nils-herrmann/clean_scirepeval huggingface dataset:
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images

We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and complex shaped objects with similar color and textures, such as buildings, surfaces/roads, and trees in very high resolution remote sensing images. The proposed DDCM-Net consists of dense dilated convolutions merged with varying dilation rates. This can effectively enlarge the kernels’ receptive fields, and, more importantly, obtain fused local and global context information to promote surrounding discriminative capability. We demonstrate the effectiveness of the proposed DDCM-Net on the publicly available ISPRS Potsdam dataset and achieve a performance of 92.3% F1-score and 86.0% mean intersection over union accuracy by only using the RGB bands, without any post-processing. We also show results on the ISPRS Vaihingen dataset, where the DDCM-Net trained with IRRG bands, also obtained better mapping accuracy (89.8% F1-score) than previous state-of-the-art approaches.
Optimal Legislative County Clustering in North Carolina

North Carolina’s constitution requires that state legislative districts should not split counties. However, counties must be split to comply with the “one person, one vote” mandate of the U.S. Supreme Court. Given that counties must be split, the North Carolina legislature and the courts have provided guidelines that seek to reduce counties split across districts while also complying with the “one person, one vote” criterion. Under these guidelines, the counties are separated into clusters; each cluster contains a specified number of districts and that are drawn independent from other clusters. The primary goal of this work is to develop, present, and publicly release an algorithm to optimally cluster counties according to the guidelines set by the court in 2015. We use this tool to investigate the optimality and uniqueness of the enacted clusters under the 2017 redistricting process. We verify that the enacted clusters are optimal, but find other optimal choices. We emphasize that the tool we provide lists all possible optimal county clusterings. We also explore the stability of clustering under changing statewide populations and project what the county clusters may look like in the next redistricting cycle beginning in 2020/2021.
Learning Rich Representations For Structured Visual Prediction Tasks

We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Our key idea is to map small image elements to feature representations extracted from a sequence of nested regions of increasing spatial extent. These regions are obtained by "zooming out" from the pixel/superpixel all the way to scene-level resolution, and hence we call these zoom-out features. Applied to semantic segmentation and other structured prediction tasks, our approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead image elements are classified by a feedforward multilayer network with skip-layer connections spanning the zoom-out levels. When used in conjunction with modern neural architectures such as ResNet, DenseNet and NASNet (to which it is complementary) our approach achieves competitive accuracy on segmentation benchmarks. 
In addition, we propose an approach for learning category-level semantic segmentation purely from image-level classification tag. It exploits localization cues that emerge from training a modified zoom-out architecture tailored for classification tasks, to drive a weakly supervised process that automatically labels a sparse, diverse training set of points likely to belong to classes of interest. Finally, we introduce data-driven regularization functions for the supervised training of CNNs. Our innovation takes the form of a regularizer derived by learning an autoencoder over the set of annotations. This approach leverages an improved representation of label space to inform extraction of features from images
Multi-Temporal High Resolution Aerial Image Registration Using Semantic Features

A new type of segmentation-based semantic feature (SegSF) for multi-temporal aerial image registration is proposed in this paper. These features encode information about temporally invariant objects such as roads which help deal with the issues such as changing foliage that classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and show good accuracy in registering aerial images across years and seasons.
PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification

Most existing work on adversarial data generation focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) consists of challenging English paraphrase identification pairs from Wikipedia and Quora. We remedy this gap with PAWS-X, a new dataset of 23,659 human translated PAWS evaluation pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. We provide baseline numbers for three models with different capacity to capture non-local context and sentence structure, and using different multilingual training and evaluation regimes. Multilingual BERT fine-tuned on PAWS English plus machine-translated data performs the best, with a range of 83.1-90.8 accuracy across the non-English languages and an average accuracy gain of 23% over the next best model. PAWS-X shows the effectiveness of deep, multilingual pre-training while also leaving considerable headroom as a new challenge to drive multilingual research that better captures structure and contextual information.
Efficient limited time reachability estimation in temporal networks

Time-limited states characterize many dynamical processes on networks: disease-infected individuals recover after some time, people forget news spreading on social networks, or passengers may not wait forever for a connection. These dynamics can be described as limited-waiting-time processes, and they are particularly important for systems modeled as temporal networks. These processes have been studied via simulations, which is equivalent to repeatedly finding all limited-waiting-time temporal paths from a source node and time. We propose a method yielding an orders-of-magnitude more efficient way of tracking the reachability of such temporal paths. Our method gives simultaneous estimates of the in- or out-reachability (with any chosen waiting-time limit) from every possible starting point and time. It works on very large temporal networks with hundreds of millions of events on current commodity computing hardware. This opens up the possibility to analyze reachability and dynamics of spreading processes on large temporal networks in completely new ways. For example, one can now compute centralities based on global reachability for all events or can find with high probability the infected node and time, which would lead to the largest epidemic outbreak.
Rethinking Irregular Scene Text Recognition

Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc.. The perspective distortion and non-linear spatial arrangement of characters make it further difficult. While rectification based method is intuitively grounded and has pushed the envelope by far, its potential is far from being well exploited. In this paper, we present a bag of tricks that prove to significantly improve the performance of rectification based method. On curved text dataset, our method achieves an accuracy of 89.6% on CUTE-80 and 76.3% on Total-Text, an improvement over previous state-of-the-art by 6.3% and 14.7% respectively. Furthermore, our combination of tricks helps us win the ICDAR 2019 Arbitrary-Shaped Text Challenge (Latin script), achieving an accuracy of 74.3% on the held-out test set. We release our code as well as data samples for further exploration at this https URL
The influence of impurities on the charge carrier mobility of small molecule organic semiconductors

Amorphous organic semiconductors based on small molecules and polymers are used in many applications, most prominently organic light emitting diodes (OLEDs) and organic solar cells. Impurities and charge traps are omnipresent in most currently available organic semiconductors and limit charge transport and thus device efficiency. The microscopic cause as well as the chemical nature of these traps are presently not well understood. Using a multiscale model we characterize the influence of impurities on the density of states and charge transport in small-molecule amorphous organic semiconductors. We use the model to quantitatively describe the influence of water molecules and water-oxygen complexes on the electron and hole mobilities. These species are seen to impact the shape of the density of states and to act as explicit charge traps within the energy gap. Our results show that trap states introduced by molecular oxygen can be deep enough to limit the electron mobility in widely used materials.
Systematic Analysis of Image Generation using GANs

Generative Adversarial Networks have been crucial in the developments made in unsupervised learning in recent times. Exemplars of image synthesis from text or other images, these networks have shown remarkable improvements over conventional methods in terms of performance. Trained on the adversarial training philosophy, these networks aim to estimate the potential distribution from the real data and then use this as input to generate the synthetic data. Based on this fundamental principle, several frameworks can be generated that are paragon implementations in several real-life applications such as art synthesis, generation of high resolution outputs and synthesis of images from human drawn sketches, to name a few. While theoretically GANs present better results and prove to be an improvement over conventional methods in many factors, the implementation of these frameworks for dedicated applications remains a challenge. This study explores and presents a taxonomy of these frameworks and their use in various image to image synthesis and text to image synthesis applications. The basic GANs, as well as a variety of different niche frameworks, are critically analyzed. The advantages of GANs for image generation over conventional methods as well their disadvantages amongst other frameworks are presented. The future applications of GANs in industries such as healthcare, art and entertainment are also discussed.
Throwing away antimatter via neutrino oscillations during the reheating era

The simplest possibility to explain the baryon asymmetry of the Universe is to assume that radiation is created asymmetrically between baryons and anti-baryons after the inflation. We propose a new mechanism of this kind where CP-violating flavor oscillations of left-handed leptons in the reheating era distribute the lepton asymmetries partially into the right-handed neutrinos while net asymmetry is not created. The asymmetry stored in the right-handed neutrinos is later washed out by the lepton number violating decays, and it ends up with the net lepton asymmetry in the Standard Model particles, which is converted into the baryon asymmetry by the sphaleron process. This scenario works for a range of masses of the right-handed neutrinos while no fine-tuning among the masses is required. The reheating temperature of the Universe can be as low as Ø(10) TeV if we assume that the decays of inflatons in the perturbative regime are responsible for the reheating. For the case of the reheating via the dissipation effects, the reheating temperature can be as low as Ø(100) GeV.
Particle re-acceleration and Faraday-complex structures in the RXC J1314.4-2515 galaxy cluster

