pwc-archive/papers-with-abstracts huggingface dataset:
A Systematic and Analytical Review of the Socioeconomic and Environmental Impact of the Deployed High-Speed Rail (HSR) Systems on the World
Date: 2020-03-18 00:00:00
Tasks: Miscellaneous

The installation of high-speed rail in the world during the last two decades
resulted in significant socioeconomic and environmental changes. The U.S. has
the longest rail network in the world, but the focus is on carrying a wide
variety of loads including coal, farm crops, industrial products, commercial
goods, and miscellaneous mixed shipments. Freight and passenger services in the
U.S. dates to 1970, with both carried out by private railway companies.
Railways were the main means of transport between cities from the late 19th
century through the middle of the 20th century. However, rapid growth in
production and improvements in technologies changed those dynamics. The fierce
competition for comfortability and pleasantness in passenger travel and the
proliferation of aviation services in the U.S. channeled federal and state
budgets towards motor vehicle infrastructure, which brought demand for
railroads to a halt in the 1950s. Presently, the U.S. has no high-speed trains,
aside from sections of Amtrak s Acela line in the Northeast Corridor that can
reach 150 mph for only 34 miles of its 457-mile span. The average speed between
New York and Boston is about 65 mph. On the other hand, China has the world s
fastest and largest high-speed rail network, with more than 19,000 miles, of
which the vast majority was built in the past decade. Japan s bullet trains can
reach nearly 200 miles per hour and dates to the 1960s. That system moved more
than 9 billion people without a single passenger casualty. In this systematic
review, we studied the effect of High-Speed Rail (HSR) on the U.S. and other
countries including France, Japan, Germany, Italy, and China in terms of energy
consumption, land use, economic development, travel behavior, time use, human
health, and quality of life.
A New Approach for Macroscopic Analysis to Improve the Technical and Economic Impacts of Urban Interchanges on Traffic Networks
Date: 2020-09-17 00:00:00

Pursuing three important elements including economic, safety, and traffic are
the overall objective of decision evaluation across all transport projects. In
this study, we investigate the feasibility of the development of city
interchanges and road connections for network users. To achieve this goal, a
series of minor goals are required to be met in advance including determining
benefits, costs of implement-ing new highway interchanges, quantifying the
effective parameters, the increase in fuel consumption, the reduction in travel
time, and finally influence on travel speed. In this study, geometric
advancement of Hakim highway, and Yadegar-e-Emam Highway were investigated in
the Macro view from the cloverleaf inter-section with a low capacity to a
three-level directional intersection of the enhanced cloverleaf. For this
purpose, the simulation was done by EMME software of INRO Company. The results
of the method were evaluated by the objective of net present value (NPV), and
the benefit and cost of each one was stated precisely in different years. At
the end, some suggestion has been provided.
A Novel Alternative Optimization Method for Joint Power and Trajectory Design in UAV-Enabled Wireless Network
Date: 2020-03-10 00:00:00

This letter aims to maximize the average throughput via the joint design of
the transmit power and trajectory for unmanned aerial vehicle (UAV)-enabled
network. The conventional way to tackle this problem is based on the
alternating optimization (AO) method by iteratively updating power and
trajectory until convergence, resulting in a non-convex trajectory subproblem
which is difficult to deal with. To develop more efficient methods, we propose
a novel AO method by incorporating both power and trajectory into an
intermediate variable, and then iteratively updating power and the newly
introduced variable. This novel variable transformation makes it easier to
decompose the original problem into two convex subproblems, namely a throughput
maximization subproblem and a feasibility subproblem. Consequently, both of
these subproblems can be solved in a globally optimal fashion. We further
propose a low-complexity algorithm for the feasibility subproblem by exploiting
the alternating directional method of multipliers (ADMM), whose updating step
is performed in closed-form solutions. Simulation results demonstrate that our
proposed method reduces the computation time by orders of magnitude, while
achieving higher performance than the conventional methods.
A Fast Recursive Algorithm for G-STBC
Date: 2020-03-10 00:00:00

This paper proposes a fast recursive algorithm for Group-wise Space-Time
Block Code (G-STBC), which takes full advantage of the Alamouti structure in
the equivalent channel matrix to reduce the computational complexity. With
respect to the existing efficient algorithms for G-STBC, the proposed algorithm
achieves better performance and usually requires less computational complexity.
Old Drugs for Newly Emerging Viral Disease, COVID-19: Bioinformatic Prospective
Date: 2020-03-10 00:00:00
Tasks: Drug Design

Coronavirus (COVID-19) outbreak in late 2019 and 2020 comprises a serious and
more likely a pandemic threat worldwide. Given that the disease has not
approved vaccines or drugs up to now, any efforts for drug design and or
clinical trails of old drugs based on their mechanism of action are worthy and
creditable in such circumstances. Experienced docking experiments using the
newly released coordinate structure for COVID-19 protease as a receptor and
thoughtfully selected chemicals among antiviral and antibiotics drugs as
ligands may be leading in this context. We selected nine drugs from HIV-1
protease inhibitors and twenty-one candidates from anti bronchitis drugs based
on their chemical structures and enrolled them in blind and active
site-directed dockings in different modes and in native-like conditions of
interactions. Our findings suggest the binding capacity and the inhibitory
potency of candidates are as follows
Tipranavir>Indinavir>Atazanavir>Darunavir>Ritonavir>Amprenavir for HIV-1
protease inhibitors and Cefditoren>Cefixime>Erythromycin>Clarithromycin for
anti bronchitis medicines. The drugs bioavailability, their hydrophobicity and
the hydrophobic properties of their binding sites and also the rates of their
metabolisms and deactivations in the human body are the next determinants for
their overall effects on viral infections, the net results that should survey
by clinical trials to assess their therapeutic usefulness for coronavirus
infections.
Controllability Issues of Linear Ensemble Systems over Multi-dimensional Parameterization Spaces
Authors: Xudong Chen
Date: 2020-03-10 00:00:00

We address an open problem in ensemble control: Whether there exist controllable linear ensemble systems over multi-dimensional parameterization spaces? We provide a negative result: Any real-analytic linear ensemble system is not $\mathrm{L}^p$-controllable, for $2\leq p \leq \infty$, if its parameterization space contains an open set in $\mathbb{R}^d$ for $d \geq 2$.
Metastatic melanoma-A review of current and future perspective
Authors: Qurat-ul-Ain, Muhammad Ismail
Date: 2020-03-06 00:00:00

Metastatic Melanoma, the fifth most common cancer in the western countries and the most common malignancy diagnosed in United States present itself as the most lethal treatment resistant cancer worldwide. In addition to the reactive oxygen species(ROS), mutations in the genes encoding receptors and non-receptor tyrosine/serene/threonine protein kinases are known to be involved in its etiology. Kinases are molecular players of cell survival, growth, and proliferation and migration that mediate their effects via various signal transduction pathways. A number of such molecular players have been previously found to be mutated and hyper phosphorylated in melanoma. Although, several systemic therapies including cytotoxic chemotherapy, targeted drugs, hormonal therapy, radiation therapy, bio-chemotherapy, and therapies that inhibit negative regulation of immune system have been approved from U. S. Food and Drug Administration (FDA) for metastatic melanoma treatment. However, no systemic therapy has meaningfully changed its survival end points so far and surgery still presents primary treatment option for advanced and metastatic melanomaa due to its highly resistant nature towards systemic drugs, high rate of severe, life-threatening, or fatal side effects, and un satisfactory overall response rate. Therefore, there is still a need to develop therapies that target the unique molecular profile of melanoma tumors.
Instability and fingering of interfaces in growing tissue
Authors: Tobias Büscher, Angel L. Diez, Gerhard Gompper, Jens Elgeti
Date: 2020-03-10 00:00:00
Tasks: Friction

Interfaces in tissues are ubiquitous, both between tissue and environment as well as between populations of different cell types. The propagation of an interface can be driven mechanically. % e.g. by a difference in the respective homeostatic stress of the different cell types. Computer simulations of growing tissues are employed to study the stability of the interface between two tissues on a substrate. From a mechanical perspective, the dynamics and stability of this system is controlled mainly by four parameters of the respective tissues: (i) the homeostatic stress (ii) cell motility (iii) tissue viscosity and (iv) substrate friction. For propagation driven by a difference in homeostatic stress, the interface is stable for tissue-specific substrate friction even for very large differences of homeostatic stress; however, it becomes unstable above a critical stress difference when the tissue with the larger homeostatic stress has a higher viscosity. A small difference in directed bulk motility between the two tissues suffices to result in propagation with a stable interface, even for otherwise identical tissues. Larger differences in motility force, however, result in a finite-wavelength instability of the interface. Interestingly, the instability is apparently bound by nonlinear effects and the amplitude of the interface undulations only grows to a finite value in time.
Pricing Interest Rate Derivatives under Volatility Uncertainty
Authors: Julian Hölzermann
Date: 2020-03-10 00:00:00

In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility.
Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data
Date: 2020-03-10 00:00:00
Tasks: Voice Conversion

This research presents a neural network based voice conversion (VC) model.
While it is a known fact that voiced sounds and prosody are the most important
component of the voice conversion framework, what is not known is their
objective contributions particularly in a noisy and uncontrolled environment.
This model uses a 2-layer feedforward neural network to map the Linear
prediction analysis coefficients of a source speaker to the acoustic vector
space of the target speaker with a view to objectively determine the
contributions of the voiced, unvoiced and supra-segmental components of sounds
to the voice conversion model. Results showed that vowels 'a', 'i', 'o' have
the most significant contribution in the conversion success. The voiceless
sounds were also found to be most affected by the noisy training data. An
average noise level of 40 dB above the noise floor were found to degrade the
voice conversion success by 55.14 percent relative to the voiced sounds. The
result also shows that for cross-gender voice conversion, prosody conversion is
more significant in scenarios where a female is the target speaker.
Equations and Shape of the Optimal Band Strategy
Date: 2020-03-17 00:00:00

We consider the problem of the optimal trading strategy in the presence of a
price predictor, linear trading costs and a quadratic risk control. The
solution is known to be a band system, a policy that induces a no-trading zone
in the positions space. Using a path-integral method introduced in a previous
work, we give equations for the upper and lower edges of this band, and solve
them explicitly in the case of an Ornstein-Uhlenbeck predictor. We then explore
the shape of this solution and derive its asymptotic behavior for large values
of the predictor, without requiring trading costs to be small.
Increased SNR in acousto-optic imaging via coded ultrasound transmission
Date: 2020-04-16 00:00:00

Acousto-optic imaging (AOI) is a non-invasive method that uses acoustic
modulation to map the light fluence inside biological tissue. In many AOI
implementations, ultrasound pulses are used in a time-gated measurement to
perform depth-resolved imaging without the need for mechanical scanning.
However, to achieve high axial resolution, it is required that ultrasound
pulses with few cycles are used, limiting the modulation strength. In this
Letter, we developed a new approach to pulse-based AOI in which a coded
ultrasound transmission is used. In coded-transmission AOI (CT-AOI), one may
achieve an axial resolution that corresponds to a single cycle, but with a
signal-to-noise ratio (SNR) that scales as the square root of the number of
cycles. Using CT-AOI with 79 cycles, we experimentally demonstrate over 4-fold
increase in SNR in comparison to a single-cycle AOI scheme.
Analysis of P-time Event Graphs in (Max,+) and (Min,+) Algebras
Date: 2020-03-10 00:00:00

In this work we investigate the behavior of P-time event graphs, a class of
time Petri nets with nondeterministic timing of places. Our approach is based
on combined linear descriptions in both (max,+) and (min,+) semirings, where
lower bounds on the state vector are (max,+)-linear and upper bounds are
(min,+)-linear. We present necessary and sufficient conditions for the
existence of extremal (fastest and slowest) periodic trajectories that are
derived from the new description. The results are illustrated by a realistic
example of an electroplating process.
Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy
Date: 2020-02-29 00:00:00
Tasks: Transfer Learning

