pwc-archive/papers-with-abstracts huggingface dataset:
Robust Arbitrage Conditions for Financial Markets
Authors: Derek Singh, Shuzhong Zhang
Date: 2020-04-20 00:00:00

This paper investigates arbitrage properties of financial markets under distributional uncertainty using Wasserstein distance as the ambiguity measure. The weak and strong forms of the classical arbitrage conditions are considered. A relaxation is introduced for which we coin the term statistical arbitrage. The simpler dual formulations of the robust arbitrage conditions are derived. A number of interesting questions arise in this context. One question is: can we compute a critical Wasserstein radius beyond which an arbitrage opportunity exists? What is the shape of the curve mapping the degree of ambiguity to statistical arbitrage levels? Other questions arise regarding the structure of best (worst) case distributions and optimal portfolios. Towards answering these questions, some theory is developed and computational experiments are conducted for specific problem instances. Finally some open questions and suggestions for future research are discussed.
A perspective on correlation-based financial networks and entropy measures
Authors: Vishwas Kukreti, Hirdesh K. Pharasi, Priya Gupta, Sunil Kumar
Date: 2020-04-20 00:00:00

In this brief review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. We, also, present our perspective on the use of recently developed entropy measures such as structural entropy and eigen-entropy for continuous monitoring of correlation-based networks.
A Hybrid Bayesian Approach Towards Clock Offset and Skew Estimation in 5G Networks
Date: 2020-04-20 00:00:00

In this work, we propose a hybrid Bayesian approach towards clock offset and
skew estimation, thereby synchronizing large scale networks. In particular, we
demonstrate the advantage of Bayesian Recursive Filtering (BRF) in alleviating
time-stamping errors for pairwise synchronization. Moreover, we indicate the
benefit of Factor Graph (FG), along with Belief Propagation (BP) algorithm in
achieving high precision end-to-end network synchronization. Finally, we reveal
the merit of hybrid synchronization, where a large-scale network is divided
into local synchronization domains, for each of which a suitable
synchronization algorithm (BP- or BRF-based) is utilized. The simulation
results show that, despite the simplifications in the hybrid approach, the Root
Mean Square Errors (RMSEs) of clock offset and skew estimation remain below 5
ns and 0.3 ppm, respectively.
Semi-closed form prices of barrier options in the Hull-White model
Authors: Andrey Itkin, Dmitry Muravey
Date: 2020-04-20 00:00:00
Tasks: Form

In this paper we derive semi-closed form prices of barrier (perhaps, time-dependent) options for the Hull-White model, ie., where the underlying follows a time-dependent OU process with a mean-reverting drift. Our approach is similar to that in (Carr and Itkin, 2020) where the method of generalized integral transform is applied to pricing barrier options in the time-dependent OU model, but extends it to an infinite domain (which is an unsolved problem yet). Alternatively, we use the method of heat potentials for solving the same problems. By semi-closed solution we mean that first, we need to solve numerically a linear Volterra equation of the first kind, and then the option price is represented as a one-dimensional integral. Our analysis shows that computationally our method is more efficient than the backward and even forward finite difference methods (if one uses them to solve those problems), while providing better accuracy and stability.
Frequency Control Analysis based on Unit Commitment Schemes with High Wind Power Integration: a Spanish Isolated Power System Case Study
Date: 2020-04-20 00:00:00

