Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Levenberg-Marquardt Method with Global Complexity Bound

A new Levenberg--Marquardt (LM) method for solving nonlinear least squares problems with convex constraints is described. Various versions of this method have been proposed, their main differences being in the choice of damping parameter. In this paper, we propose a new rule for updating the damping parameter that is based on the perspective of majorization-minimization method. Our method finds an $\epsilon$-stationary point within $O(\epsilon^{-2})$ iterations and converges locally quadratically under a local error bound condition. These properties are guaranteed even when the subproblems are solved inexactly, which reduces the computational cost per iteration of our method. To the best of our knowledge, this is the first LM method that has an iteration complexity bound for constrained problems. Numerical results show that our method achieves better global performance than those of two existing LM methods and the projected gradient method.
Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network

Human bodies exhibit various shapes for different identities or poses, but the body shape has certain similarities in structure and thus can be embedded in a low-dimensional space. This article presents an autoencoder-like network architecture to learn disentangled shape and pose embedding specifically for the 3D human body. This is inspired by recent progress of deformation-based latent representation learning. To improve the reconstruction accuracy, we propose a hierarchical reconstruction pipeline for the disentangling process and construct a large dataset of human body models with consistent connectivity for the learning of the neural network. Our learned embedding can not only achieve superior reconstruction accuracy but also provide great flexibility in 3D human body generation via interpolation, bilinear interpolation, and latent space sampling. The results from extensive experiments demonstrate the powerfulness of our learned 3D human body embedding in various applications.
Spectral properties of a Co-decorated quasi-two-dimensional GaSe layer

Based on reliable $ab\:initio$ computations and the numerical renormalization group method, systematic studies on a two-dimensional GaSe monolayer with a Co adatom have been carried out. It is shown that the stable lowest-energy configuration of the system involves the Co adatom located over Ga atom. For such configuration, it is demonstrated that the electronic and magnetic properties of the system can be effectively controlled by means of external factors, such as magnetic field, gate voltage or temperature. Moreover, if properly tuned, the GaSe-Co system can also exhibit the Kondo effect. The development of the Kondo phenomenon is revealed in the local density of states of the Co adatom, its magnetic field dependence, which presents the splitting of the Kondo peak, as well as in the temperature dependence of the conductance, which exhibits scaling typical of the spin one-half Kondo effect.
Simulation of Covid-19 epidemic evolution: are compartmental models really predictive?

Computational models for the simulation ofthe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic evolution would be extremely useful to support authorities in designing healthcare policies and lockdown measures to contain its impact on public health and economy. In Italy, the devised forecasts have been mostly based on a pure data-driven approach, by fitting and extrapolating open data on the epidemic evolution collected by the Italian Civil Protection Center. In this respect, SIR epidemiological models, which start from the description of the nonlinear interactions between population compartments, would be a much more desirable approach to understand and predict the collective emergent response. The present contribution addresses the fundamental question whether a SIR epidemiological model, suitably enriched with asymptomatic and dead individual compartments, could be able to provide reliable predictions on the epidemic evolution. To this aim, a machine learning approach based on particle swarm optimization (PSO) is proposed to automatically identify the model parameters based on a training set of data of progressive increasing size, considering Lombardy in Italy as a case study. The analysis of the scatter in the forecasts shows that model predictions are quite sensitive to the size of the dataset used for training, and that further data are still required to achieve convergent - and therefore reliable- predictions.
Training with Quantization Noise for Extreme Model Compression

We tackle the problem of producing compact models, maximizing their accuracy for a given model size. A standard solution is to train networks with Quantization Aware Training, where the weights are quantized during training and the gradients approximated with the Straight-Through Estimator. In this paper, we extend this approach to work beyond int8 fixed-point quantization with extreme compression methods where the approximations introduced by STE are severe, such as Product Quantization. Our proposal is to only quantize a different random subset of weights during each forward, allowing for unbiased gradients to flow through the other weights. Controlling the amount of noise and its form allows for extreme compression rates while maintaining the performance of the original model. As a result we establish new state-of-the-art compromises between accuracy and model size both in natural language processing and image classification. For example, applying our method to state-of-the-art Transformer and ConvNet architectures, we can achieve 82.5% accuracy on MNLI by compressing RoBERTa to 14MB and 80.0 top-1 accuracy on ImageNet by compressing an EfficientNet-B3 to 3.3MB.
Convergence of Large Population Games to Mean Field Games with Interaction Through the Controls

