Miracle-dz/UniFAR-FTUs huggingface dataset:
TinySurveillance: A Low-power Event-based Surveillance Method for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have always been faced with power management challenges. Managing power consumption becomes critical, especially in surveillance applications where the longer flight time results in wider coverage and a cheaper solution. While most current studies focus on utilizing new models for improving event detection without considering the power constraints, our design’s first priority is our platform’s power efficiency. Implementing an algorithm on a portable device with minimal access to power supply sources requires special hardware and software considerations. An improved algorithm may need more powerful hardware, which can surge power consumption. Therefore, we aim to propose a method to be suitable for such devices with power consumption constraints. In this work, we propose an event-driven surveillance method with an efficient video transmission algorithm that reduces power consumption while preserving image quality. The surveillance will start automatically once the low-power AI-based onboard processor detects the desired event. The drone repeatedly solves a classification problem by employing a lightweight deep learning algorithm. When the UAV detects the defined event, a sample image is sent to the server for validation. Afterwards, if the server validates the drone decision, the drone, which can be a UAV, starts sending a colored image accompanied by a group of N grayscale images. Then, in the server, the grayscale images will be colorized using a convolution neural network trained by the colored images. By adopting this method, the sent data rate decreases and the server’s computation load increases. The former part results in a drop in the UAV’s power consumption, which is our aim. In this work, an application of wildfire detection and surveillance has been implemented to show the proof of concept of the TinySurveillance method. Using four videos of similar scenarios with different spatial and temporal information that a UAV may face, with various spatial and temporal characteristics, we show the effectiveness of our method. Our results show that the power consumption of the onboard processing unit in detection mode will be reduced by at least 4 times, reaching a detection accuracy of 85%, while in surveillance mode, we can decrease the data transmission rate by almost 66% while achieving a competent image quality with PSNRAvg of 41.35 dB, PSNR of 30.94 dB, and output frame rate of 5.2. Also, the reproduced images show the outstanding performance of the algorithm by generating colorized images identical to the original scenes. There are main features that affect our method’s power consumption and output quality, like the number of grayscale images, sent video bitrate, learning rate, and video characteristics that are discussed comprehensively.
Assessing and dissociating virtues from the ‘bottom up’: A case study of generosity vs. fairness

ABSTRACT The empirical study of virtue is plagued by imprecise definitions and assessment. Here we propose a three-stage, data-driven (‘bottom-up’) method to differentiate lay perceptions of virtues. Employing two virtues – generosity (as cooperation) and fairness (as impartiality) – as a case study, we present findings utilizing data from three studies (total N = 2,667). First, natural language processing of free-response data indicated that participants used different ‘topics’ (i.e. clusters of words) to describe behaviours representing generosity (topics: ‘charity’ and ‘kindness’) and fairness (‘equality’). Second, participants in a survey experiment rated behaviours expressing generosity and fairness differently across 6 out of 9 underlying features measured. Third, participants perceive that actors in vignette-based experiments engaging in behaviours expressing generosity versus fairness were motivated differently on 5 out of 6 motivations measured. Our findings support the distinction of the virtues of generosity (as cooperation) and fairness (as impartiality) and indicate the utility of our bottom-up method for assessing and distinguishing virtues.
Harmonic Analysis: Real-Variable Methods, Orthogonality, and Oscillatory Integrals

PrefaceGuide to the ReaderPrologue3IReal-Variable Theory7IIMore About Maximal Functions49IIIHardy Spaces87IVH[superscript 1] and BMO139VWeighted Inequalities193VIPseudo-Differential and Singular Integral Operators: Fourier Transform228VIIPseudo-Differential and Singular Integral Operators: Almost Orthogonality269VIIIOscillatory Integrals of the First Kind329IXOscillatory Integrals of the Second Kind375XMaximal Operators: Some Examples433XIMaximal Averages and Oscillatory Integrals467XIIIntroduction to the Heisenberg Group527XIIIMore About the Heisenberg Group587Bibliography645Author Index679Subject Index685
East‐west asymmetry in coronal mass ejection geoeffectiveness

