6,855 research outputs found

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    A Broadband Mid-infrared Metasurface for Polarisation Manipulation and Utilisation

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    A pair of enantiomers are distinct from each other due to chiral structural arrangement which leads to selective interaction with chiral light. Vibrational circular dichroism spectroscopy in mid-infrared region provide a powerful label-free method to distinguish chiral enantiomers. Besides, mid-infrared sensing also significantly benefit from resolving compositional information of molecules due to molecular vibrational fingerprints which holds promising application in biological and medical sensing. However, the low signal-to-noise ratio associated with weak light-matter interaction is a continuing obstacle hindering the practical application. Recent demonstrations of chiral metamaterials have shown that due to the chirality of structure, local superchiral field can be produced in the vicinity of structure to interact with molecules and enhance vibrational circular dichroism response. However, a limitation factor in development of chiral structure is the narrow effective working bandwidth and the requirement of circular polarization excitation. This thesis introduces an achiral nanorod-based metausrface that enable to overcome these limitations. First, the nanorod-based metasurface is present to achieve high efficient linear-to-circular polarization conversion in a broadband mid-infrared wavelength range in reflection mode. The model was firstly studied and optimised through simulation tool based on Finite Difference Time Domain method. The device was fabricated in a top-down approach based on electron beam lithography and characterised using Fourier transform infrared spectroscopy. We identified two distinct resonances originated from gap-plasmon mode at 3.4ÎĽm and Fabry-Perot mode at 7.9ÎĽm. The demonstration of polarization state based on the measured Stokes parameters within off-resonance range from 4-7ÎĽm show that the reflected beam has converted into circular polarization state. For practical application of vibrational circular dichroism spectroscopy, we numerically demonstrate the induced chirality in near-field under excitation of linear polarization with various polarization angles. These analysis suggest that superchiral field can be produced by nanorod-based metasurface and distributed spatially under linear polarization excitation. When polarization is parallel or orthogonal to rod, namely the symmetry exist in the combination of rod and incident polarization, the absolute chirality is zero due to the fact that same amount of optical chirality density with opposite handedness offset by each other. However it is showed that one handedness of the optical chirality density is dominant when the symmetry is broken, hence, holds potential for circular dichroism spectroscopy sensing. In an experimental feasibility study, we measured the polarization states of light in far-field and demonstrate that the absolute chirality in the far-field show similar behaviour as that in near-field. Finally, we conduct a molecular sensing measurement based on the rod-shape metausrface for enantiomers (alanine) identification through circular dichroism spectroscopy. This thesis demonstrates with FDTD simulations that the metasurface can generate superchiral fields which enable to enhance interaction with molecules upon linear polarization excitation. By simply rotating sample with 90 degree, molecules can then interact with superchiral field with opposite handedness. The circular dichroism is to record the intensity of reflected beam and characterise the differential intensity between the two. Despite the measured data do not show inverse pattern for L- and D-alanine, we confirmed that metausrface enable to enhance the light-matter interaction

    Rational development of stabilized cyclic disulfide redox probes and bioreductive prodrugs to target dithiol oxidoreductases

