Weapon-Target Assignment Problem with Interference Constraints using Mixed-Integer Linear Programming. (arXiv:1911.12567v1 [eess.SY])

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In this paper, we propose an approach to formulate the Weapon Target Assignment (WTA) problem with physical and seeker interference constraints which is solvable in Mixed Integer Linear Programming (MILP). To handle the interference constraint which is intractable in continuous time domain, we discretize the time window and generate predicted intercept point (PIP) set. Also,…

Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning. (arXiv:1911.13300v1 [q-fin.ST])

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A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way…

Data Transmission based on Exact Inverse Periodic Nonlinear Fourier Transform, Part I: Theory. (arXiv:1911.12614v1 [eess.SP])

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The nonlinear Fourier transform (NFT) decomposes waveforms propagating through optical fiber into nonlinear degrees of freedom, which are preserved during transmission. By encoding information on the nonlinear spectrum, a transmission scheme inherently compatible with the nonlinear fiber is obtained. Despite potential advantages, the periodic NFT (PNFT) has been studied less compared to its counterpart based…

Unbiased Evaluation of Deep Metric Learning Algorithms. (arXiv:1911.12528v1 [cs.LG])

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Deep metric learning (DML) is a popular approach for images retrieval, solving verification (same or not) problems and addressing open set classification. Arguably, the most common DML approach is with triplet loss, despite significant advances in the area of DML. Triplet loss suffers from several issues such as collapse of the embeddings, high sensitivity to…

Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage. (arXiv:1901.11491v2 [stat.CO] UPDATED)

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The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness of samplers based on different parameterizations varies significantly. We derive novel algorithms for the centered and the non-centered parameterizations of the practically highly relevant SV model with leverage,…

U-CNNpred: A Universal CNN-based Predictor for Stock Markets. (arXiv:1911.12540v1 [cs.LG])

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The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general…

Data Transmission based on Exact Inverse Periodic Nonlinear Fourier Transform, Part II: Waveform Design and Experiment. (arXiv:1911.12615v1 [eess.SP])

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The nonlinear Fourier transform has the potential to overcome limits on performance and achievable data rates which arise in modern optical fiber communication systems when nonlinear interference is treated as noise. The periodic nonlinear Fourier transform (PNFT) has been much less investigated compared to its counterpart based on vanishing boundary conditions. In this paper, we…

Performance Comparison of UCA and UCCA based Real-time Sound Source Localization Systems using Circular Harmonics SRP Method. (arXiv:1911.12616v1 [eess.AS])

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Many sound source localization (SSL) algorithms based on circular microphone array (CMA), including uniform circular array (UCA) and uniform concentric circular array (UCCA), have been well developed and verified via computer simulations and offline processing. On the other hand, beamforming in the harmonic domain has been shown to be a very efficient tool for broadband…

Free-riders in Federated Learning: Attacks and Defenses. (arXiv:1911.12560v1 [cs.LG])

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Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully…