pwc-archive/papers-with-abstracts huggingface dataset:
Deregulation of calcium homeostasis in Bcr-Abl-dependent chronic myeloid leukemia
Authors: Hélène Cabanas, Thomas Harnois, Christophe Magaud, Laëtitia Cousin, Bruno Constantin, Nicolas Bourmeyster, Nadine Déliot
Date: 2019-12-17 00:00:00

Background: Chronic myeloid leukemia (CML) results from hematopoietic stem cell transformation by the bcr-abl chimeric oncogene, encoding a 210 kDa protein with constitutive tyrosine kinase activity. In spite of the efficiency of tyrosine kinase inhibitors (TKI; Imatinib), other strategies are explored to eliminate CML leukemia stem cells, such as calcium pathways. Results: In this work, we showed that Store-Operated Calcium Entry (SOCE) and thrombin induced calcium influx were decreased in Bcr-Abl expressing 32d cells (32d-p210). The 32d-p210 cells showed modified Orai1/STIM1 ratio and reduced TRPC1 expression that could explain SOCE reduction. Decrease in SOCE and thrombin induced calcium entry was associated to reduced Nuclear Factor of Activated T cells (NFAT) nucleus translocation in 32d-p210 cells. We demonstrated that SOCE blockers enhanced cell mobility of 32d-p210 cells and reduced the proliferation rate in both 32d cell lines. TKI treatment slightly reduced the thrombin-induced response, but imatinib restored SOCE to the wild type level. Bcr-Abl is also known to deregulate Protein Kinase C (PKC), which was described to modulate calcium entries. We showed that PKC enhances SOCE and thrombin induced calcium entries in control cells while this effect is lost in Bcr-Abl-expressing cells. Conclusion: The tyrosine kinase activity seems to regulate calcium entries probably not directly but through a global cellular reorganization involving a PKC pathway. Altogether, calcium entries are deregulated in Bcr-Abl-expressing cells and could represent an interesting therapeutic target in combination with TKI.
Capacity Characterization for Reconfigurable Intelligent Surfaces Assisted Multiple-Antenna Multicast
Authors: Linsong Du, Shihai Shao Gang Yang, Jianhui Ma, Qinpeng Liang, Youxi Tang
Date: 2019-12-17 00:00:00

The reconfigurable intelligent surface (RIS), which consists of a large number of passive and low-cost reflecting elements, has been recognized as a revolutionary technology to enhance the performance of future wireless networks. This paper considers an RIS assisted multicast transmission, where a base station (BS) with multiple-antenna multicasts common message to multiple single-antenna mobile users (MUs) under the assistance of an RIS. An equivalent channel model for the considered multicast transmission is analyzed, and then an optimization problem for the corresponding channel capacity is formulated to obtain the optimal covariance matrix and phase shifts. In order to solve the above non-convex and non-differentiable problem, this paper first exploits the gradient descent method and alternating optimization, to approach the locally optimal solution for any number of MUs. Then, this paper considers a special case, which can obtain the global optimal solution, and shows the sufficient and necessary condition for this special case. Finally, the order growth of the maximal capacity is obtained when the numbers of the reflecting elements, the BS antennas, and the MUs go to infinity.
Two-Timescale Channel Estimation for Reconfigurable Intelligent Surface Aided Wireless Communications
Authors: Chen Hu, Linglong Dai
Date: 2019-12-17 00:00:00
Tasks: Vocal Bursts Valence Prediction

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS and the user equipments (UEs) is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel be, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead.
A Two-Step Biopolymer Nucleation Model Shows a Nonequilibrium Critical Point
Date: 2019-12-17 00:00:00
Tasks: Vocal Bursts Valence Prediction