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    Countless biological processes allow cells to develop, survive, and proliferate. Among these, tightly balanced regulatory enzymatic pathways that can respond rapidly to external impacts maintain dynamic physiological homeostasis. More specifically, redox homeostasis broadly affects cellular metabolism and proliferation, with major contributions by thiol/disulfide oxidoreductase systems, in particular, the Thioredoxin Reductase Thioredoxin (TrxR/Trx) and the Glutathione Reductase-Glutathione-Glutaredoxin (GR/GSH/Grx) systems. These cascades drive vital cellular functions in many ways through signaling, regulating other proteins' activity by redox switches, and by stoichiometric reductant transfers in metabolism and antioxidant systems. Increasing evidence argues that there is a persistent alteration of the redox environment in certain pathological states, such as cancer, that heavily involve the Trx system: upregulation and/or overactivity of the Trx system may support or drive cancer progression, making both TrxR and Trx promising targets for anti-cancer drug development. Understanding the biochemical mechanisms and connections between certain redox cascades requires research tools that interact with them. The state-of-the-art genetic tools are mostly ratiometric reporters that measure reduced:oxidized ratios of selected redox pairs or the general thiol pool. However, the precise cellular roles of the central oxidoreductase systems, including TrxR and Trx, remain inaccessible due to the lack of probes to selectively measure turnover by either of these proteins. However, such probes would allow measuring their effective reductive activity apart from expression levels in native systems, including in cells, animals, or patient samples. They are also of high interest to identify chemical inhibitors for TrxR/Trx in cells and to validate their potential use as anti-cancer agents (to date, there is no selective cellular Trx inhibitor, and most known TrxR inhibitors were not comprehensively evaluated considering selectivity and potential off-targets). However, small molecule redox imaging tools are underdeveloped: their protein specificity, spectral properties, and applicability remain poorly precedented. This work aimed to address this opportunity gap and develop novel, small molecule diagnostic and therapeutic tools to selectively target the Trx system based on a modular trigger cargo design: artificial cyclic disulfide substrates (trigger) for oxidoreductases are tethered to molecular agents (cargo) such that the cargo’s activity is masked and is re-established only through reduction by a target protein. The rational design of these novel reduction sensors to target the cell's strongest disulfide-reducing enzymes was driven by the following principles: (i) cyclic disulfide triggers with stabilized ring systems were used to gain low reduction potentials that should resist reduction except by the strongest cellular reductases, such as Trx; and (ii) the cyclic topology also offers the potential for kinetic reversibility that should select for dithiol-type redox proteins over the cellular monothiol background. Creating imaging agents based on such two-component designs to selectively measure redox protein activity in native cells required to combine the correct trigger reducibility, probe activation kinetics, and imaging modalities and to consider the overall molecular architecture. The major prior art in this field has applied cyclic 5-membered disulfides (1,2 dithiolanes) as substrates for TrxR in a similar way to create such tools. However, this motif was described elsewhere as thermodynamically instable and was due to widely used for dynamic covalent cascade reactions. By comparing a novel 1,2 dithiolane-based probe to the state-of-the-art probes, including commercial TrxR sensors, by screening a conclusive assay panel of cellular TrxR modulations, I clarified that 1,2 dithiolanes are not selective substrates for TrxR in biological settings (Nat Commun 2022). Instead, aiming for more stable ring systems and thus more robust redox probes, during this work, I developed bicyclic 6 membered disulfides (piperidine fused 1,2 dithianes) with remarkably low reduction potentials. I showed that molecular probes using them as reduction sensors can be mostly processed by thioredoxins while being stable against reduction by GSH. The thermodynamically stabilized decalin like topology of the cis-annelated 1,2 dithianes requires particularly strong reductants to be cleaved. They also select for dithiol type redox proteins, like Trx, based on kinetic reversibility and offer fast cyclization due to the preorganization by annelation (JACS 2021). This work further expanded the system’s modularity with structural cores based on