This paper extends the domain of applicability of the Gosling‐McComas space weather forecast rule that applies to the postshock sheaths of fast coronal mass ejections at Earth (ICMEs). The rule is based on the draping of the sheath magnetic field around the ICME body. The original treatment considered only the radial‐from‐the‐Sun component of the preshock interplanetary magnetic field (IMF), which implied that the domain of applicability of the rule was the entire sheath region ahead of the leading face of the ICME. We show here that because of the generally prevailing Parker spiral orientation of the IMF, the domain of applicability of the rule is instead generally strongly shifted to the east side of the ICME sheath. We suggest that the eastward shift of the domain of applicability of the rule accounts for an observed greater geoeffectiveness of west hemisphere CMEs compared with east hemisphere CMEs. The approach used here to demonstrate the eastward shift of the region of potential ICME sheath geoeffectiveness, and thus to increase the accuracy of the forecast rule, is to present intensity contours of the geoeffective draping component of the IMF as computed by global MHD simulations. Since the shift depends only on a spiral magnetic field and a blunt object to drape it around, we demonstrate the generality of the principle on which the rule is based by treating both the case of the ICME and the case of Earth's magnetosphere.
Cosmic-ray acceleration at collisionless astrophysical shocks using Monte-Carlo simulations

Context. The di usive shock acceleration mechanism has been widely accepted as the acceleration mechanism for galactic cosmic rays. While self-consistent hybrid simulations have shown how power-law spectra are produced, detailed information on the interplay of di usive particle motion and the turbulent electromagnetic fields responsible for repeated shock crossings are still elusive. Aims. The framework of test-particle theory is applied to investigate the e ect of di usive shock acceleration by inspecting the obtained cosmic-ray energy spectra. The resulting energy spectra can be obtained this way from the particle motion and, depending on the prescribed turbulence model, the influence of stochastic acceleration through plasma waves can be studied. Methods. A numerical Monte-Carlo simulation code is extended to include collisionless shock waves. This allows one to trace the trajectories of test particle while they are being accelerated. In addition, the di usion coe cients can be obtained directly from the particle motion, which allows for a detailed understanding of the acceleration process. Results. The classic result of an energy spectrum with E 2 is only reproduced for parallel shocks, while, for all other cases, the energy spectral index is reduced depending on the shock obliqueness. Qualitatively, this can be explained in terms of the di usion coe cients in the directions that are parallel and perpendicular to the shock front.
MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques

The popularity of Windows attracts the attention of hackers/cyber-attackers, making Windows devices the primary target of malware attacks in recent years. Several sophisticated malware variants and anti-detection methods have been significantly enhanced and as a result, traditional malware detection techniques have become less effective. This work presents MalBehavD-V1, a new behavioural dataset of Windows Application Programming Interface (API) calls extracted from benign and malware executable files using the dynamic analysis approach. In addition, we present MalDetConV, a new automated behaviour-based framework for detecting both existing and zero-day malware attacks. MalDetConv uses a text processing-based encoder to transform features of API calls into a suitable format supported by deep learning models. It then uses a hybrid of convolutional neural network (CNN) and bidirectional gated recurrent unit (CNN-BiGRU) automatic feature extractor to select high-level features of the API Calls which are then fed to a fully connected neural network module for malware classification. MalDetConv also uses an explainable component that reveals features that contributed to the final classification outcome, helping the decision-making process for security analysts. The performance of the proposed framework is evaluated using our MalBehavD-V1 dataset and other benchmark datasets. The detection results demonstrate the effectiveness of MalDetConv over the state-of-the-art techniques with detection accuracy of 96.10%, 95.73%, 98.18%, and 99.93% achieved while detecting unseen malware from MalBehavD-V1, Allan and John, Brazilian, and Ki-D datasets, respectively. The experimental results show that MalDetConv is highly accurate in detecting both known and zero-day malware attacks on Windows devices.
Plane-wave and wave-packet neutrino flavor oscillations in vacuum in conformal coupling models

We investigate neutrino flavor oscillations in vacuum within a general conformal coupling model. We first examine the flavor oscillations within the plane-wave description of neutrinos, and then we extend our analysis to the wavepacket-based formalism. In both cases, we derive the general formulas for the flavor transition probability in arbitrary static and spherically symmetric spacetimes. We thoroughly discuss and assess in both cases the different possible ways -- dictated by the presence of the conformal coupling -- of computing the flavor transition probability. We show that the conformal invariance of the Dirac equation implies that the effect of conformal coupling on neutrino flavor oscillations modifies the oscillation length, but preserves the coherence length and unitarity. A detailed application to two-flavor neutrinos is then made and a numerical analysis is conducted within the well-known chameleon conformal coupling model.
Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy.
Lightweight Long-Range Generative Adversarial Networks

