Miracle-dz/UniFAR-FTUs huggingface dataset:
Walking droplets through the lens of dynamical systems

Over the past decade the study of fluidic droplets bouncing and skipping (or “walking”) on a vibrating fluid bath has gone from an interesting experiment to a vibrant research field. The field exhibits challenging fluids problems, potential connections with quantum mechanics, and complex nonlinear dynamics. We detail advancements in the field of walking droplets through the lens of Dynamical Systems Theory, and outline questions that can be answered using dynamical systems analysis. The paper begins by discussing the history of the fluidic experiments and their resemblance to quantum experiments. With this physics backdrop, we paint a portrait of the complex nonlinear dynamics present in physical models of various walking droplet systems. Naturally, these investigations lead to even more questions, and some unsolved problems that are bound to benefit from rigorous Dynamical Systems Analysis are outlined.
Redesigning the classification layer by randomizing the class representation vectors

Neural image classification models typically consist of two components. The first is an image encoder, which is responsible for encoding a given raw image into a representative vector. The second is the classification component, which is often implemented by projecting the representative vector onto target class vectors. The target class vectors, along with the rest of the model parameters, are estimated so as to minimize the loss function. In this paper, we analyze how simple design choices for the classification layer affect the learning dynamics. We show that the standard cross-entropy training implicitly captures visual similarities between different classes, which might deteriorate accuracy or even prevents some models from converging. We propose to draw the class vectors randomly and set them as fixed during training, thus invalidating the visual similarities encoded in these vectors. We analyze the effects of keeping the class vectors fixed and show that it can increase the inter-class separability, intra-class compactness, and the overall model accuracy, while maintaining the robustness to image corruptions and the generalization of the learned concepts.
Trend to equilibrium for granular media equations under non-convex potential and application to log-Coulomb gases

We investigate the long-time asymptotics of solutions of granular media equations with non-convex potentials, by establishing a HWI inequality. Our result extends the work of Carrillo et al. Moreover, we consider multidimensional log-Coulomb gases. We prove existence and uniqueness of an equilibrium for the entropy and show exponential convergence of symmetric solutions towards this equilibrium under non-convex polynomial potential, with respect to the Wasserstein metric. Finally, we study the case of the confining and the non-confining quartic potentials, and answer conjectures made by Li et al. and Biane et al.
Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders.

We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some form of sparsity for the regression functions. In this work, we introduce a debiased inverse propensity score weighting (DIPW) scheme for average treatment effect estimation that delivers $\sqrt{n}$-consistent estimates of the average treatment effect when the propensity score follows a sparse logistic regression model; the regression functions are permitted to be arbitrarily complex. Our theoretical results quantify the price to pay for permitting the regression functions to be unestimable, which shows up as an inflation of the variance of the estimator compared to the semiparametric efficient variance by at most O(1) under mild conditions. Given the lack of assumptions on the regression functions, averages of transformed responses under each treatment may also be estimated at the $\sqrt{n}$ rate, and so for example, the variances of the potential outcomes may be estimated. We show how confidence intervals centred on our estimates may be constructed, and also discuss an extension of the method to estimating projections of the heterogeneous treatment effect function.
Characterization of Unsaturated Soils Using Acoustic Techniques

Recently there has been a great interest in the ability to relate the hydro-mechanical properties of soils to their acoustic response. This ability could enhance high resolution non-destructive evaluation of the shallow subsurface, and would have applications in a variety of fields including groundwater and contaminant hydrogeology, oil recovery, soil dynamics, and the detection of buried objects. Groundwater hydrologists and environmental engineers are challenged with the task of characterizing the material, mechanical and hydraulic properties of the subsurface with limited information generally collected from discrete points. Geophysical testing offers a suite of measurement techniques that allow for the non destructive evaluation of potentially large areas in a continuous manner. Acoustic testing is one geophysical method used by many professions to characterize the subsurface. Unsaturated and multiphase flow modeling relies on the relationship between the capillary pressure and the level of saturation of the porous media. It has been previously suggested that this relationship may be non-unique and rate dependent. A theory which relates this dynamic relationship to the acoustic properties of the soil was developed by others. This research attempts to experimentally verify this theory by meeting the following three objectives: (1) develop an apparatus and procedure to collect acoustic waveforms on laboratory sized unsaturated soil samples, (2) develop a forward modeling technique using a one-dimensional wave propagation model as an alternative analysis method for waves collected on relatively small laboratory specimens, and (3) apply the theory to the measured acoustic data in an attempt to predict the dynamic behavior of the capillary pressure relationship. The acoustic data collected showed variation in compressional wave velocity and attenuation with saturation, and the trends were consistent with data collected by others in partially saturated rocks. The forward modeling technique was shown to provide objective results with reasonable accuracy and low computational time. The dynamic effects predicted with these acoustic measurements did not sufficiently explain the dynamic behavior seen in the laboratory. This is attributed to other causes of significant attenuation not accounted for in the wave propagation theory that was evaluated.
Irradiation dose affects the composition of organic refractory materials in space

