Miracle-dz/UniFAR-FTUs huggingface dataset:
Manifold Learning and Deep Clustering with Local Dictionaries

We introduce a novel clustering algorithm for data sampled from a union of nonlinear manifolds. Our algorithm extends a popular manifold clustering framework, which first computes a sparse similarity graph over the input data and then uses spectral methods to find clusters in this graph. While previous manifold learning algorithms directly compute similarity scores between pairs of data points, our algorithm first augments the data set with a small handful of representative atoms and then computes similarity scores between data points and atoms. To measure similarity, we express each data point as sparse convex combination of nearby atoms. To learn the atoms, we employ algorithm unrolling, an increasingly popular technique for structured deep learning. Ultimately, this departure from established manifold learning techniques leads to improvements in clustering accuracy and scalability.
Design of multicomponent indomethacin‐paracetamol and famotidine loaded nanoparticles for sustained and effective anti‐inflammatory therapy

Indomethacin is one of the nonsteroidal anti‐inflammatory drugs (NSAIDs) that are widely prescribed drug for pain and inflammation. However, its notoriety of causing gastrointestinal effect, low water solubility, and its short half‐life would affect patient compliance and its oral absorption and accordingly justify the need to develop a formula with a controlled and sustained release manner in combination with anti‐ulcer drugs. Herein, we synthesized indomethacin‐paracetamol co‐drug loaded in nanoemulsion and encapsulated in famotiditine loaded polycaprolactone (PCL) nanoparticles. The synthesis of the co‐drug was achieved by the formation of a hydrolyzable ester between the indomethacin and paracetamol. The synthesized co‐drug was preloading in nanoemulsion (Co‐NE), which encapsulated into famotidine PCL nanoparticles utilizing the nanoprecipitation approach. The developed nanosystem showed hydrodynamic size less than 200 nm and the zeta potential value above −30 mV. TEM images confirmed the morphological structure of the formed nanoemulsion and the loaded PCL nanoparticles. Stability studies revealed that the developed nanosystem was stable at different temperatures and pHs over 1 month. Moreover, improvement of the solubilities of these three drugs leading to have a controlled‐release multicomponent system of both co‐drug and famotidine over 3 days. This multicomponent nanoparticle might be a potential platform to overcome the obstacles of NSAIDs, synergize drugs with different mechanisms of actions by co‐encapsulating a small‐sized nanoemulsion into PCL nanoparticles for reaching the goal of effective anti‐inflammatory therapy.
Auto-correlation functions of astrophysical processes, and their relation to Gaussian processes

Context. Accounting for the effects of stellar magnetic phenomena is indispensable to fully exploit radial velocities (RVs) obtained using modern exoplanet-hunting spectrometers. Correlated time variations are often mitigated by non-trivial noise models in the framework of Gaussian processes. These models rely on fitting kernel functions that are motivated on mathematical grounds, and whose physical interpretation is often elusive. Aims. We aim to establish a clear connection between stellar magnetic activity affecting RVs and their corresponding correlations with physical parameters, and compare this connection with kernels used in the literature. Methods. We use simple activity models to investigate the relationship between the physical processes generating the signals and the covariances typically found in data, and to demonstrate the qualitative behaviour of this relationship. We use the StarSim code to calculate RVs of an M dwarf with different realistic evolving spot configurations. The auto-correlation function (ACF) of a synthetic data set shows a very specific behaviour and is explicitly related to the kernel. Gaussian process regression is performed using the quasi-periodic (QP) and simple harmonic oscillator kernels of the george and celerite codes, respectively. Comparison of the resulting kernels with the exact ACFs allows us to cross-match the kernel hyper-parameters with the introduced physical values, study the overall capabilities of the kernels, and improve their definition. Results. We find that the QP kernel provides a more straightforward interpretation of the physics. It is able to consistently recover both the introduced rotation period Prot and the spot lifetime. Our study indicates that the performance can be enhanced by fixing the form factor w and adding a physically motivated cosine term with period Prot∕2, where the contribution to the ACF for the different spot configurations differs significantly. The newly proposed quasi-periodic with cosine (QPC) kernel leads to significantly better model likelihoods, can potentially distinguish between different spot configurations, and can thereby improve the sensitivity of RV exoplanet searches.
Intervention Design for Effective Sim2Real Transfer

