pwc-archive/papers-with-abstracts huggingface dataset:
Exact Replication of the Best Rebalancing Rule in Hindsight
Authors: Alex Garivaltis
Date: 2018-10-05 00:00:00

This paper prices and replicates the financial derivative whose payoff at $T$ is the wealth that would have accrued to a $\$1$ deposit into the best continuously-rebalanced portfolio (or fixed-fraction betting scheme) determined in hindsight. For the single-stock Black-Scholes market, Ordentlich and Cover (1998) only priced this derivative at time-0, giving $C_0=1+\sigma\sqrt{T/(2\pi)}$. Of course, the general time-$t$ price is not equal to $1+\sigma\sqrt{(T-t)/(2\pi)}$. I complete the Ordentlich-Cover (1998) analysis by deriving the price at any time $t$. By contrast, I also study the more natural case of the best levered rebalancing rule in hindsight. This yields $C(S,t)=\sqrt{T/t}\cdot\,\exp\{rt+\sigma^2b(S,t)^2\cdot t/2\}$, where $b(S,t)$ is the best rebalancing rule in hindsight over the observed history $[0,t]$. I show that the replicating strategy amounts to betting the fraction $b(S,t)$ of wealth on the stock over the interval $[t,t+dt].$ This fact holds for the general market with $n$ correlated stocks in geometric Brownian motion: we get $C(S,t)=(T/t)^{n/2}\exp(rt+b'\Sigma b\cdot t/2)$, where $\Sigma$ is the covariance of instantaneous returns per unit time. This result matches the $\mathcal{O}(T^{n/2})$ "cost of universality" derived by Cover in his "universal portfolio theory" (1986, 1991, 1996, 1998), which super-replicates the same derivative in discrete-time. The replicating strategy compounds its money at the same asymptotic rate as the best levered rebalancing rule in hindsight, thereby beating the market asymptotically. Naturally enough, we find that the American-style version of Cover's Derivative is never exercised early in equilibrium.
Rate-Loss Mitigation for a Millimeter-Wave Beamspace MIMO Lens Antenna Array System Using a Hybrid Beam-Selection Scheme
Date: 2019-04-17 00:00:00
Tasks: Quantization

The rate loss is an important issue for multi-user beamspace multiple-input
multiple-output (MIMO) millimeter-wave systems. In such systems, multi-cluster
channel propagation leads as a function of the quantization error and, as a
result, each active user suffers a significant rate loss. Particularly, a
conventional single beam selection (SBS) scheme is used in an equivalent
beamspace MIMO channel to reduce the required number of radio frequency (RF)
chains, but this scheme does not properly mitigate the quantization error due
to the limited number of the channel propagation clusters per-user. In
addition, optimally selected beams or RF chains are operated at the same number
of the precoded data streams. Consequently, each active user inherently loses a
huge number of data streams utilizing the conventional SBS scheme. Therefore,
we propose a hybrid beam selection (HBS) scheme in an equivalent beamspace MIMO
channel to implement a multiple beam or RF chain group selection opportunity
and choose a reliable channel propagation cluster per-active-user, which
directly affects to mitigate the quantization error. The numerical result is
verified by computer simulations in terms of a millimeter-wave MIMO downlink
channel environment.
Comment on "Analysis of a Charge-Pump PLL: A New Model" by M. van Paemel
Date: 2019-02-23 00:00:00

In this short communication we comment on the non-linear mathematical model
of CP-PLL introduced by V.Paemel. We reveal and obviate shortcomings in the
model.
Diffusive Molecular Communication in a Biological Spherical Environment with Partially Absorbing Boundary
Date: 2018-11-13 00:00:00

