Optical Focusing and Imaging through Scattering Media

Optical techniques, which have been widely used in various fields including bio-medicine, remote sensing, astronomy, and industrial production, play an important role in modern life. Optical focusing and imaging, which correspond to the basic methods of utilizing light, are key to the implementation of optical techniques. In free space or a nearly transparent medium, optical imaging and focusing can be easily realized by using conventional optical elements, such as lenses and mirrors, due to the ballistic propagation of light in these media. However, in scattering media like biological tissue and fog, refractive index inhomogeneities cause diffusive propagation of light that increases with depth, which restricts the use of optical methods in thick, scattering media. Generally speaking, scattering media poses three challenges to optical focusing and imaging: wavefront aberrations, glare, and decorrelation. Wavefront aberrations can randomize light traveling through a scattering medium, disrupt the formation of focus, and break the conjugate relation in imaging. Glare caused by backscattering will largely impair the visibility of imaging, and decorrelation in dynamic media requires systems that counter the effect of scattering to operate faster than the decorrelation time. In this thesis, we explored solutions to the problem of scattering from different aspects. We presented Time Reversal by Analysis of Changing wavefronts from Kinetic targets (TRACK) technique to realize noninvasive optical focusing through a scattering medium. We showed that by taking the difference between time-varying scattering fields caused by a moving object and applying optical phase conjugation, light can be focused back to the location previously occupied by the object. To tackle the decorrelation of living tissue, we built up a fast digital optical phase conjugation (DOPC) system based on FPGA and DMD, which has a response time of 5.3 ms and was the fastest DOPC system in the world before 2017. We demonstrated that the system is fast enough to focus light through 2.3mm-thick living mouse skin. As for glare, inspired by noise canceling headphones, we invented an optical analogue termed coherence gated negation (CGN) technique. CGN can optically cancel out the glare in an active illumination imaging scenario to realize imaging through scattering media, like fog. In the experiment, we suppressed the glare by an order of magnitude and allowed improved imaging of a weak target. Finally, we demonstrated a method to image a moving target through scattering media noninvasively. Its principle roots are in the speckle-correlation-based imaging (SCI) invented by Ori Katz. We improved the technique and extended its application to bright field imaging of a moving target.</p

Image and Video-Based Autism Spectrum Disorder Detection via Deep Learning

People with Autism Spectrum Disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories but was particularly successful at classifying photos of people with \u3e80% accuracy. Importantly, the visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time, we showed that photos were taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed the classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. People with ASD also show atypical behavior when they are doing the same action with peers without ASD. However, it is challenging to efficiently extract this feature from spatial and temporal information. In this study, we applied Graph Convolutional Network (GCN) to the 2D skeleton sequence to classify video recording the same action (brush teeth and wash face) as either from individuals with ASD or by peers without ASD. Furthermore, we adopted an adaptive graph mechanism that allows the model to learn a kernel flexibly and exclusively for each layer, which means the model can learn more useful and robust features. Our classifier can effectively reach80% accuracy. Our method may play an important role in the evaluations of individuals with ASD

