pwc-archive/papers-with-abstracts huggingface dataset:
Marked point processes and intensity ratios for limit order book modeling
Authors: Ioane Muni Toke, Nakahiro Yoshida
Date: 2020-01-23 00:00:00
Tasks: Clustering, Model Selection, Point Processes

This paper extends the analysis of Muni Toke and Yoshida (2020) to the case of marked point processes. We consider multiple marked point processes with intensities defined by three multiplicative components, namely a common baseline intensity, a state-dependent component specific to each process, and a state-dependent component specific to each mark within each process. We show that for specific mark distributions, this model is a combination of the ratio models defined in Muni Toke and Yoshida (2020). We prove convergence results for the quasi-maximum and quasi-Bayesian likelihood estimators of this model and provide numerical illustrations of the asymptotic variances. We use these ratio processes in order to model transactions occuring in a limit order book. Model flexibility allows us to investigate both state-dependency (emphasizing the role of imbalance and spread as significant signals) and clustering. Calibration, model selection and prediction results are reported for high-frequency trading data on multiple stocks traded on Euronext Paris. We show that the marked ratio model outperforms other intensity-based methods (such as "pure" Hawkes-based methods) in predicting the sign and aggressiveness of market orders on financial markets.
Routing of Electric Vehicles in a Stochastic Network with Non-recurrent Incidents
Date: 2020-04-16 00:00:00

An approach for mapping an electric vehicle EV driver s travel time
constraints and risk taking behavior to real time routing in a probabilistic
time-dependent or stochastic network is proposed in this paper. The proposed
approach is based on a heuristic algorithm that finds the shortest path
according to the driver s preferences. Accounting for en route delays and
alternate routes, the EV routing problem in stochastic networks is shown to
exhibit other than the First In First Out FIFO property i.e. the traveling time
for those who depart earlier may not sooner of those who depart later or wait
en route in the charging stations. The proposed approach provides EV drivers
the option to manage their trip and reach the destination on time, while by
taking advantage of the non FIFO characteristics of the traffic network, charge
their cars en route. The proposed routing algorithm is tested on a given
stochastic transportation network. The best routes based on the driver s
preferences are identified while accounting for the best-planned delays at the
charging stations or en-route.
A Coalition Formation Game Framework for Peer-to-Peer Energy Trading
Date: 2019-12-24 00:00:00
Tasks: energy trading

This paper studies social cooperation backed peer-to-peer energy trading
technique by which prosumers can decide how they can use their batteries
opportunistically for participating in the peer-to-peer trading. The objective
is to achieve a solution in which the ultimate beneficiaries are the prosumers,
i.e., a prosumer-centric solution. To do so, a coalition formation game is
designed, which enables a prosumer to compare its benefit of participating in
the peer-to-peer trading with and without using its battery and thus, allows
the prosumer to form suitable social coalition groups with other similar
prosumers in the network for conducting peer-to-peer trading. The properties of
the formed coalitions are studied, and it is shown that 1) the coalition
structure that stems from the social cooperation between participating
prosumers at each time slot is both stable and optimal, and 2) the outcomes of
the proposed peer- to-peer trading scheme is prosumer-centric. Case studies are
conducted based on real household energy usage and solar generation data to
highlight how the proposed scheme can benefit prosumers through exhibiting
prosumer-centric properties.
Polyphase Waveform Design for MIMO Radar Space Time Adaptive Processing
Date: 2020-01-13 00:00:00

We consider the design of polyphase waveforms for ground moving target
detection with airborne multiple-input-multiple-output (MIMO) radar. Due to the
constant-modulus and finite-alphabet constraint on the waveforms, the
associated design problem is non-convex and in general NP-hard. To tackle this
problem, we develop an efficient algorithm based on relaxation and cyclic
optimization. Moreover, we exploit a reparameterization trick to avoid the
significant computational burden and memory requirement brought about by
relaxation. We prove that the objective values during the iterations are
guaranteed to converge. Finally, we provide an effective randomization approach
to obtain polyphase waveforms from the relaxed solution at convergence.
Numerical examples show the effectiveness of the proposed algorithm for
designing polyphase waveforms.
Finer Metagenomic Reconstruction via Biodiversity Optimization
Authors: Simon Foucart, David Koslicki
Date: 2020-01-23 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Compressive Sensing, Diversity

