pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of e-Biodiversity: Concepts, Practices, and Challenges
Date: 2018-09-29 00:00:00
Tasks: Survey

The unprecedented size of the human population, along with its associated
economic activities, have an ever increasing impact on global environments.
Across the world, countries are concerned about the growing resource
consumption and the capacity of ecosystems to provide them. To effectively
conserve biodiversity, it is essential to make indicators and knowledge openly
available to decision-makers in ways that they can effectively use them. The
development and deployment of mechanisms to produce these indicators depend on
having access to trustworthy data from field surveys and automated sensors,
biological collections, molecular data, and historic academic literature. The
transformation of this raw data into synthesized information that is fit for
use requires going through many refinement steps. The methodologies and
techniques used to manage and analyze this data comprise an area often called
biodiversity informatics (or e-Biodiversity). Biodiversity data follows a life
cycle consisting of planning, collection, certification, description,
preservation, discovery, integration, and analysis. Researchers, whether
producers or consumers of biodiversity data, will likely perform activities
related to at least one of these steps. This article explores each stage of the
life cycle of biodiversity data, discussing its methodologies, tools, and
challenges.
Proxy Controls and Panel Data
Authors: Ben Deaner
Date: 2018-09-30 00:00:00

We provide new results for nonparametric identification, estimation, and inference of causal effects using `proxy controls': observables that are noisy but informative proxies for unobserved confounding factors. Our analysis applies to cross-sectional settings but is particularly well-suited to panel models. Our identification results motivate a simple and `well-posed' nonparametric estimator. We derive convergence rates for the estimator and construct uniform confidence bands with asymptotically correct size. In panel settings, our methods provide a novel approach to the difficult problem of identification with non-separable, general heterogeneity and fixed $T$. In panels, observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure. We apply our methods to two empirical settings. We estimate consumer demand counterfactuals using panel data and we estimate causal effects of grade retention on cognitive performance.
Wearable, Epidermal, and Implantable Sensors for Medical Applications
Date: 2018-09-30 00:00:00

Continuous health monitoring using wireless body area networks (WBANs) of
wearable, epidermal and implantable medical devices is envisioned as a
transformative approach to healthcare. Rapid advances in biomedical sensors,
low-power electronics, and wireless communications have brought this vision to
the verge of reality. However, key challenges still remain to be addressed.
This paper surveys the current state-of-the-art in the area of wireless sensors
for medical applications. Specifically, it focuses on presenting the recent
advancements in wearable, epidermal and implantable technologies, and discusses
reported ways of powering up such sensors. Furthermore, this paper addresses
the challenges that exist in the various Open Systems Interconnection (OSI)
layers and illustrates future research areas concerning the utilization of
wireless sensors in healthcare applications.
Aspiration dynamics generate robust predictions in structured populations
Date: 2018-09-30 00:00:00

Evolutionary game dynamics in structured populations are strongly affected by
updating rules. Previous studies usually focus on imitation-based rules, which
rely on payoff information of social peers. Recent behavioral experiments
suggest that whether individuals use such social information for strategy
updating may be crucial to the outcomes of social interactions. This hints at
the importance of considering updating rules without dependence on social
peers' payoff information, which, however, is rarely investigated. Here, we
study aspiration-based self-evaluation rules, with which individuals
self-assess the performance of strategies by comparing own payoffs with an
imaginary value they aspire, called the aspiration level. We explore the fate
of strategies on population structures represented by graphs or networks. Under
weak selection, we analytically derive the condition for strategy dominance,
which is found to coincide with the classical condition of risk-dominance. This
condition holds for all networks and all distributions of aspiration levels,
and for individualized ways of self-evaluation. Our condition can be
intuitively interpreted: one strategy prevails over the other if the strategy
brings more satisfaction to individuals than the other does. Our work thus
sheds light on the intrinsic difference between evolutionary dynamics induced
by aspiration-based and imitation-based rules.
A Volumetric Approach to Point Cloud Compression
Date: 2018-09-30 00:00:00
Tasks: Attribute

Compression of point clouds has so far been confined to coding the positions
of a discrete set of points in space and the attributes of those discrete
points. We introduce an alternative approach based on volumetric functions,
which are functions defined not just on a finite set of points, but throughout
space. As in regression analysis, volumetric functions are continuous functions
that are able to interpolate values on a finite set of points as linear
combinations of continuous basis functions. Using a B-spline wavelet basis, we
are able to code volumetric functions representing both geometry and
attributes. Geometry is represented implicitly as the level set of a volumetric
function (the signed distance function or similar). Attributes are represented
by a volumetric function whose coefficients can be regarded as a critically
sampled orthonormal transform that generalizes the recent successful
region-adaptive hierarchical (or Haar) transform to higher orders. Experimental
results show that both geometry and attribute compression using volumetric
functions improve over those used in the emerging MPEG Point Cloud Compression
standard.
A New Form of Banking -- Concept and Mathematical Model of Venture Banking
Date: 2020-11-17 00:00:00
Tasks: Form

