Inductive and Electrostatic Acceleration in Relativistic Jet-Plasma Interactions

We report on the observation of rapid particle acceleration in numerical simulations of relativistic jet-plasma interactions and discuss the underlying mechanisms. The dynamics of a charge-neutral, narrow, electron-positron jet propagating through an unmagnetized electron-ion plasma was investigated using a three-dimensional, electromagnetic, particle-in-cell computer code. The interaction excited magnetic filamentation as well as electrostatic plasma instabilities. In some cases, the longitudinal electric fields generated inductively and electrostatically reached the cold plasma wave-breaking limit, and the longitudinal momentum of about half the positrons increased by 50% with a maximum gain exceeding a factor of 2 during the simulation period. Particle acceleration via these mechanisms occurred when the criteria for Weibel instability were satisfied.Comment: Revised for Phys. Rev. Lett. Please see publised version for best graphic

Radio Polarization Observations of the Snail: A Crushed Pulsar Wind Nebula in G327.1-1.1 with a Highly Ordered Magnetic Field

Pulsar wind nebulae (PWNe) are suggested to be acceleration sites of cosmic rays in the Galaxy. While the magnetic field plays an important role in the acceleration process, previous observations of magnetic field configurations of PWNe are rare, particularly for evolved systems. We present a radio polarization study of the "Snail" PWN inside the supernova remnant G327.1-1.1 using the Australia Telescope Compact Array. This PWN is believed to have been recently crushed by the supernova (SN) reverse shock. The radio morphology is composed of a main circular body with a finger-like protrusion. We detected a strong linear polarization signal from the emission, which reflects a highly ordered magnetic field in the PWN and is in contrast to the turbulent environment with a tangled magnetic field generally expected from hydrodynamical simulations. This could suggest that the characteristic turbulence scale is larger than the radio beam size. We built a toy model to explore this possibility, and found that a simulated PWN with a turbulence scale of about one-eighth to one-sixth of the nebula radius and a pulsar wind filling factor of 50--75% provides the best match to observations. This implies substantial mixing between the SN ejecta and pulsar wind material in this system.Comment: 13 pages, 10 figures, Accepted for publication in Ap

Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation

This paper presents a new method for the discovery of latent domains in diverse speech data, for the use of adaptation of Deep Neural Networks (DNNs) for Automatic Speech Recognition. Our work focuses on transcription of multi-genre broadcast media, which is often only categorised broadly in terms of high level genres such as sports, news, documentary, etc. However, in terms of acoustic modelling these categories are coarse. Instead, it is expected that a mixture of latent domains can better represent the complex and diverse behaviours within a TV show, and therefore lead to better and more robust performance. We propose a new method, whereby these latent domains are discovered with Latent Dirichlet Allocation, in an unsupervised manner. These are used to adapt DNNs using the Unique Binary Code (UBIC) representation for the LDA domains. Experiments conducted on a set of BBC TV broadcasts, with more than 2,000 shows for training and 47 shows for testing, show that the use of LDA-UBIC DNNs reduces the error up to 13% relative compared to the baseline hybrid DNN models

On the propagation of a two-dimensional viscous density current under surface waves

This study aims to develop an asymptotic theory for the slow spreading of a thin layer of viscous immiscible dense liquid on the bottom of a waterway under the combined effects of surface waves and density current. By virtue of the sharply different length and time scales (wave periodic excitation being effective at fast scales, while gravity and streaming currents at slow scales), a multiple-scale perturbation analysis is conducted. Evolution equations are deduced for the local and global profile distributions of the dense liquid layer as functions of the slow-time variables. When reflected waves are present, the balance between gravity and streaming will result, on a time scale one order of magnitude longer than the wave period, in an undulating water/liquid interface whose displacement amplitude is much smaller than the thickness of the dense liquid layer. On the global scale, the streaming current can predominate and drive the dense liquid to propagate with a distinct pattern in the direction of the surface waves. © 2002 American Institute of Physics.published_or_final_versio

Quantum Dot in 2D Topological Insulator: The Two-channel Kondo Fixed Point

In this work, a quantum dot couples to two helical edge states of a 2D topological insulator through weak tunnelings is studied. We show that if the electron interactions on the edge states are repulsive, with Luttinger liquid parameter $K < 1$, the system flows to a stable two-channel fixed point at low temperatures. This is in contrast to the case of a quantum dot couples to two Luttinger liquid leads. In the latter case, a strong electron-electron repulsion is needed, with $K<1/2$, to reach the two-channel fixed point. This two-channel fixed point is described by a boundary Sine-Gordon Hamiltonian with a $K$ dependent boundary term. The impurity entropy at zero temperature is shown to be $\ln\sqrt{2K}$. The impurity specific heat is $C \propto T^{\frac{2}{K}-2}$ when $2/3 < K < 1$, and $C \propto T$ when $K<2/3$. We also show that the linear conductance across the two helical edges has non-trivial temperature dependence as a result of the renormalization group flow.Comment: 4+\epsilon page

3d augmented mirror: a multimodal interface for string instrument learning and teaching with gesture support

Multimodal interfaces can open up new possibilities for music education, where the traditional model of teaching is based predominantly on verbal feedback. This paper explores the development and use of multimodal interfaces in novel tools to support music practice training. The design of multimodal interfaces for music education presents a challenge in several respects. One is the integration of multimodal technology into the music learning process. The other is the technological development, where we present a solution that aims to support string practice training with visual and auditory feedback. Building on the traditional function of a physical mirror as a teaching aid, we describe the concept and development of an "augmented mirror" using 3D motion capture technology

Rational Symplectic Field Theory for Legendrian knots

We construct a combinatorial invariant of Legendrian knots in standard contact three-space. This invariant, which encodes rational relative Symplectic Field Theory and extends contact homology, counts holomorphic disks with an arbitrary number of positive punctures. The construction uses ideas from string topology.Comment: 58 pages, many figures; v3: minor corrections; final version, to appear in Inventiones Mathematica

The 2015 Sheffield System for Longitudinal Diarisation of Broadcast Media

Speaker diarisation is the task of answering "who spoke when" within a multi-speaker audio recording. Diarisation of broadcast media typically operates on individual television shows, and is a particularly difficult task, due to a high number of speakers and challenging background conditions. Using prior knowledge, such as that from previous shows in a series, can improve performance. Longitudinal diarisation allows to use knowledge from previous audio files to improve performance, but requires finding matching speakers across consecutive files. This paper describes the University of Sheffield system for participation in the 2015 Multi-Genre Broadcast (MGB) challenge. The challenge required longitudinal diarisation of data from BBC archives, under very constrained resource settings. Our system consists of three main stages: speech activity detection using DNNs with novel adaptation and decoding methods; speaker segmentation and clustering, with adaptation of the DNN-based clustering models; and finally speaker linking to match speakers across shows. The final result on the development set of 19 shows from five different television series provided a Diarisation Error Rate of 50.77% in the diarisation and linking task

Predicting Auction Price of Vehicle License Plate with Deep Residual Learning

Due to superstition, license plates with desirable combinations of characters are highly sought after in China, fetching prices that can reach into the millions in government-held auctions. Despite the high stakes involved, there has been essentially no attempt to provide price estimates for license plates. We present an end-to-end neural network model that simultaneously predict the auction price, gives the distribution of prices and produces latent feature vectors. While both types of neural network architectures we consider outperform simpler machine learning methods, convolutional networks outperform recurrent networks for comparable training time or model complexity. The resulting model powers our online price estimator and search engine