How Does Technology Affect Skill Demand? Technical Changes and Capital-Skill Complementarity in the 21st Century

This paper attempts to examine technology’s impact on the labor market through the lens of skilled labor. Technical changes in the late 20th century are skill-biased in nature, because they are found to complement with skilled labor who are adept at adopting new technologies. However, recent studies document a lower demand for high-skilled labor in the 21st century, compared with the late 20th century. Are technologies starting to substitute for human skills instead of complementing them? Drawing on the wage share data from 1975 to 2015 for 18 sectors in the United States, I find strong and robust evidence of complementary relationships between technical changes and demand for skilled labor. Furthermore, my results suggest that technologies have become more skilled-biased, not less, in the 21st century

Attentive Adversarial Learning for Domain-Invariant Training

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, AADIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.Comment: 5 pages, 1 figure, ICASSP 201

Adversarial Speaker Adaptation

We propose a novel adversarial speaker adaptation (ASA) scheme, in which adversarial learning is applied to regularize the distribution of deep hidden features in a speaker-dependent (SD) deep neural network (DNN) acoustic model to be close to that of a fixed speaker-independent (SI) DNN acoustic model during adaptation. An additional discriminator network is introduced to distinguish the deep features generated by the SD model from those produced by the SI model. In ASA, with a fixed SI model as the reference, an SD model is jointly optimized with the discriminator network to minimize the senone classification loss, and simultaneously to mini-maximize the SI/SD discrimination loss on the adaptation data. With ASA, a senone-discriminative deep feature is learned in the SD model with a similar distribution to that of the SI model. With such a regularized and adapted deep feature, the SD model can perform improved automatic speech recognition on the target speaker's speech. Evaluated on the Microsoft short message dictation dataset, ASA achieves 14.4% and 7.9% relative word error rate improvements for supervised and unsupervised adaptation, respectively, over an SI model trained from 2600 hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201

Relaxing the Rational Expectations Assumption: Data-based and Model-based Approaches

The fundamental importance of beliefs about future outcomes in decision-making suggests that an accurate characterization of these beliefs is important for understanding individuals\u27 behavior and for evaluating the counterfactuals typically needed for policy analysis. Traditionally, many researchers have been using some form of Rational Expectations (RE) assumptions to characterize these beliefs. However, empirical evidence suggests that the RE assumption might not hold in many contexts, and that incorrectly imposing the RE assumption can lead to biased policy predictions. Motivated by these findings, I explore alternative approaches to conducting economic analysis without imposing the RE assumption. Chapters 2 and 3 of my thesis, which are co-authored with Todd and Ralph Stinebrickner, utilize unique survey expectations data from the Berea Panel Study (BPS) to characterize college students\u27 beliefs about various future outcomes. Specifically, in Chapter 2, we characterize how much uncertainty about post-college income is present for students at college entrance and how quickly this uncertainty is resolved. Measuring an individual\u27s income uncertainty by the variance of the distribution describing her beliefs about earnings at age 28, we find that, on average, students resolve roughly one-third of the income uncertainty present at the time of entrance during college. Consistent with the finding that the majority of initial income uncertainty remains at the end of college, We find that uncertainty about college GPA and field of study, which are the two primary income-influencing factors that are realized in college, can only account for about 19% to 27% of students\u27 initial income uncertainty. Chapter 3 provides a concrete example that illustrates the importance of quantifying the resolution of students\u27 (income) uncertainty during college. By entering college, students have the option to decide whether to remain in college after receiving relevant new information. We show that the value of this option of receiving new information is determined by a student\u27s dropout probability and how much uncertainty is resolved before the decision is made. Taking advantage of longitudinal expectations data from the BPS, we find that students have accurate perceptions about the amount of income uncertainty that is resolved during college but vastly underestimate the probability of dropping out of school. Consequently, on average, they underestimate this option value by 65%. Chapter 4 proposes an alternative, model-based approach to jointly nonparametrically identify individuals\u27 beliefs and the decision rule, which is a function that maps beliefs to decisions. My method can be applied to signal-based learning models, where individuals use signals to update their beliefs about an unknown permanent factor and repeatedly make decisions based on these beliefs. The econometrician observes individuals\u27 decisions and the signals they receive at each period. Using data from the BPS, I apply my method to estimate the relationship between college students\u27 study time and their beliefs about academic productivity as measured by the ratio of semester GPA to study time. I find that expectations about own academic productivity have a negative effect on study time. The RE assumption is rejected at a 10% level for a subgroup of students. Incorrectly imposing the RE assumption would lead to a substantially larger estimate of the effect of expectations about academic productivity on college study time

