pwc-archive/papers-with-abstracts huggingface dataset:
How old are dense core vesicles residing in en passant boutons: Simulation of the mean age of dense core vesicles in axonal arbors accounting for resident and transiting vesicle populations
Authors: I. A. Kuznetsov, A. V. Kuznetsov
Date: 2019-12-30 00:00:00

In neurons, neuropeptides are synthesized in the soma and are then transported along the axon in dense core vesicles (DCVs). DCVs are captured in varicosities located along the axon terminal called en passant boutons, which are active terminal sites that accumulate and release neurotransmitters. Recently developed experimental techniques allow for the estimation of the age of DCVs in various locations in the axon terminal. Accurate simulation of the mean age of DCVs in boutons requires the development of a model that would account for resident, transiting-anterograde, and transiting-retrograde DCV populations. In this paper, such a model is developed. The model is applied to simulating DCV transport in Drosophila type II motoneurons. The model simulates DCV transport and capture in the axon terminals and makes it possible to predict the age density distribution of DCVs in en passant boutons as well as DCV's mean age in boutons. The predicted prevalence of older organelles in distal boutons may explain the "dying back" pattern of axonal degeneration observed in dopaminergic neurons in Parkinson's disease. The predicted difference of two hours between the age of older DCVs residing in distal boutons and the age of younger DCVs residing in proximal boutons is consistent with an approximate estimate of age difference deduced from experimental observations. The age density of resident DCVs is found to be bimodal, which is because DCVs are captured from two transiting states: the anterograde transiting state that contains younger DCVs and the retrograde transiting state that contains older DCVs.
State Estimation for Future Energy Grids
Date: 2019-12-30 00:00:00
Tasks: State Estimation

Today's power generation and distribution networks are quickly moving toward
automated control and integration of renewable resources - a complex,
integrated system termed the Smart Grid. A key component in planning and
managing of Smart Grids is State Estimation (SE). The state-of-the art SE
technologies today operate on the basis of slow varying dynamics of the current
network and make simplifying linearity assumptions. However, the integration of
smart readers and green resources will result in significant non-linearity and
unpredictability in the network. Therefore in future Smart Grids, there is need
for ever more accurate and real-time algorithms. In this work, we propose and
examine new SE methods that aim to achieve these measures by approximating the
true distribution of the state variables, rather than a linearized version as
done for instance in Kalman filtering. Through simulations we show that in the
presence of non-linearities and non-Gaussian noise, our general SE framework
improves accuracy where linear and Kalman-like filters exhibit impaired
performance.
Effect of Franchised Business models on Fast Food Company Stock Prices in Recession and Recovery with Weibull Analysis
Authors: Sandip Dutta, Vignesh Prabhu
Date: 2019-12-30 00:00:00

At the initial stages of this research, the assumption was that the franchised businesses perhaps should not be affected much by recession as there are multiple cash pools available inherent to the franchised business model. However, after analyzing the available data, it indicated otherwise, the stock price performance as discussed indicates a different pattern. The stock price data is analyzed with an unconventional tool, Weibull distribution and observations confirmed the presence of either a reverse trend in franchised business than what is observed for non-franchised or the franchised stock followed large food suppliers. There is a layered ownership and cash flow in a franchised business model. The parent company run by franchiser depends on the performance of child companies run by franchisees. Both parent and child companies are run as independent businesses but only the parent company is listed as a stock ticker in stock exchange. Does this double layer of vertical operation, cash reserve, and cash flow protect them better in recession? The data analyzed in this paper indicates that the recession effect can be more severe; and if it dives with the average market, expect a slower recovery of stock prices in a franchised business model. This paper characterizes the differences and explains the natural experiment with available financial data.
Hybrid distance-angle rigidity theory with signed constraints and its applications to formation shape control
Date: 2019-12-30 00:00:00
Tasks: Translation

In this paper, we develop a hybrid distance-angle rigidity theory that
involves heterogeneous distances (or unsigned angles) and signed constraints
for a framework in the 2-D and 3-D space. The new rigidity theory determines a
(locally) unique formation shape up to a translation and a rotation by a set of
distance and signed constraints, or up to a translation, a rotation and
additionally a scaling factor by a set of unsigned angle and signed
constraints. Under this new rigidity theory, we have a clue to resolve the flip
(or reflection) and flex ambiguity for a target formation with hybrid
distance-angle constraints. In particular, we can completely eliminate the
ambiguity issues if formations are under a specific construction which is
called \myemph{signed Henneberg construction} in this paper. We then apply the
rigidity theory to formation shape control and develop a gradient-based control
system that guarantees an exponential convergence close to a desired formation
by inter-neighbor measurements. Several numerical simulations on formation
shape control with hybrid distance-angle constraints are provided to validate
the theoretical results.
Global redistribution and local migration in semi-discrete host-parasitoid population dynamic models
Date: 2019-12-30 00:00:00

