pwc-archive/papers-with-abstracts huggingface dataset:
*K-means and Cluster Models for Cancer Signatures
Date: 2017-07-18 00:00:00
Tasks: Clustering

We present *K-means clustering algorithm and source code by expanding
statistical clustering methods applied in https://ssrn.com/abstract=2802753 to
quantitative finance. *K-means is statistically deterministic without
specifying initial centers, etc. We apply *K-means to extracting cancer
signatures from genome data without using nonnegative matrix factorization
(NMF). *K-means' computational cost is a fraction of NMF's. Using 1,389
published samples for 14 cancer types, we find that 3 cancers (liver cancer,
lung cancer and renal cell carcinoma) stand out and do not have cluster-like
structures. Two clusters have especially high within-cluster correlations with
11 other cancers indicating common underlying structures. Our approach opens a
novel avenue for studying such structures. *K-means is universal and can be
applied in other fields. We discuss some potential applications in quantitative
finance.
Absolute bacterial cell enumeration using flow cytometry
Date: 2017-06-20 00:00:00

Aim: To evaluate a flow cytometry protocol that uses reference beads for the
enumeration of live and dead bacteria present in a mixture. Methods and
Results: Mixtures of live and dead Escherichia coli with live:dead
concentration ratios varying from 0 to 100% were prepared. These samples were
stained using SYTO 9 and propidium iodide and 6 {\mu}m reference beads were
added. Bacteria present in live samples were enumerated by agar plate counting.
Bacteria present in dead samples were enumerated by agar plate counting before
treatment with isopropanol. There is a linear relationship between the
presented flow cytometry method and agar plate counts for live (R2 = 0.99) and
dead E. coli (R2 = 0.93) concentrations of ca. 104 to 108 bacteria ml-1 within
mixtures of live and dead bacteria. Conclusions: Reliable enumeration of live
E. coli within a mixture of both live and dead was possible for concentration
ratios of above 2.5% live and for the enumeration of dead E. coli the lower
limit was ca. 20% dead. Significance and Impact of the Study: The ability to
obtain absolute cell concentrations is only available for selected flow
cytometers, this study describes a method for accurate enumeration that is
applicable to basic flow cytometers without specialised counting features. By
demonstrating the application of the method to count E. coli, we raised points
of consideration for using this FCM counting method and aim to lay the
foundation for future work that uses similar methods for different bacterial
strains.
Model Spaces for Risk Measures
Date: 2017-11-23 00:00:00
Tasks: model

We show how risk measures originally defined in a model free framework in
terms of acceptance sets and reference assets imply a meaningful underlying
probability structure. Hereafter we construct a maximal domain of definition of
the risk measure respecting the underlying ambiguity profile. We particularly
emphasise liquidity effects and discuss the correspondence between properties
of the risk measure and the structure of this domain as well as
subdifferentiability properties.
  Keywords: Model free risk assessment, extension of risk measures, continuity
properties of risk measures, subgradients.
Long-run dynamics of the U.S. patent classification system
Date: 2018-09-21 00:00:00
Tasks: Classification, Patent classification, Time Series Analysis

Almost by definition, radical innovations create a need to revise existing
classification systems. In this paper, we argue that classification system
changes and patent reclassification are common and reveal interesting
information about technological evolution. To support our argument, we present
three sets of findings regarding classification volatility in the U.S. patent
classification system. First, we study the evolution of the number of distinct
classes. Reconstructed time series based on the current classification scheme
are very different from historical data. This suggests that using the current
classification to analyze the past produces a distorted view of the evolution
of the system. Second, we study the relative sizes of classes. The size
distribution is exponential so classes are of quite different sizes, but the
largest classes are not necessarily the oldest. To explain this pattern with a
simple stochastic growth model, we introduce the assumption that classes have a
regular chance to be split. Third, we study reclassification. The share of
patents that are in a different class now than they were at birth can be quite
high. Reclassification mostly occurs across classes belonging to the same
1-digit NBER category, but not always. We also document that reclassified
patents tend to be more cited than non-reclassified ones, even after
controlling for grant year and class of origin.
Embeddability of Kimura 3ST Markov matrices
Date: 2019-02-22 00:00:00

