Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short‐term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental‐scale median Nash‐Sutcliffe Efficiency coefficient value of 0.86. Integrating moving‐average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow‐dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation‐energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater‐dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high‐aridity basins with 1‐day flash peaks. Despite this limitation, there is much promise for a deep‐learning‐based forecast paradigm due to its performance, automation, efficiency, and flexibility.
Pseudo-Anosov mappings and toral automorphisms

For every irreducible automorphism $\phi\in\text{SL}_3({\mathbb Z})$ of the $3$-torus, for which the product of the expanding eigenvalues is positive, we construct a pseudo-Anosov mapping $f$ of an associated surface, semi-conjugate and almost-isomorphic to $\phi$, whose stretch factor is the product of the expanding eigenvalues of $\phi$. This shows that any norm-$1$ cubic Pisot number occurs as the stretch factor of a pseudo-Anosov mapping, proving a conjecture of Fried in degree $3$. A similar construction works for the $4$-torus on condition that $\phi$ has exactly two eigenvalues outside the unit circle (and whose product is positive). Furthermore for any irreducible hyperbolic automorphism $\phi\in\text{SL}_n({\mathbb Z})$ of the $n$-torus, $n\ge 4$, we construct a pseudo-Anosov mapping semiconjugate and almost-isomorphic to any sufficiently large power of $\phi$.
Cloaking Property of a Plasmonic Structure in Doubly Complementary Media and Three-Sphere Inequalities with Partial Data

We investigate cloaking property of negative-index metamaterials in the time-harmonic electromagnetic setting for the so-called doubly complementary media. These are media consisting of negative-index metamaterials in a shell (plasmonic structure) and positive-index materials in its complement for which the shell is complementary to a part of the core and a part of the exterior of the core-shell structure. We show that an arbitrary object is invisible when it is placed close to a plasmonic structure of a doubly complementary medium as long as its cross section is smaller than a threshold given by the property of the plasmonic structure. To handle the loss of the compactness and of the ellipticity of the modeling Maxwell equations with sign-changing coefficients, we first obtain Cauchy's problems associated with two Maxwell systems using reflections. We then derive information from them, and combine it with the removing localized singularity technique to deal with the localized resonance. A central part of the analysis on the Cauchy's problems is to establish three-sphere inequalities with partial data for general elliptic systems, which are interesting in themselves. The proof of these inequalities first relies on an appropriate change of variables, inspired by conformal maps, and is then based on Carleman's estimates for a class of degenerate elliptic systems.
Neural Networks-based Regularization of Large-Scale Inverse Problems in Medical Imaging

In this paper we present a generalized Deep Learning-based approach to solve ill-posed large-scale inverse problems occurring in medical imaging. Recently, Deep Learning methods using iterative neural networks and cascaded neural networks have been reported to achieve excellent image quality for the task of image reconstruction in different imaging modalities. However, the fact that these approaches employ the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by decoupling the regularization of the solution from ensuring consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained neural network which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative networks. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude.
Benchmark for Generic Product Detection: A strong baseline for Dense Object Detection

Object detection in densely packed scenes is a new area where standard object detectors fail to train well (Goldman et al., 2019). We show that the performance of the standard object detectors on densely packed scenes is superior when it is trained on normal scenes rather than dense scenes. We train a standard object detector on a small, normally packed dataset with data augmentation techniques. This achieves significantly better results than state-of-the-art methods that are trained on densely packed scenes. We obtain 68.5% mAP on SKU110K dataset (Goldman et al., 2019), 19.3% higher and 1.4x better than the previous state-of-theart. We also create a varied benchmark for generic SKU product detection by providing full annotations for multiple public datasets. It can be accessed at this URL. We hope that this benchmark helps in building robust detectors that perform reliably across different settings.
An improved bound in Wirsing’s problem

<p>We improve the lower bound for the classical exponent of approximation <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="w Subscript n Superscript asterisk"> <mml:semantics> <mml:msubsup> <mml:mi>w</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∗<! -- ∗ --></mml:mo> </mml:mrow> </mml:msubsup> <mml:annotation encoding="application/x-tex">w_{n}^{\ast }</mml:annotation> </mml:semantics> </mml:math> </inline-formula> connected to Wirsing’s famous problem on approximation to real numbers by algebraic numbers of degree at most <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="n"> <mml:semantics> <mml:mi>n</mml:mi> <mml:annotation encoding="application/x-tex">n</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. Our bound exceeds <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="n slash StartRoot 3 EndRoot almost-equals 0.5773 n"> <mml:semantics> <mml:mrow> <mml:mi>n</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:msqrt> <mml:mn>3</mml:mn> </mml:msqrt> <mml:mo>≈<! -- ≈ --></mml:mo> <mml:mn>0.5773</mml:mn> <mml:mi>n</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">n/\sqrt {3}\approx 0.5773n</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and thus provides a considerable qualitative improvement to previous bounds of order <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="n slash 2 plus upper O left-parenthesis 1 right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mi>n</mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">n/2+O(1)</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. We further establish new relations between several classical exponents of approximation. </p>
Spectroscopy and polarimetry of the gravitationally lensed quasar SDSS J1004+4112 with the 6m SAO RAS telescope