The relevant integration of wind power into the grid has involved a
remarkable impact on power system operation, mainly in terms of security and
reliability due to the inherent loss of the rotational inertia as a consequence
of such new generation units decoupled from the grid. In these weak scenarios,
the contribution of wind turbines to frequency control is considered as a
suitable solution to improve system stability. With regard to frequency
response analysis and grid stability, most contributions introduce wind control
discuss generation tripping for isolated power systems under arbitrary power
imbalance conditions. Frequency response is then analyzed for hypothetical
imbalances usually ranged between 5% and 20%, and assuming averaged energy
schedule scenarios. In this paper, a more realistic framework is proposed to
evaluate frequency deviations by including high wind power integration. With
this aim, unit commitment schemes and frequency load shedding are considered in
this work for frequency response analysis under high wind power penetration.
The Gran Canaria Island's isolated power system (Spain) is used for evaluation
purposes. Results provide a variety of influences from wind frequency control
depending not only on the wind power integration, but also the generation units
under operation, the rotational inertia reductions as well as the available
reserves from each resource, aspects that have not been addressed previously in
the specific literature to evaluate frequency excursions under high wind power
integration.
Impairment of insulin-stimulated glucose utilization is associated with burn-induced insulin resistance in mouse muscle by hyperinsulinemic-isoglycemic clamp
Authors: Takeshi Yamagiwa, Yong-Ming Yu, Yoshitaka Inoue, Vasily V. Belov, Mikhail I. Papisov, Sadaki Inokuchi, Masao Kaneki, Morris F. White, Alan J. Fischman, Ronald G. Tompkins
Date: 2020-04-20 00:00:00

Burn-induced insulin resistance is associated with increased morbidity and mortality; however, the impact of burn injury on tissue-specific insulin sensitivity and its molecular mechanisms with consideration of insulin state remains unknown in rodent models. This study was designed to characterize a burn mouse model with tissue-specific insulin resistance under insulin clamp conditions. C57BL6/J mice were subjected to 30% full-thickness burn injury and underwent the combination of hyperinsulinemic isoglycemicclamp (HIC) and positron emission tomography (PET). Hepatic glucose production (HGP) and peripheral glucose disappearance rate (Rd) were measured at different time points up to 7 days post injury. Burned mice showed a significant fasting hypoglycemia and hypoinsulinemia (P < 0.01) on post-burn day (PBD) 3 and 7 along with significantly higher energy expenditure (P < 0.01). HICon PBD 3 demonstrated that burn injury induced systemic insulin resistance, resulting from a significant decrease in insulin-stimulated Rd (33.0 +/- 10.2 vs 68.3 +/- 5.9 mg/kg/min; P < 0.05). In contrast, HGP of burned and sham mice was comparable both in the basal and clamp period. PET on PBD 3 showed a lower insulin-stimulated 18F-labeled 2-fluoro-2-deoxy-D-glucose uptake in the quadriceps of burned mice compared with sham-burned mice. Gastrocnemius muscle harvested from burned mice on PBD 3 showed decreased insulin-stimulated tyrosine phosphorylation of insulin receptor substrate-1 to 34.7% of that in sham-burn mice by immunoblotting analysis (P < 0.05). These findings suggest that impaired insulin-stimulated Rd in skeletal muscle, not elevated HGP, plays a role in the development of burn-induced insulin resistance in a mouse model.
Misspecified and Asymptotically Minimax Robust Quickest Change Diagnosis
Date: 2020-04-21 00:00:00

The problem of quickly diagnosing an unknown change in a stochastic process
is studied. We establish novel bounds on the performance of misspecified
diagnosis algorithms designed for changes that differ from those of the
process, and pose and solve a new robust quickest change diagnosis problem in
the asymptotic regime of few false alarms and false isolations. Simulations
suggest that our asymptotically robust solution offers a computationally
efficient alternative to generalised likelihood ratio algorithms.
Machine Learning Method Used to find Discrete and Predictive Treatment of Cancer
Authors: SeyedMehdi Abtahi, Mojtaba Sharifi
Date: 2020-04-21 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Cancer is one of the most common diseases worldwide, posing a serious threat to human health and leading to the deaths of a large number of people. It was observed during the drug administration in chemotherapy that immune cells, cancer cells and normal cells are killed or at least seriously injured and also in order to keep dosage of the drug at specific level in body, drug should be delivered in specific time and dosage. Therefore, to address these problems, a decision-making process is needed to identify the most appropriate treatment for cancer cases which causes killing of cancer cells by considering the number of healthy cells that would be killed. Despite the latest technological developments, the current methods need to be improved to suggest the most optimized a dose of the drug for tumor cells discretely. It is expected that our proposed ANFIS model be able to suggest the specialists the most optimum dose of the drug, which considers all key factors including cancer cells, immune and health cells. The results of the simulations exhibit the high accuracy of the proposed intelligent controller during the treatment in predicting the behavior of all key factors and minimize the usage dose of the drug with regard this significant point that the proposed controller gives discrete data for treatment which can fill the gap between engineering and medical science.
Revealing Cluster Structures Based on Mixed Sampling Frequencies
Authors: Yeonwoo Rho, Yun Liu, Hie Joo Ahn
Date: 2020-04-21 00:00:00
Tasks: Clustering, regression

