pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Team Strategies for Multi-player Perimeter-Defense Games
Date: 2019-12-25 00:00:00

This paper studies a variant of the multi-player reach-avoid game played
between intruders and defenders with applications to perimeter defense. The
intruder team tries to score by sending as many intruders as possible to the
target area, while the defender team tries to minimize this score by
intercepting them. Finding the optimal strategies of the game is challenging
due to the high dimensionality of the joint state space, and the existing works
have proposed approximation methods to reduce the design of the defense
strategy into assignment problems. However they suffer from either suboptimal
defender performance or computational complexity. Based on a novel
decomposition method, this paper proposes a scalable (polynomial-time)
assignment algorithm that accommodates cooperative behaviors and outperforms
the existing defense strategies. For a certain class of initial configurations,
we derive the exact score by showing that the lower bound provided by the
intruder team matches the upper bound provided by the defender team, which also
proves the optimality of the team strategies.
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification
Date: 2020-05-28 00:00:00
Proceeding: L4DC 2020 6

The goal of this paper is to analyze Long Short Term Memory (LSTM) neural
networks from a dynamical system perspective. The classical recursive equations
describing the evolution of LSTM can be recast in state space form, resulting
in a time-invariant nonlinear dynamical system. A sufficient condition
guaranteeing the Input-to-State (ISS) stability property of this class of
systems is provided. The ISS property entails the boundedness of the output
reachable set of the LSTM. In light of this result, a novel approach for the
safety verification of the network, based on the Scenario Approach, is devised.
The proposed method is eventually tested on a pH neutralization process.
A Dynamic Motion Planning Framework for Autonomous Driving in Urban Environments
Date: 2019-12-10 00:00:00
Tasks: Autonomous Driving, Motion Planning, Trajectory Planning

Abstract: we present a framework for robust autonomous driving motion
planning system in urban environments which includes trajectory refinement,
trajectory interpolation, avoidance of static and dynamic obstacles, and
trajectory tracking. Given road centerline, our approach smoother the original
line via cubic spline. Fifth order Bezier curve is then used to generate more
human-like trajectories that guarantee at least second order continuity and
curvature continuity. Dynamic trajectory planning task is decoupled into
lateral spatial and longitudinal velocity planning problems. A bunch of
candidate trajectory sets are generated and evaluated by an object function
which considers kinematic feasibility, trajectory smoothness, driving comfort
and collision-checking. Meanwhile, an LQG controller is used to generate
longitudinal velocity profile to ensure safety and comfort. After that, spatial
and velocity profiles are transformed into commands executed by lateral
steering and longitudinal acceleration controllers. This framework is validated
within a simulation study and has been deployed on our autonomous vehicle shown
in Fig.1 that has travelled thousands of miles in urban environments.
Inverse LDM' and LU Factorizations of a Partitioned Matrix with the Square-root and Division Free Version for V-BLAST
Date: 2019-12-10 00:00:00

This letter proposes the inverse LDM' and LU factorizations of a matrix
partitioned into 2x2 blocks, which include the square-root and division free
version. The proposed squareroot and division free inverse LDM' factorization
is applied to compute the initial estimation error covariance matrix Q for the
recursive V-BLAST algorithm, which can save K-1 divisions (where K is the
number of transmit antennas), and requires about the same computational
complexity as the corresponding algorithm to compute Q in the existing
recursive V-BLAST algorithm [8], [9]. The proposed square-root and division
free inverse LDM' factorization can also be applied to propose the square-root
and division free implementation for the squareroot V-BLAST algorithm in [5],
where the wide-sense Givens rotation in [14] is utilized. With respect to the
existing squareroot V-BLAST algorithms [5], [6], the proposed square-root and
division free V-BLAST algorithm requires about the same computational
complexity, and can avoid the square-root and division operations.
151 Estrategias de Trading (151 Trading Strategies)
Authors: Zura Kakushadze, Juan Andrés Serur
Date: 2019-11-14 00:00:00
Tasks: Descriptive, Math

