shapes, colors and relations on accuracy, topological similarity and clustering precision. We investigate whether increasing the size of the latent meaning space improves metrics of compositionality. We carry out a case-study on using TexRel to reproduce the results of an experiment in a recent paper that used symbolic inputs, but using our own non-symbolic inputs, from TexRel, instead.
An error analysis of generative adversarial networks for learning distributions

This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples. Our main results establish the convergence rates of GANs under a collection of integral probability metrics defined through H\"older classes, including the Wasserstein distance as a special case. We also show that GANs are able to adaptively learn data distributions with low-dimensional structures or have H\"older densities, when the network architectures are chosen properly. In particular, for distributions concentrated around a low-dimensional set, we show that the learning rates of GANs do not depend on the high ambient dimension, but on the lower intrinsic dimension. Our analysis is based on a new oracle inequality decomposing the estimation error into the generator and discriminator approximation error and the statistical error, which may be of independent interest.
Iangreyite: a new secondary phosphate mineral closely related to perhamite

Abstract Iangreyite, ideally Ca2Al7(PO4)2(PO3OH)2(OH, F)15·8H2O, is anew mineral (IMA2009-087) from the Silver Coin mine, Nevada, USA and the Krásno ore district, Horní Slavkov, Czech Republic. At Silver Coin, iangreyite occurs as thin, colourless to white or cream, hexagonal tablets up to 0.4 mm in diameter and 0.02 mm thick associated with meurigite-Na, plumbogummite, kidwellite, lipscombite, strengite, chalcosiderite, wardite, leucophosphite, wavellite, goethite, barite, quartz and F-rich perhamite. At Krásno, white, yellowish or light pink iangreyite coatings consist of 0.3 mm wide clusters of minute and very thin intergrown tabular crystals with a maximum diameter of 0.2 mm. Individual iangreyite crystals are transparent with a vitreous lustre, while iangreyite clusters tend to be pearly and translucent. The estimated hardness is 3 on the Mohs scale, the fracture is irregular and the mineral is non-fluorescent under SW and LW ultraviolet light. Individual crystals are somewhat flexible and there is perfect cleavage on {001}. The density (Silver Coin), measured by the sink-float method in an aqueous solution of sodium polytungstate, is 2.46(3) g/cm3, while the calculated density is 2.451 g/cm3. Crystals from Silver Coin are uniaxial (+), with the indices of refraction: ω = 1.544(2) and ε = 1.554(2), measured in white light, and are non-pleochroic. The empirical formula for iangreyite from Silver Coin (calculated on the basis of 39 anions per formula unit) is: Ca1.42K0.22Na0.09Ba0. 03Sr0.01Al6. 51Mg0.09Fe0.02Cu0.01Zn0. 01P3.81F5. 24H30.21O33. 76, while the empirical formula from Krásno is: Ca2.15K0. 10Na0.01Ba0.02Sr0.12Al6. 28Mg0.01Fe0. 12Cu0.08Zn0. 01P3.64Si0.43F4.65H29. 62O34.35. Iangreyite is trigonal, space group P321 and Z = 1, with the unit-cell parameters (Silver Coin): a = 6.988(1), c = 16.707(3) Å and V = 706.5(2) Å3 and (Krásno): a = 6.989(1), c = 16.782(4) Å and V = 709.8(2) Å3. The structure of iangreyite, modelled from powder data, consists of blocks of the crandallite-type structure that are interconnected along c via corner-sharing of crandallite-block PO4 tetrahedra with AlO2(OH)3 bipyramids. This linkage generates large channels along [110] bounded by 10-member rings of octahedra, tetrahedra and trigonal biyramids, that are occupied by Ca and water molecules.
Room temperature superconductivity dome at a Fano resonance in superlattices of wires