When analyzing communities of microorganisms from their sequenced DNA, an important task is taxonomic profiling: enumerating the presence and relative abundance of all organisms, or merely of all taxa, contained in the sample. This task can be tackled via compressive-sensing-based approaches, which favor communities featuring the fewest organisms among those consistent with the observed DNA data. Despite their successes, these parsimonious approaches sometimes conflict with biological realism by overlooking organism similarities. Here, we leverage a recently developed notion of biological diversity that simultaneously accounts for organism similarities and retains the optimization strategy underlying compressive-sensing-based approaches. We demonstrate that minimizing biological diversity still produces sparse taxonomic profiles and we experimentally validate superiority to existing compressive-sensing-based approaches. Despite showing that the objective function is almost never convex and often concave, generally yielding NP-hard problems, we exhibit ways of representing organism similarities for which minimizing diversity can be performed via a sequence of linear programs guaranteed to decrease diversity. Better yet, when biological similarity is quantified by $k$-mer co-occurrence (a popular notion in bioinformatics), minimizing diversity actually reduces to one linear program that can utilize multiple $k$-mer sizes to enhance performance. In proof-of-concept experiments, we verify that the latter procedure can lead to significant gains when taxonomically profiling a metagenomic sample, both in terms of reconstruction accuracy and computational performance. Reproducible code is available at https://github.com/dkoslicki/MinimizeBiologicalDiversity.
A Belief Space Perspective of RFS based Multi-Target Tracking and its Relationship to MHT
Date: 2020-01-23 00:00:00

In this paper, we establish a connection between Reid{'}s HOMHT and the
modern Random Finite Set (RFS)/ Finite Set Statistics (FISST) based methods for
Multi-Target Tracking. We start with an RFS description of the Multi-Target
probability density function (MT-pdf), and derive the prediction, and update
equations of the MT-tracking problem in the RFS framework from a belief space
perspective. We show that the RFS pdf has a hypothesis dependent structure that
is similar to the HOMHT hypotheses structure. In particular, we examine the
different hypotheses, and derive the hypotheses update equations under the
FISST recursions, and clearly show its relationship to the classical HOMHT
hypotheses and hypothesis weight update formula, thereby establishing a
connection between the methods.
Intensity Discriminability of Electrocutaneous and Intraneural Stimulation Pulse Frequency in Intact Individuals and Amputees
Date: 2020-01-23 00:00:00

Electrical stimulation of residual nerves can be used to provide amputees
with intuitive sensory feedback. An important aspect of this artificial sensory
feedback is the ability to convey the magnitude of tactile stimuli. Using
classical psychophysical methods, we quantified the just-noticeable differences
for electrocutaneous stimulation pulse frequency in both intact participants
and one transradial amputee. For the transradial amputee, we also quantified
the just-noticeable difference of intraneural microstimulation pulse frequency
via chronically implanted Utah Slanted Electrode Arrays. We demonstrate that
intensity discrimination is similar across conditions: intraneural
microstimulation of the residual nerves, electrocutaneous stimulation of the
reinnervated skin on the residual limb, and electrocutaneous stimulation of
intact hands. We also show that intensity discrimination performance is
significantly better at lower pulse frequencies than at higher ones - a finding
that's unique to electrocutaneous and intraneural stimulation and suggests that
supplemental sensory cues may be present at lower pulse frequencies. These
results can help guide the implementation of artificial sensory feedback for
sensorized bionic arms.
Choosing the Right Return Distribution and the Excess Volatility Puzzle
Authors: Abootaleb Shirvani, Frank J. Fabozzi
Date: 2020-01-24 00:00:00

