pwc-archive/papers-with-abstracts huggingface dataset:
Asymptotic Performance Prediction for ADMM-Based Compressed Sensing
Authors: Ryo Hayakawa
Date: 2020-09-17 00:00:00
Tasks: compressed sensing, Prediction

In this paper, we propose a method to predict the asymptotic performance of the alternating direction method of multipliers (ADMM) for compressed sensing, where we reconstruct an unknown structured signal from its underdetermined linear measurements. The derivation of the proposed method is based on the recently developed convex Gaussian min-max theorem (CGMT), which can be applied to various convex optimization problems to obtain its asymptotic error performance. Our main idea is to analyze the convex subproblem in the update of ADMM iteratively and characterize the asymptotic distribution of the tentative estimate obtained at each iteration. However, since the original CGMT cannot be used directly for the analysis of the iterative updates, we intuitively assume an extended version of CGMT in the derivation of the proposed method. Under the assumption, the result shows that the update equations in ADMM can be decoupled into a scalar-valued stochastic process in the asymptotic regime with the large system limit. From the asymptotic result, we can predict the evolution of the error (e.g., mean-square-error (MSE) and symbol error rate (SER)) in ADMM for large-scale compressed sensing problems. Simulation results show that the empirical performance of ADMM and its prediction are close to each other in sparse vector reconstruction and binary vector reconstruction.
Inference for Large-Scale Linear Systems with Known Coefficients
Authors: Zheng Fang, Andres Santos, Azeem M. Shaikh, Alexander Torgovitsky
Date: 2020-09-18 00:00:00
Tasks: Discrete Choice Models

This paper considers the problem of testing whether there exists a non-negative solution to a possibly under-determined system of linear equations with known coefficients. This hypothesis testing problem arises naturally in a number of settings, including random coefficient, treatment effect, and discrete choice models, as well as a class of linear programming problems. As a first contribution, we obtain a novel geometric characterization of the null hypothesis in terms of identified parameters satisfying an infinite set of inequality restrictions. Using this characterization, we devise a test that requires solving only linear programs for its implementation, and thus remains computationally feasible in the high-dimensional applications that motivate our analysis. The asymptotic size of the proposed test is shown to equal at most the nominal level uniformly over a large class of distributions that permits the number of linear equations to grow with the sample size.
Hardware Accelerator for Multi-Head Attention and Position-Wise Feed-Forward in the Transformer
Authors: Siyuan Lu, Meiqi Wang, Shuang Liang, Jun Lin, Zhongfeng Wang
Date: 2020-09-18 00:00:00
Tasks: GPU, Position

Designing hardware accelerators for deep neural networks (DNNs) has been much desired. Nonetheless, most of these existing accelerators are built for either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Recently, the Transformer model is replacing the RNN in the natural language processing (NLP) area. However, because of intensive matrix computations and complicated data flow being involved, the hardware design for the Transformer model has never been reported. In this paper, we propose the first hardware accelerator for two key components, i.e., the multi-head attention (MHA) ResBlock and the position-wise feed-forward network (FFN) ResBlock, which are the two most complex layers in the Transformer. Firstly, an efficient method is introduced to partition the huge matrices in the Transformer, allowing the two ResBlocks to share most of the hardware resources. Secondly, the computation flow is well designed to ensure the high hardware utilization of the systolic array, which is the biggest module in our design. Thirdly, complicated nonlinear functions are highly optimized to further reduce the hardware complexity and also the latency of the entire system. Our design is coded using hardware description language (HDL) and evaluated on a Xilinx FPGA. Compared with the implementation on GPU with the same setting, the proposed design demonstrates a speed-up of 14.6x in the MHA ResBlock, and 3.4x in the FFN ResBlock, respectively. Therefore, this work lays a good foundation for building efficient hardware accelerators for multiple Transformer networks.
On necessary conditions of tracking control for nonlinear systems via contraction analysis
Date: 2020-11-15 00:00:00

In this paper we address the problem of tracking control of nonlinear systems
via contraction analysis. The necessary conditions of the systems which can
achieve universal asymptotic tracking are studied under several different
cases. We show the links to the well developed control contraction metric, as
well as its invariance under dynamic extension. In terms of these conditions,
we identify a differentially detectable output, based on which a simple
differential controller for trajectory tracking is designed via damping
injection. As illustration we apply to electrostatic microactuators.
Event-based update of synapses in voltage-based learning rules
Date: 2020-09-18 00:00:00
Tasks: Computational Efficiency, Efficient Neural Network

Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. Synapses therefore require continuous information to update their strength which a priori necessitates a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks.
International Trade Finance from the Origins to the Present: Market Structures, Regulation and Governance
Authors: Olivier Accominotti, Stefano Ugolini
Date: 2020-09-18 00:00:00

This chapter presents a history of international trade finance - the oldest domain of international finance - from its emergence in the Middle Ages up to today. We describe how the structure and governance of the global trade finance market changed over time and how trade credit instruments evolved. Trade finance products initially consisted of idiosyncratic assets issued by local merchants and bankers. The financing of international trade then became increasingly centralized and credit instruments were standardized through the diffusion of the local standards of consecutive leading trading centres (Antwerp, Amsterdam, London). This process of market centralization/product standardization culminated in the nineteenth century when London became the global centre for international trade finance and the sterling bill of exchange emerged as the most widely used trade finance instrument. The structure of the trade finance market then evolved considerably following the First World War and disintegrated during the interwar de-globalization and Bretton Woods period. The reconstruction of global trade finance in the post-1970 period gave way to the decentralized market structure that prevails nowadays.
Setting up and modelling of overflowing fed-batch cultures of Bacillus subtilis for the production and continuous removal of lipopeptides
Authors: J. S Guez, S. Chenikher, J. Ph Cassar, P. Jacques
Date: 2020-09-18 00:00:00

This work is related to the setup of overflowing exponential fed-batch cultures (O-EFBC) derived from carbon limited EFBC dedicated to the production of mycosubtilin, an antifungal lipopeptide belonging to the iturin family. O-EFBC permits the continuous removal of the product from the bioreactor achieving a complete extraction of mycosubtilin. This paper also provides a dynamical Monod-based growth model of this process that is accurate enough to simulate the evolution of the specific growth rate and to correlate it to the mycosubtilin specific productivity. Two particular and dependant phenomena related to the foam overflow are taken into account by the model: the outgoing flow rate of a broth volume and the loss of biomass. Interestingly, the biomass concentration in the foam was found to be lower than the biomass concentration in the bioreactor relating this process to a recycling one. Parameters of this model are the growth yield on substrate and the maximal specific growth rate estimated from experiments led at feed rates of 0.062, 0.071 and 0.086 h --1. The model was extrapolated to five additional experiments carried out at feed rates of 0.008, 0.022, 0.040, 0.042 and 0.062 h --1 enabling the correlation of the mean specific growth rates with productivity results. Finally, a feed rate of 0.086 h --1 corresponding to a mean specific growth rate of 0.070 h --1 allowed a specific productivity of 1.27 mg of mycosubtilin g --1 of dried biomass h --1 .
Revisiting Consensus-Based Energy-Management in Smart Grid with Transmission Losses and Directed Communication
Date: 2020-09-18 00:00:00
Tasks: energy management, Management, valid

We discovered a deficiency in Algorithm 1 and Theorem 3 of [1]. The algorithm
called CEMA aims to solve an energy management problem distributively. However,
by means of a counter example, we show that Theorem 2 and 3 of [1] contradict
each other in the case of a valid scenario, proving that the suggested
algorithm does not always find the optimum. Furthermore, we provide theoretic
results, showing that Theorem 3 of [1] does not hold generally. At last, we
provide a rectification by adjusting the algorithm and the corresponding proof
of Theorem 3.
Hybrid Digital-Analog Beamforming and MIMO Radar with OTFS Modulation
Authors: Lorenzo Gaudio, Mari Kobayashi, Giuseppe Caire, Giulio Colavolpe
Date: 2020-09-17 00:00:00
Tasks: parameter estimation