This work considers stochastic differential games with a large number of players, whose costs and dynamics interact through the empirical distribution of both their states and their controls. We develop a framework to prove convergence of finite-player games to the asymptotic mean field game. Our approach is based on the concept of propagation of chaos for forward and backward weakly interacting particles which we investigate by fully probabilistic methods, and which appear to be of independent interest. These propagation of chaos arguments allow to derive moment and concentration bounds for the convergence of both Nash equilibria and social optima in non-cooperative and cooperative games, respectively. Incidentally, we also obtain convergence of a system of second order parabolic partial differential equations on finite dimensional spaces to a second order parabolic partial differential equation on the Wasserstein space.
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation

Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify avenues for future model improvements. In this paper we demonstrate CrossCheck, an interactive capability for rapid cross-model comparison and reproducible error analysis. We describe the tool, discuss design and implementation details, and present three NLP use cases – named entity recognition, reading comprehension, and clickbait detection that show the benefits of using the tool for model evaluation. CrossCheck enables users to make informed decisions when choosing between multiple models, identify when the models are correct and for which examples, investigate whether the models are making the same mistakes as humans, evaluate models’ generalizability and highlight models’ limitations, strengths and weaknesses. Furthermore, CrossCheck is implemented as a Jupyter widget, which allows for rapid and convenient integration into existing model development workflows.
Electric Current Evolution at the Footpoints of Solar Eruptions

Electric currents play a critical role in the triggering of solar flares and their evolution. The aim of the present paper is to test whether the surface electric current has a surface or subsurface fixed source as predicted by the circuit approach of flare physics, or is the response of the surface magnetic field to the evolution of the coronal magnetic field as the MHD approach proposes? Out of all 19 X-class flares observed by SDO from 2011 to 2016 near the disk center, we analyzed the only nine eruptive flares for which clear ribbon hooks were identifiable. Flare ribbons with hooks are considered to be the footprints of eruptive flux ropes in MHD flare models. For the first time, fine measurements of the time evolution of electric currents inside the hooks in the observations as well as in the OHM 3D MHD simulation are performed. Our analysis shows a decrease of the electric current in the area surrounded by the ribbon hooks during and after the eruption. We interpret the decrease of the electric currents as due to the expansion of the flux rope in the corona during the eruption. Our analysis brings a new contribution to the standard flare model in 3D.
Faster Approximate Pattern Matching: A Unified Approach

