244 research outputs found

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    Novel neural architectures & algorithms for efficient inference

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    In the last decade, the machine learning universe embraced deep neural networks (DNNs) wholeheartedly with the advent of neural architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, etc. These models have empowered many applications, such as ChatGPT, Imagen, etc., and have achieved state-of-the-art (SOTA) performance on many vision, speech, and language modeling tasks. However, SOTA performance comes with various issues, such as large model size, compute-intensive training, increased inference latency, higher working memory, etc. This thesis aims at improving the resource efficiency of neural architectures, i.e., significantly reducing the computational, storage, and energy consumption of a DNN without any significant loss in performance. Towards this goal, we explore novel neural architectures as well as training algorithms that allow low-capacity models to achieve near SOTA performance. We divide this thesis into two dimensions: \textit{Efficient Low Complexity Models}, and \textit{Input Hardness Adaptive Models}. Along the first dimension, i.e., \textit{Efficient Low Complexity Models}, we improve DNN performance by addressing instabilities in the existing architectures and training methods. We propose novel neural architectures inspired by ordinary differential equations (ODEs) to reinforce input signals and attend to salient feature regions. In addition, we show that carefully designed training schemes improve the performance of existing neural networks. We divide this exploration into two parts: \textsc{(a) Efficient Low Complexity RNNs.} We improve RNN resource efficiency by addressing poor gradients, noise amplifications, and BPTT training issues. First, we improve RNNs by solving ODEs that eliminate vanishing and exploding gradients during the training. To do so, we present Incremental Recurrent Neural Networks (iRNNs) that keep track of increments in the equilibrium surface. Next, we propose Time Adaptive RNNs that mitigate the noise propagation issue in RNNs by modulating the time constants in the ODE-based transition function. We empirically demonstrate the superiority of ODE-based neural architectures over existing RNNs. Finally, we propose Forward Propagation Through Time (FPTT) algorithm for training RNNs. We show that FPTT yields significant gains compared to the more conventional Backward Propagation Through Time (BPTT) scheme. \textsc{(b) Efficient Low Complexity CNNs.} Next, we improve CNN architectures by reducing their resource usage. They require greater depth to generate high-level features, resulting in computationally expensive models. We design a novel residual block, the Global layer, that constrains the input and output features by approximately solving partial differential equations (PDEs). It yields better receptive fields than traditional convolutional blocks and thus results in shallower networks. Further, we reduce the model footprint by enforcing a novel inductive bias that formulates the output of a residual block as a spatial interpolation between high-compute anchor pixels and low-compute cheaper pixels. This results in spatially interpolated convolutional blocks (SI-CNNs) that have better compute and performance trade-offs. Finally, we propose an algorithm that enforces various distributional constraints during training in order to achieve better generalization. We refer to this scheme as distributionally constrained learning (DCL). In the second dimension, i.e., \textit{Input Hardness Adaptive Models}, we introduce the notion of the hardness of any input relative to any architecture. In the first dimension, a neural network allocates the same resources, such as compute, storage, and working memory, for all the inputs. It inherently assumes that all examples are equally hard for a model. In this dimension, we challenge this assumption using input hardness as our reasoning that some inputs are relatively easy for a network to predict compared to others. Input hardness enables us to create selective classifiers wherein a low-capacity network handles simple inputs while abstaining from a prediction on the complex inputs. Next, we create hybrid models that route the hard inputs from the low-capacity abstaining network to a high-capacity expert model. We design various architectures that adhere to this hybrid inference style. Further, input hardness enables us to selectively distill the knowledge of a high-capacity model into a low-capacity model by cleverly discarding hard inputs during the distillation procedure. Finally, we conclude this thesis by sketching out various interesting future research directions that emerge as an extension of different ideas explored in this work

    The role of intramyocellular lipid content in the physiological changes observed in inactivity, exercise, and non-alcoholic fatty liver disease

