pwc-archive/papers-with-abstracts huggingface dataset:
Experimental validation of MDL emulation and estimation techniques for SDM transmission systems
Authors: Menno van den Hout, Ruby S. B. Ospina, Sjoerd van der Heide, Juan Carlos Alvarado-Zacarias, Jose Enrique Antonio-López, Marianne Bigot-Astruc, Adrian Amezcua Correa, Pierre Sillard, Rodrigo Amezcua-Correa, Darli A. A. Mello, Chigo Okonkwo
Date: 2020-10-29 00:00:00

We experimentally validate a mode-dependent loss (MDL) estimation technique employing acorrection factor to remove the MDL estimation dependence on the SNR when using a minimum meansquare error (MMSE) equalizer. A reduction of the MDL estimation error is observed for both transmitter-side and in-span MDL emulation.
FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
Authors: Xiang Hao, Xiangdong Su, Radu Horaud, Xiaofei Li
Date: 2020-10-29 00:00:00
Tasks: Speech Enhancement

This paper proposes a full-band and sub-band fusion model, named as FullSubNet, for single-channel real-time speech enhancement. Full-band and sub-band refer to the models that input full-band and sub-band noisy spectral feature, output full-band and sub-band speech target, respectively. The sub-band model processes each frequency independently. Its input consists of one frequency and several context frequencies. The output is the prediction of the clean speech target for the corresponding frequency. These two types of models have distinct characteristics. The full-band model can capture the global spectral context and the long-distance cross-band dependencies. However, it lacks the ability to modeling signal stationarity and attending the local spectral pattern. The sub-band model is just the opposite. In our proposed FullSubNet, we connect a pure full-band model and a pure sub-band model sequentially and use practical joint training to integrate these two types of models' advantages. We conducted experiments on the DNS challenge (INTERSPEECH 2020) dataset to evaluate the proposed method. Experimental results show that full-band and sub-band information are complementary, and the FullSubNet can effectively integrate them. Besides, the performance of the FullSubNet also exceeds that of the top-ranked methods in the DNS Challenge (INTERSPEECH 2020).
UNetGAN: A Robust Speech Enhancement Approach in Time Domain for Extremely Low Signal-to-noise Ratio Condition
Authors: Xiang Hao, Xiangdong Su, Zhiyu Wang, HUI ZHANG, Batushiren
Date: 2020-10-29 00:00:00
Tasks: Speech Enhancement

Speech enhancement at extremely low signal-to-noise ratio (SNR) condition is a very challenging problem and rarely investigated in previous works. This paper proposes a robust speech enhancement approach (UNetGAN) based on U-Net and generative adversarial learning to deal with this problem. This approach consists of a generator network and a discriminator network, which operate directly in the time domain. The generator network adopts a U-Net like structure and employs dilated convolution in the bottleneck of it. We evaluate the performance of the UNetGAN at low SNR conditions (up to -20dB) on the public benchmark. The result demonstrates that it significantly improves the speech quality and substantially outperforms the representative deep learning models, including SEGAN, cGAN fo SE, Bidirectional LSTM using phase-sensitive spectrum approximation cost function (PSA-BLSTM) and Wave-U-Net regarding Short-Time Objective Intelligibility (STOI) and Perceptual evaluation of speech quality (PESQ).
Probabilistic interval predictor based on dissimilarity functions
Authors: A. Daniel Carnerero, Daniel R. Ramirez, Teodoro Alamo
Date: 2020-10-29 00:00:00

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A family of empirical probability density functions, parameterized by means of two scalars, is introduced. It is shown that the proposed family encompasses the multivariable normal probability density function as a particular case. We show that the presented approach constitutes a generalization of classical estimation methods. A validation scheme is used to tune the two parameters on which the methodology relies. In order to prove the effectiveness of the presented methodology, some numerical examples and comparisons are provided.
Fear and Volatility in Digital Assets
Authors: Faizaan Pervaiz, Christopher Goh, Ashley Pennington, Samuel Holt, James West, Shaun Ng
Date: 2020-10-29 00:00:00

We show Bitcoin implied volatility on a 5 minute time horizon is modestly predictable from price, volatility momentum and alternative data including sentiment and engagement. Lagged Bitcoin index price and volatility movements contribute to the model alongside Google Trends with markets responding often several hours later. The code and datasets used in this paper can be found at https://github.com/Globe-Research/bitfear.
Sampling and Reconstruction of Sparse Signals in Shift-Invariant Spaces: Generalized Shannon's Theorem Meets Compressive Sensing
Authors: Tin Vlašić, Damir Seršić
Date: 2020-10-29 00:00:00
Tasks: Compressive Sensing

