Language Models as Emotional Classifiers for Textual Conversations

Emotions play a critical role in our everyday lives by altering how we perceive, process and respond to our environment. Affective computing aims to instill in computers the ability to detect and act on the emotions of human actors. A core aspect of any affective computing system is the classification of a user's emotion. In this study we present a novel methodology for classifying emotion in a conversation. At the backbone of our proposed methodology is a pre-trained Language Model (LM), which is supplemented by a Graph Convolutional Network (GCN) that propagates information over the predicate-argument structure identified in an utterance. We apply our proposed methodology on the IEMOCAP and Friends data sets, achieving state-of-the-art performance on the former and a higher accuracy on certain emotional labels on the latter. Furthermore, we examine the role context plays in our methodology by altering how much of the preceding conversation the model has access to when making a classification

Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways

Principal component analysis (PCA) is a powerful data reductionmethod for Structural Health Monitoring. However, its computa-tional cost and data memory footprint pose a significant challengewhen PCA has to run on limited capability embedded platformsin low-cost IoT gateways. This paper presents a memory-efficientparallel implementation of the streaming History PCA algorithm.On our dataset, it achieves 10x compression factor and 59x memoryreduction with less than 0.15 dB degradation in the reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over, the algorithm benefits from parallelization on multiple cores,achieving a maximum speedup of 4.8x on Samsung ARTIK 710

Attentive History Selection for Conversational Question Answering

Conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. One of its major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding.Comment: Accepted to CIKM 201

Open-Retrieval Conversational Question Answering

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.Comment: Accepted to SIGIR'2

Excitation energies from time-dependent density-functional theory beyond the adiabatic approximation

doi:10.1063/1.1756865Time-dependent density-functional theory in the adiabatic approximation has been very successful for calculating excitation energies in molecular systems. This paper studies nonadiabatic effects for excitation energies, using the current-density functional of Vignale and Kohn [Phys. Rev. Lett. 77, 2037 (1996)]. We derive a general analytic expression for nonadiabatic corrections to excitation energies of finite systems and calculate singlet s→s and s→p excitations of closed-shell atoms. The approach works well for s→s excitations, giving a small improvement over the adiabatic local-density approximation, but tends to overcorrect s→p excitations. We find that the observed problems with the nonadiabatic correction have two main sources: (1) the currents associated with the s→p excitations are highly nonuniform and, in particular, change direction between atomic shells, (2) the so-called exchange-correlation kernels of the homogeneous electron gas, fxcL and fxcT, are incompletely known, in particular in the high-density atomic core regions.C.A.U. acknowledges support by the donors of the Petroleum Research Fund, administered by the ACS, and by the University of Missouri Research Board. K.B. was supported by DOE under Grant No. DE-FG02-01ER45928

Prevalence, determinants, and clinical associations of high-sensitivity cardiac troponin in patients attending emergency departments

Background: High-sensitivity cardiac troponin assays may improve the diagnosis of myocardial infarction but increase the detection of elevated cardiac troponin in patients without acute coronary syndrome. Methods: In a prospective cohort study, we evaluated the prevalence, determinants, and outcome of patients with elevated cardiac troponin attending the emergency department without suspected acute coronary syndrome. We measured high-sensitivity cardiac troponin in 918 consecutive patients attending the emergency department without suspected acute coronary syndrome who had blood sampling performed by the attending clinician. Elevated high-sensitivity cardiac troponin I was defined as concentrations above the sex-specific 99th percentile threshold. Clinical demographics, physiological measures, and all-cause mortality at 1 year associated with elevated high-sensitivity cardiac troponin concentrations were recorded. Results: Elevated cardiac troponin concentration occurred in 114 (12.4%) patients, of whom 2 (0.2%), 3 (0.3%), and 109 (11.9%) were adjudicated as type 1 myocardial infarction, type 2 myocardial infarction, and myocardial injury, respectively. Elevated troponin concentrations were associated with increasing age, worsening renal function, multimorbidity, and adverse physiology. Across a total of 912 patient-years follow-up, cardiac troponin concentration was a strong predictor of death (hazard ratio [HR] 1.26 per 2-fold increase, 95% confidence interval [CI] 1.06 to 1.49) independent of age, sex, multimorbidity, and adverse physiology. Conclusions: High-sensitivity cardiac troponin concentrations were elevated in 1 in 8 consecutive patients without suspected acute coronary syndrome attending the emergency department and were associated with increasing age, multimorbidity, adverse physiology, and death. Elevated cardiac troponin in unselected patients predominantly reflects myocardial injury rather than myocardial infarction

Welwitindolinone C synthetic studies. Construction of the welwitindolinone carbon skeleton via a transannular nitrone cycloaddition

Described is the construction of the N-methylwelwitindolinone C core via an efficient strategy that employs a sequential rhodium carbenoid-mediated O–H insertion, Claisen rearrangement and transannular [3+2] nitrone cycloaddition