piperazine-fused 1,2 dithianes with the two amines allowing independent derivatization. Diagnostic tools using them as reduction sensors proved equally robust but with highly improved activation kinetics and were thus cellularly activated. Cellular studies evolved that they are substrates for both Trxs and their protein cousins Grxs, so measuring the cellular dithiol protein pool rather than solely Trx activity (preprint 2023). Finally, a trigger based on a slightly adapted reduction sensor, a desymmetrized 1,2 thiaselenane, was designed for selective reduction by TrxR’s selenol/thiol active site, then combined with a precipitating large Stokes’ shift fluorophore and a solubilizing group, to evolve the first selective probe RX1 to measure cellular TrxR activity, which even allowed high throughput inhibitor screening (Chem 2022). The central principle of this work was further advanced to therapeutic prodrugs based on the duocarmycin cargo (CBI) with tunable potency (JACS Au 2022) that can be used to create off-to-on therapeutic prodrugs. Such CBI prodrugs employing stabilized 1,2 dichalcogenide triggers proved to be cytotoxins that depend on Trx system activity in cells. They could further be exploited for cell-line dependent reductase activity profiling by screening their redox activation indices, the reduction-dependent part of total prodrug activation, in 177 cell lines. Beyond that, these prodrugs were well-tolerated in animals and showed anti-cancer efficacy in vivo in two distinct mouse tumor models (preprint 2022). Taken together, I introduced unique monothiol-resistant reducible motifs to target the cellular Trx system with chemocompatible units for each for TrxR and Trx/Grx, where the cyclic nature of the dichalcogenides avoids activation by GSH. By using them with distinct molecular cargos, I developed novel selective fluorescent reporter probes; and introduced a new class of bioreductive therapeutic constructs based on a common modular design. These were either applied to selectively measure cellular reductase activity or to deliver cytotoxic anti cancer agents in vivo. Ongoing work aims to differentiate between the two major redox effector proteins Trx and Grx, requiring additional layers of selectivity that may be addressed by tuned molecular recognition. The flexible use of various molecular cargos allows harnessing the same cellular redox machinery by either probes or prodrugs. This allows predictive conclusions from diagnostics to be directly translated into therapy and offers great potential for future adaptation to other enzyme classes and therapeutic venues.Die zelluläre Redox-Homöostase hängt von Thiol/Disulfid-Oxidoreduktasen ab, die den Stoffwechsel, die Proliferation und die antioxidative Antwort von Zellen beeinflussen. Die wichtigsten Netzwerke sind die Thioredoxin Reduktase-Thioredoxin (TrxR/Trx) und Glutathion Reduktase-Glutathion-Glutaredoxin (GR/GSH/Grx) Systeme, die über Redox-Schalter in Substratproteinen lebenswichtige zelluläre Funktionen steuern und so an der Redox-Regulation und -Signalübertragung beteiligt sind. Persistente Veränderungen des Redoxmilieus in pathologischen Zuständen, wie z. B. bei Krebs, sind in hohem Maße mit dem Trx-System verbunden. Eine Hochregulierung und/oder Überaktivität des Trx-Systems, die bei vielen Krebsarten auftreten, unterstützt zudem das Fortschreiten des Krebswachstums, was TrxR/Trx zu vielversprechenden Zielproteinen für die Entwicklung neuer Krebsmedikamente macht. Um die biochemischen Prozesse dahinter zu erforschen, sind spezielle Techniken zur Visualisierung und Messung enzymatischer Aktivität nötig. Die hierzu geeigneten, meist genetischen Sensoren messen ratiometrisch das Verhältnis reduzierter/oxidierter Spezies in zellulärem Umfeld oder spezifisch ausgewählte Redoxpaare. Die weitere Erforschung der exakten Funktion von TrxR/Trx und deren Substrate ist jedoch durch mangelnde Nachweismethoden limitiert. Diese sind außerdem zur Validierung chemischer Hemmstoffe für TrxR/Trx in Zellen und deren potenziellen Verwendung als Krebsmittel von großem Interesse. Bislang gibt es keinen selektiven zellulären Trx-Inhibitor und potenzielle Off-Target-Effekte der bekannten TrxR-Inhibitoren wurden nicht abschließend bewertet. Ziel dieser Arbeit ist die Entwicklung niedermolekularer, diagnostischer und therapeutischer Werkzeuge, die selektiv auf das Trx-System abzielen und auf einem modularen Trigger-Cargo Design basieren. Hierzu werden zyklische Disulfid-Substrate (Trigger) für Oxidoreduktasen so mit molekularen Wirkstoffen (Cargo) verknüpft, dass dabei die Wirkstoffaktivität maskiert, und erst nach Reduktion durch ein Zielprotein wiederhergestellt wird. Diese neuartigen, synthetischen Reduktionssensoren basieren auf den folgenden Grundprinzipien: (i) Zyklische Disulfide sind thermodynamisch stabilisiert und können