This paper proposes a new linearized mixed data sampling (MIDAS) model and develops a framework to infer clusters in a panel regression with mixed frequency data. The linearized MIDAS estimation method is more flexible and substantially simpler to implement than competing approaches. We show that the proposed clustering algorithm successfully recovers true membership in the cross-section, both in theory and in simulations, without requiring prior knowledge of the number of clusters. This methodology is applied to a mixed-frequency Okun's law model for state-level data in the U.S. and uncovers four meaningful clusters based on the dynamic features of state-level labor markets.
Cooperative Extended State Observer Based Control of Vehicle Platoons With Arbitrarily Small Time Headway
Authors: Anquan Liu, Tao Li, Yu Gu, Haohui Dai
Date: 2020-04-21 00:00:00

We study platoon control of vehicles with linear third-order longitudinal dynamics under the constant time headway policy. The controller of each follower vehicle is only based on its own velocity, acceleration, inter-vehicle distance and velocity difference with respect to its immediate predecessor, which are all obtained by on-board sensors. We develop distributed cooperative extended state observers for followers to estimate the acceleration differences between adjacent vehicles. Based on estimates of the acceleration differences, distributed cooperative control laws are designed. By using the stability theory of perturbed linear systems, we show that the control parameters can be properly designed to ensure the closed-loop and L2 string stabilities for any given positive time headway. We further show that the proposed control law based on the ideal vehicle model can guarantee the closed-loop and L2 string stabilities even if there are small model parameter uncertainties. Also, simulation results demonstrate the robustness of the proposed control law against sensing noises, input delays and parameter uncertainties.
How Much Income Inequality Is Too Much?
Date: 2020-04-21 00:00:00

We propose a highly schematic economic model in which, in some cases, wage
inequalities lead to higher overall social welfare. This is due to the fact
that high earners can consume low productivity, non essential products, which
allows everybody to remain employed even when the productivity of essential
goods is high and producing them does not require everybody to work. We derive
a relation between heterogeneities in technologies and the minimum Gini
coefficient required to maximize global welfare. Stronger inequalities appear
to be economically unjustified. Our model may shed light on the role of
non-essential goods in the economy, a topical issue when thinking about the
post-Covid-19 world.
Putative cell type discovery from single-cell gene expression data
Authors: Zhichao Miao, Pablo Moreno, Ni Huang, Irene Papatheodorou, Alvis Brazma, Sarah A Teichmann
Date: 2020-04-21 00:00:00
Tasks: Clustering, Vocal Bursts Type Prediction

We present a novel method for automated identification of putative cell types from single-cell RNA-seq (scRNA-seq) data. By iteratively applying a machine learning approach to an initial clustering of gene expression profiles of a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The feature genes, which are differentially expressed in the particular cell group, jointly discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterised by the feature genes as markers. To benchmark this approach, we use expert-annotated scRNA-seq datasets from a range of experiments, as well as comparing to existing cell annotation methods, which are all based on a pre-existing reference. We show that our method automatically identifies the 'ground truth' cell assignments with high accuracy. Moreover, our method, Single Cell Clustering Assessment Framework (SCCAF) predicts new putative biologically meaningful cell-states in published data on haematopoiesis and the human cortex. SCCAF is available as an open-source software package on GitHub (https://github.com/SCCAF/sccaf) and as a Python package index and has also been implemented as a Galaxy tool in the Human Cell Atlas.
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours
Date: 2020-04-21 00:00:00
Tasks: Survival Analysis

