1,653 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Exploring topological phonons in different length scales: microtubules and acoustic metamaterials

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    The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales. Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the existence of edge modes within the energy gap. These edge modes allow energy to be transferred at specific frequencies along the edges of the material, while the bulk remains unaffected. In the case of microtubules, its ability to store vibrational energy at its edges and the sensitivity to changes in local bulk structure align with the properties of topological insulators. Furthermore, the appearance of edge modes in topological phononic insulators is determined by the local interactions of the bulk material. Even small changes in the local structure can shift the resonant frequency of the edge mode or completely extinguish it. Similarly, the ability of microtubules to shorten or overcome energy barriers is greatly affected by changes in their local bulk structure. This suggests a parallel between the impact of local bulk structure on both topological insulators and microtubules. This similarity has led to the proposal that microtubules could serve as an analog of topological phononic insulators, providing insights into their dynamics and potential applications in fields such as chemotherapy drug development and nanoscale materials. Secondly, the application of topological phonons in the realm of acoustic metamaterials will be examined. Acoustic waves have recently become a versatile platform for exploring and studying various topological phases, showcasing their universality and diverse manifestations. The unique properties of topological insulators and their surface states heavily rely on the dimension and symmetries of the material, making it possible to classify them using a periodic table of topological insulators. However, certain combinations of dimensions and symmetries can impede the achievement of topological insulation. It is of utmost importance to preserve symmetries in order to maintain the desired topological properties, which necessitates careful consideration of coupling methods. In the context of discrete acoustic resonant models, efficiently coupling resonators while simultaneously preserving symmetry poses a challenging question. In this part, a clever experimental approach is proposed and discussed to couple acoustic crystals. This modular platform not only supports the existence of topologically protected edge and interface states but also offers a convenient setup that can be easily assembled and disassembled. Furthermore, inspired by recent theoretical advancements that draw on techniques from the field of C*-algebras for identifying topological metals, the present study provides direct observations of topological phenomena in gapless acoustic crystals. Through these observations, a general experimental technique is realized and developed to demonstrate the topology of such systems. By employing the method of coupling acoustic crystals, the investigation unveils robust boundary-localized states in a topological acoustic metal and presents a reinterpretation of a composite operator as a new Hamiltonian. This reinterpretation enables the direct observation of a topological spectral flow and facilitates the measurement of topological invariants. Through these investigations, the aim of this dissertation is to deepen our understanding of the significance and potential applications of topological phonons in diverse systems

    Mechanical characterization, constitutive modeling and applications of ultra-soft magnetorheological elastomers

