Miracle-dz/UniFAR-FTUs huggingface dataset:
MXR-U-Nets for Real Time Hyperspectral Reconstruction

In recent times, CNNs have made significant contributions to applications in image generation, super-resolution and style transfer. In this paper, we build upon the work of Howard and Gugger, He et al. and Misra, D. and propose a CNN architecture that accurately reconstructs hyperspectral images from their RGB counterparts. We also propose a much shallower version of our best model with a 10% relative memory footprint and 3x faster inference, thus enabling real-time video applications while still experiencing only about a 0.5% decrease in performance.
Atmospheric stability and collapse on tidally locked rocky planets

Context. Over large timescales, a terrestrial planet may be driven towards spin-orbit synchronous rotation by tidal forces. In this particular configuration, the planet exhibits permanent dayside and nightside, which may induce strong day-night temperature gradients. The nightside temperature depends on the efficiency of the day-night heat redistribution and determines the stability of the atmosphere against collapse. Aims. To better constrain the atmospheric stability, climate, and surface conditions of rocky planets located in the habitable zone of their host star, it is thus crucial to understand the complex mechanism of heat redistribution. Methods. Building on early works and assuming dry thermodynamics, we developed a hierarchy of analytic models taking into account the coupling between radiative transfer, dayside convection, and large-scale atmospheric circulation in the case of slowly rotating planets. There are two types of these models: a zero-dimensional two-layer approach and a two-column radiative-convective-subsiding-upwelling model. They yield analytical solutions and scaling laws characterising the dependence of the collapse pressure on physical features, which are compared to the results obtained by early works using 3D global climate models (GCMs). Results. The analytical theory captures (i) the dependence of temperatures on atmospheric opacities and scattering in the shortwave and in the longwave, (ii) the behaviour of the collapse pressure observed in GCM simulations at low stellar fluxes that are due to the non-linear dependence of the atmospheric opacity on the longwave optical depth at the planet’s surface, (iii) the increase of stability generated by dayside sensible heating, and (iv) the decrease of stability induced by the increase of the planet size.
Latency-Aware Offloading in Integrated Satellite Terrestrial Networks

Next-generation communication networks are expected to integrate newly-used technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate the traffic load in dense regions. The prospective access network in 6G should hinge on satellite systems to take advantage of their wide coverage and high capacity. However, adopting satellites in 6G could be hindered because of the additional latency introduced, which is not tolerable by all traffic types. Therefore, we propose a traffic offloading scheme that integrates both the satellite and terrestrial networks to smartly allocate the traffic between them while satisfying different traffic requirements. Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency Communication (URLLC) traffic to the terrestrial backhaul to satisfy its stringent latency requirement. However, it offloads the enhanced Mobile Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but is not always sensitive to delay. Our scheme is shown to reduce the transmission delay of URLLC packets, decrease the number of dropped eMBB packets, and hence improve the network’s availability. Our findings highlight that the inter-working between satellite and terrestrial networks is crucial to mitigate the expected high load on the limited terrestrial capacity.
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis

Agent-based microsimulation has become the standard to analyze intelligent transportation systems, using disaggregate travel demand data for entire populations, data that are not typically readily available. Population synthesis approaches are thus needed. We present Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions.
Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

Doctors and nurses in these weeks and months are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. The discourse around the current epidemic makes use of war-related metaphors too, not only in public discourse and in the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a large corpus tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY frame covers a wider portion of the corpus, among the figurative frames WAR, a highly conventional one, is the frame used most frequently. Yet, this frame does not seem to be apt to elaborate the discourse around some aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options—or a metaphor menu—may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and beliefs during the current pandemic.
Crossed products of dual operator spaces and a characterization of groups with the approximation property