This paper introduces a novel framework and corresponding methods for sampling and reconstruction of sparse signals in shift-invariant (SI) spaces. We reinterpret the random demodulator, a system that acquires sparse bandlimited signals, as a system for the acquisition of linear combinations of the samples in the SI setting with the box function as the sampling kernel. The sparsity assumption is exploited by the compressive sensing (CS) paradigm for a recovery of the SI samples from a reduced set of measurements. The SI samples are subsequently filtered by a discrete-time correction filter to reconstruct expansion coefficients of the observed signal. Furthermore, we offer a generalization of the proposed framework to other compactly supported sampling kernels that span a wider class of SI spaces. The generalized method embeds the correction filter in the CS optimization problem which directly reconstructs expansion coefficients of the signal. Both approaches recast an inherently continuous-domain inverse problem in a set of finite-dimensional CS problems in an exact way. Finally, we conduct numerical experiments on signals in polynomial B-spline spaces whose expansion coefficients are assumed to be sparse in a certain transform domain. The coefficients can be regarded as parametric models of an underlying continuous-time signal, obtained from a reduced set of measurements. Such continuous signal representations are particularly suitable for signal processing without converting them into samples.
One-Bit Direct Position Determination of Narrowband Gaussian Signals
Authors: Amir Weiss, Gregory W. Wornell
Date: 2020-10-29 00:00:00
Tasks: Position, Quantization

One of the main drawbacks of the well-known Direct Position Determination (DPD) method is the requirement that raw signal data be transferred to a common processor. It would therefore be of high practical value if DPD$-$or a modified version thereof$-$could be successfully applied to a coarsely quantized version of the raw data, thus alleviating the requirements on the communication links between the different base stations. Motivated by the above, and inspired by recent work in the rejuvenated one-bit array processing field, we present One-Bit DPD: a method for direct localization based on one-bit quantized measurements. We show that despite the coarse quantization, the proposed method nonetheless yields an estimate for the unknown emitter position with appealing asymptotic properties. We further establish the underlying identifiability conditions of this model, which rely only on second-order statistics. Empirical simulation results corroborate our analytical derivations, demonstrating that much of the information regarding the unknown emitter position is preserved under this crude form of quantization.
FD Cell-Free mMIMO: Analysis and Optimization
Authors: Soumyadeep Datta, Ekant Sharma, Dheeraj Naidu Amudala, Rohit Budhiraja, Shivendra S. Panwar
Date: 2020-10-27 00:00:00
Tasks: Quantization

Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited-capacity fronthaul links. We derive closed-form spectral efficiency (SE) lower bounds for this system with maximum-ratio combining/maximum-ratio transmission processing and optimal uniform quantization. We then optimize the weighted sum EE (WSEE) via downlink and uplink power control by using a {two-layered} approach: the first layer formulates the optimization as a generalized convex program, while the second layer solves the optimization decentrally using alternating direction method of multipliers. We analytically show that the proposed two-layered formulation yields a Karush-Kuhn-Tucker point of the original WSEE optimization. We numerically show the influence of weights on the individual EE of the users, which demonstrates the utility of WSEE metric to incorporate heterogeneous EE requirements of users. We show that the low fronthaul capacity reduces the number of users each AP can support, and the cell-free system, consequently, becomes user-centric.
Resilient Energy Efficient Healthcare Monitoring Infrastructure with Server and Network Protection
Date: 2020-10-28 00:00:00

In this paper, a 1+1 server protection scheme is considered where two
servers, a primary and a secondary processing server are used to serve ECG
monitoring applications concurrently. The infrastructure is designed to be
resilient against server failure under two scenarios related to the geographic
location of primary and secondary servers and resilient against both server and
network failures. A Mixed Integer Linear Programming (MILP) model is used to
optimise the number and locations of both primary and secondary processing
servers so that the energy consumption of the networking equipment and
processing are minimised. The results show that considering a scenario for
server protection without geographical constraints compared to the
non-resilient scenario has resulted in both network and processing energy
penalty as the traffic is doubled. The results also reveal that increasing the
level of resilience to consider geographical constraints compared to case
without geographical constraints resulted in higher network energy penalty when
the demand is low as more nodes are utilised to place the processing servers
under the geographic constraints. Also, increasing the level of resilience to
consider network protection with link and node disjoint selection has resulted
in a low network energy penalty at high demands due to the activation of a
large part of the network in any case due to the demands. However, the results
show that the network energy penalty is reduced with the increasing number of
processing servers at each candidate node. Meanwhile, the same energy for
processing is consumed regardless of the increasing level of resilience as the
same number of processing servers are utilised. A heuristic is developed for
each resilience scenario for real-time implementation where the results show
that the performance of the heuristic is approaching the results of the MILP
model.
Are randomness of behavior and information flow important to opinion forming in organization?
Authors: Agnieszka Kowalska-Styczeń, Krzysztof Malarz
Date: 2020-10-29 00:00:00
Tasks: Clustering

