Miracle-dz/UniFAR-FTUs huggingface dataset:
Comment on "Mean-field solution of structural balance dynamics in nonzero temperature"

In recent numerical and analytical studies, Rabbani {\it et al.} [Phys. Rev. E {\bf 99}, 062302 (2019)] observed the first-order phase transition in social triads dynamics on complete graph with $N=50$ nodes. With Metropolis algorithm they found critical temperature on such graph equal to 26.2. In this comment we extend their main observation in more compact and natural manner. In contrast to the commented paper we estimate critical temperature $T^c$ for complete graph not only with $N=50$ nodes but for any size of the system. We have derived formula for critical temperature $T^c=(N-2)/a^c$, where $N$ is the number of graph nodes and $a^c\approx 1.71649$ comes from combination of heat-bath and mean-field approximation. Our computer simulation based on heat-bath algorithm confirm our analytical results and recover critical temperature $T^c$ obtained earlier also for $N=50$ and for systems with other sizes. Additionally, we have identified---not observed in commented paper---phase of the system, where the mean value of links is zero but the system energy is minimal since the network contains only balanced triangles with all positive links or with two negative links. Such a phase corresponds to dividing the set of agents into two coexisting hostile groups and it exists only in low temperatures.
Homogenization with fractional random fields

We consider a system of differential equations in a fast long range dependent random environment and prove a homogenization theorem involving multiple scaling constants. The effective dynamics solves a rough differential equation, which is `equivalent' to a stochastic equation driven by mixed It\^o integrals and Young integrals with respect to Wiener processes and Hermite processes. Lacking other tools we use the rough path theory for proving the convergence, our main technical endeavour is on obtaining an enhanced scaling limit theorem for path integrals (Functional CLT and non-CLT's) in a strong topology, the rough path topology, which is given by a H\"older distance for stochastic processes and their lifts. In dimension one we also include the negatively correlated case, for the second order / kinetic fractional BM model we also bound the error.
Sparse-Gan: Sparsity-Constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image

With the development of convolutional neural network, deep learning has shown its success for retinal disease detection from optical coherence tomography (OCT) images. However, deep learning often relies on large scale labelled data for training, which is oftentimes challenging especially for disease with low occurrence. Moreover, a deep learning system trained from data-set with one or a few diseases is unable to detect other unseen diseases, which limits the practical usage of the system in disease screening. To address the limitation, we propose a novel anomaly detection framework termed Sparsity-constrained Generative Adversarial Network (Sparse-GAN) for disease screening where only healthy data are available in the training set. The contributions of Sparse-GAN are two-folds: 1) The proposed Sparse-GAN predicts the anomalies in latent space rather than image-level; 2) Sparse-GAN is constrained by a novel Sparsity Regularization Net. Furthermore, in light of the role of lesions for disease screening, we present to leverage on an anomaly activation map to show the heatmap of lesions. We evaluate our proposed Sparse-GAN on a publicly available dataset, and the results show that the proposed method outperforms the state-of-the-art methods.
The magnetic field and accretion regime of CI Tau

This paper exploits spectropolarimetric data of the classical T Tauri star CI Tau collected with ESPaDOnS at the Canada–France–Hawaii Telescope, with the aims of detecting and characterizing the large-scale magnetic field that the star hosts, and of investigating how the star interacts with the inner regions of its accretion disc through this field. Our data unambiguously show that CI Tau has a rotation period of 9.0 d, and that it hosts a strong, mainly poloidal large-scale field. Accretion at the surface of the star concentrates within a bright high-latitude chromospheric region that spatially overlaps with a large dark photospheric spot, in which the radial magnetic field reaches −3.7 kG. With a polar strength of −1.7 kG, the dipole component of the large-scale field is able to evacuate the central regions of the disc up to about 50 per cent of the co-rotation radius (at which the Keplerian orbital period equals the stellar rotation period) throughout our observations, during which the average accretion rate was found to be unusually high. We speculate that the magnetic field of CI Tau is strong enough to sustain most of the time a magnetospheric gap extending to at least 70 per cent of the co-rotation radius, which would explain why the rotation period of CI Tau is as long as 9 d. Our results also imply that the 9 d radial velocity (RV) modulation that CI Tau exhibits is attributable to stellar activity, and thus that the existence of the candidate close-in massive planet CI Tau b to which these RV fluctuations were first attributed needs to be reassessed with new evidence.
Constraints in Gaussian Graphical Models