Recently room temperature superconductivity with degrees Celsius has been discovered in a pressurized complex ternary hydride, CSH x , which is a carbon- and hydrogen-doped H3S alloy. The nanoscale structure of H3S is a particular realization of the 1993 patent claim of superlattice of quantum wires for room temperature superconductors and the maximum T C occurs at the top of a superconducting dome. Here we focus on the electronic structure of materials showing nanoscale heterostructures at the atomic limit made of a superlattice of quantum wires like hole-doped cuprate perovskites, and organics focusing on A15 intermetallics and pressurized hydrides. We provide a perspective of the theory of room temperature multigap superconductivity in heterogeneous materials tuned at a shape resonance or Fano resonance in the superconducting gaps near a Lifshitz transition focusing on H3S where the maximum T C occurs where the multiband metal is tuned by pressure near a Lifshitz transition. Here the superconductivity dome of T C vs. pressure is driven by both electron-phonon coupling and contact exchange interaction. We show that the T C amplification up to room temperature is driven by the Fano resonance between a superconducting gap in the anti-adiabatic regime and other gaps in the adiabatic regime. In these cases the T C amplification via contact exchange interaction is the missing term in conventional multiband BCS and anisotropic Migdal-Eliashberg theories including only Cooper pairing.
The effect of air purifiers on aerosol dispersion and removal in multi-patient hospital rooms

Airborne particles cause indoor transmission of COVID-19. Here, aerosol dispersion and removal in an unoccupied 4-bed hospital room was characterized using a novel aerosol tracer experiment. NaCl particle concentrations were measured around the room following a tracer aerosol release for 27 configurations of air purifiers and curtains. Tests without portable air purifiers produced an air exchange rate of 3.8-7.4. The transportation time between the start of aerosol injection and detection at other sensors was 1.3 - 3.6 minutes indicating that even smaller droplets would be well-dispersed in the room before being removed by the ventilation system or settling. Short curtains surrounding the beds had little effect on aerosol distribution while long curtains helped in increasing both the Air Changes per Hour (ACH) and transportation time. The best configuration was 4 purifiers (effective ACH: 11.6) in the corners of the room (with curtains) and exhausting towards the ceiling. Conversely, using two purifiers located between beds, with the exhaust directed to the center of the room produced a high level of mixing and could spread large droplets through the room.
i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions

We present a method for localizing a single camera with respect to a point cloud map in indoor and outdoor scenes. The problem is challenging because correspondences of local invariant features are inconsistent across the domains between image and 3D. The problem is even more challenging as the method must handle various environmental conditions such as illumination, weather, and seasonal changes. Our method can match equirectangular images to the 3D range projections by extracting cross-domain symmetric place descriptors. Our key insight is to retain condition-invariant 3D geometry features from limited data samples while eliminating the condition-related features by a designed Generative Adversarial Network. Based on such features, we further design a spherical convolution network to learn viewpoint-invariant symmetric place descriptors. We evaluate our method on extensive self-collected datasets, which involve \textit{Long-term} (variant appearance conditions), \textit{Large-scale} (up to $2km$ structure/unstructured environment), and \textit{Multistory} (four-floor confined space). Our method surpasses other current state-of-the-arts by achieving around $3$ times higher place retrievals to inconsistent environments, and above $3$ times accuracy on online localization. To highlight our method's generalization capabilities, we also evaluate the recognition across different datasets. With a single trained model, i3dLoc can demonstrate reliable visual localization in random conditions.
Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving

We develop new parameter-free and scale-free algorithms for solving convex-concave saddle-point problems. Our results are based on a new simple regret minimizer, the Conic Blackwell Algorithm$^+$ (CBA$^+$), which attains $O(1/\sqrt{T})$ average regret. Intuitively, our approach generalizes to other decision sets of interest ideas from the Counterfactual Regret minimization (CFR$^+$) algorithm, which has very strong practical performance for solving sequential games on simplexes. We show how to implement CBA$^+$ for the simplex, $\ell_{p}$ norm balls, and ellipsoidal confidence regions in the simplex, and we present numerical experiments for solving matrix games and distributionally robust optimization problems. Our empirical results show that CBA$^+$ is a simple algorithm that outperforms state-of-the-art methods on synthetic data and real data instances, without the need for any choice of step sizes or other algorithmic parameters.
Angular dependence of Hall effect and magnetoresistance in 
SrRuO3−SrIrO3
 heterostructures

Sven Esser, Jiongyao Wu, Sebastian Esser, Robert Gruhl, Anton Jesche, Vladimir Roddatis, 4 Vasily Moshnyaga, Rossitza Pentcheva, and Philipp Gegenwart Experimentalphysik VI, Center for Electronic Correlations and Magnetism, Augsburg University, D-86159 Augsburg, Germany Department of Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, Lotharstr. 1, 47057 Duisburg, Germany Institut für Materialphysik, Georg-August-Universität Göttingen, D-37077 Göttingen, Germany GFZ German Research Centre for Geosciences, Telegrafenberg 14473, Potsdam, Germany I. Physikalisches Institut, Georg-August-Universität Göttingen, D-37077 Göttingen, Germany (Dated: May 28, 2021)
Student‐t stochastic volatility model with composite likelihood EM‐algorithm

