pwc-archive/papers-with-abstracts huggingface dataset:
Pricing and hedging American-style options with deep learning
Authors: Sebastian Becker, Patrick Cheridito, Arnulf Jentzen
Date: 2019-12-23 00:00:00
Tasks: Deep Learning

In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.
Few-mode fiber true time delay lines
Date: 2019-10-22 00:00:00

Space-division multiplexing optical fibers can provide not only parallel
channel transmission but also parallel distributed signal processing, being
this a feature particularly attractive in Microwave Photonics applications. We
present here few-mode fiber links with tailored modal propagation and
dispersion properties that operate as sampled true time delay lines for
radiofrequency signals.
Reducing Storage in Large-Scale Photo Sharing Services using Recompression
Date: 2019-12-23 00:00:00
Tasks: Management

The popularity of photo sharing services has increased dramatically in recent
years. Increases in users, quantity of photos, and quality/resolution of photos
combined with the user expectation that photos are reliably stored indefinitely
creates a growing burden on the storage backend of these services. We identify
a new opportunity for storage savings with application-specific compression for
photo sharing services: photo recompression.
  We explore new photo storage management techniques that are fast so they do
not adversely affect photo download latency, are complementary to existing
distributed erasure coding techniques, can efficiently be converted to the
standard JPEG user devices expect, and significantly increase compression. We
implement our photo recompression techniques in two novel codecs, ROMP and
L-ROMP. ROMP is a lossless JPEG recompression codec that compresses typical
photos 15% over standard JPEG. L-ROMP is a lossy JPEG recompression codec that
distorts photos in a perceptually un-noticeable way and typically achieves 28%
compression over standard JPEG. We estimate the benefits of our approach on
Facebook's photo stack and find that our approaches can reduce the photo
storage by 0.3-0.9x the logical size of the stored photos, and offer
additional, collateral benefits to the photo caching stack, including 5-11%
fewer requests to the backend storage, 15-31% reduction in wide-area bandwidth,
and 16% reduction in external bandwidth.
A Gated Recurrent Unit Approach to Bitcoin Price Prediction
Authors: Aniruddha Dutta, Saket Kumar, Meheli Basu
Date: 2019-12-24 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Portfolio Optimization, Prediction, Time Series, Time Series Analysis

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.
Supervised-learning-based Optimal Thermal Management in an Electric Vehicle
Date: 2019-12-24 00:00:00
Tasks: Management, Scheduling

Due to the increasing market share of electric vehicles (EVs), the optimal
thermal management (TM) of batteries has recently received significant
attention. Optimal battery temperature control is challenging, requiring a
detailed model and numerous parameters of the TM system, which includes fans,
pumps, compressors, and heat exchangers. This paper proposes a supervised
learning strategy for the optimal operation of the TM system in an EV.
Specifically, for TM subsystems, individual artificial neural networks (ANNs)
are implemented and trained with data obtained under normal EV driving
conditions. The ANNs are then interconnected based on the physical
configuration of the TM system. The trained ANNs are replicated using piecewise
linear equations, which can be explicitly integrated into an optimization
problem for optimal TM scheduling. This approach enables the application of a
mixed-integer linear programming solver to the problem, ensuring the global
optimality of the solution. Simulation case studies are performed for the two
operating modes of the TM system: i.e., integrated and separate modes. The case
study results demonstrate that the ANN-based model successfully reflects the
operating characteristics of the TM system, enabling accurate battery
temperature estimation. The proposed optimal TM strategy using the ANN-based
model is verified to be effective in reducing the total energy consumption,
while maintaining the battery temperature within an acceptable range.
Evolutionary Dynamics of Investors Expectations and Market Price Movement
Authors: Inga Ivanova
Date: 2019-12-24 00:00:00
Tasks: Time Series, Time Series Analysis