Host-parasitoid population dynamics is often probed using a
semi-discrete/hybrid modeling framework. Here, the update functions in the
discrete-time model connecting year-to-year changes in the population densities
are obtained by solving ordinary differential equations that mechanistically
describe interactions when hosts become vulnerable to parasitoid attacks. We
use this semi-discrete formalism to study two key spatial effects: local
movement (migration) of parasitoids between patches during the vulnerable
period; and yearly redistribution of populations across patches outside the
vulnerable period. Our results show that in the absence of any redistribution,
constant density-independent migration and parasitoid attack rates are unable
to stabilize an otherwise unstable host-parasitoid population dynamics.
Interestingly, inclusion of host redistribution (but not parasitoid
redistribution) before the start of the vulnerable period can lead to stable
coexistence of both species. Next, we consider a Type-III functional response
(parasitoid attack rate increases with host density), where the absence of any
spatial effects leads to a neutrally stable host-parasitoid equilibrium. As
before, density-independent parasitoid migration by itself is again
insufficient to stabilize the population dynamics and host redistribution
provides a stabilizing influence. Finally, we show that a Type-III functional
response combined with density-dependent parasitoid migration leads to stable
coexistence, even in the absence of population redistributions. In summary, we
have systematically characterized parameter regimes leading to stable/unstable
population dynamics with different forms of spatial heterogeneity coupled to
the parasitoid's functional response using mechanistically formulated
semi-discrete models.
Information costs in the control of protein synthesis
Authors: Rebecca J. Rousseau, William Bialek
Date: 2019-12-30 00:00:00

Efficient protein synthesis depends on the availability of charged tRNA molecules. With 61 different codons, shifting the balance among the tRNA abundances can lead to large changes in the protein synthesis rate. Previous theoretical work has asked about the optimization of these abundances, and there is some evidence that regulatory mechanisms bring cells close to this optimum, on average. We formulate the tradeoff between the precision of control and the efficiency of synthesis, asking for the maximum entropy distribution of tRNA abundances consistent with a desired mean rate of protein synthesis. Our analysis, using data from E. coli, indicates that reasonable synthesis rates are consistent only with rather low entropies, so that the cell's regulatory mechanisms must encode a large amount of information about the "correct" tRNA abundances.
Design of a Low Noise Amplifier
Date: 2019-12-30 00:00:00

A low-noise amplifier (LNA) amplifies a very low-power signal without
significantly degrading its signal-to-noise ratio. This paper provides the
design of a linear low noise amplifier with the transistor BFP 640 using
bilateral approach which is well-matched at the design frequency of 3.20 GHz
with maximum transducer gain and low noise figure. Noise is minimized by
considering trade-offs that include impedance matching and selecting low-noise
biasing conditions.
Recovering Latent Variables by Matching
Date: 2019-12-30 00:00:00

We propose an optimal-transport-based matching method to nonparametrically
estimate linear models with independent latent variables. The method consists
in generating pseudo-observations from the latent variables, so that the
Euclidean distance between the model's predictions and their matched
counterparts in the data is minimized. We show that our nonparametric estimator
is consistent, and we document that it performs well in simulated data. We
apply this method to study the cyclicality of permanent and transitory income
shocks in the Panel Study of Income Dynamics. We find that the dispersion of
income shocks is approximately acyclical, whereas the skewness of permanent
shocks is procyclical. By comparison, we find that the dispersion and skewness
of shocks to hourly wages vary little with the business cycle.
Aging a little: The optimality of limited senescence in Escherichia coli
Date: 2019-12-30 00:00:00

Recent studies have shown that even in the absence of extrinsic stress, the
morphologically symmetrically dividing model bacteria Escherichia coli do not
generate offspring with equal reproductive fitness. Instead, daughter cells
exhibit asymmetric division times that converge to two distinct growth states.
This represents a limited senescence / rejuvenation process derived from
asymmetric division that is stable for hundreds of generations. It remains
unclear why the bacteria do not continue the senescence beyond this asymptote.
Although there are inherent fitness benefits for heterogeneity in population
growth rates, the two growth equilibria are surprisingly similar, differing by
a few percent. In this work we derive an explicit model for the growth of a
bacterial population with two growth equilibria, based on a generalized
Fibonacci recurrence, in order to quantify the fitness benefit of a limited
senescence process and examine costs associated with asymmetry that could
generate the observed behavior. We find that with simple saturating effects of
asymmetric partitioning of subcellular components, two distinct but similar
growth states may be optimal while providing evolutionarily significant fitness
advantages.
Machine Learning based Post Processing Artifact Reduction in HEVC Intra Coding
Date: 2019-12-30 00:00:00
Tasks: BIG-bench Machine Learning, Blocking, Data Augmentation, SSIM, Transfer Learning