Diffusive molecular communication (DMC) is envisioned as a promising approach
to help realize healthcare applications within bounded biological environments.
In this paper, a DMC system within a biological spherical environment is
considered, inspired by bounded biological sphere-like structures throughout
the body. As a biological environment, it is assumed that the inner surface of
the sphere's boundary is fully covered by biological receptors that may
irreversibly react with hitting molecules. Moreover, information molecules
diffusing in the sphere may undergo a degradation reaction and be transformed
to another molecule type. Concentration Green's function (CGF) of diffusion
inside this environment is analytically obtained in terms of a convergent
infinite series. By employing the obtained CGF, the information channel between
transmitter and transparent receiver of DMC in this environment is
characterized. Interestingly, it is revealed that the information channel is
reciprocal, i.e., interchanging the position of receiver and transmitter does
not change the information channel. Our results indicate that the conventional
simplifying assumption that the environment is unbounded may lead to an
inaccurate characterization in such biological environments.
Modeling overland flow from local inflows in almost no-time, using Self Organizing Maps
Authors: Leitao Joao P., Zaghloul Mohamed, Moosavi Vahid
Date: 2018-09-23 00:00:00
Tasks: Decision Making

Physically-based overland flow models are computationally demanding,
hindering their use for real-time applications. Therefore, the development of
fast (and reasonably accurate) overland flow models is needed if they are to be
used to support flood mitigation decision making. In this study, we investigate
the potential of Self-Organizing Maps to rapidly generate water depth and flood
extent results. To conduct the study, we developed a flood-simulation specific
SOM, using cellular automata flood model results and a synthetic DEM and inflow
hydrograph. The preliminary results showed that water depth and flood extent
results produced by the SOM are reasonably accurate and obtained in a very
short period of time. Based on this, it seems that SOMs have the potential to
provide critical flood information to support real-time flood mitigation
decisions. The findings presented would however require further investigations
to obtain general conclusions; these further investigations may include the
consideration of real terrain representations, real water supply networks and
realistic inflows from pipe bursts.
Phase diagram for a logistic system under bounded stochasticity
Date: 2019-02-16 00:00:00

Extinction is the ultimate absorbing state of any stochastic birth-death
process, hence the time to extinction is an important characteristic of any
natural population. Here we consider logistic and logistic-like systems under
the combined effect of demographic and bounded environmental stochasticity.
Three phases are identified: an inactive phase where the mean time to
extinction $T$ increases logarithmically with the initial population size, an
active phase where $T$ grows exponentially with the carrying capacity $N$, and
temporal Griffiths phase, with power-law relationship between $T$ and $N$. The
system supports an exponential phase only when the noise is bounded, in which
case the continuum (diffusion) approximation breaks down within the Griffiths
phase. This breakdown is associated with a crossover between qualitatively
different survival statistics and decline modes. To study the power-law phase
we present a new WKB scheme which is applicable both in the diffusive and in
the non-diffusive regime.
A Stochastic model for dynamics of FtsZ filaments and the formation of Z-ring
Authors: Arabind Swain, A. V. Anil Kumar, Sumedha
Date: 2018-10-08 00:00:00

Understanding the mechanisms responsible for the formation and growth of FtsZ polymers and their subsequent formation of the $Z$-ring is important for gaining insight into the cell division in prokaryotic cells. In this work, we present a minimal stochastic model that qualitatively reproduces {\it in vitro} observations of polymerization, formation of dynamic contractile ring that is stable for a long time and depolymerization shown by FtsZ polymer filaments. In this stochastic model, we explore different mechanisms for ring breaking and hydrolysis. In addition to hydrolysis, which is known to regulate the dynamics of other tubulin polymers like microtubules, we find that the presence of the ring allows for an additional mechanism for regulating the dynamics of FtsZ polymers. Ring breaking dynamics in the presence of hydrolysis naturally induce rescue and catastrophe events in this model irrespective of the mechanism of hydrolysis.
Phrase-Based Attentions
Authors: Nguyen Phi Xuan, Joty Shafiq
Date: 2018-09-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, Translation

Most state-of-the-art neural machine translation systems, despite being
different in architectural skeletons (e.g. recurrence, convolutional), share an
indispensable feature: the Attention. However, most existing attention methods
are token-based and ignore the importance of phrasal alignments, the key
ingredient for the success of phrase-based statistical machine translation. In
this paper, we propose novel phrase-based attention methods to model n-grams of
tokens as attention entities. We incorporate our phrase-based attentions into
the recently proposed Transformer network, and demonstrate that our approach
yields improvements of 1.3 BLEU for English-to-German and 0.5 BLEU for
German-to-English translation tasks on WMT newstest2014 using WMT'16 training
data.
Classifying Financial Markets up to Isomorphism
Date: 2020-07-24 00:00:00

