Predicting Lyα Emission from Galaxies via Empirical Markers of Production and Escape in the KBSS

Lyα emission is widely used to detect and confirm high-redshift galaxies and characterize the evolution of the intergalactic medium (IGM). However, many galaxies do not display Lyα emission in typical spectroscopic observations, and intrinsic Lyα emitters represent a potentially biased set of high-redshift galaxies. In this work, we analyze a set of 703 galaxies at 2 ≾ z ≾ 3 with both Lyα spectroscopy and measurements of other rest-frame ultraviolet and optical properties in order to develop an empirical model for Lyα emission from galaxies and understand how the probability of Lyα emission depends on other observables. We consider several empirical proxies for the efficiency of Lyα photon production, as well as the subsequent escape of these photons through their local interstellar medium. We find that the equivalent width of metal-line absorption and the O3 ratio of rest-frame optical nebular lines are advantageous empirical proxies for Lyα escape and production, respectively. We develop a new quantity, X_(LIS)^(O3), that combines these two properties into a single predictor of net Lyα emission, which we find describes ~90% of the observed variance in Lyα equivalent width when accounting for our observational uncertainties. We also construct conditional probability distributions demonstrating that galaxy selection based on measurements of galaxy properties yield samples of galaxies with widely varying probabilities of net Lyα emission. The application of the empirical models and probability distributions described here may be used to infer the selection biases of current galaxy surveys and evaluate the significance of high-redshift Lyα (non)detections in studies of reionization and the IGM

Deception and Self-Deception

Why are people so often overconfident? We conduct an experiment to test the hypothesis that people become overconfident to more effectively persuade or deceive others. After performing a cognitively challenging task, half of our subjects are informed that they can earn money by convincing others of their superior performance. The privately elicited beliefs of informed subjects are significantly more confident than the beliefs of subjects in the control condition. By generating exogenous variation in confidence with a noisy performance signal, we are also able to show that higher confidence indeed makes subjects more persuasive in the subsequent face-to-face interactions

Cost-sensitive deep neural network ensemble for class imbalance problem

In data mining, classification is a task to build a model which classifies data into a given set of categories. Most classification algorithms assume the class distribution of data to be roughly balanced. In real-life applications such as direct marketing, fraud detection and churn prediction, class imbalance problem usually occurs. Class imbalance problem is referred to the issue that the number of examples belonging to a class is significantly greater than those of the others. When training a standard classifier with class imbalance data, the classifier is usually biased toward majority class. However, minority class is the class of interest and more significant than the majority class. In the literature, existing methods such as data-level, algorithmic-level and cost-sensitive learning have been proposed to address this problem. The experiments discussed in these studies were usually conducted on relatively small data sets or even on artificial data. The performance of the methods on modern real-life data sets, which are more complicated, is unclear. In this research, we study the background and some of the state-of-the-art approaches which handle class imbalance problem. We also propose two costsensitive methods to address class imbalance problem, namely Cost-Sensitive Deep Neural Network (CSDNN) and Cost-Sensitive Deep Neural Network Ensemble (CSDE). CSDNN is a deep neural network based on Stacked Denoising Autoencoders (SDAE). We propose CSDNN by incorporating cost information of majority and minority class into the cost function of SDAE to make it costsensitive. Another proposed method, CSDE, is an ensemble learning version of CSDNN which is proposed to improve the generalization performance on class imbalance problem. In the first step, a deep neural network based on SDAE is created for layer-wise feature extraction. Next, we perform Bagging’s resampling procedure with undersampling to split training data into a number of bootstrap samples. In the third step, we apply a layer-wise feature extraction method to extract new feature samples from each of the hidden layer(s) of the SDAE. Lastly, the ensemble learning is performed by using each of the new feature samples to train a CSDNN classifier with random cost vector. Experiments are conducted to compare the proposed methods with the existing methods. We examine their performance on real-life data sets in business domains. The results show that the proposed methods obtain promising results in handling class imbalance problem and also outperform all the other compared methods. There are three major contributions to this work. First, we proposed CSDNN method in which misclassification costs are considered in training process. Second, we incorporate random undersampling with layer-wise feature extraction to perform ensemble learning. Third, this is the first work that conducts experiments on class imbalance problem using large real-life data sets in different business domains ranging from direct marketing, churn prediction, credit scoring, fraud detection to fake review detection

Cooperative Distributed Transmission and Reception

In telecommunications, a cooperative scheme refers to a method where two or more users share or combine their information in order to increase diversity gain or power gain. In contrast to conventional point-to-point communications, cooperative communications allow different users in a wireless network to share resources so that instead of maximizing the performance of its own link, each user collaborates with its neighbours to achieve an overall improvement in performance. In this dissertation, we consider different models for transmission and reception and explore cooperative techniques that increase the reliability and capacity gains in wireless networks, with consideration to practical issues such as channel estimation errors and backhaul constraints. This dissertation considers the design and performance of cooperative communication techniques. Particularly, the first part of this dissertation focuses on the performance comparison between interference alignment and opportunistic transmission for a 3-user single-input single- output (SISO) interference channel in terms of average sum rate in the presence of channel estimation errors. In the case of interference alignment, channel estimation errors cause interference leakage which consequently results in a loss of achievable rate. In the case of opportunistic transmission, channel estimation errors result in a non-zero probability of incorrectly choosing the node with the best channel. The effect of these impairments is quantified in terms of the achievable average sum rate of these transmission techniques. Analysis and numerical examples show that SISO interference alignment can achieve better average sum rate with good channel estimates and at high SNR whereas opportunistic transmission provides better performance at low SNR and/or when the channel estimates are poor. We next considers the problem of jointly decoding binary phase shift keyed (BPSK) messages from a single distant transmitter to a cooperative receive cluster connected by a local area network (LAN). An approximate distributed receive beamforming algorithm is proposed based on the exchange of coarsely- quantized observations among some or all of the nodes in the receive cluster. By taking into account the differences in channel quality across the receive cluster, the quantized information from other nodes in the receive cluster can be appropriately combined with locally unquantized information to form an approximation of the ideal receive beamformer decision statistic. The LAN throughput requirements of this technique are derived as a function of the number of participating nodes in the receive cluster, the forward link code rate, and the quantization parameters. Using information-theoretic analysis and simulations of an LDPC coded system in fading channels, numerical results show that the performance penalty (in terms of outage probability and block error rate) due to coarse quantization is small in the low SNR regimes enabled by cooperative distributed reception. An upper/lower bound approximation is derived based on a circle approximation in the channel magnitude domain which provides a pretty fast way to compute the outage probability performance for a system with arbitrary number of receivers at a given SNR. In the final part of this dissertation, we discuss the distributed reception technique with higher- order modulation schemes in the forward link. The extension from BPSK to QPSK is straightforward and is studied in the second part of this dissertation. The extension to 8PSK, 4PAM and 16QAM forward links, however, is not trivial. For 8PSK, two techniques are proposed: pseudobeamforming and 3-bit belief combining where the first one is intuitive and turns out to be suboptimal,the latter is optimal in terms of outage probability performance. The idea of belief combining can be applied to the 4PAM and 16QAM and it is shown that better/finer quantizer design can further improve the block error rate performance. Information-theoretic analysis and numerical results are provided to show that significant reliability and SNR gains can be achieved by using the proposed schemes