Biopolymer self-assembly pathways are central to biological activity, but are
complicated by the ability of the monomeric subunits of biopolymers to adopt
different conformational states. As a result, biopolymer nucleation often
involves a two-step mechanism where the monomers first condense to form a
metastable intermediate, and this then converts to a stable polymer by
conformational rearrangement of its constituent monomers. While existing
mathematical models neglect the dynamics by which intermediates convert to
stable polymers, experiments and simulations show that these dynamics
frequently occur on comparable timescales to condensation of intermediates and
growth of mature polymers, and thus cannot be ignored. Moreover, nucleation
intermediates are responsible for cell toxicity in pathologies such as
Alzheimer's, Parkinson's, and prion diseases. Due to the relationship between
conformation and biological function, the slow conversion dynamics of these
species will strongly affect their toxicity. In this study, we present a
modified Oosawa model which explicitly accounts for simultaneous assembly and
conversion. To describe the conversion dynamics, we propose an experimentally
motivated initiation-propagation (IP) mechanism in which the stable phase
arises locally within the intermediate, and then spreads through additional
conversion events induced by nearest-neighbor interactions, analogous to
one-dimensional Glauber dynamics. Our mathematical analysis shows that the
competing timescales of assembly and conversion result in a nonequilibrium
critical point, separating a regime where intermediates are kinetically
unstable from one where conformationally mixed intermediates can accumulate.
Our work provides the first general model of two-step biopolymer nucleation,
which can be used to quantitatively predict the concentration and composition
of biologically crucial intermediates.
Simple mathematical model of aging
Authors: Yu. N. Morokov
Date: 2019-12-17 00:00:00
Tasks: model

A simple mathematical model of the aging process for long-lived organisms is considered. The key point in this model is the assumption that the body does not have internal clocks that count out the chronological time at scales of decades. At these scales, we may limit ourselves by empirical consideration only the background (smoothed, averaged) processes. The body is dealing with internal biological factors, which can be considered as the biological clocks in suitable parameterization of corresponding variables. The dynamics of these variables is described using a system of autonomous ODEs. A particular representation of the right-hand side of equations in the form of quadratic polynomials is considered. In the simplest case of one variable we deal with a logistic equation, which has an analytical solution. Such quadratic model is justified if it is used to predict the dynamic of aging process for relatively small time intervals. However, since a well-defined biological interpretation can be given for the quadratic right-hand side of the equations, we can expect that the area of applicability of this simplified empirical model can extend to relatively large time intervals. The considered model is, in our opinion, a good and simple mathematical framework for organizing experimental data. The model may be useful to quantify and arrange the chains of causal relationships that may appear significant for the development of the aging process.
Stochasticity in Feedback Loops; Great Expectations and Guaranteed Ruin
Date: 2019-12-17 00:00:00

Stochastic feedback systems give rise to a variety of notions of stability.
The conditions for the stability of the median, mean, and variance stability
conditions differ. These conditions can be stated explicitly for scalar
discrete-time systems with (almost) arbitrary distributions of the stochastic
feedback gain. The state variable in such systems evolves towards a
heavy-tailed distribution and exhibits some non-intuitive characteristics. For
example, one can use stochastic feedback to stabilise unstable systems where
one does not know the sign of the unstable pole or the sign of the system gain.
A more dramatic example is an investment scheme which simultaneously yields
unbounded expected profit and almost certain bankruptcy to every investor.
Feedback Control of Dynamical Systems with Given Restrictions on Output Signal
Date: 2019-12-17 00:00:00

A novel method for control of dynamical systems, proposed in the paper,
ensures an output signal belonging to the given set at any time. The method is
based on a special change of coordinates such that the initial problem with
given restrictions on an output variable can be performed as the problem of the
input-to-state stability analysis of a new extended system without
restrictions. The new control laws for linear plants, systems with sector
nonlinearity and systems with an arbitrary relative degree are proposed.
Examples of change of coordinates are given, and they are utilized to design
the control algorithms. The simulations confirm theoretical results and
illustrate the effectiveness of the proposed method in the presence of
parametric uncertainty and external disturbances.
Distributed Network Privacy using Error Correcting Codes
Date: 2019-12-17 00:00:00

Most current distributed processing research deals with improving the
flexibility and convergence speed of algorithms for networks of finite size
with no constraints on information sharing and no concept for expected levels
of signal privacy. In this work we investigate the concept of data privacy in
unbounded public networks, where linear codes are used to create hard limits on
the number of nodes contributing to a distributed task. We accomplish this by
wrapping local observations in a linear code and intentionally applying symbol
errors prior to transmission. If many nodes join the distributed task, a
proportional number of symbol errors are introduced into the code leading to
decoding failure if the code's predefined symbol error limit is exceeded.
Low-Rank Tensor MMSE Equalization
Date: 2019-12-17 00:00:00

New-generation wireless communication systems will employ large-scale antenna
arrays to satisfy the increasing capacity demand. This massive scenario brings
new challenges to the channel equalization problem due to the increased signal
processing complexity. We present a novel low-rank tensor equalizer to tackle
the high computational demands of the classical linear approach. Specifically,
we propose a method to design a canonical polyadic tensor filter to minimize
the mean square error criterion. Our simulation results indicate that the
proposed equalizer needs fewer calculations and is more robust to short
training sequences than the benchmark.
Improving the frequency response of Savitzky-Golay filters via colored-noise models
Authors: Hugh L Kennedy
Date: 2019-12-18 00:00:00

Savitzky-Golay (SG) filters are finite impulse response (FIR) realizations of least-squares polynomial regression and they are widely used for filtering (e.g. smoothing, interpolating, predicting, differentiating) and processing (e.g. detecting and classifying) non-stationary signals in non-Gaussian noise. For such inputs, the Wiener filter is biased and the Kalman filter is sub-optimal. Sequentially-correlated (i.e. colored) noise models are an integral part of the Wiener filter and an optional addition to the Kalman filter; however, their use in SG-filters has been overlooked in recent times. It is shown here that colored (wide-band and narrow-band) noise models are readily incorporated into a standard SG-filter and that this also addresses the well-known deficiency of their poor frequency-selectivity/configurability. A wide-band noise model sets the main-lobe/side-lobe width/height and provides physical justification for band-limited design procedures described elsewhere. The proposed narrow-band noise model, with arbitrarily placed side-lobe nulls, has the potential to outperform other SG filters when sinusoidal interferers of known frequency are present. The utility of these whitened SG-filters is illustrated in a hypothetical pulse/peak-detection application using a test statistic that is shaped by the noise model.
High-resolution Experimental Study and Numerical Modeling of Population Dynamics in a Bacteria Culture
Date: 2019-12-18 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

In this paper, experimental data is presented and a simple model is developed
for the time evolution of a F-amp \textit{E. Coli} culture population. In
general, the bacteria life cycle as revealed by monitoring a culture's
population consists of the lag phase, the growth (or exponential) phase, the
log (or stationary) phase, and finally the death phase. As the name suggests,
in the stationary phase, the population of the bacteria ceases to grow
exponentially and reaches a plateau before beginning the death phase. High
temporal resolution experimental observations using a unique light-scattering
technique in this work reveal all the expected phases in detail as well as an
oscillatory population behavior in the stationary phase. This unambiguous
oscillation behavior has been suggested previously using traditional surveys of
aliquots from a given population culture. An attempt is made to model these
experimental results by developing a differential equation that accounts for
the spatial distribution of the individual cells and the presence of the
self-organizing forces of competition and dispersion. The main phases are well
represented, and the oscillating behavior is attributed to intra-species
mixing. It is also observed, that the convective motion arising out of
intra-species mixing while plays a key role in limiting population growth,
scales as $t^{-\alpha}$, where $\alpha$ is bounded by model parameters.
Reflecting Modulation
Authors: Shuaishuai Guo, Shuheng Lv, Haixia Zhang, Jia Ye, Peng Zhang
Date: 2019-12-18 00:00:00

