pwc-archive/papers-with-abstracts huggingface dataset:
Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis
Authors: Ruijin Wu, Rahul Prabhu, Aysegul Ozkan, Meera Sitharam
Date: 2020-01-02 00:00:00
Tasks: CPU

Icosahedral viruses have their infectious genome encapsulated by a shell assembled by a multiscale process, starting from an integer multiple of 60 viral capsid or coat protein (VP) monomers. We predict and validate inter-atomic hotspot interactions between VP monomers that are important for the assembly of 3 icosahedral viral capsids: Adeno Associated Virus serotype 2 (AAV2) and Minute Virus of Mice (MVM), both T=1 single stranded DNA viruses, and Bromo Mosaic Virus (BMV), a T=3 single stranded RNA virus. Experimental validation is by in-vitro, site-directed mutagenesis data found in literature. We combine ab-initio predictions at two scales: at the interface-scale, we predict the importance (cruciality) of an interaction for successful subassembly across each interface between VP monomers; and at the capsid-scale, we predict the cruciality of an interface for successful capsid assembly. At the interface-scale, we measure cruciality by changes in the capsid free-energy landscape partition function when an interaction is removed. The partition function computation uses atlases of interface subassembly landscapes, rapidly generated by a novel geometric method and curated opensource software EASAL (efficient atlasing and search of assembly landscapes). At the capsid-scale, cruciality of an interface for successful assembly of the capsid is based on combinatorial entropy. Our study goes from resource-light, multiscale computational predictions of crucial hotspot inter-atomic interactions to validation using data on site-directed mutagenesis' effect on capsid assembly. By reliably and rapidly narrowing down target interactions, (no more than 1.5 hours per interface on a laptop with Intel Core i5-2500K 3.2Ghz CPU and 8GB of RAM) our predictions can inform and reduce time-consuming in-vitro and in-vivo experiments, or more computationally intensive in-silico analyses.
Stability of Multi-Dimensional Switched Systems with an Application to Open Multi-Agent Systems
Authors: Mengqi Xue, Yang Tang, Wei Ren, Feng Qian
Date: 2020-01-02 00:00:00

A multi-dimensional switched system or multi-mode multi-dimensional ($M^3D$) system extends the classic switched system by allowing different subsystem dimensions. The stability problem of the $M^3D$ system, whose state transitions at switching instants can be discontinuous due to the dimension-varying feature, is studied. The discontinuous state transition is formulated by an affine map that captures both the dimension variations and the state impulses, with no extra constraint imposed. In the presence of unstable subsystems, the general criteria featuring a series of Lyapunov-like conditions for the practical and asymptotic stability properties of the $M^3D$ system are provided under the proposed slow/fast transition-dependent average dwell time framework. Then, by considering linear subsystems, we propose a class of parametric multiple Lyapunov functions to verify the obtained Lyapunov-like stability conditions and explicitly reveal a connection between the practical/asymptotic stability property and the non-vanishing/vanishing property of the impulsive effects in the state transition process. Further, the obtained stability results for the $M^3D$ system are applied to the consensus problem of the open multi-agent system (MAS), whose network topology can be switching and size-varying due to the migrations of agents. It shows that through a proper transformation, the seeking of the (practical) consensus performance of the open MAS with disconnected digraphs boils down to that of the (practical) stability property of an $M^3D$ system with unstable subsystems.
On the probability flow in the Stock market I: The Black-Scholes case
Authors: Ivan Arraut, Alan Au, Alan Ching-biu Tse, Joao Alexandre Lobo Marques
Date: 2019-12-20 00:00:00

It is known that the probability is not a conserved quantity in the stock market, given the fact that it corresponds to an open system. In this paper we analyze the flow of probability in this system by expressing the ideal Black-Scholes equation in the Hamiltonian form. We then analyze how the non-conservation of probability affects the stability of the prices of the Stocks. Finally, we find the conditions under which the probability might be conserved in the market, challenging in this way the non-Hermitian nature of the Black-Scholes Hamiltonian.
An FBSDE approach to market impact games with stochastic parameters
Authors: Samuel Drapeau, Peng Luo, Alexander Schied, Dewen Xiong
Date: 2019-12-19 00:00:00

