The Term Structure of VIX

We extend the concept of CBOE constant 30-day VIX to other maturities and construct daily VIX term structure data from starting date available to August 2009. We propose a simple yet powerful two-factor stochastic volatility framework for VIXs. Our empirical analysis indicates that the framework is good at both capturing time-series dynamics of VIXs and generating rich cross-sectional shape of the term structure. In particular, we show that the two time-varying factors may be interpreted as factors corresponding to level and slope of the VIX term structure. Moreover, we explore the information content of VIXs relative to historical volatility in forecasting future realized volatility. Consistent with previous studies, we find that VIXs contain more information than historical volatility.postprin

The relation between physical and risk-neutral cumulants

Variance swaps are natural instruments for investors taking directional bets on volatility and are often used for portfolio protection. But the crucial observation suggests that derivative professionals may desire to hedge beyond volatility risk and there exists the need to hedge higher-moment market risks, such as skewness and kurtosis risks. We propose new derivative contracts: skewness swap and kurtosis swap, which trade the forward realized third and fourth cumulants. Using S&P 500 index options data from 1996 to 2005, we document the returns of these swap contracts, i.e., skewness risk premium and kurtosis risk premium. We find that the skewness risk premium is significantly negative and kurtosis risk premium for 90 day maturity is significantly positive.postprintThe 2009 Annual Meeting of the Financial Management Association (FMA), Reno, NV., 21-24 October 2009

The relation between physical and risk-neutral cumulants

Variance swaps are natural instruments for investors taking directional bets on volatility and are often used for portfolio protection. But the crucial observation suggests that derivative professionals may desire to hedge beyond volatility risk and there exists the need to hedge higher-moment market risks, such as skewness and kurtosis risks. We propose new derivative contracts: skewness swap and kurtosis swap, which trade the forward realized third and fourth cumulants. Using S&P 500 index options data from 1996 to 2005, we document the returns of these swap contracts, i.e., skewness risk premium and kurtosis risk premium. We find that the skewness risk premium is significantly negative and kurtosis risk premium for 90 day maturity is significantly positive.postprintThe 2009 Annual Meeting of the Financial Management Association (FMA), Reno, NV., 21-24 October 2009

Necessary and sufficient conditions of solution uniqueness in ℓ1\ell_1 minimization

This paper shows that the solutions to various convex $\ell_1$ minimization problems are \emph{unique} if and only if a common set of conditions are satisfied. This result applies broadly to the basis pursuit model, basis pursuit denoising model, Lasso model, as well as other $\ell_1$ models that either minimize $f(Ax-b)$ or impose the constraint $f(Ax-b)\leq\sigma$, where $f$ is a strictly convex function. For these models, this paper proves that, given a solution $x^*$ and defining I=\supp(x^*) and s=\sign(x^*_I), $x^*$ is the unique solution if and only if $A_I$ has full column rank and there exists $y$ such that $A_I^Ty=s$ and $|a_i^Ty|_\infty<1$ for $i\not\in I$. This condition is previously known to be sufficient for the basis pursuit model to have a unique solution supported on $I$. Indeed, it is also necessary, and applies to a variety of other $\ell_1$ models. The paper also discusses ways to recognize unique solutions and verify the uniqueness conditions numerically.Comment: 6 pages; revised version; submitte

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spherically symmetric distribution within the ambient space (for simplicity we may assume that all distributions are uniform on their corresponding unit spheres). We also assume mixture weights for the different components. We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces. We study the recovery of the most significant subspace by minimizing the lp-averaged distances of data points from d-dimensional subspaces, where p>0. Unlike other lp minimization problems, this minimization is non-convex for all p>0 and thus requires different methods for its analysis. We show that if 0<p<=1, then for any fraction of outliers the most significant subspace can be recovered by lp minimization with overwhelming probability (which depends on the generating distribution and its parameters). We show that when adding small noise around the underlying subspaces the most significant subspace can be nearly recovered by lp minimization for any 0<p<=1 with an error proportional to the noise level. On the other hand, if p>1 and there is more than one underlying subspace, then with overwhelming probability the most significant subspace cannot be recovered or nearly recovered. This last result does not require spherically symmetric outliers.Comment: This is a revised version of the part of 1002.1994 that deals with single subspace recovery. V3: Improved estimates (in particular for Lemma 3.1 and for estimates relying on it), asymptotic dependence of probabilities and constants on D and d and further clarifications; for simplicity it assumes uniform distributions on spheres. V4: minor revision for the published versio

