pwc-archive/papers-with-abstracts huggingface dataset:
Decentralized Search on Decentralized Web
Authors: Lai Ziliang, Liu Chris, Lo Eric, Kao Ben, Yiu Siu-Ming
Date: 2018-08-18 00:00:00
Tasks: Privacy Preserving

Decentralized Web, or DWeb, is envisioned as a promising future of the Web.
Being decentralized, there are no dedicated web servers in DWeb; Devices that
retrieve web contents also serve their cached data to peer devices with
straight privacy-preserving mechanisms. The fact that contents in DWeb are
distributed, replicated, and decentralized lead to a number of key advantages
over the conventional web. These include better resiliency against network
partitioning and distributed-denial-of-service attacks (DDoS), and better
browsing experiences in terms of shorter latency and higher throughput.
Moreover, DWeb provides tamper-proof contents because each content piece is
uniquely identified by a cryptographic hash. DWeb also clicks well with future
Internet architectures, such as Named Data Networking (NDN).Search engines have
been an inseparable element of the Web. Contemporary ("Web 2.0") search
engines, however, provide centralized services. They are thus subject to DDoS
attacks, insider threat, and ethical issues like search bias and censorship. As
the web moves from being centralized to being decentralized, search engines
ought to follow. We propose QueenBee, a decentralized search engine for DWeb.
QueenBee is so named because worker bees and honeycomb are a common metaphor
for distributed architectures, with the queen being the one that holds the
colony together. QueenBee aims to revolutionize the search engine business
model by offering incentives to both content providers and peers that
participate in QueenBee's page indexing and ranking operations.
Automating Analysis of Construction Workers Viewing Patterns for Personalized Safety Training and Management
Authors: Jeelani Idris, Han Kevin, Albert Alex
Date: 2018-08-20 00:00:00
Tasks: Management

Unrecognized hazards increase the likelihood of workplace fatalities and
injuries substantially. However, recent research has demonstrated that a large
proportion of hazards remain unrecognized in dynamic construction environments.
Recent studies have suggested a strong correlation between viewing patterns of
workers and their hazard recognition performance. Hence, it is important to
study and analyze the viewing patterns of workers to gain a better
understanding of their hazard recognition performance. The objective of this
exploratory research is to explore hazard recognition as a visual search
process to identifying various visual search factors that affect the process of
hazard recognition. Further, the study also proposes a framework to develop a
vision based tool capable of recording and analyzing viewing patterns of
construction workers and generate feedback for personalized training and
proactive safety management.
Self-sustained activity of low firing rate in balanced networks
Date: 2019-09-27 00:00:00

Self-sustained activity in the brain is observed in the absence of external
stimuli and contributes to signal propagation, neural coding, and dynamic
stability. It also plays an important role in cognitive processes. In this
work, by means of studying intracellular recordings from CA1 neurons in rats
and results from numerical simulations, we demonstrate that self-sustained
activity presents high variability of patterns, such as low neural firing rates
and activity in the form of small-bursts in distinct neurons. In our numerical
simulations, we consider random networks composed of coupled, adaptive
exponential integrate-and-fire neurons. The neural dynamics in the random
networks simulate regular spiking (excitatory) and fast-spiking (inhibitory)
neurons. We show that both the connection probability and network size are
fundamental properties that give rise to self-sustained activity in qualitative
agreement with our experimental results. Finally, we provide a more detailed
description of the self-sustained activity in terms of lifetime distributions,
synaptic conductances, and synaptic currents.
Shape-Enforcing Operators for Point and Interval Estimators
Authors: Xi Chen, Victor Chernozhukov, Iván Fernández-Val, Scott Kostyshak, Ye Luo
Date: 2018-09-04 00:00:00
Tasks: Econometrics