We analyze a market impact game between $n$ risk averse agents who compete for liquidity in a market impact model with permanent price impact and additional slippage. Most market parameters, including volatility and drift, are allowed to vary stochastically. Our first main result characterizes the Nash equilibrium in terms of a fully coupled system of forward-backward stochastic differential equations (FBSDEs). Our second main result provides conditions under which this system of FBSDEs has indeed a unique solution, which in turn yields the unique Nash equilibrium. We furthermore obtain closed-form solutions in special situations and analyze them numerically
Fractional Programming for Robust TX BF Design in Multi-User/Single-Carrier PD-NOMA
Date: 2020-04-25 00:00:00

We present a new Beamforming-based (BB) Multiple-Input Single-Output
(MISO)-Non-orthogonal Multiple Access (NOMA) scheme for Power Domain NOMA
(PD-NOMA), in which the total transmit power consumption is minimized subjected
to prescribed signal-to-interference-plus-noise ratio (SINR) requirements for
each user, and under the assumption that only imperfect channel state
information (CSI) is available at the transmitter. To this end, the fractional
programming (FP)-based quadratic transform is employed to reformulate the
non-convex SINR constraint of the original problem into a tractable quadratic
form, which contains an estimate of the CSI error vector as a parameter. Taking
advantage of the fact that the zero duality gap holds for the non-convex
quadratic problems, a closed-form expression for an estimate of the CSI error
vector is derived, completing the formulation. Finally, a novel iterative
algorithm based on both the herein derived CSI error vector and the
semidefinite relaxation (SDR) technique is contributed, which is shown to
capable of efficiently solving the constrained min-power problem. Simulation
results are given which illustrate the effectiveness of the proposed algorithm,
which is found to sacrifice only small quantities of transmit power in return
for a substantial increase in robustness against CSI imperfection.
Sharing Reserves through HVDC: Potential Cost Savings in the Nordic Countries
Date: 2020-01-02 00:00:00

During summer 2018, the Nordic system's kinetic energy dropped below a critical level. As a consequence, Svenska kraftn\"{a}t, the Swedish transmission system operator (TSO), requested the largest production unit to reduce its power output to guarantee system's security. This action resulted in a deviation from the generation dispatch determined by the market and in high costs for the Nordic TSOs. In this regard, this paper presents a tool for comparing mitigation strategies from an economic point of view and evaluates potential economic benefits of utilizing the Emergency Power Control (EPC) functionality of HVDC lines for the provision of fast reserves as a compliment to Frequency Containment Reserves (FCR). Moreover, the analysis is extended to the years 2020 and 2025 using inertia estimations from the Nordic TSOs. The findings of the paper suggest that the frequency of redispatching actions will increase in the future and that the cost of security for Nordic TSOs could be reduced by 70\% if HVDC links are used for frequency support.
Logical Differencing in Dyadic Network Formation Models with Nontransferable Utilities
Authors: Wayne Yuan Gao, Ming Li, Sheng Xu
Date: 2020-01-03 00:00:00

This paper considers a semiparametric model of dyadic network formation under nontransferable utilities (NTU). NTU arises frequently in real-world social interactions that require bilateral consent, but by its nature induces additive non-separability. We show how unobserved individual heterogeneity in our model can be canceled out without additive separability, using a novel method we call logical differencing. The key idea is to construct events involving the intersection of two mutually exclusive restrictions on the unobserved heterogeneity, based on multivariate monotonicity. We provide a consistent estimator and analyze its performance via simulation, and apply our method to the Nyakatoke risk-sharing networks.
Re-synchronization using the Hand Preceding Model for Multi-modal Fusion in Automatic Continuous Cued Speech Recognition
Date: 2020-02-23 00:00:00
Tasks: Lip Reading, Phoneme Recognition, Position, speech-recognition, Speech Recognition

