It is well known that multi-input multi-output (MIMO) wireless communication systemsthat employ precoding techniques are capable of meeting the high expectations of modernand future wireless communication standards. In order to fully utilize these techniques, thecommunication system typically requires information of the channel, commonly referred toas channel state information (CSI). In practice, the CSI at the transmitter (CSIT) is oftennot perfect which addresses the need for robust precoding designs, that can mitigate theeffects of precoding with imperfect CSIT. By modeling the imperfect CSIT as deterministic,it can be assumed that the estimated channel, as represented by the CSIT, belongs to aconvex uncertainty set. With this approach, the problem of finding a robust precoding designcan be formulated as a convex maximin problem, where the solution optimizes the systemperformance for the worst channel that belongs to the uncertainty set. How the uncertaintyset is modeled impacts the performance of the communication system, which calls for theevaluation of several robust precoding designs. While different characteristics of the convexuncertainty sets has been evaluated for MIMO flat-fading channels represented by i.i.d. zero-mean, unit variance Gaussian elements, it is of interest to apply the theory of worst-caserobust precoding designs on real-world measured MIMO channels.More concisely, this project investigates MIMO precoding designs with deterministic im-perfect CSIT for real-world measured channels that utilizes orthogonal frequency divisionmultiplexing (OFDM) schemes. The worst-case received signal-to-noise ratio (SNR) will bepresented as a result of using MIMO precoding designs on real-world channels, and the effectof the choice of model parameters and characteristics of the chosen uncertainty set will bevisualized and discussed. Furthermore, orthogonal space-time block code (OSTBC) transmis-sion designs will be employed to measure the worst case symbol error rate (SER) as a tool toevaluate the system performance in different scenarios. The results will be compared to thatwhen the channel is composed of i.i.d. zero-mean, unit variance Gaussian elements and forthe case when the channel is based on the Kronecker model.The results indicate that a further analysis of how the Kronecker model behaves in termsof capacity is required in order to draw accurate conclusions regarding the implementation ofrobust precoding strategies when each pair of antennas are correlated. Also, it is essential todevelop a framework that offers methods on how to accurately model the uncertainty set sothat it can represent errors that originates from both quantization errors, estimation errorsand outdated estimates. / Det är välkänt att trådlösa multi-input, multi-output (MIMO) system som använder förkodar-tekniker har kapabilitet att möta de höga förväntningar som är fastställt av moderna ochframtida kommunikationsstandarder. För att utnyttja dessa förkodartekniker till fullo be-hövs information om kanalen (CSI). I praktiska kommunikationssystem är kanalinformatio-nen hos sändaren (CSIT) ofta inte perfekt vilket adresserar betydelsen av att använda robustaförkodare som kan mildra den negativa effekten som uppstår av att förkoda med CSIT som in-nehåller fel. Genom att använda en deterministisk modell för CSIT med fel kan man anta attden skattade kanalen som är representerad av CSIT tillhör en konvex osäkerhetsregion. Meddetta tillvägagångssätt kan man formulera problemet att hitta en robust förkodardesign somett konvext maximin-problem, där lösningen optimerar systemets prestanda för den värstakanalskattningen i osäkerhetsregionen. Olika modeller av osäkerhetsregioner ger upphov tillolika systemprestanda vilket betyder att olika modeller med tillhörande robusta förkodare be-höver utvärderas. Medan tidigare forskningsrapporter behandlat MIMO flat fädnings-kanalerför i.i.d. Gaussisk fördelning av elementen finns det ett intresse att applicera teorin omvärsta-fallet robust förkodning på riktiga uppmätta MIMO-kanaler.Mer koncist undersöker detta projekt designs på förkodare för riktiga uppmätta MIMO-kanaler utifrån en deterministisk modell på felaktigt CSIT, där MIMO-kanalerna utnyttjarorthogonal frequency divsion multiplexing (OFDM) scheman. Värsta-fallet signal-to-noiseratio (SNR) kommer presenteras för olika förkodar-designs och MIMO-kanaler. Hur olika valav modellparametrar och karakteristik hos osäkerhetsregionerna påverkar systemprestandankommer att diskuteras. Vidare kommer även orthogonal space-time block codes (OSTBC)användas som transmissionsscheman för att mäta symbol error rate (SER). Resultaten kom-mer att jämföras med när MIMO-kanalen består av i.i.d. Gaussisk fördelning av elementenoch för fallet när kanalen är baserad på en Kronecker-modell.Resultaten indikerar att en fortsatt analys av hur Kronecker-modellen beter sig medavseende på kapacitet är nödvändig för att dra tillförlitliga slutsatser om systemprestan-dan för förkodar-designs när antennparen är korrelerade. Det är även väsentligt att utvecklaen teori som behandlar metoder för hur man kan på ett tillförlitligt sätt modellera osäker-hetsregionen så CSIT så att kvantiseringsfel, skattningsfel och utdaterade skattningar kanrepresenteras i den.

