pwc-archive/papers-with-abstracts huggingface dataset:
SoRC -- Evaluation of Computational Molecular Co-Localization Analysis in Mass Spectrometry Images
Authors: Wüllems Karsten, Nattkemper Tim W.
Date: 2020-09-24 00:00:00

The computational analysis of Mass Spectrometry Imaging (MSI) data aims at
the identification of interesting mass co-localizations and the visualization
of their lateral distribution in the sample, usually a tissue cross section.
But as the morphological structure of tissues and the different kinds of mass
co-localization naturally show a huge diversity, the selection and tuning of
the computational method is a time-consuming effort. In this work we address
the special problem of computationally grouping mass channel images according
to their similarities in their lateral distribution patterns. Such an analysis
is driven by the idea, that groups of molecules that feature a similar
distribution pattern may have a functional relation. But the selection of the
similarity function and other parameters is often done by a time-consuming and
unsatsifactory trial and error. We propose a new flexible workflow scheme
called SoRC (sum of ranked cluster indices) for automating this tuning step and
making it much more efficient. We test SoRC using three different data sets
acquired from the lab for three different kinds of samples (barley seed, mouse
bladder tissue, human PXE skin). We show, that SORC can be applied to score and
visualize the results obtained with the applied methods in short time without
too much effort. In our application example, the SoRC results for the three
data sets reveal that a) some well-known similarity functions are suited to
achieve good results for all three data sets and b) for the MSI data featuring
a higher degree of irregularity improved results can be achieved by applying
non-standard similarity functions. The SoRC scores computed with our approach
indicate that an automated testing and scoring of different methods for mass
channel image grouping can improve the final outcome of a study by finally
selecting the methods of the highest scores.
Scalable Deep Reinforcement Learning for Ride-Hailing
Authors: Feng Jiekun, Gluzman Mark, Dai J. G.
Date: 2020-09-27 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange
thousands of cars to meet ride requests throughout the day. We consider a
Markov decision process (MDP) model of a ride-hailing service system, framing
it as a reinforcement learning (RL) problem. The simultaneous control of many
agents (cars) presents a challenge for the MDP optimization because the action
space grows exponentially with the number of cars. We propose a special
decomposition for the MDP actions by sequentially assigning tasks to the
drivers. The new actions structure resolves the scalability problem and enables
the use of deep RL algorithms for control policy optimization. We demonstrate
the benefit of our proposed decomposition with a numerical experiment based on
real data from Didi Chuxing.
Regression to the Tail: Why the Olympics Blow Up
Authors: Bent Flyvbjerg, Alexander Budzier, Daniel Lunn
Date: 2020-09-28 00:00:00
Tasks: Decision Making, regression

The Olympic Games are the largest, highest-profile, and most expensive megaevent hosted by cities and nations. Average sports-related costs of hosting are $12.0 billion. Non-sports-related costs are typically several times that. Every Olympics since 1960 has run over budget, at an average of 172 percent in real terms, the highest overrun on record for any type of megaproject. The paper tests theoretical statistical distributions against empirical data for the costs of the Games, in order to explain the cost risks faced by host cities and nations. It is documented, for the first time, that cost and cost overrun for the Games follow a power-law distribution. Olympic costs are subject to infinite mean and variance, with dire consequences for predictability and planning. We name this phenomenon "regression to the tail": it is only a matter of time until a new extreme event occurs, with an overrun larger than the largest so far, and thus more disruptive and less plannable. The generative mechanism for the Olympic power law is identified as strong convexity prompted by six causal drivers: irreversibility, fixed deadlines, the Blank Check Syndrome, tight coupling, long planning horizons, and an Eternal Beginner Syndrome. The power law explains why the Games are so difficult to plan and manage successfully, and why cities and nations should think twice before bidding to host. Based on the power law, two heuristics are identified for better decision making on hosting. Finally, the paper develops measures for good practice in planning and managing the Games, including how to mitigate the extreme risks of the Olympic power law.
EMR: A New Metric to Assess the Resilience of Directional mmWave Channels to Blockages
Authors: Fatih Erden, Ozgur Ozdemir, Ismail Guvenc, David W. Matolak
Date: 2020-09-30 00:00:00