We examine how the randomness of behavior and the flow of information between agents affect the formation of opinions. Our main research involves the process of opinion evolution, opinion clusters formation and studying the probability of sustaining opinion. The results show that opinion formation (clustering of opinion) is influenced by both flow of information between agents (interactions outside the closest neighbors) and randomness in adopting opinions.
Discrete-time portfolio optimization under maximum drawdown constraint with partial information and deep learning resolution
Authors: Carmine de Franco, Johann Nicolle, Huyên Pham
Date: 2020-10-29 00:00:00
Tasks: Portfolio Optimization

We study a discrete-time portfolio selection problem with partial information and maxi\-mum drawdown constraint. Drift uncertainty in the multidimensional framework is modeled by a prior probability distribution. In this Bayesian framework, we derive the dynamic programming equation using an appropriate change of measure, and obtain semi-explicit results in the Gaussian case. The latter case, with a CRRA utility function is completely solved numerically using recent deep learning techniques for stochastic optimal control problems. We emphasize the informative value of the learning strategy versus the non-learning one by providing empirical performance and sensitivity analysis with respect to the uncertainty of the drift. Furthermore, we show numerical evidence of the close relationship between the non-learning strategy and a no short-sale constrained Merton problem, by illustrating the convergence of the former towards the latter as the maximum drawdown constraint vanishes.
Quickest Bayesian and non-Bayesian detection of false data injection attack in remote state estimation
Authors: Akanshu Gupta, Abhinava Sikdar, Arpan Chattopadhyay
Date: 2020-10-29 00:00:00
Tasks: State Estimation

In this paper, quickest detection of false data injection attack on remote state estimation is considered. A set of $N$ sensors make noisy linear observations of a discrete-time linear process with Gaussian noise, and report the observations to a remote estimator. The challenge is the presence of a few potentially malicious sensors which can start strategically manipulating their observations at a random time in order to skew the estimates. The quickest attack detection problem for a known {\em linear} attack scheme in the Bayesian setting with a Geometric prior on the attack initiation instant is posed as a constrained Markov decision process (MDP), in order to minimize the expected detection delay subject to a false alarm constraint, with the state involving the probability belief at the estimator that the system is under attack. State transition probabilities are derived in terms of system parameters, and the structure of the optimal policy is derived analytically. It turns out that the optimal policy amounts to checking whether the probability belief exceeds a threshold. Next, generalized CUSUM based attack detection algorithm is proposed for the non-Bayesian setting where the attacker chooses the attack initiation instant in a particularly adversarial manner. It turns out that computing the statistic for the generalised CUSUM test in this setting relies on the same techniques developed to compute the state transition probabilities of the MDP. Numerical results demonstrate significant performance gain under the proposed algorithms against competing algorithms.
Naive analytics equilibrium
Authors: Ron Berman, Yuval Heller
Date: 2020-10-29 00:00:00
Tasks: Sensitivity

We study interactions with uncertainty about demand sensitivity. In our solution concept (1) firms choose seemingly-optimal strategies given the level of sophistication of their data analytics, and (2) the levels of sophistication form best responses to one another. Under the ensuing equilibrium firms underestimate price elasticities and overestimate advertising effectiveness, as observed empirically. The misestimates cause firms to set prices too high and to over-advertise. In games with strategic complements (substitutes), profits Pareto dominate (are dominated by) those of the Nash equilibrium. Applying the model to team production games explains the prevalence of overconfidence among entrepreneurs and salespeople.
Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: An Unconditional MTE Approach
Authors: Julian Martinez-Iriarte, Yixiao Sun
Date: 2020-10-29 00:00:00

This paper studies the identification and estimation of policy effects when treatment status is binary and endogenous. We introduce a new class of marginal treatment effects (MTEs) based on the influence function of the functional underlying the policy target. We show that an unconditional policy effect can be represented as a weighted average of the newly defined MTEs over the individuals who are indifferent about their treatment status. We provide conditions for point identification of the unconditional policy effects. When a quantile is the functional of interest, we introduce the UNconditional Instrumental Quantile Estimator (UNIQUE) and establish its consistency and asymptotic distribution. In the empirical application, we estimate the effect of changing college enrollment status, induced by higher tuition subsidy, on the quantiles of the wage distribution.
Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks
Authors: Ahmadreza Ghanbarpour, Markus A. Lill
Date: 2020-10-29 00:00:00
Tasks: Caption Generation, Language Modelling

De novo design of molecules has recently enjoyed the power of generative deep neural networks. Current approaches aim to generate molecules either resembling the properties of the molecules of the training set or molecules that are optimized with respect to specific physicochemical properties. None of the methods generates molecules specific to a target protein. In the approach presented here, we introduce a method which is conditioned on the protein target sequence to generate de novo molecules that are relevant to the target. We use an implementation adapted from Google's "Show and Tell" image caption generation method, to generate SMILES strings of molecules from protein sequence embeddings generated by a deep bi-directional language model ELMo. ELMo is used to generate contextualized embedding vectors of the protein sequence. Using reinforcement learning, the trained model is further optimized through augmented episodic likelihood to increase the diversity of the generated compounds compared to the training set. We used the model to generate compounds for two major drug target families, i.e. for GPCRs and Tyrosine Kinase targets. The model generated compounds which are structurally different form the training set, while also being more similar to compounds known to bind to the two families of drug targets compared to a random set of molecules. The compounds further display reasonable synthesizability and drug-likeness scores.
Preference Estimation in Deferred Acceptance with Partial School Rankings
Date: 2020-10-29 00:00:00

The Deferred Acceptance algorithm is a popular school allocation mechanism
thanks to its strategy proofness. However, with application costs, strategy
proofness fails, leading to an identification problem. In this paper, I address
this identification problem by developing a new Threshold Rank setting that
models the entire rank order list as a one-step utility maximization problem. I
apply this framework to study student assignments in Chile. There are three
critical contributions of the paper. I develop a recursive algorithm to compute
the likelihood of my one-step decision model. Partial identification is
addressed by incorporating the outside value and the expected probability of
admission into a linear cost framework. The empirical application reveals that
although school proximity is a vital variable in school choice, student ability
is critical for ranking high academic score schools. The results suggest that
policy interventions such as tutoring aimed at improving student ability can
help increase the representation of low-income low-ability students in better
quality schools in Chile.
Low-frequency vibrations of water molecules in minor groove of the DNA double helix
Authors: T. L. Bubon, S. M. Perepelytsya
Date: 2020-10-29 00:00:00
Tasks: Translation

The dynamics of the structured water molecules in the hydration shell of the DNA double helix is of paramount importance for the understanding of many biological mechanisms. In particular, the vibrational dynamics of a water spine that is formed in the DNA minor groove is the aim of the present study. Within the framework of the developed phenomenological model, based on the approach of DNA conformational vibrations, the modes of H-bond stretching, backbone vibrations, and water translational vibrations have been established. The calculated frequencies of translation vibrations of water molecules vary from 167 to 205 cm$^{-1}$ depending on the nucleotide sequence. The mode of water vibrations higher than the modes of internal conformational vibrations of DNA. The calculated frequencies of water vibrations have shown a sufficient agreement with the experimental low-frequency vibrational spectra of DNA. The obtained modes of water vibrations are observed in the same region of the vibrational spectra of DNA as translation vibrations of water molecules in the bulk phase. To distinguish the vibrations of water molecules in the DNA minor groove from those in the bulk water, the dynamics of DNA with heavy water was also considered. The results have shown that in the case of heavy water the frequencies of vibrations decrease for about 10 cm$^{-1}$ that may be used in the experiment to identify the mode of water vibrations in the spine of hydration in DNA minor groove.
Spline-Based Adaptive Cancellation of Even-Order Intermodulation Distortions in LTE-A/5G RF Transceivers
Authors: Thomas Paireder, Christian Motz, Mario Huemer
Date: 2020-10-30 00:00:00

Radio frequency transceivers operating in in-band full-duplex or frequency-division duplex mode experience strong transmitter leakage. Combined with receiver nonlinearities, this causes intermodulation products in the baseband, possibly with higher power than the desired receive signal. In order to restore the receiver signal-to-noise ratio in such scenarios, we propose two novel digital self-interference cancellation approaches based on spline interpolation. Both employ a Wiener structure, thereby matching the baseband model of the intermodulation effect. Unlike most state-of-the-art spline-based adaptive learning schemes, the proposed concept allows for complex-valued in- and out-put signals. The optimization of the model parameters is based on the stochastic gradient descent concept, where the convergence is supported by an appropriate step-size normalization. Additionally, we provide a gain control scheme and enable pipelining in order to facilitate a hardware implementation. An optional input transform improves the performance consistency for correlated sequences. In a realistic interference scenario, the proposed algorithms clearly outperform a state-of-the-art least mean squares variant with comparable complexity, which is specifically tailored to second-order intermodulation distortions. The high flexibility of the spline interpolation allows the spline-based Wiener models to match the performance of the kernel recursive least squares algorithm at less than 0.6% of the arithmetic operations.