The goal of this work is to address the recent success of domain randomization and data augmentation for the sim2real setting. We explain this success through the lens of causal inference, positioning domain randomization and data augmentation as interventions on the environment which encourage invariance to irrelevant features. Such interventions include visual perturbations that have no effect on reward and dynamics. This encourages the learning algorithm to be robust to these types of variations and learn to attend to the true causal mechanisms for solving the task. This connection leads to two key findings: (1) perturbations to the environment do not have to be realistic, but merely show variation along dimensions that also vary in the real world, and (2) use of an explicit invariance-inducing objective improves generalization in sim2sim and sim2real transfer settings over just data augmentation or domain randomization alone. We demonstrate the capability of our method by performing zero-shot transfer of a robot arm reach task on a 7DoF Jaco arm learning from pixel observations.
Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing

The 2019 novel coronavirus (SARS-CoV-2) pandemic has resulted in more than a million deaths, high morbidities, and economic distress worldwide. There is an urgent need to identify medications that would treat and prevent novel diseases like the 2019 coronavirus disease (COVID-19). Drug repurposing is a promising strategy to discover new medical indications of the existing approved drugs due to several advantages in terms of the costs, safety factors, and quick results compared to new drug design and discovery. In this work, we explore computational data-driven methods for drug repurposing and propose a dedicated graph neural network (GNN) based drug repurposing model, called Dr-COVID. Although we analyze the predicted drugs in detail for COVID-19, the model is generic and can be used for any novel diseases. We construct a four-layered heterogeneous graph to model the complex interactions between drugs, diseases, genes, and anatomies. We pose drug repurposing as a link prediction problem. Specifically, we design an encoder based on the scalable inceptive graph neural network (SIGN) to generate embeddings for all the nodes in the four-layered graph and propose a quadratic norm scorer as a decoder to predict treatment for a disease. We provide a detailed analysis of the 150 potential drugs (such as Dexamethasone, Ivermectin) predicted by Dr-COVID for COVID-19 from different pharmacological classes (e.g., corticosteroids, antivirals, antiparasitic). Out of these 150 drugs, 46 drugs are currently in clinical trials. Dr-COVID is evaluated in terms of its prediction performance and its ability to rank the known treatment drugs for diseases as high as possible. For a majority of the diseases, Dr-COVID ranks the actual treatment drug in the top 15.
Locating the source of interacting signal in complex networks

We investigate the problem of locating the source of a self-interacting signal spreading in a complex networks. We use a well-known rumour model as an example of the process with self-interaction. According to this model based on the SIR epidemic dynamics, the infected nodes may interact and discourage each other from gossiping with probability $\alpha$. We compare three algorithms of source localization: Limited Pinto-Thiran-Vettarli (LPTV), Gradient Maximum Likelihood (GMLA) and one based on Pearson correlation between time and distance. The results of numerical simulations show that additional interactions between infected nodes decrease the quality of LPTV and Pearson. GMLA is the most resistant to harmful effects of the self-interactions, which is especially visible for medium and high level of stochasticity of the process, when spreading rate is below 0.5. The reason for this may be the fact that GMLA uses only the nearest observers, which are much less likely affected by the interactions between infected nodes, because these contacts become important as the epidemics develops and the number of infected agents increases.
We Dare You

In this article, we present a lifecycle study of We Dare You, a substitutional reality installation that combines visual and tactile stimuli. The installation is set up in a center for architecture, and invites visitors to explore its facade while playing with vertigo, in a visual virtual reality environment that replicates the surrounding physical space of the installation. Drawing on an ethnographic approach, including observations and interviews, we researched the exhibit from its opening, through the initial months plagued by technical problems, its subsequent success as a social and playful installation, on to its closure, due to COVID-19, and its subsequent reopening. Our findings explore the challenges caused by both the hybrid nature of the installation and the visitors’ playful use of the installation which made the experience social and performative—but also caused some problems. We also discuss the problems We Dare You faced in light of hygiene demands due to COVID-19. The analysis contrasts the design processes and expectations of stakeholders with the audience’s playful appropriation, which led the stakeholders to see the installation as both a success and a failure. Evaluating the design and redesign through use on behalf of visitors, we argue that an approach that further opens up the post-production experience to a process of continuous redesign based on the user input—what has been termed design-after-design—could facilitate the design of similar experiences in the museum and heritage sector, supporting a participatory agenda in the design process, and helping to resolve the tension between stakeholders’ expectations and visitors’ playful appropriations.
A purely acceleration-based measurement of the fundamental Galactic parameters using Gaia EDR3

