Design of a Ku Band Planner Receive Array for DBS Reception Systems

The main objective of this chapter is to present to the readers a step‐by‐step design approach when designing antenna array. Subsequently, the chapter will proceed following an example design of a passive Ku band planner receive array antenna for direct broadcast from satellite (DBS) reception for mobile systems. First, an appropriate antenna topology capable of reaching our target goals will be selected and optimized to be the base array element. During the design process of the base element, some figures‐of‐merit will be proposed in order to make a comparative study with the designed antenna and previously published antenna structures. Subarrays of microstrip line feed antennas will be combined by waveguides in order to build a low‐loss feed network for the array antenna. The main question during the design of the feed network is: “How should one form the subarrays and their accompanying waveguide feed networks?” These sections will answer this question by formulating the subarray and array feed network loss as an optimization problem with constraints on the size and the weight of the array. In the concluding sections, measurements on realized antennas will show that the design exhibits a 16.5% relative bandwidth, covering the complete downlink band, and the designed antennas have a 28.4–31.3 dBi gain for both vertical and horizontal polarizations. Results of some field tests will be given and conclusions will be made in the final section

Explaining Latent Factor Models for Recommendation with Influence Functions

Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is the lack of explainability. Extensive efforts have been made in the literature to incorporate explainability into LFMs. However, they either rely on auxiliary information which may not be available in practice, or fail to provide easy-to-understand explanations. In this paper, we propose a fast influence analysis method named FIA, which successfully enforces explicit neighbor-style explanations to LFMs with the technique of influence functions stemmed from robust statistics. We first describe how to employ influence functions to LFMs to deliver neighbor-style explanations. Then we develop a novel influence computation algorithm for matrix factorization with high efficiency. We further extend it to the more general neural collaborative filtering and introduce an approximation algorithm to accelerate influence analysis over neural network models. Experimental results on real datasets demonstrate the correctness, efficiency and usefulness of our proposed method

Disciplinary Learning From an Authentic Engineering Context

This small-scale design study describes disciplinary learning in mathematical modeling and science from an authentic engineeringthemed module. Current research in tissue engineering served as source material for the module, including science content for readings and a mathematical modeling activity in which students work in small teams to design a model in response to a problem from a client. The design of the module was guided by well-established principles of model-eliciting activities (a special class of problem-solving activities deeply studied in mathematics education) and recently published implementation design principles, which emphasize the portability of model-eliciting activities to many classroom settings. Two mathematical modeling research questions were addressed: 1. What mathematical approaches did student-teams take when they designed mathematical models to evaluate the quality of blood vessel networks? and 2. What attributes of mature mathematical models were captured in the mathematical models that the student-teams designed? One science content research question was addressed: 1. Before and after the module, what aspects of angiogenesis did students describe when they were asked what they knew about the process of blood vessel growth from existing vessels? Participants who field-tested the module included high school students in a summer enrichment program and early college students enrolled in four general-studies mathematics courses. Data collected from participants included mathematical models produced by small teams of students, as well as students’ individual responses before and after the module to a prompt asking them what they knew about the process of new blood vessel growth from existing vessels. The data were analyzed for mathematical model type and science content by adopting methods of grounded theory, in which researchers suspend expectations about what should be in the data and, instead, allow for the emergence of patterns and trends. The mathematical models were further analyzed for mathematical maturity using an a priori coding scheme of attributes of a mathematical model. Analyses showed that student-teams created mathematical models of varying maturity using four different mathematical approaches, and comparisons of students’ responses to the science prompt showed students knew essentially nothing about angiogenesis before the module but described important aspects of angiogenesis after the module. These findings were used to set up an agenda for future research about the design of the module and the relationship between disciplinary learning and authentic engineering problems

Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls

The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord. In this paper we investigate the IRA's communication strategies by analyzing trending terms on Twitter to identify cases in which the IRA leads or follows other users. Our analysis focuses on over 38M tweets posted between 2016 and 2017 from IRA users (n=3,613), journalists (n=976), members of Congress (n=526), and politically engaged users from the general public (n=71,128). We find that the IRA tends to lead on topics related to the 2016 election, race, and entertainment, suggesting that these are areas both of strategic importance as well having the highest potential impact. Furthermore, we identify topics where the IRA has been relatively ineffective, such as tweets on military, political scandals, and violent attacks. Despite many tweets on these topics, the IRA rarely leads the conversation and thus has little opportunity to influence it. We offer our proposed methodology as a way to track the strategic choices of future influence operations in real-time.Comment: ICWSM 202

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets

COST-SENSITIVE INFORMATION ACQUISITION IN STRUCTURED DOMAINS

Many real-world prediction tasks require collecting information about the domain entities to achieve better predictive performance. Collecting the additional information is often a costly process (money, time, risk, etc.) that involves acquiring the features describing the entities and annotating the entities with target concepts and labels. For example, document collections need to be manually annotated for document classification and lab tests need to be ordered for medical diagnosis. Annotating the whole document collection and ordering all possible lab tests might be infeasible due to limited resources or may prove unnecessary. Thus, we need to be selective about which entity we annotate and which features we acquire. In this thesis, I explore effective and efficient ways of choosing the right information to acquire under limited resources. Specifically, I develop and empirically evaluate algorithms for feature and label acquisition in structured domains. For the problem of feature acquisition, we are given entities with missing features and the task is to classify them with minimum misclassification cost. Thelikelihood of misclassification can be reduced by acquiring features but acquirin

Relational Classification of Biological Cells in Microscopy Images

We investigate the relational classification of biological cells in 2D microscopy images. Rather than treating each cell image independently, we investigate whether and how the neighborhood information of a cell can be informative for its prediction. We propose a Relational Long Short-Term Memory (R-LSTM) algorithm, coupled with auto-encoders and convolutional neural networks, that can learn from both annotated and unlabeled microscopy images and that can utilize both the local and neighborhood information to perform an improved classification of biological cells. Experimental results on both synthetic and real datasets show that R-LSTM performs comparable to or better than six baselines