Downside Risk

Economists have long recognized that investors care differently about downside losses versus upside gains. Agents who place greater weight on downside risk demand additional compensation for holding stocks with high sensitivities to downside market movements. We show that the cross-section of stock returns reflects a premium for downside risk. Specifically, stocks that covary strongly with the market when the market declines have high average returns. We estimate that the downside risk premium is approximately 6% per annum. The reward for bearing downside risk is not simply compensation for regular market beta, nor is it explained by coskewness or liquidity risk, or size, book-to-market, and momentum characteristics.

Detecting and Explaining Causes From Text For a Time Series Event

Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.Comment: Accepted at EMNLP 201

Downside Risk and the Momentum Effect

Stocks with greater downside risk, which is measured by higher correlations conditional on downside moves of the market, have higher returns. After controlling for the market beta, the size effect and the book-to-market effect, the average rate of return on stocks with the greatest downside risk exceeds the average rate of return on stocks with the least downside risk by 6.55% per annum. Downside risk is important for explaining the cross-section of expected returns. In particular of the profitability of investing in momentum strategies can be explained as compensation for bearing high exposure to downside risk.

Dialogue Act Recognition via CRF-Attentive Structured Network

Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2% gap.Comment: 10 pages, 4figure

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs

Geometry-based customization of bending modalities for 3D-printed soft pneumatic actuators

In this work, we propose a novel type of 3D-printed soft pneumatic actuator that allows geometry-based customization of bending modalities. While motion in the 3D-space has been achieved for several types of soft actuators, only 2D-bending has been previously modelled and characterized within the scope of 3D-printed soft pneumatic actuators. We developed the first type of 3D-printed soft pneumatic actuator which, by means of the unique feature of customizable cubes at an angle with the longitudinal axis of the structure, is capable of helical motion. Thus, we characterize its mechanical behavior and formulate mathematical and FEA models to validate the experimental results. Variation to the pattern of the inclination angle along the actuator is then demonstrated to allow for complex 3D-bending modalities and the main applications in the fields of object manipulation and wearable robotics are finally discussed

The outbreak of SARS at Tan Tock Seng Hospital--relating epidemiology to control.

INTRODUCTION: The outbreak of severe acute respiratory syndrome (SARS) began after the index case was admitted on 1 March 2003. We profile the cases suspected to have acquired the infection in Tan Tock Seng Hospital (TTSH), focussing on major transmission foci, and also describe and discuss the impact of our outbreak control measures. MATERIALS AND METHODS: Using the World Health Organization (WHO) case definitions for probable SARS adapted to the local context, we studied all cases documented to have passed through TTSH less than 10 days prior to the onset of fever. Key data were collected in liaison with clinicians and through a team of onsite epidemiologists. RESULTS: There were 105 secondary cases in TTSH. Healthcare staff (57.1%) formed the majority, followed by visitors (30.5%) and inpatients (12.4%). The earliest case had onset of fever on 4 March 2003, and the last case, on 5 April 2003. Eighty-nine per cent had exposures to 7 wards which had cases of SARS that were not isolated on admission. In 3 of these wards, major outbreaks resulted, each with more than 20 secondary cases. Attack rates amongst ward-based staff ranged from 0% to 32.5%. Of 13 inpatients infected, only 4 (30.8%) had been in the same room or cubicle as the index case for the ward. CONCLUSIONS: The outbreak of SARS at TTSH showed the challenges of dealing with an emerging infectious disease with efficient nosocomial spread. Super-spreading events and initial delays in outbreak response led to widespread dissemination of the outbreak to multiple wards