Let $ G $ be a locally compact group. We study the categories of $ L^{\infty}(G) $-comodules and $ L(G) $-comodules in the setting of dual operator spaces and the associated crossed products. It is proved that every $ L^{\infty}(G) $-comodule is non-degenerate and saturated, whereas every $ L(G) $-comodule is non-degenerate if and only if every $ L(G) $-comodule is saturated if and only if $ G $ has the approximation property in the sense of Haagerup and Kraus [14]. This allows us to extend known results from the duality theory of crossed products of von Neumann algebras (such as Takesaki-duality and the Digernes-Takesaki theorem) to the recent theory of crossed products of dual operator spaces. As applications, we obtain a characterization of groups with the approximation property in terms of the related crossed products improving a recent result of Crann and Neufang [9] and we generalize a theorem of Anoussis, Katavolos and Todorov [2] providing a less technical proof of it. Furthermore, this approach provides an answer to a question raised by the authors in [2].
Log-Concave Polynomials IV: Exchange Properties, Tight Mixing Times, and Faster Sampling of Spanning Trees

We prove tight mixing time bounds for natural random walks on bases of matroids, determinantal distributions, and more generally distributions associated with log-concave polynomials. For a matroid of rank $k$ on a ground set of $n$ elements, or more generally distributions associated with log-concave polynomials of homogeneous degree $k$ on $n$ variables, we show that the down-up random walk, started from an arbitrary point in the support, mixes in time $O(k\log k)$. Our bound has no dependence on $n$ or the starting point, unlike the previous analyses [ALOV19, CGM19], and is tight up to constant factors. The main new ingredient is a property we call approximate exchange, a generalization of well-studied exchange properties for matroids and valuated matroids, which may be of independent interest. Additionally, we show how to leverage down-up random walks to approximately sample spanning trees in a graph with $n$ edges in time $O(n\log^2 n)$, improving on the almost-linear time algorithm by Schild [Sch18]. Our analysis works on weighted graphs too, and is the first to achieve nearly-linear running time.
Some observations on the properness of identity plus linear powers: part 2

This paper develops our previous work on properness of a class of maps related to the Jacobian conjecture. The paper has two main parts: - In part 1, we explore properties of the set of non-proper values $S_f$ (as introduced by Z. Jelonek) of these maps. In particular, using a general criterion for non-properness of these maps, we show that under a "generic condition" (to be precise later) $S_f$ contains $0$ if it is non-empty. This result is related to a conjecture in our previous paper. We obtain this by use of a "dual set" to $S_f$, particularly designed for the special class of maps. - In part 2, we use the non-properness criteria obtained in our work to construct a counter-example to the proposed proof in arXiv:2002.10249 of the Jacobian conjecture. In the conclusion, we present some comments pertaining the Jacobian conjecture and properness of polynomial maps in general.
High-dimensional rank-one nonsymmetric matrix decomposition: the spherical case

We consider the problem of estimating a rank-one nonsymmetric matrix under additive white Gaussian noise. The matrix to estimate can be written as the outer product of two vectors and we look at the special case in which both vectors are uniformly distributed on spheres. We prove a replica-symmetric formula for the average mutual information between these vectors and the observations in the high-dimensional regime. This goes beyond previous results which considered vectors with independent and identically distributed elements. The method used can be extended to rank-one tensor problems.
On Box-Cox Transformation for Image Normality and Pattern Classification

A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda ( $\lambda$ ) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normality assumption underlies a variety of statistical test models. This technique, however, is best known in statistical analysis to handle one-dimensional data. Herein, this paper revolves around the utility of such a tool as a pre-processing step to transform two-dimensional data, namely, digital images and to study its effect. Moreover, to reduce time complexity, it suffices to estimate the parameter lambda in real-time for large two-dimensional matrices by merely considering their probability density function as a statistical inference of the underlying data distribution. We compare the effect of this light-weight Box-Cox transformation with well-established state-of-the-art low light image enhancement techniques. We also demonstrate the effectiveness of our approach through several test-bed data sets for generic improvement of visual appearance of images and for ameliorating the performance of a colour pattern classification algorithm as an example application. Results with and without the proposed approach, are compared using the AlexNet (transfer deep learning) pretrained model. To the best of our knowledge, this is the first time that the Box-Cox transformation is extended to digital images by exploiting histogram transformation.
FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features

