pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures
Date: 2020-01-28 00:00:00
Tasks: GPU

Uncovering the heterogeneity of cell populations is a long-standing goal in
fields ranging from antimicrobial resistance to cancer research. Emerging
technology platforms such as droplet microfluidics hold the promise to decipher
cellular heterogeneity at ultra-high-throughput. However, there is a lack of
methods able to rapidly identify and isolate single cells or 3D cell cultures.
Here we demonstrate that deep neural networks can accurately classify single
droplet images in real-time based on the presence and number of micro-objects
including single mammalian cells and multicellular spheroids. This approach
also enables the identification of specific objects within mixtures of objects
of different types and sizes. The training sets for the neural networks
consisted of several hundred images manually picked and augmented to up to
thousands of images per training class. Training required less than 10 minutes
using a single GPU, and yielded accuracies of over 90% for single mammalian
cell identification. Crucially, the same model could be used to classify
different types of objects such as polystyrene spheres, polyacrylamide beads
and MCF-7 cells. We applied the developed method to the selection of 3D cell
cultures generated with Hek293FT cells encapsulated in agarose gel beads,
highlighting the potential of the technology for the selection of objects with
high diversity of visual appearance. The real-time sorting of single droplets
was in-line with droplet generation and occurred at rates up to 40 per second
independently of image size up to 480 x 480 pixels. The presented microfluidic
device also enabled storage of sorted droplets to allow for downstream
analyses.
Characterization of a novel automated microfiltration device for the efficient isolation and analysis of circulating tumor cells from clinical blood samples
Date: 2019-12-11 00:00:00

The detection and analysis of circulating tumor cells (CTCs) may enable a
broad range of cancer-related applications, including the identification of
acquired drug resistance during treatments. However, the non-scalable
fabrication, prolonged sample processing times, and the lack of automation,
associated with most of the technologies developed to isolate these rare cells,
have impeded their transition into the clinical practice. This work describes a
novel membrane-based microfiltration device comprised of a fully automated
sample processing unit and a machine-vision-enabled imaging system that allows
the efficient isolation and rapid analysis of CTCs from blood. The device
performance was characterized using four prostate cancer cell lines, including
PC-3, VCaP, DU-145, and LNCaP, obtaining high assay reproducibility and capture
efficiencies greater than 93% after processing 7.5 mL blood samples from
healthy donors, spiked with 100 cancer cells. Cancer cells remained viable
after filtration due to the minimal shear stress exerted over cells during the
procedure, while the identification of cancer cells by immunostaining was not
affected by the number of non-specific events captured on the membrane. We were
also able to identify the androgen receptor (AR) point mutation T878A from 7.5
mL blood samples spiked with 50 LNCaP cells using RT-PCR and Sanger sequencing.
Finally, CTCs were detected in 8 of 8 samples from patients diagnosed with
metastatic prostate cancer (mean $\pm$ SEM = 21 $\pm$ 2.957 CTCs/mL, median =
21 CTC/mL), thereby validating the potential clinical utility of the device.
Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRI
Authors: Ahmad Mayeli, Obada Al Zoubi, Kaylee Henry, Chung Ki Wong, Evan J. White, Qingfei Luo, Vadim Zotev, Hazem Refai, the Tulsa 1000 Investigators, Jerzy Bodurka
Date: 2019-12-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), ERP

Objective. EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram (BCG) artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR). Approach. The pipeline integrates average template subtraction and independent component analysis (ICA) to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n=48) and task-based (i.e., event-related-potentials [ERP]; n=8) paradigms. The chosen gold standard is an expert manual review of the EEG database. Main results. We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g., amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data. Significance: APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
Fuzzy Group Identification Problems
Authors: Federico Fioravanti, Fernando Tohmé
Date: 2019-12-11 00:00:00

We present a fuzzy version of the Group Identification Problem ("Who is a J?") introduced by Kasher and Rubinstein (1997). We consider a class $N = \{1,2,\ldots,n\}$ of agents, each one with an opinion about the membership to a group J of the members of the society, consisting in a function $\pi : N \to [0; 1]$, indicating for each agent, including herself, the degree of membership to J. We consider the problem of aggregating those functions, satisfying different sets of axioms and characterizing different aggregators. While some results are analogous to those of the originally crisp model, the fuzzy version is able to overcome some of the main impossibility results of Kasher and Rubinstein.
A Novel Approach in Strategic Planning of Power Networks Against Physical Attacks
Date: 2019-12-11 00:00:00
Tasks: Misinformation

