pwc-archive/papers-with-abstracts huggingface dataset:
Arbitrage-Free Pricing of Game Options in Nonlinear Markets
Date: 2018-07-14 00:00:00

The goal is to re-examine and extend the findings from the recent paper by
Dumitrescu, Quenez and Sulem (2017) who studied game options within the
nonlinear arbitrage-free pricing approach developed in El Karoui and Quenez
(1997). We consider the setup introduced in Kim, Nie and Rutkowski (2018) where
contracts of an American style were examined. We give a detailed study of
unilateral pricing, hedging and exercising problems for the counterparties
within a general nonlinear setup. We also present a BSDE approach, which is
used to obtain more explicit results under suitable assumptions about solutions
to doubly reflected BSDEs.
Fast and Robust High-Dimensional Sparse Representation Recovery Using Generalized SL0
Date: 2018-07-15 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Sparse representation can be described in high dimensions and used in many
applications, including MRI imaging and radar imaging. In some cases, methods
have been proposed to solve the high-dimensional sparse representation problem,
but main solution is converting high-dimensional problem into one-dimension.
Solving the equivalent problem had very high computational complexity. In this
paper, the problem of high-dimensional sparse representation is formulated
generally based on the theory of tensors, and a method for solving it based on
SL0 (Smoothed Least zero-nor) is presented. Also, the uniqueness conditions for
solution of the problem are considered in the high-dimensions. At the end of
the paper, some numerical experiments are performed to evaluate the efficiency
of the proposed algorithm and the results are presented.
Optimal Credit Investment and Risk Control for an Insurer with Regime-Switching
Date: 2018-07-15 00:00:00

This paper studies an optimal investment and risk control problem for an
insurer with default contagion and regime-switching. The insurer in our model
allocates his/her wealth across multi-name defaultable stocks and a riskless
bond under regime-switching risk. Default events have an impact on the distress
state of the surviving stocks in the portfolio. The aim of the insurer is to
maximize the expected utility of the terminal wealth by selecting optimal
investment and risk control strategies. We characterize the optimal trading
strategy of defaultable stocks and risk control for the insurer. By developing
a truncation technique, we analyze the existence and uniqueness of global
(classical) solutions to the recursive HJB system. We prove the verification
theorem based on the (classical) solutions of the recursive HJB system.
A Survey on Expert Recommendation in Community Question Answering
Authors: Wang Xianzhi, Huang Chaoran, Yao Lina, Benatallah Boualem, Dong Manqing
Date: 2018-07-15 00:00:00
Tasks: Community Question Answering, Question Answering, Survey

Community question answering (CQA) represents the type of Web applications
where people can exchange knowledge via asking and answering questions. One
significant challenge of most real-world CQA systems is the lack of effective
matching between questions and the potential good answerers, which adversely
affects the efficient knowledge acquisition and circulation. On the one hand, a
requester might experience many low-quality answers without receiving a quality
response in a brief time, on the other hand, an answerer might face numerous
new questions without being able to identify their questions of interest
quickly. Under this situation, expert recommendation emerges as a promising
technique to address the above issues. Instead of passively waiting for users
to browse and find their questions of interest, an expert recommendation method
raises the attention of users to the appropriate questions actively and
promptly. The past few years have witnessed considerable efforts that address
the expert recommendation problem from different perspectives. These methods
all have their issues that need to be resolved before the advantages of expert
recommendation can be fully embraced. In this survey, we first present an
overview of the research efforts and state-of-the-art techniques for the expert
recommendation in CQA. We next summarize and compare the existing methods
concerning their advantages and shortcomings, followed by discussing the open
issues and future research directions.
Semantic Search by Latent Ontological Features
Authors: Cao Tru H., Ngo Vuong M.
Date: 2018-07-15 00:00:00
Tasks: Retrieval, Text Retrieval

Both named entities and keywords are important in defining the content of a
text in which they occur. In particular, people often use named entities in
information search. However, named entities have ontological features, namely,
their aliases, classes, and identifiers, which are hidden from their textual
appearance. We propose ontology-based extensions of the traditional Vector
Space Model that explore different combinations of those latent ontological
features with keywords for text retrieval. Our experiments on benchmark
datasets show better search quality of the proposed models as compared to the
purely keyword-based model, and their advantages for both text retrieval and
representation of documents and queries.
Discovering Latent Concepts and Exploiting Ontological Features for Semantic Text Search
Authors: Ngo Vuong M., Cao Tru H.
Date: 2018-07-15 00:00:00
Tasks: Retrieval

