pwc-archive/papers-with-abstracts huggingface dataset:
The bromodomain-containing protein Ibd1 links multiple chromatin related protein complexes to highly expressed genes in Tetrahymena thermophila
Date: 2018-03-22 00:00:00

Background: The chromatin remodelers of the SWI/SNF family are critical
transcriptional regulators. Recognition of lysine acetylation through a
bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes,
this function is attributed to SNF2/Brg1.
  Results: Using affinity purification coupled to mass spectrometry (AP-MS) we
identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila.
SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt,
two proteins with potential chromatin interacting domains and four proteins
without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits
localize exclusively to the transcriptionally active macronucleus (MAC) during
growth and development, consistent with a role in transcription. While
Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a
BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during
growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it
to be a subunit of several additional protein complexes, including putative
SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl
transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on
histones correlated with active transcription. Consistent with our AP-MS and
histone array data suggesting a role in regulation of gene expression, ChIP-Seq
analysis of Ibd1 indicated that it primarily binds near promoters and within
gene bodies of highly expressed genes during growth.
  Conclusions: Our results suggest that through recognizing specific histones
marks, Ibd1 targets active chromatin regions of highly expressed genes in
Tetrahymena where it subsequently might coordinate the recruitment of several
chromatin remodeling complexes to regulate the transcriptional landscape of
vegetatively growing Tetrahymena cells.
Calculating normal tissue complication probabilities and probabilities of complication-free tumour control from stochastic models of population dynamics
Date: 2018-03-22 00:00:00

We use a stochastic birth-death model for a population of cells to estimate
the normal tissue complication probability (NTCP) under a particular
radiotherapy protocol. We specifically allow for interaction between cells, via
a nonlinear logistic growth model. To capture some of the effects of intrinsic
noise in the population we develop several approximations of NTCP, using
Kramers-Moyal expansion techniques. These approaches provide an approximation
to the first and second moments of a general first-passage time problem in the
limit of large, but finite populations. We use this method to study NTCP in a
simple model of normal cells and in a model of normal and damaged cells. We
also study a combined model of normal tissue cells and tumour cells. Based on
existing methods to calculate tumour control probabilities, and our procedure
to approximate NTCP, we estimate the probability of complication free tumour
control.
Evaluating How Developers Use General-Purpose Web-Search for Code Retrieval
Authors: Rahman Md Masudur, Barson Jed, Paul Sydney, Kayan Joshua, Lois Federico Andres, Quezada Sebastian Fernandez, Parnin Christopher, Stolee Kathryn T., Ray Baishakhi
Date: 2018-03-22 00:00:00
Tasks: Code Search, Retrieval

Search is an integral part of a software development process. Developers
often use search engines to look for information during development, including
reusable code snippets, API understanding, and reference examples. Developers
tend to prefer general-purpose search engines like Google, which are often not
optimized for code related documents and use search strategies and ranking
techniques that are more optimized for generic, non-code related information.
In this paper, we explore whether a general purpose search engine like Google
is an optimal choice for code-related searches. In particular, we investigate
whether the performance of searching with Google varies for code vs. non-code
related searches. To analyze this, we collect search logs from 310 developers
that contains nearly 150,000 search queries from Google and the associated
result clicks. To differentiate between code-related searches and non-code
related searches, we build a model which identifies the code intent of queries.
Leveraging this model, we build an automatic classifier that detects a code and
non-code related query. We confirm the effectiveness of the classifier on
manually annotated queries where the classifier achieves a precision of 87%, a
recall of 86%, and an F1-score of 87%. We apply this classifier to
automatically annotate all the queries in the dataset. Analyzing this dataset,
we observe that code related searching often requires more effort (e.g., time,
result clicks, and query modifications) than general non-code search, which
indicates code search performance with a general search engine is less
effective.
APR: Architectural Pattern Recommender
Authors: Sharma Shipra, Sodhi Balwinder
Date: 2018-03-23 00:00:00
Tasks: Natural Language Inference

This paper proposes Architectural Pattern Recommender (APR) system which
helps in such architecture selection process. Main contribution of this work is
in replacing the manual effort required to identify and analyse relevant
architectural patterns in context of a particular set of software requirements.
Key input to APR is a set of architecturally significant use cases concerning
the application being developed. Central idea of APR's design is two folds: a)
transform the unstructured information about software architecture design into
a structured form which is suitable for recognizing textual entailment between
a requirement scenario and a potential architectural pattern. b) leverage the
rich experiential knowledge embedded in discussions on professional developer
support forums such as Stackoverflow to check the sentiment about a design
decision. APR makes use of both the above elements to identify a suitable
architectural pattern and assess its suitability for a given set of
requirements. Efficacy of APR has been evaluated by comparing its
recommendations for "ground truth" scenarios (comprising of applications whose
architecture is well known).
Measurement of the evolution of technology: A new perspective
Date: 2018-03-23 00:00:00

