pwc-archive/papers-with-abstracts huggingface dataset:
On Robustness Metrics for Learning STL Tasks
Date: 2020-03-12 00:00:00

Signal temporal logic (STL) is a powerful tool for describing complex
behaviors for dynamical systems. Among many approaches, the control problem for
systems under STL task constraints is well suited for learning-based solutions,
because STL is equipped with robustness metrics that quantify the satisfaction
of task specifications and thus serve as useful rewards. In this work, we
examine existing and potential robustness metrics specifically from the
perspective of how they can aid such learning algorithms. We show that various
desirable properties restrict the form of potential metrics, and introduce a
new one based on the results. The effectiveness of this new robustness metric
for accelerating the learning procedure is demonstrated through an insightful
case study.
The design and construction of reference pangenome graphs
Authors: Heng Li, Xiaowen Feng, Chong Chu
Date: 2020-03-13 00:00:00

The recent advances in sequencing technologies enables the assembly of individual genomes to the reference quality. How to integrate multiple genomes from the same species and to make the integrated representation accessible to biologists remain an open challenge. Here we propose a graph-based data model and associated formats to represent multiple genomes while preserving the coordinate of the linear reference genome. We implemented our ideas in the minigraph toolkit and demonstrate that we can efficiently construct a pangenome graph and compactly encode tens of thousands of structural variants missing from the current reference genome.
Managing aquatic invasions: optimal locations and operating times for watercraft inspection stations
Date: 2020-03-13 00:00:00
Tasks: Management

Aquatic invasive species (AIS) cause significant ecological and economic
damages around the world. A major spread mechanism for AIS is traffic of
boaters transporting their watercraft from invaded to uninvaded waterbodies. To
inhibit the spread of AIS, several Canadian provinces and American states set
up watercraft inspection stations at roadsides, where potentially infested
boats are screened for AIS and, if necessary, decontaminated. However, since
budgets for AIS control are limited, watercraft inspection stations can only be
operated at specific locations and daytimes. Though theoretical studies provide
managers with general guidelines for AIS management, more specific results are
needed to determine when and where watercraft inspections would be most
effective. This is the subject of this paper. We show how linear integer
programming techniques can be used to optimize watercraft inspection policies
under budget constraints. We introduce our approach as a general framework and
apply it to the prevention of the spread of zebra and quagga mussels (Dreissena
spp.) to the Canadian province British Columbia. We consider a variety of
scenarios and show how variations in budget constraints, propagule sources, and
model uncertainty affect the optimal policy. Based on these results, we
identify simple, generally applicable principles for optimal AIS management.
A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM Signals
Date: 2020-03-13 00:00:00

One of the major drawbacks of orthogonal frequency division multiplexing
(OFDM) signals is the high peak to average power ratio (PAPR) of the
transmitted signal. Many PAPR reduction techniques have been proposed in the
literature, among which, partial transmit sequence (PTS) technique has been
taken considerable investigation.However, PTS technique requires an exhaustive
search over all combinations of allowed phase factors, whose complexity
increases exponentially with the number of sub-blocks. In this paper, a newly
suboptimal method based on modified artificial bee colony (ABC-PTS) algorithm
is proposed to search the better combination of phase factors. The ABC-PTS
algorithm can significantly reduce the computational complexity for larger PTS
subblocks and offers lower PAPR at the same time. Simulation results show that
the ABC-PTS algorithm is an efficient method to achieve significant PAPR
reduction.
Genetic Algorithm Based Nearly Optimal Peak Reduction Tone Set Selection for Adaptive Amplitude Clipping PAPR Reduction
Date: 2020-03-13 00:00:00

