10,241 research outputs found
Grasping nothing: a study of minimal ontologies and the sense of music
If music were to have a proper sense â one in which it is truly given â one might reasonably place this in sound and aurality. I contend, however, that no such sense exists; rather, the sense of music takes place, and it does so with the impossible. To this end, this thesis â which is a work of philosophy and music â advances an ontology of the impossible (i.e., it thinks the being of what, properly speaking, can have no being) and considers its implications for music, articulating how ontological aporias â of the event, of thinking the absolute, and of sovereigntyâs dismemberment â imply senses of music that are anterior to sound. John Cageâs Silent Prayer, a nonwork he never composed, compels a rerethinking of silence on the basis of its contradictory status of existence; Florian Hecker et al.âs Speculative Solution offers a basis for thinking absolute music anew to the precise extent that it is a discourse of meaninglessness; and Manfred Werderâs [yearn] pieces exhibit exemplarily that musicâs sense depends on the possibility of its counterfeiting. Inso-much as these accounts produce musical senses that take the place of sound, they are also understood to be performances of these pieces. Here, then, thought is musicâs organon and its instrument
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Antecedents of business intelligence system use
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees.
This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwaitâs telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis.
The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI userâs self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture.
The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employeesâ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUTâs facilitating conditions and the limited variance of respondent demographics
A suite of quantum algorithms for the shortestvector problem
Crytography has come to be an essential part of the cybersecurity infrastructure that provides a safe environment for communications in an increasingly connected world. The advent of quantum computing poses a threat to the foundations of the current widely-used cryptographic model, due to the breaking of most of the cryptographic algorithms used to provide confidentiality, authenticity, and more. Consequently a new set of cryptographic protocols have been designed to be secure against quantum computers, and are collectively known as post-quantum cryptography (PQC). A forerunner among PQC is lattice-based cryptography, whose security relies upon the hardness of a number of closely related mathematical problems, one of which is known as the shortest vector problem (SVP).
In this thesis I describe a suite of quantum algorithms that utilize the energy minimization principle to attack the shortest vector problem. The algorithms outlined span the gate-model and continuous time quantum computing, and explore methods of parameter optimization via variational methods, which are thought to be effective on near-term quantum computers. The performance of the algorithms are analyzed numerically, analytically, and on quantum hardware where possible. I explain how the results obtained in the pursuit of solving SVP apply more broadly to quantum algorithms seeking to solve general real-world problems; minimize the effect of noise on imperfect hardware; and improve efficiency of parameter optimization.Open Acces
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Mixture Models in Machine Learning
Modeling with mixtures is a powerful method in the statistical toolkit that can be used for representing the presence of sub-populations within an overall population. In many applications ranging from financial models to genetics, a mixture model is used to fit the data. The primary difficulty in learning mixture models is that the observed data set does not identify the sub-population to which an individual observation belongs. Despite being studied for more than a century, the theoretical guarantees of mixture models remain unknown for several important settings.
In this thesis, we look at three groups of problems. The first part is aimed at estimating the parameters of a mixture of simple distributions. We ask the following question: How many samples are necessary and sufficient to learn the latent parameters? We propose several approaches for this problem that include complex analytic tools to connect statistical distances between pairs of mixtures with the characteristic function. We show sufficient sample complexity guarantees for mixtures of popular distributions (including Gaussian, Poisson and Geometric). For many distributions, our results provide the first sample complexity guarantees for parameter estimation in the corresponding mixture. Using these techniques, we also provide improved lower bounds on the Total Variation distance between Gaussian mixtures with two components and demonstrate new results in some sequence reconstruction problems.
In the second part, we study Mixtures of Sparse Linear Regressions where the goal is to learn the best set of linear relationships between the scalar responses (i.e., labels) and the explanatory variables (i.e., features). We focus on a scenario where a learner is able to choose the features to get the labels. To tackle the high dimensionality of data, we further assume that the linear maps are also sparse , i.e., have only few prominent features among many. For this setting, we devise algorithms with sub-linear (as a function of the dimension) sample complexity guarantees that are also robust to noise.