A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions. For example, distribution functions are nondecreasing and range between zero and one, height growth charts are nondecreasing in age, and production functions are nondecreasing and quasi-concave in input quantities. We propose a method to enforce these restrictions ex post on point and interval estimates of the target function by applying functional operators. If an operator satisfies certain properties that we make precise, the shape-enforced point estimates are closer to the target function than the original point estimates and the shape-enforced interval estimates have greater coverage and shorter length than the original interval estimates. We show that these properties hold for six different operators that cover commonly used shape restrictions in practice: range, convexity, monotonicity, monotone convexity, quasi-convexity, and monotone quasi-convexity. We illustrate the results with two empirical applications to the estimation of a height growth chart for infants in India and a production function for chemical firms in China.
Energy Efficient Resource Allocation for Mobile-Edge Computation Networks with NOMA
Date: 2019-02-14 00:00:00
Tasks: Edge-computing

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based
mobile-edge computing (MEC) network. Our objective is to minimize the total
energy consumption of all users including transmission energy and local
computation energy subject to computation latency and cloud computation
capacity constraints. We first prove that the total energy minimization problem
is a convex problem, and it is optimal to transmit with maximal time. Then, we
accordingly proposed an iterative algorithm with low complexity, where
closed-form solutions are obtained in each step. The proposed algorithm is
successfully shown to be globally optimal. Numerical results show that the
proposed algorithm achieves better performance than the conventional methods.
RASSA: Resistive Pre-Alignment Accelerator for Approximate DNA Long Read Mapping
Date: 2019-01-28 00:00:00

DNA read mapping is a computationally expensive bioinformatics task, required
for genome assembly and consensus polishing. It requires to find the
best-fitting location for each DNA read on a long reference sequence. A novel
resistive approximate similarity search accelerator, RASSA, exploits charge
distribution and parallel in-memory processing to reflect a mismatch count
between DNA sequences. RASSA implementation of DNA long read pre-alignment
outperforms the state-of-art solution, minimap2, by 16-77x with comparable
accuracy and provides two orders of magnitude higher throughput than
GateKeeper, a short-read pre-alignment hardware architecture implemented in
FPGA.
Parallel G-duplex and C-duplex DNA with Uninterrupted Spines of AgI-Mediated Base Pairs
Date: 2018-10-31 00:00:00

Hydrogen bonding between nucleobases produces diverse DNA structural motifs,
including canonical duplexes, guanine (G) quadruplexes and cytosine (C)
i-motifs. Incorporating metal-mediated base pairs into nucleic acid structures
can introduce new functionalities and enhanced stabilities. Here we
demonstrate, using mass spectrometry (MS), ion mobility spectrometry (IMS) and
fluorescence resonance energy transfer (FRET), that parallel-stranded
structures consisting of up to 20 G-Ag(I)-G contiguous base pairs are formed
when natural DNA sequences are mixed with silver cations in aqueous solution.
FRET indicates that duplexes formed by poly(cytosine) strands with 20
contiguous C-Ag(I)-C base pairs are also parallel. Silver-mediated G-duplexes
form preferentially over G-quadruplexes, and the ability of Ag+ to convert
G-quadruplexes into silver-paired duplexes may provide a new route to
manipulating these biologically relevant structures. IMS indicates that
G-duplexes are linear and more rigid than B-DNA. DFT calculations were used to
propose structures compatible with the IMS experiments. Such inexpensive,
defect-free and soluble DNA-based nanowires open new directions in the design
of novel metal-mediated DNA nanotechnology.
Resource and Competence (Internal) View vs. Environment and Market (External) View when defining a Business
Date: 2018-08-16 00:00:00

Startups is a popular phenomenon that has a significant impact on global
economy growth, innovation and society development. However, there is still
insufficient understanding about startups, particularly, how to start a new
business in the relation to consequent performance. Toward this knowledge, we
have performed an empirical study regarding the differences between a Resource
and Competence View (Internal) vs Environment and Market View (External) when
defining a Business. 701 entrepreneurs have reflected on their startups on nine
classes of Resources (values, vision, personal objectives, employees and
partners, buildings and rental contracts, cash and credit, patents, IPR's and
brands, products and services and finally revenues and grants) and three
elements of the Business Mission ("KeyContribution", "KeyMarket" and
"Distinction"). It seems to be a tendency to favour the Internal View over the
External View. This tendency is clearer in Stable Economies (Europe) than in
Emerging Economies (South Africa). There seems to be a co-variation between the
tendency to favour the Internal View and the tendency to focus on adding
Resources. Finally, we found that an order-based analysis seems to explain the
differences between the two views better than a number-based method.
A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach
Date: 2018-08-23 00:00:00