In the approximate pattern matching problem, given a text <tex>$T$</tex>, a pattern <tex>$P$</tex>, and a threshold <tex>$k$</tex>, the task is to find (the starting positions of) all substrings of <tex>$T$</tex> that are at distance at most <tex>$k$</tex> from <tex>$P$</tex>. We consider the two most fundamental string metrics: Under the Hamming distance, we search for substrings of <tex>$T$</tex> that have at most <tex>$k$</tex> mismatches with <tex>$P$</tex>, while under the edit distance, we search for substrings of <tex>$T$</tex> that can be transformed to <tex>$P$</tex> with at most <tex>$k$</tex> edits. Exact occurrences of <tex>$P$</tex> in <tex>$T$</tex> have a very simple structure: If we assume for simplicity that <tex>$\vert P\vert < \vert T\vert \leq {{}^{3}}/_{2}\vert P\vert$</tex> and that <tex>$P$</tex> occurs both as a prefix and as a suffix of <tex>$T$</tex>, then both <tex>$P$</tex> and <tex>$T$</tex> are periodic with a common period. However, an analogous characterization for occurrences with up to <tex>$k$</tex> mismatches was proved only recently by Bringmann et al. [SODA'19]: Either there are <tex>$\mathcal{O}(k^{2}) k$</tex>-mismatch occurrences of <tex>$P$</tex> in <tex>$T$</tex>, or both <tex>$P$</tex> and <tex>$T$</tex> are at Hamming distance <tex>$\mathcal{O}(k)$</tex> from strings with a common string period of length <tex>$\mathcal{O}(m/k)$</tex>. We tighten this characterization by showing that there are <tex>$\mathcal{O}(k) k$</tex>-mismatch occurrences in the non-periodic case, and we lift it to the edit distance setting, where we tightly bound the number of <tex>$k$</tex>-edit occurrences by <tex>$\mathcal{O}(k^{2})$</tex> in the non-periodic case. Our proofs are constructive and let us obtain a unified framework for approximate pattern matching for both considered distances. In particular, we provide meta-algorithms that only rely on a small set of primitive operations. We showcase the generality of our meta-algorithms with results for the fully compressed setting, the dynamic setting, and the standard setting.
The stellar mass Fundamental Plane: the virial relation and a very thin plane for slow rotators

Early-type galaxies -- slow and fast rotating ellipticals (E-SRs and E-FRs) and S0s/lenticulars -- define a Fundamental Plane (FP) in the space of half-light radius $R_e$, enclosed surface brightness $I_e$ and velocity dispersion $\sigma_e$. Since $I_e$ and $\sigma_e$ are distance-independent measurements, the thickness of the FP is often expressed in terms of the accuracy with which $I_e$ and $\sigma_e$ can be used to estimate sizes $R_e$. We show that: 1) The thickness of the FP depends strongly on morphology. If the sample only includes E-SRs, then the observed scatter in $R_e$ is $\sim 16\%$, of which only $\sim 9\%$ is intrinsic. Removing galaxies with $M_*<10^{11}M_\odot$ further reduces the observed scatter to $\sim 13\%$ ($\sim 4\%$ intrinsic). The observed scatter increases to the $\sim 25\%$ usually quoted in the literature if E-FRs and S0s are added. If the FP is defined using the eigenvectors of the covariance matrix of the observables, then the E-SRs again define an exceptionally thin FP, with intrinsic scatter of only $5\%$ orthogonal to the plane. 2) The structure within the FP is most easily understood as arising from the fact that $I_e$ and $\sigma_e$ are nearly independent, whereas the $R_e-I_e$ and $R_e-\sigma_e$ correlations are nearly equal and opposite. 3) If the coefficients of the FP differ from those associated with the virial theorem the plane is said to be `tilted'. If we multiply $I_e$ by the global stellar mass-to-light ratio $M_*/L$ and we account for non-homology across the population by using Sersic photometry, then the resulting stellar mass FP is less tilted. Accounting self-consistently for $M_*/L$ gradients will change the tilt. The tilt we currently see suggests that the efficiency of turning baryons into stars increases and/or the dark matter fraction decreases as stellar surface brightness increases.
The hydrogen Balmer lines and jump in absorption in accretion disc modelling – an ultraviolet–optical spectral analysis of the dwarf novae UZ Serpentis and CY Lyrae

The spectra of disc-dominated cataclysmic variables (CVs) often deviate from the spectra of accretion disc models; in particular, the Balmer jump and absorption lines are found to be shallower in the observations than in the models. We carried out a combined ultraviolet-optical spectral analysis of two dwarf novae: UZ Ser in outburst, decline, and quiescence, and CY Lyr on the rise to outburst and in outburst. We fit the Balmer jump and absorption lines, the continuum flux level and slope by adjusting the accretion rate, inclination, and disc outer radius. For both systems we find an accretion rate $\dot{M} \approx 8 \times 10^{-9}M_\odot$/yr in outburst, and $\dot{M} \approx 2-3 \times 10^{-9}M_\odot$/yr for the rise and decline phases. The outer disc radius we derive is smaller than expected ($R_{\rm disc} \approx 0.2a$, where $a$ is the binary separation), except during late rise (for CY Lyr) where $R_{\rm disc}=0.3a$. UZ Ser also reveals a 60,000~K white dwarf. These results show that during a dwarf nova cycle the radius of the disc is the largest just before the peak of the outburst, in qualitative agreement with the disc instability model for dwarf nova outbursts. We suspect that an additional emitting component (e.g. disc wind) is also at work to reduce the slope of the continuum and size of the Balmer jump and absorption lines. We stress that both the outer disc radius and disc wind need to be taken into account for more realistic disc modeling of CVs.
Smartphone camera based pointer