We introduce a novel federated learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. Conventional federated learning approaches are inefficient for cross-silo problems because they require the exchange of messages for gradient updates at every iteration, and raise security concerns over sharing such messages during learning. We propose a Federated Stochastic Block Coordinate Descent (FedBCD) algorithm, allowing each party to conduct multiple local updates before each communication to effectively reduce communication overhead. Under a practical security model, we show that parties cannot infer others' exact raw data (“deep leakage”) from collections of messages exchanged in our framework, regardless of the number of communication to be performed. Further, we provide convergence guarantees and empirical evaluations on a variety of tasks and datasets, demonstrating significant improvement inefficiency.
On the Energy Coupling Efficiency of AGN Outbursts in Galaxy Clusters

Active galactic nucleus (AGN) jets are believed to be important in solving the cooling flow problem in the intracluster medium (ICM), while the detailed mechanism is still under debate. Here we present a systematic study on the energy coupling efficiency ηcp, the fraction of AGN jet energy transferred to the ICM. We first estimate the values of ηcp analytically in two extreme cases, which are further confirmed and extended with a parameter study of spherical outbursts in a uniform medium using hydrodynamic simulations. We find that ηcp increases from ∼0.4 for a weak isobaric injection to ≳0.8 for a powerful point injection. For any given outburst energy, we find two characteristic outburst powers that separate these two extreme cases. We then investigate the energy coupling efficiency of AGN jet outbursts in a realistic ICM with hydrodynamic simulations, finding that jet outbursts are intrinsically different from spherical outbursts. For both powerful and weak jet outbursts, ηcp is typically around 0.7–0.9, partly due to the nonspherical nature of jet outbursts, which produce backflows emanating from the hot spots, significantly enhancing the ejecta–ICM interaction. While for powerful outbursts a dominant fraction of the energy transferred from the jet to the ICM is dissipated by shocks, shock dissipation only accounts for ≲30% of the injected jet energy for weak outbursts. While both powerful and weak outbursts could efficiently heat cooling flows, powerful thermal-energy-dominated jets are most effective in delaying the onset of the central cooling catastrophe.
Phase Transition and Entropy Force between Two Horizons in (n+2)-Dimensional de Sitter Space

In this paper, the effect of the space-time dimension on effective thermodynamic quantities in (n+2)-dimensional Reissner-Nordstrom-de Sitter space has been studied. Based on derived effective thermodynamic quantities, conditions for the phase transition are obtained. The result shows that the accelerating cosmic expansion can be attained by the entropy force arisen from the interaction between horizons of black holes and our universe, which provides a possible way to explain the physical mechanism for the accelerating cosmic expansion.
Spinon Fermi Surface Spin Liquid in a Triangular Lattice Antiferromagnet 
NaYbSe2

Triangular lattice of rare-earth ions with interacting effective spin-1/2 local moments is an ideal platform to explore the physics of quantum spin liquids (QSLs) in the presence of strong spin-orbit coupling, crystal electric ﬁelds, and geometrical frustration. The Yb delafossites, NaYbCh2 (Ch=O, S, Se) with Yb ions forming a perfect triangular lattice, have been suggested to be candidates for QSLs. Previous thermodynamics, nuclear magnetic resonance, and powder sample neutron scattering measurements on NaYbCh2 have supported the suggestion of the QSL ground states. The key signature of a QSL, the spin excitation continuum, arising from the spin quantum number fractionalization, has not been observed. Here we perform both elastic and inelastic neutron scattering measurements as well as detailed thermodynamic measurements on high-quality single crystalline NaYbSe2 samples to conﬁrm the absence of long-range magnetic order down to 40 mK, and further reveal a clear signature of magnetic excitation continuum extending from 0.1 to 2.5 meV. By comparing the structure of our magnetic excitation spectra with the theoretical expectation from the spinon continuum, we conclude that the ground state of NaYbSe2 is a QSL with a spinon Fermi surface.
Mining Coronavirus (COVID-19) Posts in Social Media