Named entities and WordNet words are important in defining the content of a
text in which they occur. Named entities have ontological features, namely,
their aliases, classes, and identifiers. WordNet words also have ontological
features, namely, their synonyms, hypernyms, hyponyms, and senses. Those
features of concepts may be hidden from their textual appearance. Besides,
there are related concepts that do not appear in a query, but can bring out the
meaning of the query if they are added. The traditional constrained spreading
activation algorithms use all relations of a node in the network that will add
unsuitable information into the query. Meanwhile, we only use relations
represented in the query. We propose an ontology-based generalized Vector Space
Model to semantic text search. It discovers relevant latent concepts in a query
by relation constrained spreading activation. Besides, to represent a word
having more than one possible direct sense, it combines the most specific
common hypernym of the remaining undisambiguated multi-senses with the form of
the word. Experiments on a benchmark dataset in terms of the MAP measure for
the retrieval performance show that our model is 41.9% and 29.3% better than
the purely keyword-based model and the traditional constrained spreading
activation model, respectively.
Efficiently Secure Broadcasting in 5G Wireless Fog-Based-Fronthaul Networks
Date: 2018-07-15 00:00:00
Tasks: Diversity

Enhanced Diversity and Network Coding (eDC-NC), the synergistic combination
of Diversity and modified Triangular Network Coding, was introduced recently to
provide efficient and ultra-reliable networking with near-instantaneous fault
recovery. In this paper it is shown that eDC-NC technology can efficiently and
securely broadcast messages in 5G wireless fog-computing-based Radio Access
Networks (F-RAN). In particular, this work is directed towards demonstrating
the ability of eDC-NC technology to more efficiently provide secure messages
broadcasting than standardized methods such as Secure Multicasting using Secret
(Shared) Key Cryptography, such that the adversary has no ability to acquire
information even if they wiretap the entire F-RAN network (except of course the
source and destination nodes). Our results show that using secure eDC-NC
technology in F-RAN fronthaul network will enhance secure broadcasting and
provide ultra-reliability networking, near-instantaneous fault recovery, and
retain the throughput benefits of Network Coding.
Network neuroscience for optimizing brain-computer interfaces
Date: 2018-07-15 00:00:00

Human-machine interactions are being increasingly explored to create
alternative ways of communication and to improve our daily life. Based on a
classification of the user's intention from the user's underlying neural
activity, brain-computer interfaces (BCIs) allow direct interactions with the
external environment while bypassing the traditional effector of the
musculoskeletal system. Despite the enormous potential of BCIs, there are still
a number of challenges that limit their societal impact, ranging from the
correct decoding of a human's thoughts, to the application of effective
learning strategies. Despite several important engineering advances, the basic
neuroscience behind these challenges remains poorly explored. Indeed, BCIs
involve complex dynamic changes related to neural plasticity at a diverse range
of spatiotemporal scales. One promising antidote to this complexity lies in
network science, which provides a natural language in which to model the
organizational principles of brain architecture and function as manifest in its
interconnectivity. Here, we briefly review the main limitations currently
affecting BCIs, and we offer our perspective on how they can be addressed by
means of network theoretic approaches. We posit that the emerging field of
network neuroscience will prove to be an effective tool to unlock human-machine
interactions.
Joint Modeling and Optimization of Search and Recommendation
Authors: Zamani Hamed, Croft W. Bruce
Date: 2018-07-15 00:00:00
Tasks: Recommendation Systems, Retrieval

Despite the somewhat different techniques used in developing search engines
and recommender systems, they both follow the same goal: helping people to get
the information they need at the right time. Due to this common goal, search
and recommendation models can potentially benefit from each other. The recent
advances in neural network technologies make them effective and easily
extendable for various tasks, including retrieval and recommendation. This
raises the possibility of jointly modeling and optimizing search ranking and
recommendation algorithms, with potential benefits to both. In this paper, we
present theoretical and practical reasons to motivate joint modeling of search
and recommendation as a research direction. We propose a general framework that
simultaneously learns a retrieval model and a recommendation model by
optimizing a joint loss function. Our preliminary results on a dataset of
product data indicate that the proposed joint modeling substantially
outperforms the retrieval and recommendation models trained independently. We
list a number of future directions for this line of research that can
potentially lead to development of state-of-the-art search and recommendation
models.
Inference of the Dynamic Aging-related Biological Subnetwork via Network Propagation
Authors: Khalique Newaz, Tijana Milenkovic
Date: 2018-07-15 00:00:00
Tasks: Human Aging

