Exploration and adaptation of large language models for specialized domains

Large language models have transformed the field of natural language processing (NLP). Their improved performance on various NLP benchmarks makes them a promising tool—also for the application in specialized domains. Such domains are characterized by highly trained professionals with particular domain expertise. Since these experts are rare, improving the efficiency of their work with automated systems is especially desirable. However, domain-specific text resources hold various challenges for NLP systems. These challenges include distinct language, noisy and scarce data, and a high level of variation. Further, specialized domains present an increased need for transparent systems since they are often applied in high stakes settings. In this dissertation, we examine whether large language models (LLMs) can overcome some of these challenges and propose methods to effectively adapt them to domain-specific requirements. We first investigate the inner workings and abilities of LLMs and show how they can fill the gaps that are present in previous NLP algorithms for specialized domains. To this end, we explore the sources of errors produced by earlier systems to identify which of them can be addressed by using LLMs. Following this, we take a closer look at how information is processed within Transformer-based LLMs to better understand their capabilities. We find that their layers encode different dimensions of the input text. Here, the contextual vector representation, and the general language knowledge learned during pre-training are especially beneficial for solving complex and multi-step tasks common in specialized domains. Following this exploration, we propose solutions for further adapting LLMs to the requirements of domain-specific tasks. We focus on the clinical domain, which incorporates many typical challenges found in specialized domains. We show how to improve generalization by integrating different domain-specific resources into our models. We further analyze the behavior of the produced models and propose a behavioral testing framework that can serve as a tool for communication with domain experts. Finally, we present an approach for incorporating the benefits of LLMs while fulfilling requirements such as interpretability and modularity. The presented solutions show improvements in performance on benchmark datasets and in manually conducted analyses with medical professionals. Our work provides both new insights into the inner workings of pre-trained language models as well as multiple adaptation methods showing that LLMs can be an effective tool for NLP in specialized domains

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Application of service composition mechanisms to Future Networks architectures and Smart Grids

Aquesta tesi gira entorn de la hipòtesi de la metodologia i mecanismes de composició de serveis i com es poden aplicar a diferents camps d'aplicació per a orquestrar de manera eficient comunicacions i processos flexibles i sensibles al context. Més concretament, se centra en dos camps d'aplicació: la distribució eficient i sensible al context de contingut multimèdia i els serveis d'una xarxa elèctrica intel·ligent. En aquest últim camp es centra en la gestió de la infraestructura, cap a la definició d'una Software Defined Utility (SDU), que proposa una nova manera de gestionar la Smart Grid amb un enfocament basat en programari, que permeti un funcionament molt més flexible de la infraestructura de xarxa elèctrica. Per tant, revisa el context, els requisits i els reptes, així com els enfocaments de la composició de serveis per a aquests camps. Fa especial èmfasi en la combinació de la composició de serveis amb arquitectures Future Network (FN), presentant una proposta de FN orientada a serveis per crear comunicacions adaptades i sota demanda. També es presenten metodologies i mecanismes de composició de serveis per operar sobre aquesta arquitectura, i posteriorment, es proposa el seu ús (en conjunció o no amb l'arquitectura FN) en els dos camps d'estudi. Finalment, es presenta la investigació i desenvolupament realitzat en l'àmbit de les xarxes intel·ligents, proposant diverses parts de la infraestructura SDU amb exemples d'aplicació de composició de serveis per dissenyar seguretat dinàmica i flexible o l'orquestració i gestió de serveis i recursos dins la infraestructura de l'empresa elèctrica.Esta tesis gira en torno a la hipótesis de la metodología y mecanismos de composición de servicios y cómo se pueden aplicar a diferentes campos de aplicación para orquestar de manera eficiente comunicaciones y procesos flexibles y sensibles al contexto. Más concretamente, se centra en dos campos de aplicación: la distribución eficiente y sensible al contexto de contenido multimedia y los servicios de una red eléctrica inteligente. En este último campo se centra en la gestión de la infraestructura, hacia la definición de una Software Defined Utility (SDU), que propone una nueva forma de gestionar la Smart Grid con un enfoque basado en software, que permita un funcionamiento mucho más flexible de la infraestructura de red eléctrica. Por lo tanto, revisa el contexto, los requisitos y los retos, así como los enfoques de la composición de servicios para estos campos. Hace especial hincapié en la combinación de la composición de servicios con arquitecturas Future Network (FN), presentando una propuesta de FN orientada a servicios para crear comunicaciones adaptadas y bajo demanda. También se presentan metodologías y mecanismos de composición de servicios para operar sobre esta arquitectura, y posteriormente, se propone su uso (en conjunción o no con la arquitectura FN) en los dos campos de estudio. Por último, se presenta la investigación y desarrollo realizado en el ámbito de las redes inteligentes, proponiendo varias partes de la infraestructura SDU con ejemplos de aplicación de composición de servicios para diseñar seguridad dinámica y flexible o la orquestación y gestión de servicios y recursos dentro de la infraestructura de la empresa eléctrica.This thesis revolves around the hypothesis the service composition methodology and mechanisms and how they can be applied to different fields of application in order to efficiently orchestrate flexible and context-aware communications and processes. More concretely, it focuses on two fields of application that are the context-aware media distribution and smart grid services and infrastructure management, towards a definition of a Software-Defined Utility (SDU), which proposes a new way of managing the Smart Grid following a software-based approach that enable a much more flexible operation of the power infrastructure. Hence, it reviews the context, requirements and challenges of these fields, as well as the service composition approaches. It makes special emphasis on the combination of service composition with Future Network (FN) architectures, presenting a service-oriented FN proposal for creating context-aware on-demand communication services. Service composition methodology and mechanisms are also presented in order to operate over this architecture, and afterwards, proposed for their usage (in conjunction or not with the FN architecture) in the deployment of context-aware media distribution and Smart Grids. Finally, the research and development done in the field of Smart Grids is depicted, proposing several parts of the SDU infrastructure, with examples of service composition application for designing dynamic and flexible security for smart metering or the orchestration and management of services and data resources within the utility infrastructure

Investigating the impact of a parenting intervention within a rural South African community: a longitudinal social network analysis

Colder, harsher parenting attitudes and behaviours negatively impact children's behaviour and development, and have been linked to heightened levels of violence towards children. Parenting interventions can improve outcomes by reducing violent and increasing non-violent parenting behaviours. I investigated how changes associated with a low-cost positive parenting intervention spread through a rural, low-income, South African community. Specifically, I assessed whether exposure to a community-wide social activation process and Parenting for Lifelong Health (PLH) programmes (focused on violence prevention in low-resource settings) significantly predict: (1) improved parenting, and (2) change in the communication networks of female caregivers in the whole community, while controlling for variables such as psychiatric symptoms, parenting stress, and alcohol misuse. Additionally, I investigated whether ties to parenting programme attendees in the communication network predicted improved parenting. Afrikaans-speaking female caregivers (n = 235; mean age 35.92 years), with children aged between 1½ and 18 years old, participated in the intervention; three waves of data were collected (January 2016, June 2017, and February 2019). The social network was measured based on a peer nomination procedure (of study participants whom “you talk to about parenting”). To analyse the role of interpersonal ties as pathways for spreading intervention effects, I make use of Social Network Analysis (SNA), in the form of nominations of people with whom respondents discuss parenting, together with self-report measures of parenting-related outcomes (from caregivers and their children). I then trace the extent to which both the social activation process and the parenting programmes are effective, in part, via their diffusion throughout the community. SNA was used to disentangle whether network changes improved parenting practices (i.e., selection effects) or whether reported improvements in parenting practices improved caregiver information networks (i.e., socialisation effects). Analysis of data from waves 1 and 2 indicated that community-wide improvements in parenting behaviour were evidenced. The significant predictors of improvement were social activation “dose” received, change in network centrality and the influence of indirect exposure to the parenting programmes via attendees. Furthermore, attending at least one session of a parenting programme offered in the intervention significantly predicted change in the caregivers' communication networks, indicating the spread of social influence through their network. The small subset of caregivers (n = 51; 21.7%) attending one or more sessions of a parenting programme evidenced greater activity (i.e., covariate ego effect) and potential influence (i.e., covariate alter effect) within the communication network compared to caregivers who did not attend any programme sessions. This subset of attending caregivers were more likely to reach out to other caregivers to speak about parenting after being exposed to the intervention, and both sought and received social support from other caregivers. Follow-up assessment using a third wave of data showed that while attendees remained socially influential within the caregiver network the overall community improvement was not sustained. These results illustrate the value of social network analysis for ascertaining the pathways through which the intervention achieved its impact and tracking the evolution of social norms within a community. The results indicate an association between spill-over effects from attendees to non-attendees and community-wide changes through targeted interventions