A fundamental problem in technological studies is how to measure the
evolution of technology. The literature has suggested several approaches to
measuring the level of technology (or state-of-the-art) and changes in
technology. However, the measurement of technological advances and
technological evolution is often a complex and elusive topic in science. The
study here starts by establishing a conceptual framework of technological
evolution based on the theory of technological parasitism, in broad analogy
with biology. Then, the measurement of the evolution of technology is modelled
in terms of morphological changes within complex systems considering the
interaction between a host technology and its subsystems of technology. The
coefficient of evolutionary growth of the model here indicates the grade and
type of the evolutionary route of a technology. This coefficient is quantified
in real instances using historical data of farm tractor, freight locomotive and
electricity generation technology in steam-powered plants and
internal-combustion plants. Overall, then, it seems that the approach here is
appropriate in grasping the typology of evolution of complex systems of
technology and in predicting which technologies are likeliest to evolve
rapidly.
Nonlinear Deconvolution by Sampling Biophysically Plausible Hemodynamic Models
Date: 2018-03-23 00:00:00
Tasks: Time Series, Time Series Analysis

Non-invasive methods to measure brain activity are important to understand
cognitive processes in the human brain. A prominent example is functional
magnetic resonance imaging (fMRI), which is a noisy measurement of a delayed
signal that depends non-linearly on the neuronal activity through the
neurovascular coupling. These characteristics make the inference of neuronal
activity from fMRI a difficult but important step in fMRI studies that require
information at the neuronal level. In this article, we address this inference
problem using a Bayesian approach where we model the latent neural activity as
a stochastic process and assume that the observed BOLD signal results from a
realistic physiological (Balloon) model. We apply a recently developed
smoothing method called APIS to efficiently sample the posterior given single
event fMRI time series. To infer neuronal signals with high likelihood for
multiple time series efficiently, a modification of the original algorithm is
introduced. We demonstrate that our adaptive procedure is able to compensate
the lacking of inputs in the model to infer the neuronal activity and that it
outperforms dramatically the standard bootstrap particle filter-smoother in
this setting. This makes the proposed procedure especially attractive to
deconvolve resting state fMRI data. To validate the method, we evaluate the
quality of the signals inferred using the timing information contained in them.
APIS obtains reliable event timing estimates based on fMRI data gathered during
a reaction time experiment with short stimuli. Hence, we show for the first
time that one can obtain accurate absolute timing of neuronal activity by
reconstructing the latent neural signal.
Fast swaption pricing in Gaussian term structure models
Date: 2018-03-23 00:00:00
Tasks: Numerical Integration

We propose a fast and accurate numerical method for pricing European
swaptions in multi-factor Gaussian term structure models. Our method can be
used to accelerate the calibration of such models to the volatility surface.
The pricing of an interest rate option in such a model involves evaluating a
multi-dimensional integral of the payoff of the claim on a domain where the
payoff is positive. In our method, we approximate the exercise boundary of the
state space by a hyperplane tangent to the maximum probability point on the
boundary and simplify the multi-dimensional integration into an analytical
form. The maximum probability point can be determined using the gradient
descent method. We demonstrate that our method is superior to previous methods
by comparing the results to the price obtained by numerical integration.
A structural Heath-Jarrow-Morton framework for consistent intraday, spot, and futures electricity prices
Date: 2019-01-18 00:00:00

In this paper we introduce a flexible HJM-type framework that allows for
consistent modelling of intraday, spot, futures, and option prices. This
framework is based on stochastic processes with economic interpretations and
consistent with the initial term structure given in the form of a price forward
curve. Furthermore, the framework allows for existing day-ahead spot price
models to be used in an HJM setting. We include several explicit examples of
classical spot price models but also show how structural models and factor
models can be formulated within the framework.
Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes
Authors: Shen Feichen, Larson David W, Naessens James M., Habermann Elizabeth B., Liu Hongfang, Sohn Sunghwan
Date: 2018-03-26 00:00:00

Postsurgical complications (PSCs) are known as a deviation from the normal
postsurgical course and categorized by severity and treatment requirements.
Surgical site infection (SSI) is one of major PSCs and the most common
healthcare-associated infection, resulting in increased length of hospital stay
and cost. In this work, we assessed an automated way to generate lexicon (i.e.,
keyword features) from clinical narratives using sublanguage analysis with
heuristics to detect SSI and evaluated these keywords with medical experts. To
further validate our approach, we also conducted decision tree algorithm on
cohort using automatically generated keywords. The results show that our
framework was able to identify SSI keywords from clinical narratives and to
support search-based natural language processing (NLP) approaches by augmenting
search queries.
How does monetary policy affect income inequality in Japan? Evidence from grouped data
Authors: Martin Feldkircher, Kazuhiko Kakamu
Date: 2018-03-23 00:00:00