Logics for strategic reasoning and collective decision-making

Cette thèse aborde le problème du raisonnement stratégique. Le raisonnement stratégique est un thème de recherches existant depuis e nombreuses années en théorie des jeux. Toutefois, celui-ci a le plus souvent pour objet de déterminer si des équilibres stratégiques existent sans détailler la définition en elle-même de ces stratégies. La construction d'agents artificiels capable de raisonner stratégiquement implique de se poser la question de la représentation de ces stratégies afin que les agents puissent les construire, combiner, comparer et enfin et surtout exécuter. Cette thèse propose un ensemble de logiques pour le raisonnement stratégique et la prise de décision collective. Elle établit dans un premier temps un cadre unifiée pour la définition de jeux, la représentation de stratégies et le raisonnement sur celles-ci dans le contexte des jeux à information parfaite. Ce cadre est ensuite étendu pour prendre en compte les jeux à information imparfaite. Les relations entre les connaissances de groupe, le pouvoir des coalitions ainsi que le partage d'informations dans une coalition sont ensuite étudiés. Dans un dernier temps, est introduit une logique modale permettant de de raisonner sur les choix collectifs, cette logique permet de généraliser les approches logiques existantes pour l'agrégation de jugements. La complexité de ces différents cadres logiques est aussi étudiée et nous montrons que ces différents cadres offre un équilibre pertinent entre efficacité computationnelle et pouvoir d'expression.This thesis proposes a set of logics for modelling strategic reasoning and collective decision-making. It first establishes a unified logical framework for game specifications, strategy representation and strategic reasoning in perfect information games. Based on that, it proposes an epistemic extension to address imperfect information games. To investigate the interplay of group knowledge and coalitional abilities, it further models knowledge sharing within coalitions. Finally it introduces a modal logic for collective choice and generalizes the logic-based approach to judgment aggregation. The complexity analysis of these logics indicates that these frameworks make a good balance between expressive power and computational efficiency

Logics for strategic reasoning and collective decision-making

Strategic decision-making is ubiquitous in everyday life. The analysis of game strategies has been a research theme in game theory for several decades since von Neumann and Morgenstern. Sophisticated models and analysis tools have been developed with wide applications in Economics, Management Science, Social Science and Computer Science, especially in the field of Artificial Intelligence. However, \much of game theory is about the question whether strategic equilibria exist", as Johan van Benthem, a world-leading logician and game-theorist, points out, \but there are hardly any explicit languages for defining, comparing, or combining strategies". Without such a facility it is challenging for computer scientists to build intelligent agents that are capable of strategic decision-making. In the last twenty years, logical approaches have been proposed to tackle this problem. Pioneering work includes Game Logics, Coalition Logic and Alternating-time Temporal Logic (ATL). These logics either provide facilities for expressing and combining games or offer mechanisms for reasoning about strategic abilities of players. But none of them can solve the problem. The intrinsic difficulty in establishing such a logic is that reasoning about strategies requires combinations of temporal reasoning, counterfactual reasoning, reasoning about actions, preferences and knowledge, as well as reasoning about multi-agent interactions and coalitional abilities. More recently, a few new logical formalisms have been proposed by extending ATL with strategy variables in order to express strategies explicitly. However, most of these logics tend to have high computational complexity, because ATL introduces quantifications over strategies (functions), which leaves little hope of building any tractable inference system based on such a logic. This thesis takes up the challenge by using a bottom-up approach in order to create a balance between expressive power and computational efficiency. Instead of starting with a highly complicated logic, we propose a set of logical frameworks based on a simple and practical logical language, called Game Description Language (GDL), which has been used as an official language for General Game Playing (GGP) since 2005. To represent game strategies, we extend GDL with two binary prioritized connectives for combining actions in terms of their priorities specified by these connectives, and provide it with a semantics based on the standard state transition model. To reason about the strategic abilities of players, we further extend the framework with coalition operators from ATL for specifying the strategic abilities of players. More importantly, a unified semantics is provided for both GDL- and ATL- formulas, which allows us to verify and reason about game strategies. Interestingly, the framework can be used to formalize the fundamental game-playing principles and formally derive two well-known results on two-player games: Weak Determinacy and Zermelo's Theorem. We also show that the model-checking problem of the logic is not worse than that of ATL*, an extension of ATL. To deal with imperfect information games, we extend GDL with the standard epistemic operators and provide it with a semantics based on the epistemic state transition model. The language allows us to specify an imperfect information game and formalize its epistemic properties. Meanwhile, the framework allows us to reason about players' own as well as other players' knowledge during game playing. Most importantly, the logic has a moderate computational complexity, which makes it significantly different from similar existing frameworks. To investigate the interplay between knowledge shared by a group of players and its coalitional abilities, we provide a variant of semantics for ATL with imperfect information. The relation between knowledge sharing and coalitional abilities is investigated through the interplay of epistemic and coalition modalities. Moreover, this semantics is able to preserve the desirable properties of coalitional abilities. To deal with collective decision-making, we apply the approach of combining actions via their priorities for collective choice. We extend propositional logic with the prioritized connective for modelling reason-based individual and collective choices. Not only individual preferences but also aggregation rules can be expressed within this logic. A model-checking algorithm for this logic is thus developed to automatically generate individual and collective choices. In many real-world situations, a group making collective judgments may assign individual members or subgroups different priorities to determine the collective judgment. We design an aggregation rule based on the priorities of individuals so as to investigate how the judgment from each individual affects group judgment in a hierarchical environment. We also show that this rule satisfies a set of plausible conditions and has a tractable computational complexity