Motivated by future automotive applications, we study some joint radar target detection and parameter estimation problems where the transmitter, equipped with a mono-static MIMO radar, wishes to detect multiple targets and then estimate their respective parameters, while simultaneously communicating information data using orthogonal time frequency space (OTFS) modulation. Assuming that the number of radio frequency chains is smaller than the number of antennas over the mmWave frequency band, we design hybrid digital-analog beamforming at the radar transmitter adapted to different operating phases. The first scenario considers a wide angular beam in order to perform the target detection and parameter estimation, while multicasting a common message to all possible active users. The second scenario considers narrow angular beams to send information streams individually to the already detected users and simultaneously keep tracking of their respective parameters. Under this setup, we propose an efficient maximum likelihood scheme combined with hybrid beamforming to jointly perform target detection and parameter estimation. Our numerical results demonstrate that the proposed algorithm is able to reliably detect multiple targets with a sufficient number of antennas and achieves the Cram\'er-Rao lower bound for radar parameter estimation such as delay, Doppler and angle-of-arrival (AoA).
Simplicial persistence of financial markets: filtering, generative processes and portfolio risk
Authors: Jeremy D. Turiel, Paolo Barucca, Tomaso Aste
Date: 2020-09-16 00:00:00
Tasks: Time Series, Time Series Analysis

We introduce simplicial persistence, a measure of time evolution of network motifs in subsequent temporal layers. We observe long memory in the evolution of structures from correlation filtering, with a two regime power law decay in the number of persistent simplicial complexes. Null models of the underlying time series are tested to investigate properties of the generative process and its evolutional constraints. Networks are generated with both TMFG filtering technique and thresholding showing that embedding-based filtering methods (TMFG) are able to identify higher order structures throughout the market sample, where thresholding methods fail. The decay exponents of these long memory processes are used to characterise financial markets based on their stage of development and liquidity. We find that more liquid markets tend to have a slower persistence decay. This is in contrast with the common understanding that developed markets are more random. We find that they are indeed less predictable for what concerns the dynamics of each single variable but they are more predictable for what concerns the collective evolution of the variables. This could imply higher fragility to systemic shocks.
A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation
Authors: Christoph M. Augustin, Matthias A. F. Gsell, Elias Karabelas, Erik Willemen, Frits W. Prinzen, Joost Lumens, Edward J. Vigmond, Gernot Plank
Date: 2020-09-16 00:00:00
Tasks: Computational Efficiency

Computer models of cardiac electro-mechanics (EM) show promise as an effective means for quantitative analysis of clinical data and, potentially, for predicting therapeutic responses.realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
Improving the spatial resolution of a BOTDA sensor using deconvolution algorithm
Authors: Li Shen, Zhiyong Zhao, Can Zhao, Hao Wu, Chao Lu, Ming Tang
Date: 2020-09-15 00:00:00
Tasks: Denoising

Spatial resolution improvement from an acquired measurement using long pulse is developed for Brillouin optical time domain analysis (BOTDA) systems based on the total variation deconvolution algorithm. The frequency dependency of Brillouin gain temporal envelope is investigated by simulation, and its impact on the recovered results of deconvolution algorithm is thoroughly analyzed. To implement a reliable deconvolution process, differential pulse-width pair (DPP) technique is utilized to effectively eliminate the systematic BFS distortion stemming from the frequency dependency of temporal envelope. The width of the pulse pairs should be larger than 40 ns as is analyzed theoretically and verified experimentally. It has been demonstrated that the proposed method can realize flexible adjustment of spatial resolution with enhanced signal-to-noise ratio (SNR) from an established measurement with long pump pulse. In the experiment, the spatial resolution is increased to 0.5 m and 1 m with high measurement accuracy by using the deconvolution algorithm from the measurement of 60/40 ns DPP signals. Compared with the raw DPP results with the same spatial resolution, 9.2 dB and 8.4 dB SNR improvements are obtained for 0.5 m and 1 m spatial resolution respectively, thanks to the denoising capability of the total variation deconvolution algorithm. The impact of sampling rate on the recovery results is also studied. The proposed sensing system allows for distortion-free Brillouin distributed sensing with higher spatial resolution and enhanced SNR from the conventional DPP setup with long pulse pairs.
Short dated smile under Rough Volatility: asymptotics and numerics
Authors: Peter K. Friz, Paul Gassiat, Paolo Pigato
Date: 2020-09-18 00:00:00
Tasks: Math

In [Precise Asymptotics for Robust Stochastic Volatility Models; Ann. Appl. Probab. 2021] we introduce a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small noise formulae for option prices, using the framework [Bayer et al; A regularity structure for rough volatility; Math. Fin. 2020]. We investigate here the fine structure of this expansion in large deviations and moderate deviations regimes, together with consequences for implied volatility. We discuss computational aspects relevant for the practical application of these formulas. We specialize such expansions to prototypical rough volatility examples and discuss numerical evidence.
A bounded operator approach to technical indicators without lag
Authors: Frédéric Butin
Date: 2020-09-18 00:00:00
Tasks: Algorithmic Trading