This book, which is in Spanish, provides detailed descriptions, including over 550 mathematical formulas, for over 150 trading strategies across a host of asset classes (and trading styles). This includes stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility (as an asset class), real estate, distressed assets, cash, cryptocurrencies, miscellany (such as weather, energy, inflation), global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms (such as artificial neural networks, Bayes, k-nearest neighbors). We also give: source code for illustrating out-of-sample backtesting with explanatory notes; around 2,000 bibliographic references; and over 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical. ----- Este libro proporciona descripciones detalladas, que incluyen m\'as de 550 f\'ormulas matem\'aticas, para m\'as de 150 estrategias de trading para una gran cantidad de clases de activos y estilos de trading. Esto incluye acciones, opciones, bonos (renta fija), futuros, ETFs, \'indices, commodities, divisas, bonos convertibles, activos estructurados, volatilidad (como clase de activos), bienes inmuebles, activos en distress, efectivo, criptomonedas, miscel\'aneos (como clima, energ\'ia, inflaci\'on), macro global, infraestructura y arbitraje impositivo. Algunas estrategias se basan en algoritmos de aprendizaje autom\'atico (como redes neuronales artificiales, Bayes, k vecinos m\'as cercanos). El libro tambi\'en incluye c\'odigo para backtesting fuera de la muestra con notas explicativas; cerca de 2,000 referencias bibliogr\'aficas; m\'as de 900 t\'erminos que comprenden el glosario, acr\'onimos y definiciones matem\'aticas. La presentaci\'on pretende ser descriptiva y pedag\'ogica.
Joint Estimation of Operational Topology and Outages for Unbalanced Power Distribution Systems
Date: 2019-12-10 00:00:00

An electric power distribution system is operated in several distinct radial
topologies by opening and closing of system's sectionalizing and tie switches.
The estimation of the system's current operational topology is a precursor to
implementing any optimal control actions (during normal operation) or
restorative actions (during outage condition). This paper presents a
mathematical programming approach to estimate the operational topology of a
three-phase unbalanced power distribution system under both outage and normal
operating conditions. Specifically, a minimum weighted least absolute value
estimator is proposed that uses the line flow measurements,
historical/forecasted load data, and ping measurements and solves a
mixed-integer linear program (MILP) to estimate the operational topology and
any outaged sections simultaneously. We thoroughly validate the accuracy of the
proposed approach using IEEE 123-bus and a 1069-bus three-phase multi-feeder
test system with the help of numerous simulations. It is observed that the
approach performs well even at high percentages of measurement errors.
Maximum Average Entropy-Based Quantization of Local Observations for Distributed Detection
Authors: Muath A. Wahdan, Mustafa A. Altınkaya
Date: 2019-12-10 00:00:00
Tasks: Quantization

In a wireless sensor network, multilevel quantization is necessary in order to find a compromise between the smallest possible power consumption of the sensors and the detection performance at the fusion center (FC). The general methodology is using distance measures such as J-divergence and Bhattacharyya distance in this quantization. This work proposes a different approach which is based on maximizing the average output entropy of the sensors under both hypotheses and utilizes it in a Neyman-Pearson criterion based distributed detection scheme in order to detect a point source. The receiver operating characteristics of the proposed maximum average entropy (MAE) method in quantizing sensor outputs was obtained for multilevel quantization both when the sensor outputs are available error-free at the FC and when non-coherent M-ary frequency shift keying communication is used for transmitting MAE based multilevel quantized sensor outputs over a Rayleigh fading channel. The simulation studies show the success of the MAE both in the cases of isolated error-free fusion and in the case where the effect of the wireless channel is incorporated. As expected the performance gets better as the level of quantization increases and with six-level quantization it approaches the performance of non-quantized data transmission.
Market Price of Trading Liquidity Risk and Market Depth
Authors: Masaaki Kijima, Christopher Ting
Date: 2019-12-10 00:00:00

Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear function of the order flow in Kyle (1985) for informed traders. However, when traders are not as asymmetrically informed, an S-shape function of the order flow is obtained. We perform an empirical intra-day analysis on Nikkei futures to quantify the price impact of order flow and compare our results with industry's heuristic price impact functions. Our model of order flow yields a rich framework for not only to estimate the liquidity risk parameters, but also to provide a plausible cause of why volatility and correlation are stochastic in nature. Finally, we find that the market depth encapsulates the market price of liquidity risk.
Animal daily mobility patterns analysis using resting event networks
Date: 2019-12-10 00:00:00

Characterizing the movement patterns of animals is crucial to improve our understanding of their behavior and thus develop adequate conservation strategies. Such investigations, which could not have been implemented in practice only a few years ago, have been facilitated through the recent advances in tracking methods that enable researchers to study animal movement at an unprecedented spatio-temporal resolution. However, the identification and extraction of patterns from spatio-temporal trajectories is still a general problem that has relevance for many applications. Here, we rely on the concept of resting event networks to identify the presence of daily mobility patterns in animal spatio-temporal trajectories. We illustrate our approach by analyzing spatio-temporal trajectories of several fish species in a large hydropeaking river.
Exact distributions for stochastic models of gene expression with arbitrary regulation
Date: 2019-12-10 00:00:00