Klioner et al. have used the Gaia EDR3 data to for the first time directly measure the solar system's acceleration within the Milky Way using the apparent proper motions of quasars. Here I show that this single absolute acceleration measurement in combination with relative accelerations obtained from pulsar orbital decay allows one to determine all of the parameters describing the dynamics of our local Galactic environment, including the circular velocity at the Sun and its derivative, the local angular frequency, the Oort constants, and the Sun's motion with respect to the LSR. While current measurements are less precise than indirect methods, this is the first determination of these parameters that only uses general relativity without the need for additional assumptions.
Predicting Misinformation and Engagement in COVID-19 Twitter Discourse in the First Months of the Outbreak

Disinformation entails the purposeful dissemination of falsehoods towards a greater dubious agenda and the chaotic fracturing of a society. The general public has grown aware of the misuse of social media towards these nefarious ends, where even global public health crises have not been immune to misinformation (deceptive content spread without intended malice). In this paper, we examine nearly 505K COVID-19-related tweets from the initial months of the pandemic to understand misinformation as a function of bot-behavior and engagement. Using a correlation-based feature selection method, we selected the 11 most relevant feature subsets among over 170 features to distinguish misinformation from facts, and to predict highly engaging misinformation tweets about COVID-19. We achieved an average F-score of at least 72\% with ten popular multi-class classifiers, reinforcing the relevance of the selected features. We found that (i) real users tweet both facts and misinformation, while bots tweet proportionally more misinformation; (ii) misinformation tweets were less engaging than facts; (iii) the textual content of a tweet was the most important to distinguish fact from misinformation while (iv) user account metadata and human-like activity were most important to predict high engagement in factual and misinformation tweets; and (v) sentiment features were not relevant.
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i. d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations --- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.
On the Impossibility of Convergence of Mixed Strategies with No Regret Learning

We study convergence properties of the mixed strategies that result from a general class of optimal no regret learning strategies in a repeated game setting where the stage game is any 2 by 2 competitive game (i.e. game for which all the Nash equilibria (NE) of the game are completely mixed). We consider the class of strategies whose information set at each step is the empirical average of the opponent's realized play (and the step number), that we call mean based strategies. We first show that there does not exist any optimal no regret, mean based strategy for player 1 that would result in the convergence of her mixed strategies (in probability) against an opponent that plays his Nash equilibrium mixed strategy at each step. Next, we show that this last iterate divergence necessarily occurs if player 2 uses any adaptive strategy with a minimal randomness property. This property is satisfied, for example, by any fixed sequence of mixed strategies for player 2 that converges to NE. We conjecture that this property holds when both players use optimal no regret learning strategies against each other, leading to the divergence of the mixed strategies with a positive probability. Finally, we show that variants of mean based strategies using recency bias, which have yielded last iterate convergence in deterministic min max optimization, continue to lead to this last iterate divergence. This demonstrates a crucial difference in outcomes between using the opponent's mixtures and realizations to make strategy updates.
Latex Film Formation Probed by Nonradiative Energy Transfer: Effect of Grafted and Free Poly(ethylene oxide) on a Poly(n-butyl methacrylate) Latex

The formation of solid films from latex dispersions is studied using particles labeled with phenanthrene or anthracene comprised of either poly(n-butyl methacrylate) (PBMA) or a copolymer of n-butyl methacrylate with a poly(ethylene oxide) (PEO) macromonomer, co(PBMA-PEO). Interparticle polymer diffusion was followed by nonradiative electronic energy transfer (DET) between electronically excited phenanthrene and anthracene. A model of energy transfer that considers both the topological constraints and the heterogeneous distributions of donors and acceptors is presented. The analysis of the phenanthrene decay curves allows calculation of the diffusion coefficient as a unique parameter, for several annealing times. The values recovered decrease initially with annealing time, which was attributed mainly to the polydispersity of the PBMA. The addition of a small percentage of low molecular weight PEO (in the form of nonylphenolethoxylate) to the PBMA particles increases the diffusion coefficient, this effect resulting from the increase of the polymer free volume in the film. When the same percentage of PEO is incorporated in the PBMA polymer chain in the form of a grafted macromonomer, the fraction of mixing increases on drying, but during annealing the diffusion coefficients remain equal to that of PBMA without PEO.
Analog quantum simulation of chemical dynamics