In tone reservation (TR) based OFDM systems, the peak to average power ratio
(PAPR) reduction performance mainly depends on the selection of the peak
reduction tone (PRT) set and the optimal target clipping level. Finding the
optimal PRT set requires an exhaustive search of all combinations of possible
PRT sets, which is a nondeterministic polynomial-time (NP-hard) problem, and
this search is infeasible for the number of tones used in practical systems.
The existing selection methods, such as the consecutive PRT set, equally spaced
PRT set and random PRT set, perform poorly compared to the optimal PRT set or
incur high computational complexity. In this paper, an efficient scheme based
on genetic algorithm (GA) with lower computational complexity is proposed for
searching a nearly optimal PRT set. While TR-based clipping is simple and
attractive for practical implementation, determining the optimal target
clipping level is difficult. To overcome this problem, we propose an adaptive
clipping control algorithm.Simulation results show that our proposed algorithms
efficiently obtain a nearly optimal PRT set and good PAPR reductions.
SARS-CoV-2 Entry Genes Are Most Highly Expressed in Nasal Goblet and Ciliated Cells within Human Airways
Authors: Waradon Sungnak, Ni Huang, Christophe Bécavin, Marijn Berg, HCA Lung Biological Network
Date: 2020-03-13 00:00:00

The SARS-CoV-2 coronavirus, the etiologic agent responsible for COVID-19 coronavirus disease, is a global threat. To better understand viral tropism, we assessed the RNA expression of the coronavirus receptor, ACE2, as well as the viral S protein priming protease TMPRSS2 thought to govern viral entry in single-cell RNA-sequencing (scRNA-seq) datasets from healthy individuals generated by the Human Cell Atlas consortium. We found that ACE2, as well as the protease TMPRSS2, are differentially expressed in respiratory and gut epithelial cells. In-depth analysis of epithelial cells in the respiratory tree reveals that nasal epithelial cells, specifically goblet/secretory cells and ciliated cells, display the highest ACE2 expression of all the epithelial cells analyzed. The skewed expression of viral receptors/entry-associated proteins towards the upper airway may be correlated with enhanced transmissivity. Finally, we showed that many of the top genes associated with ACE2 airway epithelial expression are innate immune-associated, antiviral genes, highly enriched in the nasal epithelial cells. This association with immune pathways might have clinical implications for the course of infection and viral pathology, and highlights the specific significance of nasal epithelia in viral infection. Our findings underscore the importance of the availability of the Human Cell Atlas as a reference dataset. In this instance, analysis of the compendium of data points to a particularly relevant role for nasal goblet and ciliated cells as early viral targets and potential reservoirs of SARS-CoV-2 infection. This, in turn, serves as a biological framework for dissecting viral transmission and developing clinical strategies for prevention and therapy.
Optimal Control of Boolean Control Networks with Discounted Cost: An Efficient Approach based on Deterministic Markov Decision Process
Date: 2020-03-13 00:00:00

This paper deals with the infinite-horizon optimal control problem for
Boolean control networks (BCNs) with a discounted-cost criterion. This problem
has been investigated in existing studies with algorithms characterized by high
computational complexity. We thus attempt to develop more efficient approaches
for this problem from a deterministic Markov decision process (DMDP)
perspective. First, we show the eligibility of a DMDP to model the control
process of a BCN and the existence of an optimal solution. Next, two approaches
are developed to handle the optimal control problem in a DMDP. One approach
adopts the well-known value iteration algorithm, and the other resorts to the
Madani's algorithm specifically designed for DMDPs. The latter approach can
find an exact optimal solution and outperform existing methods in terms of time
efficiency, while the former value iteration based approach usually obtains a
near-optimal solution much faster than all others. The 9-state-4-input
\textit{ara} operon network of the bacteria \textit{E. coli} is used to verify
the effectiveness and performance of our approaches. Results show that both
approaches can reduce the running time dramatically by several orders of
magnitude compared with existing work.
Coronavirus and oil price crash
Authors: Claudiu Albulescu
Date: 2020-03-13 00:00:00

