11,356 research outputs found

    Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

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    The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

    The future of cosmology? A case for CMB spectral distortions

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    This thesis treats the topic of CMB Spectral Distortions (SDs), which represent any deviation from a pure black body shape of the CMB energy spectrum. As such, they can be used to probe the inflationary, expansion and thermal evolution of the universe both within Λ\LambdaCDM and beyond it. The currently missing observation of this rich probe of the universe makes of it an ideal target for future observational campaigns. In fact, while the Λ\LambdaCDM signal guarantees a discovery, the sensitivity to a wide variety of new physics opens the door to an enormous uncharted territory. In light of these considerations, the thesis opens by reviewing the topic of CMB SDs in a pedagogical and illustrative fashion, aimed at waking the interest of the broader community. This introductory premise sets the stage for the first main contribution of the thesis to the field of SDs: their implementation in the Boltzmann solver CLASS and the parameter inference code MontePython. The CLASS+MontePython pipeline is publicly available, fast, it includes all sources of SDs within Λ\LambdaCDM and many others beyond that, and allows to consistently account for any observational setup. By means of these numerical tools, the second main contribution of the thesis consists in showcasing the versatility and competitiveness of SDs for several cosmological models as well as for a number of different mission designs. Among others, the results cover features in the primordial power spectrum, primordial gravitational waves, non-standard dark matter properties, primordial black holes, primordial magnetic fields and Hubble tension. Finally, the manuscript is disseminated with (20) follow-up ideas that naturally extend the work carried out so far, highlighting how rich of unexplored possibilities the field of CMB SDs still is. The hope is that these suggestions will become a propeller for further interesting developments.Comment: PhD thesis. Pedagogical review of theory, experimental status and numerical tools (CLASS+MontePython) with broad overview of applications. Includes 20 original follow-up idea

    How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

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    Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks. While they provide high-quality and diverse samples from empirical distributions, important questions remain on the reliability and trustworthiness of these sampling procedures for their responsible use in critical scenarios. Conformal prediction is a modern tool to construct finite-sample, distribution-free uncertainty guarantees for any black-box predictor. In this work, we focus on image-to-image regression tasks and we present a generalization of the Risk-Controlling Prediction Sets (RCPS) procedure, that we term KK-RCPS, which allows to (i)(i) provide entrywise calibrated intervals for future samples of any diffusion model, and (ii)(ii) control a certain notion of risk with respect to a ground truth image with minimal mean interval length. Differently from existing conformal risk control procedures, ours relies on a novel convex optimization approach that allows for multidimensional risk control while provably minimizing the mean interval length. We illustrate our approach on two real-world image denoising problems: on natural images of faces as well as on computed tomography (CT) scans of the abdomen, demonstrating state of the art performance