The lossy compression techniques produce various artifacts like blurring,
distortion at block bounders, ringing and contouring effects on outputs
especially at low bit rates. To reduce those compression artifacts various
Convolutional Neural Network (CNN) based post processing techniques have been
experimented over recent years. The latest video coding standard HEVC adopts
two post processing filtering operations namely de-blocking filter (DBF)
followed by sample adaptive offset (SAO). These operations consumes extra
signaling bit and becomes an overhead to network. In this paper we proposed a
new Deep learning based algorithm on SAO filtering operation. We designed a
variable filter size Sub-layered Deeper CNN (SDCNN) architecture to improve
filtering operation and incorporated large stride convolutional, deconvolution
layers for further speed up. We also demonstrated that deeper architecture
model can effectively be trained with the features learnt in a shallow network
using data augmentation and transfer learning based techniques. Experimental
results shows that our proposed network outperforms other networks in terms on
PSNR and SSIM measurements on widely available benchmark video sequences and
also perform an average of 4.1 % bit rate reduction as compared to HEVC
baseline.
Open Markets
Date: 2019-12-30 00:00:00

An open market is a subset of an entire equity market composed of a certain
fixed number of top capitalization stocks. Though the number of stocks in the
open market is fixed, the constituents of the market change over time as each
company's rank by its market capitalization fluctuates. When one is allowed to
invest also in the money market, the open market resembles the entire 'closed'
equity market in the sense that the equivalence of market viability (lack of
arbitrage) and the existence of numeraire portfolio (portfolio which cannot be
outperformed) holds. When access to the money market is prohibited, some topics
such as Capital Asset Pricing Model (CAPM), construction of functionally
generated portfolios, and the concept of the universal portfolio are presented
in the open market setting.
Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey
Date: 2019-12-31 00:00:00
Tasks: energy trading

The advancements in smart power grid and the advocation of ``green
communications'' have inspired the wireless communication networks to harness
energy from ambient environments and operate in an energy-efficient manner for
economic and ecological benefits. This article presents a contemporary review
of recent breakthroughs on the utilization, redistribution, trading and
planning of energy harvested in future wireless networks interoperating with
smart grids. This article starts with classical models of renewable energy
harvesting technologies. We embark on constrained operation and optimization of
different energy harvesting wireless systems, such as point-to-point,
multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell
systems. We also review wireless power and information transfer technologies
which provide a special implementation of energy harvesting wireless
communications. A significant part of the article is devoted to the
redistribution of redundant (unused) energy harvested within cellular networks,
the energy planning under dynamic pricing when smart grids are in place, and
two-way energy trading between cellular networks and smart grids. Applications
of different optimization tools, such as convex optimization, Lagrangian
dual-based method, subgradient method, and Lyapunov-based online optimization,
are compared. This article also collates the potential applications of energy
harvesting techniques in emerging (or upcoming) 5G/B5G communication systems.
It is revealed that an effective redistribution and two-way trading of energy
can significantly reduce the electricity bills of wireless service providers
and decrease the consumption of brown energy. A list of interesting research
directions are provided, requiring further investigation.
Series Resonant Matrix Converter Topology for EV DC Fast Charging
Date: 2019-12-31 00:00:00
Tasks: Blocking

In this paper, we presented a matrix converter with a high-frequency DC link
for EV DC fast charging applications. Introducing this topology allows
eliminating electrolytic capacitors in DC link of the converter, which
potentially increases the reliability and power density of the system. The
converter uses bidirectionally blocking three-phase rectifier to generate a
high-frequency square wave voltage for the series resonant LC DC/DC converter,
eliminating one H-bridge in front of the resonant tank. Principles of
high-frequency and low-frequency modulations are described. A low voltage
prototype has been built and tested which demonstrated ZVS soft switching of
all the transistors in the converter. Peak efficiency estimated as 98.0 % for
state-of-the-art switches.
Systemic liquidity contagion in the European interbank market
Authors: V. Macchiati, G. Brandi, G. Cimini, G. Caldarelli, D. Paolotti, T. Di Matteo
Date: 2019-12-31 00:00:00
Tasks: valid