In this paper, we consider the problem of finding the constraints in bow-free acyclic directed mixed graphs (ADMGs). ADMGs are a generalisation of directed acyclic graphs (DAGs) that allow for certain latent variables. We first show that minimal generators for the ideal $\I(G)$ containing all the constraints of a Gaussian ADMG $G$ corresponds precisely to the pairs of non-adjacent vertices in $G$. The proof of this theorem naturally leads to an efficient algorithm that fits a bow-free Gaussian ADMG by maximum likelihood. In particular, we can test for the goodness of fit of a given data set to a bow-free ADMG.
Procedural Content Generation: From Automatically Generating Game Levels to Increasing Generality in Machine Learning

The idea behind procedural content generation (PCG) in games is to create content automatically, using algorithms, instead of relying on user-designed content. While PCG approaches have traditionally focused on creating content for video games, they are now being applied to all kinds of virtual environments, thereby enabling training of machine learning systems that are significantly more general. For example, PCG's ability to generate never-ending streams of new levels has allowed DeepMind's Capture the Flag agent to reach beyond human-level-performance. Additionally, PCG-inspired methods such as domain randomization enabled OpenAI's robot arm to learn to manipulate objects with unprecedented dexterity. Level generation in 2D arcade games has also illuminated some shortcomings of standard deep RL methods, suggesting potential ways to train more general policies. This Review looks at key aspect of PCG approaches, including its ability to (1) enable new video games (such as No Man's Sky), (2) create open-ended learning environments, (3) combat overfitting in supervised and reinforcement learning tasks, and (4) create better benchmarks that could ultimately spur the development of better learning algorithms. We hope this article can introduce the broader machine learning community to PCG, which we believe will be a critical tool in creating a more general machine intelligence.
An Optimal Streaming Algorithm for Non-monotone Submodular Maximization

We study the problem of maximizing a non-monotone submodular function subject to a size constraint in the streaming model. Our main contribution is a single-pass streaming algorithm that uses $k\cdot\mathrm{poly}(1/\epsilon)$ memory, where $k$ is the size constraint. At the end of the stream, we post-process the output of the algorithm using any offline algorithm for submodular maximization, and we obtain a solution whose approximation guarantee is $\frac{\alpha}{1+\alpha}-\epsilon$, where $\alpha$ is the approximation of the offline algorithm. If we use an exact (exponential time) post-processing algorithm, we obtain a $\frac{1}{2}-\epsilon$ approximation, almost matching the lower bound of [Alaluf-Feldman, 2019]. If we post-process with the algorithm of [Buchbinder-Feldman, Math of OR 2019] that achieves the currently best approximation guarantee $\alpha=0.385$, we obtain a $0.2779$ approximation in polynomial time, improving over the previously best polynomial-time approximation of $0.2335$ due to [Alaluf-Feldman, 2019]. In addition to its improved approximation guarantee, our algorithm enjoys a fast update time and overall running time.
Mean Shift Rejection: Training Deep Neural Networks Without Minibatch Statistics or Normalization

Deep convolutional neural networks are known to be unstable during training at high learning rate unless normalization techniques are employed. Normalizing weights or activations allows the use of higher learning rates, resulting in faster convergence and higher test accuracy. Batch normalization requires minibatch statistics that approximate the dataset statistics but this incurs additional compute and memory costs and causes a communication bottleneck for distributed training. Weight normalization and initialization-only schemes do not achieve comparable test accuracy. We introduce a new understanding of the cause of training instability and provide a technique that is independent of normalization and minibatch statistics. Our approach treats training instability as a spatial common mode signal which is suppressed by placing the model on a channel-wise zero-mean isocline that is maintained throughout training. Firstly, we apply channel-wise zero-mean initialization of filter kernels with overall unity kernel magnitude. At each training step we modify the gradients of spatial kernels so that their weighted channel-wise mean is subtracted in order to maintain the common mode rejection condition. This prevents the onset of mean shift. This new technique allows direct training of the test graph so that training and test models are identical. We also demonstrate that injecting random noise throughout the network during training improves generalization. This is based on the idea that, as a side effect, batch normalization performs deep data augmentation by injecting minibatch noise due to the weakness of the dataset approximation. Our technique achieves higher accuracy compared to batch normalization and for the first time shows that minibatches and normalization are unnecessary for state-of-the-art training.
Conditional symmetries and exact solutions of a nonlinear three-component reaction-diffusion model

Abstract Q-conditional (non-classical) symmetries of the known three-component reaction-diffusion (RD) system [K. Aoki et al. Theor. Popul. Biol. 50, 1–17 (1996)] modelling interaction between farmers and hunter-gatherers are constructed for the first time. A wide variety of Q-conditional symmetries are found, and it is shown that these symmetries are not equivalent to the Lie symmetries. Some operators of Q-conditional (non-classical) symmetry are applied for finding exact solutions of the RD system in question. Properties of the exact solutions (in particular, their asymptotic behaviour) are identified and possible biological interpretation is discussed.
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