Background Brain tumours represent the highest cause of mortality in the
paediatric oncological population. Diagnosis is commonly performed with
magnetic resonance imaging and spectroscopy. Survival biomarkers are
challenging to identify due to the relatively low numbers of individual tumour
types, especially for rare tumour types such as atypical rhabdoid tumours.
  Methods 69 children with biopsy-confirmed brain tumours were recruited into
this study. All participants had both perfusion and diffusion weighted imaging
performed at diagnosis. Data were processed using conventional methods, and a
Bayesian survival analysis performed. Unsupervised and supervised machine
learning were performed with the survival features, to determine novel
sub-groups related to survival. Sub-group analysis was undertaken to understand
differences in imaging features, which pertain to survival.
  Findings Survival analysis showed that a combination of diffusion and
perfusion imaging were able to determine two novel sub-groups of brain tumours
with different survival characteristics (p <0.01), which were subsequently
classified with high accuracy (98%) by a neural network. Further analysis of
high-grade tumours showed a marked difference in survival (p=0.029) between the
two clusters with high risk and low risk imaging features.
  Interpretation This study has developed a novel model of survival for
paediatric brain tumours, with an implementation ready for integration into
clinical practice. Results show that tumour perfusion plays a key role in
determining survival in brain tumours and should be considered as a high
priority for future imaging protocols.
Power and subcarrier allocation in multicarrier NOMA-FD systems
Date: 2020-04-21 00:00:00

In this paper, we study the problem of power and channel allocation for
multicarrier non-orthogonal multiple access (NOMA) full duplex (FD) systems. In
such a system there are multiple interfering users transmitting over the same
channel and the allocation task is a non-convex and extremely challenging
problem. The objective of our work is to propose a solution that is close to
the theoretic optimum but is of limited complexity. Following a block
coordinate descent approach, we propose two algorithms based on the
decomposition of the original allocation problem in lower-complexity
sub-problems, which can be solved in the Lagrangian dual domain with a great
reduction of the computational load. Numerical results show the effectiveness
of approach we propose, which outperforms other schemes designed to address
NOMA-FD allocation and attains performance similar to the optimal solution with
much lower complexity.
Adaptive Channel-Matched Detection for C-Band 64-Gbit/s Optical OOK System over 100-km Dispersion-Uncompensated Link
Date: 2020-04-21 00:00:00

In this paper, we propose adaptive channel-matched detection (ACMD) for
C-band 64-Gbit/s intensity-modulation and direct-detection (IM/DD) optical
on-off keying (OOK) system over a 100-km dispersion-uncompensated link. The
proposed ACMD can adaptively compensate most of the link distortions based on
channel and noise characteristics, which includes a polynomial nonlinear
equalizer (PNLE), a decision feedback equalizer (DFE) and maximum likelihood
sequence estimation (MLSE). Based on the channel characteristics, PNLE
eliminates the linear and nonlinear distortions, while the followed DFE
compensates the spectral nulls caused by chromatic dispersion. Finally, based
on the noise characteristics, a post filter can whiten the noise for
implementing optimal signal detection using MLSE. To the best of our knowledge,
we present a record C-band 64-Gbit/s IM/DD optical OOK system over a 100 km
dispersion-uncompensated link achieving 7\% hard-decision forward error
correction limit using only the proposed ACMD at the receiver side. In
conclusion, ACMD-based C-band 64-Gbit/s optical OOK system shows great
potential for future optical interconnects.
Cooperative Speed Estimation of an RF Jammer in Wireless Vehicular Networks
Date: 2020-04-21 00:00:00