We examine the effects of monetary policy on income inequality in Japan using a novel econometric approach that jointly estimates the Gini coefficient based on micro-level grouped data of households and the dynamics of macroeconomic quantities. Our results indicate different effects on income inequality for different types of households: A monetary tightening increases inequality when income data is based on households whose head is employed (workers' households), while the effect reverses over the medium term when considering a broader definition of households. Differences in the relative strength of the transmission channels can account for this finding. Finally we demonstrate that the proposed joint estimation strategy leads to more informative inference while results based on the frequently used two-step estimation approach yields inconclusive results.
Lyapunov Event-triggered Stabilization with a Known Convergence Rate
Date: 2020-07-14 00:00:00

A constructive tool of nonlinear control systems design, the method of
Control Lyapunov Functions (CLF) has found numerous applications in
stabilization problems for continuous time, discrete-time and hybrid systems.
In this paper, we address the fundamental question: given a CLF, corresponding
to the continuous-time controller with some predefined (e.g. exponential)
convergence rate, can the same convergence rate be provided by an
event-triggered controller? Under certain assumptions, we give an affirmative
answer to this question and show that the corresponding event-based controllers
provide positive dwelltimes between the consecutive events. Furthermore, we
prove the existence of self-triggered and periodic event-triggered controllers,
providing stabilization with a known convergence rate.
Quantitative characterization of the imaging limits of diffuse low-grade oligodendrogliomas
Date: 2018-03-23 00:00:00

Background : Supratentorial diffuse low-grade gliomas in adults extend beyond
maximal visible MRI-defined abnormalities, and a gap exists between the imaging
signal changes and the actual tumor margins. Direct quantitative comparisons
between imaging and histological analyses are lacking to date. However, they
are of the utmost importance if one wishes to develop realistic models for
diffuse glioma growth.
  Methods : In this study, we quantitatively compare the cell concentration and
the edema fraction from human histological biopsy samples (BSs) performed
inside and outside imaging abnormalities during serial imaging-based
stereotactic biopsy of diffuse low-grade gliomas.
  Results : The cell concentration was significantly higher in BSs located
inside (1189 $\pm$ 378 cell/mm$^2$) than outside (740 $\pm$ 124 cell/mm$^2$)
MRI-defined abnormalities (p=0.0003). The edema fraction was significantly
higher in BSs located inside (mean, 45 $\pm$ 23%) than outside (mean, 5 $\pm$
9%) MRI-defined abnormalities (p<0.0001). At borders of the MRI-defined
abnormalities, 20% of the tissue surface area was occupied by edema, and only
3% by tumor cells. The cycling cell concentration was significantly higher in
BSs located inside (10 $\pm$ 12 cell/mm$^2$) compared to outside (0.5 $\pm$ 0.9
cell/mm$^2$) MRI-defined abnormalities (p=0.0001).
  Conclusions : We show that the margins of T2-weighted signal changes are
mainly correlated with the edema fraction. In 62.5% of patients, the cycling
tumor cell fraction (defined as the ratio of the cycling tumor cell
concentration to the total number of tumor cells) was higher at the limits of
the MRI-defined abnormalities than closer to the center of the tumor. In the
remaining patients, the cycling tumor cell fraction increased towards the
center of the tumor.
Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments
Date: 2018-03-23 00:00:00
Tasks: speech-recognition, Speech Recognition

This paper evaluates the robustness of a DNN-HMM-based speech recognition
system in highly-reverberant real environments using the HRRE database. The
performance of locally-normalized filter bank (LNFB) and Mel filter bank
(MelFB) features in combination with Non-negative Matrix Factorization (NMF),
Suppression of Slowly-varying components and the Falling edge (SSF) and
Weighted Prediction Error (WPE) enhancement methods are discussed and
evaluated. Two training conditions were considered: clean and reverberated
(Reverb). With Reverb training the use of WPE and LNFB provides WERs that are
3% and 20% lower in average than SSF and NMF, respectively. WPE and MelFB
provides WERs that are 11% and 24% lower in average than SSF and NMF,
respectively. With clean training, which represents a significant mismatch
between testing and training conditions, LNFB features clearly outperform MelFB
features. The results show that different types of training, parametrization,
and enhancement techniques may work better for a specific combination of
speaker-microphone distance and reverberation time. This suggests that there
could be some degree of complementarity between systems trained with different
enhancement and parametrization methods.
Difference-in-Differences with Multiple Time Periods
Date: 2020-08-18 00:00:00
Tasks: valid