Logics for strategic reasoning and collective decision-making

Strategic decision-making is ubiquitous in everyday life. The analysis of game strategies has been a research theme in game theory for several decades since von Neumann and Morgenstern. Sophisticated models and analysis tools have been developed with wide applications in Economics, Management Science, Social Science and Computer Science, especially in the field of Artificial Intelligence. However, \much of game theory is about the question whether strategic equilibria exist", as Johan van Benthem, a world-leading logician and game-theorist, points out, \but there are hardly any explicit languages for defining, comparing, or combining strategies". Without such a facility it is challenging for computer scientists to build intelligent agents that are capable of strategic decision-making. In the last twenty years, logical approaches have been proposed to tackle this problem. Pioneering work includes Game Logics, Coalition Logic and Alternating-time Temporal Logic (ATL). These logics either provide facilities for expressing and combining games or offer mechanisms for reasoning about strategic abilities of players. But none of them can solve the problem. The intrinsic difficulty in establishing such a logic is that reasoning about strategies requires combinations of temporal reasoning, counterfactual reasoning, reasoning about actions, preferences and knowledge, as well as reasoning about multi-agent interactions and coalitional abilities. More recently, a few new logical formalisms have been proposed by extending ATL with strategy variables in order to express strategies explicitly. However, most of these logics tend to have high computational complexity, because ATL introduces quantifications over strategies (functions), which leaves little hope of building any tractable inference system based on such a logic. This thesis takes up the challenge by using a bottom-up approach in order to create a balance between expressive power and computational efficiency. Instead of starting with a highly complicated logic, we propose a set of logical frameworks based on a simple and practical logical language, called Game Description Language (GDL), which has been used as an official language for General Game Playing (GGP) since 2005. To represent game strategies, we extend GDL with two binary prioritized connectives for combining actions in terms of their priorities specified by these connectives, and provide it with a semantics based on the standard state transition model. To reason about the strategic abilities of players, we further extend the framework with coalition operators from ATL for specifying the strategic abilities of players. More importantly, a unified semantics is provided for both GDL- and ATL- formulas, which allows us to verify and reason about game strategies. Interestingly, the framework can be used to formalize the fundamental game-playing principles and formally derive two well-known results on two-player games: Weak Determinacy and Zermelo's Theorem. We also show that the model-checking problem of the logic is not worse than that of ATL*, an extension of ATL. To deal with imperfect information games, we extend GDL with the standard epistemic operators and provide it with a semantics based on the epistemic state transition model. The language allows us to specify an imperfect information game and formalize its epistemic properties. Meanwhile, the framework allows us to reason about players' own as well as other players' knowledge during game playing. Most importantly, the logic has a moderate computational complexity, which makes it significantly different from similar existing frameworks. To investigate the interplay between knowledge shared by a group of players and its coalitional abilities, we provide a variant of semantics for ATL with imperfect information. The relation between knowledge sharing and coalitional abilities is investigated through the interplay of epistemic and coalition modalities. Moreover, this semantics is able to preserve the desirable properties of coalitional abilities. To deal with collective decision-making, we apply the approach of combining actions via their priorities for collective choice. We extend propositional logic with the prioritized connective for modelling reason-based individual and collective choices. Not only individual preferences but also aggregation rules can be expressed within this logic. A model-checking algorithm for this logic is thus developed to automatically generate individual and collective choices. In many real-world situations, a group making collective judgments may assign individual members or subgroups different priorities to determine the collective judgment. We design an aggregation rule based on the priorities of individuals so as to investigate how the judgment from each individual affects group judgment in a hierarchical environment. We also show that this rule satisfies a set of plausible conditions and has a tractable computational complexity