In this paper, we introduce novel lightweight generative adversarial networks, which can effectively capture long-range dependencies in the image generation process, and produce high-quality results with a much simpler architecture. To achieve this, we first introduce a long-range module, allowing the network to dynamically adjust the number of focused sampling pixels and to also augment sampling locations. Thus, it can break the limitation of the fixed geometric structure of the convolution operator, and capture long-range dependencies in both spatial and channel-wise directions. Also, the proposed long-range module can highlight negative relations between pixels, working as a regularization to stabilize training. Furthermore, we propose a new generation strategy through which we introduce metadata into the image generation process to provide basic information about target images, which can stabilize and speed up the training process. Our novel long-range module only introduces few additional parameters and is easily inserted into existing models to capture long-range dependencies. Extensive experiments demonstrate the competitive performance of our method with a lightweight architecture.
Aerial View Goal Localization with Reinforcement Learning

With an increased amount and availability of unmanned aerial vehicles (UAVs) and other remote sensing devices (e.g. satellites), we have recently seen a vast increase in computer vision methods for aerial view data. One application of such technologies is within search-and-rescue (SAR), where the task is to localize and assist one or several people who are missing, for example after a natural disaster. In many cases the rough location may be known and a UAV can be deployed to explore a given, conﬁned area to precisely localize the missing people. Due to time and battery constraints it is often critical that localization is performed as efﬁciently as possible. In this work, we approach this type of problem by abstracting it as an aerial view goal localization task in a framework that emulates a SAR-like setup without requiring access to actual UAVs. In this framework, an agent operates on top of an aerial image (proxy for a search area) and is tasked with localizing a goal that is described in terms of visual cues. To further mimic the situation on an actual UAV, the agent is not able to observe the search area in its entirety, not even at low resolution, and thus it has to operate solely based on partial glimpses when navigating towards the goal. To tackle this task, we propose AiRLoc , a reinforcement learning (RL)-based model that decouples exploration (searching for distant goals) and exploitation (localizing nearby goals). Extensive evaluations show that AiRLoc outperforms heuristic search methods as well as alternative learnable approaches. We also conduct a proof-of-concept study which indicates that the learnable methods outperform humans on average. Code has been made publicly available: https://github.com/aleksispi/airloc .
Few-mode to mesoscopic junctions in gatemon qubits

We investigate a semiconductor nanowire-based gatemon qubit with epitaxial Al on two facets of the nanowire, allowing gate control of wire density. Two segments have the Al removed, one forming a Josephson junction and the other operating as a transistor, providing in-situ switching between dc transport and qubit operation. Gating the NW changes the bulk wire potential distribution, while gating the Josephson junction changes the number of junction modes. Both effects are revealed by the dependence of qubit frequency on parallel magnetic field. A detailed model of the wire and junction yields behavior consistent with experiment. In the multi-mode regime, fluctuations in qubit frequency are considerably smaller than the theoretical"universal"value, also smaller than numerics, and consistent with previous measurements of fluctuating critical current.
ScGAN: a generative adversarial network to predict hypothetical superconductors

Despite having been discovered more than three decades ago, high temperature superconductors (HTSs) lack both an explanation for their mechanisms and a systematic way to search for them. To aid this search, this project proposes ScGAN, a generative adversarial network (GAN) to efficiently predict new superconductors. ScGAN was trained on compounds in Open Quantum Materials Database and then transfer learned onto the SuperCon database or a subset of it. Once trained, the GAN was used to predict superconducting candidates, and approximately 70% of them were determined to be superconducting by a classification model–a 23-fold increase in discovery rate compared to manual search methods. Furthermore, more than 99% of predictions were novel materials, demonstrating that ScGAN was able to potentially predict completely new superconductors, including several promising HTS candidates. This project presents a novel, efficient way to search for new superconductors, which may be used in technological applications or provide insight into the unsolved problem of high temperature superconductivity.
Pointwise convergence of Haar type wavelets on self-similar sets