In this note, we characterize the embeddability of generic Kimura 3ST Markov
matrices in terms of their eigenvalues. As a consequence, we are able to
compute the volume of such matrices relative to the volume of all Markov
matrices within the model. We also provide examples showing that, in general,
mutation rates are not identifiable from substitution probabilities. These
examples also illustrate that symmetries between mutation probabilities do not
necessarily arise from symmetries between the corresponding mutation rates.
Characterization of Fully Coupled FBSDE in Terms of Portfolio Optimization
Date: 2019-09-30 00:00:00
Tasks: Portfolio Optimization

We provide a verification and characterization result of optimal maximal
sub-solutions of BSDEs in terms of fully coupled forward backward stochastic
differential equations. We illustrate the application thereof in utility
optimization with random endowment under probability and discounting
uncertainty. We show with explicit examples how to quantify the costs of
incompleteness when using utility indifference pricing, as well as a way to
find optimal solutions for recursive utilities.
Phase separation and single-chain compactness of charged disordered proteins are strongly correlated
Date: 2017-05-18 00:00:00

Liquid-liquid phase separation of intrinsically disordered proteins (IDPs) is
a major undergirding factor in the regulated formation of membraneless
organelles in the cell. The phase behavior of an IDP is sensitive to its amino
acid sequence. Here we apply a recent random-phase-approximation polymer theory
to investigate how the tendency for multiple chains of a protein to phase
separate, as characterized by the critical temperature $T^*_{\rm cr}$, is
related to the protein's single-chain average radius of gyration $\langle
R_{\rm g} \rangle$. For a set of sequences containing different permutations of
an equal number of positively and negatively charged residues, we found a
striking correlation $T^*_{\rm cr}\sim \langle R_{\rm g} \rangle^{-\gamma}$
with $\gamma$ as large as $\sim 6.0$, indicating that electrostatic effects
have similarly significant impact on promoting single-chain conformational
compactness and phase separation. Moreover, $T^*_{\rm cr}\propto -{\rm SCD}$,
where SCD is a recently proposed "sequence charge decoration" parameter
determined solely by sequence information. Ramifications of our findings for
deciphering the sequence dependence of IDP phase separation are discussed.
Dynamic Intention-Aware Recommendation System
Authors: Zhang Shuai, Yao Lina
Date: 2017-03-10 00:00:00
Tasks: Recommendation Systems

Recommender systems have been actively and extensively studied over past
decades. In the meanwhile, the boom of Big Data is driving fundamental changes
in the development of recommender systems. In this paper, we propose a dynamic
intention-aware recommender system to better facilitate users to find desirable
products and services. Compare to prior work, our proposal possesses the
following advantages: (1) it takes user intentions and demands into account
through intention mining techniques. By unearthing user intentions from the
historical user-item interactions, and various user digital traces harvested
from social media and Internet of Things, it is capable of delivering more
satisfactory recommendations by leveraging rich online and offline user data;
(2) it embraces the benefits of embedding heterogeneous source information and
shared representations of multiple domains to provide accurate and effective
recommendations comprehensively; (3) it recommends products or services
proactively and timely by capturing the dynamic influences, which can
significantly reduce user involvements and efforts.
Identifying feasible operating regimes for early T-cell recognition: The speed, energy, accuracy trade-off in kinetic proofreading and adaptive sorting
Date: 2018-08-16 00:00:00
Tasks: Specificity

In the immune system, T cells can quickly discriminate between foreign and
self ligands with high accuracy. There is evidence that T-cells achieve this
remarkable performance utilizing a network architecture based on a
generalization of kinetic proofreading (KPR). KPR-based mechanisms actively
consume energy to increase the specificity beyond what is possible in
equilibrium.An important theoretical question that arises is to understand the
trade-offs and fundamental limits on accuracy, speed, and dissipation (energy
consumption) in KPR and its generalization. Here, we revisit this question
through numerical simulations where we simultaneously measure the speed,
accuracy, and energy consumption of the KPR and adaptive sorting networks for
different parameter choices. Our simulations highlight the existence of a
'feasible operating regime' in the speed-energy-accuracy plane where T-cells
can quickly differentiate between foreign and self ligands at reasonable energy
expenditure. We give general arguments for why we expect this feasible
operating regime to be a generic property of all KPR-based biochemical networks
and discuss implications for our understanding of the T cell receptor circuit.
Does Greed Help a Forager Survive?
Date: 2017-06-20 00:00:00