Several studies explore inferences based on stochastic volatility (SV)
models, taking into account the stylized facts of return data. The common
problem is that the latent parameters of many volatility models are
high-dimensional and analytically intractable, which means inferences require
approximations using, for example, the Markov Chain Monte Carlo or Laplace
methods. Some SV models are expressed as a linear Gaussian state-space model
that leads to a marginal likelihood, reducing the dimensionality of the
problem. Others are not linearized, and the latent parameters are integrated
out. However, these present a quite restrictive evolution equation. Thus, we
propose a Bayesian GED-Gamma SV model with a direct marginal likelihood that is
a product of the generalized Student's t-distributions in which the latent
states are related across time through a stationary Gaussian evolution
equation. Then, an approximation is made for the prior distribution of
log-precision/volatility, without the need for model linearization. This also
allows for the computation of the marginal likelihood function, where the
high-dimensional latent states are integrated out and easily sampled in blocks
using a smoothing procedure. In addition, extensions of our GED-Gamma model are
easily made to incorporate skew heavy-tailed distributions. We use the Bayesian
estimator for the inference of static parameters, and perform a simulation
study on several properties of the estimator. Our results show that the
proposed model can be reasonably estimated. Furthermore, we provide case
studies of a Brazilian asset and the pound/dollar exchange rate to show the
performance of our approach in terms of fit and prediction.
  Keywords: SV model, New sequential and smoothing procedures, Generalized
Student's t-distribution, Non-Gaussian errors, Heavy tails, Skewness
Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding
Date: 2020-08-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This study considers various semiparametric difference-in-differences models
under different assumptions on the relation between the treatment group
identifier, time and covariates for cross-sectional and panel data. The
variance lower bound is shown to be sensitive to the model assumptions imposed
implying a robustness-efficiency trade-off. The obtained efficient influence
functions lead to estimators that are rate double robust and have desirable
asymptotic properties under weak first stage convergence conditions. This
enables to use sophisticated machine-learning algorithms that can cope with
settings where common trend confounding is high-dimensional. The usefulness of
the proposed estimators is assessed in an empirical example. It is shown that
the efficiency-robustness trade-offs and the choice of first stage predictors
can lead to divergent empirical results in practice.
Hierarchical Distribution Matching for Probabilistically Shaped Coded Modulation
Date: 2018-12-26 00:00:00

The implementation difficulties of combining distribution matching (DM) and
dematching (invDM) for probabilistic shaping (PS) with soft-decision forward
error correction (FEC) coding can be relaxed by reverse concatenation, for
which the FEC coding and decoding lies inside the shaping algorithms. PS can
seemingly achieve performance close to the Shannon limit, although there are
practical implementation challenges that need to be carefully addressed. We
propose a hierarchical DM (HiDM) scheme, having fully parallelized input/output
interfaces and a pipelined architecture that can efficiently perform the
DM/invDM without the complex operations of previously proposed methods such as
constant composition DM (CCDM). Furthermore, HiDM can operate at a
significantly larger post-FEC bit error rate (BER) for the same post-invDM BER
performance, which facilitates simulations. These benefits come at the cost of
a slightly larger rate loss and required signal-to-noise ratio at a given
post-FEC BER.
Data Lakes, Clouds and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data
Date: 2018-12-24 00:00:00
Tasks: Cloud Computing, Management

