pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive and optimal online linear regression on $\ell^1$-balls
Authors: Sébastien Gerchinovitz, Jia Yuan Yu
Date: 2011-05-20 00:00:00
Tasks: regression

We consider the problem of online linear regression on individual sequences.
The goal in this paper is for the forecaster to output sequential predictions
which are, after $T$ time rounds, almost as good as the ones output by the best
linear predictor in a given $\ell^1$-ball in $\\R^d$. We consider both the
cases where the dimension~$d$ is small and large relative to the time horizon
$T$. We first present regret bounds with optimal dependencies on $d$, $T$, and
on the sizes $U$, $X$ and $Y$ of the $\ell^1$-ball, the input data and the
observations. The minimax regret is shown to exhibit a regime transition around
the point $d = \sqrt{T} U X / (2 Y)$. Furthermore, we present efficient
algorithms that are adaptive, \ie, that do not require the knowledge of $U$,
$X$, $Y$, and $T$, but still achieve nearly optimal regret bounds.
MRI to CT Translation with GANs
Authors: Bodo Kaiser, Shadi Albarqouni
Date: 2019-01-16 00:00:00
Tasks: Computed Tomography (CT), Generative Adversarial Network, Image Registration, Translation

We present a detailed description and reference implementation of
preprocessing steps necessary to prepare the public Retrospective Image
Registration Evaluation (RIRE) dataset for the task of magnetic resonance
imaging (MRI) to X-ray computed tomography (CT) translation. Furthermore we
describe and implement three state of the art convolutional neural network
(CNN) and generative adversarial network (GAN) models where we report
statistics and visual results of two of them.
IEDM, an Ontology for Irradiation Experiment Data Management
Authors: Blerina Gkotse, Pierre Jouvelot, Federico Ravotti
Date: 2019-01-16 00:00:00
Tasks: Management

Irradiation experiments (IE) are an essential step in the development of
High-Energy Physics (HEP) particle accelerators and detectors. They assess the
radiation hardness of materials used in HEP experimental devices by simulating,
in a short time, the common long-term degradation effects due to their
bombardment by high-energy particles. IEs are also used in other scientific and
industrial fields such as medicine (e.g., for cancer treatment, medical
imaging, etc.), space/avionics (e.g., for radiation testing of payload
equipment) as well as in industry (e.g., for food sterilization). Usually
carried out with ionizing radiation, these complex processes require highly
specialized infrastructures: the irradiation facilities. Currently, hundreds of
such facilities exist worldwide. To help develop best practices and promote
computer-assisted handling and management of IEs, we introduce IEDM, a new
OWL-based Irradiation Experiment Data Management ontology. This paper provides
an overview of the classes and properties of IEDM. Since one of the key design
choices for IEDM was to maximize the reuse of existing foundational ontologies
such as the Ontology of Scientific Experiments (EXPO), the Ontology of Units of
Measure (OM) and the Friend-of-a-Friend Ontology (FOAF), we discuss the
methodological issues of the integration of IEDM with these imported
ontologies. We illustrate the use of IEDM via an actual IE recently performed
at IRRAD, the CERN proton irradiation facility. Finally, we discuss other
motivations for this work, including the use of IEDM for the generation of user
interfaces for IE management, and their impact on our methodology.
Machine learning applied to quantum synchronization-assisted probing
Authors: Gabriel Garau Estarellas, Gian Luca Giorgi, Miguel C. Soriano, Roberta Zambrini
Date: 2019-01-16 00:00:00
Tasks: BIG-bench Machine Learning

A probing scheme is considered with an accessible and controllable qubit,
used to probe an out-of equilibrium system consisting of a second qubit
interacting with an environment. Quantum spontaneous synchronization between
the probe and the system emerges in this model and, by tuning the probe
frequency, can occur both in-phase and in anti-phase. We analyze the capability
of machine learning in this probing scheme based on quantum synchronization. An
artificial neural network is used to infer, from a probe observable, main
dissipation features, such as the environment Ohmicity index. The efficiency of
the algorithm in the presence of some noise in the dataset is also considered.
We show that the performance in either classification and regression is
significantly improved due to the in/anti-phase synchronization transition.
This opens the way to the characterization of environments with arbitrary
spectral densities.
Sentence transition matrix: An efficient approach that preserves sentence semantics
Authors: Myeongjun Jang, Pilsung Kang
Date: 2019-01-16 00:00:00
Tasks: Document Summarization, Semantic Textual Similarity, Sentence, Sentence Classification, Sentence Embedding, Sentence-Embedding, STS

