Miracle-dz/UniFAR-FTUs huggingface dataset:
Clean Images are Hard to Reblur: A New Clue for Deblurring

The goal of dynamic scene deblurring is to remove the motion blur present in a given image. Most learning-based approaches implement their solutions by minimizing the L1 or L2 distance between the output and reference sharp image. Recent attempts improve the perceptual quality of the deblurred image by using features learned from visual recognition tasks. However, those features are originally designed to capture the high-level contexts rather than the low-level structures of the given image, such as blurriness. We propose a novel low-level perceptual loss to make image sharper. To better focus on image blurriness, we train a reblurring module amplifying the unremoved motion blur. Motivated that a well-deblurred clean image should contain zero-magnitude motion blur that is hard to be ampli-ﬁed, we design two types of reblurring loss functions. The supervised reblurring loss at training stage compares the ampliﬁed blur between the deblurred image and the reference sharp image. The self-supervised reblurring loss at inference stage inspects if the deblurred image still contains noticeable blur to be ampliﬁed. Our experimental re-sults demonstrate the proposed reblurring losses improve the perceptual quality of the deblurred images in terms of NIQE and LPIPS scores as well as visual sharpness.
Dimensional transmutation and nonconventional scaling behaviour in a model of self-organized criticality

The paper addresses two unusual scaling regimes (types of critical behaviour) predicted by the field-theoretic renormalization group analysis for a self-organized critical system with turbulent motion of the environment. The system is modelled by the anisotropic stochastic equation for a"running sandpile"introduced by Hwa and Kardar in [{\it Phys. Rev. Lett. } {\bf 62}: 1813 (1989)]. The turbulent motion is described by the isotropic Kazantsev-Kraichnan"rapid-change"velocity ensemble for an incompressible fluid. The original Hwa-Kardar equation allows for independent scaling of the spatial coordinates $x_{\parallel}$ (the coordinate along the preferred dimension) and ${\bf x_{\bot}}$ (the coordinates in the orthogonal subspace to the preferred direction) that becomes impossible once the isotropic velocity ensemble is coupled to the equation. However, it is found that one of the regimes of the system's critical behaviour (the one where the isotropic turbulent motion is irrelevant) recovers the anisotropic scaling through"dimensional transmutation. "The latter manifests as a dimensionless ratio acquiring nontrivial canonical dimension. The critical regime where both the velocity ensemble and the nonlinearity of the Hwa-Kardar equation are relevant simultaneously is also characterized by"atypical"scaling. While the ordinary scaling with fixed infra-red irrelevant parameters is impossible in this regime, the"restricted"scaling where the times, the coordinates, and the dimensionless ratio are scaled becomes possible. This result brings to mind scaling hypotheses modifications (Stell's weak scaling or Fisher's generalized scaling) for systems with significantly different characteristic scales.
One-Round Active Learning

In this work, we initiate the study of one-round active learning, which aims to select a subset of unlabeled data points that achieve the highest model performance after being labeled with only the information from initially labeled data points. The challenge of directly applying existing data selection criteria to the one-round setting is that they are not indicative of model performance when available labeled data is limited. We address the challenge by explicitly modeling the dependence of model performance on the dataset. Specifically, we propose DULO, a data-driven framework for one-round active learning, wherein we learn a model to predict the model performance for a given dataset and then leverage this model to guide the selection of unlabeled data. Our results demonstrate that DULO leads to the state-of-the-art performance on various active learning benchmarks in the one-round setting.
Parallel mesh reconstruction streams for pose estimation of interacting hands

We present a new multi-stream 3D mesh reconstruction network (MSMR-Net) for hand pose estimation from a single RGB image. Our model consists of an image encoder followed by a mesh-convolution decoder composed of connected graph convolution layers. In contrast to previous models that form a single mesh decoding path, our decoder network incorporates multiple cross-resolution trajectories that are executed in parallel. Thus, global and local information are shared to form rich decoding representations at minor additional parameter cost compared to the single trajectory network. We demonstrate the effectiveness of our method in hand-hand and hand-object interaction scenarios at various levels of interaction. To evaluate the former scenario, we propose a method to generate RGB images of closely interacting hands. Moreoever, we suggest a metric to quantify the degree of interaction and show that close hand interactions are particularly challenging. Experimental results show that the MSMR-Net outperforms existing algorithms on the hand-object FreiHAND dataset as well as on our own hand-hand dataset.
MusCaps: Generating Captions for Music Audio

Content-based music information retrieval has seen rapid progress with the adoption of deep learning. Current approaches to high-level music description typically make use of classification models, such as in auto-tagging or genre and mood classification. In this work, we propose to address music description via audio captioning, defined as the task of generating a natural language description of music audio content in a human-like manner. To this end, we present the first music audio captioning model, MusCaps, consisting of an encoder-decoder with temporal attention. Our method combines convolutional and recurrent neural network architectures to jointly process audio-text inputs through a multimodal encoder and leverages pretraining on audio data to obtain representations that effectively capture and summarise musical features in the input. Evaluation of the generated captions through automatic metrics shows that our method outperforms a baseline designed for non-music audio captioning. Through an ablation study, we unveil that this performance boost can be mainly attributed to pre-training of the audio encoder, while other design choices - modality fusion, decoding strategy and the use of attention - contribute only marginally. Our model represents a shift away from classification-based music description and combines tasks requiring both auditory and linguistic understanding to bridge the semantic gap in music information retrieval11Code available at https://github.com/ilaria-manco/muscaps.
Turbulence in the Magnetospheres of the Outer Planets

The magnetospheres of the outer planets exhibit turbulent phenomena in an environment which is qualitatively different compared to the solar wind or the interstellar medium. The key differences are the finite sizes of the magnetospheres limited by their physical boundaries, the presence of a strong planetary background magnetic field and spatially very inhomogeneous plasma properties within the magnetospheres. Typical turbulent fluctuations possess amplitudes much smaller than the background field and are characterized by Alfvén times, which can be smaller than the non-linear interaction time scales. The magnetospheres of the outer planets are thus interesting laboratories of plasma turbulence. In Jupiter's and Saturn's magnetospheres, turbulence is well-established thanks to the in-situ measurements by several spacecraft, in particular the Galileo and Cassini orbiter. In contrast, the fluctuations in Uranus' and Neptune's magnetospheres are poorly understood due to the lack of sufficient data. Turbulence in the outer planets' magnetospheres have important effects on the systems as a whole. The dissipation of the turbulent fluctuations through wave-particle interaction is a significant heat source, which can explain the large magnetospheric plasma temperatures. Similarly, turbulent wave fluctuations strongly contribute to the acceleration of particles responsible for the planet's auroras.
On the Comparison between Cyclic Sampling and Random Reshuffling

When applying a stochastic/incremental algo-rithm, one must choose the order to draw samples. Among the most popular approaches are cyclic sampling and random reshufﬂing, which are empirically faster and more cache-friendly than uniform-iid-sampling. Cyclic sampling draws the samples in a ﬁxed, cyclic order, which is less robust than reshufﬂing the samples periodically. In-deed, existing works have established worst case convergence rates for cyclic sampling, which are generally worse than that of random reshufﬂing. In this paper, however, we found a certain cyclic order can be much faster than reshufﬂing and one can discover it at a low cost! Studying and comparing different sampling orders typically require new analytic techniques. In this paper, we introduce a norm, which is de-ﬁned based on the sampling order, to measure the distance to solution. Applying this technique on proximal Finito/MISO algorithm allows us to identify the optimal ﬁxed ordering, which can beat random reshufﬂing by a factor up to log( n ) /n in terms of the best-known upper bounds. We also propose a strategy to discover the optimal ﬁxed ordering numerically. The established rates are state-of-the-art compared to previous works.
Weathering the Storm: How Foreign Aid and Institutions Affect Entrepreneurship Activity Following Natural Disasters

This study examines how foreign aid and institutions affect entrepreneurship activity following natural disasters. We use insights from the entrepreneurship, development, and institutions literature to develop a model of entrepreneurship activity in the aftermath of natural disasters. First, we hypothesize the effect of natural disasters on entrepreneurship activity depends on the amount of foreign aid received. Second, we hypothesize that natural disasters and foreign aid either encourages or discourages entrepreneurship activity depending on two important institutional conditions: the quality of government and economic freedom. The findings from our panel of 85 countries from 2006 to 2016 indicate that natural disasters are negatively associated with entrepreneurship activity, but both foreign aid and economic freedom attenuate this effect. In addition, we observe that foreign aid is positively associated with entrepreneurship activity but only in countries with high quality government. Hence, we conclude that the effect of natural disasters on entrepreneurship depends crucially on the quality of government, economic freedom, and foreign aid. Our findings provide new insights into how natural disasters and foreign aid affect entrepreneurship and highlight the important role of the institutional context.
Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids Using Graph Neural Networks