Two markets should be considered isomorphic if they are financially
indistinguishable. We define a notion of isomorphism for financial markets in
both discrete and continuous time. We then seek to identify the distinct
isomorphism classes, that is to classify markets.
  We classify complete one-period markets. We define an invariant of continuous
time complete markets which we call the absolute market price of risk. This
invariant plays a role analogous to the curvature in Riemannian geometry. We
classify markets when the absolute market price of risk is deterministic.
  We show that, in general, markets with non-trivial automorphism groups admit
mutual fund theorems. We prove a number of such theorems.
The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation
Date: 2018-10-08 00:00:00

In this paper we consider the properties of the Pesaran (2004, 2015a) CD test for cross-section correlation when applied to residuals obtained from panel data models with many estimated parameters. We show that the presence of period-specific parameters leads the CD test statistic to diverge as length of the time dimension of the sample grows. This result holds even if cross-section dependence is correctly accounted for and hence constitutes an example of the Incidental Parameters Problem. The relevance of this problem is investigated both for the classical Time Fixed Effects estimator as well as the Common Correlated Effects estimator of Pesaran (2006). We suggest a weighted CD test statistic which re-establishes standard normal inference under the null hypothesis. Given the widespread use of the CD test statistic to test for remaining cross-section correlation, our results have far reaching implications for empirical researchers.
Iterative Decision Feedback Equalization Using Online Prediction
Date: 2019-08-22 00:00:00
Tasks: Prediction

In this article, a new category of soft-input soft-output (SISO) minimum-mean
square error (MMSE) finite-impulse response (FIR) decision feedback equalizers
(DFEs) with iteration-wise static filters (i.e. iteration variant) is
investigated.
  It has been recently shown that SISO MMSE DFE with dynamic filters (i.e.
time-varying) reaches very attractive operating points for high-data rate
applications, when compared to alternative turbo-equalizers of the same
category, thanks to sequential estimation of data symbols [1].
  However the dependence of filters on the feedback incurs high amount of
latency and computational costs, hence SISO MMSE DFEs with static filters
provide an attractive alternative for computational complexity-performance
trade-off.
  However, the latter category of receivers faces a fundamental design issue on
the estimation of the decision feedback reliability for filter computation.
  To address this issue, a novel approach to decision feedback reliability
estimation through online prediction is proposed and applied for SISO FIR DFE
with either a posteriori probability (APP) or expectation propagation (EP)
based soft feedback.
  This novel method for filter computation is shown to improve detection
performance compared to previously known alternative methods, and finite-length
and asymptotic analysis show that DFE with static filters still remains
well-suited for high-spectral efficiency applications.
Interactive Surveillance Technologies for Dense Crowds
Authors: Bera Aniket, Manocha Dinesh
Date: 2018-09-27 00:00:00
Tasks: Anomaly Detection

We present an algorithm for realtime anomaly detection in low to medium
density crowd videos using trajectory-level behavior learning. Our formulation
combines online tracking algorithms from computer vision, non-linear pedestrian
motion models from crowd simulation, and Bayesian learning techniques to
automatically compute the trajectory-level pedestrian behaviors for each agent
in the video. These learned behaviors are used to segment the trajectories and
motions of different pedestrians or agents and detect anomalies. We demonstrate
the interactive performance on the PETS ARENA dataset as well as indoor and
outdoor crowd video benchmarks consisting of tens of human agents. We also
discuss the implications of recent public policy and law enforcement issues
relating to surveillance and our research.
Adaptive Image Stream Classification via Convolutional Neural Network with Intrinsic Similarity Metrics
Authors: Gao Yang, Chandra Swarup, Wang Zhuoyi, Khan Latifur
Date: 2018-09-27 00:00:00
Tasks: General Classification, Multi-Task Learning