Proponents of behavioral finance have identified several "puzzles" in the market that are inconsistent with rational finance theory. One such puzzle is the "excess volatility puzzle". Changes in equity prices are too large given changes in the fundamentals that are expected to change equity prices. In this paper, we offer a resolution to the excess volatility puzzle within the context of rational finance. We empirically show that market inefficiency attributable to the volatility of excess return across time is caused by fitting an improper distribution to the historical returns. Our results indicate that the variation of gross excess returns is attributable to poorly fitting the tail of the return distribution and that the puzzle disappears by employing a more appropriate distribution for the return data. The new distribution that we introduce in this paper that better fits the historical return distribution of stocks explains the excess volatility in the market and thereby explains the volatility puzzle. Failing to estimate the historical returns using the proper distribution is only one possible explanation for the existence of the volatility puzzle. However, it offers statistical models within the rational finance framework which can be used without relying on behavioral finance assumptions when searching for an explanation for the volatility puzzle.
Pricing commodity swing options
Authors: Roberto Daluiso, Emanuele Nastasi, Andrea Pallavicini, Giulio Sartorelli
Date: 2020-01-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In commodity and energy markets swing options allow the buyer to hedge against futures price fluctuations and to select its preferred delivery strategy within daily or periodic constraints, possibly fixed by observing quoted futures contracts. In this paper we focus on the natural gas market and we present a dynamical model for commodity futures prices able to calibrate liquid market quotes and to imply the volatility smile for futures contracts with different delivery periods. We implement the numerical problem by means of a least-square Monte Carlo simulation and we investigate alternative approaches based on reinforcement learning algorithms.
Refined model of the covariance/correlation matrix between securities
Authors: Sebastien Valeyre
Date: 2020-01-24 00:00:00

A new methodology has been introduced to clean the correlation matrix of single stocks returns based on a constrained principal component analysis using financial data. Portfolios were introduced, namely "Fundamental Maximum Variance Portfolios", to capture in an optimal way the risks defined by financial criteria ("Book", "Capitalization", etc.). The constrained eigenvectors of the correlation matrix, which are the linear combination of these portfolios, are then analyzed. Thanks to this methodology, several stylized patterns of the matrix were identified: i) the increase of the first eigenvalue with a time scale from 1 minute to several months seems to follow the same law for all the significant eigenvalues with 2 regimes; ii) a universal law seems to govern the weights of all the "Maximum variance" portfolios, so according to that law, the optimal weights should be proportional to the ranking based on the financial studied criteria; iii) the volatility of the volatility of the "Maximum Variance" portfolios, which are not orthogonal, could be enough to explain a large part of the diffusion of the correlation matrix; iv) the leverage effect (increase of the first eigenvalue with the decline of the stock market) occurs only for the first mode and cannot be generalized for other factors of risk. The leverage effect on the beta, which is the sensitivity of stocks with the market mode, makes variable the weights of the first eigenvector.
Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma
Authors: Daniela Schenonea, Rita Lai, Michele Cea, Federica Rossi, Lorenzo Torri, Bianca Bignotti, Giulia Succio, Stefano Gualco, Alessio Conte, Alida Dominietto, Anna Maria Massone, Michele Piana, Cristina Campi, Francesco Frassoni, Gianmario Sambuceti, Alberto Stefano Tagliafico
Date: 2020-01-24 00:00:00
Tasks: BIG-bench Machine Learning

Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement, renal damages and osteolytic lesions. The skeleton involvement of MM is at the core of the present paper, exploiting radiomics and artificial intelligence to identify image-based biomarkers for MM. Preliminary results show that MM is associated to an extension of the intrabone volume for the whole body and that machine learning can identify CT image features mostly correlating with the disease evolution. This computational approach allows an automatic stratification of MM patients relying of these biomarkers and the formulation of a prognostic procedure for determining the disease follow-up.
Big Data based Research on Mechanisms of Sharing Economy Restructuring the World
Date: 2020-01-24 00:00:00