This thesis consists of an introduction and a summary, followed by two papers, both of them on the topic of function spaces on fractals. Paper I: Andreas Brodin, Pointwise Convergence of Haar type Wavelets on Self-Similar Sets, Manuscript. Paper II: Andreas Brodin, Regularization of Wavelet Expansion characterizes Besov Spaces on Fractals, Manuscript. Properties of wavelets, originally constructed by A. Jonsson, are studied in both papers. The wavelets are piecewise polynomial functions on self-similar fractal sets. In Paper I, pointwise convergence of partial sums of the wavelet expansion is investigated. On a specific fractal set, the Sierpinski gasket, pointwise convergence of the partial sums is shown by calculating the kernel explicitly, when the wavelets are piecewise constant functions. For more general self-similar fractals, pointwise convergence of the partial sums and their derivatives, in case the expanded function has higher regularity, is shown using a different technique based on Markov's inequality. A. Jonsson has shown that on a class of totally disconnected self-similar sets it is possible to characterize Besov spaces by means of the magnitude of the coefficients in the wavelet expansion of a function. M. Bodin has extended his results to a class of graph directed self-similar sets introduced by Mauldin and Williams. Unfortunately, these results only holds for fractals such that the sets in the first generation of the fractal are disjoint. In Paper II we are able to characterize Besov spaces on a class of fractals not necessarily sharing this condition by making the wavelet expansion smooth. We create continuous regularizations of the partial sums of the wavelet expansion and show that properties of these regularizations can be used to characterize Besov spaces.
Line absorption of He-like triplet lines by Li-like ions. Caveats of using line ratios of triplets for plasma diagnostics

He-like ions produce distinctive series of triplet lines under various astrophysical conditions. However, this emission can be affected by line absorption from Li-like ions in the same medium. We investigate this absorption of He-like triplets and present the implications for diagnostics of plasmas in photoionisation equilibrium using the line ratios of the triplets. Our computations were carried out for the O VI and Fe XXIV absorption of the O VII and Fe XXV triplet emission lines, respectively. The fluorescent emission by the Li-like ions and continuum absorption of the He-like ion triplet lines are also investigated. We determine the absorption of the triplet lines as a function of Li-like ion column density and velocity dispersion of the emitting and absorbing medium. We find O VI line absorption can significantly alter the O VII triplet line ratios in optically-thin plasmas, by primarily absorbing the intercombination lines, and to a lesser extent, the forbidden line. Because of intrinsic line absorption by O VI inside a photoionised plasma, the predicted ratio of forbidden to intercombination line intensity for the O VII triplet increases from 4 up to an upper limit of 16. This process can explain the triplet line ratios that are higher than expected and that are seen in some X-ray observations of photoionised plasmas. For the Fe XXV triplet, line absorption by Fe XXIV becomes less apparent owing to significant fluorescent emission by Fe XXIV. Without taking the associated Li-like ion line absorption into account, the density diagnosis of photoionised plasmas using the observed line ratios of the He-like ion triplet emission lines can be unreliable, especially for low-Z ions.
Data Feedback Loops: Model-driven Amplification of Dataset Biases

Datasets scraped from the internet have been critical to the successes of large-scale machine learning. Yet, this very success puts the utility of future internet-derived datasets at potential risk, as model outputs begin to replace human annotations as a source of supervision. In this work, we first formalize a system where interactions with one model are recorded as history and scraped as training data in the future. We then analyze its stability over time by tracking changes to a test-time bias statistic (e.g. gender bias of model predictions). We find that the degree of bias amplification is closely linked to whether the model's outputs behave like samples from the training distribution, a behavior which we characterize and define as consistent calibration. Experiments in three conditional prediction scenarios - image classification, visual role-labeling, and language generation - demonstrate that models that exhibit a sampling-like behavior are more calibrated and thus more stable. Based on this insight, we propose an intervention to help calibrate and stabilize unstable feedback systems. Code is available at https://github.com/rtaori/data_feedback.
Taking Advice from (Dis)Similar Machines: The Impact of Human-Machine Similarity on Machine-Assisted Decision-Making

Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. While neither the algorithm nor the human are perfectly accurate, one could expect that their complementary expertise might lead to improved outcomes. In this study, we demonstrate that in practice decision aids that are not complementary, but make errors similar to human ones may have their own benefits. In a series of human-subject experiments with a total of 901 participants, we study how the similarity of human and machine errors influences human perceptions of and interactions with algorithmic decision aids. We find that (i) people perceive more similar decision aids as more useful, accurate, and predictable, and that (ii) people are more likely to take opposing advice from more similar decision aids, while (iii) decision aids that are less similar to humans have more opportunities to provide opposing advice, resulting in a higher influence on people’s decisions overall.