Large screen displays are omnipresent today as a part of infrastructure for presentations and entertainment. Also powerful smartphones with integrated camera(s) are ubiquitous. However, there are not many ways in which smartphones and screens can interact besides casting the video from a smartphone. In this paper, we present a novel idea that turns a smartphone into a direct virtual pointer on the screen using the phone's camera. The idea and its implementation are simple, robust, efficient and fun to use. Besides the mathematical concepts of the idea we accompany the paper with a small javascript project (this http URL) which demonstrates the possibility of the new interaction technique presented as a massive multiplayer game in the HTML5 framework.
Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data

The broad concept of emergence is instrumental in various of the most challenging open scientific questions—yet, few quantitative theories of what constitutes emergent phenomena have been proposed. This article introduces a formal theory of causal emergence in multivariate systems, which studies the relationship between the dynamics of parts of a system and macroscopic features of interest. Our theory provides a quantitative definition of downward causation, and introduces a complementary modality of emergent behaviour—which we refer to as causal decoupling. Moreover, the theory allows practical criteria that can be efficiently calculated in large systems, making our framework applicable in a range of scenarios of practical interest. We illustrate our findings in a number of case studies, including Conway’s Game of Life, Reynolds’ flocking model, and neural activity as measured by electrocorticography.
Cascaded Context Enhancement for Automated Skin Lesion Segmentation

Skin lesion segmentation is an important step for automated melanoma diagnosis. Due to the non-negligible diversity of lesions from different patients, extracting powerful context for fine-grained semantic segmentation is still challenging today. In this paper, we formulate a cascaded context enhancement neural network for skin lesion segmentation. The proposed method adopts encoder-decoder architecture, a new cascaded context aggregation (CCA) module with gate-based information integration approach is proposed for sequentially and selectively aggregating original image and encoder network features from low-level to high-level. The generated context is further utilized to guide discriminative features extraction by the designed context-guided local affinity module. Furthermore, an auxiliary loss is added to the CCA module for refining the prediction. In our work, we evaluate our approach on three public datasets. We achieve the Jaccard Index (JA) of 87.1%, 80.3% and 86.6% on ISIC-2016, ISIC-2017 and PH2 datasets, which are higher than other state-of-the-art methods respectively.
Thermodynamic signatures of an antiferromagnetic quantum critical point inside a superconducting dome

Recent experiments in unconventional superconductors, and in particular iron-based materials, have reported evidence of an antiferromagnetic quantum critical point (AFM-QCP) emerging inside the superconducting dome of the phase diagram. Fluctuations associated with such an AFM-QCP are expected to promote unusual temperature dependencies of thermodynamic quantities. Here, we compute the $T$ dependence of the specific heat $C(T)$ deep inside a fully gapped $s^{+-}$ superconducting state as the AFM-QCP is approached. We find that, at the AFM-QCP, the specific heat $C(T)$ vanishes quadratically with temperature, as opposed to the typical exponential suppression seen in fully-gapped BCS superconductors. This robust result is due to a non-analytic contribution to the free-energy arising from the general form of the bosonic (AFM) propagator in the SC state. Away from the AFM-QCP, as temperature is lowered, $C(T)$ shows a crossover from a $T^2$ behavior to an exponential behavior, with the crossover temperature scale set by the value of the superconducting gap and the distance to the QCP. We argue that these features in the specific heat can be used to unambiguously determine the existence of AFM-QCPs inside the superconducting domes of iron-based and other fully gapped unconventional superconductors.
3-D Context Entropy Model for Improved Practical Image Compression