In this paper, we are concerned with the problem of estimating the speed of
an RF jammer that moves towards a group/platoon of moving wireless
communicating nodes. In our system model, the group of nodes receives an
information signal from a master node, that they want to decode, while the
Radio Frequency (RF) jammer desires to disrupt this communication as it
approaches them. For this system model, we propose first a transmission scheme
where the master node remains silent for a time period while it transmits in a
subsequent slot. Second, we develop a joint data and jamming estimation
algorithm that uses Linear Minimum Mean Square Error (LMMSE) estimation. We
develop analytical closed-form expressions that characterize the Mean Square
Error (MSE) of the data and jamming signal estimates. Third, we propose a
cooperative jammer speed estimation algorithm based on the jamming signal
estimates at each node of the network. Our numerical and simulation results for
different system configurations prove the ability of our overall system to
estimate with high accuracy and the RF jamming signals and the speed of the
jammer.
Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States
Date: 2020-04-21 00:00:00

In March of 2020, many U.S. state governments encouraged or mandated
restrictions on social interactions to slow the spread of COVID-19, the disease
caused by the novel coronavirus SARS-CoV-2 that has spread to nearly 180
countries. Estimating the effectiveness of these social-distancing strategies
is challenging because surveillance of COVID-19 has been limited, with tests
generally being prioritized for high-risk or hospitalized cases according to
temporally and regionally varying criteria. Here we show that reductions in
mobility across U.S. counties with at least 100 confirmed cases of COVID-19 led
to reductions in fever incidences, as captured by smart thermometers, after a
mean lag of 6.5 days ($90\%$ within 3--10 days) that is consistent with the
incubation period of COVID-19. Furthermore, counties with larger decreases in
mobility subsequently achieved greater reductions in fevers ($p<0.01$), with
the notable exception of New York City and its immediate vicinity. These
results indicate that social distancing has reduced the transmission of
influenza like illnesses, including COVID 19, and support social distancing as
an effective strategy for slowing the spread of COVID-19.
Subcarrier Assignment and Power Allocation for SCMA Energy Efficiency
Date: 2020-04-21 00:00:00

In this paper we propose resource allocation algorithm for uplink sparse code
multiple access (SCMA) networks to maximize the energy efficiency (EE). Due to
the joint optimization of factor graph matrix and power allocation matrix, the
EE maximization is a non-convex mixed-integer non-linear program (MINLP)
problem. After transforming the non-convex form of the uplink sum rate to a
convex one and separating subcarrier assignment and power allocation, we
propose an energy efficient subcarrier assignment algorithm. By applying the
fractional programming theory based on Dinkelbach method, we then propose power
allocation algorithm to maximize EE. Finally, the simulation results show that
the proposed resource allocation algorithm can significantly increase the EE of
the uplink SCMA network.
Dynamic Pricing and Mean Field Analysis for Controlling Age of Information
Authors: Xuehe Wang, Lingjie Duan
Date: 2020-04-18 00:00:00

Today many mobile users in various zones are invited to sense and send back real-time useful information (e.g., traffic observation and sensor data) to keep the freshness of the content updates in such zones. However, due to the sampling cost in sensing and transmission, a user may not have the incentive to contribute the real-time information to help reduce the age of information (AoI). We propose dynamic pricing for each zone to offer age-dependent monetary returns and encourage users to sample information at different rates over time. This dynamic pricing design problem needs to well balance the monetary payments as rewards to users and the AoI evolution over time, and is challenging to solve especially under the incomplete information about users' arrivals and their private sampling costs. After formulating the problem as a nonlinear constrained dynamic program, to avoid the curse of dimensionality, we first propose to approximate the dynamic AoI reduction as a time-average term and successfully solve the approximate dynamic pricing in closed-form. Further, by providing the steady-state analysis for an infinite time horizon, we show that the pricing scheme (though in closed-form) can be further simplified to an $\varepsilon$-optimal version without recursive computing over time. Finally, we extend the AoI control from a single zone to many zones with heterogeneous user arrival rates and initial ages, where each zone cares not only its own AoI dynamics but also the average AoI of all the zones in a mean field game system to provide a holistic service. Accordingly, we propose decentralized mean field pricing for each zone to self-operate by using a mean field term to estimate the average age dynamics of all the zones, which does not even require many zones to exchange their local data with each other.