Systemic liquidity risk, defined by the IMF as "the risk of simultaneous liquidity difficulties at multiple financial institutions", is a key topic in macroprudential policy and financial stress analysis. Specialized models to simulate funding liquidity risk and contagion are available but they require not only banks' bilateral exposures data but also balance sheet data with sufficient granularity, which are hardly available. Alternatively, risk analyses on interbank networks have been done via centrality measures of the underlying graph capturing the most interconnected and hence more prone to risk spreading banks. In this paper, we propose a model which relies on an epidemic model which simulate a contagion on the interbank market using the funding liquidity shortage mechanism as contagion process. The model is enriched with country and bank risk features which take into account the heterogeneity of the interbank market. The proposed model is particularly useful when full set of data necessary to run specialized models is not available. Since the interbank network is not fully available, an economic driven reconstruction method is also proposed to retrieve the interbank network by constraining the standard reconstruction methodology to real financial indicators. We show that the contagion model is able to reproduce systemic liquidity risk across different years and countries. This result suggests that the proposed model can be successfully used as a valid alternative to more complex ones.
Radio Positioning with EM Processing of the Spherical Wavefront
Authors: Francesco Guidi, Davide Dardari
Date: 2019-12-31 00:00:00

Next 5G and beyond applications have brought a tremendous interest towards array systems employing an extremely large number of antennas, so that the technology that might be in place for communication can be also exploited for positioning. In particular, in this paper we investigate the possibility to infer the position of an omnidirectional transmitter by retrieving the information from the incident spherical wavefront through its EM processing. Despite such a post-processing of the curvature wavefront has been mainly considered in the past at microwave and acoustic frequencies using extremely large antennas, it is of interest to explore the opportunities offered in the context of next 5G and beyond systems. Thus, differently from the state-of-the-art, here we first introduce a dedicated general model for different EM processing configurations, and successively we investigate the trade-off between the attainable positioning performance and the complexity offered by the different architectures, that might entail or not the use of a lens, that can be either reconfigurable or not. Indeed, we analyze also the effect of the interference, in order to evaluate the robustness of the considered system to the presence of multiple simultaneous transmitting sources. Results, obtained for different number of antennas, i.e., for different array apertures, confirm the possibility to achieve interesting positioning performance using a single antenna array with limited dimensions.
Optimal evolutionary control for artificial selection on molecular phenotypes
Authors: Armita Nourmohammad, Ceyhun Eksin
Date: 2019-12-31 00:00:00

Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for desirable actions in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to counter evolutionary tradeoffs among multi-variate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict molecular evolution. We develop an information theoretical framework and show that molecular time-scales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions.
Pricing Multi-Interval Dispatch under Uncertainty Part II: Generalization and Performance
Date: 2020-10-27 00:00:00

Pricing multi-interval economic dispatch of electric power under operational
uncertainty is considered in this two-part paper. Part I investigates
dispatch-following incentives for generators under the locational marginal
pricing (LMP) and temporal locational marginal pricing (TLMP) policies.
Extending the theoretical results developed in Part I, Part II evaluates a
broader set of performance measures under a general network model. For networks
with power flow constraints, TLMP is shown to have an energy-congestion-ramping
price decomposition. Under the one-shot dispatch and pricing model, this
decomposition leads to a nonnegative merchandising surplus equal to the sum of
congestion and ramping surpluses. It is also shown that, comparing with LMP,
TLMP imposes a penalty on generators with limited ramping capabilities, thus
giving incentives for generators to reveal their ramping limits truthfully and
improve their ramping capacities. Several benchmark pricing mechanisms are
evaluated under the rolling-window dispatch and pricing models. The performance
measures considered are the level of out-of-the-market uplifts, the revenue
adequacy of the system operator, consumer payment, generator profit, level of
discriminative payments, and price volatility.
Practical Issues of Energy Harvesting and Data Transmissions in Sustainable IoT
Date: 2019-12-31 00:00:00

The sustainable Internet of Things (IoT) is becoming a promising solution for
the green living and smart industries. In this article, we investigate the
practical issues in the radio energy harvesting and data communication systems
through extensive field experiments. A number of important characteristics of
energy harvesting circuits and communication modules have been studied,
including the non-linear energy consumption of the communication system
relative to the transmission power, the wake-up time associated with the
payload, and the varying system power during consecutive packet transmissions.
In order to improve the efficiency of energy harvest and energy utilization, we
propose a new model to accurately describe the energy harvesting process and
the power consumption for sustainable IoT devices. Experiments are performed
using commercial IoT devices and RF energy harvesters to verify the accuracy of
the proposed model. The experiment results show that the new model matches the
performance of sustainable IoT devices very well in the real scenario.