Enhancing touch interactions with passive finger acoustics

In this report, we introduce a unique method of interacting with mobile devices through passive finger acoustics. Phone users typically use one of two fingers when interacting with a touchscreen device, leaving the other fingers idle. The motivation is that users can make use of their idle fingers to enhance the experience of an application. By wearing minimally obtrusive rings on the thumb and index finger, users can make distinguishable clicking sounds to quickly perform actions, without having to interact directly with the screen. Our system leverages the microphone embedded in the mobile device to capture sound and recognize sound in real-time without requiring Internet connection. The different sounds introduce new ways for users to interact with their devices, without cluttering the screen. We evaluated our system on an Android drawing application, which allows the user to switch tools based on clicks. We optimized our system based on accuracy, classification speed, and ease of use, in order to create a comfortable user experienceInformatio

IDENTIFYING THE GENETIC MECHANISMS IN SEIZURE THRESHOLD REGULATION

Epilepsy is a brain disease defined by having recurrent and spontaneous seizures. The susceptibility to seizure is determined by seizure threshold, which describes the balance between excitatory and inhibitory neurotransmission in the brain. Epileptogenesis, the transition from normal brain to epileptic brain, is accompanied by a progressive reduction of seizure threshold and has been shown to have genetic influences. Expression of neuronal cyclooxygenase-2 (COX-2) gene, PTGS2, a primary gene that regulates prostaglandin synthesis in the normal brain, is enhanced by excitatory neurotransmission and is under tight regulation of N-Methyl-D-Aspartate type glutamate receptor (NMDAR) activity in cortical neurons. The 3’ untranslated region (3’UTR) of PTGS2 gene was found to be a key site of post-transcriptional regulation by NMDAR activity. However, deletion of T-cell intracellular antigen-1 (TIA-1), an RNA binding protein of COX-2 mRNA 3’UTR, did not affect COX-2 protein expression in mouse brain. Although TIA-1 deletion did not alter innate seizure threshold, it facilitated the acquisition of epilepsy and enhanced epileptogenesis-associated mortality. Further investigation revealed that TIA-1 knockout mice have an altered transcriptome in their hippocampi. Together, my findings illustrate an NMDAR-dependent regulatory mechanism of a known modulator of seizure threshold (COX-2) in neurons, and provide insight into the regulation of epileptogenesis by a novel genetic modifier (TIA-1)

Conditional Teacher-Student Learning

The teacher-student (T/S) learning has been shown to be effective for a variety of problems such as domain adaptation and model compression. One shortcoming of the T/S learning is that a teacher model, not always perfect, sporadically produces wrong guidance in form of posterior probabilities that misleads the student model towards a suboptimal performance. To overcome this problem, we propose a conditional T/S learning scheme, in which a "smart" student model selectively chooses to learn from either the teacher model or the ground truth labels conditioned on whether the teacher can correctly predict the ground truth. Unlike a naive linear combination of the two knowledge sources, the conditional learning is exclusively engaged with the teacher model when the teacher model's prediction is correct, and otherwise backs off to the ground truth. Thus, the student model is able to learn effectively from the teacher and even potentially surpass the teacher. We examine the proposed learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker adaptation on Microsoft short message dictation dataset. The proposed method achieves 9.8% and 12.8% relative word error rate reductions, respectively, over T/S learning for environment adaptation and speaker-independent model for speaker adaptation.Comment: 5 pages, 1 figure, ICASSP 201

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high