Context. Near- and mid-infrared observations have revealed the presence of organic refractory materials in the Solar System, in cometary nuclei and on the surface of centaurs, Kuiper-belt and trans-neptunian objects. In these astrophysical environments, organic materials can be formed because of the interaction of frozen volatile compounds with cosmic rays and solar particles, and favoured by thermal processing. The analysis of laboratory analogues of such materials gives information on their properties, complementary to observations. Aims. We present new experiments to contribute to the understanding of the chemical composition of organic refractory materials in space. Methods. We bombard frozen water, methanol and ammonia mixtures with 40 keV H+ and we warmed the by-products up to 300 K. The experiments enabled the production of organic residues that we analysed by means of infrared spectroscopy and by very high resolution mass spectrometry to study their chemical composition and their high molecular diversity, including the presence of hexamethylenetetramine and its derivatives. Results. We find that the accumulated irradiation dose plays a role in determining the composition of the residue. Conclusions. Based on the laboratory doses, we estimate the astrophysical timescales to be short enough to induce an efficient formation of organic refractory materials at the surface of icy bodies in the outer Solar System.
5G Air-to-Ground Network Design and Optimization: A Deep Learning Approach

Direct air-to-ground (A2G) communications leveraging the fifth-generation (5G) new radio (NR) can provide high-speed broadband in-flight connectivity to aircraft in the sky. A2G network deployment entails optimizing various design parameters such as inter-site distances, number of sectors per site, and the up-tilt angles of sector antennas. The system-level design guidelines in the existing work on A2G network are rather limited. In this paper, a novel deep learning-based framework is proposed for efficient design and optimization of a 5G A2G network. The devised architecture comprises two deep neural networks (DNNs): the first DNN is used for approximating the 5G A2G network behavior in terms of user throughput, and the second DNN is developed as a function optimizer to find the throughput-optimal deployment parameters including antenna up-tilt angles and inter-site distances. Simulation results are provided to validate the proposed model and reveal system-level design insights.
Detecting Receptivity for mHealth Interventions in the Natural Environment

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach - Ally - that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study.
Avoiding Tampering Incentives in Deep RL via Decoupled Approval

How can we design agents that pursue a given objective when all feedback mechanisms are influenceable by the agent? Standard RL algorithms assume a secure reward function, and can thus perform poorly in settings where agents can tamper with the reward-generating mechanism. We present a principled solution to the problem of learning from influenceable feedback, which combines approval with a decoupled feedback collection procedure. For a natural class of corruption functions, decoupled approval algorithms have aligned incentives both at convergence and for their local updates. Empirically, they also scale to complex 3D environments where tampering is possible.
Properties of polarized synchrotron emission from fluctuation-dynamo action -- I. Application to galaxy clusters.

Using magnetohydrodynamic simulations of fluctuation dynamos, we perform broad-bandwidth synthetic observations to investigate the properties of polarized synchrotron emission and the role that Faraday rotation plays in inferring the polarized structures in the intracluster medium (ICM) of galaxy clusters. In the saturated state of the dynamo, we find a Faraday depth (FD) dispersion $\sigma_{\rm FD} \approx 100$ rad m$^{-2}$, in agreement with observed values in the ICM. Remarkably, the FD power spectrum is qualitatively similar to $M(k)/k$, where $M(k)$ is the magnetic spectrum and $k$ the wavenumber. However, this similarity is broken at high $k$ when FD is obtained by applying RM synthesis to polarized emission from the ICM due to poor resolution and complexities of spectrum in FD space. Unlike the Gaussian probability distribution function (PDF) obtained for FD, the PDF of the synchrotron intensity is lognormal. A relatively large $\sigma_{\rm FD}$ in the ICM gives rise to strong frequency-dependent variations of the pixel-wise mean and peak polarized intensities at low frequencies ($\lesssim 1.5\,{\rm GHz}$). The mean fractional polarization $\langle p \rangle$ obtained at the resolution of the simulations increases from $<0.1$ at 0.5 GHz to its intrinsic value of $\sim0.3$ at 6 GHz. Beam smoothing significantly affects the polarization properties below $\lesssim 1.5\,{\rm GHz}$, reducing $\langle p \rangle$ to $\lesssim 0.01$ at 0.5 GHz. At frequencies $\gtrsim 5\,{\rm GHz}$, polarization remains largely unaffected, even when recovered using RM synthesis. Thus, our results underline the need for high-frequency ($\gtrsim 5\,{\rm GHz}$) observations with future radio telescopes to effectively probe the properties of polarized emission in the ICM.
A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces

Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture. Method: The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities. Results: Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI. Conclusion: The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets. Significance: Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.
Denoising Score-Matching for Uncertainty Quantification in Inverse Problems

Deep neural networks have proven extremely efficient at solving a wide rangeof inverse problems, but most often the uncertainty on the solution they provideis hard to quantify. In this work, we propose a generic Bayesian framework forsolving inverse problems, in which we limit the use of deep neural networks tolearning a prior distribution on the signals to recover. We adopt recent denoisingscore matching techniques to learn this prior from data, and subsequently use it aspart of an annealed Hamiltonian Monte-Carlo scheme to sample the full posteriorof image inverse problems. We apply this framework to Magnetic ResonanceImage (MRI) reconstruction and illustrate how this approach not only yields highquality reconstructions but can also be used to assess the uncertainty on particularfeatures of a reconstructed image.
Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations

In this paper we present a solution to the task of “unsupervised domain adaptation (UDA) of a given pre-trained semantic segmentation model without relying on any source domain representations”. Previous UDA approaches for semantic segmentation either employed simultaneous training of the model in the source and target domains, or they relied on an additional network, replaying source domain knowledge to the model during adaptation. In contrast, we present our novel Unsupervised BatchNorm Adaptation (UBNA) method, which adapts a given pre-trained model to an unseen target domain without using—beyond the existing model parameters from pre-training—any source domain representations (neither data, nor networks) and which can also be applied in an online setting or using just a few unlabeled images from the target domain in a few-shot manner. Specifically, we partially adapt the normalization layer statistics to the target domain using an exponentially decaying momentum factor, thereby mixing the statistics from both domains. By evaluation on standard UDA benchmarks for semantic segmentation we show that this is superior to a model without adaptation and to baseline approaches using statistics from the target domain only. Compared to standard UDA approaches we report a trade-off between performance and usage of source domain representations. 11Code is available at https://git;hub.com/ifnspaml/UBNR
Confounding Feature Acquisition for Causal Effect Estimation

Reliable treatment effect estimation from observational data depends on the availability of all confounding information. While much work has targeted treatment effect estimation from observational data, there is relatively little work in the setting of confounding variable missingness, where collecting more information on confounders is often costly or time-consuming. In this work, we frame this challenge as a problem of feature acquisition of confounding features for causal inference. Our goal is to prioritize acquiring values for a fixed and known subset of missing confounders in samples that lead to efficient average treatment effect estimation. We propose two acquisition strategies based on i) covariate balancing (CB), and ii) reducing statistical estimation error on observed factual outcome error (OE). We compare CB and OE on five common causal effect estimation methods, and demonstrate improved sample efficiency of OE over baseline methods under various settings. We also provide visualizations for further analysis on the difference between our proposed methods.
Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors

The recent advancement in computational and communication systems has led to the introduction of high-performing neural networks and high-speed wireless vehicular communication networks. As a result, new technologies such as cooperative perception and cognition have emerged, addressing the inherent limitations of sensory devices by providing solutions for the detection of partially occluded targets and expanding the sensing range. However, designing a reliable cooperative cognition or perception system requires addressing the challenges caused by limited network resources and discrepancies between the data shared by different sources. In this paper, we examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS). We explore different cooperative object detection designs and evaluate their performance in terms of average precision. We use the Volony dataset for our experimental study. The results confirm that the DFS methods are significantly less sensitive to the localization error caused by GPS noise. Furthermore, the results attest that detection gain of DFS methods caused by adding more cooperative participants in the scenes is comparable to raw information sharing technique while DFS enables flexibility in design toward satisfying communication requirements.
Learning outside the Black-Box: The pursuit of interpretable models

Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models. This paper proposes an algorithm that produces a continuous global interpretation of any given continuous black-box function. Our algorithm employs a variation of projection pursuit in which the ridge functions are chosen to be Meijer G-functions, rather than the usual polynomial splines. Because Meijer G-functions are differentiable in their parameters, we can tune the parameters of the representation by gradient descent; as a consequence, our algorithm is efficient. Using five familiar data sets from the UCI repository and two familiar machine learning algorithms, we demonstrate that our algorithm produces global interpretations that are both highly accurate and parsimonious (involve a small number of terms). Our interpretations permit easy understanding of the relative importance of features and feature interactions. Our interpretation algorithm represents a leap forward from the previous state of the art.
Asteroseismology of the heartbeat star KIC 5006817

This paper summarizes the project work on asteroseismology at the ERASMUS+ GATE 2020 Summer school on space satellite data. The aim was to do a global asteroseismic analysis of KIC 5006817 and quantify its stellar properties using the high-quality, state of the art space missions data. We employed the aperture photometry to analyze the data from the Kepler space telescope and the Transiting Exoplanet Survey Satellite (TESS). Using the lightkurve Python package, we have derived the asteroseismic parameters and calculated the stellar parameters using the scaling relations. Our analysis of KIC 5006817 confirmed its classification as a heartbeat binary. The rich oscillation spectrum facilitate estimating power excess ($\nu_{\rm max}$) at 145.50$\pm$0.50 $\mu$Hz and large frequency separation ($\Delta\nu$) to be 11.63$\pm$0.10 $\mu$Hz. Our results showed that the primary component is a low-luminosity, red-giant branch star with a mass, radius, surface gravity, and luminosity of 1.53$\pm$0.07 M$_\odot$, 5.91$\pm$0.12 R$_\odot$, 3.08$\pm$0.01 dex, and 19.66$\pm$0.73 L$_\odot$, respectively. The orbital period of the system is 94.83$\pm$0.05 d.