nur durch die stärksten Reduktasen gespalten werden; und (ii) die zyklische Topologie ermöglicht die kinetische Reversibilität der zwei Thiol-Disulfid-Austauschreaktionen, die eine erste Reaktion mit Monothiolen, wie z. B. GSH, sofort umkehrt und so eine vollständige Reduktion verhindert. Die meisten früheren Arbeiten auf diesem Gebiet verwendeten ein zyklisches, fünfgliedriges Disulfid (1,2 Dithiolan) als Substrat für TrxR. Das gleiche Strukturmotiv wurde jedoch an anderer Stelle als thermodynamisch instabil beschrieben und aufgrund dieser Eigenschaft explizit für dynamische Kaskadenreaktionen verwendet. Deshalb vergleicht diese Arbeit zu Beginn einen neuen 1,2 Dithiolan basierten fluorogenen Indikator mit bestehenden, z. T. kommerziellen, Redox Sonden für TrxR in einer Reihe von Zellkultur-Experimenten unter Modulation der zellulären TrxR Aktivität und stellt so einen Widerspruch in der Literatur klar: 1,2 Dithiolane eignen sich nicht als selektive Substrate für TrxR, da sie labil sowohl gegen die Reduktion durch andere Redoxproteine, als auch gegen den Monothiol Hintergrund in Zellen sind (Nat. Commun. 2022). Als alternatives Strukturmotiv wird in dieser Arbeit ein bizyklisches sechsgliedriges Disulfid (anneliertes 1,2 Dithian) etabliert. Durch sein niedriges Reduktionspotenzial, also seine hohe Resistenz gegen Reduktion, werden molekulare Sonden basierend auf diesem 1,2 Dithian als Reduktionssensor fast ausschließlich von Trx aktiviert, nicht aber von TrxR oder GSH (JACS 2021). Dieses Kernmotiv bestimmt dabei die Reduzierbarkeit, und damit die Enzymspezifität, durch seine zyklische Natur und die Annelierung, auch unter Verwendung unterschiedlicher Farb-/Wirkstoffe. Auf dieser Grundlage konnte die molekulare Struktur durch einen weiteren Modifikationspunkt für die flexible Verwendung weiterer funktioneller Einheiten ergänzt werden. Obwohl zelluläre Studien ergaben, dass diese neuartigen 1,2 Dithian Einheiten in Zellen sowohl Trx als auch das strukturell verwandte Grx adressieren, sind die daraus resultierenden diagnostischen Moleküle wertvoll, um den katalytischen Umsatz zellulärer Dithiol-Reduktasen, der sogenannten Trx Superfamilie, selektiv anzuzeigen (Preprint 2023). Begünstigt durch das modulare Moleküldesign stellt diese Arbeit zudem das erste Reportersystem RX1 zum selektiven Nachweis der TrxR-Aktivität in Zellen vor. Es basiert auf der Verwendung eines zyklischen, unsymmetrischen Selenenylsulfid-Sensors (1,2 Thiaselenan), der selektiv von dem einzigartigen Selenolat der TrxR angegriffen wird, und dadurch letztlich nur von TrxR reduziert werden kann. RX1 eignete sich zudem für eine Hochdurchsatz-Validierung bestehender TrxR Inhibitoren und unterstreicht dadurch den kommerziellen Nutzen derartiger Diagnostika (Chem 2022). Das zentrale Trigger-Cargo Konzept dieser Arbeit wurde für therapeutische Zwecke weiterentwickelt und nutzt dabei den einzigartigen Wirkmechanismus der Duocarmycin-Naturstoffklasse (CBI) (JACS Au 2022) zur Entwicklung reduktiv aktivierbarer Therapeutika. CBI Prodrugs basierend auf stabilisierten Redox-Schaltern (1,2 Dithiane für Trx; 1,2 Thiaselenan für TrxR) reagierten signifikant auf TrxR-Modulation in Zellen. Sie wurden darüber hinaus durch das Referenzieren ihrer Aktivität gegenüber nicht-reduzierbaren Kontrollmoleküle für die Erstellung zelllinienabhängiger Profile der Reduktaseaktivität in 177 Zelllinien genutzt. Schließlich waren diese neuen Krebsmittel im Tiermodell gut verträglich und zeigten in zwei verschiedenen Mausmodellen eine krebshemmende Wirkung (Preprint 2022b). Zusammenfassend präsentiert diese Dissertation monothiol-resistente reduzierbare Trigger-Einheiten für das zelluläre Trx-System zur Entwicklung neuartiger, selektiver Reporter-Sonden, sowie eine neue Klasse reduktiv aktivierbarer Krebsmittel auf Basis eines adaptierbaren Trigger-Cargo Designs. Diese fanden entweder zur selektiven Messung zellulärer Proteinaktivität oder zum Einsatz als Antikrebsmittel Verwendung. Es wurden chemokompatible Motive sowohl für TrxR als auch für Trx/Grx identifiziert, wobei deren zyklische Natur eine Aktivierung durch GSH verhindert. Eine weitere Differenzierung zwischen den beiden Redox-Proteinen Trx und Grx und anderen Proteinen der Trx-Superfamilie erfordert eine zusätzliche Ebene der Selektierung, z. B. durch molekulare Erkennung, und ist Gegenstand laufender Arbeiten. Die flexible Verwendung verschiedener molekularer Wirkstoffe ermöglicht dabei die „Pipeline-Entwicklung“ von Diagnostika und Therapeutika, die von der zellulären Redox-Maschinerie analog umgesetzt werden, und dadurch Schlussfolgerungen aus der Diagnostik direkt auf eine Therapie übertragbar machen. Dies birgt großes Potenzial für künftige Entwicklungen bei einer potenziellen Übertragung des modularen Konzepts auf andere Enzymklassen und therapeutische Einsatzgebiete