Sentence embedding is a significant research topic in the field of natural
language processing (NLP). Generating sentence embedding vectors reflecting the
intrinsic meaning of a sentence is a key factor to achieve an enhanced
performance in various NLP tasks such as sentence classification and document
summarization. Therefore, various sentence embedding models based on supervised
and unsupervised learning have been proposed after the advent of researches
regarding the distributed representation of words. They were evaluated through
semantic textual similarity (STS) tasks, which measure the degree of semantic
preservation of a sentence and neural network-based supervised embedding models
generally yielded state-of-the-art performance. However, these models have a
limitation in that they have multiple parameters to update, thereby requiring a
tremendous amount of labeled training data. In this study, we propose an
efficient approach that learns a transition matrix that refines a sentence
embedding vector to reflect the latent semantic meaning of a sentence. The
proposed method has two practical advantages; (1) it can be applied to any
sentence embedding method, and (2) it can achieve robust performance in STS
tasks irrespective of the number of training examples.
The Discrete Langevin Machine: Bridging the Gap Between Thermodynamic and Neuromorphic Systems
Authors: Lukas Kades, Jan M. Pawlowski
Date: 2019-01-16 00:00:00

A formulation of Langevin dynamics for discrete systems is derived as a class of generic stochastic processes. The dynamics simplify for a two-state system and suggest a network architecture which is implemented by the Langevin machine. The Langevin machine represents a promising approach to compute successfully quantitative exact results of Boltzmann distributed systems by LIF neurons. Besides a detailed introduction of the dynamics, different simplified models of a neuromorphic hardware system are studied with respect to a control of emerging sources of errors.
Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding
Authors: Liviu Marina, Bogdan Trasnea, Cocias Tiberiu, Andrei Vasilcoi, Florin Moldoveanu, Sorin Grigorescu
Date: 2019-01-16 00:00:00
Tasks: Autonomous Driving, Motion Planning

Grid maps obtained from fused sensory information are nowadays among the most
popular approaches for motion planning for autonomous driving cars. In this
paper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designed
for understanding the context in which an autonomous car is driving. DGN
incorporates a learned driving environment representation based on Occupancy
Grids (OG) obtained from raw Lidar data and constructed on top of the
Dempster-Shafer (DS) theory. The predicted driving context is further used for
switching between different driving strategies implemented within EB robinos,
Elektrobit's Autonomous Driving (AD) software platform. Based on genetic
algorithms (GAs), we also propose a neuroevolutionary approach for learning the
tuning hyperparameters of DGN. The performance of the proposed deep network has
been evaluated against similar competing driving context estimation
classifiers.
Formal models of Structure Building in Music, Language and Animal Songs
Authors: Willem Zuidema, Dieuwke Hupkes, Geraint Wiggins, Constance Scharff, Martin Rohrmeier
Date: 2019-01-16 00:00:00

Human language, music and a variety of animal vocalisations constitute ways
of sonic communication that exhibit remarkable structural complexity. While the
complexities of language and possible parallels in animal communication have
been discussed intensively, reflections on the complexity of music and animal
song, and their comparisons are underrepresented. In some ways, music and
animal songs are more comparable to each other than to language, as
propositional semantics cannot be used as as indicator of communicative success
or well-formedness, and notions of grammaticality are less easily defined. This
review brings together accounts of the principles of structure building in
language, music and animal song, relating them to the corresponding models in
formal language theory, with a special focus on evaluating the benefits of
using the Chomsky hierarchy (CH). We further discuss common misunderstandings
and shortcomings concerning the CH, as well as extensions or augmentations of
it that address some of these issues, and suggest ways to move beyond.
A Functional Representation for Graph Matching
Authors: Fu-Dong Wang, Gui-Song Xia, Nan Xue, Yi-Peng Zhang, Marcello Pelillo
Date: 2019-01-16 00:00:00
Tasks: Graph Matching

Graph matching is an important and persistent problem in computer vision and
pattern recognition for finding node-to-node correspondence between
graph-structured data. However, as widely used, graph matching that
incorporates pairwise constraints can be formulated as a quadratic assignment
problem (QAP), which is NP-complete and results in intrinsic computational
difficulties. In this paper, we present a functional representation for graph
matching (FRGM) that aims to provide more geometric insights on the problem and
reduce the space and time complexities of corresponding algorithms. To achieve
these goals, we represent a graph endowed with edge attributes by a linear
function space equipped with a functional such as inner product or metric, that
has an explicit geometric meaning. Consequently, the correspondence between
graphs can be represented as a linear representation map of that functional.
Specifically, we reformulate the linear functional representation map as a new
parameterization for Euclidean graph matching, which is associative with
geometric parameters for graphs under rigid or nonrigid deformations. This
allows us to estimate the correspondence and geometric deformations
simultaneously. The use of the representation of edge attributes rather than
the affinity matrix enables us to reduce the space complexity by two orders of
magnitudes. Furthermore, we propose an efficient optimization strategy with low
time complexity to optimize the objective function. The experimental results on
both synthetic and real-world datasets demonstrate that the proposed FRGM can
achieve state-of-the-art performance.
The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition
Authors: Anna Guitart, Pei Pei Chen, África Periáñez
Date: 2019-01-16 00:00:00

