Miracle-dz/UniFAR-FTUs huggingface dataset:
Pixel to policy: DQN Encoders for within & cross-game reinforcement learning

Reinforcement Learning can be applied to various tasks, and environments. Many of these environments have a similar shared structure, which can be exploited to improve RL performance on other tasks. Transfer learning can be used to take advantage of this shared structure, by learning policies that are transferable across different tasks and environments and can lead to more efficient learning as well as improved performance on a wide range of tasks. This work explores as well as compares the performance between RL models being trained from the scratch and on different approaches of transfer learning. Additionally, the study explores the performance of a model trained on multiple game environments, with the goal of developing a universal game-playing agent as well as transfer learning a pre-trained encoder using DQN, and training it on the same game or a different game. Our DQN model achieves a mean episode reward of 46.16 which even beats the human-level performance with merely 20k episodes which is significantly lower than deepmind's 1M episodes. The achieved mean rewards of 533.42 and 402.17 on the Assault and Space Invader environments respectively, represent noteworthy performance on these challenging environments.
Relationship between Collider Bias and Interactions on the Log-Additive Scale

Collider bias occurs when conditioning on a common effect (collider) of two variables $X, Y$. In this manuscript, we quantify the collider bias in the estimated association between exposure $X$ and outcome $Y$ induced by selecting on one value of a binary collider $S$ of the exposure and the outcome. In the case of logistic regression, it is known that the magnitude of the collider bias in the exposure-outcome regression coefficient is proportional to the strength of interaction $\delta_3$ between $X$ and $Y$ in a log-additive model for the collider: $\mathbb{P} (S = 1 | X, Y) = \exp \left\{ \delta_0 + \delta_1 X + \delta_2 Y + \delta_3 X Y \right\}$. We show that this result also holds under a linear or Poisson regression model for the exposure-outcome association. We then illustrate by simulation that even if a log-additive model with interactions is not the true model for the collider, the interaction term in such a model is still informative about the magnitude of collider bias. Finally, we discuss the implications of these findings for methods that attempt to adjust for collider bias, such as inverse probability weighting which is often implemented without including interactions between variables in the weighting model.
DroneReqValidator: Facilitating High Fidelity Simulation Testing for Uncrewed Aerial Systems Developers

Rigorous testing of small Uncrewed Aerial Systems (sUAS) is crucial to ensure their safe and reliable deployment in the real world. sUAS developers aim to validate the reliability and safety of their applications through simulation testing. However, the dynamic nature of the real-world environment, including factors such as challenging weather conditions and wireless interference, causes unique software faults that may only be revealed through field testing. Considering the high cost and impracticality of conducting field testing in thousands of environmental contexts and conditions, there exists a pressing need to develop automated techniques that can generate high-fidelity, realistic environments enabling sUAS developers to deploy their applications and conduct thorough simulation testing in close- to- reality environmental conditions. To address this need, DroneReqValidator (DRV) offers a comprehensive small Unmanned Aerial Vehicle (sUAV) simulation ecosystem that automatically generates realistic environments based on developer-specified constraints, monitors sUAV activities against predefined safety parameters, and generates detailed acceptance test reports for effective debugging and analysis of sUAV applications. Providing these capabilities, DRV offers a valuable solution for enhancing the testing and development process of sUAS. The comprehensive demo of DRV is available at https://www.youtube. com/watch? v=Fd9ft55gbO8
Validation of wall boundary conditions for simulating complex fluid flows via the Boltzmann equation: Momentum transport and skin friction

The influence and validity of wall boundary conditions for non-equilibrium fluid flows described by the Boltzmann equation remains an open problem. The substantial computational cost of directly solving the Boltzmann equation has limited the extent of numerical validation studies to simple, often two-dimensional, flow problems. Recent algorithmic advancements for the Boltzmann–Bhatnagar–Gross–Krook equation introduced by the authors [Dzanic et al., J. Comput. Phys. 486, 112146 (2023)], consisting of a highly efficient high-order spatial discretization augmented with a discretely conservative velocity model, have made it feasible to accurately simulate unsteady three-dimensional flow problems across both the rarefied and continuum regimes. This work presents a comprehensive evaluation and validation of wall boundary conditions across a variety of flow regimes, primarily for the purpose of exploring their effects on momentum transfer in the low Mach limit. Results are presented for a range of steady and unsteady wall-bounded flow problems across both the rarefied and continuum regimes, from canonical two-dimensional laminar flows to unsteady three-dimensional transitional and turbulent flows, the latter of which are the first instances of wall-bounded turbulent flows computed by directly solving the Boltzmann equation. We show that approximations of the molecular gas dynamics equations can accurately predict both non-equilibrium phenomena and complex hydrodynamic flow instabilities and show how spatial and velocity domain resolution affect the accuracy. The results indicate that an accurate approximation of particle transport (i.e., high spatial resolution) is significantly more important than particle collision (i.e., high velocity domain resolution) for predicting flow instabilities and momentum transfer consistent with that predicted by the hydrodynamic equations and that these effects can be computed accurately even with very few degrees of freedom in the velocity domain. These findings suggest that highly accurate spatial schemes (e.g., high-order schemes) are a promising approach for solving molecular gas dynamics for complex flows and that the direct solution of the Boltzmann equation can be performed at a reasonable cost when compared to hydrodynamic simulations at the same level of resolution.
Exploiting Sparsity for Localization of Large-Scale Wireless Sensor Networks

