1,225 research outputs found

    Addiction in context

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    The dissertation provides a comprehensive exploration of the interplay between social and cultural factors in substance use, specifically focusing on alcohol use disorder (AUD) and cannabis use disorder (CUD). It begins by introducing the concept of social plasticity, which posits that adolescents' susceptibility to AUD is influenced by their heightened sensitivity to their social environment, but this sensitivity increases the potential for recovery in the transition to adulthood.A series of studies delves into how social cues impact alcohol craving and consumption. One study using functional magnetic resonance imaging (fMRI) investigated social alcohol cue reactivity and its relationship to social drinking behavior, revealing increased craving but no significant change in brain activity in response to alcohol cues. Another fMRI study compared social processes in alcohol cue reactivity between adults and adolescents, showing age-related differences in how social attunement affects drinking behavior. Shifting focus to cannabis, this dissertation discusses how cultural factors, including norms, legal policies, and attitudes, influence cannabis use and processes underlying CUD. The research presented examined various facets of cannabis use, including how cannabinoid concentrations in hair correlate with self-reported use, the effects of cannabis and cigarette co-use on brain reactivity, and cross-cultural differences in CUD between Amsterdam and Texas. Furthermore, the evidence for the relationship between cannabis use, CUD, and mood disorders is reviewed, suggesting a bidirectional relationship, with cannabis use potentially preceding the onset of bipolar disorder and contributing to the development and worse prognosis of mood disorders and mood disorders leading to more cannabis use

    Automated identification and behaviour classification for modelling social dynamics in group-housed mice

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    Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results. This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing. A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups. The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup. Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic

    Understanding Agreement and Disagreement in Listeners’ Perceived Emotion in Live Music Performance

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    Emotion perception of music is subjective and time dependent. Most computational music emotion recognition (MER) systems overlook time- and listener-dependent factors by averaging emotion judgments across listeners. In this work, we investigate the influence of music, setting (live vs lab vs online), and individual factors on music emotion perception over time. In an initial study, we explore changes in perceived music emotions among audience members during live classical music performances. Fifteen audience members used a mobile application to annotate time-varying emotion judgments based on the valence-arousal model. Inter-rater reliability analyses indicate that consistency in emotion judgments varies significantly across rehearsal segments, with systematic disagreements in certain segments. In a follow-up study, we examine listeners' reasons for their ratings in segments with high and low agreement. We relate these reasons to acoustic features and individual differences. Twenty-one listeners annotated perceived emotions while watching a recorded video of the live performance. They then reflected on their judgments and provided explanations retrospectively. Disagreements were attributed to listeners attending to different musical features or being uncertain about the expressed emotions. Emotion judgments were significantly associated with personality traits, gender, cultural background, and music preference. Thematic analysis of explanations revealed cognitive processes underlying music emotion perception, highlighting attributes less frequently discussed in MER studies, such as instrumentation, arrangement, musical structure, and multimodal factors related to performer expression. Exploratory models incorporating these semantic features and individual factors were developed to predict perceived music emotion over time. Regression analyses confirmed the significance of listener-informed semantic features as independent variables, with individual factors acting as moderators between loudness, pitch range, and arousal. In our final study, we analyzed the effects of individual differences on music emotion perception among 128 participants with diverse backgrounds. Participants annotated perceived emotions for 51 piano performances of different compositions from the Western canon, spanning various era. Linear mixed effects models revealed significant variations in valence and arousal ratings, as well as the frequency of emotion ratings, with regard to several individual factors: music sophistication, music preferences, personality traits, and mood states. Additionally, participants' ratings of arousal, valence, and emotional agreement were significantly associated to the historical time periods of the examined clips. This research highlights the complexity of music emotion perception, revealing it to be a dynamic, individual and context-dependent process. It paves the way for the development of more individually nuanced, time-based models in music psychology, opening up new avenues for personalised music emotion recognition and recommendation, music emotion-driven generation and therapeutic applications

