pwc-archive/papers-with-abstracts huggingface dataset:
Attentive Long Short-Term Preference Modeling for Personalized Product Search
Authors: Guo Yangyang, Cheng Zhiyong, Nie Liqiang, Wang Yinglong, Ma Jun, Kankanhalli Mohan
Date: 2018-11-26 00:00:00

E-commerce users may expect different products even for the same query, due
to their diverse personal preferences. It is well-known that there are two
types of preferences: long-term ones and short-term ones. The former refers to
user' inherent purchasing bias and evolves slowly. By contrast, the latter
reflects users' purchasing inclination in a relatively short period. They both
affect users' current purchasing intentions. However, few research efforts have
been dedicated to jointly model them for the personalized product search. To
this end, we propose a novel Attentive Long Short-Term Preference model, dubbed
as ALSTP, for personalized product search. Our model adopts the neural networks
approach to learn and integrate the long- and short-term user preferences with
the current query for the personalized product search. In particular, two
attention networks are designed to distinguish which factors in the short-term
as well as long-term user preferences are more relevant to the current query.
This unique design enables our model to capture users' current search
intentions more accurately. Our work is the first to apply attention mechanisms
to integrate both long- and short-term user preferences with the given query
for the personalized search. Extensive experiments over four Amazon product
datasets show that our model significantly outperforms several state-of-the-art
product search methods in terms of different evaluation metrics.
Bull Bear Balance: A Cluster Analysis of Socially Informed Financial Volatility
Date: 2018-11-26 00:00:00
Tasks: Clustering, Financial Analysis

Using a method rooted in information theory, we present results that have
identified a large set of stocks for which social media can be informative
regarding financial volatility. By clustering stocks based on the joint feature
sets of social and financial variables, our research provides an important
contribution by characterizing the conditions in which social media signals can
lead financial volatility. The results indicate that social media is most
informative about financial market volatility when the ratio of bullish to
bearish sentiment is high, even when the number of messages is low. The
robustness of these findings is verified across 500 stocks from both NYSE and
NASDAQ exchanges. The reported results are reproducible via an open-source
library for social-financial analysis made freely available.
Single-pixel imaging with sampling distributed over simplex vertices
Date: 2019-04-24 00:00:00

We propose a method of reduction of experimental noise in single-pixel
imaging by expressing the subsets of sampling patterns as linear combinations
of vertices of a multidimensional regular simplex. This method may be also
directly extended to complementary sampling. The modified sampling consists
only of non-negative patterns. The measurement becomes theoretically
independent of the ambient illumination, and in practice becomes more robust to
the varying conditions of the experiment. We show how the optimal
dimensionality of the simplex depends on the level of measurement noise. We
present experimental results of single-pixel imaging using binarized sampling
and a real-time reconstruction with the Fourier domain regularized inversion
method.
Waveforms of molecular oscillations reveal circadian timekeeping mechanisms
Authors: Hang-Hyun Jo, Yeon Jeong Kim, Jae Kyoung Kim, Mathias Foo, David E. Somers, Pan-Jun Kim
Date: 2018-11-26 00:00:00

Circadian clocks play a pivotal role in orchestrating numerous physiological and developmental events. Waveform shapes of the oscillations of protein abundances can be informative about the underlying biochemical processes of circadian clocks. We derive a mathematical framework where waveforms do reveal hidden biochemical mechanisms of circadian timekeeping. We find that the cost of synthesizing proteins with particular waveforms can be substantially reduced by rhythmic protein half-lives over time, as supported by previous plant and mammalian data, as well as our own seedling experiment. We also find that previously-enigmatic, cyclic expression of positive arm components within the mammalian and insect clocks allows both a broad range of peak time differences between protein waveforms and the symmetries of the waveforms about the peak times. Such various peak-time differences may facilitate tissue-specific or developmental stage-specific multicellular processes. Our waveform-guided approach can be extended to various biological oscillators, including cell-cycle and synthetic genetic oscillators.
Decentralizing Multicell Beamforming via Deterministic Equivalents
Date: 2018-11-26 00:00:00