    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

    Robustness and Interpretability of Neural Networks’ Predictions under Adversarial Attacks

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    Le reti neurali profonde (DNNs) sono potenti modelli predittivi, che superano le capacità umane in una varietà di task. Imparano sistemi decisionali complessi e flessibili dai dati a disposizione e raggiungono prestazioni eccezionali in molteplici campi di apprendimento automatico, dalle applicazioni dell'intelligenza artificiale, come il riconoscimento di immagini, parole e testi, alle scienze più tradizionali, tra cui medicina, fisica e biologia. Nonostante i risultati eccezionali, le prestazioni elevate e l’alta precisione predittiva non sono sufficienti per le applicazioni nel mondo reale, specialmente in ambienti critici per la sicurezza, dove l'utilizzo dei DNNs è fortemente limitato dalla loro natura black-box. Vi è una crescente necessità di comprendere come vengono eseguite le predizioni, fornire stime di incertezza, garantire robustezza agli attacchi avversari e prevenire comportamenti indesiderati. Anche le migliori architetture sono vulnerabili a piccole perturbazioni nei dati di input, note come attacchi avversari: manipolazioni malevole degli input che sono percettivamente indistinguibili dai campioni originali ma sono in grado di ingannare il modello in predizioni errate. In questo lavoro, dimostriamo che tale fragilità è correlata alla geometria del manifold dei dati ed è quindi probabile che sia una caratteristica intrinseca delle predizioni dei DNNs. Questa condizione suggerisce una possibile direzione al fine di ottenere robustezza agli attacchi: studiamo la geometria degli attacchi avversari nel limite di un numero infinito di dati e di pesi per le reti neurali Bayesiane, dimostrando che, in questo limite, sono immuni agli attacchi avversari gradient-based. Inoltre, proponiamo alcune tecniche di training per migliorare la robustezza delle architetture deterministiche. In particolare, osserviamo sperimentalmente che ensembles di reti neurali addestrati su proiezioni casuali degli input originali in spazi basso-dimensionali sono più resistenti agli attacchi. Successivamente, ci concentriamo sul problema dell'interpretabilità delle predizioni delle reti nel contesto delle saliency-based explanations. Analizziamo la stabilità delle explanations soggette ad attacchi avversari e dimostriamo che, nel limite di un numero infinito di dati e di pesi, le interpretazioni Bayesiane sono più stabili di quelle fornite dalle reti deterministiche. Confermiamo questo comportamento in modo sperimentale nel regime di un numero finito di dati. Infine, introduciamo il concetto di attacco avversario alle sequenze di amminoacidi per protein Language Models (LM). I modelli di Deep Learning per la predizione della struttura delle proteine, come AlphaFold2, sfruttano le architetture Transformer e il loro meccanismo di attention per catturare le proprietà strutturali e funzionali delle sequenze di amminoacidi. Nonostante l'elevata precisione delle predizioni, perturbazioni biologicamente piccole delle sequenze di input, o anche mutazioni di un singolo amminoacido, possono portare a strutture 3D sostanzialmente diverse. Al contempo, i protein LMs sono insensibili alle mutazioni che inducono misfolding o disfunzione (ad esempio le missense mutations). In particolare, le predizioni delle coordinate 3D non rivelano l'effetto di unfolding indotto da queste mutazioni. Pertanto, esiste un'evidente incoerenza tra l'importanza biologica delle mutazioni e il conseguente cambiamento nella predizione strutturale. Ispirati da questo problema, introduciamo il concetto di perturbazione avversaria delle sequenze proteiche negli embedding continui dei protein LMs. Il nostro metodo utilizza i valori di attention per rilevare le posizioni degli amminoacidi più vulnerabili nelle sequenze di input. Le mutazioni avversarie sono biologicamente diverse dalle sequenze di riferimento e sono in grado di alterare in modo significativo le strutture 3D.Deep Neural Networks (DNNs) are powerful predictive models, exceeding human capabilities in a variety of tasks. They learn complex and flexible decision systems from the available data and achieve exceptional performances in multiple machine learning fields, spanning from applications in artificial intelligence, such as image, speech and text recognition, to the more traditional sciences, including medicine, physics and biology. Despite the outstanding achievements, high performance and high predictive accuracy are not sufficient for real-world applications, especially in safety-critical settings, where the usage of DNNs is severely limited by their black-box nature. There is an increasing need to understand how predictions are performed, to provide uncertainty estimates, to guarantee robustness to malicious attacks and to prevent unwanted behaviours. State-of-the-art DNNs are vulnerable to small perturbations in the input data, known as adversarial attacks: maliciously crafted manipulations of the inputs that are perceptually indistinguishable from the original samples but are capable of fooling the model into incorrect predictions. In this work, we prove that such brittleness is related to the geometry of the data manifold and is therefore likely to be an intrinsic feature of DNNs’ predictions. This negative condition suggests a possible direction to overcome such limitation: we study the geometry of adversarial attacks in the large-data, overparameterized limit for Bayesian Neural Networks and prove that, in this limit, they are immune to gradient-based adversarial attacks. Furthermore, we propose some training techniques to improve the adversarial robustness of deterministic architectures. In particular, we experimentally observe that ensembles of NNs trained on random projections of the original inputs into lower dimensional spaces are more resilient to the attacks. Next, we focus on the problem of interpretability of NNs’ predictions in the setting of saliency-based explanations. We analyze the stability of the explanations under adversarial attacks on the inputs and we prove that, in the large-data and overparameterized limit, Bayesian interpretations are more stable than those provided by deterministic networks. We validate this behaviour in multiple experimental settings in the finite data regime. Finally, we introduce the concept of adversarial perturbations of amino acid sequences for protein Language Models (LMs). Deep Learning models for protein structure prediction, such as AlphaFold2, leverage Transformer architectures and their attention mechanism to capture structural and functional properties of amino acid sequences. Despite the high accuracy of predictions, biologically small perturbations of the input sequences, or even single point mutations, can lead to substantially different 3d structures. On the other hand, protein language models are insensitive to mutations that induce misfolding or dysfunction (e.g. missense mutations). Precisely, predictions of the 3d coordinates do not reveal the structure-disruptive effect of these mutations. Therefore, there is an evident inconsistency between the biological importance of mutations and the resulting change in structural prediction. Inspired by this problem, we introduce the concept of adversarial perturbation of protein sequences in continuous embedding spaces of protein language models. Our method relies on attention scores to detect the most vulnerable amino acid positions in the input sequences. Adversarial mutations are biologically diverse from their references and are able to significantly alter the resulting 3D structures