Data commons collate data with cloud computing infrastructure and commonly
used software services, tools and applications to create biomedical resources
for the large-scale management, analysis, harmonization, and sharing of
biomedical data. Over the past few years, data commons have been used to
analyze, harmonize and share large scale genomics datasets. Data ecosystems can
be built by interoperating multiple data commons. It can be quite labor
intensive to curate, import and analyze the data in a data commons. Data lakes
provide an alternative to data commons and simply provide access to data, with
the data curation and analysis deferred until later and delegated to those that
access the data. We review software platforms for managing, analyzing and
sharing genomic data, with an emphasis on data commons, but also covering data
ecosystems and data lakes.
A Limitation of V-Matrix based Methods
Authors: Gauraha Niharika, Chaturvedi Akshay
Date: 2018-08-27 00:00:00

To estimate the conditional probability functions based on the direct problem
setting, V-matrix based method was proposed. We construct V-matrix based
constrained quadratic programming problems for which the inequality constraints
are inconsistent. In particular, we would like to present that the constrained
quadratic optimization problem for conditional probability estimation using
V-matrix method may not have a consistent solution always.
Mitochondrial heterogeneity
Authors: Juvid Aryaman, Iain G. Johnston, Nick S. Jones
Date: 2018-09-06 00:00:00

Cell-to-cell heterogeneity drives a range of (patho)physiologically important phenomena, such as cell fate and chemotherapeutic resistance. The role of metabolism, and particularly mitochondria, is increasingly being recognised as an important explanatory factor in cell-to-cell heterogeneity. Most eukaryotic cells possess a population of mitochondria, in the sense that mitochondrial DNA (mtDNA) is held in multiple copies per cell, where the sequence of each molecule can vary. Hence intra-cellular mitochondrial heterogeneity is possible, which can induce inter-cellular mitochondrial heterogeneity, and may drive aspects of cellular noise. In this review, we discuss sources of mitochondrial heterogeneity (variations between mitochondria in the same cell, and mitochondrial variations between supposedly identical cells) from both genetic and non-genetic perspectives, and mitochondrial genotype-phenotype links. We discuss the apparent homeostasis of mtDNA copy number, the observation of pervasive intra-cellular mtDNA mutation (we term `microheteroplasmy') and developments in the understanding of inter-cellular mtDNA mutation (`macroheteroplasmy'). We point to the relationship between mitochondrial supercomplexes, cristal structure, pH and cardiolipin as a potential amplifier of the mitochondrial genotype-phenotype link. We also discuss mitochondrial membrane potential and networks as sources of mitochondrial heterogeneity, and their influence upon the mitochondrial genome. Finally, we revisit the idea of mitochondrial complementation as a means of dampening mitochondrial genotype-phenotype links in light of recent experimental developments. The diverse sources of mitochondrial heterogeneity, as well as their increasingly recognised role in contributing to cellular heterogeneity, highlights the need for future single-cell mitochondrial measurements in the context of cellular noise studies.
Multi-User Visible Light Communications: State-of-the-Art and Future Directions
Date: 2018-11-14 00:00:00
Tasks: Management

Visible light communication (VLC) builds upon the dual use of existing
lighting infrastructure for wireless data transmission. VLC has recently gained
interest as cost-effective, secure, and energy-efficient wireless access
technology particularly for indoor user-dense environments. While initial
studies in this area are mainly limited to single-user point-to-point links,
more recent efforts have focused on multi-user VLC systems in an effort to
transform VLC into a scalable and fully networked wireless technology. In this
paper, we provide a comprehensive overview of multi-user VLC systems discussing
the recent advances on multi-user precoding, multiple access, resource
allocation, and mobility management. We further provide possible directions of
future research in this emerging topic.
Continuous viscosity solutions to linear-quadratic stochastic control problems with singular terminal state constraint
Date: 2020-04-28 00:00:00

This paper establishes the existence of a unique nonnegative continuous
viscosity solution to the HJB equation associated with a Markovian
linear-quadratic control problems with singular terminal state constraint and
possibly unbounded cost coefficients. The existence result is based on a novel
comparison principle for semi-continuous viscosity sub- and supersolutions for
PDEs with singular terminal value. Continuity of the viscosity solution is
enough to carry out the verification argument.
Suboptimal Control of Dividends under Exponential Utility
Date: 2019-01-21 00:00:00