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    Mención Internacional en el título de doctorSmart materials are bringing sweeping changes in the way humans interact with engineering devices. A myriad of state-of-the-art applications are based on novel ways to actuate on structures that respond under different types of stimuli. Among them, materials that respond to magnetic fields allow to remotely modify their mechanical properties and macroscopic shape. Ultra-soft magnetorheological elastomers (MREs) are composed of a highly stretchable soft elastomeric matrix in the order of 1 kPa and magnetic particles embedded in it. This combination allows large deformations with small external actuations. The type of the magnetic particles plays a crucial role as it defines the reversibility or remanence of the material magnetization. According to the fillers used, MREs are referred to as soft-magnetic magnetorheological elastomers (sMREs) and hard-magnetic magnetorheological elastomers (hMREs). sMREs exhibit strong changes in their mechanical properties when an external magnetic field is applied, whereas hMREs allow sustained magnetic effects along time and complex shape-morphing capabilities. In this regard, end-of-pipe applications of MREs in the literature are based on two major characteristics: the modification of their mechanical properties and macrostructural shape changes. For instance, smart actuators, sensors and soft robots for bioengineering applications are remotely actuated to perform functional deformations and autonomous locomotion. In addition, hMREs have been used for industrial applications, such as damping systems and electrical machines. From the analysis of the current state of the art, we identified some impediments to advance in certain research fields that may be overcome with new solutions based on ultrasoft MREs. On the mechanobiology area, we found no available experimental methodologies to transmit complex and dynamic heterogeneous strain patterns to biological systems in a reversible manner. To remedy this shortcoming, this doctoral research proposes a new mechanobiology experimental setup based on responsive ultra-soft MRE biological substrates. Such an endeavor requires deeper insights into the magneto-viscoelastic and microstructural mechanisms of ultra-soft MREs. In addition, there is still a lack of guidance for the selection of the magnetic fillers to be used for MREs and the final properties provided to the structure. Eventually, the great advances on both sMREs and hMREs to date pose a timely question on whether the combination of both types of particles in a hybrid MRE may optimize the multifunctional response of these active structures. To overcome these roadblocks, this thesis provides an extensive and comprehensive experimental characterization of ultra-soft sMREs, hMREs and hybrid MREs. The experimental methodology uncovers magneto-mechanical rate dependences under numerous loading and manufacturing conditions. Then, a set of modeling frameworks allows to delve into such mechanisms and develop three ground-breaking applications. Therefore, the thesis has lead to three main contributions. First and motivated on mechanobiology research, a computational framework guides a sMRE substrate to transmit complex strain patterns in vitro to biological systems. Second, we demonstrate the ability of remanent magnetic fields in hMREs to arrest cracks propagations and improve fracture toughness. Finally, the combination of soft- and hard-magnetic particles is proved to enhance the magnetorheological and magnetostrictive effects, providing promising results for soft robotics.Los materiales inteligentes están generando cambios radicales en la forma que los humanos interactúan con dispositivos ingenieriles. Distintas aplicaciones punteras se basan en formas novedosas de actuar sobre materiales que responden a diferentes estímulos. Entre ellos, las estructuras que responden a campos magnéticos permiten la modificación de manera remota tanto de sus propiedades mecánicas como de su forma. Los elastómeros magnetorreológicos (MREs) ultra blandos están compuestos por una matriz elastomérica con gran ductilidad y una rigidez en torno a 1 kPa, reforzada con partículas magnéticas. Esta combinación permite inducir grandes deformaciones en el material mediante la aplicación de campos magnéticos pequeños. La naturaleza de las partículas magnéticas define la reversibilidad o remanencia de la magnetización del material compuesto. De esta manera, según el tipo de partículas que contengan, los MREs pueden presentar magnetización débil (sMRE) o magnetización fuerte (hMRE). Los sMREs experimentan grandes cambios en sus propiedades mecánicas al aplicar un campo magnético externo, mientras que los hMREs permiten efectos magneto-mecánicos sostenidos a lo largo del tiempo, así como programar cambios de forma complejos. En este sentido, las aplicaciones de los MREs se basan en dos características principales: la modificación de sus propiedades mecánicas y los cambios de forma macroestructurales. Por ejemplo, los campos magnéticos pueden emplearse para inducir deformaciones funcionales en actuadores y sensores inteligentes, o en robótica blanda para bioingeniería. Los hMREs también se han aplicado en el ámbito industrial en sistemas de amortiguación y máquinas eléctricas. A partir del análisis del estado del arte, se identifican algunas limitaciones que impiden el avance en ciertos campos de investigación y que podrían resolverse con nuevas soluciones basadas en MREs ultra blandos. En el área de la mecanobiología, no existen metodologías experimentales para transmitir patrones de deformación complejos y dinámicos a sistemas biológicos de manera reversible. En esta investigación doctoral se propone una configuración experimental novedosa basada en sustratos biológicos fabricados con MREs ultra blandos. Dicha solución requiere la identificación de los mecanismos magneto-viscoelásticos y microestructurales de estos materiales, según el tipo de partículas magnéticas, y las consiguientes propiedades macroscópicas del material. Además, investigaciones recientes en sMREs y hMREs plantean la pregunta sobre si la combinación de distintos tipos de partículas magnéticas en un MRE híbrido puede optimizar su respuesta multifuncional. Para superar estos obstáculos, la presente tesis proporciona una caracterización experimental completa de sMREs, hMREs y MREs híbridos ultra blandos. Estos resultados muestran las dependencias del comportamiento multifuncional del material con la velocidad de aplicación de cargas magneto-mecánicas. El desarrollo de un conjunto de modelos teórico-computacionales permite profundizar en dichos mecanismos y desarrollar aplicaciones innovadoras. De este modo, la tesis doctoral ha dado lugar a tres bloques de aportaciones principales. En primer lugar, este trabajo proporciona un marco computacional para guiar el diseño de sustratos basados en sMREs para transmitir patrones de deformación complejos in vitro a sistemas biológicos. En segundo lugar, se demuestra la capacidad de los campos magnéticos remanentes en los hMRE para detener la propagación de grietas y mejorar la tenacidad a la fractura. Finalmente, se establece que la combinación de partículas magnéticas de magnetización débil y fuerte mejora el efecto magnetorreológico y magnetoestrictivo, abriendo nuevas posibilidades para el diseño de robots blandos.I want to acknowledge the support from the Ministerio de Ciencia, Innovación y Universidades, Spain (FPU19/03874), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 947723, project: 4D-BIOMAP).Programa de Doctorado en Ingeniería Mecánica y de Organización Industrial por la Universidad Carlos III de MadridPresidente: Ramón Eulalio Zaera Polo.- Secretario: Abdón Pena Francesch.- Vocal: Laura de Lorenzi

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Induced superconductivity in HgTe based topological insulator nanowires