    Circulation Statistics in Homogeneous and Isotropic Turbulence

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    This is the committee version of a Thesis presented to the PostGrad Program in Physics of the Physics Institute of the Federal University of Rio de Janeiro (UFRJ), as a necessary requirement for the title of Ph.D. in Science (Physics). The development of the Vortex Gas Model (VGM) introduces a novel statistical framework for describing the characteristics of velocity circulation. In this model, the underlying foundations rely on the statistical attributes of two fundamental constituents. The first is a GMC field that governs intermittent behavior and the second constituent is a Gaussian Free field responsible for the partial polarization of the vortices in the gas. The model is revisited in a more sophisticated language, where volume exclusion among vortices is addressed. These additions were subsequently validated through numerical simulations of turbulent Navier-Stokes equations. This revised approach harmonizes with the multifractal characteristics exhibited by circulation statistics, offering a compelling elucidation for the phenomenon of linearization of the statistical circulation moments, observed in recent numerical simulation. In the end, a field theoretical approach, known as Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) functional method is carried out in the context of circulation probability density function. This approach delves into the realm of extreme circulation events, often referred to as Instantons, through two distinct methodologies: The First investigates the linear solutions and, by a renormalization group argument a time-rescaling symmetry is discussed. Secondly, a numerical strategy is implemented to tackle the nonlinear instanton equations in the axisymmetric approximation. This approach addresses the typical topology exhibited by the velocity field associated with extreme circulation events.Comment: Ph.D. Thesis - preliminary versio