Cued Speech (CS) is an augmented lip reading complemented by hand coding, and
it is very helpful to the deaf people. Automatic CS recognition can help
communications between the deaf people and others. Due to the asynchronous
nature of lips and hand movements, fusion of them in automatic CS recognition
is a challenging problem. In this work, we propose a novel re-synchronization
procedure for multi-modal fusion, which aligns the hand features with lips
feature. It is realized by delaying hand position and hand shape with their
optimal hand preceding time which is derived by investigating the temporal
organizations of hand position and hand shape movements in CS. This
re-synchronization procedure is incorporated into a practical continuous CS
recognition system that combines convolutional neural network (CNN) with
multi-stream hidden markov model (MSHMM). A significant improvement of about
4.6\% has been achieved retaining 76.6\% CS phoneme recognition correctness
compared with the state-of-the-art architecture (72.04\%), which did not take
into account the asynchrony of multi-modal fusion in CS. To our knowledge, this
is the first work to tackle the asynchronous multi-modal fusion in the
automatic continuous CS recognition.
Judicial Favoritism of Politicians: Evidence from Small Claims Court
Date: 2020-01-31 00:00:00

Multiple studies have documented racial, gender, political ideology, or
ethnical biases in comparative judicial systems. Supplementing this literature,
we investigate whether judges rule cases differently when one of the litigants
is a politician. We suggest a theory of power collusion, according to which
judges might use rulings to buy cooperation or threaten members of the other
branches of government. We test this theory using a sample of small claims
cases in the state of S\~ao Paulo, Brazil, where no collusion should exist. The
results show a negative bias of 3.7 percentage points against litigant
politicians, indicating that judges punish, rather than favor, politicians in
court. This punishment in low-salience cases serves as a warning sign for
politicians not to cross the judiciary when exercising checks and balances,
suggesting yet another barrier to judicial independence in development
settings.
The Lie Detector
Date: 2019-12-13 00:00:00

How many free variables do we really need to build a credible model of a
physical system? Currently there is no systematic approach; we appeal to some
physical principles, tune free variables by comparing with canonical cases, and
hope our real-world applications interpolate between them. In this work we
combine two pioneering and entirely disparate pieces of mathematics: the
century-old techniques of Sophus Lie for solving differential equtions and
recent work initiated by Field's medallist Terence Tao on converting
NP-complete combinatorical problems into neighbouring convex optimisations. We
present a novel and fully systematic procedure for designing models of physical
systems with necessary and just-sufficient complexity, in marked contrast with
the approach to function approximation taken by neural networks and other
current approaches to machine learning. Our methodology replaces the ad-hoc
development of models to recover structure and understanding from
observational, experimental or simulated data. At its core, our method seeks to
find invariant properties of differential equations known as Lie symmetries,
and for this reason we have called our algorithm the Lie Detector.
Performance Evaluation of a Substrate Integrated Waveguide Antenna for Vehicular Networks
Date: 2019-12-26 00:00:00

This paper describes the design and evaluation of a Substrate Integrated
Waveguide (SIW) antenna operating at 2.4 GHz. This antenna was designed as a
possible solution for the implementation of the vehicular networks (VANETs).
The main advantages of the SIW antennas, such as their simplicity, small size
and low cost, make this kind of antennas particularly suitable for using in
wireless nodes where the size is a critical factor. For example, in Vehicular
Networks (VANETs) which is one of the most promising wireless communication
systems. We present in this paper the design of the SIW antenna using the
software ANSYS HFSS as well as the results of the assessment of some parameters
like the radiation pattern, operation frequency and bandwidth. Also, we present
the simulation of a VANET that include the designed parameters of the SIW
antenna in order to evaluate its integration into the vehicular nodes. This
simulation was performed in the NS2 simulator and some network performance
metrics, like packet delay and the packet loss rate, were evaluated. Other
additional software, for example MOVE and SUMO, were also used to generate the
routes and the movement of the vehicles
Revenue Analysis of Stationary and Transportable Battery Storage for Power Systems with High Penetration of Renewable Sources: A Market Participant Perspective
Authors: Zhongyang Zhao, Caisheng Wang, Masoud H. Nazari
Date: 2020-01-06 00:00:00