We consider an insurance company modelling its surplus process by a Brownian
motion with drift. Our target is to maximise the expected exponential utility
of discounted dividend payments, given that the dividend rates are bounded by
some constant. The utility function destroys the linearity and the time
homogeneity of the considered problem. The value function depends not only on
the surplus, but also on time. Numerical considerations suggest that the
optimal strategy, if it exists, is of a barrier type with a non-linear barrier.
In the related article by granditz et al., it has been observed that standard
numerical methods break down in certain parameter cases and no close form
solution has been found.
  For these reasons, we offer a new method allowing to estimate the distance of
an arbitrary smooth enough function to the value function. Applying this
method, we investigate the goodness of the most obvious suboptimal strategies -
payout on the maximal rate, and constant barrier strategies - by measuring the
distance of its performance function to the value function.
The Impact of LIBOR Linked Borrowing to Cover Venture Bank Investment Loans Creates a New Systemic Risk
Authors: Brian P. Hanley
Date: 2018-08-10 00:00:00

A scenario in which regulators take the drastic step of requiring coverage of all venture bank investment loans using interbank borrowed funds is considered. In this scenario, a minimal amount of default insurance is used, such that Tier 1 and 2 capital requirements are still met. To do this, the default insurance percentage on all investment loans is cut to 3.88%, although the minimum is 2.88%. Results: For a portfolio of 1.31X (ten year total conventional return) or better, at interest rates of 2% or better, the venture bank survives and can have excellent returns. For a portfolio of 1.5X (ten year total conventional return) the bank can have extraordinary returns below 1.5% interest and survive up to 3%. interest. However, if returns fall, or interest rates rise, then venture banks go underwater quite rapidly. Conclusion: Using LIBOR funds limits profitability, and damages stability of the bank, with no visible benefit to any party, thus creating a new systemic risk to the banking system.
Sequence and structural patterns detected in entangled proteins reveal the importance of co-translational folding
Date: 2019-05-31 00:00:00

Proteins must fold quickly to acquire their biologically functional
three-dimensional native structures. Hence, these are mainly stabilized by
local contacts, while intricate topologies such as knots are rare. Here, we
reveal the existence of specific patterns adopted by protein sequences and
structures to deal with backbone self-entanglement. A large scale analysis of
the Protein Data Bank shows that loops significantly intertwined with another
chain portion are typically closed by weakly bound amino acids. Why is this
energetic frustration maintained? A possible picture is that entangled loops
are formed only toward the end of the folding process to avoid kinetic traps.
Consistently, these loops are more frequently found to be wrapped around a
portion of the chain on their N-terminal side, the one translated earlier at
the ribosome. Finally, these motifs are less abundant in natural native states
than in simulated protein-like structures, yet they appear in 32% of proteins,
which in some cases display an amazingly complex intertwining.
A geometrical analysis of global stability in trained feedback networks
Date: 2019-01-18 00:00:00

Recurrent neural networks have been extensively studied in the context of
neuroscience and machine learning due to their ability to implement complex
computations. While substantial progress in designing effective learning
algorithms has been achieved in the last years, a full understanding of trained
recurrent networks is still lacking. Specifically, the mechanisms that allow
computations to emerge from the underlying recurrent dynamics are largely
unknown. Here we focus on a simple, yet underexplored computational setup: a
feedback architecture trained to associate a stationary output to a stationary
input. As a starting point, we derive an approximate analytical description of
global dynamics in trained networks which assumes uncorrelated connectivity
weights in the feedback and in the random bulk. The resulting mean-field theory
suggests that the task admits several classes of solutions, which imply
different stability properties. Different classes are characterized in terms of
the geometrical arrangement of the readout with respect to the input vectors,
defined in the high-dimensional space spanned by the network population. We
find that such approximate theoretical approach can be used to understand how
standard training techniques implement the input-output task in finite-size
feedback networks. In particular, our simplified description captures the local
and the global stability properties of the target solution, and thus predicts
training performance.