Machine learning competitions such as those organized by Kaggle or KDD
represent a useful benchmark for data science research. In this work, we
present our winning solution to the Game Data Mining competition hosted at the
2017 IEEE Conference on Computational Intelligence and Games (CIG 2017). The
contest consisted of two tracks, and participants (more than 250, belonging to
both industry and academia) were to predict which players would stop playing
the game, as well as their remaining lifetime. The data were provided by a
major worldwide video game company, NCSoft, and came from their successful
massively multiplayer online game Blade and Soul. Here, we describe the long
short-term memory approach and conditional inference survival ensemble model
that made us win both tracks of the contest, as well as the validation
procedure that we followed in order to prevent overfitting. In particular,
choosing a survival method able to deal with censored data was crucial to
accurately predict the moment in which each player would leave the game, as
censoring is inherent in churn. The selected models proved to be robust against
evolving conditions---since there was a change in the business model of the
game (from subscription-based to free-to-play) between the two sample datasets
provided---and efficient in terms of time cost. Thanks to these features and
also to their a ability to scale to large datasets, our models could be readily
implemented in real business settings.
Predicting Variable Types in Dynamically Typed Programming Languages
Authors: Abhinav Jangda, Gaurav Anand
Date: 2019-01-16 00:00:00

Dynamic Programming Languages are quite popular because they increase the
programmer's productivity. However, the absence of types in the source code
makes the program written in these languages difficult to understand and
virtual machines that execute these programs cannot produced optimized code. To
overcome this challenge, we develop a technique to predict types of all
identifiers including variables, and function return types.
  We propose the first implementation of $2^{nd}$ order Inside Outside
Recursive Neural Networks with two variants (i) Child-Sum Tree-LSTMs and (ii)
N-ary RNNs that can handle large number of tree branching. We predict the types
of all the identifiers given the Abstract Syntax Tree by performing just two
passes over the tree, bottom-up and top-down, keeping both the content and
context representation for all the nodes of the tree. This allows these
representations to interact by combining different paths from the parent,
siblings and children which is crucial for predicting types. Our best model
achieves 44.33\% across 21 classes and top-3 accuracy of 71.5\% on our gathered
Python data set from popular Python benchmarks.
Deep Supervised Hashing leveraging Quadratic Spherical Mutual Information for Content-based Image Retrieval
Authors: Nikolaos Passalis, Anastasios Tefas
Date: 2019-01-16 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Information Retrieval, Retrieval

Several deep supervised hashing techniques have been proposed to allow for
efficiently querying large image databases. However, deep supervised image
hashing techniques are developed, to a great extent, heuristically often
leading to suboptimal results. Contrary to this, we propose an efficient deep
supervised hashing algorithm that optimizes the learned codes using an
information-theoretic measure, the Quadratic Mutual Information (QMI). The
proposed method is adapted to the needs of large-scale hashing and information
retrieval leading to a novel information-theoretic measure, the Quadratic
Spherical Mutual Information (QSMI). Apart from demonstrating the effectiveness
of the proposed method under different scenarios and outperforming existing
state-of-the-art image hashing techniques, this paper provides a structured way
to model the process of information retrieval and develop novel methods adapted
to the needs of each application.
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
Authors: Rixon Crane, Fred Roosta
Date: 2019-01-16 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Distributed Computing, Vocal Bursts Type Prediction

For optimization of a sum of functions in a distributed computing environment, we present a novel communication efficient Newton-type algorithm that enjoys a variety of advantages over similar existing methods. Similar to Newton-MR, our algorithm, DINGO, is derived by optimization of the gradient's norm as a surrogate function. DINGO does not impose any specific form on the underlying functions, and its application range extends far beyond convexity. In addition, the distribution of the data across the computing environment can be arbitrary. Further, the underlying sub-problems of DINGO are simple linear least-squares, for which a plethora of efficient algorithms exist. Lastly, DINGO involves a few hyper-parameters that are easy to tune. Moreover, we theoretically show that DINGO is not sensitive to the choice of its hyper-parameters in that a strict reduction in the gradient norm is guaranteed, regardless of the selected hyper-parameters. We demonstrate empirical evidence of the effectiveness, stability and versatility of our method compared to other relevant algorithms.
Attention-aware Multi-stroke Style Transfer
Authors: Yuan Yao, Jianqiang Ren, Xuansong Xie, Weidong Liu, Yong-Jin Liu, Jun Wang
Date: 2019-01-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Style Transfer