Pencil beam all-optical ultrasound imaging

A miniature, directional fibre-optic acoustic source is presented that employs geometrical focussing to generate a nearly-collimated acoustic pencil beam. When paired with a fibre-optic acoustic detector, an all-optical ultrasound probe with an outer diameter of 2.5 mm is obtained that acquires a pulse-echo image line at each probe position without the need for image reconstruction. B-mode images can be acquired by translating the probe and concatenating the image lines, and artefacts resulting from probe positioning uncertainty are shown to be significantly lower than those observed for conventional synthetic aperture scanning of a non-directional acoustic source. The high image quality obtained for excised vascular tissue suggests that the all-optical ultrasound probe is ideally suited for in vivo, interventional applications

Canine distemper virus neutralization activity is low in human serum and it is sensitive to an amino acid substitution in the hemagglutinin protein

© 2015 Elsevier Inc.Serum was analyzed from 146 healthy adult volunteers in eastern Africa to evaluate measles virus (MV) and canine distemper virus (CDV) neutralizing antibody (nAb) prevalence and potency. MV plaque reduction neutralization test (PRNT) results indicated that all sera were positive for MV nAbs. Furthermore, the 50% neutralizing dose (ND50) for the majority of sera corresponded to antibody titers induced by MV vaccination. CDV nAbs titers were low and generally were detected in sera with high MV nAb titers. A mutant CDV was generated that was less sensitive to neutralization by human serum. The mutant virus genome had 10 nucleotide substitutions, which coded for single amino acid substitutions in the fusion (F) and hemagglutinin (H) glycoproteins and two substitutions in the large polymerase (L) protein. The H substitution occurred in a conserved region involved in receptor interactions among morbilliviruses, implying that this region is a target for cross-reactive neutralizing antibodies

Deep interactive evolution

This paper describes an approach that combines generative adversarial networks (GANs) with interactive evolutionary computation (IEC). While GANs can be trained to produce lifelike images, they are normally sampled randomly from the learned distribution, providing limited control over the resulting output. On the other hand, interactive evolution has shown promise in creating various artifacts such as images, music and 3D objects, but traditionally relies on a hand-designed evolvable representation of the target domain. The main insight in this paper is that a GAN trained on a specific target domain can act as a compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes do resemble valid domain artifacts). Once such a GAN is trained, the latent vector given as input to the GAN's generator network can be put under evolutionary control, allowing controllable and high-quality image generation. In this paper, we demonstrate the advantage of this novel approach through a user study in which participants were able to evolve images that strongly resemble specific target images.Comment: 16 pages, 5 figures, Published at EvoMUSART EvoStar 201

A reconfigurable all-optical ultrasound transducer array for 3D endoscopic imaging

A miniature all-optical ultrasound imaging system is presented that generates three-dimensional images using a stationary, real acoustic source aperture. Discrete acoustic sources were sequentially addressed by scanning a focussed optical beam across the proximal end of a coherent fibre bundle; high-frequency ultrasound (156% fractional bandwidth centred around 13.5 MHz) was generated photoacoustically in the corresponding regions of an optically absorbing coating deposited at the distal end. Paired with a single fibre-optic ultrasound detector, the imaging probe (3.5 mm outer diameter) achieved high on-axis resolutions of 97 μm, 179 μm and 110 μm in the x, y and z directions, respectively. Furthermore, the optical scan pattern, and thus the acoustic source array geometry, was readily reconfigured. Implementing four different array geometries revealed a strong dependency of the image quality on the source location pattern. Thus, by employing optical technology, a miniature ultrasound probe was fabricated that allows for arbitrary source array geometries, which is suitable for three-dimensional endoscopic and laparoscopic imaging, as was demonstrated on ex vivo porcine cardiac tissue

Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201