This theoretical model contains concept, equations, and graphical results for
venture banking. A system of 27 equations describes the behavior of the
venture-bank and underwriter system allowing phase-space type graphs that show
where profits and losses occur. These results confirm and expand those obtained
from the original spreadsheet based model. An example investment in a castle at
a loss is provided to clarify concept. This model requires that all investments
are in enterprises that create new utility value. The assessed utility value
created is the new money out of which the venture bank and underwriter are
paid. The model presented chooses parameters that ensure that the venture-bank
experiences losses before the underwriter does. Parameters are: DIN Premium,
0.05; Clawback lien fraction, 0.77; Clawback bonds and equity futures discount,
1.5 x (USA 12 month LIBOR); Range of clawback bonds sold, 0 to 100%; Range of
equity futures sold 0 to 70%.
Control of cell colony growth by contact inhibition
Date: 2019-04-10 00:00:00

We investigate the dynamics of a colony of crawling, proliferating cells with
a minimal, mechanical cell model. The cells consist of two disks, modelling the
cell body and a pseudopod, connected by a finite extensible spring. The cells
exhibit locomotion due to a linear coupling of the motility force to the cell
extension. With a simple mechanism for contact inhibition of proliferation, we
find the typical regimes of colony growth, with exponential growth at short
times turning into sub-exponential growth at long times. In the latter regime
the colony boundary moves outwards with a constant speed. We identify simple
scaling relations for both regimes and the crossover between them. We find that
the shape of the cells (the ratio between the two disk's radii) determines the
efficiency with which cells orient themselves away from the colony. The better
the cells are at aligning themselves away from the colony, the faster the
colony expands.
Cascading Failure Mitigation via Transmission Switching
Authors: Sayed Abdullah Sadat, Mostafa Sahraei-Ardakani
Date: 2018-08-21 00:00:00

After decades of research, cascading blackouts remain one of the unresolved challenges in the bulk power system operations. A new perspective for measuring the susceptibility of the system to cascading failures is clearly needed. The newly developed concept of system stress metrics may be able to provide new insights into this problem. The method measures stress as susceptibility to cascading failures by analyzing the network structure and electrical properties. This paper investigates the effectiveness of transmission switching in reducing the risk of cascading failures, measured in system stress metrics. Based on line-outage distribution factors, an algorithm is developed to identify and test the switching candidates quickly. A case study analyzing different stress metrics on the IEEE 118-bus test system is presented. The results show that transmission switching identified by our proposed algorithm could be used in preventive as well as corrective mechanisms to reduce the system's susceptibility to cascading failures. Contrary to the conventional operation wisdom that switching lines out of service jeopardizes reliability, our results suggest the opposite; system operators can often use transmission switching, when the system is under stress, as a tool to reduce the risk of cascading failures.
Attention-based Encoder-Decoder Networks for Spelling and Grammatical Error Correction
Authors: Ahmadi Sina
Date: 2018-09-21 00:00:00
Tasks: Decoder, Grammatical Error Correction, Machine Translation, Sentence, Translation

Automatic spelling and grammatical correction systems are one of the most
widely used tools within natural language applications. In this thesis, we
assume the task of error correction as a type of monolingual machine
translation where the source sentence is potentially erroneous and the target
sentence should be the corrected form of the input. Our main focus in this
project is building neural network models for the task of error correction. In
particular, we investigate sequence-to-sequence and attention-based models
which have recently shown a higher performance than the state-of-the-art of
many language processing problems. We demonstrate that neural machine
translation models can be successfully applied to the task of error correction.
  While the experiments of this research are performed on an Arabic corpus, our
methods in this thesis can be easily applied to any language.
Hindi-English Code-Switching Speech Corpus
Authors: Sreeram Ganji, Dhawan Kunal, Sinha Rohit
Date: 2018-09-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Identification, Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition, Speech Synthesis

Code-switching refers to the usage of two languages within a sentence or
discourse. It is a global phenomenon among multilingual communities and has
emerged as an independent area of research. With the increasing demand for the
code-switching automatic speech recognition (ASR) systems, the development of a
code-switching speech corpus has become highly desirable. However, for training
such systems, very limited code-switched resources are available as yet. In
this work, we present our first efforts in building a code-switching ASR system
in the Indian context. For that purpose, we have created a Hindi-English
code-switching speech database. The database not only contains the speech
utterances with code-switching properties but also covers the session and the
speaker variations like pronunciation, accent, age, gender, etc. This database
can be applied in several speech signal processing applications, such as
code-switching ASR, language identification, language modeling, speech
synthesis etc. This paper mainly presents an analysis of the statistics of the
collected code-switching speech corpus. Later, the performance results for the
ASR task have been reported for the created database.
Text Similarity in Vector Space Models: A Comparative Study
Authors: Shahmirzadi Omid, Lugowski Adam, Younge Kenneth
Date: 2018-09-24 00:00:00
Tasks: text similarity, Topic Models