Reconfigurable intelligent surface (RIS) has emerged as a promising technique for future wireless communication networks. How to reliably transmit information in a RIS-based communication system arouses much interest. This paper proposes a reflecting modulation (RM) scheme for RIS-based communications, where both the reflecting patterns and transmit signals can carry information. Depending on that the transmitter and RIS jointly or independently deliver information, RM is further classified into two categories: jointly mapped RM (JRM) and separately mapped RM (SRM). JRM and SRM are naturally superior to existing schemes, because the transmit signal vectors, reflecting patterns, and bit mapping methods of JRM and SRM are more flexibly designed. To enhance transmission reliability, this paper proposes a discrete optimization-based joint signal mapping, shaping, and reflecting (DJMSR) design for JRM and SRM to minimize the bit error rate (BER) with a given transmit signal candidate set and a given reflecting pattern candidate set. To further improve the performance, this paper optimizes multiple reflecting patterns and their associated transmit signal sets in continuous fields for JRM and SRM. Numerical results show that JRM and SRM with the proposed system optimization methods considerably outperform existing schemes in BER.
Reduced Switching-Frequency Modulation Design for Model Predictive Control Based Modular Multilevel Converters
Date: 2019-12-18 00:00:00
Tasks: Model Predictive Control

This paper proposes a novel switching algorithm for modular multilevel
converters (MMCs) that significantly reduces the switching frequency while
fulfilling all control objectives required for their proper operation. Unlike
in the conventional capacitor voltage-balancing strategies, in addition to
submodule (SM) capacitor voltages, the proposed algorithm considers previous
switching statuses during sorting. The algorithm is applied to a seven-level
back-to-back MMC-HVDC system and tested under various operating conditions.
Significant reduction in the switching frequency with trivial impacts on
submodule capacitor voltages are observed.
A Novel Optimal Modulation Strategy for Modular Multilevel Converter Based HVDC Systems
Date: 2019-12-18 00:00:00

Unlike conventional converters, modular multilevel converter (MMC) has a
higher switching frequency -- which has direct implication on important
parameters like converter loss and reliability -- mainly due to increased
number of switching components. However, conventional switching techniques,
where submodule sorting is just based on capacitor voltage balancing, are not
able to achieve switching frequency reduction objective. A novel modulation
algorithm for modular multilevel converters (MMCs) is proposed in this paper to
reduce the switching frequency of MMC operation by defining a constrained
multi-objective optimization model. The optimized switching algorithm
incorporates all control objectives required for the proper operation of MMC
and adds new constraints to limit the number of submodule switching events at
each time step. Variation of severity of the constraints leads to a desired
level of controllability in MMC switching algorithm to trade-off between
capacitor voltage regulation and switching frequency reduction. Finally,
performance of the proposed algorithm is validated against a seven-level
back-to-back MMC-HVDC system under various operating conditions.
Lambda-Policy Iteration with Randomization for Contractive Models with Infinite Policies: Well-Posedness and Convergence (Extended Version)
Date: 2020-06-11 00:00:00

Abstract dynamic programming models are used to analyze $\lambda$-policy
iteration with randomization algorithms. Particularly, contractive models with
infinite policies are considered and it is shown that well-posedness of the
$\lambda$-operator plays a central role in the algorithm. The operator is known
to be well-posed for problems with finite states, but our analysis shows that
it is also well-defined for the contractive models with infinite states
studied. Similarly, the algorithm we analyze is known to converge for problems
with finite policies, but we identify the conditions required to guarantee
convergence with probability one when the policy space is infinite regardless
of the number of states. Guided by the analysis, we exemplify a data-driven
approximated implementation of the algorithm for estimation of optimal costs of
constrained linear and nonlinear control problems. Numerical results indicate
potentials of this method in practice.
Optimizing Pilots and Analog Processing for Channel Estimation in Cell-Free Massive MIMO With One-Bit ADCs
Date: 2019-12-18 00:00:00