Gene expression (GE) data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.) However, GE analyses ignore physical interactions between gene products, i.e., proteins. Since proteins function by interacting with each other, and since biological networks (BNs) capture these interactions, BN analyses are promising. However, current BN data fail to capture condition-specific information. Recently, GE and BN data have been integrated using network propagation (NP) to infer condition-specific BNs. However, existing NP-based studies result in a static condition-specific subnetwork, even though cellular processes are dynamic. A dynamic process of our interest is human aging. We use prominent existing NP methods in a new task of inferring a dynamic rather than static condition-specific (aging-related) subnetwork. Then, we study evolution of network structure with age - we identify proteins whose network positions significantly change with age and predict them as new aging-related candidates. We validate the predictions via e.g., functional enrichment analyses and literature search. Dynamic network inference via NP yields higher prediction quality than the only existing method for inferring a dynamic aging-related BN, which does not use NP.
True Contextuality Beats Direct Influences in Human Decision Making
Date: 2020-05-30 00:00:00
Tasks: Decision Making

In quantum physics there are well-known situations when measurements of the
same property in different contexts (under different conditions) have the same
probability distribution, but cannot be represented by one and the same random
variable. Such systems of random variables are called contextual. More
generally, true contextuality is observed when different contexts force
measurements of the same property (in psychology, responses to the same
question) to be more dissimilar random variables than warranted by the
difference of their distributions. The difference in distributions is itself a
form of context-dependence, but of another nature: it is attributable to direct
causal influences exerted by contexts upon the random variables. The
Contextuality-by-Default (CbD) theory allows one to separate true contextuality
from direct influences in the overall context-dependence. The CbD analysis of
numerous previous attempts to demonstrate contextuality in human judgments
shows that all context-dependence in them can be accounted for by direct
influences, with no true contextuality present. However, contextual systems in
human behavior can be found. In this paper we present a series of crowdsourcing
experiments that exhibit true contextuality in simple decision making.}{The
design of these experiments is an elaboration of one introduced in the "Snow
Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the
first time demonstrated unequivocally.
On SDEs with Lipschitz coefficients, driven by continuous, model-free martingales
Authors: Lesiba Ch. Galane, Rafał M. Łochowski, Farai J. Mhlanga
Date: 2018-07-16 00:00:00

We prove the existence and uniqueness of solutions of SDEs with Lipschitz coefficients, driven by continuous, model-free martingales. The main tool in our reasoning is Picard's iterative procedure and a model-free version of the Burkholder-Davis-Gundy inequality for integrals driven by model-free, continuous martingales. We work with a new outer measure which assigns zero value exactly to those properties which are instantly blockable.
Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth
Date: 2018-07-16 00:00:00
Tasks: Position

In a multiuser context, the Bluetooth data from the smartphone could give an
approximation of the distance between users. Meanwhile, the Wi-Fi data can be
used to calculate the user's position directly. However, both the Wi-Fi-based
position outputs and Bluetooth-based distances are affected by some degree of
noise. In our work, we propose several approaches to combine the two types of
outputs for improving the tracking accuracy in the context of collaborative
positioning. The two proposed approaches attempt to build a model for measuring
the errors of the Bluetooth output and Wi-Fi output. In a non-temporal
approach, the model establishes the relationship in a specific interval of the
Bluetooth output and Wi-Fi output. In a temporal approach, the error
measurement model is expanded to include the time component between users'
movement. To evaluate the performance of the two approaches, we collected the
data from several multiuser scenarios in indoor environment. The results show
that the proposed approaches could reach a distance error around 3.0m for 75
percent of time, which outperforms the positioning results of the standard
Wi-Fi fingerprinting model.
A Collective Variational Autoencoder for Top-$N$ Recommendation with Side Information
Authors: Chen Yifan, de Rijke Maarten
Date: 2018-07-16 00:00:00
Tasks: Recommendation Systems

Recommender systems have been studied extensively due to their practical use
in many real-world scenarios. Despite this, generating effective
recommendations with sparse user ratings remains a challenge. Side information
associated with items has been widely utilized to address rating sparsity.
Existing recommendation models that use side information are linear and, hence,
have restricted expressiveness. Deep learning has been used to capture
non-linearities by learning deep item representations from side information but
as side information is high-dimensional existing deep models tend to have large
input dimensionality, which dominates their overall size. This makes them
difficult to train, especially with small numbers of inputs.
  Rather than learning item representations, which is problematic with
high-dimensional side information, in this paper, we propose to learn feature
representation through deep learning from side information. Learning feature
representations, on the other hand, ensures a sufficient number of inputs to
train a deep network. To achieve this, we propose to simultaneously recover
user ratings and side information, by using a Variational Autoencoder (VAE).
Specifically, user ratings and side information are encoded and decoded
collectively through the same inference network and generation network. This is
possible as both user ratings and side information are data associated with
items. To account for the heterogeneity of user rating and side information,
the final layer of the generation network follows different distributions
depending on the type of information. The proposed model is easy to implement
and efficient to optimize and is shown to outperform state-of-the-art top-$N$
recommendation methods that use side information.
An Adjustable Heat Conduction based KNN Approach for Session-based Recommendation
Authors: Guo Huifeng, Tang Ruiming, Ye Yunming, Liu Feng, Zhang Yuzhou
Date: 2018-07-16 00:00:00
Tasks: Recommendation Systems, Session-Based Recommendations

