pwc-archive/papers-with-abstracts huggingface dataset:
Closed-loop robots driven by short-term synaptic plasticity: Emergent explorative vs. limit-cycle locomotion
Date: 2018-03-14 00:00:00
Tasks: Position

We examine the hypothesis, that short-term synaptic plasticity (STSP) may
generate self-organized motor patterns. We simulated sphere-shaped autonomous
robots, within the LPZRobots simulation package, containing three weights
moving along orthogonal internal rods. The position of a weight is controlled
by a single neuron receiving excitatory input from the sensor, measuring its
actual position, and inhibitory inputs from the other two neurons. The
inhibitory connections are transiently plastic, following physiologically
inspired STSP-rules. We find that a wide palette of motion patterns are
generated through the interaction of STSP, robot, and environment (closed-loop
configuration), including various forward meandering and circular motions,
together with chaotic trajectories. The observed locomotion is robust with
respect to additional interactions with obstacles. In the chaotic phase the
robot is seemingly engaged in actively exploring its environment. We believe
that our results constitute a concept of proof that transient synaptic
plasticity, as described by STSP, may potentially be important for the
generation of motor commands and for the emergence of complex locomotion
patterns, adapting seamlessly also to unexpected environmental feedback. We
observe spontaneous and collision induced mode switchings, finding in addition,
that locomotion may follow transiently limit cycles which are otherwise
unstable. Regular locomotion corresponds to stable limit cycles in the
sensorimotor loop, which may be characterized in turn by arbitrary angles of
propagation. This degeneracy is, in our analysis, one of the drivings for the
chaotic wandering observed for selected parameter settings, which is induced by
the smooth diffusion of the angle of propagation.
Computational Limitations of First-Order Repressor Systems
Date: 2016-08-10 00:00:00

Almost all current approaches for engineering modular logic components in
synthetic biology use first-order regulators, including most CRISPR/CAS, TAL,
zinc finger, and RNA interference systems. Many practitioners understand
intuitively that second and higher order binding is necessary for scalability,
and this is easy to show for single-input single-output systems. However, no
study to date has analysed whether a more complex system, utilizing e.g.
feedback or error correction, can produce scalable computation from first-order
regulators. We prove here that first order repressor systems cannot support
bistability. In the process, we introduce a function G to measure signal
quality in molecular systems, and we show that G always decreases in dynamic
feedback systems as well as static feed-forward logic cascades of first-order
repressors. As a result, first order repressors cannot build memory or signal
buffering elements. Finally, we suggest G as a potential new property for
characterization of standard biological parts.
Dynamic structure of stock communities: A comparative study between stock returns and turnover rates
Date: 2016-08-10 00:00:00
Tasks: Time Series Analysis

The detection of community structure in stock market is of theoretical and
practical significance for the study of financial dynamics and portfolio risk
estimation. We here study the community structures in Chinese stock markets
from the aspects of both price returns and turnover rates, by using a
combination of the PMFG and infomap methods based on a distance matrix. We find
that a few of the largest communities are composed of certain specific industry
or conceptional sectors and the correlation inside a sector is generally larger
than the correlation between different sectors. In comparison with returns, the
community structure for turnover rates is more complex and the sector effect is
relatively weaker. The financial dynamics is further studied by analyzing the
community structures over five sub-periods. Sectors like banks, real estate,
health care and New Shanghai take turns to compose a few of the largest
communities for both returns and turnover rates in different sub-periods.
Several specific sectors appear in the communities with different rank orders
for the two time series even in the same sub-period. A comparison between the
evolution of prices and turnover rates of stocks from these sectors is
conducted to better understand their differences. We find that stock prices
only had large changes around some important events while turnover rates surged
after each of these events relevant to specific sectors, which may offer a
possible explanation for the complexity of stock communities for turnover
rates.
Dynamic portfolio strategy using clustering approach
Date: 2016-08-10 00:00:00
Tasks: Asset Management, Clustering, Management, Portfolio Optimization