In this article, we consider identification, estimation, and inference
procedures for treatment effect parameters using Difference-in-Differences
(DID) with (i) multiple time periods, (ii) variation in treatment timing, and
(iii) when the "parallel trends assumption" holds potentially only after
conditioning on observed covariates. We show that a family of causal effect
parameters are identified in staggered DID setups, even if differences in
observed characteristics create non-parallel outcome dynamics between groups.
Our identification results allow one to use outcome regression, inverse
probability weighting, or doubly-robust estimands. We also propose different
aggregation schemes that can be used to highlight treatment effect
heterogeneity across different dimensions as well as to summarize the overall
effect of participating in the treatment. We establish the asymptotic
properties of the proposed estimators and prove the validity of a
computationally convenient bootstrap procedure to conduct asymptotically valid
simultaneous (instead of pointwise) inference. Finally, we illustrate the
relevance of our proposed tools by analyzing the effect of the minimum wage on
teen employment from 2001--2007. Open-source software is available for
implementing the proposed methods.
Schooling Choice, Labour Market Matching, and Wages
Date: 2019-08-27 00:00:00
Tasks: valid

We develop inference for a two-sided matching model where the characteristics
of agents on one side of the market are endogenous due to pre-matching
investments. The model can be used to measure the impact of frictions in labour
markets using a single cross-section of matched employer-employee data. The
observed matching of workers to firms is the outcome of a discrete, two-sided
matching process where firms with heterogeneous preferences over education
sequentially choose workers according to an index correlated with worker
preferences over firms. The distribution of education arises in equilibrium
from a Bayesian game: workers, knowing the distribution of worker and firm
types, invest in education prior to the matching process. Although the observed
matching exhibits strong cross-sectional dependence due to the matching
process, we propose an asymptotically valid inference procedure that combines
discrete choice methods with simulation.
Conscious Perception: Time for an Update?
Date: 2018-03-24 00:00:00

Understanding the neural mechanism underlying subjective representation has
become a central endeavor in cognitive-neuroscience. In theories of conscious
perception, stimulus gaining conscious access is usually considered as a
discrete neuronal event to be characterized in time or space, sometimes refer
to as a 'conscious episode'. Surprisingly, the alternative hypothesis according
to which conscious perception is a dynamic process has been rarely considered.
Here, we discuss this hypothesis and envisage its implications. We show how it
can reconcile inconsistent empirical findings on the timing of the neural
correlates of consciousness (NCCs), and make testable predictions. According to
this hypothesis, a stimulus is consciously perceived for as long as it is
recoded to fit an ongoing stream composed of all other perceived stimuli. We
suggest that this 'updating' process is governed by at least three factors (1)
context, (2) stimulus saliency and (3) observer's goal. Finally, this framework
forces us to reconsider the typical distinction between conscious and
unconscious information processing.
Distributed Energy and Resource Management for Full-Duplex Dense Small Cells for 5G
Date: 2018-03-24 00:00:00
Tasks: Management, Scheduling

We consider a multi-carrier and densely deployed small cell network, where
small cells are powered by renewable energy source and operate in a full-duplex
mode. We formulate an energy and traffic aware resource allocation optimization
problem, where a joint design of the beamformers, power and sub-carrier
allocation, and users scheduling is proposed. The problem minimizes the sum
data buffer lengths of each user in the network by using the harvested energy.
A practical uplink user rate-dependent decoding energy consumption is included
in the total energy consumption at the small cell base stations. Hence,
harvested energy is shared with both downlink and uplink users. Owing to the
non-convexity of the problem, a faster convergence sub-optimal algorithm based
on successive parametric convex approximation framework is proposed. The
algorithm is implemented in a distributed fashion, by using the alternating
direction method of multipliers, which offers not only the limited information
exchange between the base stations, but also fast convergence. Numerical
results advocate the redesigning of the resource allocation strategy when the
energy at the base station is shared among the downlink and uplink
transmissions.
Multi-SpaM: a Maximum-Likelihood approach to Phylogeny reconstruction based on Multiple Spaced-Word Matches
Date: 2018-04-28 00:00:00

Motivation: Word-based or `alignment-free' methods for phylogeny
reconstruction are much faster than traditional approaches, but they are
generally less accurate. Most of these methods calculate pairwise distances for
a set of input sequences, for example from word frequencies, from so-called
spaced-word matches or from the average length of common substrings.
  Results: In this paper, we propose the first word-based approach to tree
reconstruction that is based on multiple sequence comparison and Maximum
Likelihood. Our algorithm first samples small, gap-free alignments involving
four taxa each. For each of these alignments, it then calculates a quartet tree
and, finally, the program Quartet MaxCut is used to infer a super tree topology
for the full set of input taxa from the calculated quartet trees. Experimental
results show that trees calculated with our approach are of high quality.
  Availability: The source code of the program is available at
https://github.com/tdencker/multi-SpaM
  Contact: thomas.dencker@stud.uni-goettingen.de