Pose estimation and tracking is essential for applications involving human controls. Specifically, as the primary operating tool for human activities, hand pose estimation plays a significant role in applications such as hand tracking, gesture recognition, human-computer interaction and VR/AR. As the field develops, there has been a trend to utilize deep learning to estimate the 2D/3D hand poses using color-based information without depth data. Within the depth-based as well as color-based approaches, the research community has primarily focused on single-hand scenarios in a localized/normalized coordinate system. Due to the fact that both hands are utilized in most applications, we propose to push the frontier by addressing two-hand pose estimation in the global coordinate system using only color information. Our first chapter introduces the first system capable of estimating global 3D joint locations for both hands via only monocular RGB input images. To enable training and evaluation of the learning-based models, we propose to introduce a large-scale synthetic 3D hand pose dataset Ego3DHands. As knowledge in synthetic data cannot be directly applied to the real-world domain, a natural two-hand pose dataset is necessary for real-world applications. To this end, we present a large-scale RGB-based egocentric hand dataset Ego2Hands in two chapters. In chapter 2, we address the task of two-hand segmentation/detection using images in the wild. In chapter 3, we focus on the task of two-hand 2D/3D pose estimation using real-world data. In addition to research in hand pose estimation, chapter 4 includes our work on interactive refinement that generalizes the backpropagating refinement technique for dense prediction models.

The concept of function is crucial to a great extent in modern mathematics and is considered a major barrier to many mathematics students. Students have difficulty interpreting information related to functions in general, and quadratic functions in particular. Quadratic Function is one of the topics which are covered in a course which is compulsory for a large number of students in the General Education Program of Zayed University. This program leads to different majors, including Mathematics Education, Business, Information Technology, and other majors. The challenge in teaching Quadratic Function in a course like this is mostly based on the fact that many students think that Quadratic Function is a difficult topic to understand and learn, and some teachers would agree with them that it is difficult to teach. In this paper, I demonstrate real world problems aimed to improve the students understanding of Quadratic Functions; life problems on this topic support developing student’s knowledge, critical thinking, quantitative reasoning, and analytical skills. This paper also includes examples of the techniques used with graphing of quadratic function, the algebra, and inverses of the same function. International move to improve mathematics curriculum have supported new goals for student’s learning which highlights problem solving skills, reasoning, ability to work in groups and individually, and use of technology. Knowing that information technology plays considerable role in achieving the above goals, teaching students the concept of Quadratic Functions can be smoothly achieved by using Information Technology in solving real world problems.