Radio relics are sites of electron (re)acceleration in merging galaxy clusters but the mechanism of acceleration and the topology of the magnetic field in and near relics are yet to be understood. We are carrying out an observational campaign on double relic galaxy clusters starting with RXC J1314.4-2515. With Jansky Very Large Array multi-configuration observations in the frequency range 1-4 GHz, we perform both spectral and polarization analyses, using the Rotation Measure synthesis technique. We use archival XMM-Newton observations to constrain the properties of the shocked region. We discover a possible connection between the activity of a radio galaxy and the emission of the eastern radio relic. In the northern elongated arc of the western radio relic, we detect polarized emission with an average polarization fraction of 31 % at 3 GHz and we derive the Mach number of the underlying X-ray shock. Our observations reveal low levels of fractional polarization and Faraday-complex structures in the southern region of the relic, which point to the presence of thermal gas and filamentary magnetic field morphology inside the radio emitting volume. We measured largely different Rotation Measure dispersion from the two relics. Finally, we use cosmological magneto-hydrodynamical simulations to constrain the magnetic field, viewing angle, and to derive the acceleration efficiency of the shock. We find that the polarization properties of RXC J1314.4-2515 are consistent with a radio relic observed at 70○ with respect to the line of sight and that efficient re-acceleration of fossil electrons has taken place.
Properties of the circumgalactic medium in cosmic ray-dominated galaxy haloes

We investigate the impact of cosmic rays (CRs) on the circumgalactic medium (CGM) in FIRE-2 simulations, for ultra-faint dwarf through Milky Way (MW)-mass halos hosting star-forming (SF) galaxies. Our CR treatment includes injection by supernovae, anisotropic streaming and diffusion along magnetic field lines, collisional and streaming losses, with constant parallel diffusivity $\kappa\sim3\times10^{29}\,\mathrm{cm^2\ s^{-1}}$ chosen to match $\gamma$-ray observations. With this, CRs become more important at larger halo masses and lower redshifts, and dominate the pressure in the CGM in MW-mass halos at $z\lesssim 1-2$. The gas in these ``CR-dominated'' halos differs significantly from runs without CRs: the gas is primarily cool (a few $\sim10^{4}\,$K), and the cool phase is volume-filling and has a thermal pressure below that needed for virial or local thermal pressure balance. Ionization of the ``low'' and ``mid'' ions in this diffuse cool gas is dominated by photo-ionization, with O VI columns $\gtrsim 10^{14.5}\,\mathrm{cm^{-2}}$ at distances $\gtrsim 150\,\mathrm{kpc}$. CR and thermal gas pressure are locally anti-correlated, maintaining total pressure balance, and the CGM gas density profile is determined by the balance of CR pressure gradients and gravity. Neglecting CRs, the same halos are primarily warm/hot ($T\gtrsim 10^{5}\,$K) with thermal pressure balancing gravity, collisional ionization dominates, O VI columns are lower and Ne VIII higher, and the cool phase is confined to dense filaments in local thermal pressure equilibrium with the hot phase.
Constraining axionlike particles from rare pion decays

Ultraviolet completions for axion-like particles (ALPs) lighter than the neutral pion generically induce ALP-neutral pion mixing, and are therefore sensitive to direct constraints on the mixing angle. For ALPs below the pion mass, we demonstrate that strong and novel bounds on the ALP-pion mixing angle can be extracted from existing rare pion decay data, measured by the PIENU and PIBETA experiments.
Simulating the complexity of the dark matter sheet I: numerical algorithms

At early times, dark matter has a thermal velocity dispersion of unknown amplitude which, for warm dark matter (WDM) models, can influence the formation of non-linear structure on observable scales. We propose a new scheme to simulate cosmologies with a small-scale suppression of perturbations that combines two previous methods in a way that avoids the numerical artefacts which have so far prevented either from producing fully reliable results. At low densities and throughout most of the cosmological volume, we represent the dark matter phase sheet directly using high-accuracy interpolation, thereby avoiding the artificial fragmentation which afflicts particle-based methods in this regime. Such phase-sheet methods are, however, unable to follow the rapidly increasing complexity of the denser regions of dark matter haloes, so for these we switch to an N-body scheme which uses the geodesic deviation equation to track phase-sheet properties local to each particle. In addition, we present a novel high-resolution force calculation scheme based on an oct-tree of cubic force resolution elements which is well suited to approximate the force field of our combined sheet+particle distribution. Our hybrid simulation scheme enables the first reliable simulations of the internal structure of low-mass haloes in a WDM cosmology.
Low-
Q2
 empirical parametrizations of the 
N*
 helicity amplitudes