Millimeter-wave (mmWave) communication systems require narrow beams to increase communication range. If the dominant communication direction is blocked by an obstacle, an alternative and reliable spatial communication path should be quickly identified to maintain connectivity. In this paper, we introduce a new metric to quantify the effective multipath richness (EMR) of a directional communication channel by considering the strength and spatial diversity of the resolved paths, while also taking into account beamwidth and blockage characteristics. The metric is defined as a weighted sum of the number of multipath component (MPC) clusters, where clustering is performed based on the cosine-distance between the MPCs that have power above a certain threshold. This process returns a single scalar value for a transmitter (TX)/receiver (RX) location pair in a given environment. It is also possible to represent the EMR of the whole environment with a probability distribution function of the metric by considering a set of TX/RX locations. Using this proposed metric, one can assess the scattering richness of different communication environments to achieve a particular quality of service (QoS). This metric is especially informative and useful at higher frequencies, such as mmWave and terahertz (THz), where the propagation path loss and penetration loss are high, and directional non-light-of-sight (NLOS) communication is critical for the success of the network. We evaluate the proposed metric using our channel measurements at 28 GHz in a large indoor environment at a library setting for LOS and NLOS scenarios.
Statistical Field Theory and Networks of Spiking Neurons
Authors: Pierre Gosselin, Aïleen Lotz, Marc Wambst
Date: 2020-09-30 00:00:00

This paper models the dynamics of a large set of interacting neurons within the framework of statistical field theory. We use a method initially developed in the context of statistical field theory [44] and later adapted to complex systems in interaction [45][46]. Our model keeps track of individual interacting neurons dynamics but also preserves some of the features and goals of neural field dynamics, such as indexing a large number of neurons by a space variable. Thus, this paper bridges the scale of individual interacting neurons and the macro-scale modelling of neural field theory.
A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems
Authors: Florian Geissler, Alex Unnervik, Michael Paulitsch
Date: 2020-09-30 00:00:00
Tasks: Fault Detection

Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots. The problem remains that such trust is notoriously difficult to guarantee in the presence of systematic faults, e.g. non-traceable errors caused by machine learning functions. One way to tackle this issue without making rather specific assumptions about the perception process is plausibility checking. Similar to the reasoning of human intuition, the final outcome of a complex black-box procedure is verified against given expectations of an object's behavior. In this article, we apply and evaluate collaborative, sensor-generic plausibility checking as a mean to detect empirical perception faults from their statistical fingerprints. Our real use case is next-generation automated driving that uses a roadside sensor infrastructure for perception augmentation, represented here by test scenarios at a German highway and a city intersection. The plausibilization analysis is integrated naturally in the object fusion process, and helps to diagnose known and possibly yet unknown faults in distributed sensing systems.
Calibrating Local Volatility Models with Stochastic Drift and Diffusion
Authors: Orcan Ogetbil, Narayan Ganesan, Bernhard Hientzsch
Date: 2020-09-30 00:00:00

We propose Monte Carlo calibration algorithms for three models: local volatility with stochastic interest rates, stochastic local volatility with deterministic interest rates, and finally stochastic local volatility with stochastic interest rates. For each model, we include detailed derivations of the corresponding SDE systems, and list the required input data and steps for calibration. We give conditions under which a local volatility can exist given European option prices, stochastic interest rate model parameters, and correlations. The models are posed in a foreign exchange setting. The drift term for the exchange rate is given as a difference of two stochastic short rates, domestic and foreign, each modeled by a G1++ process. For stochastic volatility, we model the variance for the exchange rate by a CIR process. We include tests to show the convergence and the accuracy of the proposed algorithms.
On Detecting Spoofing Strategies in High Frequency Trading
Date: 2020-09-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Spoofing is an illegal act of artificially modifying the supply to drive temporarily prices in a given direction for profit. In practice, detection of such an act is challenging due to the complexity of modern electronic platforms and the high frequency at which orders are channeled. We present a micro-structural study of spoofing in a simple static setting. A multilevel imbalance which influences the resulting price movement is introduced upon which we describe the optimization strategy of a potential spoofer. We provide conditions under which a market is more likely to admit spoofing behavior as a function of the characteristics of the market. We describe the optimal spoofing strategy after optimization which allows us to quantify the resulting impact on the imbalance after spoofing. Based on these results we calibrate the model to real Level 2 datasets from TMX, and provide some monitoring procedures based on the Wasserstein distance to detect spoofing strategies in real time.
Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration
Date: 2020-09-30 00:00:00
Tasks: BIG-bench Machine Learning

We present a physical interpretation of machine learning functions, opening up the possibility to control properties of statistical systems via the inclusion of these functions in Hamiltonians. In particular, we include the predictive function of a neural network, designed for phase classification, as a conjugate variable coupled to an external field within the Hamiltonian of a system. Results in the two-dimensional Ising model evidence that the field can induce an order-disorder phase transition by breaking or restoring the symmetry, in contrast with the field of the conventional order parameter which causes explicit symmetry breaking. The critical behavior is then studied by proposing a Hamiltonian-agnostic reweighting approach and forming a renormalization group mapping on quantities derived from the neural network. Accurate estimates of the critical point and of the critical exponents related to the operators that govern the divergence of the correlation length are provided. We conclude by discussing how the method provides an essential step toward bridging machine learning and physics.
Constructions and Comparisons of Pooling Matrices for Pooled Testing of COVID-19
Authors: Yi-Jheng Lin, Che-Hao Yu, Tzu-Hsuan Liu, Cheng-Shang Chang, Wen-Tsuen Chen
Date: 2020-09-30 00:00:00