    Quantum coherent manipulation of spin information in molecular nanomagnets

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    Los sistemas cuánticos de dos niveles basados en estados de espín, conocidos como ``qubits de espín'', son bloques prometedores para el desarrollo de tecnologías cuánticas. Entre las distintas plataformas físicas, los qubits de espín definidos en imanes de molécula única (SMM) son candidatos prometedores porque su estructura electrónica puede ajustarse fácilmente mediante ingeniería química (es decir, el Hamiltoniano de espín molecular puede modificarse con facilidad). Sin embargo, los qubits moleculares de espín generados en SMM se enfrentan a varios retos: coherencia cuántica frágil, control coherente insuficiente de los estados de espín y generación de entrelazamiento entre los qubits de espín para aplicaciones de procesamiento de información cuántica. Para abordar estos retos y lograr la manipulación coherente de la información de espín, necesitamos comprender la relación entre los estados de espín, los movimientos moleculares (vibraciones o fonones) y la polarización de la carga (por ejemplo, la generada por un campo E externo). La presente Tesis explora la relación entre los estados de espín, las vibraciones y la polarización desde una perspectiva teórica. Inicialmente, estudiamos la interacción entre los estados de espín y las vibraciones (acoplamiento vibrónico) como una fuente importante de disipación de información de espín. En particular, se emplea un modelado detallado de los acoplamientos vibrónicos, apoyado por pruebas experimentales, para descifrar las vías de decoherencia en diferentes SMM. Nuestros resultados revelan que sólo algunas distorsiones moleculares asociadas a determinados modos vibracionales son capaces de acoplarse fuertemente a grados de libertad de espín y, por tanto, promover la decoherencia. Además, también identificamos que los espectros dispersos entre los estados de espín y fonón son cruciales para preservar las superposiciones cuánticas durante más tiempo. En segundo lugar, presentamos un estudio exhaustivo del control coherente de los estados de espín mediante campos eléctricos en un sistema qubit molecular que presenta transiciones de reloj (HoW10). Este control coherente se modela evaluando el acoplamiento espín-eléctrico (SEC); es decir, encontrando una relación entre los estados de espín, la polarización de la carga y las distorsiones moleculares. El fuerte SEC observado en HoW10 es suficiente para permitir el direccionamiento selectivo de los espines mediante un campo E local a nivel práctico. Por último, exploramos la posibilidad de construir una puerta de entrelazamiento de dos qubits en un par de dos reloj-qubit acoplados dipolarmente (HoW10--HoW10), donde el campo eléctrico se utiliza para controlar localmente los estados de los qubits. El trabajo presentado en esta Tesis avanza en la comprensión de los qubits de espín moleculares para su potencial aplicación en el procesamiento cuántico de la información.Quantum two-level systems based on spin states known as ``spin-qubits’’ are promising building blocks for the development of quantum technologies. Among different physical platforms, spin-qubits defined in single-molecule-magnets (SMMs) are promising candidates because their electronic structure can be easily tuned by chemical engineering (i.e., the molecular spin Hamiltonian can be easily modified). However, molecular spin qubits generated in SMMs faces several challenges: fragile quantum coherence, insufficient coherent control over spin states and generation of entanglement between the spin-qubits for quantum information processing applications. To address these challenges and achieve the coherent manipulation of spin information, we need to understand the relationship between spin states, molecular motions (vibrations or phonons) and charge polarization (e.g., that generated by an external E-field). The current Thesis explores the relationship between spin states, vibrations and polarization from a theoretical perspective. Initially, we study the interaction between spin states and vibrations (vibronic coupling) as an important source of spin information dissipation. In particular, a detailed modelling of vibronic couplings, supported by experimental evidence, is employed to decipher the decoherence pathways in different SMMs. Our outcomes reveal that only some molecular distortions associated to certain vibrational modes are able to strongly couple to spin degrees of freedom and, thus, promoting decoherence. Additionally, we also identified that sparse spectra between spin and phonon states are crucial to preserve quantum superpositions longer times. Secondly, we present a comprehensive study of coherent control over spin states using electrical fields in a molecular qubit system that exhibits clock transitions (HoW10). This coherent control is modelled by evaluating the spin-electric coupling (SEC); that is, finding a relation between spin states, charge polarization, and molecular distortions. The strong SEC observed in HoW10 is sufficient to allow selective addressing of the spins using a local E-field at practical level. Finally, we explore the possibility of constructing two-qubit entanglement gate in a pair of two dipolar-coupled clock-qubit (HoW10--HoW10), where electrical field is used to locally control the qubit states. The work presented in this Thesis advances the understanding of molecular spin qubits for their potential application in quantum information processing

    A review of abnormal behavior detection in activities of daily living

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    Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

    Resonant and polarization effects in the processes of quantum electrodynamics in a strong magnetic field

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    The monograph considers resonance and polarization effects in quantum electrodynamics processes that take place in a strong external magnetic field. A method for analyzing spin-polarization effects has been developed. The factorization of process cross sections in resonant conditions and the representation of these cross sections in the form of Breit-Wigner are considered. The possibility of testing these effects in modern international projects to test quantum electrodynamics in strong fields is shown.Comment: 283 pages, 24 figures, monograp

    Modified Theories of Gravity and Cosmological Applications

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    This reprint focuses on recent aspects of gravitational theory and cosmology. It contains subjects of particular interest for modified gravity theories and applications to cosmology, special attention is given to Einstein–Gauss–Bonnet, f(R)-gravity, anisotropic inflation, extra dimension theories of gravity, black holes, dark energy, Palatini gravity, anisotropic spacetime, Einstein–Finsler gravity, off-diagonal cosmological solutions, Hawking-temperature and scalar-tensor-vector theories