    ACOUSTIC SPEECH MARKERS FOR TRACKING CHANGES IN HYPOKINETIC DYSARTHRIA ASSOCIATED WITH PARKINSON’S DISEASE

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    Previous research has identified certain overarching features of hypokinetic dysarthria associated with Parkinson’s Disease and found it manifests differently between individuals. Acoustic analysis has often been used to find correlates of perceptual features for differential diagnosis. However, acoustic parameters that are robust for differential diagnosis may not be sensitive to tracking speech changes. Previous longitudinal studies have had limited sample sizes or variable lengths between data collection. This study focused on using acoustic correlates of perceptual features to identify acoustic markers able to track speech changes in people with Parkinson’s Disease (PwPD) over six months. The thesis presents how this study has addressed limitations of previous studies to make a novel contribution to current knowledge. Speech data was collected from 63 PwPD and 47 control speakers using an online podcast software at two time points, six months apart (T1 and T2). Recordings of a standard reading passage, minimal pairs, sustained phonation, and spontaneous speech were collected. Perceptual severity ratings were given by two speech and language therapists for T1 and T2, and acoustic parameters of voice, articulation and prosody were investigated. Two analyses were conducted: a) to identify which acoustic parameters can track perceptual speech changes over time and b) to identify which acoustic parameters can track changes in speech intelligibility over time. An additional attempt was made to identify if these parameters showed group differences for differential diagnosis between PwPD and control speakers at T1 and T2. Results showed that specific acoustic parameters in voice quality, articulation and prosody could differentiate between PwPD and controls, or detect speech changes between T1 and T2, but not both factors. However, specific acoustic parameters within articulation could detect significant group and speech change differences across T1 and T2. The thesis discusses these results, their implications, and the potential for future studies

    Development of Small-Angle X-Ray Scattering on a Nanometer and Femtosecond Scale for the Investigation of Laser-Driven Matter