Coronavirus (COVID-19) creates fear and uncertainty, hitting the global economy and amplifying the financial markets volatility. The oil price reaction to COVID-19 was gradually accommodated until March 09, 2020, when, 49 days after the release of the first coronavirus monitoring report by the World Health Organization (WHO), Saudi Arabia floods the market with oil. As a result, international prices drop with more than 20% in one single day. Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. Our ARDL estimation shows that the COVID-19 daily reported cases of new infections have a marginal negative impact on the crude oil prices in the long run. Nevertheless, by amplifying the financial markets volatility, COVID-19 also has an indirect effect on the recent dynamics of crude oil prices.
Natural pigments from microalgae grown in industrial wastewater
Date: 2020-03-13 00:00:00

The aim of this study was to investigate the cultivation of Nostoc sp.,
Arthrospira platensis and Porphyridium purpureum in industrial wastewater to
produce phycobiliproteins. Initially, light intensity and growth medium
composition were optimized, indicating that light conditions influenced the
phycobiliproteins production more than the medium composition. Conditions were
then selected, according to biomass growth, nutrients removal and
phycobiliproteins production, to cultivate these microalgae in food-industry
wastewater. The three species could efficiently remove up to 98%, 94% and 100%
of COD, inorganic nitrogen and PO43--P, respectively. Phycocyanin,
allophycocyanin and phycoerythrin were successfully extracted from the biomass
reaching concentrations up to 103, 57 and 30 mg/g dry weight, respectively.
Results highlight the potential use of microalgae for industrial wastewater
treatment and related high-value phycobiliproteins recovery.
Life Cycle Assessment of high rate algal ponds for wastewater treatment and resource recovery
Date: 2020-03-13 00:00:00

The aim of this study was to assess the potential environmental impacts
associated with high rate algal ponds (HRAP) systems for wastewater treatment
and resource recovery in small communities. To this aim, a Life Cycle
Assessment (LCA) and an economic assessment were carried out evaluating two
alternatives: i) a HRAPs system for wastewater treatment where microalgal
biomass is valorised for energy recovery (biogas production); ii) a HRAPs
system for wastewater treatment where microalgal biomass is reused for
nutrients recovery (biofertiliser production). Additionally, both alternatives
were compared to a typical small-sized activated sludge system. The results
showed that HRAPs system coupled with biogas production appeared to be more
environmentally friendly than HRAPs system coupled with biofertiliser
production in the climate change, ozone layer depletion, photochemical oxidant
formation, and fossil depletion impact categories. Different climatic
conditions have strongly influenced the results obtained in the eutrophication
and metal depletion impact categories, with the HRAPs system located where warm
temperatures and high solar radiation are predominant showing lower impact. In
terms of costs, HRAPs systems seemed to be more economically feasible when
combined with biofertiliser production instead of biogas.
High-Accuracy Facial Depth Models derived from 3D Synthetic Data
Date: 2020-03-26 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Scene Understanding, Vocal Bursts Intensity Prediction

In this paper, we explore how synthetically generated 3D face models can be
used to construct a high accuracy ground truth for depth. This allows us to
train the Convolutional Neural Networks (CNN) to solve facial depth estimation
problems. These models provide sophisticated controls over image variations
including pose, illumination, facial expressions and camera position. 2D
training samples can be rendered from these models, typically in RGB format,
together with depth information. Using synthetic facial animations, a dynamic
facial expression or facial action data can be rendered for a sequence of image
frames together with ground truth depth and additional metadata such as head
pose, light direction, etc. The synthetic data is used to train a CNN based
facial depth estimation system which is validated on both synthetic and real
images. Potential fields of application include 3D reconstruction, driver
monitoring systems, robotic vision systems, and advanced scene understanding.
Asymptotic expansion for the transition densities of stochastic differential equations driven by the gamma processes
Authors: Fan Jiang, Xin Zang, Jingping Yang
Date: 2020-03-13 00:00:00