Many researches have discussed the phenomenon and definition of sharing
economy, but an understanding of sharing economy's reconstructions of the world
remains elusive. We illustrate the mechanism of sharing economy's
reconstructions of the world in detail based on big data including the
mechanism of sharing economy's reconstructions of society, time and space,
users, industry, and self-reconstruction in the future, which is very important
for society to make full use of the reconstruction opportunity to upgrade our
world through sharing economy. On the one hand, we established the mechanisms
for sharing economy rebuilding society, industry, space-time, and users through
qualitative analyses, and on the other hand, we demonstrated the rationality of
the mechanisms through quantitative analyses of big data.
Symmetries and periodic orbits in simple hybrid Routhian systems
Date: 2020-01-24 00:00:00

Symmetries are ubiquitous in a wide range of nonlinear systems. Particularly
in systems whose dynamics are determined by a Lagrangian or Hamiltonian
function. For hybrid systems which possess a continuous-time dynamics
determined by a Lagrangian function, with a cyclic variable, the degrees of
freedom for the corresponding hybrid Lagrangian system can be reduced by means
of a method known as \textit{hybrid Routhian reduction}. In this paper we study
sufficient conditions for the existence of periodic orbits in hybrid Routhian
systems which also exhibit time-reversal symmetry. Likewise, we explore some
stability aspects of such orbits through the characterization of the
eigenvalues for the corresponding linearized Poincar\'e map. Finally, we apply
the results to find periodic solutions in underactuated hybrid Routhian control
systems.
Investigation of the cellular response to bone fractures: evidence for flexoelectricity
Authors: Raquel Núñez-Toldrà, Fabian Vasquez-Sancho, Nathalie Barroca, Gustau Catalan
Date: 2020-01-24 00:00:00

The recent discovery of bone flexoelectricity (electrical polarization induced by strain gradient) suggests that flexoelectricity could have physiological effects in bones, and specifically near bone fractures, where flexoelectricity is theoretically highest. Here, we report a cytological study of the interaction between crack stress and bone cells. We have cultured MC3T3-E1 mouse osteoblastic cells in biomimetic microcracked hydroxyapatite substrates, differentiated into osteocytes and applied a strain gradient to the samples. The results show a strong apoptotic cellular response, whereby mechanical stimulation causes those cells near the crack to die, as indicated by live-dead and caspase staining. In addition, analysis two weeks after stimulation shows increased cell attachment and mineralization around microcracks and a higher expression of osteocalcin, an osteogenic protein known to be promoted by physical exercise. The results are consistent with flexoelectricity playing at least two different roles in bone remodelling: apoptotic trigger of the repair protocol, and electrostimulant of the bone-building activity of osteoblasts.
Virtualized C-RAN Orchestration with Docker, Kubernetes and OpenAirInterface
Date: 2020-01-13 00:00:00
Tasks: Management

Virtualization is a key feature in Cloud Radio Access Network (C-RAN). It can
help to save costs since it allows the use of virtualized base stations instead
of physically deployment in different areas. However, the creation and
management of virtual base stations pools is not trivial and introduces new
challenges to C-RAN deployment. This paper reports a method to orchestrate and
manage a container-based C-RAN. We used several instances of the
OpenAirInterface software running on Docker containers, and orchestrated them
using Kubernetes. We demonstrate that using Kubernetes it is possible to
dynamically scale remote radio heads (RRHs) and baseband units (BBUs) according
to requirements of the network and parameters such as the server resources
usage.
Reactive Navigation under Non-Parametric Uncertainty through Hilbert Space Embedding of Probabilistic Velocity Obstacles
Date: 2020-01-21 00:00:00
Tasks: Model Predictive Control