When performing data classification over a stream of continuously occurring
instances, a key challenge is to develop an open-world classifier that
anticipates instances from an unknown class. Studies addressing this problem,
typically called novel class detection, have considered classification methods
that reactively adapt to such changes along the stream. Importantly, they rely
on the property of cohesion and separation among instances in feature space.
Instances belonging to the same class are assumed to be closer to each other
(cohesion) than those belonging to different classes (separation).
Unfortunately, this assumption may not have large support when dealing with
high dimensional data such as images. In this paper, we address this key
challenge by proposing a semisupervised multi-task learning framework called
CSIM which aims to intrinsically search for a latent space suitable for
detecting labels of instances from both known and unknown classes.
Particularly, we utilize a convolution neural network layer that aids in the
learning of a latent feature space suitable for novel class detection. We
empirically measure the performance of CSIM over multiple realworld image
datasets and demonstrate its superiority by comparing its performance with
existing semi-supervised methods.
Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks
Date: 2018-09-27 00:00:00
Tasks: Classification, ECG Classification, Feature Engineering

The recent advances in ECG sensor devices provide opportunities for user
self-managed auto-diagnosis and monitoring services over the internet. This
imposes the requirements for generic ECG classification methods that are
inter-patient and device independent. In this paper, we present our work on
using the densely connected convolutional neural network (DenseNet) and gated
recurrent unit network (GRU) for addressing the inter-patient ECG
classification problem. A deep learning model architecture is proposed and is
evaluated using the MIT-BIH Arrhythmia and Supraventricular Databases. The
results obtained show that without applying any complicated data pre-processing
or feature engineering methods, both of our models have considerably
outperformed the state-of-the-art performance for supraventricular (SVEB) and
ventricular (VEB) arrhythmia classifications on the unseen testing dataset
(with the F1 score improved from 51.08 to 61.25 for SVEB detection and from
88.59 to 89.75 for VEB detection respectively). As no patient-specific or
device-specific information is used at the training stage in this work, it can
be considered as a more generic approach for dealing with scenarios in which
varieties of ECG signals are collected from different patients using different
types of sensor devices.
Computer-Aided Arrhythmia Diagnosis by Learning ECG Signal
Date: 2018-09-28 00:00:00
Tasks: Arrhythmia Detection, Self-Learning

Electrocardiogram (ECG) is one of the non-invasive and low-risk methods to
monitor the condition of the human heart. Any abnormal pattern(s) in the ECG
signal is an indicative measure of malfunctioning of the heart, termed as
arrhythmia. Due to the lack of human expertise and high probability to
misdiagnose, computer-aided diagnosis and analysis are preferred. In this work,
we perform arrhythmia detection with an optimized neural network having
piecewise linear approximation based activation function to alleviate the
complex computations in the traditional activation functions. Further, we
propose a self-learning method for arrhythmia detection by learning and
analyzing the characteristics (period) of the ECG signal. Self-learning based
approach achieves 97.28% of arrhythmia detection accuracy, and neural network
with optimized activation functions achieve an arrhythmia detection accuracy of
99.56%.
A Bidomain Model for Lens Microcirculation
Date: 2019-05-22 00:00:00
Tasks: model

There exists a large body of research on the lens of mammalian eye over the
past several decades. The objective of the current work is to provide a link
between the most recent computational models to some of the pioneering work in
the 1970s and 80s. We introduce a general non-electro-neutral model to study
the microcirculation in lens of eyes. It describes the steady state
relationships among ion fluxes, water flow and electric field inside cells, and
in the narrow extracellular spaces between cells in the lens. Using asymptotic
analysis, we derive a simplified model based on physiological data and compare
our results with those in the literature. We show that our simplified model can
be reduced further to the first generation models while our full model is
consistent with the most recent computational models. In addition, our
simplified model captures the main features of the full model. Our results
serve as a useful link intermediate between the computational models and the
first generation analytical models.
Analysis of Maximal Topologies Achieving Optimal DoF and DoF $\frac{1}{n}$ in Topological Interference Management
Date: 2018-10-10 00:00:00
Tasks: Management