The data associated with the electromagnetic excitations of the nucleon ($\gamma^\ast N \to N^\ast$) are usually parametrized by helicity amplitudes at the resonance $N^\ast$ rest frame. The properties of the $\gamma^\ast N \to N^\ast$ transition current at low $Q^2$ can be, however, better understood when expressed in terms of structure form factors, particularly near the pseudothreshold, when the magnitude of the photon three-momentum vanishes ($|{\bf q}| =0$). At the pseudothreshold the invariant four-momentum square became $q^2= (M_R-M_N)^2$, well in the timelike region $Q^2 =- q^2 < 0$ [$M_N$ and $M_R$ are the mass of the nucleon and of the resonance, respectively]. In the helicity amplitude representation, the amplitudes have well-defined dependences on $|{\bf q}|$, near the pseudothreshold, and there are correlations between different amplitudes. Those constraints are often ignored in the empirical parametrizations of the helicity amplitudes. In the present work, we show that the structure of the transition current near the pseudothreshold has an impact on the parametrizations of the data. We present a method which modifies analytic parametrizations of the data at low $Q^2$, in order to take into account the constraints of the transition amplitudes near the pseudothreshold. The model dependence of the parametrizations on the low-$Q^2$ data is studied in detail.
Adaptively Sparse Transformers

Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. The multiple heads learn diverse types of word relationships. However, with standard softmax attention, all attention heads are dense, assigning a non-zero weight to all context words. In this work, we introduce the adaptively sparse Transformer, wherein attention heads have flexible, context-dependent sparsity patterns. This sparsity is accomplished by replacing softmax with alpha-entmax: a differentiable generalization of softmax that allows low-scoring words to receive precisely zero weight. Moreover, we derive a method to automatically learn the alpha parameter – which controls the shape and sparsity of alpha-entmax – allowing attention heads to choose between focused or spread-out behavior. Our adaptively sparse Transformer improves interpretability and head diversity when compared to softmax Transformers on machine translation datasets. Findings of the quantitative and qualitative analysis of our approach include that heads in different layers learn different sparsity preferences and tend to be more diverse in their attention distributions than softmax Transformers. Furthermore, at no cost in accuracy, sparsity in attention heads helps to uncover different head specializations.
A single-layer RNN can approximate stacked and bidirectional RNNs, and topologies in between

To enhance the expressiveness and representational capacity of recurrent neural networks (RNN), a large body of work has emerged exploring stacked architectures with additional topological modifications like shortcut connections or bidirectionality. However, choosing the best network for a particular problem requires a combinatorial search over architectures and their hyperparameters. In this work, we show that a single-layer RNN can perfectly mimic an arbitrarily deep stacked RNN under specific constraints on its weight matrix and a delay between input and output. This obviates the need to manually select hyperparameters like the number of layers. Additionally, we show that weakening weight constraints while keeping the delay gives rise to partial acausality in the single-layer RNN, much like a bidirectional network. Synthetic experiments confirm that the delayed RNN can mimic bidirectional networks in perfectly solving some acausal tasks, outperforming them in others. Finally, we show that in a challenging language processing task, the delayed RNN performs within 0.3\% of the accuracy of the bidirectional network while reducing computational costs.
Racial Disparities in Voting Wait Times: Evidence from Smartphone Data

Equal access to voting is a core feature of democratic government. Using data from hundreds of thousands of smartphone users, we quantify a racial disparity in voting wait times across a nationwide sample of polling places during the 2016 U.S. presidential election. Relative to entirely-white neighborhoods, residents of entirely-black neighborhoods waited 29% longer to vote and were 74% more likely to spend more than 30 minutes at their polling place. This disparity holds when comparing predominantly white and black polling places within the same states and counties, and survives numerous robustness and placebo tests. We shed light on the mechanism for these results and discuss how geospatial data can be an effective tool to both measure and monitor these disparities going forward.
Meta-Learning with Warped Gradient Descent

Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better initialisations or scaling factors for a gradient-based update rule. Both of these approaches pose challenges. On one hand, directly producing an update forgoes a useful inductive bias and can easily lead to non-converging behaviour. On the other hand, approaches that try to control a gradient-based update rule typically resort to computing gradients through the learning process to obtain their meta-gradients, leading to methods that can not scale beyond few-shot task adaptation. In this work, we propose Warped Gradient Descent (WarpGrad), a method that intersects these approaches to mitigate their limitations. WarpGrad meta-learns an efficiently parameterised preconditioning matrix that facilitates gradient descent across the task distribution. Preconditioning arises by interleaving non-linear layers, referred to as warp-layers, between the layers of a task-learner. Warp-layers are meta-learned without backpropagating through the task training process in a manner similar to methods that learn to directly produce updates. WarpGrad is computationally efficient, easy to implement, and can scale to arbitrarily large meta-learning problems. We provide a geometrical interpretation of the approach and evaluate its effectiveness in a variety of settings, including few-shot, standard supervised, continual and reinforcement learning.