The paper presents a step forward into the development of the theory of meaning. Stock and financial markets are examined from communication-theoretical perspective on the dynamics of information and meaning. This study focuses on the link between the dynamics of investors' expectations and market price movement. The model for market asset price dynamiscs, based on non-linear evolutionary equation linking investors' expectations and market asset price movement, is provided. Model predictions are tested on various FX, energy, food, and indices markets along different time frames. The results suggest that model predicted time series is co-integrated with asset time series which implies that the prop[osed model can be used to forecast future price movement.
A Low-Complexity LoRa Synchronization Algorithm Robust to Sampling Time Offsets
Authors: Mathieu Xhonneux, Orion Afisiadis, David Bol, Jérôme Louveaux
Date: 2019-12-24 00:00:00

LoRaWAN is nowadays one of the most popular protocols for low-power Internet-of-Things communications. Although its physical layer, namely LoRa, has been thoroughly studied in the literature, aspects related to the synchronization of LoRa receivers have received little attention so far. The estimation and correction of carrier frequency and sampling time offsets is however crucial to attain the low sensitivity levels offered by the LoRa spread-spectrum modulation. The goal of this paper is to build a low-complexity, yet efficient synchronization algorithm capable of correcting both offsets. To this end, a complete analytical model of a LoRa signal corrupted by these offsets is first derived. Using this model, we propose a new estimator for the sampling time offset. We also show that the estimations of the carrier frequency and the sampling time offsets cannot be performed independently. Therefore, to avoid a complex joint estimation of both offsets, an iterative low-complexity synchronization algorithm is proposed. To reach a packet error rate of 10^-3, performance evaluations show that the proposed receiver requires only 1 or 2 dB higher SNR than a theoretical perfectly synchronized receiver, while incurring a very low computational overhead.
Healthy Access for Healthy Places: A Multidimensional Food Access Measure
Date: 2019-12-23 00:00:00

When it comes to preventive healthcare, place matters. It is increasingly
clear that social factors, particularly reliable access to healthy food, are as
determinant to health and health equity as medical care. However, food access
studies often only present one-dimensional measurements of access. We
hypothesize that food access is a multidimensional concept and evaluated
Penchansky and Thomas's 1981 definition of access. In our approach, we identify
ten variables contributing to food access in the City of Chicago and use
principal component analysis to identify vulnerable populations with low
access. Our results indicate that within the urban environment of the case
study site, affordability is the most important factor in low food
accessibility, followed by urban youth, reduced mobility, and higher immigrant
population.
Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid
Date: 2019-12-24 00:00:00
Tasks: State Estimation

Recent studies have considered thwarting false data injection (FDI) attacks
against state estimation in power grids by proactively perturbing branch
susceptances. This approach is known as moving target defense (MTD). However,
despite of the deployment of MTD, it is still possible for the attacker to
launch stealthy FDI attacks generated with former branch susceptances. In this
paper, we prove that, an MTD has the capability to thwart all FDI attacks
constructed with former branch susceptances only if (i) the number of branches
$l$ in the power system is not less than twice that of the system states $n$
(i.e., $l \geq 2n$, where $n + 1$ is the number of buses); (ii) the
susceptances of more than $n$ branches, which cover all buses, are perturbed.
Moreover, we prove that the state variable of a bus that is only connected by a
single branch (no matter it is perturbed or not) can always be modified by the
attacker. Nevertheless, in order to reduce the attack opportunities of
potential attackers, we first exploit the impact of the susceptance
perturbation magnitude on the dimension of the \emph{stealthy attack space}, in
which the attack vector is constructed with former branch susceptances. Then,
we propose that, by perturbing an appropriate set of branches, we can minimize
the dimension of the \emph{stealthy attack space} and maximize the number of
covered buses. Besides, we consider the increasing operation cost caused by the
activation of MTD. Finally, we conduct extensive simulations to illustrate our
findings with IEEE standard test power systems.
p75NTR as a Molecular Memory Switch
Date: 2019-12-24 00:00:00

In recent years, many molecular and environmental factors have been studied
to understand how synaptic plasticity is modulated. Sleep, as an evolutionary
conserved biological function, has shown to be a critical player for the
consolidation and filtering of synaptic circuitry underlying memory traces.
Although sleep disturbances do not alter normal memory consolidation, they may
reflect fundamental circuit malfunctions that can play a significant role in
exacerbating diseases, such as autism and Alzheimer's disease. Very recently,
scientists sought to answer part of this enigma and they identified p75
neurotrophic receptor (p75NTR) as a critical player in mediating impairments in
hippocampal-dependent associative plasticity upon sleep deprivation. This paper
will review the role of the p75NTR, critically discuss the impact and
implications of this research as the bridge for sleep research and neurological
diseases.
Wideband Source Enumeration Using Sparse Array Periodogram Averaging in Low Snapshot Scenarios
Date: 2019-12-24 00:00:00