Context. We present new spectroscopic and polarimetric observations of the gravitational lens SDSS J1004+4112 taken with the 6 m telescope of the Special Astrophysical Observatory (Russia). Aims. In order to explain the variability that is observed only in the blue wing of the C IV emission line, corresponding to image A, we analyze the spectroscopy and polarimetry of the four images of the lensed system. Methods. Spectra of the four images were taken in 2007, 2008, and 2018, and polarization was measured in the period 2014–2017. Additionally, we modeled the microlensing effect in the polarized light, assuming that the source of polarization is the equatorial scattering in the inner part of the torus. Results. We find that a blue enhancement in the C IV line wings affects component A in all three epochs. We also find that the UV continuum of component D was amplified in the period 2007–2008, and that the red wings of CIII] and C IV appear brighter in D than in the other three components. We report significant changes in the polarization parameters of image D, which can be explained by microlensing. Our simulations of microlensing of an equatorial scattering region in the dusty torus can qualitatively explain the observed changes in the polarization degree and angle of image D. We do not detect significant variability in the polarization parameters of the other images (A, B, and C), although the averaged values of the polarization degree and angle are different for the different images. Conclusions. Microlensing of a broad line region model including a compact outflowing component can qualitatively explain the C IV blue wing enhancement (and variation) in component A. However, to confirmed this hypothesis, we need additional spectroscopic observation in future.
Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks

Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong. This has led to a situation in which mistakes are less likely to be made than before, but are equally likely to be absurd or catastrophic when they do occur. Past works have recognised and tried to address this issue of mistake severity, often by using graph distances in class hierarchies, but this has largely been neglected since the advent of the current deep learning era in computer vision. In this paper, we aim to renew interest in this problem by reviewing past approaches and proposing two simple methods which outperform the prior art under several metrics on two large datasets with complex class hierarchies: tieredImageNet and iNaturalist’19.
Deconfined Critical Point in a Doped Random Quantum Heisenberg Magnet

We describe the phase diagram of electrons on a fully connected lattice with random hopping, subject to a random Heisenberg spin exchange interactions between any pair of sites and a constraint of no double occupancy. A perturbative renormalization group analysis yields a critical point with fractionalized excitations at a non-zero critical value $p_c$ of the hole doping $p$ away from the half-filled insulator. We compute the renormalization group to two loops, but some exponents are obtained to all loop order. We argue that the critical point $p_c$ is flanked by confining phases: a disordered Fermi liquid with carrier density $1+p$ for $p>p_c$, and a metallic spin glass with carrier density $p$ for $p<p_c$. Additional evidence for the critical behavior is obtained from a large $M$ analysis of a model which extends the SU(2) spin symmetry to SU($M$). We discuss the relationship of the vicinity of this deconfined quantum critical point to key aspects of cuprate phenomenology.
Comparing optimized renormalization schemes for QCD observables

Based on the renormalization group summation method of McKeon ${\it et\; al.}$, it is shown that the renormalization group equation, while related to the radiatively mass scale $\mu$, would perform a summation over QCD perturbative terms. Employing the full QCD $\beta$-function within this summation, all logarithmic corrections can be presented as log-independent contributions. In another step of this approach, the renormalization scheme dependence of QCD observables, characterized by Stevenson, is required to be examined. In this regard, two choices of renormalization scheme would be exposed. In one of them, the QCD observable is expressed in terms of two powers of running coupling constant. In the other one, the perturbative series expansion is written as an infinite series in terms of the two-loop running coupling represented by the Lambert $W$-function. In both cases, the QCD observable involves parameters which are renormalization scheme invariant and a coupling constant which is independent of the renormalization scale. We then consider the approach of complete renormalization group improvement (CORGI). In this approach, using the self consistency principle, it is possible to reconstruct the conventional perturbative series in terms of scheme invariant quantities and the coupling constant as a function of Lambert $W$-function. One of the key differences between CORGI and the renormalization group summation method of McKeon ${\it et\; al.}$ is that the later treats renormalization scale and scheme independently while the former would encounter both dependencies together. In continuation, numerical results of the two approaches are compared for $R_{e^+e^-}$ ratio and the Higgs decay width to gluon-gluon.
n-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers

This paper proposes a new defense called $n$-ML against adversarial examples, i.e., inputs crafted by perturbing benign inputs by small amounts to induce misclassifications by classifiers. Inspired by $n$-version programming, $n$-ML trains an ensemble of $n$ classifiers, and inputs are classified by a vote of the classifiers in the ensemble. Unlike prior such approaches, however, the classifiers in the ensemble are trained specifically to classify adversarial examples differently, rendering it very difficult for an adversarial example to obtain enough votes to be misclassified. We show that $n$-ML roughly retains the benign classification accuracies of state-of-the-art models on the MNIST, CIFAR10, and GTSRB datasets, while simultaneously defending against adversarial examples with better resilience than the best defenses known to date and, in most cases, with lower classification-time overhead.
Identifying Adversarial Sentences by Analyzing Text Complexity