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    Laser-Plasma-Beschleunigung mittels ultraintensiver Laserstrahlung ist eine vielversprechende Technologie fĂŒr die Entwicklung kompakter Strahlungsquellen. Diese werden in einem breiten Spektrum technischer AnwendungsfĂ€lle genutzt, zum Beispiel zur Krebstherapie, in der Laborastrophysik und fĂŒr die TrĂ€gheitsfusion, weshalb viele interdisziplinĂ€ren Forschungsfelder ein großes Interesse an ihrer Entwicklung haben. Die ersten Machbarkeitsstudien zur Nutzung gepulster Protonenstrahlung zur Tumorbehandlung haben bereits erfreuliche Ergebnisse geliefert. Dennoch lagen die erzielten Parameter des Protonenstrahls weit unter den erwarteten Werten. Die bekannten Faktoren, die diese Performance einschrĂ€nken, wurden fast ausschließlich durch Simulationen identifiziert. Der experimentelle Zugang zur Laser-Plasma-Wechselwirkung ist bisher auf die Auswertung der resultierenden Strahlung und auf makroskopische OberflĂ€cheneffekte beschrĂ€nkt, die mit optischen Messtechniken untersucht werden können. Diese Diagnostiken liefern allerdings keinerlei Informationen ĂŒber die VorgĂ€nge im Inneren des Plasmas, die letztlich die Parameter der beschleunigten Protonen bestimmen. Diese Prozesse werden in ihrer GrĂ¶ĂŸe und Zeitskala durch die Plasmaoszillation bzw. deren Frequenz und WellenlĂ€nge bestimmt. Das Ziel dieses Forschungsprojekts war es, diese LĂŒcke in der Auflösung bestehender Messmethoden zu schließen und eine Diagnostik zu entwickeln, die in der Lage ist, nanoskopische Plasma-PhĂ€nomene im Inneren der lasergetriebenen Probe zu untersuchen. Dieses Ziel konnten wir durch die EinfĂŒhrung von Röntgenkleinwinkelstreuung (SAXS) in Laserexperimenten an Röntgen-Freie-Elektronen-Lasern (XFELs) erreichen. In dieser Arbeit erlĂ€utere ich das technische Design und die methodische Auswertung des ersten dedizierten SAXS Experiments, das an der Matter in Extreme Conditions Messstation (auch MEC, Materie unter extremen Bedingungen) der Linac Coherent Light Source (auch LCLS, Linearbeschleuniger als kohĂ€rente Lichtquelle) durchgefĂŒhrt wurde. Dieses Experiment war vorrangig eine Machbarkeitsstudie, die als Basis fĂŒr die weitere Verwendung von SAXS in Laserexperimenten dienen soll. Meine Arbeit wird ausfĂŒhrlich die dafĂŒr nötigen experimentellen Techniken, den Aufbau, die Reinigung des gemessenen Beugungsbilds, das Probendesign und den Auswerteprozess erlĂ€utern. Um die experimentelle DurchfĂŒhrbarkeit dieser Methode zu testen, nutzten wir SAXS, um die Ausbreitung einer nanostrukturierten Probe in der Zeit kurz vor und wĂ€hrend des Beginns des Laserpulses zu messen. Der Ausbreitungsparameter, den wir so aus den experimentellen Daten gewinnen konnten, liegt im einstelligen Nanometer- und teilweise im Subnanometer-Bereich und stimmte gut mit den Ergebnissen einer Particle In Cell (PIC) Simulation zur frĂŒhen Ausbreitungsphase ĂŒberein. Dies zeigt, dass SAXS in der Lage ist, Plasma Prozesse zu messen, die fĂŒr andere Diagnostiken bisher nicht zugĂ€nglich waren. Außerdem beobachteten wir eine Abweichung der experimentellen Daten von dem von uns entwickelten Modell zur Beschreibung der ungehinderten Ausbreitung des Plasmas ins Vakuum. Dies veranlasste uns zu einer genaueren Untersuchung der Ausbreitung mittels PIC Simulation und tatsĂ€chlich sahen wir darin die Bildung von Plasma-Strömen, die auch in der SAXS-Auswertung qualitativ bestĂ€tigt werden konnten. Die KomplexitĂ€t des Ausbreitungsprozesses, die wir in diesem Forschungsprojekt aufdecken konnten, zeigt, dass weitere Studien dazu durchgefĂŒhrt werden sollten. Wenn wir die Ergebnisse der hier prĂ€sentierten SAXS Modelle nutzen, um unser VerstĂ€ndnis des Effekts von Vorpulsen und IntensitĂ€ts-Plateaus auf die Protonenbeschleunigung mit nanostrukturierten Proben zu verbessern, werden wir zukĂŒnftig in der Lage sein, die damit erzielten Strahlparameter zu verbessern. Der entwickelte SAXS Aufbau wurde auch an die Gegebenheiten von Experimenten zur Schockwellenverdichtung mittels Hochenergielasern angepasst und angewendet. Es gibt großes wissenschaftliches Interesse an der Entmischung von Kohlenwasserstoffen im Zustand warmer dichter Materie (WDM). Viele Laborastrophysikexperimente untersuchen das Innere von Eisriesen wie Uranus und Neptun, insbesondere den Verlauf der Phasentrennung von leichten Elementen wie Kohlenstoff und Wasserstoff, die zu Diamantregen fĂŒhrt. Bisher war es bei diesen Messungen nicht möglich, nanoskopische DichteĂ€nderungen im Inneren einer dichten Probe unter extremen Bedingungen zu untersuchen. Im Rahmen dieser Forschungsarbeit wurde SAXS als ergĂ€nzende Diagnostik in Hochenergiedichte-Experimenten mit Lasern an Einrichtungen wie an der MEC Messstation und an anderen XFELs etabliert. Ich wendete bekannte SAXS Auswerteroutinen auf den besonderen Fall eines sich von Schuss zu Schuss Ă€ndernden Dichtekontrasts an. Die verschiedenen Komponenten der SAXS Daten wurden mit den Informationen korreliert, die aus anderen Diagnostiken wie Beugung und VISAR gewonnen wurden. So konnte ich durch die Auswertung der Nanodiamant-Komponente eine SchĂ€tzung der DiamantgrĂ¶ĂŸe und des Diamant-Volumenanteils ableiten, indem ich spezifische Modelle fittete, die auf hydrodynamischen Simulationen basieren. ZukĂŒnftig möchten wir diese experimentellen Grundlagen auch auf die Untersuchung von FlĂŒssig-FlĂŒssig-Entmischung leichter Elemente im WDM Zustand anwenden. In dieser Arbeit erlĂ€utere ich die von mir entwickelten Auswerteprozesse, die auf weitere Messungen angewendet werden können, sobald deren Messbereich und SensitivitĂ€t so verbessert wurde, dass die Parameter von Interesse bestimmbar sind. Dieses Projekt half dabei, SAXS als Standarddiagnostik in Forschungseinrichtungen