Secure location-aware communications in energy-constrained wireless networks

Wireless ad hoc network has enabled a variety of exciting civilian, industrial and military applications over the past few years. Among the many types of wireless ad hoc networks, Wireless Sensor Networks (WSNs) has gained popularity because of the technology development for manufacturing low-cost, low-power, multi-functional motes. Compared with traditional wireless network, location-aware communication is a very common communication pattern and is required by many applications in WSNs. For instance, in the geographical routing protocol, a sensor needs to know its own and its neighbors\u27 locations to forward a packet properly to the next hop. The application-aware communications are vulnerable to many malicious attacks, ranging from passive eavesdropping to active spoofing, jamming, replaying, etc. Although research efforts have been devoted to secure communications in general, the properties of energy-constrained networks pose new technical challenges: First, the communicating nodes in the network are always unattended for long periods without physical maintenance, which makes their energy a premier resource. Second, the wireless devices usually have very limited hardware resources such as memory, computation capacity and communication range. Third, the number of nodes can be potentially of very high magnitude. Therefore, it is infeasible to utilize existing secure algorithms designed for conventional wireless networks, and innovative mechanisms should be designed in a way that can conserve power consumption, use inexpensive hardware and lightweight protocols, and accommodate with the scalability of the network. In this research, we aim at constructing a secure location-aware communication system for energy-constrained wireless network, and we take wireless sensor network as a concrete research scenario. Particularly, we identify three important problems as our research targets: (1) providing correct location estimations for sensors in presence of wormhole attacks and pollution attacks, (2) detecting location anomalies according to the application-specific requirements of the verification accuracy, and (3) preventing information leakage to eavesdroppers when using network coding for multicasting location information. Our contributions of the research are as follows: First, we propose two schemes to improve the availability and accuracy of location information of nodes. Then, we study monitoring and detection techniques and propose three lightweight schemes to detect location anomalies. Finally, we propose two network coding schemes which can effectively prevent information leakage to eavesdroppers. Simulation results demonstrate the effectiveness of our schemes in enhancing security of the system. Compared to previous works, our schemes are more lightweight in terms of hardware cost, computation overhead and communication consumptions, and thus are suitable for energy-constrained wireless networks

Multi-frame reconstruction using super-resolution, inpainting, segmentation and codecs