Ultrafast chemical reactions are difficult to simulate because they involve entangled, many-body wavefunctions whose computational complexity grows rapidly with molecular size. In photochemistry, the breakdown of the Born–Oppenheimer approximation further complicates the problem by entangling nuclear and electronic degrees of freedom. Here, we show that analog quantum simulators can efficiently simulate molecular dynamics using commonly available bosonic modes to represent molecular vibrations. Our approach can be implemented in any device with a qudit controllably coupled to bosonic oscillators and with quantum hardware resources that scale linearly with molecular size, and offers significant resource savings compared to digital quantum simulation algorithms. Advantages of our approach include a time resolution orders of magnitude better than ultrafast spectroscopy, the ability to simulate large molecules with limited hardware using a Suzuki–Trotter expansion, and the ability to implement realistic system-bath interactions with only one additional interaction per mode. Our approach can be implemented with current technology; e.g., the conical intersection in pyrazine can be simulated using a single trapped ion. Therefore, we expect our method will enable classically intractable chemical dynamics simulations in the near term.
Cardiac Autonomic Neuropathy: A Progressive Consequence of Chronic Low-Grade Inflammation in Type 2 Diabetes and Related Metabolic Disorders

Cardiac autonomic neuropathy (CAN) is one of the earliest complications of type 2 diabetes (T2D), presenting a silent cause of cardiovascular morbidity and mortality. Recent research relates the pathogenesis of cardiovascular disease in T2D to an ensuing chronic, low-grade proinflammatory and pro-oxidative environment, being the hallmark of the metabolic syndrome. Metabolic inflammation emerges as adipose tissue inflammatory changes extending systemically, on the advent of hyperglycemia, to reach central regions of the brain. In light of changes in glucose and insulin homeostasis, dysbiosis or alteration of the gut microbiome (GM) emerges, further contributing to inflammatory processes through increased gut and blood–brain barrier permeability. Interestingly, studies reveal that the determinants of oxidative stress and inflammation progression exist at the crossroad of CAN manifestations, dictating their evolution along the natural course of T2D development. Indeed, sympathetic and parasympathetic deterioration was shown to correlate with markers of adipose, vascular, and systemic inflammation. Additionally, evidence points out that dysbiosis could promote a sympatho-excitatory state through differentially affecting the secretion of hormones and neuromodulators, such as norepinephrine, serotonin, and γ-aminobutyric acid, and acting along the renin–angiotensin–aldosterone axis. Emerging neuronal inflammation and concomitant autophagic defects in brainstem nuclei were described as possible underlying mechanisms of CAN in experimental models of metabolic syndrome and T2D. Drugs with anti-inflammatory characteristics provide potential avenues for targeting pathways involved in CAN initiation and progression. The aim of this review is to delineate the etiology of CAN in the context of a metabolic disorder characterized by elevated oxidative and inflammatory load.
Image inpainting using frequency-domain priors

Abstract. We present an image inpainting technique using frequency-domain information. Prior works on image inpainting predict the missing pixels by training neural networks using only the spatial-domain information. However, these methods still struggle to reconstruct high-frequency details for real complex scenes, leading to a discrepancy in color, boundary artifacts, distorted patterns, and blurry textures. To alleviate these problems, we investigate if it is possible to obtain better performance by training the networks using frequency-domain information (discrete Fourier transform) along with the spatial-domain information. To this end, we propose a frequency-based deconvolution module that enables the network to learn the global context while selectively reconstructing the high-frequency components. We evaluate our proposed method on the publicly available datasets: celebFaces attribute (CelebA) dataset, Paris streetview, and describable textures dataset and show that our method outperforms current state-of-the-art image inpainting techniques both qualitatively and quantitatively.