pwc-archive/papers-with-abstracts huggingface dataset:
Foot-print of Claudin and Occludin Transcriptome in Colorectal Cancer
Authors: Maryam Ghoojaei, Reza Shirkoohi, Mojtaba Saffari, Amirnader Emamirazavi, Mehrdad Hashemi
Date: 2020-09-29 00:00:00

Background and Purpose: Colorectal cancer as a leading cause of mortality worldwide, can be regarded as a relatively common and fatal disease with increasing incidence over recent years. Colorectal cancer is characterized by uncontrolled growth of abnormal cells occurring in different parts of the colon. About 90% of deaths associated with cancers occur due to metastasis which overcome overcomes the body's cellular connection, including tight junctions. Claudin and Occludin are integral membrane proteins found in tight junctions. The aim of the present study was to investigate the expression level of claudin and occludin in human colorectal cancer. Method: In this study, 38 colorectal cancer patients who referred to Cancer Institute of Imam Khomeini Hospital in Tehran, Iran were studied after obtaining the informed consent. First, quantitative extraction of RNA was performed, then the expression levels of claudin and Occludin genes were examined by reverse transcription, PCR, and Real-time PCR. Findings: The expression levels of both claudin and Occludin genes in cases with higher stage and grade of disease, in the state of metastasis were more than those of the control samples. Conclusion: The increased expression level of the mentioned genes can be considered as an influential factor in turning the normal healthy tissues into cancerous cells.
DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging using Deep Learning
Date: 2020-09-29 00:00:00
Tasks: Deep Learning

Optical diffraction tomography measures the three-dimensional refractive
index map of a specimen and visualizes biochemical phenomena at the nanoscale
in a non-destructive manner. One major drawback of optical diffraction
tomography is poor axial resolution due to limited access to the
three-dimensional optical transfer function. This missing cone problem has been
addressed through regularization algorithms that use a priori information, such
as non-negativity and sample smoothness. However, the iterative nature of these
algorithms and their parameter dependency make real-time visualization
impossible. In this article, we propose and experimentally demonstrate a deep
neural network, which we term DeepRegularizer, that rapidly improves the
resolution of a three-dimensional refractive index map. Trained with pairs of
datasets (a raw refractive index tomogram and a resolution-enhanced refractive
index tomogram via the iterative total variation algorithm), the
three-dimensional U-net-based convolutional neural network learns a
transformation between the two tomogram domains. The feasibility and
generalizability of our network are demonstrated using bacterial cells and a
human leukaemic cell line, and by validating the model across different
samples. DeepRegularizer offers more than an order of magnitude faster
regularization performance compared to the conventional iterative method. We
envision that the proposed data-driven approach can bypass the high time
complexity of various image reconstructions in other imaging modalities.
Far-Field Minimum-Fuel Spacecraft Rendezvous using Koopman Operator and $\ell_2/\ell_1$ Optimization
Date: 2020-09-29 00:00:00
Tasks: Compressive Sensing

We propose a method to compute approximate solutions to the minimum-fuel
far-field rendezvous problem for thrust-vectoring spacecraft. It is well-known
that the use of linearized spacecraft rendezvous equations may not give
sufficiently accurate results for far-field rendezvous. In particular, as the
distance between the active and the target spacecraft becomes significantly
greater than the distance between the target spacecraft and the center of
gravity of the planet, the accuracy of linearization-based control design
approaches may decline substantially. In this paper, we use a nonlinear state
space model which corresponds to more accurate description of dynamics than
linearized models but at the same time poses the known challenges of nonlinear
control design. To overcome these challenges, we utilize a Koopman operator
based approach with which the nonlinear spacecraft rendezvous dynamics is
lifted into a higher dimensional space over which the nonlinear dynamics can be
approximated by a linear system which is more suitable for control design
purposes than the original nonlinear model. An Iteratively Recursive Least
Squares (IRLS) algorithm from compressive sensing is then used to solve the
minimum fuel control problem based on the lifted linear system. Numerical
simulations are performed to show the efficacy of the proposed Koopman operator
based approach.
Internet Photonic Sensing: Using Internet Fiber Optics for Vibration Measurement and Monitoring
Authors: Shreeshrita Patnaik, Paul Barford, Dante Fratta, Bill Jensen, Neal Lord, Matt Malloy, Herb Wang
Date: 2020-09-29 00:00:00