In the framework of technical analysis for algorithmic trading we use a linear algebra approach in order to define classical technical indicators as bounded operators of the space $l^\infty(\mathbb{N})$. This more abstract view enables us to define in a very simple way the no-lag versions of these tools. Then we apply our results to a basic trading system in order to compare the classical Elder's impulse system with its no-lag version and the so-called Nyquist-Elder's impulse system.
Generalized distance to a simplex and a new geometrical method for portfolio optimization
Authors: Frédéric Butin
Date: 2020-09-18 00:00:00
Tasks: Portfolio Optimization

Risk aversion plays a significant and central role in investors' decisions in the process of developing a portfolio. In this framework of portfolio optimization we determine the portfolio that possesses the minimal risk by using a new geometrical method. For this purpose, we elaborate an algorithm that enables us to compute any generalized Euclidean distance to a standard simplex. With this new approach, we are able to treat the case of portfolio optimization without short-selling in its entirety, and we also recover in geometrical terms the well-known results on portfolio optimization with allowed short-selling. Then, we apply our results in order to determine which convex combination of the CAC 40 stocks possesses the lowest risk: not only we get a very low risk compared to the index, but we also get a return rate that is almost three times better than the one of the index.
Natural Graph Wavelet Packet Dictionaries
Authors: Alexander Cloninger, Haotian Li, Naoki Saito
Date: 2020-09-18 00:00:00

We introduce a set of novel multiscale basis transforms for signals on graphs that utilize their "dual" domains by incorporating the "natural" distances between graph Laplacian eigenvectors, rather than simply using the eigenvalue ordering. These basis dictionaries can be seen as generalizations of the classical Shannon wavelet packet dictionary to arbitrary graphs, and do not rely on the frequency interpretation of Laplacian eigenvalues. We describe the algorithms (involving either vector rotations or orthogonalizations) to construct these basis dictionaries, use them to efficiently approximate graph signals through the best basis search, and demonstrate the strengths of these basis dictionaries for graph signals measured on sunflower graphs and street networks.
Combined approach for automatic and robust calculation of dominant frequency of electrogastrogram
Authors: Nebojša Jovanović, Nenad B. Popović, Nadica Miljković
Date: 2020-09-18 00:00:00
Tasks: Density Estimation

We present a novel method for automatic and robust detection of dominant frequency (DF) in the electrogastrogram (EGG). Our new approach combines Fast Fourier Transform (FFT), Welch's method for spectral density estimation, and autocorrelation. The proposed combined method as well as other separate procedures were tested on a freely available dataset consisted of EGG recordings in 20 healthy individuals. DF was calculated in relation (1) to the fasting and postprandial states, (2) to the three recording locations, and (3) to the subjects' body mass index. For the estimation of algorithms performance in the presence of noise, we created a synthetic dataset by adding white Gaussian noise to the artifact-free EGG waveform in one subject. The individual algorithms and novel combined approach were evaluated in relation to the signal-to-noise ratio (SNR) in range from -40 dB to 20 dB. Our results showed that the novel combined method significantly outperformed the commonly used approach for DF calculation - FFT in noise presence when compared to the benchmark data being was manually corrected by an expert. The novel method outperformed autocorrelation and Welch's method in accuracy. Additionally, we presented a method for optimal window width selection when using Welch's spectrogram that showed that for DF detection, window length of N/4 (300 s), where N is the length of EGG waveform in samples, performed the best when compared to the benchmark data. The combined approach proved efficient for automatic and robust calculation of dominant frequency on openly available EGG dataset recorded in healthy individuals and is promising approach for DF detection.
Explicit solution simulation method for the 3/2 model
Authors: Iro René Kouarfate, Michael A. Kouritzin, Anne MacKay
Date: 2020-09-18 00:00:00

An explicit weak solution for the 3/2 stochastic volatility model is obtained and used to develop a simulation algorithm for option pricing purposes. The 3/2 model is a non-affine stochastic volatility model whose variance process is the inverse of a CIR process. This property is exploited here to obtain an explicit weak solution, similarly to Kouritzin (2018). A simulation algorithm based on this solution is proposed and tested via numerical examples. The performance of the resulting pricing algorithm is comparable to that of other popular simulation algorithms.