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    Lipid stored within droplets in skeletal muscle, referred to as intramyocellular lipid (IMCL), has established and emerging roles in health and disease. Lipid droplets (LDs) act as the first destination for activated fatty acids (FAs) following their esterification to triacylglycerol (TAG). Under normal physiological conditions these FAs are then released from LDs to supply adjacent mitochondria with substrate for ATP production during fasting and exercise. It has been proposed that dysregulation of adipose tissue storage, in the context of chronic overfeeding, and basal and insulin-mediated impairments in muscle lipid oxidation in response to inactivity are responsible for the ectopic accumulation of lipid in the skeletal muscles. This accumulation can result in increased sarcoplasmic and sarcolemmal expression of intermediates of TAG synthesis and lipolysis, which attenuate the insulin signalling pathway, resulting in skeletal muscle and whole-body insulin resistance, and potentially contributing to the aetiology of non-alcoholic fatty liver disease (NAFLD). However, the associations between IMCL accumulation and insulin resistance in inactivity and NAFLD are equivocal, and the adaptations in IMCL to resistance exercise training are poorly defined. Therefore, primarily using the hyperinsulinaemic-euglycaemic clamp technique, the gold standard method in the assessment of insulin action in humans in vivo, and histochemical quantification of total and fibre-type specific IMCL content, the results of the work in this thesis contribute to our understanding of the role of IMCL in inactivity, resistance exercise training, and NAFLD. This thesis comprises primarily of retrospective analyses of four comprehensive human volunteer studies. The studies described in Chapter 3 explored the role of IMCL in the development of whole-body insulin resistance during acute (3 days) and chronic (56 days) bed rest in healthy, male participants maintained in energy balance throughout. Glucose disposal was decreased by a similar magnitude after 3 and 56 days of bed rest, and these observations could not be explained by IMCL accumulation. This suggests that inactivity per se is the primary driver of whole-body insulin resistance during bed rest and that IMCL accumulation is likely to be a confounding response that occurs when participants are in positive energy balance. It has been proposed that overfeeding, which contributes to the pathogenesis of obese NAFLD by increasing plasma FA concentration and hepatic lipid content, also leads to the ectopic accumulation of IMCL. Given that the skeletal muscles are the main sites for the disposal of glucose and that IMCL accumulation is associated with muscle insulin resistance, increased muscle lipid content may contribute to the development of whole-body insulin resistance in those with NAFLD. The study described in Chapter 4 investigated differences in IMCL content, skeletal muscle glucose disposal, and whole-body glucose disposal between individuals with NAFLD and healthy controls to determine if muscle lipid content does in fact contribute to insulin resistance in those with NAFLD. It was observed that IMCL content was not different between healthy males and males with NAFLD, even though skeletal muscle and whole-body glucose disposal were significantly reduced in those with NAFLD. These findings suggest that IMCL accumulation is not a contributor to the development of insulin resistance in NAFLD. The study described in Chapter 5 explored changes in IMCL and perilipin 5 (PLIN5) content in response to a 12-week resistance training intervention, which has not been investigated in detail to date. A secondary aim was to determine the impact of the non-steroidal anti-inflammatory drug (NSAID), diclofenac, on the mRNA expression of genes involved in FA metabolism and oxidation. It was hypothesised that diclofenac would have a role in these processes based on evidence of its affinity for Peroxisome proliferator-activated receptor gamma (PPAR-γ) in vitro. This study comprised a randomised, placebo controlled, double-blind protocol in which one group of exercise-trained participants ingested diclofenac, 75 mg/daily, concurrent with the exercise protocol. IMCL content and muscle PLIN5 content did not change in response to the resistance exercise intervention, though diclofenac administration robustly altered the mRNA expression of genes involved in lipid metabolism. This thesis presents novel insights into the role of IMCL content in the development of insulin resistance in the context of bed rest-induced immobilisation and NAFLD. It also identifies a new trajectory for future research into diclofenac, an NSAID which may alter muscle FA oxidation via a previously underexplored mechanism

    KI-Realitäten: Modelle, Praktiken und Topologien maschinellen Lernens

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    Maschinelles Lernen stellt zunehmend einen wichtigen Faktor soziotechnischen Wandels dar. Zugleich ist es selbst Produkt der Realitäten, an deren Reproduktion es in Form praktischer Anwendungen wie auch als Spekulationsobjekt beteiligt ist. Die Beiträge des Bandes verhandeln gegenwärtige Manifestationen maschinellen Lernens als Phänomene, die für epistemische Verunsicherungen sorgen und die Bedingungen von Sozialität rekonfigurieren. Sie begegnen dieser Herausforderung, indem sie konkrete Verfahren in ihrer gesellschaftlichen Einbettung analysieren sowie bestehende theoretische Charakterisierungen sogenannter Künstlicher Intelligenz kritisch reflektieren

    Information Refinement Technologies for Crisis Informatics: User Expectations and Design Implications for Social Media and Mobile Apps in Crises