In this paper, we introduce Internet Photonic Sensing (IPS), a new framework for deformation and vibration measurement and monitoring based on signals that are available from standard fiber optic communication hardware deployed in the Internet. IPS is based on the hypothesis that atmospheric, seismic, anthropogenic and other natural activity cause vibrations in the earth that trigger detectable changes in standard optical signals that transmit data through Internet fiber. We assume a simple system component model for optical communication hardware and identify two candidate signals that may reflect deformation and vibrations and that can be measured through standard interfaces: Optical Signal Strength (OSS) and Bit Error Rate (BER). We investigate the efficacy of IPS through a series of controlled, laboratory experiments that consider how the candidate signals respond when fiber is subjected to a range of stresses. We believe that IPS offers the potential to transform the practice of scientific, commercial and public safety-related vibration monitoring applications by providing a highly-sensitive platform that is available at a global scale.
Expectations, Networks, and Conventions
Authors: Benjamin Golub, Stephen Morris
Date: 2020-09-29 00:00:00

In coordination games and speculative over-the-counter financial markets, solutions depend on higher-order average expectations: agents' expectations about what counterparties, on average, expect their counterparties to think, etc. We offer a unified analysis of these objects and their limits, for general information structures, priors, and networks of counterparty relationships. Our key device is an interaction structure combining the network and agents' beliefs, which we analyze using Markov methods. This device allows us to nest classical beauty contests and network games within one model and unify their results. Two applications illustrate the techniques: The first characterizes when slight optimism about counterparties' average expectations leads to contagion of optimism and extreme asset prices. The second describes the tyranny of the least-informed: agents coordinating on the prior expectations of the one with the worst private information, despite all having nearly common certainty, based on precise private signals, of the ex post optimal action.
Unboxing mutations: Connecting mutation types with evolutionary consequences
Authors: Emma L. Berdan, Alexandre Blanckaert, Tanja Slotte, Alexander Suh, Anja M. Westram, Inês Fragata
Date: 2020-09-29 00:00:00

Mutations are typically classified by their effects on the nucleotide sequence and by their size. Here, we argue that if our main aim is to understand the effect of mutations on evolutionary outcomes (such as adaptation or speciation), we need to instead consider their population genetic and genomic effects, from altering recombination rate to modifying chromatin. We start by reviewing known population genetic and genomic effects of different mutation types and connect these to the major evolutionary processes of drift and selection. We illustrate how mutation type can thus be linked with evolutionary outcomes and provide suggestions for further exploring and quantifying these relationships. This reframing lays a foundation for determining the evolutionary significance of different mutation types.
Channel Modeling and Performance Analysis of Airplane-Satellite Terahertz Band Communications
Authors: J. Kokkoniemi, J. M. Jornet, V. Petrov, E. Koucheryavy, M. Juntti
Date: 2020-09-29 00:00:00

Wireless connectivity in airplanes is becoming more important, demanded, and common. One of the largest bottlenecks with the in-flight Internet is that the airplane is far away from both the satellites and the ground base stations during most of the flight time. Maintaining a reliable and high-rate wireless connection with the airplane over such a long-range link thus becomes a challenge. Microwave frequencies allow for long link distances but lack the data rate to serve up to several hundreds of potential onboard customers. Higher bands in the millimeter-wave spectrum (30 GHz-300 GHz) have, therefore, been utilized to overcome the bandwidth limitations. Still, the per-user throughput with state-of-the-art millimeter-wave systems is an order of magnitude lower than the one available with terrestrial wireless networks. In this paper, we take a step further and study the channel characteristics for the terahertz band (THz, 0.3 THz-10 THz) in order to map the feasibility of this band for aviation. We first propose a detailed channel model for aerial THz communications taking into account both the non-flat Earth geometry and the main features of the frequency-selective THz channel. We then apply this model to estimate the characteristics of aerial THz links in different conditions. We finally determine the altitudes where the use of airplane-to-satellite THz connection becomes preferable over the airplane-to-ground THz link. Our results reveal that the capacity of the airborne THz link may reach up to 120 Gbit/s, thus enabling cellular-equivalent data rates to the passengers and staff during the entire flight.
A Computational Approach to Identification of Treatment Effects for Policy Evaluation
Authors: Sukjin Han, Shenshen Yang
Date: 2020-09-29 00:00:00
Tasks: counterfactual

For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables are only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to fully incorporate statistical independence (rather than mean independence) of instruments and a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services.
Structural asymmetry along protein sequences and co-translational folding
Authors: John M McBride, Tsvi Tlusty
Date: 2020-09-29 00:00:00
Tasks: Translation