This paper focuses on developing a decentralized framework for coordinated
minimum power beamforming wherein $L$ base stations (BSs), each equipped with
$N$ antennas, serve $K$ single-antenna users with specific rate constraints.
This is realized by considering user specific intercell interference (ICI)
strength as the principal coupling parameter among BSs. First, explicit
deterministic expressions for transmit powers are derived for spatially
correlated channels in the asymptotic regime in which $N$ and $K$ grow large
with a non-trivial ratio $K/N$. These asymptotic expressions are then used to
compute approximations of the optimal ICI values that depend only on the
channel statistics. By relying on the approximate ICI values as coordination
parameters, a distributed non-iterative coordination algorithm, suitable for
large networks with limited backhaul, is proposed. A heuristic algorithm is
also proposed relaxing coordination requirements even further as it only needs
pathloss values for non-local channels. The proposed algorithms satisfy the
target rates for all users even when $N$ and $K$ are relatively small. Finally,
the potential benefits of grouping users with similar statistics are
investigated to further reduce the overhead and computational effort of the
proposed solutions. Simulation results show that the proposed methods yield
near-optimal performance.
Direct Coupling Analysis of Epistasis in Allosteric Materials
Date: 2020-03-13 00:00:00
Tasks: Drug Design

In allosteric proteins, the binding of a ligand modifies function at a
distant active site. Such allosteric pathways can be used as target for drug
design, generating considerable interest in inferring them from sequence
alignment data. Currently, different methods lead to conflicting results, in
particular on the existence of long-range evolutionary couplings between
distant amino-acids mediating allostery. Here we propose a resolution of this
conundrum, by studying epistasis and its inference in models where an
allosteric material is evolved in silico to perform a mechanical task. We find
in our model the four types of epistasis (Synergistic, Sign, Antagonistic,
Saturation), which can be both short or long-range and have a simple mechanical
interpretation. We perform a Direct Coupling Analysis (DCA) and find that DCA
predicts well the cost of point mutations but is a rather poor generative
model. Strikingly, it can predict short-range epistasis but fails to capture
long-range epistasis, in consistence with empirical findings. We propose that
such failure is generic when function requires subparts to work in concert. We
illustrate this idea with a simple model, which suggests that other methods may
be better suited to capture long-range effects.
Universal and generalizable restoration strategies for degraded ecological networks
Date: 2018-11-13 00:00:00
Tasks: Diversity

Humans are increasingly stressing ecosystems via habitat destruction, climate
change and global population movements leading to the widespread loss of
biodiversity and the disruption of key ecological services. Ecosystems
characterized primarily by mutualistic relationships between species such as
plant-pollinator interactions may be particularly vulnerable to such
perturbations because the loss of biodiversity can cause extinction cascades
that can compromise the entire network. Here, we develop a general restoration
strategy based on network-science for degraded ecosystems. Specifically, we
show that network topology can be used to identify the optimal sequence of
species reintroductions needed to maximize biodiversity gains following partial
and full ecosystem collapse. This restoration strategy generalizes across
topologically-disparate and geographically-distributed ecosystems.
Additionally, we find that although higher connectance and diversity promote
persistence in pristine ecosystems, these attributes reduce the effectiveness
of restoration efforts in degraded networks. Hence, focusing on restoring the
factors that promote persistence in pristine ecosystems may yield suboptimal
recovery strategies for degraded ecosystems. Overall, our results have
important insights for designing effective ecosystem restoration strategies to
preserve biodiversity and ensure the delivery of critical natural services that
fuel economic development, food security and human health around the globe
Scalable graph-based individual named entity identification
Authors: Khalife Sammy, Vazirgiannis Michalis
Date: 2018-11-26 00:00:00
Tasks: Entity Linking, Graph Mining, Information Retrieval, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Re-Ranking, Retrieval, TAG