Alemtuzumab is a humanized monoclonal antibody approved for the treatment of relapsing-remitting multiple sclerosis (RRMS), given as two annual courses on five consecutive days at baseline and on three consecutive days 12 months later. Here we provide an update on the long-term efficacy and safety of alemtuzumab in RRMS, including realworld experience, and advances in our understanding of its mechanism of action. Recent data from the phase II/III extension study have demonstrated that alemtuzumab reduces relapse rates, disability worsening, and the rate of brain volume loss over the long term, with many patients achieving no evidence of disease activity. In high proportions of patients, preexisting disability remained stable or improved. Alemtuzumab is associated with a consistent safety profile over the long term, with no new safety signals emerging and the overall annual incidence of reported adverse events decreasing after the first year on treatment. Acyclovir prophylaxis reduces herpetic infections, and monitoring has been shown to mitigate the risk of autoimmune adverse events, allowing early detection and overall effective management. Data from clinical practice and ongoing observational studies are providing additional information on the real-world use of alemtuzumab. Recent evidence on the mechanism of action of alemtuzumab indicates that in addition to its previously known effects of inducing depletion and repopulation of T and B lymphocytes, it also results in a relative increase of cells with memory and regulatory phenotypes and a decrease in cells with a proinflammatory signature, and may further promote an immunoregulatory environment through an impact on other innate immune cells (e.g. dendritic cells) that play a role in MS. These effects may allow preservation of innate immunity and immunosurveillance. Together, these lines of evidence help explain the durable clinical efficacy of alemtuzumab, in the absence of continuous treatment, in patients with RRMS.

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Antenna tilt is the angle subtended by the radiation beam and horizontal plane. This angle plays a vital role in determining the coverage and the interference of the network with neighbouring cells and adjacent base stations. Traditional methods for network optimization rely on rule-based heuristics to do decision making for antenna tilt optimization to achieve desired network characteristics. However, these methods are quite brittle and are incapable of capturing the dynamics of communication traffic. Recent advancements in reinforcement learning have made it a viable solution to overcome this problem but even this learning approach is either limited to its simulation environment or is limited to off-policy offline learning. So far, there has not been any effort to overcome the previously mentioned limitations, so as to make it applicable in the real world. This work proposes a method that consists of transferring reinforcement learning policies from a simulated environment to a real environment i.e. sim-to-real transfer through the use of offline learning. The approach makes use of a simulated environment and a fixed dataset to compensate for the underlined limitations. The proposed sim-to-real transfer technique utilizes a hybrid policy model, which is composed of a portion trained in simulation and a portion trained on the offline real-world data from the cellular networks. This enables to merge samples from the real-world data to the simulated environment consequently modifying the standard reinforcement learning training procedures through knowledge sharing between the two environment’s representations. On the one hand, simulation enables to achieve better generalization performance with respect to conventional offline learning as it complements offline learning with learning through unseen simulated trajectories. On the other hand, the offline learning procedure enables to close the sim-to-real gap by exposing the agent to real-world data samples. Consequently, this transfer learning regime enable us to establish optimal antenna tilt control which in turn results in improved coverage and reduced interference with neighbouring cells in the cellular network. / Antennlutning är den vinkel som dämpas av strålningsstrålen och det horisontella planet. Denna vinkel spelar en viktig roll för att bestämma täckningen och störningen av nätverket med angränsande celler och intilliggande basstationer. Traditionella metoder för nätverksoptimering förlitar sig på regelbaserad heuristik för att göra beslutsfattande för antennlutningsoptimering för att uppnå önskade nätverksegenskaper. Dessa metoder är dock ganska styva och är oförmögna att fånga dynamiken i kommunikationstrafiken. De senaste framstegen inom förstärkningsinlärning har gjort det till en lönsam lösning att lösa detta problem, men även denna inlärningsmetod är antingen begränsad till dess simuleringsmiljö eller är begränsad till off-policy offline inlärning. Hittills har inga ansträngningar gjorts för att övervinna de tidigare nämnda begränsningarna för att göra det tillämpligt i den verkliga världen. Detta arbete föreslår en metod som består i att överföra förstärkningsinlärningspolicyer från en simulerad miljö till en verklig miljö, dvs. sim-till-verklig överföring genom användning av offline-lärande. Metoden använder en simulerad miljö och en fast dataset för att kompensera för de understrukna begränsningarna. Den föreslagna sim-till-verkliga överföringstekniken använder en hybridpolicymodell, som består av en del utbildad i simulering och en del utbildad på offline-verkliga data från mobilnätverk. Detta gör det möjligt att slå samman prover från verklig data till den simulerade miljön och därmed modifiera standardutbildningsförfarandena för förstärkning genom kunskapsdelning mellan de två miljöernas representationer. Å ena sidan möjliggör simulering att uppnå bättre generaliseringsprestanda med avseende på konventionellt offlineinlärning eftersom det kompletterar offlineinlärning med inlärning genom osynliga simulerade banor. Å andra sidan möjliggör offline-inlärningsförfarandet att stänga sim-till-real-klyftan genom att exponera agenten för verkliga dataprov. Följaktligen möjliggör detta överföringsinlärningsregime att upprätta optimal antennlutningskontroll som i sin tur resulterar i förbättrad täckning och minskad störning med angränsande celler i mobilnätet.