The power system needs more transmission capacity to deal with the increasing integration of renewable energy resources. The battery energy storage systems (BESSs) are effective to enhance the grid capacity and relieve the transmission congestion. A comprehensive revenue analysis of BESSs is critical for market participants to install such systems in a market-based power system. Taking PJM as an example, this paper carries out a thorough revenue analysis for the entire system. Highly profitable nodes in the system are revealed and characterized for BESS participants. A comparison study of stationary and transportable BESSs shows the transportable energy storage can produce higher potential revenue in the energy and regulation markets. Based on the results of the revenue analysis and characterization of commercial pricing nodes, an optimal placement algorithm is proposed for finding the profitable sites for market participants to install BESSs in the system and the algorithm is validated with real PJM market data.
Line-based compressive sensing for low-power visual applications
Date: 2019-12-31 00:00:00
Tasks: Compressive Sensing, Decoder

In this paper, a Line based Compressive Sensing (LCS) scheme is discussed and
proposed for low power visual applications, in which image acquisition is
performed in a line-by-line manner at the encoder side using same measurement
operator. Such approach reduces the computational burden and makes the
implementation process easier (at encoder) plus provides better and more
efficient initial reconstruction (at decoder) than other CS techniques. The
reconstruction algorithm is based on the combination of the conventional
augmented Lagrangian method with variable splitting and alternating direction
method and is referred as TV-AL3. The simulation results show that the proposed
line based CS scheme not only improves the quality of image by 1dB to ~3dB at
various subrates, when compared to the conventional CS schemes but also reduces
the computational complexity at the encoder side.
False Data Injection Attacks and the Distributed Countermeasure in DC Microgrids
Authors: Mengxiang Liu, Peng Cheng, Chengcheng Zhao, Ruilong Deng, Wenhai Wang, Jiming Chen
Date: 2020-01-07 00:00:00

In this paper, we consider a hierarchical control based DC microgrid (DCmG) equipped with unknown input observer (UIO) based detectors, where the potential false data injection (FDI) attacks and the distributed countermeasure are investigated. First, we find that the vulnerability of the UIO-based detector originates from the lacked knowledge of true unknown inputs. Zero trace stealthy (ZTS) attacks can be launched by secretly faking the unknown inputs, under which the detection residual will not be altered, and the impact on the DCmG in terms of voltage balancing and current sharing is theoretically analyzed. Then, to mitigate the ZTS attack, we propose an automatic and timely countermeasure based on the average point of common coupling (PCC) voltage obtained from the dynamic average consensus (DAC) estimator. The integrity of the communicated data utilized in DAC estimators is guaranteed via UIO-based detectors, where the DAC parameters are perturbed in a fixed period to be concealed from attackers. Finally, the detection and mitigation performance of the proposed countermeasure is rigorously investigated, and extensive simulations are conducted in Simulink/PLECS to validate the theoretical results.
Flexible Architecture for Real-time Processing of Multiple Video Signals
Date: 2019-12-29 00:00:00

Simultaneous processing of multiple video sources requires each pixel in a
frame from a video source to be processed synchronously with the pixels at the
same spatial positions in corresponding frames from the other video sources.
However, simultaneous processing is challenging as corresponding frames from
different video signals provided by multiple sources have time-varying delay
because of the electrical and mechanical restrictions inside the video sources
hardware that cause deviation in the corresponding frame rates. Researchers
overcome the aforementioned challenges either by utilizing ready-made video
processing systems or designing and implementing a custom system tailored to
their specific application. These video processing systems lack flexibility in
handling different applications requirements such as the required number of
video sources and outputs, video standards, or frame rates of the input/output
videos. In this paper, we present a design for a flexible simultaneous video
processing architecture that is suitable for various applications. The proposed
architecture is upgradeable to deal with multiple video standards, scalable to
process/produce a variable number of input/output videos, and compatible with
most video processors. Moreover, we present in details the analog/digital
mixed-signals and power distribution considerations used in designing the
proposed architecture. As a case study application of the proposed flexible
architecture, we utilized the architecture for a realization of a simultaneous
video processing system that performs video fusion from visible and
near-infrared video sources in real time. We make available the source files of
the hardware design along with the bill of material (BOM) of the case study to
be a reference for researchers who intend to design and implement simultaneous
multi-video processing systems.
Event-based Processing of Single Photon Avalanche Diode Sensors
Date: 2019-12-25 00:00:00