In comparison with individual testing, group testing (also known as pooled testing) is more efficient in reducing the number of tests and potentially leading to tremendous cost reduction. As indicated in the recent article posted on the US FDA website, the group testing approach for COVID-19 has received a lot of interest lately. There are two key elements in a group testing technique: (i) the pooling matrix that directs samples to be pooled into groups, and (ii) the decoding algorithm that uses the group test results to reconstruct the status of each sample. In this paper, we propose a new family of pooling matrices from packing the pencil of lines (PPoL) in a finite projective plane. We compare their performance with various pooling matrices proposed in the literature, including 2D-pooling, P-BEST, and Tapestry, using the two-stage definite defectives (DD) decoding algorithm. By conducting extensive simulations for a range of prevalence rates up to 5%, our numerical results show that there is no pooling matrix with the lowest relative cost in the whole range of the prevalence rates. To optimize the performance, one should choose the right pooling matrix, depending on the prevalence rate. The family of PPoL matrices can dynamically adjust their column weights according to the prevalence rates and could be a better alternative than using a fixed pooling matrix.
The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes
Date: 2020-09-30 00:00:00
Tasks: valid

The simulation of stochastic reaction-diffusion systems using fine-grained
representations can become computationally prohibitive when particle numbers
become large. If particle numbers are sufficiently high then it may be possible
to ignore stochastic fluctuations and use a more efficient coarse-grained
simulation approach. Nevertheless, for multiscale systems which exhibit
significant spatial variation in concentration, a coarse-grained approach may
not be appropriate throughout the simulation domain. Such scenarios suggest a
hybrid paradigm in which a computationally cheap, coarse-grained model is
coupled to a more expensive, but more detailed fine-grained model enabling the
accurate simulation of the fine-scale dynamics at a reasonable computational
cost.
  In this paper, in order to couple two representations of reaction-diffusion
at distinct spatial scales, we allow them to overlap in a "blending region".
Both modelling paradigms provide a valid representation of the particle density
in this region. From one end of the blending region to the other, control of
the implementation of diffusion is passed from one modelling paradigm to
another through the use of complementary "blending functions" which scale up or
down the contribution of each model to the overall diffusion. We establish the
reliability of our novel hybrid paradigm by demonstrating its simulation on
four exemplar reaction-diffusion scenarios.
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction
Authors: Jun Yang, Qing Li, Yixuan Sun
Date: 2020-09-30 00:00:00
Tasks: Time Series Analysis

Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecast model, which could investigate the variation trend of stability coefficient of tailing dam and issue early warnings. In order to fill the gap, this work presents an hybrid network - Wavelet-based Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), namely Wavelet-CNN-LSTM netwrok for predicting the tailings pond risk. Firstly, we construct the especial nonlinear data processing method to impute the missing value with the numerical inversion (NI) method, which combines correlation analysis, sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new forecasting model was proposed to monitor the saturation line, which is the lifeline of the tailings pond and can directly reflect the stability of the tailings pond. After using the discrete wavelet transform (DWT) to decompose the original saturation line data into 4-layer wavelets and de-noise the data, the CNN was used to identify and learn the spatial structures in the time series, followed by LSTM cells for detecting the long-short-term dependence. Finally, different experiments were conducted to evaluate the effectiveness of our model by comparing it with other state-of-the-art algorithms. The results show that Wavelet-CNN-LSTM achieves the best score both in mean absolute percentage error (MAPE), root-mean-square error (RMSE) and R 2 .
On the Compression of Translation Operator Tensors in FMM-FFT-Accelerated SIE Simulators via Tensor Decompositions
Authors: Cheng Qian, Abdulkadir C. Yucel
Date: 2020-09-25 00:00:00
Tasks: Tensor Decomposition, Translation

Tensor decomposition methodologies are proposed to reduce the memory requirement of translation operator tensors arising in the fast multipole method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation (SIE) simulators. These methodologies leverage Tucker, hierarchical Tucker (H-Tucker), and tensor train (TT) decompositions to compress the FFT'ed translation operator tensors stored in three-dimensional (3D) and four-dimensional (4D) array formats. Extensive numerical tests are performed to demonstrate the memory saving achieved by and computational overhead introduced by these methodologies for different simulation parameters. Numerical results show that the H-Tucker-based methodology for 4D array format yields the maximum memory saving while Tucker-based methodology for 3D array format introduces the minimum computational overhead. For many practical scenarios, all methodologies yield a significant reduction in the memory requirement of translation operator tensors while imposing negligible/acceptable computational overhead.
Did circoviruses intermediate the recombination between bat and pangolin coronaviruses, yielding SARS-CoV-2?
Authors: Nabil Abid, Giovanni Chillemi, Ahmed Rebai
Date: 2020-09-29 00:00:00
Tasks: Articles