In the final part, we study Mixtures of Sparse Linear Classifiers in the same setting as above. Given a set of features and the binary labels, the objective of this task is to find a set of hyperplanes in the space of features such that for any (feature, label) pair, there exists a hyperplane in the set that justifies the mapping. We devise efficient algorithms with sub-linear sample complexity guarantees for learning the unknown hyperplanes under similar sparsity assumptions as above. To that end, we propose several novel techniques that include tensor decomposition methods and combinatorial designs
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MODELING CHAIN PACKING IN COMPLEX PHASES OF SELF-ASSEMBLED BLOCK COPOLYMERS
Block copolymer (BCP) melts undergo microphase seperation and form ordered soft matter crystals with varying domain shapes and symmetries. We study the con- nection between diblock copolymer molecular designs and thermodynamic selection of ordered crystals by modeling features of variable sub-domain geometry filled with individual blocks within non-canonical sphere-like and network phases that together with layered, cylindrical and canonical spherical phases forms ânatural formsâ of self- assembled amphiphilic soft matter at large. First, we present a model to revise our understanding of optimal Frank-Kasper sphere-like morphologies by advancing the- ory to account for varying domain volumes. We then develop generic approaches to quantify local changes to domain thickness or packing frustration using medial sets and show its application to morphologies with arbitrary domain topologies and sym- metries in both theoretical models and experimental data. We further use medial sets as a proxy for terminal boundaries of blocks within different domains and revise thermodynamic models of BCP assembly in the strong segregation limit. Finally, we use this revised model to study effect of elastic stiffness asymmetry on relaxing packing frustration experienced by BCPs in tubular and matrix domains leading to equilibrium double gyroid network morphology in diblock copolymers
Robustness against adversarial attacks on deep neural networks
While deep neural networks have been successfully applied in several different domains, they exhibit vulnerabilities to artificially-crafted perturbations in data. Moreover, these perturbations have been shown to be transferable across different networks where the same perturbations can be transferred between different models. In response to this problem, many robust learning approaches have emerged. Adversarial training is regarded as a mainstream approach to enhance the robustness of deep neural networks with respect to norm-constrained perturbations. However, adversarial training requires a large number of perturbed examples (e.g., over 100,000 examples are required for MNIST dataset) trained on the deep neural networks before robustness can be considerably enhanced. This is problematic due to the large computational cost of obtaining attacks. Developing computationally effective approaches while retaining robustness against norm-constrained perturbations remains a challenge in the literature.
In this research we present two novel robust training algorithms based on Monte-Carlo Tree Search (MCTS) [1] to enhance robustness under norm-constrained perturbations [2, 3]. The first algorithm searches potential candidates with Scale Invariant Feature Transform method and makes decisions with Monte-Carlo Tree Search method [2]. The second algorithm adopts Decision Tree Search method (DTS) to accelerate the search process while maintaining efficiency [3]. Our overarching objective is to provide computationally effective approaches that can be deployed to train deep neural networks robust against perturbations in data. We illustrate the robustness with these algorithms by studying the resistances to adversarial examples obtained in the context of the MNIST and CIFAR10 datasets. For MNIST, the results showed an average training efforts saving of 21.1\% when compared to Projected Gradient Descent (PGD) and 28.3\% when compared to Fast Gradient Sign Methods (FGSM). For CIFAR10, we obtained an average improvement of efficiency of 9.8\% compared to PGD and 13.8\% compared to FGSM. The results suggest that these two methods here introduced are not only robust to norm-constrained perturbations but also efficient during training.
In regards to transferability of defences, our experiments [4] reveal that across different network architectures, across a variety of attack methods from white-box to black-box and across various datasets including MNIST and CIFAR10, our algorithms outperform other state-of-the-art methods, e.g., PGD and FGSM. Furthermore, the derived attacks and robust models obtained on our framework are reusable in the sense that the same norm-constrained perturbations can facilitate robust training across different networks. Lastly, we investigate the robustness of intra-technique and cross-technique transferability and the relations with different impact factors from adversarial strength to network capacity. The results suggest that known attacks on the resulting models are less transferable than those models trained by other state-of-the-art attack algorithms.
Our results suggest that exploiting these tree search frameworks can result in significant improvements in the robustness of deep neural networks while saving computational cost on robust training. This paves the way for several future directions, both algorithmic and theoretical, as well as numerous applications to establish the robustness of deep neural networks with increasing trust and safety.Open Acces
Full stack development toward a trapped ion logical qubit
Quantum error correction is a key step toward the construction of a large-scale quantum computer, by preventing small infidelities in quantum gates from accumulating over the course of an algorithm. Detecting and correcting errors is achieved by using multiple physical qubits to form a smaller number of robust logical
qubits. The physical implementation of a logical qubit requires multiple qubits, on which high fidelity gates
can be performed.
