Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition

Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition (ASR). In this paper, we propose a data selection strategy named LM Filter to improve the performances of NST on non-target domain data in ASR tasks. Hypothesis with and without Language Model are generated and CER differences between them are utilized as a filter threshold. Results reveal that significant improvements of 10.4% compared with no data filtering baselines. We can achieve 3.31% CER in AISHELL-1 test set, which is best result from our knowledge without any other supervised data. We also perform evaluations on supervised 1000 hour AISHELL-2 dataset and competitive results of 4.72% CER can be achieved

On q-deformed infinite-dimensional n-algebra

The $q$-deformation of the infinite-dimensional $n$-algebra is investigated. Based on the structure of the $q$-deformed Virasoro-Witt algebra, we derive a nontrivial $q$-deformed Virasoro-Witt $n$-algebra which is nothing but a sh-$n$-Lie algebra. Furthermore in terms of the pseud-differential operators on the quantum plane, we construct the (co)sine $n$-algebra and the $q$-deformed $SDiff(T^2)$ $n$-algebra. We prove that they are the sh-$n$-Lie algebras for the case of even $n$. An explicit physical realization of the (co)sine $n$-algebra is given.Comment: 22 page

2D Heisenberg model from rotating membrane

We study a rotating probe membrane in S^3 inside AdS_4 x S^7 background of M-theory. With (partial) gauge fixing, we show that in the fast limit the worldvolume of tensionless membrane reduces to either the XXX_1/2 spin chain or the two-dimensional SU(2) Heisenberg spin model. Later we introduce the anisotropy and couple it to the external magnetic field. We also establish the correspondence for higher dimensional (D)p-branes.Comment: 15 pages, revtex file, no figur

New multiple target tracking strategy using domain knowledge and optimisation

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only

Determination and Validation of Parameters for Riedel-Hiermaier-Thoma Concrete Model

Numerical modelling of the complex physical processes such as concrete structures subjected to high-impulsive loads relies on suitable material models appropriate for impact and explosion problems. One of theextensive used concrete material models, the RHT model, contains all essential features of concrete materialssubjected to high dynamic loading. However, the application of the RHT model requires a set of material propertiesand model parameters without which reliable results cannot be expected. The present paper provides adetailed valuation of the RHT model and proposes a method of determining the model parameters for C40 concrete.Furthermore, the dynamic compressive and tensile strength function of the model formulation are modified toenhance the performance of the model as implemented in the hydrocode AUTODYN. The performance of thedetermined parameters of the modified RHT model is demonstrated by comparing to available experimentaldata, and further verified via simulations of physical experiments of concrete penetration by steel projectiles.The results of numerical analyses are found closely match the penetration depth and the crater size in the frontsurface of the concrete targets.Defence Science Journal, 2013, 63(5), pp.524-530, DOI:http://dx.doi.org/10.14429/dsj.63.386

A Switched Approach to Robust Stabilization of Multiple Coupled Networked Control Systems

This paper proposes a switched approach to robust stabilization of a collection of coupled networked controlled systems (NCSs) with node devices acting over a limited communication channel. We suppose that the state information of every subsystem is split into different packets and only one packet of the subsystem can be transmitted at a time. Multiple NCSs with norm-bounded parameter uncertainties and multiple transmissions are modeled as a periodic switched system in this paper. State feedback controllers can be constructed in terms of linear matrix inequalities. A numerical example is given to show that a collection of uncertain NCSs with the problem of limited communication can be effectively stabilized via the designed controller

Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://github.com/Chasel-Tsui/mmrotate-dcfl.Comment: Accepted by CVPR202