    Edge-resolved non-line-of-sight imaging

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    Over the past decade, the possibility of forming images of objects hidden from line-of-sight (LOS) view has emerged as an intriguing and potentially important expansion of computational imaging and computer vision technology. This capability could help soldiers anticipate danger in a tunnel system, autonomous vehicles avoid collision, and first responders safely traverse a building. In many scenarios where non-line-of-sight (NLOS) vision is desired, the LOS view is obstructed by a wall with a vertical edge. In this thesis we show that through modeling and computation, the impediment to LOS itself can be exploited for enhanced resolution of the hidden scene. NLOS methods may be active, where controlled illumination of the hidden scene is used, or passive, relying only on already present light sources. In both active and passive NLOS imaging, measured light returns to the sensor after multiple diffuse bounces. Each bounce scatters light in all directions, eliminating directional information. When the scene is hidden behind a wall with a vertical edge, that edge occludes light as a function of its incident azimuthal angle around the edge. Measurements acquired on the floor adjacent to the occluding edge thus contain rich azimuthal information about the hidden scene. In this thesis, we explore several edge-resolved NLOS imaging systems that exploit the occlusion provided by a vertical edge. In addition to demonstrating novel edge-resolved NLOS imaging systems with real experimental data, this thesis includes modeling, performance bound analyses, and inversion algorithms for the proposed systems. We first explore the use of a single vertical edge to form a 1D (in azimuthal angle) reconstruction of the hidden scene. Prior work demonstrated that temporal variation in a video of the floor may be used to image moving components of the hidden scene. In contrast, our algorithm reconstructs both moving and stationary hidden scenery from a single photograph, without assuming uniform floor albedo. We derive a forward model that describes the measured photograph as a nonlinear combination of the unknown floor albedo and the light from behind the wall. The inverse problem, which is the joint estimation of floor albedo and a 1D reconstruction of the hidden scene, is solved via optimization, where we introduce regularizers that help separate light variations in the measured photograph due to floor pattern and hidden scene, respectively. Next, we combine the resolving power of a vertical edge with information from the relationship between intensity and radial distance to form 2D reconstructions from a single passive photograph. We derive a new forward model, accounting for radial falloff, and propose two inversion algorithms to form 2D reconstructions from a single photograph of the penumbra. The performances of both algorithms are demonstrated on experimental data corresponding to several different hidden scene configurations. A Cramer-Rao bound analysis further demonstrates the feasibility and limitations of this 2D corner camera. Our doorway camera exploits the occlusion provided by the two vertical edges of a doorway for more robust 2D reconstruction of the hidden scene. This work provides and demonstrates a novel inversion algorithm to jointly estimate two views of change in the hidden scene, using the temporal difference between photographs acquired on the visible side of the doorway. A Cramer-Rao bound analysis is used to demonstrate the 2D resolving power of the doorway camera over other passive acquisition strategies and to motivate the novel biangular reconstruction grid. Lastly, we present the active corner camera. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches are inherently limited by the need for laser scanning, a process that is generally too slow to image hidden objects in motion. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a `map’ of the stationary scenery behind them. This new ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene could greatly improve indoor situational awareness in a variety of applications

    iid2022: a workshop on statistical methods for event data in astronomy

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    We review the iid2022 workshop on statistical methods for X-ray and γ-ray astronomy and high–energy astrophysics event data in astronomy, held in Guntersville, AL, on Nov. 15–18 2022. New methods for faint source detection, spatial point processes, variability and spectral analysis, and machine learning are discussed. Ideas for future developments of advanced methodology are shared

    Identification and Quantification of Sperm Head Plasma Membrane Proteins Associated with Male Fertility

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    The major objective was to characterize proteins in head plasma membrane (HPM) of sperm from animals of two species to identify species’ and proteins’ differences related to fertility. HPM’s sodium/potassium-ATPase (Na+⁄K+-ATPase) acts as a receptor, inducing capacitation when bound by its hormone ouabain. Na+/K+-ATPase is an α/β dimer, each with several isoforms (α1, α2, α3, α4, β1, β2, β3) whose exact relationship to in vivo fertility and capacitation is unknown. In the first study, specific Na+/K+-ATPase isoforms in sperm HPM of boars with different Direct Boar Effects (DBEs) for farrowing rate (FR) and litter size, differed between low and high fertility boars (LF, HF, n=6/each; DBE-based). SDS-PAGE and immunoblotting detected more α3 (P or <100; Semex evaluated). Statistical analysis identified 67 differential abundance proteins (DAPs) between HF and LF (n=3/group; P<0.05), which associated by meta-analysis to BFI. Gene ontology assigned 48 up-regulated HF proteins to sperm fertilization, and 19 down-regulated to catalytic and transporter activity. 38-up-regulated DAPs (HF and LF, n=16) correlated positively (r2=0.29 to 0.66; P≤0.05) and 6 down-regulated negatively (r2=0.26 to 0.44; P≤0.05) to BFI. The third study characterized HPM Na+/K+-ATPase in 16 bulls with differing BFI but similar sperm motility kinetics. Normalized Spectral Abundance Factor (NSAF) of α1 was significantly greater in 8 higher- vs 8 lower-fertility bulls. Linear regression positively correlated BFI to NSAF of α1 and β2 (r2=0.42 and 0.47, respectively; P≤0.05), and negatively correlated BFI to α4 (r2=0.37; P≤0.05), confirmed by bioinformatics predictions. These results suggest involvement of α1 and β2 in fertilization as potential fertility biomarkers. Overall, specific Na+/K+-ATPase isoforms identified in boar and bull sperm HPM significantly correlate with in vivo fertility, as do other specific bull HPM proteins. Elucidating potential fertility biomarkers in two species improves understanding of key proteins and their roles in various, complex mechanisms that enable successful sperm fertilization
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