The project aims to realize a logical qubit based on ions confined on a microfabricated surface trap. Each
physical qubit will be a microwave dressed state qubit based on 171Yb+ ions. Gates are intended to be realized through RF and microwave radiation in combination with magnetic field gradients. The project vertically integrates software down to hardware compilation layers in order to deliver, in the near future, a fully functional small device demonstrator.
This thesis presents novel results on multiple layers of a full stack quantum computer model. On the hardware level a robust quantum gate is studied and ion displacement over the X-junction geometry is demonstrated.
The experimental organization is optimized through automation and compressed waveform data transmission. A new quantum assembly language purely dedicated to trapped ion quantum computers is introduced. The demonstrator is aimed at testing implementation of quantum error correction codes while preparing for larger
scale iterations.Open Acces
Structure and adsorption properties of gas-ionic liquid interfaces
Supported ionic liquids are a diverse class of materials that have been considered
as a promising approach to design new surface properties within solids for gas
adsorption and separation applications. In these materials, the surface morphology and
composition of a porous solid are modified by depositing ionic liquid. The resulting
materials exhibit a unique combination of structural and gas adsorption properties
arising from both components, the support, and the liquid. Naturally, theoretical and
experimental studies devoted to understanding the underlying principles of exhibited
interfacial properties have been an intense area of research. However, a complete
understanding of the interplay between interfacial gas-liquid and liquid-solid
interactions as well as molecular details of these processes remains elusive.
The proposed problem is challenging and in this thesis, it is approached from
two different perspectives applying computational and experimental techniques. In
particular, molecular dynamics simulations are used to model gas adsorption in films
of ionic liquids on a molecular level. A detailed description of the modeled systems is
possible if the interfacial and bulk properties of ionic liquid films are separated. In this
study, we use a unique method that recognizes the interfacial and bulk structures of
ionic liquids and distinguishes gas adsorption from gas solubility. By combining
classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous
support.
The developed approach was applied to a range of ionic liquids that feature
different interaction behavior with gas and porous support. Using molecular
simulations with interfacial analysis, it was discovered that gas adsorption capacity
can be directly related to gas solubility data, allowing the development of a predictive
model for the gas adsorption performance of ionic liquid films. Furthermore, it was
found that this CO2 adsorption on the surface of ionic liquid films is determined by the
specific arrangement of cations and anions on the surface. A particularly important
result is that, for the first time, a quantitative relation between these structural and
adsorption properties of different ionic liquid films has been established. This link
between two types of properties determines design principles for supported ionic
liquids.
However, the proposed predictive model and design principles rely on the
assumption that the ionic liquid is uniformly distributed on the surface of the porous
support. To test how ionic liquids behave under confinement, nitrogen physisorption
experiments were conducted for microâ and mesopore analysis of supported ionic
liquid materials. In conjunction with mean-field density functional theory applied to
the lattice gas and pore models, we revealed different scenarios for the pore-filling
mechanism depending on the strength of the liquid-solid interactions.
In this thesis, a combination of computational and experimental studies provides
a framework for the characterization of complex interfacial gas-liquid and liquid-solid
processes. It is shown that interfacial analysis is a powerful tool for studying
molecular-level interactions between different phases. Finally, nitrogen sorption
experiments were effectively used to obtain information on the structure of supported
ionic liquids
Cis-Regulation of Gremlin1 Expression during Mouse Limb Bud Development and its Diversification during Vertebrate Evolution
Embryonic development and organogenesis rely on tightly controlled gene expression, which is achieved by cis-regulatory modules (CRMs) interacting with distinct transcription factors (TFs) that control spatio-temporal and tissue-specific gene expression. During organogenesis, gene regulatory networks (GRNs) with selfregulatory feedback properties coordinately control growth and patterning and provide systemic robustness against genetic and/or environmental perturbations. During limb bud development, various interlinked GRNs control outgrowth and patterning along all three limb axes. A paradigm network is the epithelial-mesenchymal (e-m) SHH/GREM1/AER-FGF feedback signaling system which controls limb bud outgrowth and digit patterning. The BMP antagonist GREMLIN1 (GREM1) is central to this e-m interactions as its antagonism of BMP activity is essential to maintain both AER-Fgf and Shh expression. In turn, SHH signaling upregulates Grem1 expression, which results in establishment of a self-regulatory signaling network. One previous study provided evidence that several CRMs could regulate Grem1 expression during limb bud development. However, the cis-regulatory logics underlying the spatio-temporal regulation of the Grem1 expression dynamics remained obscure. From an evolutionary point of view, diversification of CRMs can result in diversification of gene regulation which can drive the establishment of morphological novelties and adaptions. This was evidenced by the observed differences in Grem1 expression in different species that correlates with the evolutionary plasticity of tetrapod digit patterning. Hence, a better understanding of spatio-temporal regulation of the Grem1 expression dynamics and underlying cis-regulatory logic is of interest from both adevelopmental and an evolutionary perspective.