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    In the past 20 years, mobile technologies and social media have not only been established in everyday life, but also in crises, disasters, and emergencies. Especially large-scale events, such as 2012 Hurricane Sandy or the 2013 European Floods, showed that citizens are not passive victims but active participants utilizing mobile and social information and communication technologies (ICT) for crisis response (Reuter, Hughes, et al., 2018). Accordingly, the research field of crisis informatics emerged as a multidisciplinary field which combines computing and social science knowledge of disasters and is rooted in disciplines such as human-computer interaction (HCI), computer science (CS), computer supported cooperative work (CSCW), and information systems (IS). While citizens use personal ICT to respond to a disaster to cope with uncertainty, emergency services such as fire and police departments started using available online data to increase situational awareness and improve decision making for a better crisis response (Palen & Anderson, 2016). When looking at even larger crises, such as the ongoing COVID-19 pandemic, it becomes apparent the challenges of crisis informatics are amplified (Xie et al., 2020). Notably, information is often not available in perfect shape to assist crisis response: the dissemination of high-volume, heterogeneous and highly semantic data by citizens, often referred to as big social data (Olshannikova et al., 2017), poses challenges for emergency services in terms of access, quality and quantity of information. In order to achieve situational awareness or even actionable information, meaning the right information for the right person at the right time (Zade et al., 2018), information must be refined according to event-based factors, organizational requirements, societal boundary conditions and technical feasibility. In order to research the topic of information refinement, this dissertation combines the methodological framework of design case studies (Wulf et al., 2011) with principles of design science research (Hevner et al., 2004). These extended design case studies consist of four phases, each contributing to research with distinct results. This thesis first reviews existing research on use, role, and perception patterns in crisis informatics, emphasizing the increasing potentials of public participation in crisis response using social media. Then, empirical studies conducted with the German population reveal positive attitudes and increasing use of mobile and social technologies during crises, but also highlight barriers of use and expectations towards emergency services to monitor and interact in media. The findings led to the design of innovative ICT artefacts, including visual guidelines for citizens’ use of social media in emergencies (SMG), an emergency service web interface for aggregating mobile and social data (ESI), an efficient algorithm for detecting relevant information in social media (SMO), and a mobile app for bidirectional communication between emergency services and citizens (112.social). The evaluation of artefacts involved the participation of end-users in the application field of crisis management, pointing out potentials for future improvements and research potentials. The thesis concludes with a framework on information refinement for crisis informatics, integrating event-based, organizational, societal, and technological perspectives

    An assessment of the management of information sharing in the order processing system at Diplomat South Africa.

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    Masters Degree. University of KwaZulu-Natal, Durban.Supply chain management is essential in steering an enterprise to success through coordinated activities of the value chain partners. The achievement of fast-moving consumer goods business organisations (FMCGs) has a direct relationship with the overall performance of supply chains, which are their principal distribution channel. Although it is known that sharing information improves the overall performance of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. The supply chain of Diplomat South Africa (DSA), a Sales and Distribution company, and the corresponding supply chains were studied to establish whether the internal relationships enhanced the response to the customers’ requirements. The study was grounded in collaboration and integration theory, and a qualitative research methodology was used. Non-probability sampling was used, and five senior managers from the Sales Department, Demand Planning, Operations, Masta Data, and Credit control/Finance Department were selected from the firm and were interviewed. The data collected were transcribed, coded, and thematically interpreted using content analysis. The aim of the study was to assess the management of information sharing in the order processing systems at DSA. Further, to determine whether they can appropriately use the information sharing tool and the level of transparency of information sharing amongst the departments involved in the order processing. The outcome of the study indicated that supply chain problems were department-specific, and it is recommended that information sharing and supply chain management be cohesive throughout each department at Diplomat South Africa. Employees’ collaboration in the information sharing of the FMCGs at Diplomat South Africa would enhance the response to the client’s requirements

    Value Loss of Activities Propelled by Digital Transformation: Theoretical Evaluation and Empirical Modelling to Identify Efficiency Potentials to Maximize Value in the Field of Marketing & Sales.

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    Digital transformation of firms and the adoption of digital technologies is progressing inexorably. Decision-makers are preoccupied with the endeavor to identify the potentials of existing as well as newly emerging technologies and underutilize the entailed profits. This research study proposes a newly developed conceptualization and model to compute efficiency potentials in the field of marketing and sales, a business function with an intense consumer focus. While this conjoint business unit mainly fosters and propels the performance measure of effectiveness, the full exploitation of internal workforce efficiency stays neglected and barely treated by practice and science. By employing expert interviews with managers in this field, a tailored efficiency determination model is created with in total eight efficiency potentials allocated to three digital technology effects, acceleration, automation, and outsourcing. The efficiency coefficient of time weights the human labor input while the additive connection with digital technologies as input factor engenders either a complementary, substitutional, or no effect. With a sequential mixed-methods research approach, a further quantitative study with 251 employees in the field of marketing and sales uses the qualitative model to determine the efficiency potential based on individual task assessments, including the identification of task values. While distinguishing between office and customer interaction-related work, the study finds that 45 percent of the working time underlies an efficiency potential by utilizing the ONET database, which contains 214 individual tasks in the career cluster marketing and professional sales.Administración y Dirección de Empresa
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