13,814 research outputs found
An incremental input-to-state stability condition for a generic class of recurrent neural networks
This paper proposes a novel sufficient condition for the incremental
input-to-state stability of a generic class of recurrent neural networks
(RNNs). The established condition is compared with others available in the
literature, showing to be less conservative. Moreover, it can be applied for
the design of incremental input-to-state stable RNN-based control systems,
resulting in a linear matrix inequality constraint for some specific RNN
architectures. The formulation of nonlinear observers for the considered system
class, as well as the design of control schemes with explicit integral action,
are also investigated. The theoretical results are validated through simulation
on a referenced nonlinear system
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
Reinforcement Learning from Passive Data via Latent Intentions
Passive observational data, such as human videos, is abundant and rich in
information, yet remains largely untapped by current RL methods. Perhaps
surprisingly, we show that passive data, despite not having reward or action
labels, can still be used to learn features that accelerate downstream RL. Our
approach learns from passive data by modeling intentions: measuring how the
likelihood of future outcomes change when the agent acts to achieve a
particular task. We propose a temporal difference learning objective to learn
about intentions, resulting in an algorithm similar to conventional RL, but
which learns entirely from passive data. When optimizing this objective, our
agent simultaneously learns representations of states, of policies, and of
possible outcomes in an environment, all from raw observational data. Both
theoretically and empirically, this scheme learns features amenable for value
prediction for downstream tasks, and our experiments demonstrate the ability to
learn from many forms of passive data, including cross-embodiment video data
and YouTube videos.Comment: Accompanying website at https://dibyaghosh.com/icvf
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
YouTube and political ideologies: Technology, populism and rhetorical form
Digital media are driving profound changes in contemporary politics, including, this article argues, to the production, reception and dissemination of political ideas and ideologies. Platforms increase the number and political range of ‘ideological entrepreneurs’ using distinct rhetorics through which ideas are articulated and experienced. Developing and justifying these claims I draw on the political theory of ideologies, digital media studies and rhetorical political analysis. I show how a populist ‘style’ and appeal to rhetorical ethos, linked to mediatisation, are intensified by digital media, affecting ideological form and content. Explaining in particular how YouTube constitutes political-ideological communication I examine in detail the British-based political YouTuber Paul Joseph Watson. I show that his political ideology is a blend of conservatism and libertarianism, with a populist style and rhetorical ethos of ‘charismatic’ authority. Centred on the revelation of political truths, presented as of therapeutic benefit for individuals, it is characteristic of the medium
Where Does Music End and Nonmusic Begin? Fine-tuning the “Naturalist Response” Problem for Nontonal Music’s Naturalistic Critics
As to what distinguishes music from other sound, some investigators in both
philosophy and cognitive scientists have answered “tonality.” It seems subservient even
to rhythm. Tonality is considered to be the central factor around which the piece is
oriented; it gives a sense of home, expectation, and completeness. Most important, much
of this inquiry builds on naturalistic, evolutionary explanation to account for human nature
and behavior. The conclusion of such line of thought is that sounds missing tonality or
tonal focus cannot be music. This article challenges such sort of naturalistic criteria
distinguishing music from nonmusic. Permitting certain sets of sounds to be considered
music does not necessitate denial or approval of naturalistic explanations but does allow
nontonal music to serve a part of human and musical evolution
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
Recent Hong Kong cinema and the generic role of film noir in relation to the politics of identity and difference
