Miracle-dz/UniFAR-FTUs huggingface dataset:
Instability mechanisms and intermittency distribution in adverse pressure gradient attached and separated boundary layers

Direct Numerical Simulation of a boundary layer subjected to an adverse pressure gradient has been carried out to investigate the instability mechanisms and transition-zone characteristics, as the boundary layer changes its character from an attached to a separated flow. The freestream turbulence intensity is fixed at a moderately low value of 0.3% and the inlet Reynolds number (Re) is reduced to obtain one attached and two separated flow (small and large separation) cases. A detailed characterisation of the pre-transitional boundary layer has been carried out that reveals a “mixed-mode” instability, involving contribution from instability waves associated with inflectional instability and streamwise streaks generated by the lift-up effect. A unified picture is presented of the changes in the relative significance of these two modes, as Re is varied, leading up to the transition onset marked by a breakdown of spanwise vortical rollers for all the three cases. Next, we carry out a time–frequency analysis of the transitional velocity signals and show that as Re decreases, the character of the time traces evolves continuously from a “spotty” behaviour (i.e., exhibiting distinct turbulent spots) for the attached case to a “non-spotty” behaviour (i.e., involving a more “uniform” distribution of turbulent fluctuations in time) for the large separation case, encompassing the entire spectrum of transition scenarios. The intermittency factor (γγ) has been calculated within the transition zone and its variation is seen to compare well with the “universal” γγ-distribution of Dhawan and Narasimha (J. Fluid Mech., Vol.3, pp.418, 1958) for all the three cases. One of the basic premises of the universal γγ-distribution is the concentrated breakdown hypothesis (requiring generation of spots randomly in time and span) and we investigate whether the transition
DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acoustics

The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning. This challenge is an open call for researchers to analyze a dataset of sound recordings collected from COVID-19 infected and non-COVID-19 individuals for a two-class classification. These recordings were collected via crowdsourcing from multiple countries, through a website application. The challenge features two tracks, one focusing on cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings. In this paper, we introduce the challenge and provide a detailed description of the task, and present a baseline system for the task.
Is it enough to optimize CNN architectures on ImageNet?

Classification performance based on ImageNet is the de-facto standard metric for CNN development. In this work we challenge the notion that CNN architecture design solely based on ImageNet leads to generally effective convolutional neural network (CNN) architectures that perform well on a diverse set of datasets and application domains. To this end, we investigate and ultimately improve ImageNet as a basis for deriving such architectures. We conduct an extensive empirical study for which we train 500 CNN architectures, sampled from the broad AnyNetX design space, on ImageNet as well as 8 additional well-known image classification benchmark datasets from a diverse array of application domains. We observe that the performances of the architectures are highly dataset dependent. Some datasets even exhibit a negative error correlation with ImageNet across all architectures. We show how to significantly increase these correlations by utilizing ImageNet subsets restricted to fewer classes. These contributions can have a profound impact on the way we design future CNN architectures and help alleviate the tilt we see currently in our community with respect to over-reliance on one dataset.
Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.
Please Don't Go — A Comprehensive Approach to Increase Women's Participation in Open Source Software

Women represent less than 24% of employees in the software development industry and experience various types of prejudice and bias. Despite various efforts to increase diversity and multi-gendered participation, women are even more underrepresented in Open Source Software (OSS) projects. In my Ph.D., I investigate the following question: How can OSS communities increase women's participation in their projects? I will identify different OSS career pathways and develop a holistic view of women's motivations to join or leave OSS, as well as their definitions of success. Based on this empirical investigation, I will work together with the Linux Foundation to design attraction and retention strategies focused on women. Before and after implementing the strategies, I will conduct empirical studies to evaluate the state of the practice and understand the implications of the strategies.
H($\text{curl}^2$)-conforming quadrilateral spectral element method for quad-curl problems