Proteins are translated from the N- to the C-terminus, raising the basic question of how this innate directionality affects their evolution. To explore this question, we analyze 16,200 structures from the protein data bank (PDB). We find remarkable enrichment of $\alpha$-helices at the C terminus and $\beta$-strands at the N terminus. Furthermore, this $\alpha$-$\beta$ asymmetry correlates with sequence length and contact order, both determinants of folding rate, hinting at possible links to co-translational folding (CTF). Hence, we propose the 'slowest-first' scheme, whereby protein sequences evolved structural asymmetry to accelerate CTF: the slowest of the cooperatively-folding segments are positioned near the N terminus so they have more time to fold during translation. A phenomenological model predicts that CTF can be accelerated by asymmetry, up to double the rate, when folding time is commensurate with translation time; analysis of the PDB reveals that structural asymmetry is indeed maximal in this regime. This correspondence is greater in prokaryotes, which generally require faster protein production. Altogether, this indicates that accelerating CTF is a substantial evolutionary force whose interplay with stability and functionality is encoded in sequence asymmetry.
When Local Governments' Stay-at-Home Orders Meet the White House's "Opening Up America Again"
Date: 2020-10-29 00:00:00

On April 16th, The White House launched "Opening up America Again" (OuAA)
campaign while many U.S. counties had stay-at-home orders in place. We created
a panel data set of 1,563 U.S. counties to study the impact of U.S. counties'
stay-at-home orders on community mobility before and after The White House's
campaign to reopen the country. Our results suggest that before the OuAA
campaign stay-at-home orders brought down time spent in retail and recreation
businesses by about 27% for typical conservative and liberal counties. However,
after the launch of OuAA campaign, the time spent at retail and recreational
businesses in a typical conservative county increased significantly more than
in liberal counties (15% increase in a typical conservative county Vs. 9%
increase in a typical liberal county). We also found that in conservative
counties with stay-at-home orders in place, time spent at retail and
recreational businesses increased less than that of conservative counties
without stay-at-home orders. These findings illuminate to what extent
residents' political ideology could determine to what extent they follow local
orders and to what extent the White House's OuAA campaign polarized the
obedience between liberal and conservative counties. The silver lining in our
study is that even when the federal government was reopening the country, the
local authorities that enforced stay-at-home restrictions were to some extent
effective.
On the Pricing of Currency Options under Variance Gamma Process
Authors: Azwar Abdulsalam, Gowri Jayprakash, Abhijeet Chandra
Date: 2020-09-29 00:00:00
Tasks: Management

The pricing of currency options is largely dependent on the dynamic relationship between a pair of currencies. Typically, the pricing of options with payoffs dependent on multi-assets becomes tricky for reasons such as the non-Gaussian distribution of financial variable and non-linear macroeconomic relations between these markets. We study the options based on the currency pair US dollar and Indian rupee (USD-INR) and test several pricing formulas to evaluate the performance under different volatility regimes. We show the performance of the variance gamma and the symmetric variance gamma models during different volatility periods as well as for different moneyness, in comparison to the modified Black-Scholes model. In all cases, variance gamma model outperforms Black-Scholes. This can be attributed to the control of kurtosis and skewness of the distribution that is possible using the variance gamma model. Our findings support the superiority of variance gamma process of currency option pricing in better risk management strategies.
The role of clearance mechanisms in the kinetics of toxic protein aggregates involved in neurodegenerative diseases
Authors: Travis B. Thompson, Georg Meisl, Tuomas Knowles, Alain Goriely
Date: 2020-09-29 00:00:00

Protein aggregates in the brain play a central role in cognitive decline and structural damage associated with neurodegenerative diseases. For instance, in Alzheimer's disease the formation of Amyloid-beta plaques and tau proteins neurofibrillary tangles follows from the accumulation of different proteins into large aggregates through specific mechanisms such as nucleation and elongation. These mechanisms have been studied in vitro where total protein mass is conserved. However, in vivo, clearance mechanisms may play an important role in limiting the formation of aggregates. Here, we generalise classical models of protein aggregation to take into account both production of monomers and the clearance of protein aggregates. Depending on the clearance model, we show that there may be a critical clearance value above which aggregation does not take place. Our result offers further evidence in support of the hypotheses that clearance mechanisms play a potentially crucial role in neurodegenerative disease initiation and progression; and as such, are a possible therapeutic target.
A Comprehensive Multi-Period Optimal Power Flow Framework for Smart LV Networks
Date: 2020-09-29 00:00:00