This thesis identifies a connection in Hong Kong cinema with classical Hollywood film noir and examines what it will call a 'reinvestment' in film noir in recent films. It will show that this reinvestment is a discursive strategy that both engages the spectator-subject in the cinematic practice and disengages him or her from the hegemony of the discourse by decentring the narrative. The thesis argues that a cinematic practice has occurred in the recent reinvestment of film noir in Hong Kong, which restages the intertextual relay of the historical genre that gives rise to an expectation of ideas about social instability. The noir vision that is seen as related to the fixed categories of film narratives, characterizations and visual styles is reassessed in the course of the thesis using Derridian theory. The focus of analysis is the way in which the constitution of meanings is dependent on generic characteristics that are different. Key to the phenomenon is a film strategy that destabilizes, differs and defers the interpretation of crises-personal, social, political and/or cultural-by soliciting self-conscious re-reading of suffering, evil, fate, chance and fortune. It will be argued that such a strategy evokes the genre expectation as the film invokes a network of ideas regarding a world perceived by the audience in association with the noirish moods of claustrophobia, paranoia, despair and nihilism. The noir vision is thus mutated and transformed when the film device differs and defers the conception of the crises as tragic in nature by exposing the workings of the genre amalgamation and the ideological function of the cinematic discourse. Thus, noirishness becomes both an affect and an agent that contrives a self-reflexive re-reading of the tragic vision and of the conventional comprehension of reality within the discursive practice. The film strategy, as an agent that problematizes the film form and narrative, gives rise to what I call a politics of difference, which may also be understood as the Lyotardian 'language game' or a practice of 'pastiche' in Jameson's terminology. Under the influence of the film strategy, the spectator is enabled to negotiate his or her understanding of recent Hong Kong cinema diegetically and extra-diegetically by traversing different positions of cinematic identification. When the practice of genre amalgamation adopts the visual impact of the noirish film form, the film turns itself into a playing field of 'fatal' misrecognition or a site of question. Through cinematic identification and alienation from the identification, the spectator-subject is enabled to experience the misrecognition as the film slowly foregrounds the way in which the viewer's presence is implicated in the narrative. This thesis demonstrates that certain contemporary Hong Kong films introduce this selfconscious mode of explication and interpretation, which solicits the spectator to negotiate his or her subject-position in the course of viewing. The notions of identity and subjectivity under scrutiny will thus be reread. With reference to The Private Eye Blue, Swordsman II, City a/Glass and Happy Together, the thesis shall explore the ways in which the Hong Kong films enable and facilitate a negotiation of cultural identity
Anytime algorithms for ROBDD symmetry detection and approximation
Reduced Ordered Binary Decision Diagrams (ROBDDs) provide a dense and memory efficient representation of Boolean functions. When ROBDDs are applied in logic synthesis, the problem arises of detecting both classical and generalised symmetries. State-of-the-art in symmetry detection is represented by Mishchenko's algorithm. Mishchenko showed how to detect symmetries in ROBDDs without the need for checking equivalence of all co-factor pairs. This work resulted in a practical algorithm for detecting all classical symmetries in an ROBDD in O(|G|³) set operations where |G| is the number of nodes in the ROBDD. Mishchenko and his colleagues subsequently extended the algorithm to find generalised symmetries. The extended algorithm retains the same asymptotic complexity for each type of generalised symmetry. Both the classical and generalised symmetry detection algorithms are monolithic in the sense that they only return a meaningful answer when they are left to run to completion. In this thesis we present efficient anytime algorithms for detecting both classical and generalised symmetries, that output pairs of symmetric variables until a prescribed time bound is exceeded. These anytime algorithms are complete in that given sufficient time they are guaranteed to find all symmetric pairs. Theoretically these algorithms reside in O(n³+n|G|+|G|³) and O(n³+n²|G|+|G|³) respectively, where n is the number of variables, so that in practice the advantage of anytime generality is not gained at the expense of efficiency. In fact, the anytime approach requires only very modest data structure support and offers unique opportunities for optimisation so the resulting algorithms are very efficient. The thesis continues by considering another class of anytime algorithms for ROBDDs that is motivated by the dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This thesis proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate an ROBDD from above (i.e. derive a weaker function) or below (i.e. infer a stronger function). The thesis also considers how randomised techniques may be deployed to improve the speed of computing an approximation by avoiding potentially expensive ROBDD manipulation
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