In this paper, we propose an $H(\text{curl}^2)$-conforming quadrilateral spectral element method to solve quad-curl problems. Starting with generalized Jacobi polynomials, we first introduce quasi-orthogonal polynomial systems for vector fields over rectangles. $H(\text{curl}^2)$-conforming elements over arbitrary convex quadrilaterals are then constructed explicitly in a hierarchical pattern using the contravariant transform together with the bilinear mapping from the reference square onto each quadrilateral. It is astonishing that both the simplest rectangular and quadrilateral spectral elements possess only 8 degrees of freedom on each physical element. In the sequel, we propose our $H(\text{curl}^2)$-conforming quadrilateral spectral element approximation based on the mixed weak formulation to solve the quad-curl equation and its eigenvalue problem. Numerical results show the effectiveness and efficiency of our method.
Sterile neutrino dark matter from generalized 
CPT
-symmetric early-Universe cosmologies

We generalize gravitational particle production in a radiation-dominated $CPT$-symmetric universe to non-standard, but also $CPT$-symmetric early universe cosmologies. We calculate the mass of a right-handed"sterile"neutrino needed for it to be the cosmological dark matter. Since generically sterile neutrinos mix with the Standard Model active neutrinos, we use state-of-the-art tools to compute the expected spectrum of gamma rays and high-energy active neutrinos from ultra-heavy sterile neutrino dark matter decay. We demonstrate that the sterile neutrinos are never in thermal equilibrium in the early universe. We show that very high-energy Cherenkov telescopes might detect a signal for sterile neutrino lifetimes up to around 10$^{27}$ s, while a signal in high-energy neutrino telescopes such as IceCube could be detectable for lifetimes up to 10$^{30}$ s, offering a better chance of detection across a vast landscape of possible masses.
Embedding Code Contexts for Cryptographic API Suggestion: New Methodologies and Comparisons

Despite recent research efforts, the vision of automatic code generation through API recommendation has not been realized. Accuracy and expressiveness challenges of API recommendation needs to be systematically addressed. We present a new neural network-based approach, Multi-HyLSTM for API recommendation --targeting cryptography-related code. Multi-HyLSTM leverages program analysis to guide the API embedding and recommendation. By analyzing the data dependence paths of API methods, we train embedding and specialize a multi-path neural network architecture for API recommendation tasks that accurately predict the next API method call. We address two previously unreported programming language-specific challenges, differentiating functionally similar APIs and capturing low-frequency long-range influences. Our results confirm the effectiveness of our design choices, including program-analysis-guided embedding, multi-path code suggestion architecture, and low-frequency long-range-enhanced sequence learning, with high accuracy on top-1 recommendations. We achieve a top-1 accuracy of 91.41% compared with 77.44% from the state-of-the-art tool SLANG. In an analysis of 245 test cases, compared with the commercial tool Codota, we achieve a top-1 recommendation accuracy of 88.98%, which is significantly better than Codota's accuracy of 64.90%. We publish our data and code as a large Java cryptographic code dataset.
Autonomous Drone Racing with Deep Reinforcement Learning

In many robotic tasks, such as autonomous drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge for this task is planning the timeoptimal trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. The resulting solution is either highly specialized for a single-track layout, or suboptimal due to simplifying assumptions about the platform dynamics. In this work, a new approach to near-time-optimal trajectory generation for quadrotors is presented. Leveraging deep reinforcement learning and relative gate observations, our approach can compute near-time-optimal trajectories and adapt the trajectory to environment changes. Our method exhibits computational advantages over approaches based on trajectory optimization for non-trivial track configurations. The proposed approach is evaluated on a set of race tracks in simulation and the real world, achieving speeds of up to 60kmh−1 with a physical quadrotor.
ARXON: A Framework for Approximate Communication Over Photonic Networks-on-Chip

The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to improve the energy-efficiency of silicon photonic networks-on-chip (PNoCs). Silicon photonic interconnects suffer from high power dissipation because of laser sources, which generate carrier wavelengths, and tuning power required for regulating photonic devices under different uncertainties. In this article, we propose a framework called AppRoXimation framework for On-chip photonic Networks (ARXON) to reduce such power dissipation overhead by enabling intelligent and aggressive approximation during communication over silicon photonic links in PNoCs. Our framework reduces laser and tuning-power overhead while intelligently approximating communication, such that application output quality is not distorted beyond an acceptable limit. Simulation results show that our framework can achieve up to 56.4% lower laser power consumption and up to 23.8% better energy-efficiency than the best-known prior work on approximate communication with silicon photonic interconnects and for the same application output quality.
Clandestino or Rifugiato?Anti-immigration Facebook Ad Targeting in Italy✱