Clinical observations of individual patients are the cornerstones for furthering our understanding of the human body, diseases, and therapeutics. Traditionally, clinical observations were communicated through publishing case reports and case series. The effort of identifying and investigating unusual clinical observations has always rested on the shoulders of busy clinicians. To date, there has been little effort dedicated to increasing the efficiency of identifying unique and uncommon patient observations that may lead to valuable discoveries. In this thesis, we propose and implement an augmented intelligence framework to identify potential novel clinical observations by combining machine analytics through outlier analysis with the judgment of subject-matter experts. Preeclampsia is a significant cause of maternal and perinatal mortality and morbidity, and advances in its management have been slow. Considering the complex etiological nature of preeclampsia, clinical observations are essential in advancing our understanding of the disease and therapeutic approaches. Thus, the objectives and studies in this thesis aim to answer the hypothesis that using outlier analysis in preeclampsia-related medical data would lead to identifying previously uninvestigated clinical cases with new clinical insight. This thesis combines three articles published or submitted for publication in peer-reviewed journals. The first article (published) is a systematic review examining the extent to which case reports and case series in preeclampsia have contributed new knowledge or discoveries. We report that under one-third of the identified case reports and case series presented new knowledge. In our second article (submitted for publication), we provide an overview of outlier analysis and introduce the framework of augmented intelligence using our proposed extreme misclassification contextual outlier analysis approach. Furthermore, we conduct a systematic review of obstetrics-related research that used outlier analysis to answer scientific questions. Our systematic review findings indicate that such use is in its infancy. In our third article (published), we implement the proposed augmented intelligence framework using two different outlier analysis methods on two independent datasets from separate studies in preeclampsia and hypertensive disorders of pregnancy. We identify several clinical observations as potential novelties, thus supporting the feasibility and applicability of outlier analysis to accelerate clinical discovery.

The impact of driving cycle on exhaust emissions of buses in Hanoi was presented in this article. A typical driving cycle of buses in Hanoi was developed based on the real-world driving data, and it also was assessed that has a good conformity with the real-world driving data. The typical driving cycle and European Transient Cycle part 1 (ETC-part1) were used to estimate vehicle emission according to different driving cycles. The obtained results showed that emissions level of CO, VOC, PM, CO2 and NOx of the buses were very different between two driving cycles, especially CO2 and NOx. This paper, therefore, reconfirms the necessity of the development of the typical driving cycle before conducting the emission inventory for mobile sources. / Tóm tắt: Tác động của chu trình lái tới sự phát thải của xe buýt tại Hà Nội đã được trình bày trong bài báo này. Một chu trình lái đặc trưng của xe buýt Hà Nội đã được xây dựng dựa trên dữ liệu hoạt động ngoài thực tế của phương tiện, và chu trình lái này cũng đã được đánh giá có sự phù hợp rất cao với dữ liệu lái ngoài thực tế. Chu trình lái đặc trưng và chu trình thử ETC-part1 được sử dụng để đánh giá phát thải của phương tiện theo các chu trình lái khác nhau. Các kết quả đạt được cho thấy mức độ phát thải CO, VOC, PM, CO2 và NOx của xe buýt rất khác nhau giữa hai chu trình lái, đặc biệt là CO2 và NOx. Do đó, bài báo khẳng định sự cần thiết phải xây dựng chu trình lái đặc trưng trước khi thực hiện kiểm kê phát thải đối với nguồn động.

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