    Superconducting Circuit Architectures Based on Waveguide Quantum Electrodynamics

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    Quantum science and technology provides new possibilities in processing information, simulating novel materials, and answering fundamental questions beyond the reach of classical methods. Realizing these goals relies on the advancement of physical platforms, among which superconducting circuits have been one of the leading candidates offering complete control and read-out over individual qubits and the potential to scale up. However, most circuit-based multi-qubit architectures only include nearest-neighbor (NN) coupling between qubits, which limits the efficient implementation of low-overhead quantum error correction and access to a wide range of physical models using analog quantum simulation. This challenge can be overcome by introducing non-local degrees of freedom. For example, photons in a shared channel between qubits can mediate long-range qubit-qubit coupling arising from light-matter interaction. In addition, constructing a scalable architecture requires this channel to be intrinsically extensible, in which case a one-dimensional waveguide is an ideal structure providing the extensible direction as well as strong light-matter interaction. In this thesis, we explore superconducting circuit architectures based on light-matter interactions in waveguide quantum electrodynamics (QED) systems. These architectures in return allow us to study light-matter interaction, demonstrating strong coupling in the open environment of a waveguide by employing sub-radiant states resulting from collective effects. We further engineer the waveguide dispersion to enter the topological photonics regime, exploring interactions between qubits that are mediated by photons with topological properties. Finally, towards the goals of quantum information processing and simulation, we settle into a multi-qubit architecture where the photon-mediated interaction between qubits exhibits tunable range and strength. We use this multi-qubit architecture to construct a lattice with tunable connectivity for strongly interacting microwave photons, synthesizing a quantum many-body model to explore chaotic dynamics. The architectures in this thesis introduce scalable beyond-NN coupling between superconducting qubits, opening the door to the exploration of many-body physics with long-range coupling and efficient implementation of quantum information processing protocols.</p

    On factor models for high-dimensional time series

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    The aim of this thesis is to develop statistical methods for use with factor models for high-dimensional time series. We consider three broad areas: estimation, changepoint detection, and determination of the number of factors. In Chapter 1, we sketch the backdrop for our thesis and review key aspects of the literature. In Chapter 2, we develop a method to estimate the factors and parameters in an approximate dynamic factor model. Specifically, we present a spectral expectation-maximisation (or \spectral EM") algorithm, whereby we derive the E and M step equations in the frequency domain. Our E step relies on the Wiener-Kolmogorov smoother, the frequency domain counterpart of the Kalman smoother, and our M step is based on maximisation of the Whittle Likelihood with respect to the parameters of the model. We initialise our procedure using dynamic principal components analysis (or \dynamic PCA"), and by leveraging results on lag-window estimators of spectral density by Wu and Zaffaroni (2018), we establish consistency-with-rates of our spectral EM estimator of the parameters and factors as both the dimension (N) and the sample size (T) go to infinity. We find rates commensurate with the literature. Finally, we conduct a simulation study to numerically validate our theoretical results. In Chapter 3, we develop a sequential procedure to detect changepoints in an approximate static factor model. Specifically, we define a ratio of eigenvalues of the covariance matrix of N observed variables. We compute this ratio each period using a rolling window of size m over time, and declare a changepoint when its value breaches an alarm threshold. We investigate the asymptotic behaviour (as N;m ! 1) of our ratio, and prove that, for specific eigenvalues, the ratio will spike upwards when a changepoint is encountered but not otherwise. We use a block-bootstrap to obtain alarm thresholds. We present simulation results and an empirical application based on Financial Times Stock Exchange 100 Index (or \FTSE 100") data. In Chapter 4, we conduct an exploratory analysis which aims to extend the randomised sequential procedure of Trapani (2018) into the frequency domain. Specifically, we aim to estimate the number of dynamically loaded factors by applying the test of Trapani (2018) to eigenvalues of the estimated spectral density matrix (as opposed to the covariance matrix) of the data

    Enhancing Signal Space Diversity for SCMA Over Rayleigh Fading Channels

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    Sparse code multiple access (SCMA) is a promising technique for the enabling of massive connectivity in future machine-type communication networks, but it suffers from a limited diversity order which is a bottleneck for significant improvement of error performance. This paper aims for enhancing the signal space diversity of sparse code multiple access (SCMA) by introducing quadrature component delay to the transmitted codeword of a downlink SCMA system in Rayleigh fading channels. Such a system is called SSD-SCMA throughout this work. By looking into the average mutual information (AMI) and the pairwise error probability (PEP) of the proposed SSD-SCMA, we develop novel codebooks by maximizing the derived AMI lower bound and a modified minimum product distance (MMPD), respectively. The intrinsic asymptotic relationship between the AMI lower bound and proposed MMPD based codebook designs is revealed. Numerical results show significant error performance improvement in the both uncoded and coded SSD-SCMA systems

    Shadow estimation of gate-set properties from random sequences

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    With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment—random sequence estimation—solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk
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