Automatic measurement of semantic text similarity is an important task in
natural language processing. In this paper, we evaluate the performance of
different vector space models to perform this task. We address the real-world
problem of modeling patent-to-patent similarity and compare TFIDF (and related
extensions), topic models (e.g., latent semantic indexing), and neural models
(e.g., paragraph vectors). Contrary to expectations, the added computational
cost of text embedding methods is justified only when: 1) the target text is
condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF
performs surprisingly well in other cases: in particular for longer and more
technical texts or for making finer-grained distinctions between nearest
neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun
phrases or calculating term weights incrementally, were not helpful in our
context.
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
Authors: Kasewa Sudhanshu, Stenetorp Pontus, Riedel Sebastian
Date: 2018-09-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Grammatical Error Correction, Grammatical Error Detection, Machine Translation, Sentence, Translation

Grammatical error correction, like other machine learning tasks, greatly
benefits from large quantities of high quality training data, which is
typically expensive to produce. While writing a program to automatically
generate realistic grammatical errors would be difficult, one could learn the
distribution of naturallyoccurring errors and attempt to introduce them into
other datasets. Initial work on inducing errors in this way using statistical
machine translation has shown promise; we investigate cheaply constructing
synthetic samples, given a small corpus of human-annotated data, using an
off-the-rack attentive sequence-to-sequence model and a straight-forward
post-processing procedure. Our approach yields error-filled artificial data
that helps a vanilla bi-directional LSTM to outperform the previous state of
the art at grammatical error detection, and a previously introduced model to
gain further improvements of over 5% $F_{0.5}$ score. When attempting to
determine if a given sentence is synthetic, a human annotator at best achieves
39.39 $F_1$ score, indicating that our model generates mostly human-like
instances.
Automatic Data Expansion for Customer-care Spoken Language Understanding
Authors: Jalalvand Shahab, Ljolje Andrej, Bangalore Srinivas
Date: 2018-09-27 00:00:00
Tasks: Language Modelling, Natural Language Understanding, Spoken Language Understanding

Spoken language understanding (SLU) systems are widely used in handling of
customer-care calls.A traditional SLU system consists of an acoustic model (AM)
and a language model (LM) that areused to decode the utterance and a natural
language understanding (NLU) model that predicts theintent. While AM can be
shared across different domains, LM and NLU models need to be
trainedspecifically for every new task. However, preparing enough data to train
these models is prohibitivelyexpensive. In this paper, we introduce an
efficient method to expand the limited in-domain data. Theprocess starts with
training a preliminary NLU model based on logistic regression on the
in-domaindata. Since the features are based onn= 1,2-grams, we can detect the
most informative n-gramsfor each intent class. Using these n-grams, we find the
samples in the out-of-domain corpus that1) contain the desired n-gram and/or 2)
have similar intent label. The ones which meet the firstconstraint are used to
train a new LM model and the ones that meet both constraints are used to train
anew NLU model. Our results on two divergent experimental setups show that the
proposed approachreduces by 30% the absolute classification error rate (CER)
comparing to the preliminary modelsand it significantly outperforms the
traditional data expansion algorithms such as the ones based onsemi-supervised
learning, TF-IDF and embedding vectors.
Direct optimization of F-measure for retrieval-based personal question answering
Authors: Fakoor Rasool, Kainth Amanjit, Shakeri Siamak, Winestock Christopher, Mohamed Abdel-rahman, Sarikaya Ruhi
Date: 2018-09-28 00:00:00
Tasks: Question Answering, reinforcement-learning, Reinforcement Learning (RL), Retrieval

Recent advances in spoken language technologies and the introduction of many
customer facing products, have given rise to a wide customer reliance on smart
personal assistants for many of their daily tasks. In this paper, we present a
system to reduce users' cognitive load by extending personal assistants with
long-term personal memory where users can store and retrieve by voice,
arbitrary pieces of information. The problem is framed as a neural retrieval
based question answering system where answers are selected from previously
stored user memories. We propose to directly optimize the end-to-end retrieval
performance, measured by the F1-score, using reinforcement learning, leading to
better performance on our experimental test set(s).
Learning Robust, Transferable Sentence Representations for Text Classification
Authors: Ahmad Wasi Uddin, Bai Xueying, Peng Nanyun, Chang Kai-Wei
Date: 2018-09-28 00:00:00
Tasks: Classification, General Classification, Sentence, text-classification, Text Classification, Transfer Learning