Named entity discovery (NED) is an important information retrieval problem
that can be decomposed into two sub-problems. The first sub-problem, named
entity recognition (NER), aims to tag pre-defined sets of words in a vocabulary
(called "named entities": names, places, locations, ...) when they appear in
natural language. The second subproblem, named entity linking/identification
(NEL), considers these entity mentions as queries to be identified in a
pre-existing database. In this paper, we consider the NEL problem, and assume a
set of queries (or mentions) that have to be identified within a knowledge
base. This knowledge base is represented by a text database paired with a
semantic graph. We present state-of-the-art methods in NEL, and propose a
2-step method for individual identification of named entities. Our approach is
well-motivated by the limitations brought by recent deep learning approaches
that lack interpratability, and require lots of parameter tuning along with
large volume of annotated data.
  First of all, we propose a filtering algorithm designed with information
retrieval and text mining techniques, aiming to maximize precision at K
(typically for 5 <= K <=20). Then, we introduce two graph-based methods for
named entity identification to maximize precision at 1 by re-ranking the
remaining top entity candidates. The first identification method is using
parametrized graph mining, and the second similarity with graph kernels. Our
approach capitalizes on a fine-grained classification of entities from
annotated web data. We present our algorithms in details, and show
experimentally on standard datasets (NIST TAC-KBP, CONLL/AIDA) their
performance in terms of precision are better than any graph-based method
reported, and competitive with state-of-the-art systems. Finally, we conclude
on the advantages of our graph-based approach compared to recent deep learning
methods.
Using stigmergy to incorporate the time into artificial neural networks
Authors: Galatolo Federico A., Cimino Mario G. C. A., Vaglini Gigliola
Date: 2018-10-26 00:00:00

A current research trend in neurocomputing involves the design of novel
artificial neural networks incorporating the concept of time into their
operating model. In this paper, a novel architecture that employs stigmergy is
proposed. Computational stigmergy is used to dynamically increase (or decrease)
the strength of a connection, or the activation level, of an artificial neuron
when stimulated (or released). This study lays down a basic framework for the
derivation of a stigmergic NN with a related training algorithm. To show its
potential, some pilot experiments have been reported. The XOR problem is solved
by using only one single stigmergic neuron with one input and one output. A
static NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have
been trained to solve the MNIST digits recognition benchmark.
Base-Stations Up in the Air: Multi-UAV Trajectory Control for Min-Rate Maximization in Uplink C-RAN
Date: 2018-11-26 00:00:00
Tasks: Fairness

In this paper we study the impact of unmanned aerial vehicles (UAVs)
trajectories on terrestrial users' spectral efficiency (SE). Assuming a strong
line of sight path to the users, the distance from all users to all UAVs
influence the outcome of an online trajectory optimization. The trajectory
should be designed in a way that the fairness rate is maximized over time. That
means, the UAVs travel in the directions that maximize the minimum of the
users' SE. From the free-space path-loss channel model, a data-rate gradient is
calculated and used to direct the UAVs in a long-term perspective towards the
local optimal solution on the two-dimensional spatial grid. Therefore, a
control system implementation is designed. Thereby, the UAVs follow the
data-rate gradient direction while having a more smooth trajectory compared
with a gradient method. The system can react to changes of the user locations
online; this system design captures the interaction between multiple UAV
trajectories by joint processing at the central unit, e.g., a ground base
station. Because of the wide spread of user locations, the UAVs end up in
optimal locations widely apart from each other. Besides, the SE expectancy is
enhancing continuously while moving along this trajectory.
LM-BIC Model Selection in Semiparametric Models
Date: 2018-11-26 00:00:00
Tasks: model, Model Selection