The KNN approach, which is widely used in recommender systems because of its
efficiency, robustness and interpretability, is proposed for session-based
recommendation recently and outperforms recurrent neural network models. It
captures the most recent co-occurrence information of items by considering the
interaction time. However, it neglects the co-occurrence information of items
in the historical behavior which is interacted earlier and cannot discriminate
the impact of items and sessions with different popularity. Due to these
observations, this paper presents a new contextual KNN approach to address
these issues for session-based recommendation. Specifically, a diffusion-based
similarity method is proposed for considering the popularity of vertices in
session-item bipartite network, and a candidate selection method is proposed to
capture the items that are co-occurred with different historical clicked items
in the same session efficiently. Comprehensive experiments are conducted to
demonstrate the effectiveness of our KNN approach over the state-of-the-art KNN
approach for session-based recommendation on three benchmark datasets.
PAM-4 Transmission at 1550nm using Photonic Reservoir Computing Post-processing
Date: 2019-03-29 00:00:00

The efficacy of data decoding in contemporary ultrafast fiber transmission
systems is greatly determined by the capabilities of the signal processing
tools that are used. The received signal must not exceed a certain level of
complexity, beyond which the applied signal processing solutions become
insufficient or slow. Moreover, the required signal-to-noise ratio of the
received signal can be challenging, especially when adopting modulation formats
with multi-level encoding. Lately, photonic reservoir computing (RC) - a
hardware machine learning technique with recurrent connectivity - has been
proposed as a post-processing tool that deals with deterministic distortions
from fiber transmission. Here we show that RC post-processing is remarkably
efficient for multilevel encoding and for the use of very high launched optical
peak power for fiber transmission up to 14dBm. Higher power levels provide the
desired high signal-to-noise ratio (SNR) values at the receiver end, at the
expense of a complex nonlinear transformation of the transmission signal. Our
demonstration evaluates a direct fiber communication link with 4-level pulse
amplitude modulation (PAM-4) encoding and direct detection, without including
optical amplification, dispersion compensation, pulse shaping or other digital
signal processing (DSP) techniques. By applying RC post-processing on the
distorted signal, we numerically estimate fiber transmission distances of 27km
at 56Gb/s and of 5.5 km at 112Gb/s data encoding rates, while fulfilling the
hard-decision forward error correction (HD-FEC) bit-error-rate (BER) limit for
data recovery. In an experimental equivalent demonstration of our photonic
reservoir, the achieved distances are 21km and 4.6km respectively.
Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors
Date: 2018-07-20 00:00:00
Tasks: Time Series, Time Series Analysis

This article demonstrates the possibility of constructing indicators of
critical and crisis phenomena in the volatile market of cryptocurrency. For
this purpose, the methods of the theory of complex systems such as recurrent
analysis of dynamic systems and the calculation of permutation entropy are
used. It is shown that it is possible to construct dynamic measures of
complexity, both recurrent and entropy, which behave in a proper way during
actual pre-crisis periods. This fact is used to build predictors of crisis
phenomena on the example of the main five crises recorded in the time series of
the key cryptocurrency bitcoin, the effectiveness of the proposed
indicators-precursors of crises has been identified.
Machine Learning Approaches to Hybrid Music Recommender Systems
Authors: Vall Andreu, Widmer Gerhard
Date: 2018-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Music recommender systems have become a key technology supporting the access
to increasingly larger music catalogs in on-line music streaming services,
on-line music shops, and private collections. The interaction of users with
large music catalogs is a complex phenomenon researched from different
disciplines. We survey our works investigating the machine learning and data
mining aspects of hybrid music recommender systems (i.e., systems that
integrate different recommendation techniques). We proposed hybrid music
recommender systems based solely on data and robust to the so-called
"cold-start problem" for new music items, favoring the discovery of relevant
but non-popular music. We thoroughly studied the specific task of music
playlist continuation, by analyzing fundamental playlist characteristics, song
feature representations, and the relationship between playlists and the songs
therein.