Cut-out: music, profanity, and subcultural politics in 1990s China

This thesis examines the subcultural politics of the “cut-out generation” in 1990s China. It is driven by a double aspiration, and aims to bridge gaps in two main fields of literature. The first aspiration is a historical one: I offer a fine historical account of the “cut-out generation” as a slice of 1990s Chinese society, which remained overlooked in Chinese studies. The second aspiration is a theoretical one: I interrogate existing theories and concepts in the larger field of cultural studies, including music and materiality, subculture and profanity, regime of value and structure of feeling, with evidence found in this Chinese case, and point toward a comprehensive framework for the analysis of subcultural politics. The methodological approach consists of 72 in-depth life history interview and archival analysis of a “cut-out archive”, both conducted through an “ethnographic imagination” lens. The thesis unpacks subcultural politics by looking into the materiality of subcultural objects, the sensuous interactions between people and things, and the dialectical interplay between a subculture and its historical, structural settings. It has three main empirical foci corresponding to each empirical chapter and organized under three theoretical themes: politics of value, dialectics of profanity, and structure of feeling. The first empirical chapter (politics of value) investigates three contested materialities of the cut-outs as plastic scrap, defective records, and “music info”, demonstrating in this way the different regimes of value which the cut-outs travelled through. The second one (dialectics of profanity) reveals how the cut-outs became a music object whose profanity was realized in various sensuous and creative relationships formed with Chinese consumers. The last empirical chapter (structure of feeling) provides a historicized analysis of the structure of feeling of the “cut-out generation”, which functioned as an affective infrastructure which both “emerged” from and “penetrated” the historical context of 1990s China

Non-linear galaxy clustering in modified gravity cosmologies

We present \textsc{MG-GLAM}, a code developed for the very fast production of full $N$-body cosmological simulations in modified gravity (MG) models. We describe the implementation, numerical tests, and first results of a large suite of cosmological simulations for a wide range of viable MG models. The code is highly optimised, with a tremendous speedup of a factor of more than a hundred compared with earlier $N$-body codes, while still giving accurate predictions of the matter power spectrum and dark matter halo abundance. \textsc{MG-GLAM} is ideal for the generation of large numbers of MG simulations that can be used in the construction of mock galaxy catalogues and the production of accurate emulators for ongoing and future galaxy surveys. The coming generation of galaxy surveys will provide measurements of galaxy clustering with unprecedented accuracy and data size, which will allow us to test cosmological models at a much higher precision than previously achievable. This means that we must have more accurate theoretical predictions to compare with future observational data. As a first step toward more accurate modelling of the redshift space distortions (RSD) of small-scale galaxy clustering in modified gravity cosmologies, we investigate the validity of the so-called Skew-T (ST) probability distribution function (PDF) of halo pairwise peculiar velocities in these models. We show that combined with the streaming model, the ST PDF substantially improves the small-scale predictions by incorporating skewness and kurtosis, for both $\Lambda$ cold dark matter ($\Lambda$CDM) and two leading MG models: $f(R)$ gravity and the DGP braneworld model. The ST model reproduces the velocity PDF and redshift-space halo clustering measured from MG $N$-body simulations down to highly non-linear scales. By performing a simple Fisher analysis, we find a significant increase in constraining power to detect modifications of General Relativity by introducing small-scale information in the RSD analyses. We introduce an emulator-based halo model approach for non-linear clustering of galaxies in modified gravity cosmologies. We construct accurate emulators, i.e. simulation-based theoretical templates, using neural networks for basic halo properties than can be calculated robustly from $N$-body simulations. The dark matter halo emulators can be combined with a halo-galaxy connection model to predict the galaxy clustering statistics down to non-linear scales through the halo model