A new robust stochastic volatility (SV) model having Student‐ t marginals is proposed. Our process is defined through a linear normal regression model driven by a latent gamma process that controls temporal dependence. This gamma process is strategically chosen to enable us to find an explicit expression for the pairwise joint density function of the Student‐t response process. With this at hand, we propose a composite likelihood (CL) based inference for our model, which can be straightforwardly implemented with a low computational cost. This is a remarkable feature of our Student‐t process over existing SV models in the literature that involve computationally heavy algorithms for estimating parameters. Aiming at a precise estimation of the parameters related to the latent process, we propose a CL expectation–maximization algorithm and discuss a bootstrap approach to obtain standard errors. The finite‐sample performance of our CL methods is assessed through Monte Carlo simulations. The methodology is motivated by an empirical application in the financial market. We analyze the relationship, across multiple time periods, between various US sector Exchange‐Traded Funds returns and individual companies' stock price returns based on our novel Student‐t model. This relationship is further utilized in selecting optimal financial portfolios. Generalizations of the Student‐t SV model are also proposed.
cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery

Detection and classification of objects in overhead images are two important and challenging problems in computer vision. Among various research areas in this domain, the task of fine-grained classification of objects in overhead images has become ubiquitous in diverse real-world applications, due to recent advances in high-resolution satellite and airborne imaging systems. The small inter-class variations and the large intra class variations caused by the fine grained nature make it a challenging task, especially in low-resource cases. In this paper, we introduce COFGA a new open dataset for the advancement of fine-grained classification research. The 2,104 images in the dataset are collected from an airborne imaging system at 5 15 cm ground sampling distance, providing higher spatial resolution than most public overhead imagery datasets. The 14,256 annotated objects in the dataset were classified into 2 classes, 15 subclasses, 14 unique features, and 8 perceived colors a total of 37 distinct labels making it suitable to the task of fine-grained classification more than any other publicly available overhead imagery dataset. We compare COFGA to other overhead imagery datasets and then describe some distinguished fine-grain classification approaches that were explored during an open data-science competition we have conducted for this task.
Rethinking Grid-Forming and Grid-Following Inverters: The Duality Theory

Grid-forming and grid-following inverters are recognized as two main types of power electronic converters for integrating renewable energy resources into power systems. They hold certain similarities, but more differences, which makes their relationship quite subtle and ambiguous. In this article, a new perspective called duality is proposed to overcome this problem, which successfully unifies the grid interfacing characteristics and grid synchronization of them in a very symmetric and graceful form. As analyzed, the grid-forming and grid-following inverters are essentially dual to each other, which is further based on the duality of a) synchronization controllers: frequency droop control and phase-locked loop (PLL); b) grid-interfacing characteristics: current-following voltage-forming and voltagefollowing current-forming; c) swing characteristics: current-angle swing and voltage-angle swing; d) controllers: output impedance shaping and output admittance shaping; e) grid strength: stronggrid instability and weak-grid instability; etc. The detailed swing characteristics are also derived in dual form for both inverters, which reveals the dynamic interaction among grid strength, synchronization controllers, and inner-loop controllers, as well as their effects on stability. Typical cases, namely single-inverterinfinite-bus systems and multi-inverter power systems, are studied and simulated to validate the theoretical analysis.
On the Complexity of Weight-Dynamic Network Algorithms

While operating communication networks adaptively may improve utilization and performance, frequent adjustments also introduce an algorithmic challenge: the re-optimization of traffic engineering solutions is time-consuming and may limit the granularity at which a network can be adjusted. This paper is motivated by question whether the reactivity of a network can be improved by re-optimizing solutions dynamically rather than from scratch, especially if inputs such as link weights do not change significantly. This paper explores to what extent dynamic algorithms can be used to speed up fundamental tasks in network operations. We specifically investigate optimizations related to traffic engineering (namely shortest paths and maximum flow computations), but also consider spanning tree and matching applications. While prior work on dynamic graph algorithms focusses on link insertions and deletions, we are interested in the practical problem of link weight changes. We revisit existing upper bounds in the weight-dynamic model, and present several novel lower bounds on the amortized runtime for recomputing solutions. In general, we find that the potential performance gains depend on the application, and there are also strict limitations on what can be achieved, even if link weights change only slightly.
Corruption Determinants, Geography, and Model Uncertainty