This paper proposes a new sparse array source enumeration algorithm for
underdetermined scenarios with more sources than sensors. The proposed
algorithm decomposes the wideband signals into multiple uncorrelated frequency
bands, computes the narrowband spatial periodograms and then averages
periodograms to reinforce the sources' spectral information. The inverse
spatial Fourier transform of the wideband periodogram estimates the spatial
correlation function, which then populates the diagonals of a Hermitian
Toeplitz augmented covariance matrix (ACM) after lag redundancy averaging. A
modified minimum description length (MDL) criteria, termed MDLgap, is proposed
for source enumeration using the eigenvalues of the constructed ACM. MDLgap
provably never overestimates the number of sources present, and is
asymptotically consistent when the signals present span a limited dynamic
range. Numerical simulations show that the proposed MDLgap algorithm achieves
improved performance over existing approaches for underdetermined source
enumeration, especially in low snapshot scenarios.
Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC
Date: 2019-12-25 00:00:00

This paper addresses neural network based post-processing for the
state-of-the-art video coding standard, High Efficiency Video Coding (HEVC). We
first propose a partition-aware Convolution Neural Network (CNN) that utilizes
the partition information produced by the encoder to assist in the
post-processing. In contrast to existing CNN-based approaches, which only take
the decoded frame as input, the proposed approach considers the coding unit
(CU) size information and combines it with the distorted decoded frame such
that the artifacts introduced by HEVC are efficiently reduced. We further
introduce an adaptive-switching neural network (ASN) that consists of multiple
independent CNNs to adaptively handle the variations in content and distortion
within compressed-video frames, providing further reduction in visual
artifacts. Additionally, an iterative training procedure is proposed to train
these independent CNNs attentively on different local patch-wise classes.
Experiments on benchmark sequences demonstrate the effectiveness of our
partition-aware and adaptive-switching neural networks. The source code can be
found at http://min.sjtu.edu.cn/lwydemo/HEVCpostprocessing.html.
Cache-Enabled Coordinated Mobile Edge Network: Opportunities and Challenges
Date: 2019-12-25 00:00:00

Cache-enabled coordinated mobile edge network is an emerging network
architecture, wherein serving nodes located at the network edge have the
capabilities of baseband signal processing and caching files at their local
cache. The main goals of such an emerging network architecture are to alleviate
the burden on fronthaul links, while achieving low-latency high rate content
delivery, say on the order of the millisecond level end-to-end content delivery
latency. Consequently, the application of delaysensitive and content-aware has
been executed in close proximity to end users. In this article, an outlook of
research directions, challenges, and opportunities is provided and discussed in
depth. We first introduce the cache-enabled coordinated mobile edge network
architecture, and then discuss the key technical challenges and opening
research issues that need to be addressed for this architecture. Then, to
reduce the delivery latency and the burden on fronthaul links, new
cache-enabled physical layer transmission schemes are discussed. Finally,
artificial intelligence based cache-enabled communications are discussed as
future research directions. Numerical studies show that several gains are
achieved by caching popular content at the network edge with proper coordinated
transmission schemes.
A design framework for all-digital mmWave massive MIMO with per-antenna nonlinearities
Authors: Mohammed Abdelghany, Ali A. Farid, Upamanyu Madhow, Mark J. W. Rodwell
Date: 2019-12-25 00:00:00
Tasks: All