The probabilistic velocity obstacle (PVO) extends the concept of velocity
obstacle (VO) to work in uncertain dynamic environments. In this paper, we show
how a robust model predictive control (MPC) with PVO constraints under
non-parametric uncertainty can be made computationally tractable. At the core
of our formulation is a novel yet simple interpretation of our robust MPC as a
problem of matching the distribution of PVO with a certain desired
distribution. To this end, we propose two methods. Our first baseline method is
based on approximating the distribution of PVO with a Gaussian Mixture Model
(GMM) and subsequently performing distribution matching using Kullback Leibler
(KL) divergence metric. Our second formulation is based on the possibility of
representing arbitrary distributions as functions in Reproducing Kernel Hilbert
Space (RKHS). We use this foundation to interpret our robust MPC as a problem
of minimizing the distance between the desired distribution and the
distribution of the PVO in the RKHS. Both the RKHS and GMM based formulation
can work with any uncertainty distribution and thus allowing us to relax the
prevalent Gaussian assumption in the existing works. We validate our
formulation by taking an example of 2D navigation of quadrotors with a
realistic noise model for perception and ego-motion uncertainty. In particular,
we present a systematic comparison between the GMM and the RKHS approach and
show that while both approaches can produce safe trajectories, the former is
highly conservative and leads to poor tracking and control costs. Furthermore,
RKHS based approach gives better computational times that are up to one order
of magnitude lesser than the computation time of the GMM based approach.
Optimal terminal sliding-mode control for second-order motion systems
Authors: Michael Ruderman
Date: 2020-01-24 00:00:00

Terminal sliding mode (TSM) control algorithm and its non-singular refinement have been elaborated for two decades and belong, since then, to a broader class of the finite-time controllers, which are known to be robust against the matched perturbations. While TSM manifold allows for different forms of the sliding variable, which are satisfying the $q/p$ power ratio of the measurable output state, we demonstrate that $q/p=0.5$ is the optimal one for the second-order Newton's motion dynamics with a bounded control action. The paper analyzes the time-optimal sliding surface and, based thereupon, claims the optimal TSM control for the second-order motion systems. It is stressed that the optimal TSM control is fully inline with the Fuller's problem of optimal switching which minimizes the settling time, i.e. with time-optimal control of an unperturbed double-integrator. Is is also shown that for the given plant characteristics, i.e. the overall inertia and control bound, there is no need for additional control parameters. The single surface design parameter might (but not necessarily need to) be used for driving system on the boundary layer of the twisting mode, or for forcing it to the robust terminal sliding mode. Additional insight is given into the finite-time convergence of TSM and robustness against the bounded perturbations. Numerical examples with different upper-bounded perturbations are demonstrated.
Aggregation and Data Driven Identification of Building Thermal Dynamic Model and Unmeasured Disturbance
Date: 2020-01-24 00:00:00

An aggregate model is a single-zone equivalent of a multi-zone building, and
is useful for many purposes, including model based control of large heating,
ventilation and air conditioning (HVAC) equipment. This paper deals with the
problem of simultaneously identifying an aggregate thermal dynamic model and
unknown disturbances from input-output data. The unknown disturbance is a key
challenge since it is not measurable but non-negligible. We first present a
principled method to aggregate a multi-zone building model into a single zone
model, and show the aggregation is not as trivial as it has been assumed in the
prior art. We then provide a method to identify the parameters of the model and
the unknown disturbance for this aggregate (single-zone) model. Finally, we
test our proposed identification algorithm to data collected from a multi-zone
building testbed in Oak Ridge National Laboratory. A key insight provided by
the aggregation method allows us to recognize under what conditions the
estimation of the disturbance signal will be necessarily poor and uncertain,
even in the case of a specially designed test in which the disturbances
affecting each zone are known (as the case of our experimental testbed). This
insight is used to provide a heuristic that can be used to assess when the
identification results are likely to have high or low accuracy.