Attackers create adversarial text to deceive both human perception and the current AI systems to perform malicious purposes such as spam product reviews and fake political posts. We investigate the difference between the adversarial and the original text to prevent the risk. We prove that the text written by a human is more coherent and fluent. Moreover, the human can express the idea through the flexible text with modern words while a machine focuses on optimizing the generated text by the simple and common words. We also suggest a method to identify the adversarial text by extracting the features related to our findings. The proposed method achieves high performance with 82.0% of accuracy and 18.4% of equal error rate, which is better than the existing methods whose the best accuracy is 77.0% corresponding to the error rate 22.8%.
Revealing the CO X-factor in Dark Molecular Gas through Sensitive ALMA Absorption Observations

Carbon-bearing molecules, particularly CO, have been widely used as tracers of molecular gas in the interstellar medium (ISM). In this work, we aim to study the properties of molecules in diffuse, cold environments, where CO tends to be underabundant and/or subthermally excited. We performed one of the most sensitive (down to τ rms CO ∼ 0.002 and τ rms HCO + ∼ 0.0008 ) submillimeter molecular absorption line observations toward 13 continuum sources with the ALMA. CO absorption was detected in diffuse ISM down to A v < 0.32 mag and HCO+ was detected down to A v < 0.2 mag , where atomic gas and dark molecular gas start to dominate. Multiple transitions measured in absorption toward 3C454.3 allow for a direct determination of excitation temperatures Tex of 4.1 and 2.7 K, for CO and for HCO+, respectively, which are close to the cosmic microwave background and explain their being undercounted in emission surveys. A stronger linear correlation was found between N HCO + and N H 2 (Pearson correlation coefficient P ∼ 0.93) than that of NCO and N H 2 (P ∼ 0.33), suggesting HCO+ is a better tracer of H2 than CO in diffuse gas. The derived CO-to-H2 conversion factor (the CO X-factor) of (14 ± 3) × 1020 cm−2 (K km s − 1 )−1 is approximately six times larger than the average value found in the Milky Way.
Adaptive Loss-Aware Quantization for Multi-Bit Networks

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantization-induced error on the loss function involving neither gradient approximation nor full precision maintenance. ALQ also exploits strategies including adaptive bitwidth, smooth bitwidth reduction, and iterative trained quantization to allow a smaller network size without loss in accuracy. Experiment results on popular image datasets show that ALQ outperforms state-of-the-art compressed networks in terms of both storage and accuracy.
A Theory of Trotter Error

The Lie-Trotter formula, together with its higher-order generalizations, provides a direct approach to decomposing the exponential of a sum of operators. Despite significant effort, the error scaling of such product formulas remains poorly understood. We develop a theory of Trotter error that overcomes the limitations of prior approaches based on truncating the Baker-Campbell-Hausdorff expansion. Our analysis directly exploits the commutativity of operator summands, producing tighter error bounds for both real- and imaginary-time evolutions. Whereas previous work achieves similar goals for systems with geometric locality or Lie-algebraic structure, our approach holds in general. We give a host of improved algorithms for digital quantum simulation and quantum Monte Carlo methods, including simulations of second-quantized plane-wave electronic structure, $k$-local Hamiltonians, rapidly decaying power-law interactions, clustered Hamiltonians, the transverse field Ising model, and quantum ferromagnets, nearly matching or even outperforming the best previous results. We obtain further speedups using the fact that product formulas can preserve the locality of the simulated system. Specifically, we show that local observables can be simulated with complexity independent of the system size for power-law interacting systems, which implies a Lieb-Robinson bound as a byproduct. Our analysis reproduces known tight bounds for first- and second-order formulas. Our higher-order bound overestimates the complexity of simulating a one-dimensional Heisenberg model with an even-odd ordering of terms by only a factor of $5$, and is close to tight for power-law interactions and other orderings of terms. This suggests that our theory can accurately characterize Trotter error in terms of both asymptotic scaling and constant prefactor.
Distributed Reinforcement Learning for Decentralized Linear Quadratic Control: A Derivative-Free Policy Optimization Approach

This article considers a distributed reinforcement learning problem for decentralized linear quadratic (LQ) control with partial state observations and local costs. We propose a zero-order distributed policy optimization algorithm (ZODPO) that learns linear local controllers in a distributed fashion, leveraging the ideas of policy gradient, zero-order optimization, and consensus algorithms. In ZODPO, each agent estimates the global cost by consensus, and then conducts local policy gradient in parallel based on zero-order gradient estimation. ZODPO only requires limited communication and storage even in large-scale systems. Further, we investigate the nonasymptotic performance of ZODPO and show that the sample complexity to approach a stationary point is polynomial with the error tolerance’s inverse and the problem dimensions, demonstrating the scalability of ZODPO. We also show that the controllers generated throughout ZODPO are stabilizing controllers with high probability. Last, we numerically test ZODPO on multizone HVAC systems.