Recently, multiple candidate CRMs have been identified that might be functionally relevant for Grem1 expression during mouse limb bud development. For my PhD project, I genetically analyzed which of these CRMs are involved in the regulation of the spatial-temporal Grem1 expression dynamics in limb buds. Therefore, we generated various single and compound CRM mutant alleles using CRISPR/Cas9. Our CRMs allelic series revealed a complex Grem1 cis-regulation among a minimum of six CRMs, where a subset of CRMs regulates Grem1 transcript levels in an additive manner. Surprisingly, phenotypic robustness depends not on threshold transcript levels but the spatial integrity of the Grem1 expression domain. In particular, interactions among five CRMs control the characteristic asymmetrical and posteriorly biased Grem1 expression in mouse limb buds. Our results provide an example of how multiple seemingly redundant limb-specific CRMs provide phenotypical robustness by cooperative/synergistic regulation of the spatial Grem1 expression dynamics.
Three CRMs are conserved along the phylogeny of extant vertebrates with paired appendages. Of those, the activities of two CRMs recapitulate the major spatiotemporal aspects of Grem1 expression in mouse limb buds. In order to study their functions in species-specific regulation of Grem1 expression and their functional diversification in tetrapods, I tested the orthologous of both CRMs from representative species using LacZ reporter assays in transgenic mice, in comparison to the endogenous Grem1 expression in limb buds of the species of origin. Surprisingly, the activities of CRM orthologues display high evolutionary plasticity, which correlates better with the Grem1 expression pattern in limb buds of the species of origin than its mouse orthologue. This differential responsiveness to the GRNs in mouse suggests that TF binding site alterations in CRMs could underlie the spatial diversification of Grem1 in limb buds during tetrapod evolution.
While the fish fin and tetrapod limb share some homologies of proximal bones, the autopod is a neomorphic feature of tetrapods. The Grem1 requirement for digit patterning and conserved expression in fin buds prompted us to assess the enhancer activity of fish CRM orthologues in transgenic mice. Surprisingly, all tested fish CRMs are active in the mouse autopod primordia providing strong evidence that Grem1 CRMs are active in fin buds and that they predate the fin-to-limb transition. Our results corroborate increasing evidence that CRMs governing autopodial gene expression have been co-opted during the emergence of tetrapod autopod.
Furthermore, as part of a collaboration with Dr. S. Jhanwar, I contributed to the study of shared and species-specific epigenomic and genomic variations during mouse and chicken limb bud development. In this analysis, Dr. S. Jhanwar identified putative enhancers that show higher chicken-specific sequence turnover rates in comparison to their mouse orthologues, which defines them as so-called chicken accelerated regions (CARs). Here, I analyzed the CAR activities in comparison to their mouse orthologues by transgenic LacZ reporter assays, which was complemented by analysis of the endogenous gene expression in limb buds of both species. This analysis indicates that diversified activity of CARs and their mouse orthologues could be linked to the differential gene expression patterns in limb buds of both species
Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach
In this paper, we propose a pre-trained-combined neural network (PTCN) as a
comprehensive solution to the inverse design of an integrated photonic circuit.
By utilizing both the initially pre-trained inverse and forward model with a
joint training process, our PTCN model shows remarkable tolerance to the
quantity and quality of the training data. As a proof of concept demonstration,
the inverse design of a wavelength demultiplexer is used to verify the
effectiveness of the PTCN model. The correlation coefficient of the prediction
by the presented PTCN model remains greater than 0.974 even when the size of
training data is decreased to 17%. The experimental results show a good
agreement with predictions, and demonstrate a wavelength demultiplexer with an
ultra-compact footprint, a high transmission efficiency with a transmission
loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB
simultaneously
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