End-to-End Algebraic Network Coding for Wireless TCP/IP Networks

The Transmission Control Protocol (TCP) was designed to provide reliable transport services in wired networks. In such networks, packet losses mainly occur due to congestion. Hence, TCP was designed to apply congestion avoidance techniques to cope with packet losses. Nowadays, TCP is also utilized in wireless networks where, besides congestion, numerous other reasons for packet losses exist. This results in reduced throughput and increased transmission round-trip time when the state of the wireless channel is bad. We propose a new network layer, that transparently sits below the transport layer and hides non congestion-imposed packet losses from TCP. The network coding in this new layer is based on the well-known class of Maximum Distance Separable (MDS) codes.Comment: Accepted for the 17th International Conference on Telecommunications 2010 (ICT2010), Doha, Qatar, April 4 - 7, 2010. 6 pages, 7 figure

Graph Priors, Optimal Transport, and Deep Learning in Biomedical Discovery

Recent advances in biomedical data collection allows the collection of massive datasets measuring thousands of features in thousands to millions of individual cells. This data has the potential to advance our understanding of biological mechanisms at a previously impossible resolution. However, there are few methods to understand data of this scale and type. While neural networks have made tremendous progress on supervised learning problems, there is still much work to be done in making them useful for discovery in data with more difficult to represent supervision. The flexibility and expressiveness of neural networks is sometimes a hindrance in these less supervised domains, as is the case when extracting knowledge from biomedical data. One type of prior knowledge that is more common in biological data comes in the form of geometric constraints. In this thesis, we aim to leverage this geometric knowledge to create scalable and interpretable models to understand this data. Encoding geometric priors into neural network and graph models allows us to characterize the models’ solutions as they relate to the fields of graph signal processing and optimal transport. These links allow us to understand and interpret this datatype. We divide this work into three sections. The first borrows concepts from graph signal processing to construct more interpretable and performant neural networks by constraining and structuring the architecture. The second borrows from the theory of optimal transport to perform anomaly detection and trajectory inference efficiently and with theoretical guarantees. The third examines how to compare distributions over an underlying manifold, which can be used to understand how different perturbations or conditions relate. For this we design an efficient approximation of optimal transport based on diffusion over a joint cell graph. Together, these works utilize our prior understanding of the data geometry to create more useful models of the data. We apply these methods to molecular graphs, images, single-cell sequencing, and health record data

Stabilized Radiation Pressure Dominated Ion Acceleration from Thin-foil Targets

We study transverse and longitudinal electron heating effects on the target stability and the ion spectra in the radiation pressure dominated regime of ion acceleration by means of multi dimensional particle-in-cell (PIC) simulations. Efficient ion acceleration occurs when the longitudinal electron temperature is kept as low as possible. However, tailoring of the transverse electron temperature is required in view of suppressing the transverse instability, which can keep the target structure intact for longer duration during the acceleration stage. We suggest using the surface erosion of the target to increase the transverse temperature, which improves both the final peak energy and the spectral quality of the ions in comparison with a normal flat target.Comment: 5 pages, 3 picture

Target shape effects on monoenergetic GeV proton acceleration

When a circularly polarized laser pulse interacts with a foil target, there are three stages: pre-hole-boring, hole-boring and the light sail acceleration. We study the electron and ion dynamics in the first stage and find the minimum foil thickness requirement for a given laser intensity. Based on this analysis, we propose to use a shaped foil for ion acceleration, whose thickness varies transversely to match the laser intensity. Then, the target evolves into three regions: the acceleration, transparency and deformation regions. In the acceleration region, the target can be uniformly accelerated producing a mono-energetic and spatially collimated ion beam. Detailed numerical simulations are performed to check the feasibility and robustness of this scheme, such as the influence of shape factors and surface roughness. A GeV mono-energetic proton beam is observed in the three dimensional particle-in-cell simulations when a laser pulse with the focus intensity of 1022W=cm2 is used. The energy conversion efficiency of laser pulse to accelerated proton beam is more than 23%. Synchrotron radiation and damping effects are also checked in the interaction.Comment: 11 pages, 9 figure

Market Mill Dependence Pattern in the Stock Market: Modeling of Predictability and Asymmetry via Multi-Component Conditional Distribution

Recent studies have revealed a number of striking dependence patterns in high frequency stock price dynamics characterizing probabilistic interrelation between two consequent price increments x (push) and y (response) as described by the bivariate probability distribution P(x,y) [1,2,3,4]. There are two properties, the market mill asymmetries of P(x,y) and predictability due to nonzero z-shaped mean conditional response, that are of special importance. Main goal of the present paper is to put together a model reproducing both the z-shaped mean conditional response and the market mill asymmetry of P(x,y) with respect to the axis y=0. We develop a probabilistic model based on a multi-component ansatz for conditional distribution P(y|x) with push-dependent weights and means describing both properties. A relationship between the market mill asymmetry and predictability is discussed. A possible connection of the model to agent-based picture is outlined