Multi-task learning is an open and challenging problem in computer vision. The typical way of conducting multi-task learning with deep neural networks is either through handcrafting schemes that share all initial layers and branch out at an adhoc point or through using separate task-specific networks with an additional feature sharing/fusion mechanism. Unlike existing methods, we propose an adaptive sharing approach, called AdaShare, that decides what to share across which tasks for achieving the best recognition accuracy, while taking resource efficiency into account. Specifically, our main idea is to learn the sharing pattern through a task-specific policy that selectively chooses which layers to execute for a given task in the multi-task network. We efficiently optimize the task-specific policy jointly with the network weights using standard back-propagation. Experiments on three challenging and diverse benchmark datasets with a variable number of tasks well demonstrate the efficacy of our approach over state-of-the-art methods.
Galactic dust evolution with rapid dust formation in the interstellar medium due to hypersonic turbulence

Turbulence can significantly accelerate the growth of dust grains by accretion of molecules. For dust dynamically coupled to the gas, the growth rate scales with the square of the Mach number, which means that the growth time-scale can easily be reduced by more than an order of magnitude. The limiting time-scale is therefore rather the rate of molecular cloud formation, which means that dust production in the interstellar medium can rapidly reach the levels needed to explain the dust masses observed at high redshifts. Thus, turbulence may be the solution to the replenishment problem in models of dust evolution in high-redshift galaxies and explain the dust masses seen at $z$ = 7–8. A simple analytic galactic dust-evolution model is presented, where grain growth nicely compensates for the expected higher rate of dust destruction by supernova shocks. This model is simpler, relies on fewer assumptions and seems to yields a better fit to data derived from observations, compared to previous models of the same type.
Tropical Polynomial Division and Neural Networks

In this work, we examine the process of Tropical Polynomial Division, a geometric method which seeks to emulate the division of regular polynomials, when applied to those of the max-plus semiring. This is done via the approximation of the Newton Polytope of the dividend polynomial by that of the divisor. This process is afterwards generalized and applied in the context of neural networks with ReLU activations. In particular, we make use of the intuition it provides, in order to minimize a two-layer fully connected network, trained for a binary classification problem. This method is later evaluated on a variety of experiments, demonstrating its capability to approximate a network, with minimal loss in performance.
The Gaia–ESO Survey: Carbon Abundance in the Galactic Thin and Thick Disks

This paper focuses on carbon, which is one of the most abundant elements in the universe and is of high importance in the field of nucleosynthesis and galactic and stellar evolution. The origin of carbon and the relative importance of massive and low- to intermediate-mass stars in producing it is still a matter of debate. We aim at better understanding the origin of carbon by studying the trends of [C/H], [C/Fe], and [C/Mg] versus [Fe/H] and [Mg/H] for 2133 FGK dwarf stars from the fifth Gaia–ESO Survey internal data release (GES iDR5). The availability of accurate parallaxes and proper motions from Gaia DR2 and radial velocities from GES iDR5 allows us to compute Galactic velocities, orbits, absolute magnitudes, and, for 1751 stars, Bayesian-derived ages. Three different selection methodologies have been adopted to discriminate between thin- and thick-disk stars. In all the cases, the two stellar groups show different [C/H], [C/Fe], and [C/Mg] and span different age intervals, with the thick-disk stars being, on average, older than the thin-disk ones. The behaviors of [C/H], [C/Fe], and [C/Mg] versus [Fe/H], [Mg/H], and age all suggest that C is primarily produced in massive stars. The increase of [C/Mg] for young thin-disk stars indicates a contribution from low-mass stars or the increased C production from massive stars at high metallicities due to the enhanced mass loss. The analysis of the orbital parameters Rmed and supports an “inside–out” and “upside–down” formation scenario for the disks of the Milky Way.
Evolution of Facial Musculature and Cutaneous Field of Trigeminus. Part I

In order to present a comprehensive picture of the marvelous evolution of the facial musculature I shall briefly view conditions in a few selected types of the "ascending scale of Vertebrates," beginning with the Selachians, where the motor facialis field is represented by a comparatively insignificant muscle territory. While a series of scattered earlier publications by Vetter, Tiesing, Stannius, Adams, Walter, Fischer, Ecker, Gaupp, Wiedersheim, Killian, Fiirbringer and others referred to facial musculature of certain representatives ofthe lower Vertebrates, Ruge's classical monograph (I896) finally outlined the evolution of this muscle field in the main groups of the lower vertebrates up to the reptiles, with an attempt to link these with the monotreme mammals. This work of Ruge's afforded a solid basis for further elaboration of this fascinating chapter of comparative anatomy. The writer has had opportunity to study representatives of the main groups of Vertebrates, including the fishes, amphibians, reptiles and birds of which only a few were examined, while concentrated efforts were directed towards a systematic investigation of the facial musculature in mammals. Fishes: In the dogfish we find, connected with the branchial skeleton (fig. ia) a number of serially arranged muscle sections forming the "constrictor musculature" of the gill arches (fig. ib). This muscula-
Finite-size effects in the nonphononic density of states in computer glasses.