The reported work points at developing a practical approach for power
transmission planners to secure power networks from potential deliberate
attacks. We study the interaction between a system planner (defender) and a
rational attacker who threatens the operation of the power grid. In addition to
the commonly used hardening strategy for protecting the network, a new sort of
resource is introduced under the deception concept. Feint and deception are
acknowledged as effective tools for misleading the attacker in strategic
planning. To this end, the defender deception is mathematically formulated by
releasing misinformation about his plan in the shared cognition-based model. To
reduce the risk of damage in case of deception failure, preemptive-goal
programming is utilized to prioritize the hardening strategy for the vital
components. Furthermore, the value of posturing is introduced which is the
benefits that the deception brings to the system. The problems are formulated
as tri-level mixed-integer linear programming and solved by the
constraint-and-column generation method. Comprehensive simulation studies
performed on WSCC 9-bus and IEEE 118-bus systems indicate how the defender will
save significant cost from protecting his network with posturing rather than
hardening and the proposed approach is a promising development to ensure the
secure operation of power networks.
Rand-OFDM: A Secured Wireless Signal
Date: 2019-12-11 00:00:00
Tasks: Cryptanalysis

Wireless communication has been a broadcast system since its inception, which
violates security and privacy issues at the physical layer between the intended
transmit and receive pairs. As we move towards advanced spectrum sharing
methodologies involving billions of devices connected over wireless networks,
it is essential to secure the wireless signal such that only the intended
receiver can realize the properties of the signal. In this paper, we propose
Rand-OFDM, a new waveform, secured with a shared secret key, where the signal
properties can be recovered only at the expected receiver. We achieve the
signal level security by modifying the OFDM signal in time-domain, thus erasing
the OFDM properties and in turn obfuscating the signal properties to an
eavesdropper. We introduce a key-based secured training signal for channel
estimation, which can be used only at the intended receiver with prior
knowledge of the key. As a final step for the recovery of the signal, we use a
clustering-based technique to correct the phase of the received signal. Our
cryptanalysis shows that Rand-OFDM is especially useful in future wideband
signals. Extensive simulation and over-the-air experiments show that the
performance of Rand-OFDM is comparable to legacy OFDM and SNR penalty due to
the secured waveform varies between 1-4dB. In all these scenarios, Rand-OFDM
remains unrecognized by the adversary even at the highest possible SNR.
A Dynamic MST- deltaCovar Model Of Systemic Risk In The European Insurance Sector
Authors: Anna Denkowska, Stanisław Wanat
Date: 2019-11-30 00:00:00

This work is an answer to the EIOPA 2017 report. It follows from the latter that in order to assess the potential systemic risk we should take into account the build-up of risk and in particular the risk that arises in time, as well as the interlinkages in the financial sector and the whole economy. Our main tools used to analyse the systemic risk dynamics in the European insurance sector during the years 2005-2019 are the topological indices of minimum spanning trees (MST) and the deltaCoVaR measure. We address the following questions: 1) What is the contribution to systemic risk of each of the 28 largest European insurance companies whose list includes also those appearing on the G-SIIs list? 2) Does the analysis of the deltaCoVaR of those 28 insurance companies and the conclusions we draw agree with the our claims from our latest article [Wanat S., Denkowska A. 2019]. In clear: does the most important contribution to systemic risk come from the companies that have the highest betweenness centrality or the highest degree in the MST obtained?
Adaptive Markov State Model estimation using short reseeding trajectories
Date: 2019-12-12 00:00:00
Tasks: Protein Folding

In the last decade, advances in molecular dynamics (MD) and Markov State
Model (MSM) methodologies have made possible accurate and efficient estimation
of kinetic rates and reactive pathways for complex biomolecular dynamics
occurring on slow timescales. A promising approach to enhanced sampling of MSMs
is to use so-called "adaptive" methods, in which new MD trajectories are
"seeded" preferentially from previously identified states. Here, we investigate
the performance of various MSM estimators applied to reseeding trajectory data,
for both a simple 1D free energy landscape, and for mini-protein folding MSMs
of WW domain and NTL9(1-39). Our results reveal the practical challenges of
reseeding simulations, and suggest a simple way to reweight seeding trajectory
data to better estimate both thermodynamic and kinetic quantities.
Brexit Risk Implied by the SABR Martingale Defect in the EUR-GBP Smile
Authors: Petteri Piiroinen, Lassi Roininen, Martin Simon
Date: 2019-12-12 00:00:00
Tasks: parameter estimation

We construct a data-driven statistical indicator for quantifying the tail risk perceived by the EURGBP option market surrounding Brexit-related events. We show that under lognormal SABR dynamics this tail risk is closely related to the so-called martingale defect and provide a closed-form expression for this defect which can be computed by solving an inverse calibration problem. In order to cope with the the uncertainty which is inherent to this inverse problem, we adopt a Bayesian statistical parameter estimation perspective. We probe the resulting posterior densities with a combination of optimization and adaptive Markov chain Monte Carlo methods, thus providing a careful uncertainty estimation for all of the underlying parameters and the martingale defect indicator. Finally, to support the feasibility of the proposed method, we provide a Brexit "fever curve" for the year 2019.
Anomalous thermal fluctuation distribution sustains proto-metabolic cycles and biomolecule synthesis
Date: 2019-12-12 00:00:00