Monitoring advertising around controversial issues is an important step in ensuring accountability and transparency of political processes. To that end, we use the Facebook Ads Library to collect 2312 migration-related advertising campaigns in Italy over one year. Our pro- and anti-immigration classifier (F1=0.85) reveals a partisan divide among the major Italian political parties, with anti-immigration ads accounting for nearly 15M impressions. Although composing 47.6% of all migration-related ads, anti-immigration ones receive 65.2% of impressions. We estimate that about two thirds of all captured campaigns use some kind of demographic targeting by location, gender, or age. We find sharp divides by age and gender: for instance, anti-immigration ads from major parties are 17% more likely to be seen by a male user than a female. Unlike pro-migration parties, we find that anti-immigration ones reach a similar demographic to their own voters. However their audience change with topic: an ad from anti-immigration parties is 24% more likely to be seen by a male user when the ad speaks about migration, than if it does not. Furthermore, the viewership of such campaigns tends to follow the volume of mainstream news around immigration, supporting the theory that political advertisers try to “ride the wave” of current news. We conclude with policy implications for political communication: since the Facebook Ads Library does not allow to distinguish between advertisers intentions and algorithmic targeting, we argue that more details should be shared by platforms regarding the targeting configuration of socio-political campaigns.
The morphology of the X-ray afterglows and of the jetted GeV emission in long GRBs

We recall evidence that long gamma-ray bursts (GRBs) have binary progenitors and give new examples. Binary-driven hypernovae (BdHNe) consist of a carbon-oxygen core (COcore) and a neutron star (NS) companion. For binary periods ∼ 5 min, the COcore collapse originates the subclass BdHN I characterized by: 1) an energetic supernova (the “SN-rise”); 2) a black hole (BH), born from the NS collapse by SN matter accretion, leading to a GeV emission with luminosity LGeV = AGeV t−αGeV , observed only in some cases; 3) a new NS (νNS), born from the SN, originating the X-ray afterglow with LX = AX t−αX , observed in all BdHN I. We record 378 sources and present for four prototypes GRBs 130427A, 160509A, 180720B and 190114C: 1) spectra, luminosities, SN-rise duration; 2) AX , αX = 1.48± 0.32, and 3) the νNS spin time-evolution. We infer a) AGeV, αGeV = 1.19± 0.04; b) the BdHN I morphology from time-resolved spectral analysis, three-dimensional simulations, and the GeV emission presence/absence in 54 sources within the Fermi-LAT boresight angle. For 25 sources, we give the integrated and time-varying GeV emission, 29 sources have no GeV emission detected and show X/gamma-ray flares previously inferred as observed along the binary plane. The 25/54 ratio implies the GeV radiation is emitted within a cone of half-opening angle≈ 60◦ from the normal to the orbital plane. We deduce BH masses 2.3–8.9 M and spin 0.27–0.87 by explaining the GeV emission from the BH energy extraction, while their time evolution validates the BH mass-energy formula.
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way, using a sequential gating-based mechanism that distributes contextual information across 2-D space. We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class. Furthermore, we demonstrate that SDN can be applied to large images by synthesizing samples of high quality and coherence. In a vanilla VAE setting, we find that a powerful SDN decoder also improves learning disentangled representations, indicating that neural architectures play an important role in this task. Our results suggest favoring spatial dependency over convolutional layers in various VAE settings. The accompanying source code is given at https://github.com/djordjemila/sdn.
Revisiting Dynamic Convolution via Matrix Decomposition

Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels.It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging. In this paper, we revisit it from a new perspective of matrix decomposition and reveal the key issue is that dynamic convolution applies dynamic attentions over channel groups after projecting into a higher dimensional intermediate space. To address this issue, we propose dynamic channel fusion to replace dynamic attentions over channel groups. Dynamic channel fusion not only enables significant dimension reduction of the intermediate space, but also mitigates the joint optimization difficulty. As a result, our method is easier to train and requires significantly fewer parameters without sacrificing accuracy.
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose

Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple learning algorithms. Many regress precise geometric quantities, like poses or 3D points, from an input image. This either fails to generalize to new viewpoints or ties the model parameters to a specific scene. In this paper, we go Back to the Feature: we argue that deep networks should focus on learning robust and invariant visual features, while the geometric estimation should be left to principled algorithms. We introduce PixLoc, a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. Our approach is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. The system can localize in large environments given coarse pose priors but also improve the accuracy of sparse feature matching by jointly refining keypoints and poses with little overhead. The code will be publicly available at github.com/cvg/pixloc.
Two-gluon production of 
ϕ
 and 
η′
 mesons in proton-proton collisions at high energies

We discuss gluon-gluon mechanisms for production of mesons with hidden strangeness, such as η′ and φ meson, in proton-proton collisions at large energies. The g∗g∗ → η′ and g∗g∗ → φg mechanisms are considered only and the corresponding cross sections are calculated in the kt-factorization approach. The Fγ∗γ∗→η ′ and Fg∗g∗→η ′ form factors are calculated from quarkantiquark η′ light-cone wave function including quark/antiquark transverse momenta. The result for two-photon transition form factor demonstrates that higher twists may survive even to large photon virtualities. The result is compared with the result of a recent leading-twist NLO analysis which uses phenomenological distribution amplitudes fitted to exclusive production of η′ in e+e− → e+e−η′ reaction. We calculate transverse momentum distributions of both η′ and φ mesons in proton-proton collisions for RHIC and LHC energies. The results are compared to experimental data whenever available. The results of the Lund string model are shown for comparison for η′ production at LHC energies. It seems that the two-gluon fusion is not the dominant mechanism for both φ and η′ production, although the situation for η′, especially at larger energies, is less clear due to lack of experimental data. † also at University of Rzeszów, PL-35-959 Rzeszów, Poland ∗Electronic address: acisek@ur.edu. pl ‡Electronic address: antoni.szczurek@ifj.edu. pl
Exact Sparse Orthogonal Dictionary Learning

Over the past decade, learning a dictionary from input images for sparse modeling has been one of the topics which receive most research attention in image processing and compressed sensing. Most existing dictionary learning methods consider an over-complete dictionary, such as the K-SVD method, which may result in high mutual incoherence and therefore has a negative impact in recognition. On the other side, the sparse codes are usually optimized by adding the $\ell_0$ or $\ell_1$-norm penalty, but with no strict sparsity guarantee. In this paper, we propose an orthogonal dictionary learning model which can obtain strictly sparse codes and orthogonal dictionary with global sequence convergence guarantee. We find that our method can result in better denoising results than over-complete dictionary based learning methods, and has the additional advantage of high computation efficiency.
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportation scheduling. This problem involves a high-dimensional design space and is challenging especially with uncertainty present. To tackle this challenge, the developed framework has a hierarchical structure with reinforcement learning and network-based mixed-integer linear programming (MILP), where the former optimizes campaign-level decisions (e.g., design of the vehicle used throughout the campaign, destination demand assigned to each mission in the campaign), whereas the latter optimizes the detailed mission-level decisions (e.g., when to launch what from where to where). The framework is applied to a set of human lunar exploration campaign scenarios with uncertain in situ resource utilization performance as a case study. The main value of this work is its integration of the rapidly growing reinforcement learning research and the existing MILP-based space logistics methods through a hierarchical framework to handle the otherwise intractable complexity of space mission design under uncertainty. This unique framework is expected to be a critical steppingstone for the emerging research direction of artificial intelligence for space mission design.
Analyticity and infinite breakdown of regularity in mass-subcritical Hartree scattering

We study the asymptotic behavior of solutions to the defocusing mass-subcritical Hartree NLS $iu_t + \Delta u = F(u) = (|x|^{-\gamma}*|u|^2)u$ on $\mathbb{R}^d$, $d\geq 2$, $\frac{4}{3}<\gamma<2$. We show that the scattering problem associated to this equation is analytically well-posed in the weighted spaces $\Sigma = H^1\cap\mathcal{F}H^1$ and $\mathcal{F}H^1$. Furthermore, we show that the same problem fails to be analytically well-posed for data in $L^2$. This constitutes an infinite loss of regularity between the scattering problems in weighted spaces and in $L^2$. This further develops an earlier investigation initiated by the author in which a finite breakdown of regularity was proved for the $L^2$ scattering problem for the mass-subcritical NLS with power nonlinearity $F(u) = |u|^pu$.