Distributed Bayesian Probabilistic Matrix Factorization

Matrix factorization is a common machine learning technique for recommender systems. Despite its high prediction accuracy, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of its high computational cost. In this paper we propose a distributed high-performance parallel implementation of BPMF on shared memory and distributed architectures. We show by using efficient load balancing using work stealing on a single node, and by using asynchronous communication in the distributed version we beat state of the art implementations

Fast derivatives of likelihood functionals for ODE based models using adjoint-state method

We consider time series data modeled by ordinary differential equations (ODEs), widespread models in physics, chemistry, biology and science in general. The sensitivity analysis of such dynamical systems usually requires calculation of various derivatives with respect to the model parameters. We employ the adjoint state method (ASM) for efficient computation of the first and the second derivatives of likelihood functionals constrained by ODEs with respect to the parameters of the underlying ODE model. Essentially, the gradient can be computed with a cost (measured by model evaluations) that is independent of the number of the ODE model parameters and the Hessian with a linear cost in the number of the parameters instead of the quadratic one. The sensitivity analysis becomes feasible even if the parametric space is high-dimensional. The main contributions are derivation and rigorous analysis of the ASM in the statistical context, when the discrete data are coupled with the continuous ODE model. Further, we present a highly optimized implementation of the results and its benchmarks on a number of problems. The results are directly applicable in (e.g.) maximum-likelihood estimation or Bayesian sampling of ODE based statistical models, allowing for faster, more stable estimation of parameters of the underlying ODE model.Comment: 5 figure

WBSDF for simulating wave effects of light and audio

Diffraction is a common phenomenon in nature when dealing with small scale occluders. It can be observed on biological surfaces, such as feathers and butterfly wings, and man-made objects like rainbow holograms. In acoustics, the effect of diffraction is even more significant due to the much longer wavelength of sound waves. In order to simulate effects such as interference and diffraction within a ray-based framework, the phase of light or sound waves needs to be integrated

Virales Marketing : nachfragerseitige Determinanten des Weiterleitens viraler Videoclips im Internet

Aus Unternehmenssicht gewinnt die interpersonelle Kommunikation zwischen Konsumenten aufgrund der hohen Werbedichte zunehmend an Bedeutung. Eine Marketingstrategie, die sich der Mundwerbung in sozialen Netzwerken im Internet bedient, ist das so genannte Virale Marketing. Dahinter verbirgt sich die Idee, dass Werbebotschaften durch Mundpropaganda wie ein Virus von Person zu Person weitergegeben werden. Obwohl der Begriff in der Marketingpraxis bereits weit verbreitet ist, hat sich die wissenschaftliche Literatur mit diesem Phänomen bisher kaum auseinander gesetzt. In der vorliegenden Studie werden zunächst die Grundzüge des Viralen Marketing dargestellt. Auf Basis der gewonnenen Erkenntnisse wird im Anschluss daran für einen Teilbereich des Viralen Marketing, dem Viral Advertising, ein Hypothesensystem zur Erklärung des nachfragerinduzierten Weiterleitens viraler Videoclips im Internet entwickelt. Die empirische Überprüfung dieses Hypothesensystems erfolgt abschlie- ßend mit Hilfe eines linearen Strukturgleichungsmodells. Die Ergebnisse der Studie zeigen, dass die Einstellung gegenüber dem viralen Videoclip, das wahrgenommene Community-Erlebnis, die wahrgenommene Überraschung sowie Market Mavenism zentrale Determinanten der Einstellung zum Weiterleiten eines viralen Videoclips darstellen. Diese wiederum bestimmt neben der sozialen Norm die Absicht zum Weiterleiten eines viralen Videoclips

Surgery-Guided Removal of Ovarian Cancer Using Up-Converting Nanoparticles

Ovarian cancer survival and the recurrence rate are drastically affected by the amount of tumor that can be surgically removed prior to chemotherapy. Surgeons are currently limited to visual inspection, making smaller tumors difficult to be removed surgically. Enhancing the surgeon’s ability to selectively remove cancerous tissue would have a positive effect on a patient’s prognosis. One approach to aid in surgical tumor removal involves using targeted fluorescent probes to selectively label cancerous tissue. To date, there has been a trade-off in balancing two requirements for the surgeon: the ability to see maximal tumors and the ability to identify these tumors by eye while performing the surgery. The ability to see maximal tumors has been prioritized and this has led to the use of fluorophores activated by near-infrared (NIR) light as NIR penetrates most deeply in this surgical setting, but the light emitted by traditional NIR fluorophores is invisible to the naked eye. This has necessitated the use of specialty detectors and monitors that the surgeon must consult while performing the surgery. In this study, we develop nanoparticles that selectively label ovarian tumors and are activated by NIR light but emit visible light. This potentially allows for maximal tumor observation and real-time detection by eye during surgery. We designed two generations of up-converting nanoparticles that emit green light when illuminated with NIR light. These particles specifically label ovarian tumors most likely via tumor-associated macrophages, which are prominent in the tumor microenvironment. Our results demonstrate that this approach is a viable means of visualizing tumors during surgery without the need for complicated, expensive, and bulky detection equipment. Continued improvement and experimentation could expand our approach into a much needed surgical technique to aid ovarian tumor removal

Focusing light inside scattering media with magnetic-particle-guided wavefront shaping

Optical scattering has traditionally limited the ability to focus light inside scattering media such as biological tissue. Recently developed wavefront shaping techniques promise to overcome this limit by tailoring an optical wavefront to constructively interfere at a target location deep inside scattering media. To find such a wavefront solution, a “guidestar” mechanism is required to identify the target location. However, developing guidestars of practical usefulness is challenging, especially in biological tissue, which hinders the translation of wavefront shaping techniques. Here, we demonstrate a guidestar mechanism that relies on magnetic modulation of small particles. This guidestar method features an optical modulation efficiency of 29% and enables micrometer-scale focusing inside biological tissue with a peak intensity-to-background ratio (PBR) of 140; both numbers are one order of magnitude higher than those achieved with the ultrasound guidestar, a popular guidestar method. We also demonstrate that light can be focused on cells labeled with magnetic particles, and to different target locations by magnetically controlling the position of a particle. Since magnetic fields have a large penetration depth even through bone structures like the skull, this optical focusing method holds great promise for deep-tissue applications such as optogenetic modulation of neurons, targeted light-based therapy, and imaging