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.
Attention vs non-attention for a Shapley-based explanation method

The field of explainable AI has recently seen an explosion in the number of explanation methods for highly non-linear deep neural networks. The extent to which such methods – that are often proposed and tested in the domain of computer vision – are appropriate to address the explainability challenges in NLP is yet relatively unexplored. In this work, we consider Contextual Decomposition (CD) – a Shapley-based input feature attribution method that has been shown to work well for recurrent NLP models – and we test the extent to which it is useful for models that contain attention operations. To this end, we extend CD to cover the operations necessary for attention-based models. We then compare how long distance subject-verb relationships are processed by models with and without attention, considering a number of different syntactic structures in two different languages: English and Dutch. Our experiments confirm that CD can successfully be applied for attention-based models as well, providing an alternative Shapley-based attribution method for modern neural networks. In particular, using CD, we show that the English and Dutch models demonstrate similar processing behaviour, but that under the hood there are consistent differences between our attention and non-attention models.
On the Correlation between Atomic Gas and Bars in Galaxies

We analyze the correlation between properties of large-scale bars and atomic gas content of galaxies to explore the role of H i gas on bar evolution in galaxies. We show that the absolute bar size depends strongly on total stellar mass of galaxies and does not change significantly with H i gas fraction at fixed stellar mass. Furthermore, the physical size of the bar is small and nearly constant in high H i gas fraction and low-mass galaxies and becomes larger with increasing galactic stellar mass in low gas fraction galaxies. When the stellar masses are fixed, the relative bar length normalized to the disk shows a decrease with increasing H i gas fraction due to the larger disks in gas-richer galaxies. We measure the gas deficiency of the samples and find that the gas-rich galaxies have longer and stronger bars compared with the H i gas-deficient galaxies at fixed stellar mass, especially for the massive ones. When splitting the samples into star-forming and quiescent subgroups, the star-forming galaxies tend to have longer bars than the quiescent ones at fixed stellar mass and gas deficiency. In addition, our results suggest two different types of bar properties, in which the bars in gas-rich galaxies grow longer but retain similar axial ratio over time, while they grow longer and fatter over time in gas-poor galaxies.
Compressive Oscillations in Hot Coronal Loops: Are Sloshing Oscillations and Standing Slow Waves Independent?

Employing high-resolution EUV imaging observations from SDO/AIA, we analyze a compressive plasma oscillation in a hot coronal loop triggered by a C-class flare near one of its footpoints, as first studied by Kumar et al. We investigate the oscillation properties in both the 131 Å and 94 Å channels and find that what appears as a pure sloshing oscillation in the 131 Å channel actually transforms into a standing wave in the 94 Å channel at a later time. This is the first clear evidence of such transformation and confirms the results of a recent numerical study that suggests that these two oscillations are not independent phenomena. We introduce a new analytical expression to properly fit the sloshing phase of an oscillation and extract the oscillation properties. For the AIA 131 Å channel, the obtained oscillation period and damping time are 608 ± 4 s and 431 ± 20 s, respectively, during the sloshing phase. The corresponding values for the AIA 94 Å channel are 617 ± 3 s and 828 ± 50 s. During the standing phase that is observed only in the AIA 94 Å channel, the oscillation period and damping time have increased to 791 ± 5 s and 1598 ± 138 s, respectively. The plasma temperature obtained from the differential emission measure analysis indicates substantial cooling of the plasma during the oscillation. Considering this, we show that the observed oscillation properties and the associated changes are compatible with damping due to thermal conduction. We further demonstrate that the absence of a standing phase in the 131 Å channel is a consequence of cooling plasma besides the faster decay of oscillation in this channel.
Low emittance muon beam in the 2 to 40 GeV energy range for muon and neutrino experiments

I present a scheme to obtain a 2 to 40 GeV low emittance muon beam, not requiring cooling and within today’s technological resources, to be used for early commissioning of muon accelerator projects, or alternatively dedicated muon and neutrino parameter measurements. In particular, a muon rate of 5×10 μ/s in a normalized transverse emittance of 5 π μm at 22 GeV, and energy spread of 1 GeV obtained from O(10) e/s on target at 44 GeV. This emittance is below the expected results of advanced emittance cooling techniques for muons produced from protons-on-target, and represents an alternative for the duration of complete muon cooling studies. The scheme has beam designed to adjust the muon beam energy in the GeV energy range to the needs for precise parameter measurements of muons and neutrinos. Although the rate is small compared to other muon sources, it does not seem to represent a big limitation for its usage. Furthermore, the muon rate could be in principle increased proportionally to the availability of higher positron rates, already foreseen for future collider projects.
Learning Passage Impacts for Inverted Indexes

Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT. In this paper, we propose DeepImpact, a new document term-weighting scheme suitable for efficient retrieval using a standard inverted index. Compared to existing methods, DeepImpact improves impact-score modeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact leverages DocT5Query to enrich the document collection and, using a contextualized language model, directly estimates the semantic importance of tokens in a document, producing a single-value representation for each token in each document. Our experiments show that DeepImpact significantly outperforms prior first-stage retrieval approaches by up to 17% on effectiveness metrics w.r. t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the same effectiveness of state-of-the-art approaches with up to 5.1x speedup in efficiency.
Evanescent particles

Massive Klein–Gordon theory is quantized on a timelike hyperplane in Minkowski space using the framework of general boundary quantum field theory. In contrast to previous work, not only the propagating sector of the phase space is quantized, but also the evanescent sector, with the correct physical vacuum. This yields for the first time a description of the quanta of the evanescent field alone. The key tool is the novel [Formula: see text]-Kähler quantization prescription based on a ∗-twisted observable algebra. The spatial evolution of states between timelike hyperplanes is established and turns out to be non-unitary if different choices are made for the quantization ambiguity for initial and final hyperplane. Nevertheless, a consistent notion of transition probability is established also in the non-unitary case, thanks to the use of the positive formalism. Finally, it is shown how a conducting boundary condition on the timelike hyperplane gives rise to what we call the Casimir state. This is a pseudo-state which can be interpreted as an alternative vacuum and which gives rise to a sea of particle pairs even in this static case.
Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs

Author(s): Huang, Luna Yue; Hsiang, Solomon; Gonzalez-Navarro, Marco | Abstract: The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional evaluation approaches rely heavily on repeated in-person field surveys to measure changes in economic well-being and thus program effects. However, this is known to be costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in the context of a recent anti poverty program in rural Kenya. The approach we use is based on a large literature documenting a reliable relationship between housing quality and household wealth. We infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach can be used to obtain inexpensive and timely insights on program effectiveness in international developmentprograms.
TensorLib: A Spatial Accelerator Generation Framework for Tensor Algebra

Tensor algebra finds applications in various domains, and these applications, especially when accelerated on spatial hardware accelerators, can deliver high performance and low power. Spatial hardware accelerator exhibits complex design space. Prior approaches based on manual implementation lead to low programming productivity, rendering thorough design space exploration impossible. In this paper, we propose TensorLib, a framework for generating spatial hardware accelerator for tensor algebra applications. TensorLib is motivated by the observation that, different dataflows share common hardware modules, which can be reused across different designs. To build such a framework, TensorLib first uses Space-Time Transformation to explore different dataflows, which can compactly represent the hardware dataflow using a simple transformation matrix. Next, we identify the common structures of different dataflows and build parameterized hardware module templates with Chisel. Our generation framework can select the needed hardware modules for each dataflow, connect the modules using a specified interconnection pattern, and automatically generate the complete hardware accelerator design. TensorLib remarkably improves the productivity for the development and optimization of spatial hardware architecture, providing a rich design space with tradeoffs in performance, area, and power. Experiments show that TensorLib can automatically generate hardware designs with different dataflows and achieve 21% performance improvement on FPGA compared to the state-of-the-arts.
Highly sensitive spin-flop transition in antiferromagnetic van der Waals material 
MPS3
 (
M=Ni
 and Mn)

Recent developments in two-dimensional (2D) magnetism have motivated the search for novel van-der Waals (vdWs) magnetic materials to explore new magnetic phenomenon in the 2D limit. Metal thiophosphates, MPX3, is a class of magnetic vdWs materials with antiferromagnetic (AFM) ordering persisting down to the atomically thin limit. The magnetism in this material family has been found to be highly dependent on the choice of transition metal M. In this work, we have synthesized the intermediate compounds Ni1-xMnxPS3 (0 ≤ x ≤ 1) and investigated their magnetic properties. Our study reveals that the variation of Ni and Mn content in Ni1-xMnxPS3 can efficiently tune the spin-flop transition, likely due to the modulation of the magnetic anisotropy. Such effective tunning offers a promising candidate to engineer 2D magnetism for future device