This paper presents an extensive multi-period optimal power flow framework, with new modelling elements, for smart LV distribution systems that rely on residential flexibility for combating operational issues. A detailed performance assessment of different setups is performed, including: ZIP flexible loads (FLs), varying degrees of controllability of conventional residential devices, such as electric vehicles (EVs) or photovoltaics (PVs), by the distribution system operator (DSO) (adhering to customer-dependent restrictions) and full exploitation of the capabilities offered by state-of-the-art inverter technologies. A comprehensive model-dependent impact assessment is performed, including phase imbalances, neutral and ground wires and load dependencies. The de-congestion potential of common residential devices is highlighted, analyzing capabilities such as active power redistribution, reactive power support and phase balancing. Said potential is explored on setups where the DSO can make only partial adjustments on customer profiles, rather than (as is common) deciding on the full profiles. The extensive analysis can be used by DSOs and researchers alike to make informed decisions on the required levels of modelling detail, the connected devices and the degrees of controlability. The formulation is computationally efficient, scaling well to medium-size systems, and can serve as an excellent basis for building more tractable or more targeted approaches.
Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020
Date: 2020-09-29 00:00:00
Tasks: Speaker Recognition

This report describes our submission to the VoxCeleb Speaker Recognition
Challenge (VoxSRC) at Interspeech 2020. We perform a careful analysis of
speaker recognition models based on the popular ResNet architecture, and train
a number of variants using a range of loss functions. Our results show
significant improvements over most existing works without the use of model
ensemble or post-processing. We release the training code and pre-trained
models as unofficial baselines for this year's challenge.
Abusive Language Detection and Characterization of Twitter Behavior
Authors: Davis Dincy, Murali Reena, Babu Remesh
Date: 2020-09-26 00:00:00
Tasks: Abusive Language

In this work, abusive language detection in online content is performed using
Bidirectional Recurrent Neural Network (BiRNN) method. Here the main objective
is to focus on various forms of abusive behaviors on Twitter and to detect
whether a speech is abusive or not. The results are compared for various
abusive behaviors in social media, with Convolutional Neural Netwrok (CNN) and
Recurrent Neural Network (RNN) methods and proved that the proposed BiRNN is a
better deep learning model for automatic abusive speech detection.
Reduced neural activity during volatile anesthesia compared to TIVA: evidence from a novel EEG signal processing analysis
Authors: Amitai Bickel, Alexey Gavrilov, Shimon Ivry, Neta B Maimon, Lior Molcho, Nathan Intrator
Date: 2020-09-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Post-operative cognitive decline is a well-known phenomenon and of crucial importance especially in the elderly. General anesthesia can be accomplished by inhalation-based (volatile) or total intravenous anesthesia (TIVA). While their effects on post-operative symptoms have been investigated, little is known about their influence on brain functionalities during the surgery itself. To assess differences 17 patients were divided to receive either volatile anesthesia (n=9), or TIVA (n=8). The level of anesthesia was kept to be equal in both groups. A single bipolar EEG electrode (Neurosteer system) was placed on the participants foreheads. It presented real-time activity and collected their data during the surgery. The dependent variables included frequency bands (delta, theta, alpha, and beta), and three features (VC9, ST4, and A0) previously extracted with the device and provided by Neurosteer. All surgeries were uneventful, and all patients showed bispectral index (BIS) score less than 60. Feature activity under volatile anesthesia (in comparison to TIVA) was significantly lower for the delta, theta and alpha frequency bands and for the three features. Further analysis showed that the largest difference between anesthesia types was for feature A0. The EEG frequency bands and novel brain activity features provide evidence that volatile anesthesia further reduces components of brain activity in comparison to TIVA anesthesia. Specifically, A0, which previously showed a correlation with cognitive decline severity and cognitive load, exhibited the most prominent difference between anesthesia types. Together, this study suggests that measuring brain activity during anesthesia using sensitive features, enables revealing that different anesthesia types may affect brain activity differently, which could affect the recovery from anesthesia, and consequently reduce post-operative cognitive decline
Predicting Non Farm Employment
Authors: Tarun Bhatia
Date: 2020-09-12 00:00:00
Tasks: BIG-bench Machine Learning, regression

U.S. Nonfarm employment is considered one of the key indicators for assessing the state of the labor market. Considerable deviations from the expectations can cause market moving impacts. In this paper, the total U.S. nonfarm payroll employment is predicted before the release of the BLS employment report. The content herein outlines the process for extracting predictive features from the aggregated payroll data and training machine learning models to make accurate predictions. Publically available revised employment report by BLS is used as a benchmark. Trained models show excellent behaviour with R2 of 0.9985 and 99.99% directional accuracy on out of sample periods from January 2012 to March 2020. Keywords Machine Learning; Economic Indicators; Ensembling; Regression, Total Nonfarm Payroll