The universal form of the density of nonphononic, quasilocalized vibrational modes of frequency ω in structural glasses, D(ω), was predicted theoretically decades ago, but only recently revealed in numerical simulations. In particular, it has been recently established that, in generic computer glasses, D(ω) increases from zero frequency as ω^{4}, independent of spatial dimension and of microscopic details. However, it has been shown [Lerner and Bouchbinder, Phys. Rev. E 96, 020104(R) (2017)2470-004510.1103/PhysRevE.96. 020104] that the preparation protocol employed to create glassy samples may affect the form of their resulting D(ω): glassy samples rapidly quenched from high-temperature liquid states were shown to feature D(ω)∼ω^{β} with β<4, presumably limiting the degree of universality of the ω^{4} law. Here we show that exponents β<4 are seen only in small glassy samples quenched from high-temperature liquid states-whose sizes are comparable to or smaller than the size of the disordered core of soft quasilocalized vibrations-while larger glassy samples made with the same protocol feature the universal ω^{4} law. Our results demonstrate that observations of β<4 in the nonphononic density of states stem from finite-size effects, and we thus conclude that the ω^{4} law should be featured by any sufficiently large glass quenched from a melt.
Stochastic Event Generation Through Markovian Jumps: Generalized Distribution and Minimal Representations

Stochastic Event Timing is a fundamental issue in developing both analytic and simulation models for stochastic systems. Generalized Erlang distributions are quite useful for generating those random events in a quite general way by inserting intermediary states with markovian jumps. One very important and celebrated generalization of the Erlang distribution was made by D. R. Cox in the middle 50's. This paper discuss further the Cox generalization and presents an even more general topology, capable of representing any practical distribution. As an application, we revisit the classical problem of the first two moments matching, and derive minimal topologies in terms of number of states, then the results are compared with those found in literature. At the end of the paper, we show how the generalized structure can be use for timing general stochastic discrete-event models for analytic and simulation purposes.
Unbiased Evaluation of Deep Metric Learning Algorithms

Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common DML approach is with triplet loss, despite significant advances in the area of DML. Triplet loss suffers from several issues such as collapse of the embeddings, high sensitivity to sampling schemes and more importantly a lack of performance when compared to more modern methods. We attribute this adoption to a lack of fair comparisons between various methods and the difficulty in adopting them for novel problem statements. In this paper, we perform an unbiased comparison of the most popular DML baseline methods under same conditions and more importantly, not obfuscating any hyper parameter tuning or adjustment needed to favor a particular method. We find, that under equal conditions several older methods perform significantly better than previously believed. In fact, our unified implementation of 12 recently introduced DML algorithms achieve state-of-the art performance on CUB200, CAR196, and Stanford Online products datasets which establishes a new set of baselines for future DML research. The codebase and all tuned hyperparameters will be open-sourced for reproducibility and to serve as a source of benchmark.
A two-year audit of emergency contraception in a general practice.

An audit of a semi-rural general practice revealed that the incidence of requests for emergency contraception is 11/1000 women age 15-44 with 1/2 of the requests coming from teenagers. Reviewing data from the years 1989-90 the audit determined that of the practices total population of 8765 2081 were women age 15-44. From this pool of female patients 46 requests were made 16 in 1989 and 30 in 1990. (The total number of patients stood at 44 since two of the women made requests twice during the 2-year period). In line with previous studies the majority of these requests came from women who were not of proven fertility. Over 1.2 of the couples had used contraception during their most recent intercourse but many had experienced problems with condoms -- bursting in particular. Of the 46 requests 3 did not result in treatment. And of the remaining 43 cases 42 women received the Yuzpe regimen (ethinylestradiol 100 mcg and levonorgestrel 500 mcg repeated after 12 hours). The remaining patient was fitted with a Multiload Cu-375 IUD. The age of the patients at the time of treatment ranged from 13-39. 1/2 of the patients were teenagers 5 of whom were under 16 years of age. 10 of the patients age 15-18 were starting the combined pill for the first time. While pointing out that ratio of women age 15-19 was larger than those reported by studies of community clinics the author expresses relief over the fact that teenagers feel comfortable seeking treatment. And noting that the number of requests for emergency contraception nearly doubled during the second of audit the author attributes this change to the publics increased awareness of this free service.