In this thesis, different aspects of video and light field reconstruction are considered such as super-resolution, inpainting, segmentation and codecs. For this purpose, each of these strategies are analyzed based on a specific goal and a specific database. Accordingly, databases which are relevant to film industry, sport videos, light fields and hyperspectral videos are used for the sake of improvement. This thesis is constructed around six related manuscripts, in which several approaches are proposed for multi-frame reconstruction. Initially, a novel multi-frame reconstruction strategy is proposed for lightfield super-resolution in which graph-based regularization is applied along with edge preserving filtering for improving the spatio-angular quality of lightfield. Second, a novel video reconstruction is proposed which is built based on compressive sensing (CS), Gaussian mixture models (GMM) and sparse 3D transform-domain block matching. The motivation of the proposed technique is the improvement in visual quality performance of the video frames and decreasing the reconstruction error in comparison with the former video reconstruction methods. In the next approach, student-t mixture models and edge preserving filtering are applied for the purpose of video super-resolution. Student-t mixture model has a heavy tail which makes it robust and suitable as a video frame patch prior and rich in terms of log likelihood for information retrieval. In another approach, a hyperspectral video database is considered, and a Bayesian dictionary learning process is used for hyperspectral video super-resolution. To that end, Beta process is used in Bayesian dictionary learning and a sparse coding is generated regarding the hyperspectral video super-resolution. The spatial super-resolution is followed by a spectral video restoration strategy, and the whole process leveraged two different dictionary learnings, in which the first one is trained for spatial super-resolution and the second one is trained for the spectral restoration. Furthermore, in another approach, a novel framework is proposed for replacing advertisement contents in soccer videos in an automatic way by using deep learning strategies. For this purpose, a UNET architecture is applied (an image segmentation convolutional neural network technique) for content segmentation and detection. Subsequently, after reconstructing the segmented content in the video frames (considering the apparent loss in detection), the unwanted content is replaced by new one using a homography mapping procedure. In addition, in another research work, a novel video compression framework is presented using autoencoder networks that encode and decode videos by using less chroma information than luma information. For this purpose, instead of converting Y'CbCr 4:2:2/4:2:0 videos to and from RGB 4:4:4, the video is kept in Y'CbCr 4:2:2/4:2:0 and merged the luma and chroma channels after the luma is downsampled to match the chroma size. An inverse function is performed for the decoder. The performance of these models is evaluated by using CPSNR, MS-SSIM, and VMAF metrics. The experiments reveal that, as compared to video compression involving conversion to and from RGB 4:4:4, the proposed method increases the video quality by about 5.5% for Y'CbCr 4:2:2 and 8.3% for Y'CbCr 4:2:0 while reducing the amount of computation by nearly 37% for Y'CbCr 4:2:2 and 40% for Y'CbCr 4:2:0. The thread that ties these approaches together is reconstruction of the video and light field frames based on different aspects of problems such as having loss of information, blur in the frames, existing noise after reconstruction, existing unpleasant content, excessive size of information and high computational overhead. In three of the proposed approaches, we have used Plug-and-Play ADMM model for the first time regarding reconstruction of videos and light fields in order to address both information retrieval in the frames and tackling noise/blur at the same time. In two of the proposed models, we applied sparse dictionary learning to reduce the data dimension and demonstrate them as an efficient linear combination of basis frame patches. Two of the proposed approaches are developed in collaboration with industry, in which deep learning frameworks are used to handle large set of features and to learn high-level features from the data

Development and applications of high speed and hyperspectral nonlinear microscopy

Nonlinear microscopy refers to a range of laser scanning microscopy techniques that are based on nonlinear optical processes such as two-photon excited fluorescence and second harmonic generation. Nonlinear microscopy techniques are powerful because they enable the visualization of highly scattering biological samples with subcellular resolution. This capability is especially valuable for in vivo and live tissue imaging since it can provide both structural and functional information about tissues in their native environment. With the use of a range of exogenous dyes and intrinsic contrast, in vivo nonlinear microscopy can be used to characterize and measure dynamic processes of tissues in their normal environment. These advances have been particularly relevant in neuroscience, where truly understanding the function of the brain requires that its neural and vascular networks be observed while undisturbed. Despite these advantages, in vivo nonlinear microscopy still faces several major challenges. First, observing dynamics that occur in large areas over short time scales, such as neuronal signaling and blood flow, is challenging because nonlinear microscopy generally requires scanning to create an image. This limits the study of dynamic behavior to either a single plane or to a small subset of regions within a volume. Second, applications that rely on the use of exogenous dyes can be limited by the need to stain tissues before imaging, the availability of dyes, and specificity that can be achieved. Usually considered a nuisance, endogenous tissue contrast from autofluorescence or structures exhibiting second harmonic generation can produce stunning images for visualizing subcellular morphology. Imaging endogenous contrast can also provide valuable information about the chemical makeup and metabolic state of the tissue. Few methods have been developed to carefully and quantitatively examine endogenous fluorescence in living tissues. In this thesis, these two challenges in nonlinear microscopy are addressed. The development of a novel hyperspectral two-photon microscopy method to acquire spectroscopic data from tissues and increase the information available from endogenous contrast is presented. This system was applied to visualize and identify sources of endogenous contrast in gastrointestinal tissues, providing robust references for the assessment of normal and diseased tissues. Secondly, three methods for high speed volumetric imaging using laser scanning nonlinear microscopy were developed to address the need for improved high-speed imaging in living tissues. A spectrally-encoded high-speed imaging method that can provide simultaneous imaging of multiple regions of the living brain in parallel is presented and used to study spontaneous changes in vascular tone in the brain. This technique is then extended for use with second harmonic generation microscopy, which has the potential to greatly increase the degree of multiplexing. Finally, a complete system design capable of volumetric scan rates >1Hz is shown, offering improved performance and versatility to image brain activity