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    This thesis describes the studies of Josephson junctions made from topological insulator nanowires. Such nanowires in proximity to conventional superconductors have been proposed as a tunable platform to realize topological superconductivity and Majorana zero modes. The tuning is done using an axial magnetic flux Φ, which allows transforming the system from trivial at Φ=0 to topologically nontrivial when half a magnetic flux quantum Φ=Φ₀/2=h/2e penetrates the cross-sectional area of the wire. In this work, we investigate Josephson junctions based on HgTe nanowires under the influence of a magnetic field. Additionally, we probe the expected transition from trivial to topological superconductivity as a function of the axial magnetic flux. In an out-of-plane magnetic field, some devices studied show a Fraunhofer pattern, i.e. a modulation of the critical current as a function of the magnetic field. The effects of magnetic flux focusing and spin-orbit interaction are taken into account to agree with the theoretically predicted evolution of the pattern. The absence of the pattern in other devices is explained by bad transmissions of the nanowire/superconductor interface or the extreme thinness of some wires. For an axial magnetic field, we also observe a modulation of the critical current for devices with low transmissions. Here, the modulations show periodicities of Φ=h/4e or Φ=h/8e. For devices with high transmission, no modulation occurs, and the critical current monotonously decays with the magnetic field. We relate these findings both experimentally and theoretically to the coupling of the superconducting contacts to the topological insulator nanowire. For a high transmission, all sides of the nanowire become superconducting due to the proximity effect. In contrast, only the interfaces directly covered by the superconductor, i.e. the top and the sides, are proximitized if the average transmission is rather low. Here, different types of transport paths exist while some of them pick up an additional phase induced by the magnetic flux. This additional phase causes a modification of the current-phase relation and, thus, leads to a modulation of the critical current as a function of the magnetic flux. In Josephson junctions, an indication of emanating Majorana bound states is a 4π-periodic current-phase relation. A convenient way to probe the periodicity of the current-phase relation is the measurement of Shapiro steps. In the trivial situation, all Shapiro steps are visible while the odd steps are supposed to disappear if the current-phase relation is 4π-periodic. Hence, we probe the Shapiro step spectrum for different microwave frequencies and intensities. From the suppression of odd Shapiro steps, we extract the 4π- and 2π-periodic fraction of the critical current using both a resistively shunted junction (RSJ) and a resistively and capacitively shunted junction (RCSJ) model. The ratio of the 4π-periodic current on the total critical current depends strongly on the axial magnetic flux. While the total critical current decreases with an increasing flux, the ratio changes from approximately 6% at Φ=0 up to a maximum around Φ=Φ₀/2. The presence of a finite 4π-periodic current at Φ=0 and small magnetic fields is ascribed to Landau-Zener transitions between trivial Andreev bound states causing the 4π-periodic current. A method to distinguish between the trivial and the topological origin of the 4π-periodic currents is achieved by comparing the results for in-plane magnetic fields parallel to the wire as well as perpendicular to the wire. A topological 4π-periodic current is only proposed for the first configuration, while a trivial 4π-periodic current appear for both cases. Thus, our data suggest that the 4π-periodic current, which only appears for parallel magnetic fields ⪎Φ₀/4, is mainly of topological origin. Therefore, our experiments provide an indication for the switching between trivial and topological superconductivity with an axial magnetic flux in topological insulator nanowires

    Patient Zero and Patient Six: Zero-Value and Correlation Attacks on CSIDH and SIKE

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    Recent works have started side-channel analysis on SIKE and show the vulnerability of isogeny-based systems to zero-value attacks. In this work, we expand on such attacks by analyzing the behavior of the zero curve E0E_0 and six curve E6E_6 in CSIDH and SIKE. We demonstrate an attack on static-key CSIDH and SIKE implementations that recovers bits of the secret key by observing via zero-value-based resp. exploiting correlation-collision-based side-channel analysis whether secret isogeny walks pass over the zero or six curve. We apply this attack to fully recover secret keys of SIKE and two state-of-the-art CSIDH-based implementations: CTIDH and SQALE. We show the feasibility of exploiting side-channel information for the proposed attacks based on simulations with various realistic noise levels. Additionally, we discuss countermeasures to prevent zero-value and correlation-collision attacks against CSIDH and SIKE in our attacker model

    Temporal Mapper: Transition networks in simulated and real neural dynamics

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    AbstractCharacterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method—Temporal Mapper—built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects’ behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics

    Dimensional Analysis: Essays on the Metaphysics and Epistemology of Quantities

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    This dissertation draws upon historical studies of scientific practice and contemporary issues in the metaphysics and epistemology of science to account for the nature of physical quantities. My dissertation applies this integrated HPS approach to dimensional analysis—a logic for quantitative physical equations which respects the distinct dimensions of quantities (e.g. mass, length, charge). Dimensional analysis and its historical development serve both as subjects of study and as a sources for solutions to contemporary problems. The dissertation consists primarily of three related papers on: (1) the methodological and metaphysical foundations of dimensional analysis, (2) the use of dimensional analysis in determining physical symmetries, (3) the use of dimensional analysis in securing metrological extension
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