World Health Organization (WHO) characterized the novel coronavirus (COVID-19) as a global pandemic on March 11th, 2020. Before this and in late January, more specifically on January 27th, while the majority of the infection cases were still reported in China and a few cruise ships, we began crawling social media user postings using the Twitter search API. Our goal was to leverage machine learning and linguistic tools to better understand the impact of the outbreak in China. Unlike our initial expectation to monitor a local outbreak, COVID-19 rapidly spread across the globe. In this short article we report the preliminary results of our study on automatically detecting the positive reports of COVID-19 from social media user postings using state-of-the-art machine learning models.
Detection of anisotropic galaxy assembly bias in BOSS DR12

We present evidence of anisotropic galaxy assembly bias in the Baryon Oscillation Spectroscopic Survey Data Release 12 galaxy sample at a level exceeding 5σ. We use measurements of the line-of-sight velocity dispersion σ⋆ and stellar mass M⋆ to perform a simple split into subsamples of galaxies. We show that the amplitude of the monopole and quadrupole moments of the power spectrum depend differently on σ⋆ and M⋆, allowing us to split the galaxy sample into subsets with matching monopoles but significantly different quadrupoles on all scales. Combining data from the LOWZ and CMASS NGC galaxy samples, we find >5σ evidence for anisotropic bias on scales k<0.15 h Mpc−1. We also examine splits using other observed properties. For galaxy samples split using M⋆ and projected size R0, we find no significant evidence of anisotropic bias. Galaxy samples selected using additional properties exhibit strongly varying degrees of anisotropic assembly bias, depending on which combination of properties is used to split into subsets. This may explain why previous searches for this effect using the Fundamental Plane found inconsistent results. We conclude that any selection of a galaxy sample that depends on σ⋆ can give biased and incorrect Redshift Space Distortion measurements.
How Is Video Game Development Different from Software Development in Open Source?

Recent research has provided evidence that, in the industrial context, developing video games diverges from developing software systems in other domains, such as office suites and system utilities. In this paper, we consider video game development in the open source system (OSS) context. Specifically, we investigate how developers contribute to video games vs. non-games by working on different kinds of artifacts, how they handle malfunctions, and how they perceive the development process of their projects. To this purpose, we conducted a mixed, qualitative and quantitative study on a broad suite of 60 OSS projects. Our results confirm the existence of significant differences between game and non-game development, in terms of how project resources are organized and in the diversity of developers' specializations. Moreover, game developers responding to our survey perceive more difficulties than other developers when reusing code as well as performing automated testing, and they lack a clear overview of their system's requirements.
Neural Status Registers

Neural networks excel at approximating functions and finding patterns in complex and challenging domains. Yet, they fail to learn simple but precise computation. Recent work addressed the ability to add, subtract, and multiply numbers but is lacking a component to drive control flow. True computer intelligence should also be able to decide when to perform what operation. In this paper, we introduce the Neural Status Register (NSR), inspired by physical Status Registers. At the heart of the NSR are arithmetic comparisons between inputs. With theoretically principled changes to physical Status Registers, the NSR allows end-to-end differentiation and learns such comparisons reliably. But the NSR also extrapolates: it generalizes to unseen data distributions. For example, the NSR trains on single digits and correctly predicts numbers that are up to 14 orders of magnitude larger. This suggests that the NSR captures the true underlying arithmetic. In follow-up experiments, we use the NSR to control the computation of a downstream arithmetic unit to learn piecewise functions. We can also learn more challenging tasks through redundancy. Finally, we use the NSR to learn an upstream convolutional neural network to compare images of MNIST digits to decide which image contains the larger digit.