In this paper, enlightened by the asymptotic expansion methodology developed by Li(2013b) and Li and Chen (2016), we propose a Taylor-type approximation for the transition densities of the stochastic differential equations (SDEs) driven by the gamma processes, a special type of Levy processes. After representing the transition density as a conditional expectation of Dirac delta function acting on the solution of the related SDE, the key technical method for calculating the expectation of multiple stochastic integrals conditional on the gamma process is presented. To numerically test the efficiency of our method, we examine the pure jump Ornstein--Uhlenbeck (OU) model and its extensions to two jump-diffusion models. For each model, the maximum relative error between our approximated transition density and the benchmark density obtained by the inverse Fourier transform of the characteristic function is sufficiently small, which shows the efficiency of our approximated method.
Quantifying Musical Style: Ranking Symbolic Music based on Similarity to a Style
Date: 2020-03-10 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Modelling human perception of musical similarity is critical for the
evaluation of generative music systems, musicological research, and many Music
Information Retrieval tasks. Although human similarity judgments are the gold
standard, computational analysis is often preferable, since results are often
easier to reproduce, and computational methods are much more scalable.
Moreover, computation based approaches can be calculated quickly and on demand,
which is a prerequisite for use with an online system. We propose StyleRank, a
method to measure the similarity between a MIDI file and an arbitrary musical
style delineated by a collection of MIDI files. MIDI files are encoded using a
novel set of features and an embedding is learned using Random Forests.
Experimental evidence demonstrates that StyleRank is highly correlated with
human perception of stylistic similarity, and that it is precise enough to rank
generated samples based on their similarity to the style of a corpus. In
addition, similarity can be measured with respect to a single feature, allowing
specific discrepancies between generated samples and a particular musical style
to be identified.
Robustness study of noisy annotation in deep learning based medical image segmentation
Date: 2020-03-10 00:00:00
Tasks: Deep Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Partly due to the use of exhaustive-annotated data, deep networks have
achieved impressive performance on medical image segmentation. Medical imaging
data paired with noisy annotation are, however, ubiquitous, but little is known
about the effect of noisy annotation on deep learning-based medical image
segmentation. We studied the effects of noisy annotation in the context of
mandible segmentation from CT images. First, 202 images of Head and Neck cancer
patients were collected from our clinical database, where the organs-at-risk
were annotated by one of 12 planning dosimetrists. The mandibles were roughly
annotated as the planning avoiding structure. Then, mandible labels were
checked and corrected by a physician to get clean annotations. At last, by
varying the ratios of noisy labels in the training data, deep learning-based
segmentation models were trained, one for each ratio. In general, a deep
network trained with noisy labels had worse segmentation results than that
trained with clean labels, and fewer noisy labels led to better segmentation.
When using 20% or less noisy cases for training, no significant difference was
found on the prediction performance between the models trained by noisy or
clean. This study suggests that deep learning-based medical image segmentation
is robust to noisy annotations to some extent. It also highlights the
importance of labeling quality in deep learning
Optimal hedging of a perpetual American put with a single trade
Authors: Cheng Cai, Tiziano De Angelis, Jan Palczewski
Date: 2020-03-13 00:00:00

It is well-known that using delta hedging to hedge financial options is not feasible in practice. Traders often rely on discrete-time hedging strategies based on fixed trading times or fixed trading prices (i.e., trades only occur if the underlying asset's price reaches some predetermined values). Motivated by this insight and with the aim of obtaining explicit solutions, we consider the seller of a perpetual American put option who can hedge her portfolio once until the underlying stock price leaves a certain range of values $(a,b)$. We determine optimal trading boundaries as functions of the initial stock holding, and an optimal hedging strategy for a bond/stock portfolio. Optimality here refers to the variance of the hedging error at the (random) time when the stock leaves the interval $(a,b)$. Our study leads to analytical expressions for both the optimal boundaries and the optimal stock holding, which can be evaluated numerically with no effort.
Optimized Control of Variable Speed Hydropower for Provision of Fast Frequency Reserves
Date: 2020-03-13 00:00:00