Wireless Sensor Network (WSN) localization refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information. In many cases, such as in distance and bearing-based localization, the measurement model is a nonlinear function of the agents' positions, leading to pairwise interconnections between the agents. As the optimal solution for the WSN localization problem is known to be computationally expensive in these cases, an efficient approximation is desired. In this paper, we show that the inherent sparsity in this problem can be exploited to greatly reduce the computational effort of using an Extended Kalman Filter (EKF) for large-scale WSN localization. In the proposed method, which we call the Low-Bandwidth Extended Kalman Filter (LB-EKF), the measurement information matrix is converted into a banded matrix by relabeling (permuting the order of) the vertices of the graph. Using a combination of theoretical analysis and numerical simulations, it is shown that typical WSN configurations (which can be modeled as random geometric graphs) can be localized in a scalable manner using the proposed LB-EKF approach.
Multipiezo Effect in Altermagnetic V2SeTeO Monolayer.

Strain engineering has been used as an efficient method to modulate various properties of quantum materials and electronic devices. One may establish piezo effects based on a disciplined response to the strain in multifunctional nanosystems. Inspired by a recent theoretical proposal on the interesting piezomagnetism and C-paired valley polarization in the V2Se2O monolayer, we predict a stable altermagnetic Janus monolayer V2SeTeO using density functional theory calculations. It exhibits a novel "multipiezo" effect combining piezoelectricity, piezovalley, and piezomagnetism. Most interestingly, the valley polarization and the net magnetization under strain in V2SeTeO exceed these in V2Se2O, along with the additional large piezoelectric coefficient. The "multipiezo" effect makes Janus monolayer V2SeTeO as a tantalizing material for potential applications in nanoelectronics, optoelectronics, spintronics, and valleytronics.
SkullGAN: Synthetic Skull CT Generation with Generative Adversarial Networks

Purpose: Deep learning offers potential for various healthcare applications involving the human skull, yet requires extensive datasets of curated medical images. To overcome this challenge, we propose SkullGAN, a generative adversarial network (GAN), to create large datasets of synthetic skull CT slices, thus reducing reliance on real images and accelerating the integration of machine learning into healthcare. Materials and Methods: CT slices of 38 subjects were fed to SkullGAN, a neural network comprising over 200 million parameters. The generated synthetic skull images were then evaluated based on three quantitative radiological features: skull density ratio (SDR), mean thickness, and mean intensity, They were further analyzed using t-distributed stochastic neighbor embedding (t-SNE), and by applying the SkullGAN discriminator as a classifier. Results: SkullGAN-generated images demonstrated similar key quantitative radiological features to real skulls. Additionally, more definitive analysis was undertaken by applying the discriminator of SkullGAN. The SkullGAN discriminator classified 56.5% of a test set of real skull images and 55.9% of the SkullGAN-generated images as reals (the theoretical optimum being 50%), demonstrating that the SkullGAN-generated skull set is indistinguishable from the real skull set – within the limits of our nonlinear classifier. Conclusion: SkullGAN makes it possible for researchers to generate large numbers of synthetic skull CT segments, necessary for training neural networks for medical applications involving the human skull. By doing so, it mitigates challenges associated with preparing large, high-quality training datasets, such as access, capital, time, and the need for domain expertise.
Heat flow map and geothermal resources in Mexico

Heat flow maps are a powerful tool for regional exploration of geothermal resources. Mexico is one of the main producers ofgeothermal energy and the search for undiscovered resources at a regional level should be based on heat flow values. Here, we present a heat flow map at 1:4,000,000 scale, produced with heat flow data compiled from open data bases and previously unpub-lished data. The compiled heat flow data includes bottom hole temperature, temperature logs, transient temperature measurements and measured temperature logs. The new data were calculated from temperature gradient information and estimating a mean con-ductivity value characteristic for the type of rock present in the stratigraphic column or assigning the mean conductivity value for the crust. Geothermal gradient and the thermal resistivity (inverse thermal conductivity) were plotted and heat flow was calculatedusing the Bullard method. The map covers the whole continental territory of Mexico and shows that most of the country has valueshigher than the world average. The highest heat flow values are concentrated in two provinces: the Gulf of California extensionalprovince and the Trans-Mexican Volcanic Belt.
Space Debris: Are Deep Learning-based Image Enhancements part of the Solution?