Posttraumatic Stress Symptoms are Associated with Diminished Learning from Reward

This item is only available electronically.Posttraumatic stress disorder (PTSD) and subthreshold posttraumatic stress symptoms (PTSS) are associated with differences related to learning, particularly impaired fear extinction. The extinction of learned fear responses relies on neural mechanisms of reward learning, in that the absence of the expected aversive event triggers unexpected relief, which is a form of reward. Impaired learning from reward may therefore be important in the aetiology and maintenance of PTSS. PTSS have been linked to reduced reward responsiveness, but evidence regarding learning from reward is limited. This study examined whether reward learning is associated with PTSS. Participants (N = 150, 110 female) completed the Life Events Checklist and the PTSD Checklist for DSM-5 to indicate their trauma history and current PTSS. The mean PCL-5 score was 14.82, and 14 participants also reported having PTSD. Participants’ learning from reward and from punishment were derived from their performance on a probabilistic reinforcement learning task. In a linear regression model, lower reward learning, more directly experienced traumatic events, and lower age were all associated with more severe PCL-5 scores. The relationship between PTSS and reward learning appeared to be driven by the DSM-5 symptom cluster of intrusive re-experiencing, for which reward learning predicted 4.2% of the variance. Participants who reported having PTSD had poorer reward learning than those who did not. These results provide evidence for ties between poor reward learning and PTSS. This contributes to potential therapeutic approaches to the disorder, as reward learning deficits may contribute to the severity and longevity of PTSS.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202

Broadcast in sparse conversion optical networks using fewest converters

Wavelengths and converters are shared by communication requests in optical networks. When a message goes through a node without a converter, the outgoing wavelength must be the same as the incoming one. This constraint can be removed if the node uses a converter. Hence, the usage of converters increases the utilization of wavelengths and allows more communication requests to succeed. Since converters are expensive, we consider sparse conversion networks, where only some specified nodes have converters. Moreover, since the usage of converters induces delays, we should minimize the use of available converters. The Converters Usage Problem (CUP) is to use a minimum number of converter so that each node can send messages to all the others (broadcasting). In this dissertation, we study the CUP in sparse conversion networks. We design a linear algorithm to find a wavelength assignment in tree networks such that, with the usage of a minimum number of available converters, every node can send messages to all the others. This is a generalization of [35], where each node has a converter. Our algorithm can assign wavelengths efficiently and effectively for one-to-one, multicast, and broadcast communication requests. A converter wavelength-dominates a node if there is a uniform wavelength path between them. The Minimal Wavelength Dominating Set Problem (MWDSP) is to locate a minimum number of converters so that all the other nodes in the network are wavelength-dominated. We use a linear complexity dynamic programming algorithm to solve the MWDSP for networks with bounded treewidth. One such solution provides a low bound for the optimal solution to the CUP

Targeting prolyl-isomerase Pin1 prevents mitochondrial oxidative stress and vascular dysfunction: insights in patients with diabetes

The present study demonstrates that Pin1 is a common activator of key pathways involved in diabetic vascular disease in different experimental settings including primary human endothelial cells, knockout mice, and diabetic patients. Gene silencing and genetic disruption of Pin1 prevent hyperglycaemia-induced mitochondrial oxidative stress, endothelial dysfunction, and vascular inflammation. Moreover, we have translated our findings to diabetic patients. In line with our experimental observations, Pin1 up-regulation is associated with impaired flow-mediated dilation, increased oxidative stress, and plasma levels of adhesion molecules. In perspective, these findings may provide the rationale for mechanism-based therapeutic strategies in patients with diabete