Millimeter wave MIMO combines the benefits of compact antenna arrays with a large number of elements and massive bandwidths, so that fully digital beamforming has the potential of supporting a large number of simultaneous users with {\it per user} data rates of multiple gigabits/sec (Gbps). In this paper, we develop an analytical model for the impact of nonlinearities in such a system, and illustrate its utility in providing hardware design guidelines regarding two key challenges: the low available precision of analog-to-digital conversion at high sampling rates, and nonlinearities in ultra-high speed radio frequency (RF) and baseband circuits. We consider linear minimum mean square error (LMMSE) reception for a multiuser MIMO uplink, and provide performance guarantees based on two key concepts: (a) summarization of the impact of per-antenna nonlinearities via a quantity that we term the "intrinsic SNR", (b) using linear MMSE performance in an ideal system without nonlinearities to bound that in our non-ideal system. For our numerical results, we employ nominal parameters corresponding to outdoor picocells operating at a carrier frequency of 140 GHz, with a data rate of 10 Gbps per user.
Enhancing the Performance of Low Priority SUs Using Reserved Channels in CRN
Date: 2019-12-25 00:00:00

Cognitive radio networks (CRNs) are considered a promising solution for
spectrum resources scarcity and efficient channel utilization. In this letter,
multi-dimensional analytical Markov model based on reservation channel access
scheme and channel aggregation method is proposed to enhance spectrum
utilization, capacity of low priority secondary users (SUs) and reducing
handoff probability of SUs. Moreover, the proposed method improves the
performance of high priority SUs by providing the capability to resume the
connection after dropping. The numerical results indicate that the modified
reservation access model can enhance the performance of SUs compared to the
traditional basic random access model.
Dynamics of the Price Behavior in Stock Market: A Statistical Physics Approach
Authors: Hung T. Diep, Gabriel Desgranges
Date: 2019-12-25 00:00:00

We study in this paper the time evolution of stock markets using a statistical physics approach. Each agent is represented by a spin having a number of discrete states $q$ or continuous states, describing the tendency of the agent for buying or selling. The market ambiance is represented by a parameter $T$ which plays the role of the temperature in physics. We show that there is a critical value of $T$, say $T_c$, where strong fluctuations between individual states lead to a disordered situation in which there is no majority: the numbers of sellers and buyers are equal, namely the market clearing. We have considered three models: $q=3$ ( sell, buy, wait), $q=5$ (5 states between absolutely buy and absolutely sell), and $q=\infty$. The specific measure, by the government or by economic organisms, is parameterized by $H$ applied on the market at the time $t_1$ and removed at the time $t_2$. We have used Monte Carlo simulations to study the time evolution of the price as functions of those parameters. Many striking results are obtained. In particular we show that the price strongly fluctuates near $T_c$ and there exists a critical value $H_c$ above which the boosting effect remains after $H$ is removed. This happens only if $H$ is applied in the critical region. Otherwise, the effect of $H$ lasts only during the time of the application of $H$. The second party of the paper deals with the price variation using a time-dependent mean-field theory. By supposing that the sellers and the buyers belong to two distinct communities with their characteristics different in both intra-group and inter-group interactions, we find the price oscillation with time.
Convergence of a Distributed Least Squares
Authors: Siyu Xie, Yaqi Zhang, Lei Guo
Date: 2019-12-25 00:00:00

In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed LS.
Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems
Authors: R. Liu, M. Li, Q. Liu, A. L. Swindlehurst
Date: 2019-12-26 00:00:00

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this paper, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.
Stabilization with Closed-loop DOA Enlargement: An Interval Analysis Approach
Date: 2020-08-11 00:00:00

In this paper, the stabilization problem with closed-loop domain of
attraction (DOA) enlargement for discrete-time general nonlinear plants is
solved. First, a sufficient condition for asymptotic stabilization and
estimation of the closed-loop DOA is given. It shows that, for a given Lyapunov
function, the negative-definite and invariant set in the state-control space is
a stabilizing controller set and its projection along the control space to the
state space can be an estimate of the closed-loop DOA. Then, an algorithm is
proposed to approximate the negative-definite and invariant set for the given
Lyapunov function, in which an interval analysis algorithm is used to find an
inner approximation of sets as precise as desired. Finally, a solvable
optimization problem is formulated to enlarge the estimate of the closed-loop
DOA by selecting an appropriate Lyapunov function from a positive-definite
function set. The proposed method try to find a unstructured controller set
(namely, the negative-definite and invariant set) in the state-control space
rather than design parameters of a structured controller in traditional
synthesis methods.