We investigate the role of greed on the lifetime of a random-walking forager
on an initially resource-rich lattice. Whenever the forager lands on a
food-containing site, all the food there is eaten and the forager can hop
$\mathcal{S}$ more steps without food before starving. Upon reaching an empty
site, the forager comes one time unit closer to starvation. The forager is also
greedy---given a choice to move to an empty or to a food-containing site in its
local neighborhood, the forager moves preferentially towards food.
Surprisingly, the forager lifetime varies non-monotonically with greed, with
different senses of the non-monotonicity in one and two dimensions. Also
unexpectedly, the forager lifetime in one dimension has a huge peak for very
negative greed.
An Algorithm for Cellular Reprogramming
Date: 2017-07-14 00:00:00

The day we understand the time evolution of subcellular elements at a level
of detail comparable to physical systems governed by Newton's laws of motion
seems far away. Even so, quantitative approaches to cellular dynamics add to
our understanding of cell biology, providing data-guided frameworks that allow
us to develop better predictions about and methods for control over specific
biological processes and system-wide cell behavior. In this paper we describe
an approach to optimizing the use of transcription factors in the context of
cellular reprogramming. We construct an approximate model for the natural
evolution of a synchronized population of fibroblasts, based on data obtained
by sampling the expression of some 22,083 genes at several times along the cell
cycle. (These data are based on a colony of cells that have been cell cycle
synchronized) In order to arrive at a model of moderate complexity, we cluster
gene expression based on the division of the genome into topologically
associating domains (TADs) and then model the dynamics of the expression levels
of the TADs. Based on this dynamical model and known bioinformatics, we develop
a methodology for identifying the transcription factors that are the most
likely to be effective toward a specific cellular reprogramming task. The
approach used is based on a device commonly used in optimal control. From this
data-guided methodology, we identify a number of validated transcription
factors used in reprogramming and/or natural differentiation. Our findings
highlight the immense potential of dynamical models models, mathematics, and
data guided methodologies for improving methods for control over biological
processes.
Inferring repeat protein energetics from evolutionary information
Date: 2017-03-15 00:00:00

Natural protein sequences contain a record of their history. A common
constraint in a given protein family is the ability to fold to specific
structures, and it has been shown possible to infer the main native ensemble by
analyzing covariations in extant sequences. Still, many natural proteins that
fold into the same structural topology show different stabilization energies,
and these are often related to their physiological behavior. We propose a
description for the energetic variation given by sequence modifications in
repeat proteins, systems for which the overall problem is simplified by their
inherent symmetry. We explicitly account for single amino acid and pair-wise
interactions and treat higher order correlations with a single term. We show
that the resulting force field can be interpreted with structural detail. We
trace the variations in the energetic scores of natural proteins and relate
them to their experimental characterization. The resulting energetic force
field allows the prediction of the folding free energy change for several
mutants, and can be used to generate synthetic sequences that are statistically
indistinguishable from the natural counterparts.
A neuronal dynamics study on a neuromorphic chip
Date: 2017-03-10 00:00:00

Neuronal firing activities have attracted a lot of attention since a large
population of spatiotemporal patterns in the brain is the basis for adaptive
behavior and can also reveal the signs for various neurological disorders
including Alzheimer's, schizophrenia, epilepsy and others. Here, we study the
dynamics of a simple neuronal network using different sets of settings on a
neuromorphic chip. We observed three different types of collective neuronal
firing activities, which agree with the clinical data taken from the brain. We
constructed a brain phase diagram and showed that within the weak noise region,
the brain is operating in an expected noise-induced phase (N-phase) rather than
at a so-called self-organized critical boundary. The significance of this study
is twofold: first, the deviation of neuronal activities from the normal brain
could be symptomatic of diseases of the central nervous system, thus paving the
way for new diagnostics and treatments; second, the normal brain states in the
N-phase are optimal for computation and information processing. The latter may
provide a way to establish powerful new computing paradigm using collective
behavior of networks of spiking neurons.
On representing and hedging claims for coherent risk measures
Date: 2018-02-18 00:00:00