Single Photon Avalanche Diode sensor arrays operating in direct time of
flight mode can perform 3D imaging using pulsed lasers. Operating at high frame
rates, SPAD imagers typically generate large volumes of noisy and largely
redundant spatio-temporal data. This results in communication bottlenecks and
unnecessary data processing. In this work, we propose a set of neuromorphic
processing solutions to this problem. By processing the SPAD generated
spatio-temporal patterns locally and in an event-based manner, the proposed
methods reduce the size of output data transmitted from the sensor by orders of
magnitude while increasing the utility of the output data in the context of
challenging recognition tasks. To demonstrate these results, the first large
scale complex SPAD imaging dataset is presented involving high-speed
view-invariant recognition of airplanes with background clutter. The
frame-based SPAD imaging dataset is converted via several alternative methods
into event-based data streams and processed using a range of feature extractor
networks and pooling methods. The results of the event-based processing methods
are compared to processing the original frame-based dataset via frame-based but
otherwise identical architectures. The results show the event-based methods are
superior to the frame-based approach both in terms of classification accuracy
and output data-rate.
Optimal insurance contract with benefits in kind under adverse selection
Date: 2020-04-28 00:00:00

An income loss can have a negative impact on households, forcing them to
reduce their consumption of some staple goods. This can lead to health issues
and, consequently, generate significant costs for society. We suggest that
consumers can, to prevent these negative consequences, buy insurance to secure
sufficient consumption of a staple good if they lose part of their income. We
develop a two-period/two-good principal-agent problem with adverse selection
and endogenous reservation utility to model insurance with in-kind benefits.
This model allows us to obtain semi-explicit solutions for the insurance
contract and is applied to the context of fuel poverty. For this application,
our model allows to conclude that, even in the least efficient scenario from
the households point of view, i.e., when the insurance is provided by a
monopoly, this mechanism improves significantly the living conditions of the
riskiest households by ensuring them a sufficient consumption of energy.
A unified energy optimality criterion predicts human navigation paths and speeds
Authors: Geoffrey L. Brown, Nidhi Seethapathi, Manoj Srinivasan
Date: 2020-01-07 00:00:00

Navigating our physical environment requires changing directions and turning. Despite its ecological importance, we do not have a unified theoretical account of non-straight-line human movement. Here, we present a unified optimality criterion that predicts disparate non-straight-line walking phenomena, with straight-line walking as a special case. We first characterized the metabolic cost of turning, deriving the cost landscape as a function of turning radius and rate. We then generalized this cost landscape to arbitrarily complex trajectories, allowing the velocity direction to deviate from body orientation (holonomic walking). We used this generalized optimality criterion to mathematically predict movement patterns in multiple contexts of varying complexity: walking on prescribed paths, turning in place, navigating an angled corridor, navigating freely with end-point constraints, walking through doors, and navigating around obstacles. In these tasks, humans moved at speeds and paths predicted by our optimality criterion, slowing down to turn and never using sharp turns. We show that the shortest path between two points is, counterintuitively, often not energy optimal, and indeed, humans do not use the shortest path in such cases. Thus, we have obtained a unified theoretical account that predicts human walking paths and speeds in diverse contexts. Our model focuses on walking in healthy adults; future work could generalize this model to other human populations, other animals, and other locomotor tasks.