In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthened with residual structures. In short, we make three noteworthy improvements here. First, we propose a 3-D context entropy model which can take advantage of known latent representation in current spatial locations for better entropy estimation. Second, a light-weighted residual structure is adopted for feature learning during entropy estimation. Finally, an effective training strategy is introduced for practical adaptation with different resolutions. Experiment results indicate our image compression method achieves 0.9775 MS-SSIM on CLIC validation set and 0.9809 MS-SSIM on test set.
Introducing the HD+B model for pulsar wind nebulae: a hybrid hydrodynamics/radiative approach

Identification and characterization of a rapidly increasing number of pulsar wind nebulae is, and will continue to be, a challenge of high-energy gamma-ray astrophysics. Given that such systems constitute -- by far -- the most numerous expected population in the TeV regime, such characterization is important not only to learn about the sources per se from an individual and population perspective, but also to be able to connect them with observations at other frequencies, especially in radio and X-rays. Also, we need to remove the emission from nebulae in highly confused regions of the sky for revealing other underlying emitters. In this paper we present a new approach for theoretical modelling of pulsar wind nebulae: a hybrid hydrodynamic-radiative model able to reproduce morphological features and spectra of the sources, with relatively limited numerical cost.
Verification of Continuous-time Markov Chains

In this paper, we fill the long-standing gap in the field of the verification of continuous-time systems by proposing a linear-time temporal logic, named continuous linear logic (CLL) and the corresponding model checking algorithm for continuous-time Markov chains. The correctness of our model checking algorithm depends on Schanuel's conjecture, a central open question in transcendental number theory.
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation

Depth estimation is an active area of research in the field of computer vision, and has garnered significant interest due to its rising demand in a large number of applications ranging from robotics and unmanned aerial vehicles to autonomous vehicles. A particularly challenging problem in this area is monocular depth estimation, where the goal is to infer depth from a single image. An effective strategy that has shown considerable promise in recent years for tackling this problem is the utilization of deep convolutional neural networks. Despite these successes, the memory and computational requirements of such networks have made widespread deployment in embedded scenarios very challenging. In this study, we introduce DepthNet Nano, a highly compact self normalizing network for monocular depth estimation designed using a human machine collaborative design strategy, where principled network design prototyping based on encoder-decoder design principles are coupled with machine-driven design exploration. The result is a compact deep neural network with highly customized macroarchitecture and microarchitecture designs, as well as self-normalizing characteristics, that are highly tailored for the task of embedded depth estimation. The proposed DepthNet Nano possesses a highly efficient network architecture (e.g., 24X smaller and 42X fewer MAC operations than Alhashim et al. on KITTI), while still achieving comparable performance with state-of-the-art networks on the NYU-Depth V2 and KITTI datasets. Furthermore, experiments on inference speed and energy efficiency on a Jetson AGX Xavier embedded module further illustrate the efficacy of DepthNet Nano at different resolutions and power budgets (e.g., ~14 FPS and >0.46 images/sec/watt at 384 X 1280 at a 30W power budget on KITTI).
A polynomial-degree-robust a posteriori error estimator for Nédélec discretizations of magnetostatic problems

We present an equilibration-based a posteriori error estimator for Nedelec element discretizations of the magnetostatic problem. The estimator is obtained by adding a gradient correction to the estimator for Nedelec elements of arbitrary degree presented in [J. Gedicke, S. Geevers, and I. Perugia. An equilibrated a posteriori error estimator for arbitrary-order Nedelec elements for magnetostatic problems. arXiv preprint, arXiv:1909.01853 [math.NA], 2019]. This new estimator is proven to be reliable, with reliability constant 1, and efficient, with an efficiency constant that is independent of the polynomial degree of the approximation. These properties are demonstrated in a series of numerical experiments on three-dimensional test problems.