In order to find effective treatments for Alzheimer's disease (AD), we need
to identify subjects at risk of AD as early as possible. To this end, recently
developed disease progression models can be used to perform early diagnosis, as
well as predict the subjects' disease stages and future evolution. However,
these models have not yet been applied to rare neurodegenerative diseases, are
not suitable to understand the complex dynamics of biomarkers, work only on
large multimodal datasets, and their predictive performance has not been
objectively validated. In this work I developed novel models of disease
progression and applied them to estimate the progression of Alzheimer's disease
and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing
visual deficits. My first contribution is a study on the progression of
Posterior Cortical Atrophy, using models already developed: the Event-based
Model (EBM) and the Differential Equation Model (DEM). My second contribution
is the development of DIVE, a novel spatio-temporal model of disease
progression that estimates fine-grained spatial patterns of pathology,
potentially enabling us to understand complex disease mechanisms relating to
pathology propagation along brain networks. My third contribution is the
development of Disease Knowledge Transfer (DKT), a novel disease progression
model that estimates the multimodal progression of rare neurodegenerative
diseases from limited, unimodal datasets, by transferring information from
larger, multimodal datasets of typical neurodegenerative diseases. My fourth
contribution is the development of novel extensions for the EBM and the DEM,
and the development of novel measures for performance evaluation of such
models. My last contribution is the organization of the TADPOLE challenge, a
competition which aims to identify algorithms and features that best predict
the evolution of AD.
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning
Authors: Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau
Date: 2020-03-10 00:00:00
Tasks: Deep Reinforcement Learning, Management, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time. However, this could leak sensitive information about the consumers to the UP or a third-party. Recent works have leveraged the availability of energy storage devices, e.g., a rechargeable battery (RB), in order to provide privacy to the consumers with minimal additional energy cost. In this paper, a privacy-cost management unit (PCMU) is proposed based on a model-free deep reinforcement learning algorithm, called deep double Q-learning (DDQL). Empirical results evaluated on actual SMs data are presented to compare DDQL with the state-of-the-art, i.e., classical Q-learning (CQL). Additionally, the performance of the method is investigated for two concrete cases where attackers aim to infer the actual demand load and the occupancy status of dwellings. Finally, an abstract information-theoretic characterization is provided.
Impact of Temperature and Relative Humidity on the Transmission of COVID-19: A Modeling Study in China and the United States
Authors: Jingyuan Wang, Ke Tang, Kai Feng, Xin Li, Weifeng Lv, Kun Chen, Fei Wang
Date: 2020-03-09 00:00:00
Tasks: regression

Objectives: We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design: A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting: We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants: A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures: Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results: Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions: Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.; an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic.
Phase-based Variant Maximum Likelihood Positioning for Passive UHF-RFID Tags
Date: 2020-03-10 00:00:00
Tasks: Management, TAG

Radio frequency identification (RFID) technology brings tremendous
advancement in Internet-of-Things, especially in supply chain and smart
inventory management. Phase-based passive ultra high frequency RFID tag
localization has attracted great interest, due to its insensitivity to the
propagation environment and tagged object properties compared with the signal
strength based method. In this paper, a phase-based maximum-likelihood tag
positioning estimation is proposed. To mitigate the phase uncertainty, the
likelihood function is reconstructed through trigonometric transformation.
Weights are constructed to reduce the impact of unexpected interference and to
augment the positioning performance. The experiment results show that the
proposed algorithms realize fine-grained tag localization, which achieve
centimeter-level lateral accuracy, and less than 15-centimeters vertical
accuracy along the altitude of the racks.
Data Driven Online Learning of Power System Dynamics
Date: 2020-03-10 00:00:00

With the advancement of sensing and communication in power networks,
high-frequency real-time data from a power network can be used as a resource to
develop better monitoring capabilities. In this work, a systematic approach
based on data-driven operator theoretic methods involving Koopman operator is
proposed for the online identification of power system dynamics. In particular,
a new algorithm is provided, which unlike any previously existing algorithms,
updates the Koopman operator iteratively as new data points are acquired. The
proposed algorithm has three advantages: a) allows for real-time monitoring of
the power system dynamics b) linear power system dynamics (this linear system
is usually in a higher dimensional feature space and is not same as
linearization of the underlying nonlinear dynamics) and c) computationally fast
and less intensive when compared to the popular Extended Dynamic Mode
Decomposition (EDMD) algorithm. The efficiency of the proposed algorithm is
illustrated on an IEEE 9 bus system using synthetic data from the nonlinear
model and on IEEE 39 bus system using synthetic data from the linearized model.