zu etablieren, die XFELs mit Hochleistungslaserexperimenten verbinden. Es bereitet sowohl die technische als auch die methodische Grundlage fĂŒr weitere Experimente.Laser plasma acceleration with ultra-high intensity (UHI) lasers is a promising technology for building compact radiation sources. These hold immense potential for a wide array of applications including cancer therapy, laboratory astrophysics and inertial confinement fusion and there is great interest in their development in many interdisciplinary fields of research. But while proof of concept experiments using proton pulses for tumor irradiation have delivered encouraging results, the achieved proton beam parameters fell short of the originally expected values. The limiting factors to this performance have mostly been identified in simulation only. Experimental access to the interaction between the drive laser and the dense plasma is so far limited to the analysis of the emitted radiation and the macroscopic surface effects that can be probed by visible light. These diagnostics cannot provide information about the processes in the bulk of the plasma that eventually determine the properties of the accelerated particles. Their spatial and temporal domain is dominated by the plasma oscillation frequency and wavelength. The aim of this project was to bridge this resolution gap with a diagnostic that is capable of investigating nanoscopic plasma features in the bulk of a laser-driven sample on a femtosecond scale. This was achieved by establishing the use of Small Angle X-Ray Scattering (SAXS) at UHI laser experiments at X-Ray Free Electron Lasers. My thesis will outline the technical design and scientific analysis of the first dedicated SAXS experiment at the Matter in Extreme Conditions (MEC) instrument of the Linac Coherent Light Source. The primary goal of the experiment was proof of concept as a foundation for regular use of SAXS in UHI experiments in the future. I will discuss the experimental procedures, the setup, the cleaning of the diffraction pattern, the target design and the analysis process that were developed for this new diagnostic in detail. To test the feasibility of this method, we used SAXS to measure the expansion of a nanostructured target in the femtosecond time span before and around the onset of a low intensity drive laser pulse. The expansion parameter that was extracted from the experimental data is in the in the sub- to single nanometer range and was in good agreement with the results of a particle-in-cell (PIC) simulation describing the early expansion phase. This demonstrates that SAXS is capable of measuring plasma processes on scales that were previously unobtainable by other diagnostics. We also identified a deviation of the experimental data from the simple model that we developed to describe an unobstructed expansion of plasma into vacuum. This lead us to examine the expansion in more detail via PIC simulation and indeed we discovered the formation of plasma jets at a later phase of the plasma expansion in simulation for a grating target. This additional effect was confirmed qualitatively by the SAXS analysis. The complexity of the plasma expansion process for a structured target we found in this project demonstrates the need for further studies. If we use the SAXS models presented here to improve our understanding of the effect of prepulses and pedestals on proton acceleration using nanostructured targets, we can apply this knowledge to the improvement of the proton beam parameters in future developments. %Additionally the technical implementation of SAXS for UHI laser experiments was developed in the framework of this thesis and established as a useful tool for the investigation of other nanoscopic plasma features. The developed experimental setup for SAXS was also adapted and applied to laser shock compression experiments using high energy drive lasers. There is great research interest in the demixing of hydrocarbons in the Warm Dense Matter (WDM) state. Many laboratory astrophysics experiments investigate the internal structure of ice giants like Uranus and Neptune, specifically the dynamics of the phase separation of light elements like carbon and hydrogen which can result in diamond rain. So far these measurements lacked a diagnostic that is capable of probing nanoscopic density modulations in the bulk of a dense target in an extreme state of matter. SAXS allowed us to gain access to the parameters of the demixing process. In the framework of this project SAXS was established as a complementary diagnostic to the standard setup for high energy density laser experiments at the MEC instrument and at other XFELs. I applied existing SAXS analysis procedures to the special case of a density contrast that changes on every shot. The different components of the SAXS data were correlated to information from other standard diagnostics including diffraction and VISAR. I was able to quantitatively analyze the component caused by nanodiamonds and retrieved an estimate of the diamond size and volume fraction from fits to custom models that are based on hydrodynamic simulations. In the future, we would like to extend this experimental basis to the investigation of liquid-liquid demixing of light elements in the WDM state. In this thesis I will discuss the SAXS analysis procedures that I dweveloped so that they can be applied to future measurements, once the experimental range and sensitivity has been improved to retrieve the parameters of interest. This project helped to establish SAXS as a standard diagnostic at facilities combining XFELs with high power laser experiments. It is supposed to lay both the technical and methodical groundwork for further experiments