The problem of portfolio optimization is one of the most important issues in
asset management. This paper proposes a new dynamic portfolio strategy based on
the time-varying structures of MST networks in Chinese stock markets, where the
market condition is further considered when using the optimal portfolios for
investment. A portfolio strategy comprises two stages: selecting the portfolios
by choosing central and peripheral stocks in the selection horizon using five
topological parameters, i.e., degree, betweenness centrality, distance on
degree criterion, distance on correlation criterion and distance on distance
criterion, then using the portfolios for investment in the investment horizon.
The optimal portfolio is chosen by comparing central and peripheral portfolios
under different combinations of market conditions in the selection and
investment horizons. Market conditions in our paper are identified by the
ratios of the number of trading days with rising index or the sum of the
amplitudes of the trading days with rising index to the total number of trading
days. We find that central portfolios outperform peripheral portfolios when the
market is under a drawup condition, or when the market is stable or drawup in
the selection horizon and is under a stable condition in the investment
horizon. We also find that the peripheral portfolios gain more than central
portfolios when the market is stable in the selection horizon and is drawdown
in the investment horizon. Empirical tests are carried out based on the optimal
portfolio strategy. Among all the possible optimal portfolio strategy based on
different parameters to select portfolios and different criteria to identify
market conditions, $65\%$ of our optimal portfolio strategies outperform the
random strategy for the Shanghai A-Share market and the proportion is $70\%$
for the Shenzhen A-Share market.
Uniform spatial distribution of collagen fibril radii within tendon implies local activation of pC-collagen at individual fibrils
Date: 2016-08-10 00:00:00

Collagen fibril cross-sectional radii show no systematic variation between
the interior and the periphery of fibril bundles, indicating an effectively
constant rate of collagen incorporation into fibrils throughout the bundle.
Such spatially homogeneous incorporation constrains the extracellular diffusion
of collagen precursors from sources at the bundle boundary to sinks at the
growing fibrils. With a coarse-grained diffusion equation we determine
stringent bounds, using parameters extracted from published experimental
measurements of tendon development. From the lack of new fibril formation after
birth, we further require that the concentration of diffusing precursors stays
below the critical concentration for fibril nucleation. We find that the
combination of the diffusive bound, which requires larger concentrations to
ensure homogeneous fibril radii, and lack of nucleation, which requires lower
concentrations, is only marginally consistent with fully-processed collagen
using conservative bounds. More realistic bounds may leave no consistent
concentrations. Therefore, we propose that unprocessed pC-collagen diffuses
from the bundle periphery followed by local C-proteinase activity and
subsequent collagen incorporation at each fibril. We suggest that C-proteinase
is localized within bundles, at fibril surfaces, during radial fibrillar
growth. The much greater critical concentration of pC-collagen, as compared to
fully-processed collagen, then provides broad consistency between homogeneous
fibril radii and the lack of fibril nucleation during fibril growth.
Managing counterparty credit risk via BSDEs
Date: 2016-08-16 00:00:00

We discuss a general dynamic replication approach to counterparty credit risk
modeling. This leads to a fundamental jump-process backward stochastic
differential equation (BSDE) for the credit risk adjusted portfolio value. We
then reduce the fundamental BSDE to a continuous BSDE. Depending on the close
out value convention, the reduced fundamental BSDE's solution can be
represented explicitly or through an accurate approximate expression.
Furthermore, we discuss practical aspects of the approach, important for the
its industry applications: (i) efficient numerical methodology for solving a
BSDE driven by a moderate number of Brownian motions, and (ii) factor reduction
methodology that allows one to approximately replace a portfolio driven by a
large number of risk factors with a portfolio driven by a moderate number of
risk factors.
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision
Date: 2017-11-14 00:00:00