This paper deals with the design of controllers for variable speed hydropower
(VSHP) plants with the objective of optimize the plants' performance. The
control objectives imply enabling fast responses to frequency deviations while
keeping the electric and hydraulic variables within their constraints. A model
predictive controller (MPC) was developed to coordinate the turbine controller
with the virtual synchronous generator (VSG) control of the power electronics
converter. The simulation results show that the VSG is able to deliver fast
power responses by utilizing the rotational energy of the turbine and the
generator. The MPC controls the guide vane opening of the turbine to regain the
nominal turbine rotational speed. If this is not possible due to the
constraints of the hydraulic system, the MPC adjusts the power output of the
VSHP by changing the VSG power reference. The proposed control system allows
the VSHP to provide fast frequency reserves (FFR).
KatzDriver: A network Based method to predict cancer causal genes in GR Network
Authors: Mostafa Akhavansafar, Babak Teimourpour
Date: 2020-03-13 00:00:00

One of the important issues in oncology is finding the genes that perturbation the cell functionality, and result in cancer propagation. The genes, namely driver genes, when they mutate in expression, result in cancer through activation of the mutated proteins. So, many methods have been introduced to predict this group of genes. These are mostly computational methods based on the number of mutations of each gene. Recently, some network-based methods have been proposed to predict Cancer Driver Genes (CDGs). In this study, we use a network-based approach and relative importance of each gene in the propagation and absorption of genes anomalies in the network to recognize CDGs. The experimental results are compared with 19 previous methods that show our proposed algorithm is better than the others in terms of accuracy, precision, and the number of recognized CDGs.
Targeting customers under response-dependent costs
Authors: Johannes Haupt, Stefan Lessmann
Date: 2020-03-13 00:00:00
Tasks: Causal Inference, Marketing

This study provides a formal analysis of the customer targeting problem when the cost for a marketing action depends on the customer response and proposes a framework to estimate the decision variables for campaign profit optimization. Targeting a customer is profitable if the impact and associated profit of the marketing treatment are higher than its cost. Despite the growing literature on uplift models to identify the strongest treatment-responders, no research has investigated optimal targeting when the costs of the treatment are unknown at the time of the targeting decision. Stochastic costs are ubiquitous in direct marketing and customer retention campaigns because marketing incentives are conditioned on a positive customer response. This study makes two contributions to the literature, which are evaluated on an e-commerce coupon targeting campaign. First, we formally analyze the targeting decision problem under response-dependent costs. Profit-optimal targeting requires an estimate of the treatment effect on the customer and an estimate of the customer response probability under treatment. The empirical results demonstrate that the consideration of treatment cost substantially increases campaign profit when used for customer targeting in combination with an estimate of the average or customer-level treatment effect. Second, we propose a framework to jointly estimate the treatment effect and the response probability by combining methods for causal inference with a hurdle mixture model. The proposed causal hurdle model achieves competitive campaign profit while streamlining model building. Code is available at https://github.com/Humboldt-WI/response-dependent-costs.
Transfer Function Estimation in System Identification Toolbox via Vector Fitting
Date: 2020-03-13 00:00:00

This paper considers black- and grey-box continuous-time transfer function
estimation from frequency response measurements. The first contribution is a
bilinear mapping of the original problem from the imaginary axis onto the
unitdisk. This improves the numerics of the underlying Sanathanan-Koerner
iterations and the more recent instrumental-variable iterations. Orthonormal
rational basis functions on the unit disk are utilized. Each iteration step
necessitates a minimal state-space realization with these basis functions. One
such derivation is the second contribution. System identification with these
basis functions yield zero-pole-gain models. The third contribution is an
efficient method to express transfer function coefficient constraints in terms
of the orthonormal rational basis functions. This allows for estimating
transfer function models with arbitrary relative degrees (including improper
models), along with other fixed and bounded parameter values. The algorithm is
implemented in the tfest function in System Identification Toolbox (Release
2016b, for use with MATLAB) for frequency domain data. Two examples are
presented to demonstrate the algorithm performance.