In a cell-free cloud radio access network (C-RAN) architecture, uplink
channel estimation is carried out by a centralized baseband processing unit
(BBU) connected to distributed remote radio heads (RRHs). When the RRHs have
multiple antennas and limited radio front-end resources, the design of uplink
channel estimation is faced with the challenges posed by reduced radio
frequency (RF) chains and one-bit analog-to-digital converters (ADCs) at the
RRHs. This work tackles the problem of jointly optimizing the pilot sequences
and the pre-RF chains analog combiners with the goal of minimizing the sum of
mean squared errors (MSEs) of the estimated channel vectors at the BBU. The
problem formulation models the impact of the ADC operation by leveraging
Bussgang's theorem. An efficient solution is developed by means of an iterative
alternating optimization algorithm. Numerical results validate the advantages
of the proposed joint design compared to baseline schemes that randomly choose
either pilots or analog combiners.
Cognitive Twins for Supporting Decision-Makings of Internet of Things Systems
Date: 2020-01-14 00:00:00
Tasks: Decision Making

Cognitive Twins (CT) are proposed as Digital Twins (DT) with augmented
semantic capabilities for identifying the dynamics of virtual model evolution,
promoting the understanding of interrelationships between virtual models and
enhancing the decision-making based on DT. The CT ensures that assets of
Internet of Things (IoT) systems are well-managed and concerns beyond technical
stakeholders are addressed during IoT system development. In this paper, a
Knowledge Graph (KG) centric framework is proposed to develop CT. Based on the
framework, a future tool-chain is proposed to develop the CT for the
initiatives of H2020 project FACTLOG. Based on the comparison between DT and
CT, we infer the CT is a more comprehensive approach to support IoT-based
systems development than DT.
Action at a distance in transcriptional regulation
Authors: William Bialek, Thomas Gregor, Gašper Tkačik
Date: 2019-12-18 00:00:00

There is increasing evidence that protein binding to specific sites along DNA can activate the reading out of genetic information without coming into direct physical contact with the gene. There also is evidence that these distant but interacting sites are embedded in a liquid droplet of proteins which condenses out of the surrounding solution. We argue that droplet-mediated interactions can account for crucial features of gene regulation only if the droplet is poised at a non-generic point in its phase diagram. We explore a minimal model that embodies this idea, show that this model has a natural mechanism for self-tuning, and suggest direct experimental tests.
Kalman Filter Tuning with Bayesian Optimization
Date: 2019-12-17 00:00:00
Tasks: Bayesian Optimization, State Estimation

Many state estimation algorithms must be tuned given the state space process
and observation models, the process and observation noise parameters must be
chosen. Conventional tuning approaches rely on heuristic hand-tuning or
gradient-based optimization techniques to minimize a performance cost function.
However, the relationship between tuned noise values and estimator performance
is highly nonlinear and stochastic. Therefore, the tuning solutions can easily
get trapped in local minima, which can lead to poor choices of noise parameters
and suboptimal estimator performance. This paper describes how Bayesian
Optimization (BO) can overcome these issues. BO poses optimization as a
Bayesian search problem for a stochastic ``black box'' cost function, where the
goal is to search the solution space to maximize the probability of improving
the current best solution. As such, BO offers a principled approach to
optimization-based estimator tuning in the presence of local minima and
performance stochasticity. While extended Kalman filters (EKFs) are the main
focus of this work, BO can be similarly used to tune other related state space
filters. The method presented here uses performance metrics derived from
normalized innovation squared (NIS) filter residuals obtained via sensor data,
which renders knowledge of ground-truth states unnecessary. The robustness,
accuracy, and reliability of BO-based tuning is illustrated on practical
nonlinear state estimation problems,losed-loop aero-robotic control.