Convolutional neural network (CNN) driven by image recognition has been shown
to be able to explain cortical responses to static pictures at ventral-stream
areas. Here, we further showed that such CNN could reliably predict and decode
functional magnetic resonance imaging data from humans watching natural movies,
despite its lack of any mechanism to account for temporal dynamics or feedback
processing. Using separate data, encoding and decoding models were developed
and evaluated for describing the bi-directional relationships be-tween the CNN
and the brain. Through the encoding models, the CNN-predicted areas covered not
only the ventral stream, but also the dorsal stream, albe-it to a lesser
degree; single-voxel response was visualized as the specific pixel pattern that
drove the response, revealing the distinct representation of individual
cortical location; cortical activation was synthesized from natural images with
high-throughput to map category representation, con-trast, and selectivity.
Through the decoding models, fMRI signals were directly decoded to estimate the
feature representations in both visual and semantic spaces, for direct visual
reconstruction and seman-tic categorization, respectively. These results
cor-roborate, generalize, and extend previous findings, and highlight the value
of using deep learning, as an all-in-one model of the visual cortex, to
understand and decode natural vision.
A Gaussian Markov alternative to fractional Brownian motion for pricing financial derivatives
Date: 2016-08-11 00:00:00

Replacing Black-Scholes' driving process, Brownian motion, with fractional
Brownian motion allows for incorporation of a past dependency of stock prices
but faces a few major downfalls, including the occurrence of arbitrage when
implemented in the financial market. We present the development, testing, and
implementation of a simplified alternative to using fractional Brownian motion
for pricing derivatives. By relaxing the assumption of past independence of
Brownian motion but retaining the Markovian property, we are developing a
competing model that retains the mathematical simplicity of the standard
Black-Scholes model but also has the improved accuracy of allowing for past
dependence. This is achieved by replacing Black-Scholes' underlying process,
Brownian motion, with a particular Gaussian Markov process, proposed by
Vladimir Dobri\'{c} and Francisco Ojeda.
Black-boxing and cause-effect power
Date: 2019-05-14 00:00:00

Reductionism assumes that causation in the physical world occurs at the micro
level, excluding the emergence of macro-level causation. We challenge this
reductionist assumption by employing a principled, well-defined measure of
intrinsic cause-effect power - integrated information ({\Phi}), and showing
that, according to this measure, it is possible for a macro level to "beat" the
micro level. Simple systems were evaluated for {\Phi} across different spatial
and temporal scales by systematically considering all possible black boxes.
These are macro elements that consist of one or more micro elements over one or
more micro updates. Cause-effect power was evaluated based on the inputs and
outputs of the black boxes, ignoring the internal micro elements that support
their input-output function. We show how black-box elements can have more
common inputs and outputs than the corresponding micro elements, revealing the
emergence of high-order mechanisms and joint constraints that are not apparent
at the micro level. As a consequence, a macro, black-box system can have higher
{\Phi} than its micro constituents by having more mechanisms (higher
composition) that are more interconnected (higher integration). We also show
that, for a given micro system, one can identify local maxima of {\Phi} across
several spatiotemporal scales. The framework is demonstrated on a simple
biological system, the Boolean network model of the fission-yeast cell-cycle,
for which we identify stable local maxima during the course of its simulated
biological function. These local maxima correspond to macro levels of
organization at which emergent cause-effect properties of physical systems come
into focus, and provide a natural vantage point for scientific inquiries.
A New Statistical Model of Electroencephalogram Noise Spectra for Real-time Brain-Computer Interfaces
Date: 2016-07-24 00:00:00
Tasks: EEG, Electroencephalogram (EEG), SSVEP

