State Dependent Preferences Can Explain the Equity Premium Puzzle

We introduce state dependent recursive preferences into the Mehra-Prescott economy. We show that such preferences can match the historical first two moments of the returns on equity and the risk free rate. Other authors have reported similar results using state dependent expected utility preferences. These authors have tended to emphasize the importance of countercyclical risk aversion in explaining the equity premium puzzle. We find that countercyclical risk aversion plays an important role but only when combined with modest cyclical variation in intertemporal substitution.

Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing

Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs

An Alarm System For Segmentation Algorithm Based On Shape Model

It is usually hard for a learning system to predict correctly on rare events that never occur in the training data, and there is no exception for segmentation algorithms. Meanwhile, manual inspection of each case to locate the failures becomes infeasible due to the trend of large data scale and limited human resource. Therefore, we build an alarm system that will set off alerts when the segmentation result is possibly unsatisfactory, assuming no corresponding ground truth mask is provided. One plausible solution is to project the segmentation results into a low dimensional feature space; then learn classifiers/regressors to predict their qualities. Motivated by this, in this paper, we learn a feature space using the shape information which is a strong prior shared among different datasets and robust to the appearance variation of input data.The shape feature is captured using a Variational Auto-Encoder (VAE) network that trained with only the ground truth masks. During testing, the segmentation results with bad shapes shall not fit the shape prior well, resulting in large loss values. Thus, the VAE is able to evaluate the quality of segmentation result on unseen data, without using ground truth. Finally, we learn a regressor in the one-dimensional feature space to predict the qualities of segmentation results. Our alarm system is evaluated on several recent state-of-art segmentation algorithms for 3D medical segmentation tasks. Compared with other standard quality assessment methods, our system consistently provides more reliable prediction on the qualities of segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures

Highly Mutable Linker Regions Regulate HIV-1 Rev Function and Stability.

HIV-1 Rev is an essential viral regulatory protein that facilitates the nuclear export of intron-containing viral mRNAs. It is organized into structured, functionally well-characterized motifs joined by less understood linker regions. Our recent competitive deep mutational scanning study confirmed many known constraints in Rev's established motifs, but also identified positions of mutational plasticity, most notably in surrounding linker regions. Here, we probe the mutational limits of these linkers by testing the activities of multiple truncation and mass substitution mutations. We find that these regions possess previously unknown structural, functional or regulatory roles, not apparent from systematic point mutational approaches. Specifically, the N- and C-termini of Rev contribute to protein stability; mutations in a turn that connects the two main helices of Rev have different effects in different contexts; and a linker region which connects the second helix of Rev to its nuclear export sequence has structural requirements for function. Thus, Rev function extends beyond its characterized motifs, and is tuned by determinants within seemingly plastic portions of its sequence. Additionally, Rev's ability to tolerate many of these massive truncations and substitutions illustrates the overall mutational and functional robustness inherent in this viral protein

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated by sampling from this distribution. The model consists of two sub-networks: a deep recurrent neural network for sentences and a deep convolutional network for images. These two sub-networks interact with each other in a multimodal layer to form the whole m-RNN model. The effectiveness of our model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K, Flickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In addition, we apply the m-RNN model to retrieval tasks for retrieving images or sentences, and achieves significant performance improvement over the state-of-the-art methods which directly optimize the ranking objective function for retrieval. The project page of this work is: www.stat.ucla.edu/~junhua.mao/m-RNN.html .Comment: Add a simple strategy to boost the performance of image captioning task significantly. More details are shown in Section 8 of the paper. The code and related data are available at https://github.com/mjhucla/mRNN-CR ;. arXiv admin note: substantial text overlap with arXiv:1410.109