This paper studies model selection in semiparametric econometric models. It
develops a consistent series-based model selection procedure based on a
Bayesian Information Criterion (BIC) type criterion to select between several
classes of models. The procedure selects a model by minimizing the
semiparametric Lagrange Multiplier (LM) type test statistic from Korolev (2018)
but additionally rewards simpler models. The paper also develops consistent
upward testing (UT) and downward testing (DT) procedures based on the
semiparametric LM type specification test. The proposed semiparametric LM-BIC
and UT procedures demonstrate good performance in simulations. To illustrate
the use of these semiparametric model selection procedures, I apply them to the
parametric and semiparametric gasoline demand specifications from Yatchew and
No (2001). The LM-BIC procedure selects the semiparametric specification that
is nonparametric in age but parametric in all other variables, which is in line
with the conclusions in Yatchew and No (2001). The results of the UT and DT
procedures heavily depend on the choice of tuning parameters and assumptions
about the model errors.
Estimation of a Heterogeneous Demand Function with Berkson Errors
Date: 2019-08-27 00:00:00

Berkson errors are commonplace in empirical microeconomics. In consumer
demand this form of measurement error occurs when the price an individual pays
is measured by the (weighted) average price paid by individuals in a specified
group (e.g., a county), rather than the true transaction price. We show the
importance of such measurement errors for the estimation of demand in a setting
with nonseparable unobserved heterogeneity. We develop a consistent estimator
using external information on the true distribution of prices. Examining the
demand for gasoline in the U.S., we document substantial within-market price
variability, and show that there are significant spatial differences in the
magnitude of Berkson errors across regions of the U.S. Accounting for Berkson
errors is found to be quantitatively important for estimating price effects and
for welfare calculations. Imposing the Slutsky shape constraint greatly reduces
the sensitivity to Berkson errors.
Private Shotgun DNA Sequencing
Date: 2018-11-23 00:00:00

Current techniques in sequencing a genome allow a service provider (e.g. a
sequencing company) to have full access to the genome information, and thus the
privacy of individuals regarding their lifetime secret is violated. In this
paper, we introduce the problem of private DNA sequencing, where the goal is to
keep the DNA sequence private to the sequencer. We propose an architecture,
where the task of reading fragments of DNA and the task of DNA assembly are
separated, the former is done at the sequencer(s), and the later is completed
at a local trusted data collector. To satisfy the privacy constraint at the
sequencer and reconstruction condition at the data collector, we create an
information gap between these two relying on two techniques: (i) we use more
than one non-colluding sequencer, all reporting the read fragments to the
single data collector, (ii) adding the fragments of some known DNA molecules,
which are still unknown to the sequencers, to the pool. We prove that these two
techniques provide enough freedom to satisfy both conditions at the same time.
Maintaining the stem cell niche in multicellular models of epithelia
Authors: Claire Miller, Edmund Crampin, James Osborne
Date: 2018-11-27 00:00:00

The maintenance of the proliferative cell niche is critical to epithelial tissue morphology and function. In this paper we investigate how current modelling methods can result in the erroneous loss of proliferative cells from the proliferative cell niche. Using an established model of the inter-follicular epidermis we find there is a limit to the proliferative cell densities that can be maintained in the basal layer (the niche) if we do not include additional mechanisms to stop the loss of proliferative cells from the niche. We suggest a new methodology that enables maintenance of a desired homeostatic population of proliferative cells in the niche: a rotational force is applied to the two daughter cells during the mitotic phase of division to enforce a particular division direction. We demonstrate that this new methodology achieves this goal. This methodology reflects the regulation of the orientation of cell division.
A Scalable Optimization Mechanism for Pairwise based Discrete Hashing
Authors: Shi Xiaoshuang, Xing Fuyong, Zhang Zizhao, Sapkota Manish, Guo Zhenhua, Yang Lin
Date: 2018-11-27 00:00:00