    Transiting Exoplanet Yields for the Roman Galactic Bulge Time Domain Survey Predicted from Pixel-Level Simulations

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    The Nancy Grace Roman Space Telescope (Roman) is NASA's next astrophysics flagship mission, expected to launch in late 2026. As one of Roman's core community science surveys, the Galactic Bulge Time Domain Survey (GBTDS) will collect photometric and astrometric data for over 100 million stars in the Galactic bulge to search for microlensing planets. To assess the potential with which Roman can detect exoplanets via transit, we developed and conducted pixel-level simulations of transiting planets in the GBTDS. From these simulations, we predict that Roman will find between ∌\sim60,000 and ∌\sim200,000 transiting planets, over an order of magnitude more planets than are currently known. While the majority of these planets will be giants (Rp>4R⊕R_p>4R_\oplus) on close-in orbits (a<0.3a<0.3 au), the yield also includes between ∌\sim7,000 and ∌\sim12,000 small planets (Rp<4R⊕R_p<4 R_\oplus). The yield for small planets depends sensitively on the observing cadence and season duration, with variations on the order of ∌\sim10-20% for modest changes in either parameter, but is generally insensitive to the trade between surveyed area and cadence given constant slew/settle times. These predictions depend sensitively on the Milky Way's metallicity distribution function, highlighting an opportunity to significantly advance our understanding of exoplanet demographics, particularly across stellar populations and Galactic environments.Comment: Accepted to ApJS; 64 pages, 18 figure

    Towards trustworthy computing on untrustworthy hardware

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    Historically, hardware was thought to be inherently secure and trusted due to its obscurity and the isolated nature of its design and manufacturing. In the last two decades, however, hardware trust and security have emerged as pressing issues. Modern day hardware is surrounded by threats manifested mainly in undesired modifications by untrusted parties in its supply chain, unauthorized and pirated selling, injected faults, and system and microarchitectural level attacks. These threats, if realized, are expected to push hardware to abnormal and unexpected behaviour causing real-life damage and significantly undermining our trust in the electronic and computing systems we use in our daily lives and in safety critical applications. A large number of detective and preventive countermeasures have been proposed in literature. It is a fact, however, that our knowledge of potential consequences to real-life threats to hardware trust is lacking given the limited number of real-life reports and the plethora of ways in which hardware trust could be undermined. With this in mind, run-time monitoring of hardware combined with active mitigation of attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed as the last line of defence. This last line of defence allows us to face the issue of live hardware mistrust rather than turning a blind eye to it or being helpless once it occurs. This thesis proposes three different frameworks towards trustworthy computing on untrustworthy hardware. The presented frameworks are adaptable to different applications, independent of the design of the monitored elements, based on autonomous security elements, and are computationally lightweight. The first framework is concerned with explicit violations and breaches of trust at run-time, with an untrustworthy on-chip communication interconnect presented as a potential offender. The framework is based on the guiding principles of component guarding, data tagging, and event verification. The second framework targets hardware elements with inherently variable and unpredictable operational latency and proposes a machine-learning based characterization of these latencies to infer undesired latency extensions or denial of service attacks. The framework is implemented on a DDR3 DRAM after showing its vulnerability to obscured latency extension attacks. The third framework studies the possibility of the deployment of untrustworthy hardware elements in the analog front end, and the consequent integrity issues that might arise at the analog-digital boundary of system on chips. The framework uses machine learning methods and the unique temporal and arithmetic features of signals at this boundary to monitor their integrity and assess their trust level

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
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