The volume of space debris currently orbiting the Earth is reaching an unsustainable level at an accelerated pace. The detection, tracking, identification, and differentiation between orbit-defined, registered spacecraft, and rogue/inactive space ``objects'', is critical to asset protection. The primary objective of this work is to investigate the validity of Deep Neural Network (DNN) solutions to overcome the limitations and image artefacts most prevalent when captured with monocular cameras in the visible light spectrum. In this work, a hybrid UNet-ResNet34 Deep Learning (DL) architecture pre-trained on the ImageNet dataset, is developed. Image degradations addressed include blurring, exposure issues, poor contrast, and noise. The shortage of space-generated data suitable for supervised DL is also addressed. A visual comparison between the URes34P model developed in this work and the existing state of the art in deep learning image enhancement methods, relevant to images captured in space, is presented. Based upon visual inspection, it is determined that our UNet model is capable of correcting for space-related image degradations and merits further investigation to reduce its computational complexity.
DAVIS: High-Quality Audio-Visual Separation with Generative Diffusion Models

We propose DAVIS, a Diffusion model-based Audio-VIusal Separation framework that solves the audio-visual sound source separation task through a generative manner. While existing discriminative methods that perform mask regression have made remarkable progress in this field, they face limitations in capturing the complex data distribution required for high-quality separation of sounds from diverse categories. In contrast, DAVIS leverages a generative diffusion model and a Separation U-Net to synthesize separated magnitudes starting from Gaussian noises, conditioned on both the audio mixture and the visual footage. With its generative objective, DAVIS is better suited to achieving the goal of high-quality sound separation across diverse categories. We compare DAVIS to existing state-of-the-art discriminative audio-visual separation methods on the domain-specific MUSIC dataset and the open-domain AVE dataset, and results show that DAVIS outperforms other methods in separation quality, demonstrating the advantages of our framework for tackling the audio-visual source separation task.
Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review

The advent of deep learning has brought a revolutionary transformation to image denoising techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised methods in real-world scenarios remains formidable, necessitating the exploration of more practical self-supervised image denoising. This paper focuses on self-supervised image denoising methods that offer effective solutions to address this challenge. Our comprehensive review thoroughly analyzes the latest advancements in self-supervised image denoising approaches, categorizing them into three distinct classes: General methods, Blind Spot Network (BSN)-based methods, and Transformer-based methods. For each class, we provide a concise theoretical analysis along with their practical applications. To assess the effectiveness of these methods, we present both quantitative and qualitative experimental results on various datasets, utilizing classical algorithms as benchmarks. Additionally, we critically discuss the current limitations of these methods and propose promising directions for future research. By offering a detailed overview of recent developments in self-supervised image denoising, this review serves as an invaluable resource for researchers and practitioners in the field, facilitating a deeper understanding of this emerging domain and inspiring further advancements.
simcadi: Similarity Indices for Categorical Distributions

In this article we introduce the Stata command simcadi. It helps to calculate indicators for the similarity of categorically ordered variables and distribution, respectively. In particular, it permits the calculation of the Cosine index, and indices introduced by Finger and Kreinin (1979), Bray and Curtis (1957), Dice (1945), Sorenson (1948), Jaccard (1912), Grubel and Lloyd (1971), Ruzicka (1958), and Gower (1971). Moreover, it allows us to compute the development of a distribution over time. The command offers various options for an efficient handling of datasets, because it permits the calculation of benchmarks of comparison automatically, and the incoporation of complex weighting schemes.
Hovering Control of Flapping Wings in Tandem with Multi-Rotors

This work briefly covers our efforts to stabilize the flight dynamics of Northeatern's tailless bat-inspired micro aerial vehicle, Aerobat. Flapping robots are not new. A plethora of examples is mainly dominated by insect-style design paradigms that are passively stable. However, Aerobat, in addition for being tailless, possesses morphing wings that add to the inherent complexity of flight control. The robot can dynamically adjust its wing platform configurations during gaitcycles, increasing its efficiency and agility. We employ a guard design with manifold small thrusters to stabilize Aerobat's position and orientation in hovering, a flapping system in tandem with a multi-rotor. For flight control purposes, we take an approach based on assuming the guard cannot observe Aeroat's states. Then, we propose an observer to estimate the unknown states of the guard which are then used for closed-loop hovering control of the Guard-Aerobat platform.
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches

Autonomous flying robots, such as multirotors, often rely on deep learning models that make predictions based on a camera image, e.g. for pose estimation. These models can predict surprising results if applied to input images outside the training domain. This fault can be exploited by adversarial attacks, for example, by computing small images, so-called adversarial patches, that can be placed in the environment to manipulate the neural network’s prediction. We introduce flying adversarial patches, where multiple images are mounted on at least one other flying robot and therefore can be placed anywhere in the field of view of a victim multirotor. By introducing the attacker robots, the system is extended to an adversarial multi-robot system. For an effective attack, we compare three methods that simultaneously optimize multiple adversarial patches and their position in the input image. We show that our methods scale well with the number of adversarial patches. Moreover, we demonstrate physical flights with two robots, where we employ a novel attack policy that uses the computed adversarial patches to kidnap a robot that was supposed to follow a human.
Theoretical analysis of nBn infrared photodetectors

Abstract. The depletion and surface leakage dark current suppression properties of unipolar barrier device architectures such as the nBn have been highly beneficial for III–V semiconductor-based infrared detectors. Using a one-dimensional drift-diffusion model, we theoretically examine the effects of contact doping, minority carrier lifetime, and absorber doping on the dark current characteristics of nBn detectors to explore some basic aspects of their operation. We found that in a properly designed nBn detector with highly doped excluding contacts the minority carriers are extracted to nonequilibrium levels under reverse bias in the same manner as the high operating temperature (HOT) detector structure. Longer absorber Shockley–Read–Hall (SRH) lifetimes result in lower diffusion and depletion dark currents. Higher absorber doping can also lead to lower diffusion and depletion dark currents, but the benefit should be weighted against the possibility of reduced diffusion length due to shortened SRH lifetime. We also briefly examined nBn structures with unintended minority carrier blocking barriers due to excessive n-doping in the unipolar electron barrier, or due to a positive valence band offset between the barrier and the absorber. Both types of hole blocking structures lead to higher turn-on bias, although barrier n-doping could help suppress depletion dark current.
Influence of Utterance and Speaker Characteristics on the Classification of Children with Cleft Lip and Palate

Recent findings show that pre-trained wav2vec 2.0 models are reliable feature extractors for various speaker characteristics classification tasks. We show that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be used as features for binary classification to distinguish between children with Cleft Lip and Palate (CLP) and a healthy control group. The results indicate that the distinction between CLP and healthy voices, especially with latent representations from the lower and middle encoder layers, reaches an accuracy of 100%. We test the classifier to find influencing factors for classification using unseen out-of-domain healthy and pathologic corpora with varying characteristics: age, spoken content, and acoustic conditions. Cross-pathology and cross-healthy tests reveal that the trained classifiers are unreliable if there is a mismatch between training and out-of-domain test data in, e.g., age, spoken content, or acoustic conditions.
Contact feedback helps snake robots propel against uneven terrain using vertical bending

Snakes can bend their elongate bodies in various forms to traverse various environments. We understand well how snakes use lateral body bending to push against asperities on flat ground for propulsion, and snake robots can do so effectively. However, snakes can also use vertical bending to push against uneven terrain of large height variation for propulsion, and they can adjust this bending to adapt to novel terrain presumably using mechano-sensing feedback control. Although some snake robots can traverse uneven terrain, few have used vertical bending for propulsion, and how to control this process in novel environments is poorly understood. Here we systematically studied a snake robot with force sensors pushing against large bumps using vertical bending to understand the role of sensory feedback control. We compared a feedforward controller and four feedback controllers that use different sensory information and generate distinct bending patterns and body-terrain interaction. We challenged the robot with increasing backward load and novel terrain geometry that break its contact with the terrain. We further varied how much the feedback control modulated body bending to conform to or push against the terrain to test their effects. Feedforward propagation of vertical bending generated large propulsion when the bending shape matched terrain geometry. However, when perturbations caused loss of contact, the robot easily lost propulsion or had motor overload. Contact feedback control resolved these issues by helping the robot regain contact. Yet excessive conformation interrupted shape propagation and excessive pushing stalled motors frequently. Unlike that using lateral bending, for propulsion generation using vertical bending, body weight that can help maintain contact with the environment but may also overload motors. Our results will help snake robots better traverse uneven terrain with large height variation and can inform how snakes use sensory feedback to control vertical body bending for propulsion.