An environment far from equilibrium is thought to be a necessary condition
for the origin and persistence of life. In this context we report open-flow
simulations of a non-enzymic proto-metabolic system, in which hydrogen peroxide
acts both as oxidant and driver of thermochemical cycling. We find that a
Gaussian perturbed input produces a non-Boltzmann output fluctuation
distribution around the mean oscillation maximum. Our main result is that net
biosynthesis can occur under fluctuating cyclical but not steady drive.
Consequently we may revise the necessary condition to "dynamically far from
equilibrium".
Online Distributed Learning over Graphs with Multitask Graph-Filter Models
Date: 2019-12-12 00:00:00

In this work, we are interested in adaptive and distributed estimation of
graph filters from streaming data. We formulate this problem as a consensus
estimation problem over graphs, which can be addressed with diffusion LMS
strategies. Most popular graph-shift operators such as those based on the graph
Laplacian matrix, or the adjacency matrix, are not energy preserving. This may
result in an ill-conditioned estimation problem, and reduce the convergence
speed of the distributed algorithms. To address this issue and improve the
transient performance, we introduce a preconditioned graph diffusion LMS
algorithm. We also propose a computationally efficient version of this
algorithm by approximating the Hessian matrix with local information.
Performance analyses in the mean and mean-square sense are provided. Finally,
we consider a more general problem where the filter coefficients to estimate
may vary over the graph. To avoid a large estimation bias, we introduce an
unsupervised clustering method for splitting the global estimation problem into
local ones. Numerical results show the effectiveness of the proposed algorithms
and validate the theoretical results.
Single-Switch-Regulated Resonant WPT Receiver
Date: 2019-12-18 00:00:00

A single-switch-regulated wireless power transfer (WPT) receiver is presented
in this letter. Aiming at low-cost applications, the system involves only a
single-switch class-E resonant rectifier, a frequency synchronization circuit,
and a microcontroller. The number of power semiconductor devices required in
this circuit is minimal. Only one active switch is used and no diode is
required. As a single-switch solution, this simplifies circuit implementation,
improves reliability, and lowers hardware cost. The single-switch resonant
rectifier provides a relatively constant quasi-sinusoidal voltage waveform to
pick up the wireless power from the receiver coil. Due to the resonant nature
of the rectifier, ZVS turn on and turn off are achieved. The steady-state
analysis and discussions on the component sizing and the control design are
provided. A prototype is built and experimental works are performed to verify
the features.
Modified Computation of Correlation Integral for Analyzing Epileptic Signals
Date: 2019-12-12 00:00:00
Tasks: Time Series, Time Series Analysis

Epilepsy is a chronic neurological disorder characterized by recurrent
seizures. One method for analyzing seizure activity is to compute the
correlation dimension of time-series electroencephalographic signals. The
Grasserberg and Proccacia algorithm is commonly used to compute this
correlation dimension. The algorithm uses the Heaviside function to determine
the correlation integral by counting the number of distances between vectors
(d_ij) that are greater than a threshold. However, information about the
chaotic nature of the signal is not completely retained by this function. In
this work, instead of using the Heaviside function, we calculated the
correlation integral by using an exponential function of d_ij. Greater
sensitivity to the interictal and ictal signals using this modified algorithm
was verified using three datasets. Comparing heatmaps of d_ij obtained using
the original and modified methods showed additional information that was
retained with the new algorithm.
RARE: Image Reconstruction using Deep Priors Learned without Ground Truth
Date: 2020-05-23 00:00:00
Tasks: Denoising, Image Reconstruction

Regularization by denoising (RED) is an image reconstruction framework that
uses an image denoiser as a prior. Recent work has shown the state-of-the-art
performance of RED with learned denoisers corresponding to pre-trained
convolutional neural nets (CNNs). In this work, we propose to broaden the
current denoiser-centric view of RED by considering priors corresponding to
networks trained for more general artifact-removal. The key benefit of the
proposed family of algorithms, called regularization by artifact-removal
(RARE), is that it can leverage priors learned on datasets containing only
undersampled measurements. This makes RARE applicable to problems where it is
practically impossible to have fully-sampled groundtruth data for training. We
validate RARE on both simulated and experimentally collected data by
reconstructing a free-breathing whole-body 3D MRIs into ten respiratory phases
from heavily undersampled k-space measurements. Our results corroborate the
potential of learning regularizers for iterative inversion directly on
undersampled and noisy measurements.
Alternative Axioms in Group Identification Problems
Date: 2019-12-11 00:00:00