Topological interference management (TIM) can obtain degrees of freedom (DoF)
gains with no channel state information at the transmitters (CSIT) except
topological information of network in the interference channel. It was shown
that TIM achieves the optimal symmetric DoF when internal conflict does not
exist among messages. However, it is difficult to assure whether a specific
topology can achieve the optimal DoF without scrutinizing internal conflict,
which requires lots of works. Also, it is hard to design a specific optimal
topology directly from the conventional condition for the optimal DoF. With
these problems in mind, we propose a method to derive maximal topology directly
in TIM, named as alliance construction in K-user interference channel. That is,
it is proved that a topology is maximal if and only if it is derived from
alliance construction. We translate a topology design by alliance construction
in message graph into topology matrix and propose conditions for maximal
topology matrix (MTM). Moreover, we propose a generalized alliance construction
that derives a topology achieving DoF 1/n for n>=3 by generalizing
sub-alliances. A topology matrix can also be used to analyze maximality of
topology with DoF 1/n.
Complex Valued Risk Diversification
Date: 2018-10-10 00:00:00

Risk diversification is one of the dominant concerns for portfolio managers.
Various portfolio constructions have been proposed to minimize the risk of the
portfolio under some constrains including expected returns. We propose a
portfolio construction method that incorporates the complex valued principal
component analysis into the risk diversification portfolio construction. The
proposed method is verified to outperform the conventional risk parity and risk
diversification portfolio constructions.
V3C - a Research Video Collection
Authors: Rossetto Luca, Schuldt Heiko, Awad George, Butt Asad A.
Date: 2018-10-11 00:00:00
Tasks: Management, Retrieval, Video Retrieval

With the widespread use of smartphones as recording devices and the massive
growth in bandwidth, the number and volume of video collections has increased
significantly in the last years. This poses novel challenges to the management
of these large-scale video data and especially to the analysis of and retrieval
from such video collections. At the same time, existing video datasets used for
research and experimentation are either not large enough to represent current
collections or do not reflect the properties of video commonly found on the
Internet in terms of content, length, or resolution. In this paper, we
introduce the Vimeo Creative Commons Collection, in short V3C, a collection of
28'450 videos (with overall length of about 3'800 hours) published under
creative commons license on Vimeo. V3C comes with a shot segmentation for each
video, together with the resulting keyframes in original as well as reduced
resolution and additional metadata. It is intended to be used from 2019 at the
International large-scale TREC Video Retrieval Evaluation campaign (TRECVid).
Synthesis for Vesicle Traffic Systems
Authors: Ashutosh Gupta, Somya Mani, Ankit Shukla
Date: 2018-10-10 00:00:00

Vesicle Traffic Systems (VTSs) are the material transport mechanisms among the compartments inside the biological cells. The compartments are viewed as nodes that are labeled with the containing chemicals and the transport channels are similarly viewed as labeled edges between the nodes. Understanding VTSs is an ongoing area of research and for many cells they are partially known. For example, there may be undiscovered edges, nodes, or their labels in a VTS of a cell. It has been speculated that there are properties that the VTSs must satisfy. For example, stability, i.e., every chemical that is leaving a compartment comes back. Many synthesis questions may arise in this scenario, where we want to complete a partially known VTS under a given property. In the paper, we present novel encodings of the above questions into the QBF (quantified Boolean formula) satisfiability problems. We have implemented the encodings in a highly configurable tool and applied to a couple of found-in-nature VTSs and several synthetic graphs. Our results demonstrate that our method can scale up to the graphs of interest.
Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-Atlas Selection and Registration
Date: 2018-10-10 00:00:00

This paper presents a fully automated method to segment the complex left
atrial (LA) cavity, from 3D Gadolinium-enhanced magnetic resonance imaging
(GE-MRI) scans. The proposed method consists of four steps: (1) preprocessing
to convert the original GE-MRI to a probability map, (2) atlas selection to
match the atlases to the target image, (3) multi-atlas registration and fusion,
and (4) level-set refinement. The method was evaluated on the datasets provided
by the MICCAI 2018 STACOM Challenge with 100 dataset for training. Compared to
manual annotation, the proposed method achieved an average Dice overlap index
of 0.88.