Despite deep recurrent neural networks (RNNs) demonstrate strong performance
in text classification, training RNN models are often expensive and requires an
extensive collection of annotated data which may not be available. To overcome
the data limitation issue, existing approaches leverage either pre-trained word
embedding or sentence representation to lift the burden of training RNNs from
scratch. In this paper, we show that jointly learning sentence representations
from multiple text classification tasks and combining them with pre-trained
word-level and sentence level encoders result in robust sentence
representations that are useful for transfer learning. Extensive experiments
and analyses using a wide range of transfer and linguistic tasks endorse the
effectiveness of our approach.
A belief combination rule for a large number of sources
Authors: Zhou Kuang  NPU, Martin Arnaud  DRUID, Pan Quan  NPU
Date: 2018-09-28 00:00:00

The theory of belief functions is widely used for data from multiple sources.
Different evidence combination rules have been proposed in this framework
according to the properties of the sources to combine. However, most of these
combination rules are not efficient when there are a large number of sources.
This is due to either the complexity or the existence of an absorbing element
such as the total conflict mass function for the conjunctive based rules when
applied on unreliable evidence. In this paper, based on the assumption that the
majority of sources are reliable, a combination rule for a large number of
sources is proposed using a simple idea: the more common ideas the sources
share, the more reliable these sources are supposed to be. This rule is
adaptable for aggregating a large number of sources which may not all be
reliable. It will keep the spirit of the conjunctive rule to reinforce the
belief on the focal elements with which the sources are in agreement. The mass
on the emptyset will be kept as an indicator of the conflict. The proposed
rule, called LNS-CR (Conjunctive combinationRule for a Large Number of
Sources), is evaluated on synthetic mass functions. The experimental results
verify that the rule can be effectively used to combine a large number of mass
functions and to elicit the major opinion.
Shannon Entropy for Neutrosophic Information
Authors: Patrascu Vasile
Date: 2018-09-24 00:00:00

The paper presents an extension of Shannon entropy for neutrosophic
information. This extension uses a new formula for distance between two
neutrosophic triplets. In addition, the obtained results are particularized for
bifuzzy, intuitionistic and paraconsistent fuzzy information.
A Preliminary Report on Probabilistic Attack Normal Form for Constellation Semantics
Authors: Mantadelis Theofrastos, Bistarelli Stefano
Date: 2018-09-24 00:00:00
Tasks: Abstract Argumentation, Form

After Dung's founding work in Abstract Argumentation Frameworks there has
been a growing interest in extending the Dung's semantics in order to describe
more complex or real life situations. Several of these approaches take the
direction of weighted or probabilistic extensions. One of the most prominent
probabilistic approaches is that of constellation Probabilistic Abstract
Argumentation Frameworks from Li~et~al. In this paper, we present a normal form
for constellation probabilistic abstract argumentation frameworks. Furthermore,
we present a transformation from general constellation probabilistic abstract
argumentation frameworks to the presented normal form. In this way we
illustrate that the simpler normal form has equal representation power with the
general one.
Multiscale Modelling of Fibres Dynamics and Cell Adhesion within Moving Boundary Cancer Invasion
Date: 2018-10-10 00:00:00

Cancer cell invasion is recognised as one of the hallmarks of cancer and
involves several inner-related multiscale processes that ultimately contribute
to its spread into the surrounding tissue. In order to gain a deeper
understanding of the tumour invasion process, we pay special attention to the
interacting dynamics between the cancer cell population and various
constituents of the surrounding tumour microenvironment. To that end, we
consider the key role that ECM plays within the human body tissue, providing
not only structure and support to surrounding cells, but also acting as a
platform for cells communication and spatial movement. There are several other
vital structures within the ECM, however we are going to focus primarily on
fibrous proteins, such as fibronectin. These fibres play a crucial role in
tumour progression, enabling the anchorage of tumour cells to the ECM. In this
work we consider the two-scale dynamic cross-talk between cancer cells and a
two component ECM (consisting of both a fibre and a non-fibre phase). To that
end, we incorporate the interlinked two-scale dynamics of cells-ECM
interactions within the tumour support that contributes simultaneously both to
cell-adhesion and to the dynamic rearrangement and restructuring of the ECM
fibres. Furthermore, this is embedded within a multiscale moving boundary
approach for the invading cancer cell population, in the presence of
cell-adhesion at the tissue scale and cell-scale fibre redistribution activity
and leading edge matrix degrading enzyme molecular proteolytic processes. The
overall modelling framework will be accompanied by computational results that
will explore the impact on cancer invasion patterns of different levels of cell
adhesion in conjunction with the continuous ECM fibres rearrangement.