Forecast Combination Under Heavy-Tailed Errors

Forecast combination has been proven to be a very important technique to obtain accurate predictions. In many applications, forecast errors exhibit heavy tail behaviors for various reasons. Unfortunately, to our knowledge, little has been done to deal with forecast combination for such situations. The familiar forecast combination methods such as simple average, least squares regression, or those based on variance-covariance of the forecasts, may perform very poorly. In this paper, we propose two nonparametric forecast combination methods to address the problem. One is specially proposed for the situations that the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student's t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to shortage of data and/or evolving data generating process. Adaptive risk bounds of both methods are developed. Simulations and a real example show superior performance of the new methods

THE ROLE OF DIGLYCERIDES IN LIPID-INDUCED INSULIN RESISTANCE

The cause of lipid-induced insulin resistance has been an intensively-discussed question since the last century. Many experiments have proven the positive correlation between insulin resistance and diglyceride (DAG), which is a bioactive lipid, and an essential intermediate in TAG synthesis and lipolysis. Among the three structural and stereoisomers, sn-1,2 DAG can activate protein kinase C enzymes, mainly PKCε in the liver and PKCθ in the skeletal muscle, and thereby inhibit insulin signaling pathways. While sn-1,2 DAG is primarily formed during TAG synthesis, multiple mechanisms are available for its metabolism. The Bradford Hill criteria are applied and discussed to better evaluate the role of DAG in the development of lipid-induced insulin resistance. To help understand how DAG affects insulin signaling and type 2 diabetes, a network containing glucose metabolism, lipid metabolism, the effect of exercise, and insulin signaling is generated for the liver, muscle, and adipose tissue, and how each PKC isoenzymes influence the network are discussed. Beyond its direct inhibition of hepatic insulin signaling, PKCε might alter hepatic metabolism through indirect effect from adipose tissue. The crosstalk between the liver and adipose tissue has been further demonstrated through adipose lipolysis, secretion of signaling molecules, inflammation, and how these mechanisms deteriorate hepatic insulin sensitivity

Quasi-compactons in inverted nonlinear photonic crystals

We study large-amplitude one-dimensional solitary waves in photonic crystals featuring competition between linear and nonlinear lattices, with minima of the linear potential coinciding with maxima of the nonlinear pseudopotential, and vice versa (inverted nonlinear photonic crystals, INPhCs), in the case of the saturable self-focusing nonlinearity. Such crystals were recently fabricated using a mixture of SU-8 and Rhodamine-B optical materials. By means of numerical methods and analytical approximations, we find that large-amplitude solitons are broad sharply localized stable pulses (quasi-compactons, QCs). With the increase of the totalpower, P, the QC's centroid performs multiple switchings between minima and maxima of the linear potential. Unlike cubic INPhCs, the large-amplitude solitons are mobile in the medium with the saturable nonlinearity. The threshold value of the kick necessary to set the soliton in motion is found as a function of P. Collisions between moving QCs are considered too.Comment: 11 pages, 8 figures, Physical Review A, in pres

The Next-Generation Surgical Robots

The chronicle of surgical robots is short but remarkable. Within 20 years since the regulatory approval of the first surgical robot, more than 3,000 units were installed worldwide, and more than half a million robotic surgical procedures were carried out in the past year alone. The exceptionally high speeds of market penetration and expansion to new surgical areas had raised technical, clinical, and ethical concerns. However, from a technological perspective, surgical robots today are far from perfect, with a list of improvements expected for the next-generation systems. On the other hand, robotic technologies are flourishing at ever-faster paces. Without the inherent conservation and safety requirements in medicine, general robotic research could be substantially more agile and explorative. As a result, various technical innovations in robotics developed in recent years could potentially be grafted into surgical applications and ignite the next major advancement in robotic surgery. In this article, the current generation of surgical robots is reviewed from a technological point of view, including three of possibly the most debated technical topics in surgical robotics: vision, haptics, and accessibility. Further to that, several emerging robotic technologies are highlighted for their potential applications in next-generation robotic surgery

High-Resolution Volumetric Reconstruction for Clothed Humans

We present a novel method for reconstructing clothed humans from a sparse set of, e.g., 1 to 6 RGB images. Despite impressive results from recent works employing deep implicit representation, we revisit the volumetric approach and demonstrate that better performance can be achieved with proper system design. The volumetric representation offers significant advantages in leveraging 3D spatial context through 3D convolutions, and the notorious quantization error is largely negligible with a reasonably large yet affordable volume resolution, e.g., 512. To handle memory and computation costs, we propose a sophisticated coarse-to-fine strategy with voxel culling and subspace sparse convolution. Our method starts with a discretized visual hull to compute a coarse shape and then focuses on a narrow band nearby the coarse shape for refinement. Once the shape is reconstructed, we adopt an image-based rendering approach, which computes the colors of surface points by blending input images with learned weights. Extensive experimental results show that our method significantly reduces the mean point-to-surface (P2S) precision of state-of-the-art methods by more than 50% to achieve approximately 2mm accuracy with a 512 volume resolution. Additionally, images rendered from our textured model achieve a higher peak signal-to-noise ratio (PSNR) compared to state-of-the-art methods

AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing

With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensive attention. And these applications necessitate the mobile edge computing scheme, i.e., offloading partial computation from mobile devices to edge devices for inference acceleration and transmission load reduction. The current practices have relied on collaborative DNN partition and offloading to satisfy the predefined latency requirements, which is intractable to adapt to the dynamic deployment context at runtime. AdaMEC, a context-adaptive and dynamically-combinable DNN deployment framework is proposed to meet these requirements for mobile edge computing, which consists of three novel techniques. First, once-for-all DNN pre-partition divides DNN at the primitive operator level and stores partitioned modules into executable files, defined as pre-partitioned DNN atoms. Second, context-adaptive DNN atom combination and offloading introduces a graph-based decision algorithm to quickly search the suitable combination of atoms and adaptively make the offloading plan under dynamic deployment contexts. Third, runtime latency predictor provides timely latency feedback for DNN deployment considering both DNN configurations and dynamic contexts. Extensive experiments demonstrate that AdaMEC outperforms state-of-the-art baselines in terms of latency reduction by up to 62.14% and average memory saving by 55.21%

UbiEar: Bringing location-independent sound awareness to the hard-of-hearing people with smartphones

Non-speech sound-awareness is important to improve the quality of life for the deaf and hard-of-hearing (DHH) people. DHH people, especially the young, are not always satisfied with their hearing aids. According to the interviews with 60 young hard-of-hearing students, a ubiquitous sound-awareness tool for emergency and social events that works in diverse environments is desired. In this paper, we design UbiEar, a smartphone-based acoustic event sensing and notification system. Core techniques in UbiEar are a light-weight deep convolution neural network to enable location-independent acoustic event recognition on commodity smartphons, and a set of mechanisms for prompt and energy-efficient acoustic sensing. We conducted both controlled experiments and user studies with 86 DHH students and showed that UbiEar can assist the young DHH students in awareness of important acoustic events in their daily life.</jats:p