$Objective$: A characteristic of neurological signal processing is high
levels of noise from sub-cellular ion channels up to whole-brain processes. In
this paper, we propose a new model of electroencephalogram (EEG) background
periodograms, based on a family of functions which we call generalized van der
Ziel--McWhorter (GVZM) power spectral densities (PSDs). To the best of our
knowledge, the GVZM PSD function is the only EEG noise model which has
relatively few parameters, matches recorded EEG PSD's with high accuracy from 0
Hz to over 30 Hz, and has approximately $1/f^\theta$ behavior in the
mid-frequencies without infinities. $Methods$: We validate this model using
three approaches. First, we show how GVZM PSDs can arise in population of ion
channels in maximum entropy equilibrium. Second, we present a class of mixed
autoregressive models, which simulate brain background noise and whose
periodograms are asymptotic to the GVZM PSD. Third, we present two real-time
estimation algorithms for steady-state visual evoked potential (SSVEP)
frequencies, and analyze their performance statistically. $Results$: In
pairwise comparisons, the GVZM-based algorithms showed statistically
significant accuracy improvement over two well-known and widely-used SSVEP
estimators. $Conclusion$: The GVZM noise model can be a useful and reliable
technique for EEG signal processing. $Significance$: Understanding EEG noise is
essential for EEG-based neurology and applications such as real-time
brain-computer interfaces (BCIs), which must make accurate control decisions
from very short data epochs. The GVZM approach represents a successful new
paradigm for understanding and managing this neurological noise.
Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches
Date: 2016-08-10 00:00:00
Tasks: Open-Ended Question Answering

Neuronal avalanches measured in vitro and in vivo in different cortical
networks consistently exhibit power law behaviour for the size and duration
distributions with exponents typical for a mean field self-organized branching
process. These exponents are also recovered in neuronal network simulations
implementing various neuronal dynamics on different network topologies. They
can therefore be considered a very robust feature of spontaneous neuronal
activity. Interestingly, this scaling behaviour is also observed on regular
lattices in finite dimensions, which raises the question about the origin of
the mean field behaviour observed experimentally. In this study we provide an
answer to this open question by investigating the effect of activity dependent
plasticity in combination with the neuronal refractory time in a neuronal
network. Results show that the refractory time hinders backward avalanches
forcing a directed propagation. Hebbian plastic adaptation plays the role of
sculpting these directed avalanche patterns into the topology of the network
slowly changing it into a branched structure where loops are marginal.
Demographic noise can reverse the direction of deterministic selection
Date: 2016-08-11 00:00:00

Deterministic evolutionary theory robustly predicts that populations
displaying altruistic behaviours will be driven to extinction by mutant cheats
that absorb common benefits but do not themselves contribute. Here we show that
when demographic stochasticity is accounted for, selection can in fact act in
the reverse direction to that predicted deterministically, instead favouring
cooperative behaviors that appreciably increase the carrying capacity of the
population. Populations that exist in larger numbers experience a selective
advantage by being more stochastically robust to invasions than smaller
populations, and this advantage can persist even in the presence of
reproductive costs. We investigate this general effect in the specific context
of public goods production and find conditions for stochastic selection
reversal leading to the success of public good producers. This insight,
developed here analytically, is missed by both the deterministic analysis as
well as standard game theoretic models that enforce a fixed population size.
The effect is found to be amplified by space; in this scenario we find that
selection reversal occurs within biologically reasonable parameter regimes for
microbial populations. Beyond the public good problem, we formulate a general
mathematical framework for models that may exhibit stochastic selection
reversal. In this context, we describe a stochastic analogue to r-K theory, by
which small populations can evolve to higher densities in the absence of
disturbance.
Modulation of sensory information processing by a neuroglobin in C. elegans
Date: 2016-09-14 00:00:00

Sensory receptor neurons match their dynamic range to ecologically relevant
stimulus intensities. How this tuning is achieved is poorly understood in most
receptors. We show that in the C. elegans URX O2 sensing neurons two putative
molecular O2 sensors, a neuroglobin and O2-binding soluble guanylate cyclases,
work antagonistically to sculpt a slowly sigmoidal O2 response curve tuned to
approach saturation when O2 reaches 21%. glb-5 imposes this sigmoidal function
by inhibiting O2-evoked Ca2+ responses in URX when O2 levels fall. Without
GLB-5, the URX response curve approaches saturation at 15% O2. Behaviorally,
GLB-5 signaling broadens the O2 preference of C. elegans while maintaining
strong avoidance of 21% O2. Our computational aerotaxis model suggests that the
relationship between GLB-5-modulated URX responses and reversal behavior is
sufficient to broaden O2-preference. Thus, a neuroglobin can shift neural
information coding leading to a change in perception and altered behavior.
Cliques and Cavities in the Human Connectome
Date: 2016-12-20 00:00:00