Since the first reports of a coronavirus (CoV) disease 2019 (COVID-19) caused by severe acute respiratory syndrome virus (SARS-CoV-2) in Wuhan, Hubei province, China, scientists are working around the clock to find sound answers to the issue of its origin. While the number of scientific articles on SARS-CoV-2 is increasing, there are still many gaps as to its origin. All studies failed to find a coronavirus in other animals that is more similar to human SARS-COV2 than the bat virus, considered to be the primary reservoir. In this paper we address a new hypothesis, based on a possible recombination between a DNA and SARS-CoV viruses, to explain the rise of SRAS-CoV-2. By comparing SARS-CoV-2 and related CoVs with circoviruses (CVs), we found strong sequence similarity of the genomic region at the 3-end of Bat-CoV ORF1a and the origin of replication (Ori) of porcine CV type 2 (PCV2), as well as similar RNA secondary structures of the region encompassing the cleavage site of CoV S gene with the PCV2 Ori. This constitutes a primary evidence that supports a possible recombination, which occurrence might explain the origin of SARS-CoV-2.
Containing a pandemic: Nonpharmaceutical interventions and the "second wave"
Date: 2020-09-30 00:00:00

In response to the worldwide outbreak of the coronavirus disease COVID-19, a
variety of nonpharmaceutical interventions such as face masks and social
distancing have been implemented. A careful assessment of the effects of such
containment strategies is required to avoid exceeding social and economical
costs as well as a dangerous "second wave" of the pandemic. In this work, we
combine a recently developed dynamical density functional theory model and an
extended SIRD model with hysteresis to study effects of various measures and
strategies using realistic parameters. Depending on intervention thresholds, a
variety of phases with different numbers of shutdowns and deaths are found.
Spatiotemporal simulations provide further insights into the dynamics of a
second wave. Our results are of crucial importance for public health policy.
Debunking Rumors in Networks
Authors: Luca P. Merlino, Paolo Pin, Nicole Tabasso
Date: 2020-10-02 00:00:00

We study the diffusion of a true and a false message (the rumor) in a social network. Upon hearing a message, individuals may believe it, disbelieve it, or debunk it through costly verification. Whenever the truth survives in steady state, so does the rumor. Communication intensity in itself is irrelevant for relative rumor prevalence, and the effect of homophily depends on the exact verification process and equilibrium verification rates. Our model highlights that successful policies in the fight against rumors increase individuals' incentives to verify.
Policy evaluation of waste pricing programs using heterogeneous causal effect estimation
Authors: Marica Valente
Date: 2020-10-02 00:00:00
Tasks: Management

Using machine learning methods in a quasi-experimental setting, I study the heterogeneous effects of introducing waste prices - unit prices on household unsorted waste disposal on - waste demands, municipal costs and pollution. Using a unique panel of Italian municipalities with large variation in prices and observables, I show that waste demands are nonlinear. I find evidence of constant elasticities at low prices, and increasing elasticities at high prices driven by income effects and waste habits before policy. The policy reduces waste management costs and pollution in all municipalities after three years of adoption, when prices cause significant waste avoidance.
Optimal Echo Chambers
Authors: Gabriel Martinez, Nicholas H. Tenev
Date: 2020-10-03 00:00:00

When learning from others, people tend to focus their attention on those with similar views. This is often attributed to flawed reasoning, and thought to slow learning and polarize beliefs. However, we show that echo chambers are a rational response to uncertainty about the accuracy of information sources, and can improve learning and reduce disagreement. Furthermore, overextending the range of views someone is exposed to can backfire, slowing their learning by making them less responsive to information from others. We model a Bayesian decision maker who chooses a set of information sources and then observes a signal from one. With uncertainty about which sources are accurate, focusing attention on signals close to one's own expectation can be beneficial, as their expected accuracy is higher. The optimal echo chamber balances the credibility of views similar to one's own against the usefulness of those further away.
Inference on tissue transplantation experiments
Authors: Yue Wang, Boyu Zhang, Jérémie Kropp, Nadya Morozova
Date: 2020-10-06 00:00:00

We review studies on tissue transplantation experiments for various species: one piece of the donor tissue is excised and transplanted into a slit in the host tissue, then observe the behavior of this grafted tissue. Although we have known the results of some transplantation experiments, there are many more possible experiments with unknown results. We develop a penalty function-based method that uses the known experimental results to infer the unknown experimental results. Similar experiments without similar results get penalized and correspond to smaller probability. This method can provide the most probable results of a group of experiments or the probability of a specific result for each experiment. This method is also generalized to other situations. Besides, we solve a problem: how to design experiments so that such a method can be applied most efficiently.