Doctor of Philosophy

dissertationDeep Neural Networks (DNNs) are the state-of-art solution in a growing number of tasks including computer vision, speech recognition, and genomics. However, DNNs are computationally expensive as they are carefully trained to extract and abstract features from raw data using multiple layers of neurons with millions of parameters. In this dissertation, we primarily focus on inference, e.g., using a DNN to classify an input image. This is an operation that will be repeatedly performed on billions of devices in the datacenter, in self-driving cars, in drones, etc. We observe that DNNs spend a vast majority of their runtime to runtime performing matrix-by-vector multiplications (MVM). MVMs have two major bottlenecks: fetching the matrix and performing sum-of-product operations. To address these bottlenecks, we use in-situ computing, where the matrix is stored in programmable resistor arrays, called crossbars, and sum-of-product operations are performed using analog computing. In this dissertation, we propose two hardware units, ISAAC and Newton.In ISAAC, we show that in-situ computing designs can outperform DNN digital accelerators, if they leverage pipelining, smart encodings, and can distribute a computation in time and space, within crossbars, and across crossbars. In the ISAAC design, roughly half the chip area/power can be attributed to the analog-to-digital conversion (ADC), i.e., it remains the key design challenge in mixed-signal accelerators for deep networks. In spite of the ADC bottleneck, ISAAC is able to out-perform the computational efficiency of the state-of-the-art design (DaDianNao) by 8x. In Newton, we take advantage of a number of techniques to address ADC inefficiency. These techniques exploit matrix transformations, heterogeneity, and smart mapping of computation to the analog substrate. We show that Newton can increase the efficiency of in-situ computing by an additional 2x. Finally, we show that in-situ computing, unfortunately, cannot be easily adapted to handle training of deep networks, i.e., it is only suitable for inference of already-trained networks. By improving the efficiency of DNN inference with ISAAC and Newton, we move closer to low-cost deep learning that in turn will have societal impact through self-driving cars, assistive systems for the disabled, and precision medicine

Biostratigraphic Study of the Gurpi Formation Based on Planktonic Foraminifera In Lar Area (Kuh-e-kurdeh Section)

The study of planktonic foraminifera of the Gurpi formations at Lar area (Kuh-e-kurdeh section) enables me to find the most standard biozones defined in mediterranean regions, especially Tethysian domain. Five biozones were determined. Biozones I (Globotruncanita elevata zone) and II (Globotruncana ventricosa zone) and III (Radotruncana calcarata zone) indicate the Early Campanian and Middle and Late Campanian, respectively. Biozones IV (Globotruncanita stuarti zone) and V (Gansserina gansseri zone) suggest the Early and Middle Maastrichtian, respectively. In the Late Maastrichtian, due to decreasing in water depth at the study area, Abathomphalus mayaroensis zone defined in Tethysian domain was not recognised.

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Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

MaiT: Leverage Attention Masks for More Efficient Image Transformers

Though image transformers have shown competitive results with convolutional neural networks in computer vision tasks, lacking inductive biases such as locality still poses problems in terms of model efficiency especially for embedded applications. In this work, we address this issue by introducing attention masks to incorporate spatial locality into self-attention heads. Local dependencies are captured efficiently with masked attention heads along with global dependencies captured by unmasked attention heads. With Masked attention image Transformer - MaiT, top-1 accuracy increases by up to 1.7% compared to CaiT with fewer parameters and FLOPs, and the throughput improves by up to 1.5X compared to Swin. Encoding locality with attention masks is model agnostic, and thus it applies to monolithic, hierarchical, or other novel transformer architectures

Preparation and biological studies of 68Ga-DOTA-alendronate

Objective(s): In line with previous research on the development of conjugated bisphosphonate ligands as new bone-avid agents, in this study, DOTA conjugated alendronate (DOTA-ALN) was synthesized and evaluated after labeling with gallium-68 (68Ga).Methods: DOTA-ALN was synthesized and characterized, followed by 68Ga-DOTA-ALN preparation, using DOTA-ALN and 68GaCl3 (pH: 4-5) at 92-95°C for 10 min. Stability tests, hydroxyapatite assay, partition coefficient calculation,biodistribution studies, and imaging were performed on the developed agent in normal rats.Results: The complex was prepared with high radiochemical purity (>99% as depicted by radio thin-layer chromatography; specific activity: 310-320GBq/mmol) after solid phase purification and was stabilized for up to 90 min with a logP value of -2.91. Maximum ligand binding (65%) was observed in the presence of 50 mg of hydroxyapatite; a major portion of the activity was excreted through the kidneys. With the exception of excretory organs, gastrointestinal tract organs, including the liver, intestine, and colon, showed significant uptake; however, the bone uptake was low

Nano-scaled Diethylene Triamine Pent Acetic Acid (N-DTPA): Novel Anti-Wilson's Disease Cell Model

Abstract Wilson's disease (WD) is an autosomal recessive disorder in which copper metabolism is impaired. In fact, copper accumulates in various organs and tissues can be seen and causes toxic effects in various tissues including liver, brain, kidneys and eyes. Sulfur amino acid is a metabolite of D-penicillamine and penicillamine and copper Chlator is a factor that causes urinary excretion of copper and WD therapeutic agent as well. The interesting thing about the neurological symptoms of Wilson's disease with penicillamine is the drug may worsen or even in an asymptomatic patient, the treatment may be creating symptoms. DTPA is a pentavalent compound containing carboxylic DTPA is a chemical compound that is used in radiation therapy and MRI. It can give the metal chelate with iron, copper and other cations can be conjugated and also treatment of internal body pollution caused by various elements, including raDOIactive elements. DTPA could not be lonely absorbed by the cell. The goal is to conjugate it with the G2 Dendrimer (Nanosized anionic linear biocompatible polymer) to bring it to the nano size and increase the intracellular uptake compared to the ground state. Based on the hypothesis, nanoconjugated DTPA-Dendrimer G2 was synthesized and then evaluated on Hep G2 WD cell model in vitro and the results showed a good effectiveness without any toxicity for the conjugate in decreasing the intracellular copper level comparing to gold standard D-penicillamine respectively. Based on the findings the nanosized conjugate seems to have very good prognoses and clinical future and this needs to be further investigated

CD133-Functionalized Gold Nanoparticles as a Carrier Platform for Telaglenastat (CB-839) against Tumor Stem Cells

The failure of a long-lasting curative therapeutic benefit of currently applied chemotherapies against malignant cancers is suggested to be caused by the ineffectiveness of such interventions on cancer stem cells (CSCs). CD133/AC133 is a cell surface protein previously shown to have potential to identify CSCs in various tumors, including brain tumors. Moreover, an increase in the rate of cellular metabolism of glutamine and glucose are contributors to the fast cellular proliferation of some high-grade malignancies. Inhibition of glutaminolysis by utilizing pharmacological inhibitors of the enzyme glutaminase 1 (GLS1) can be an effective anti-CSC strategy. In this study, the clinical-stage GLS1 inhibitor Telaglenastat (CB-839) was loaded into PEGylated gold nanoparticles equipped with the covalently conjugated CD133 aptamer (Au-PEG-CD133-CB-839) and exposed to a collection of CD133-positive brain tumor models in vitro. Our results show that Au-PEG-CD133-CB-839 significantly decreased the viability of CD133-postive cancer cells in a dose-dependent manner, which was higher as compared to the effects of treatment of the cells with the individual components of the assembled nanodrug. Interestingly, the treatment effect was observed in glioblastoma stem cells modeling different transcriptomic subtypes of the disease. The presented platform is the fundament for subsequent target specificity characterization and in vivo application