Encoding brain regions and their connections as a network of nodes and edges
captures many of the possible paths along which information can be transmitted
as humans process and perform complex behaviors. Because cognitive processes
involve large and distributed networks of brain areas, examinations of
multi-node routes within larger connection patterns can offer fundamental
insights into the complexities of brain function. Here, we investigate both
densely connected groups of nodes that could perform local computations as well
as larger patterns of interactions that would allow for parallel processing.
Finding such structures necessitates we move from considering pairwise
interactions to capturing higher order relations, concepts naturally expressed
in the language of algebraic topology. These tools can be used to study
mesoscale structures arising from the arrangement of densely connected
substructures called cliques in otherwise sparsely connected brain networks. We
detect cliques (all-to-all connected sets of brain regions) in the average
structural connectomes of 8 healthy adults and discover the presence of more
large cliques than expected in null networks constructed via wiring
minimization, providing architecture through which brain network can perform
rapid, local processing. We then locate topological cavities of different
dimensions, around which information may flow in either diverging or converging
patterns. These cavities exist consistently across subjects, differ from those
observed in null model networks, and link regions of early and late
evolutionary origin in long loops, underscoring their unique role in
controlling brain function. These results offer a first demonstration that
techniques from algebraic topology offer a novel perspective on structural
connectomics, highlighting loop-like paths as crucial features in the human
brain's structural architecture.
Emergent organization in a model market
Date: 2016-08-11 00:00:00
Tasks: model

We study the collective behavior of interacting agents in a simple model of
market economics originally introduced by N{\o}rrelykke and Bak. A general
theoretical framework for interacting traders on an arbitrary network is
presented, with the interaction consisting of buying (namely, consumption) and
selling (namely, production) of commodities. Extremal dynamics is introduced by
having the agent with least profit in the market readjust prices, causing the
market to self--organize. We study this model market on regular lattices in
two--dimension as well as on random complex networks; in the critical state
fluctuations in an activity signal exhibit properties that are characteristic
of avalanches observed in models of self-organized criticality, and these can
be described by power--law distributions.
Optimal Hashing-based Time-Space Trade-offs for Approximate Near Neighbors
Authors: Andoni Alexandr, Laarhoven Thijs, Razenshteyn Ilya, Waingarten Erik
Date: 2017-05-21 00:00:00

[See the paper for the full abstract.]
  We show tight upper and lower bounds for time-space trade-offs for the
$c$-Approximate Near Neighbor Search problem. For the $d$-dimensional Euclidean
space and $n$-point datasets, we develop a data structure with space $n^{1 +
\rho_u + o(1)} + O(dn)$ and query time $n^{\rho_q + o(1)} + d n^{o(1)}$ for
every $\rho_u, \rho_q \geq 0$ such that: \begin{equation} c^2 \sqrt{\rho_q} +
(c^2 - 1) \sqrt{\rho_u} = \sqrt{2c^2 - 1}. \end{equation}
  This is the first data structure that achieves sublinear query time and
near-linear space for every approximation factor $c > 1$, improving upon
[Kapralov, PODS 2015]. The data structure is a culmination of a long line of
work on the problem for all space regimes; it builds on Spherical
Locality-Sensitive Filtering [Becker, Ducas, Gama, Laarhoven, SODA 2016] and
data-dependent hashing [Andoni, Indyk, Nguyen, Razenshteyn, SODA 2014] [Andoni,
Razenshteyn, STOC 2015].
  Our matching lower bounds are of two types: conditional and unconditional.
First, we prove tightness of the whole above trade-off in a restricted model of
computation, which captures all known hashing-based approaches. We then show
unconditional cell-probe lower bounds for one and two probes that match the
above trade-off for $\rho_q = 0$, improving upon the best known lower bounds
from [Panigrahy, Talwar, Wieder, FOCS 2010]. In particular, this is the first
space lower bound (for any static data structure) for two probes which is not
polynomially smaller than the one-probe bound. To show the result for two
probes, we establish and exploit a connection to locally-decodable codes.