Corruption is an area of contention, both in the public sphere and scientific community. Understanding its manifestations, causes, consequences, and crafting efficient policies to curb it has elicited numerous efforts from social scientists, international bodies, and governments. More specifically, the ongoing debate over its underlying driving factors has resulted in a variety of theories and contrasting empirical findings.
A Full-stack Accelerator Search Technique for Vision Applications

The rapidly-changing ML model landscape presents a unique opportunity for building hardware accelerators optimized for speciﬁc datacenter-scale workloads. We propose Full-stack Accelerator Search Technique (FAST), a hardware accelerator search framework that deﬁnes a broad optimization environment covering key design decisions within the hardware-software stack, including hardware datapath, software scheduling, and compiler passes such as operation fusion and tensor padding. Although FAST can be used on any number and type of deep learning workload, in this paper we focus on optimizing for a single or small set of vision models, resulting in signiﬁcantly faster and more power-efﬁcient designs relative to a general purpose ML accelerator. When evaluated on EfﬁcientNet [45], ResNet50v2 [18] and OCR [36] inference performance relative to a TPU-v3 [22], designs generated by FAST optimized for single workloads can improve Perf/TDP (peak power) by over 6x in the best case and 4x on average. On a limited workload subset, FAST improves Perf/TDP 2.85x on average, with a reduction to 2.35x for a single design optimized over the set of workloads. In addition, we demonstrate a potential 1.8x speedup opportunity for TPU-v3 with improved scheduling.
Local mirror symmetry via SYZ

In this note, we explain how mirror symmetry for basic local models in the Gross-Siebert program can be understood through the non-toric blowup construction described by Gross-Hacking-Keel. This is part of a program to understand the symplectic geometry of affine cluster varieties through their SYZ fibrations.
Representation by sums of unlike powers

Abstract It is proved that all sufficiently large integers n can be represented as n=x12+x23+⋯+x1314,n=x_{1}^{2}+x_{2}^{3}+\cdots+x_{13}^{14}, where x1,…,x13{x_{1},\ldots,x_{13}} are positive integers. This improves upon the current record with fourteen variables in place of thirteen.
A Universal Law of Robustness via Isoperimetry

Classically, data interpolation with a parametrized model class is possible as long as the number of parameters is larger than the number of equations to be satisfied. A puzzling phenomenon in deep learning is that models are trained with many more parameters than what this classical theory would suggest. We propose a partial theoretical explanation for this phenomenon. We prove that for a broad class of data distributions and model classes, overparametrization is necessary if one wants to interpolate the data smoothly. Namely we show that smooth interpolation requires d times more parameters than mere interpolation, where d is the ambient data dimension. We prove this universal law of robustness for any smoothly parametrized function class with polynomial size weights, and any covariate distribution verifying isoperimetry (or a mixture thereof). In the case of two-layer neural networks and Gaussian covariates, this law was conjectured in prior work by Bubeck, Li, and Nagaraj. We also give an interpretation of our result as an improved generalization bound for model classes consisting of smooth functions.
On the Globalization of the QAnon Conspiracy Theory Through Telegram

QAnon is a far-right conspiracy theory that has implications in the real world, with supporters of the theory participating in real-world violent acts like the US capitol attack in 2021. At the same time, the QAnon theory started evolving into a global phenomenon by attracting followers across the globe and, in particular, in Europe, hence it is imperative to understand how QAnon has become a worldwide phenomenon and how this dissemination has been happening in the online space. This paper performs a large-scale data analysis of QAnon through Telegram by collecting 4.4M messages posted in 161 QAnon groups/channels. Using Google’s Perspective API, we analyze the toxicity of QAnon content across languages and over time. Also, using a BERT-based topic modeling approach, we analyze the QAnon discourse across multiple languages. Among other things, we find that the German language is prevalent in our QAnon dataset, even overshadowing English after 2020. Also, we find that content posted in German and Portuguese tends to be more toxic compared to English. Our topic modeling indicates that QAnon supporters discuss various topics of interest within far-right movements, including world politics, conspiracy theories, COVID-19, and the anti-vaccination movement. Taken all together, we perform the first multilingual study on QAnon through Telegram and paint a nuanced overview of the globalization of QAnon.