Maintaining the pair similarity relationship among originally
high-dimensional data into a low-dimensional binary space is a popular strategy
to learn binary codes. One simiple and intutive method is to utilize two
identical code matrices produced by hash functions to approximate a pairwise
real label matrix. However, the resulting quartic problem is difficult to
directly solve due to the non-convex and non-smooth nature of the objective. In
this paper, unlike previous optimization methods using various relaxation
strategies, we aim to directly solve the original quartic problem using a novel
alternative optimization mechanism to linearize the quartic problem by
introducing a linear regression model. Additionally, we find that gradually
learning each batch of binary codes in a sequential mode, i.e. batch by batch,
is greatly beneficial to the convergence of binary code learning. Based on this
significant discovery and the proposed strategy, we introduce a scalable
symmetric discrete hashing algorithm that gradually and smoothly updates each
batch of binary codes. To further improve the smoothness, we also propose a
greedy symmetric discrete hashing algorithm to update each bit of batch binary
codes. Moreover, we extend the proposed optimization mechanism to solve the
non-convex optimization problems for binary code learning in many other
pairwise based hashing algorithms. Extensive experiments on benchmark
single-label and multi-label databases demonstrate the superior performance of
the proposed mechanism over recent state-of-the-art methods.
On the martingale property in the rough Bergomi model
Date: 2019-04-30 00:00:00

We consider a class of fractional stochastic volatility models (including the
so-called rough Bergomi model), where the volatility is a superlinear function
of a fractional Gaussian process. We show that the stock price is a true
martingale if and only if the correlation $\rho$ between the driving Brownian
motions of the stock and the volatility is nonpositive. We also show that for
each $\rho<0$ and $m> \frac{1}{{1-\rho^2}}$, the $m$-th moment of the stock
price is infinite at each positive time.
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors
Authors: Liu Tianlin, Sedoc João, Ungar Lyle
Date: 2018-11-17 00:00:00
Tasks: Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Word Embeddings

Distributed representations of words, better known as word embeddings, have
become important building blocks for natural language processing tasks.
Numerous studies are devoted to transferring the success of unsupervised word
embeddings to sentence embeddings. In this paper, we introduce a simple
representation of sentences in which a sentence embedding is represented as a
weighted average of word vectors followed by a soft projection. We demonstrate
the effectiveness of this proposed method on the clinical semantic textual
similarity task of the BioCreative/OHNLP Challenge 2018.
Systematic Exploration of the High Likelihood Set of Phylogenetic Tree Topologies
Date: 2018-11-27 00:00:00
Tasks: Blocking, Vocal Bursts Intensity Prediction

Bayesian Markov chain Monte Carlo explores tree space slowly, in part because
it frequently returns to the same tree topology. An alternative strategy would
be to explore tree space systematically, and never return to the same topology.
In this paper, we present an efficient parallelized method to map out the high
likelihood set of phylogenetic tree topologies via systematic search, which we
show to be a good approximation of the high posterior set of tree topologies.
Here `likelihood' of a topology refers to the tree likelihood for the
corresponding tree with optimized branch lengths. We call this method
`phylogenetic topographer' (PT). The PT strategy is very simple: starting in a
number of local topology maxima (obtained by hill-climbing from random starting
points), explore out using local topology rearrangements, only continuing
through topologies that are better than than some likelihood threshold below
the best observed topology. We show that the normalized topology likelihoods
are a useful proxy for the Bayesian posterior probability of those topologies.
By using a non-blocking hash table keyed on unique representations of tree
topologies, we avoid visiting topologies more than once across all concurrent
threads exploring tree space. We demonstrate that PT can be used directly to
approximate a Bayesian consensus tree topology. When combined with an accurate
means of evaluating per-topology marginal likelihoods, PT gives an alternative
procedure for obtaining Bayesian posterior distributions on phylogenetic tree
topologies.
Ultra-dense Radio Access Networks for Smart Cities: Cloud-RAN, Fog-RAN and "cell-free" Massive MIMO
Authors: Alister Burr, Manijeh Bashar, Dick Maryopi
Date: 2018-11-27 00:00:00

In this paper we discuss the requirements for a radio access network architecture for ultra-dense networks for "smart city" applications, and show that coordination is required between access points to overcome the effects of interference. We propose a new paradigm, Fog Massive MIMO, based on a combination of the "cell-free" massive MIMO concept and the Fog Radio Access Network (F-RAN). In particular we analyze the potential benefit of improved coordination between APs over different coordination ranges.