Stochasticity in gene expression can result in fluctuations in gene product
levels. Recent experiments indicated that feedback regulation plays an
important role in controlling the noise in gene expression. A quantitative
understanding of the feedback effect on gene expression requires analysis of
the corresponding stochastic model. However, for stochastic models of gene
expression with general regulation functions, exact analytical results for gene
product distributions have not been given so far. Here, we propose a technique
to solve a generalized ON-OFF model of stochastic gene expression with
arbitrary (positive or negative, linear or nonlinear) feedbacks including
posttranscriptional or posttranslational regulation. The obtained results,
which generalize results obtained previously, provide new insights into the
role of feedback in regulating gene expression. The proposed analytical
framework can easily be extended to analysis of more complex models of
stochastic gene expression.
Development and Evaluation of Video Recordings for the OLSA Matrix Sentence Test
Authors: Gerard Llorach, Frederike Kirschner, Giso Grimm, Melanie A. Zokoll, Kirsten C. Wagener, Volker Hohmann
Date: 2019-12-10 00:00:00
Tasks: Sentence

One of the established multi-lingual methods for testing speech intelligibility is the matrix sentence test (MST). Most versions of this test are designed with audio-only stimuli. Nevertheless, visual cues play an important role in speech intelligibility, mostly making it easier to understand speech by speechreading. In this work we present the creation and evaluation of dubbed videos for the Oldenburger female MST (OLSA). 28 normal-hearing participants completed test and retest sessions with conditions including audio and visual modalities, speech in quiet and noise, and open and closed-set response formats. The levels to reach 80% sentence intelligibility were measured adaptively for the different conditions. In quiet, the audiovisual benefit compared to audio-only was 7 dB in sound pressure level (SPL). In noise, the audiovisual benefit was 5 dB in signal-to-noise ratio (SNR). Speechreading scores ranged from 0% to 84% speech reception in visual-only sentences, with an average of 50% across participants. This large variability in speechreading abilities was reflected in the audiovisual speech reception thresholds (SRTs), which had a larger standard deviation than the audio-only SRTs. Training and learning effects in audiovisual sentences were found: participants improved their SRTs by approximately 3 dB SNR after 5 trials. Participants retained their best scores on a separate retest session and further improved their SRTs by approx. -1.5 dB.
Vigilance Overload Measured by Computerized Mackworth Clock Test
Date: 2019-11-21 00:00:00

This paper studied the change of vigilance based on stimulus coming
consecutively using the computerized version of the Mackworth Clock Test run
from PsyToolkit website. 7 participants (16.57 +/-1 years old, 2 males),
performed 10 consecutive trials in order to measure whether or not it is a
realistic goal for high school students to display the level of vigilance
expected from them in class. Success percentages were calculated by dividing
the number of correct jumps to the total number of jumps. The results indicated
that while the average success percentage for all subjects remained relatively
stable over the 10 trials (79% +/-7%), success percentages drop relatively as
the number of jumps increase. Success rate dropped from 90% (2 jumps) to 70% (7
jumps). We conclude that there is an upper limit of vigilance that should be
expected from students when they are exposed to more than 4 randomly occurring
attention requiring task within a minute.
Navigating in Virtual Reality using Thought: The Development and Assessment of a Motor Imagery based Brain-Computer Interface
Date: 2019-12-10 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Motor Imagery, Navigate

Brain-computer interface (BCI) systems have potential as assistive
technologies for individuals with severe motor impairments. Nevertheless,
individuals must first participate in many training sessions to obtain adequate
data for optimizing the classification algorithm and subsequently acquiring
brain-based control. Such traditional training paradigms have been dubbed
unengaging and unmotivating for users. In recent years, it has been shown that
the synergy of virtual reality (VR) and a BCI can lead to increased user
engagement. This study created a 3-class BCI with a rather elaborate EEG signal
processing pipeline that heavily utilizes machine learning. The BCI initially
presented sham feedback but was eventually driven by EEG associated with motor
imagery. The BCI tasks consisted of motor imagery of the feet and left and
right hands, which were used to navigate a single-path maze in VR. Ten of the
eleven recruited participants achieved online performance superior to chance (p
< 0.01), while the majority successfully completed more than 70% of the
prescribed navigational tasks. These results indicate that the proposed
paradigm warrants further consideration as neurofeedback BCI training tool. A
paradigm that allows users, from their perspective, control from the outset
without the need for prior data collection sessions.
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Authors: Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, Tucker Hybinette Balch
Date: 2019-12-10 00:00:00

Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing. Furthermore, simulation is important for validation of hand-coded trading strategies and for testing hypotheses about market structure. A challenge, however, concerns the robustness of policies validated in simulation because the simulations lack fidelity. In fact, researchers have shown that many market simulation approaches fail to reproduce statistics and stylized facts seen in real markets. As a step towards addressing this we surveyed the literature to collect a set of reference metrics and applied them to real market data and simulation output. Our paper provides a comprehensive catalog of these metrics including mathematical formulations where appropriate. Our results show that there are still significant discrepancies between simulated markets and real ones. However, this work serves as a benchmark against which we can measure future improvement.
Venue-based HIV testing at sex work hotspots to reach adolescent girls and young women living with HIV: a cross-sectional study in Mombasa, Kenya
Authors: Huiting Ma, Linwei Wang, Peter Gichangi, Vernon Mochache, Griffins Manguro, Helgar K Musyoki, Parinita Bhattacharjee, François Cholette, Paul Sandstrom, Marissa L Becker, Sharmistha Mishra
Date: 2019-12-10 00:00:00
Tasks: Sensitivity

Background: We estimated the potential number of newly diagnosed HIV infections among adolescent girls and young women (AGYW) using a venue-based approach to HIV testing at sex work hotspots. Methods: We used hotspot enumeration and cross-sectional bio-behavioural survey data from the 2015 Transitions Study of AGYW aged 14-24 years who frequented hotspots in Mombasa, Kenya. We compared the HIV cascade among AGYW who sell sex (YSW, N=408) versus those who do not (NSW, N=891); and triangulated the potential (100% test acceptance and accuracy) and feasible (accounting for test acceptance and sensitivity) number of AGYW that could be newly diagnosed via hotspot-based HIV rapid testing in Mombasa. We identified the profile of AGYW recently tested for HIV (in the past year) using multivariable logistic regression. Results: N=37/365 (10.1%) YSW and N=30/828 (3.6%) NSW were living with HIV, of whom 27.0% (N=10/37) and 30.0% (N=9/30) were diagnosed and aware (p=0.79). Rapid test acceptance was 89.3% and sensitivity was 80.4%. Hotspot enumeration estimated 15,635 (range: 12,172-19,097) AGYW in hotspots in Mombasa. The potential and feasible number of new diagnosis were 627 (310-1,081), and 450 (223-776), respectively. Thus, hotspot-based testing could feasibly reduce the undiagnosed fraction from 71.6% to 20.2%. The profile of AGYW who recently tested was similar among YSW and NSW. YSW were two-fold more likely to report a recent HIV test after adjusting for other determinants [odds ratio (95% CI): 2.1 (1.6-3.1)]. Conclusion: Reaching AGYW via hotspot-based HIV testing could fill gaps left by traditional, clinic-based HIV prevention and testing services.
Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure
Authors: Ahmad Mayeli, Obada Al Zoubi, Masaya Misaki, Jennifer L. Stewart, Vadim Zotev, Qingfei Luo, Raquel Phillips, Stefan Fischer, Marcus Goetz, Martin P. Paulus, Hazem Refai, Jerzy Bodurka
Date: 2019-12-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects.
Outage Detection in Partially Observable Distribution Systems using Smart Meters and Generative Adversarial Networks
Date: 2019-12-10 00:00:00
Tasks: Generative Adversarial Network

In this paper, we present a novel data-driven approach to detect outage
events in partially observable distribution systems by capturing the changes in
smart meters' (SMs) data distribution. To achieve this, first, a breadth-first
search (BFS)-based mechanism is proposed to decompose the network into a set of
zones that maximize outage location information in partially observable
systems. Then, using SM data in each zone, a generative adversarial network
(GAN) is designed to implicitly extract the temporal-spatial behavior in normal
conditions in an unsupervised fashion. After training, an anomaly scoring
technique is leveraged to determine if real-time measurements indicate an
outage event in the zone. Finally, to infer the location of the outage events
in a multi-zone network, a zone coordination process is proposed to take into
account the interdependencies of intersecting zones. We have provided
analytical guarantees of performance for our algorithm using the concept of
entropy, which is leveraged to quantify outage location information in
multi-zone grids. The proposed method has been tested and verified on
distribution feeder models with real SM data.