Kasher and Rubinstein (1997) introduced the problem of classifying the
members of a group in terms of the opinions of their potential members. This
involves a finite set of agents $N = \{1,2,\ldots,n\}$, each one having an
opinion about which agents should be classified as belonging to a specific
subgroup J. A Collective Identity Function (CIF) aggregates those opinions
yielding the class of members deemed $J$. Kasher and Rubinstein postulate
axioms, intended to ensure fair and socially desirable outcomes, characterizing
different CIFs. We follow their lead by replacing their liberal axiom by other
axioms, constraining the spheres of influence of the agents. We show that some
of them lead to different CIFs while in another instance we find an
impossibility result.
Stochastic and Distributionally Robust Load Ensemble Control
Date: 2020-03-27 00:00:00

Demand response (DR) programs aim to engage distributed demand-side resources
in providing ancillary services for electric power systems. Previously,
aggregated thermostatically controlled loads (TCLs) have been demonstrated as a
technically viable and economically valuable provider of such services that can
effectively compete with conventional generation resources in reducing load
peaks and smoothing demand fluctuations. Yet, to provide these services at
scale, a large number of TCLs must be accurately aggregated and operated in
sync. This paper describes a Markov Decision Process (MDP) that aggregates and
models an ensemble of TCLs. Using the MDP framework, we propose to internalize
the exogenous uncertain dynamics of TCLs by means of stochastic and
distributionally robust optimization. First, under mild assumptions on the
underlying uncertainty, we derive analytical stochastic and distributionally
robust control policies for dispatching a given TCL ensemble. Second, we
further relax these mild assumptions to allow for a more delicate treatment of
uncertainty, which leads to distributionally robust MDP formulations with
moment- and Wasserstein-based ambiguity sets that can be efficiently solved
numerically. The case study compares the analytical and numerical control
policies using a simulated ensemble of 1,000 air conditioners.
Terahertz Communications (TeraCom): Challenges and Impact on 6G Wireless Systems
Date: 2019-12-13 00:00:00

Terahertz communications are envisioned as a key technology for 6G, which
requires 100+ Gbps data rates, 1-millisecond latency, among other performance
metrics. As a fundamental wireless infrastructure, the THz communication can
boost abundant promising applications, including next-generation WLAN systems
like Tera-WiFi, THz wireless backhaul, as well as other long-awaited novel
communication paradigms. Serving as a basis of efficient wireless communication
and networking design, this paper highlights the key THz channel features and
recent advancements in device technologies. In light of these, impact and
guidelines on 6G wireless communication and networking are elaborated. We
believe the progress of THz technologies is helping finally close the so called
THz Gap, and will realize THz communications as a pillar of 6G wireless
systems.
Efficient methods for determining folding free energies in single-molecule pulling experiments
Date: 2019-12-12 00:00:00

The remarkable accuracy and versatility of single-molecule techniques make
possible new measurements that are not feasible in bulk assays. Among these,
the precise estimation of folding free energies using fluctuation theorems in
nonequilibrium pulling experiments has become a benchmark in modern biophysics.
In practice, the use of fluctuation relations to determine free energies
requires a thorough evaluation of the usually large energetic contributions
caused by the elastic deformation of the different elements of the experimental
setup (such as the optical trap, the molecular linkers and the
stretched-unfolded polymer). We review and describe how to optimally estimate
such elastic energy contributions to extract folding free energies, using DNA
and RNA hairpins as model systems pulled by laser optical tweezers. The
methodology is generally applicable to other force-spectroscopy techniques and
molecular systems.
EM-based approach to 3D reconstruction from single-waveform multispectral Lidar data
Date: 2019-12-12 00:00:00
Tasks: 3D Reconstruction

In this paper, we present a novel Bayesian approach for estimating spectral
and range profiles from single-photon Lidar waveforms associated with single
surfaces in the photon-limited regime. In contrast to classical multispectral
Lidar signals, we consider a single Lidar waveform per pixel, whereby a single
detector is used to acquire information simultaneously at multiple wavelengths.
A new observation model based on a mixture of distributions is developed. It
relates the unknown parameters of interest to the observed waveforms containing
information from multiple wavelengths. Adopting a Bayesian approach, several
prior models are investigated and a stochastic Expectation-Maximization
algorithm is proposed to estimate the spectral and depth profiles. The
reconstruction performance and computational complexity of our approach are
assessed, for different prior models, through a series of experiments using
synthetic and real data under different observation scenarios. The results
obtained demonstrate a significant speed-up without significant degradation of
the reconstruction performance when compared to existing methods in the
photon-starved regime.