We provide a dual characterisation of the weak$^*$-closure of a finite sum of
cones in $L^\infty$ adapted to a discrete time filtration $\mathcal{F}_t$: the
$t^{th}$ cone in the sum contains bounded random variables that are
$\mathcal{F}_t$-measurable. Hence we obtain a generalisation of Delbaen's
m-stability condition for the problem of reserving in a collection of
num\'eraires $\mathbf{V}$, called $\mathbf{V}$-m-stability, provided these
cones arise from acceptance sets of a dynamic coherent measure of risk. We also
prove that $\mathbf{V}$-m-stability is equivalent to time-consistency when
reserving in portfolios of $\mathbf{V}$, which is of particular interest to
insurers.
Domain Boundary Detection in Hi-C Maps: A Probabilistic Graphical Model Approach
Date: 2019-09-20 00:00:00
Tasks: Boundary Detection

To understand the nature of a cell, one needs to understand the structure of
its genome. For this purpose, experimental techniques such as Hi-C detecting
chromosomal contacts are used to probe the three-dimensional genomic structure.
These experiments yield topological information, consistently showing a
hierarchical subdivision of the genome into self-interacting domains across
many organisms. Current methods for detecting these domains using the Hi-C
contact matrix, i.e. a doubly-stochastic matrix, are mostly based on the
assumption that the domains are distinct, thus non-overlapping. For overcoming
this simplification and for being able to unravel a possible nested domain
structure, we developed a probabilistic graphical model that makes no a priori
assumptions on the domain structure. Within this approach, the Hi-C contact
matrix is analyzed using an Ising like probabilistic graphical model whose
coupling constant is proportional to each lattice point (entry in the contact
matrix). The results show clear boundaries between identified domains and the
background. These domain boundaries are dependent on the coupling constant, so
that one matrix yields several clusters of different sizes, which show the
self-interaction of the genome on different scales.
Learning to represent signals spike by spike
Date: 2017-03-16 00:00:00

A key question in neuroscience is at which level functional meaning emerges
from biophysical phenomena. In most vertebrate systems, precise functions are
assigned at the level of neural populations, while single-neurons are deemed
unreliable and redundant. Here we challenge this view and show that many
single-neuron quantities, including voltages, firing thresholds, excitation,
inhibition, and spikes, acquire precise functional meaning whenever a network
learns to transmit information parsimoniously and precisely to the next layer.
Based on the hypothesis that neural circuits generate precise population codes
under severe constraints on metabolic costs, we derive synaptic plasticity
rules that allow a network to represent its time-varying inputs with maximal
accuracy. We provide exact solutions to the learnt optimal states, and we
predict the properties of an entire network from its input distribution and the
cost of activity. Single-neuron variability and tuning curves as typically
observed in cortex emerge over the course of learning, but paradoxically
coincide with a precise, non-redundant spike-based population code. Our work
suggests that neural circuits operate far more accurately than previously
thought, and that no spike is fired in vain.
A new integrated symmetrical table for genetic codes
Date: 2017-03-10 00:00:00

Degeneracy is a salient feature of genetic codes, because there are more
codons than amino acids. The conventional table for genetic codes suffers from
an inability of illustrating a symmetrical nature among genetic base codes. In
fact, because the conventional wisdom avoids the question, there is little
agreement as to whether the symmetrical nature actually even exists. A better
understanding of symmetry and an appreciation for its essential role in the
genetic code formation can improve our understanding of nature coding
processes. Thus, it is worth formulating a new integrated symmetrical table for
genetic codes, which is presented in this paper. It could be very useful to
understand the Nobel laureate Crick wobble hypothesis: how one transfer
ribonucleic acid can recognize two or more synonymous codons, which is an
unsolved fundamental question in biological science.
Building automated vandalism detection tools for Wikidata
Authors: Sarabadani Amir, Halfaker Aaron, Taraborelli Dario
Date: 2017-03-10 00:00:00
Tasks: Feature Engineering

Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open
collaboration model is powerful in that it reduces barriers to participation
and allows a large number of people to contribute. However, it exposes the
knowledge base to the risk of vandalism and low-quality contributions. In this
work, we build on past work detecting vandalism in Wikipedia to detect
vandalism in Wikidata. This work is novel in that identifying damaging changes
in a structured knowledge-base requires substantially different feature
engineering work than in a text-based wiki like Wikipedia. We also discuss the
utility of these classifiers for reducing the overall workload of vandalism
patrollers in Wikidata. We describe a machine classification strategy that is
able to catch 89% of vandalism while reducing patrollers' workload by 98%, by
drawing lightly from contextual features of an edit and heavily from the
characteristics of the user making the edit.
Locality-sensitive hashing of curves
Authors: Driemel Anne, Silvestri Francesco
Date: 2017-03-11 00:00:00
Tasks: 2k, Dynamic Time Warping

We study data structures for storing a set of polygonal curves in ${\rm R}^d$
such that, given a query curve, we can efficiently retrieve similar curves from
the set, where similarity is measured using the discrete Fr\'echet distance or
the dynamic time warping distance. To this end we devise the first
locality-sensitive hashing schemes for these distance measures. A major
challenge is posed by the fact that these distance measures internally optimize
the alignment between the curves. We give solutions for different types of
alignments including constrained and unconstrained versions. For unconstrained
alignments, we improve over a result by Indyk from 2002 for short curves. Let
$n$ be the number of input curves and let $m$ be the maximum complexity of a
curve in the input. In the particular case where $m \leq \frac{\alpha}{4d} \log
n$, for some fixed $\alpha>0$, our solutions imply an approximate near-neighbor
data structure for the discrete Fr\'echet distance that uses space in
$O(n^{1+\alpha}\log n)$ and achieves query time in $O(n^{\alpha}\log^2 n)$ and
constant approximation factor. Furthermore, our solutions provide a trade-off
between approximation quality and computational performance: for any parameter
$k \in [m]$, we can give a data structure that uses space in $O(2^{2k}m^{k-1} n
\log n + nm)$, answers queries in $O( 2^{2k} m^{k}\log n)$ time and achieves
approximation factor in $O(m/k)$.
Locally embedded presages of global network bursts
Date: 2017-03-12 00:00:00
Tasks: Time Series, Time Series Analysis

Spontaneous, synchronous bursting of neural population is a widely observed
phenomenon in nervous networks, which is considered important for functions and
dysfunctions of the brain. However, how the global synchrony across a large
number of neurons emerges from an initially non-bursting network state is not
fully understood. In this study, we develop a new state-space reconstruction
method combined with high-resolution recordings of cultured neurons. This
method extracts deterministic signatures of upcoming global bursts in "local"
dynamics of individual neurons during non-bursting periods. We find that local
information within a single-cell time series can compare with or even
outperform the global mean field activity for predicting future global bursts.
Moreover, the inter-cell variability in the burst predictability is found to
reflect the network structure realized in the non-bursting periods. These
findings demonstrate the deterministic mechanisms underlying the locally
concentrated early-warnings of the global state transition in self-organized
networks.
Optimal control of eye-movements during visual search
Date: 2018-08-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study the problem of optimal oculomotor control during the execution of
visual search tasks. We introduce a computational model of human eye movements,
which takes into account various constraints of the human visual and oculomotor
systems. In the model, the choice of the subsequent fixation location is posed
as a problem of stochastic optimal control, which relies on reinforcement
learning methods. We show that if biological constraints are taken into
account, the trajectories simulated under learned policy share both basic
statistical properties and scaling behaviour with human eye movements. We
validated our model simulations with human psychophysical eye-tracking
experiments