Neural style transfer has drawn considerable attention from both academic and
industrial field. Although visual effect and efficiency have been significantly
improved, existing methods are unable to coordinate spatial distribution of
visual attention between the content image and stylized image, or render
diverse level of detail via different brush strokes. In this paper, we tackle
these limitations by developing an attention-aware multi-stroke style transfer
model. We first propose to assemble self-attention mechanism into a
style-agnostic reconstruction autoencoder framework, from which the attention
map of a content image can be derived. By performing multi-scale style swap on
content features and style features, we produce multiple feature maps
reflecting different stroke patterns. A flexible fusion strategy is further
presented to incorporate the salient characteristics from the attention map,
which allows integrating multiple stroke patterns into different spatial
regions of the output image harmoniously. We demonstrate the effectiveness of
our method, as well as generate comparable stylized images with multiple stroke
patterns against the state-of-the-art methods.
Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques
Authors: Dan Lu, Daniel Ricciuto
Date: 2019-01-16 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Computational Efficiency

Improving predictive understanding of Earth system variability and change
requires data-model integration. Efficient data-model integration for complex
models requires surrogate modeling to reduce model evaluation time. However,
building a surrogate of a large-scale Earth system model (ESM) with many output
variables is computationally intensive because it involves a large number of
expensive ESM simulations. In this effort, we propose an efficient surrogate
method capable of using a few ESM runs to build an accurate and
fast-to-evaluate surrogate system of model outputs over large spatial and
temporal domains. We first use singular value decomposition to reduce the
output dimensions, and then use Bayesian optimization techniques to generate an
accurate neural network surrogate model based on limited ESM simulation
samples. Our machine learning based surrogate methods can build and evaluate a
large surrogate system of many variables quickly. Thus, whenever the quantities
of interest change such as a different objective function, a new site, and a
longer simulation time, we can simply extract the information of interest from
the surrogate system without rebuilding new surrogates, which significantly
saves computational efforts. We apply the proposed method to a regional
ecosystem model to approximate the relationship between 8 model parameters and
42660 carbon flux outputs. Results indicate that using only 20 model
simulations, we can build an accurate surrogate system of the 42660 variables,
where the consistency between the surrogate prediction and actual model
simulation is 0.93 and the mean squared error is 0.02. This highly-accurate and
fast-to-evaluate surrogate system will greatly enhance the computational
efficiency in data-model integration to improve predictions and advance our
understanding of the Earth system.
Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis
Authors: Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2019-01-16 00:00:00
Tasks: Generative Adversarial Network

This paper presents a novel framework for predicting shot location and type
in tennis. Inspired by recent neuroscience discoveries we incorporate neural
memory modules to model the episodic and semantic memory components of a tennis
player. We propose a Semi Supervised Generative Adversarial Network
architecture that couples these memory models with the automatic feature
learning power of deep neural networks and demonstrate methodologies for
learning player level behavioural patterns with the proposed framework. We
evaluate the effectiveness of the proposed model on tennis tracking data from
the 2012 Australian Tennis open and exhibit applications of the proposed method
in discovering how players adapt their style depending on the match context.
Variable-sized input, character-level recurrent neural networks in lead generation: predicting close rates from raw user inputs
Authors: Giulio Giorcelli
Date: 2019-01-16 00:00:00
Tasks: Decision Making

Predicting lead close rates is one of the most problematic tasks in the lead
generation industry. In most cases, the only available data on the prospect is
the self-reported information inputted by the user on the lead form and a few
other data points publicly available through social media and search engine
usage. All the major market niches for lead generation [1], such as insurance,
health & medical and real estate, deal with life-altering decision making that
no amount of data will be ever be able to describe or predict. This paper
illustrates how character-level, deep long short-term memory networks can be
applied to raw user inputs to help predict close rates. The output of the model
is then used as an additional, highly predictive feature to significantly boost
performance of lead scoring models.
Towards Personalized Management of Type B Aortic Dissection Using STENT: a STandard cta database with annotation of the ENtire aorta and True-false lumen
Authors: Jianning Li, Long Cao, Yangyang Ge, Bowen Meng, Cheng Wang, Wei Guo
Date: 2019-01-03 00:00:00
Tasks: Decision Making, Management

Type B Aortic Dissection(TBAD) is a rare aortic disease with a high 5-year
mortality.Personalized and precise management of TBAD has been increasingly
desired in clinic which requires the geometric parameters of TBAD specific to
the patient be measured accurately.This remains to be a challenging task for
vascular surgeons as manual measurement is highly subjective and imprecise. To
solve this problem,we introduce STENT-a STandard cta database with annotation
of the ENtire aorta and True-false lumen. The database contains 274 CT
angiography (CTA) scans from 274 unique TBAD patients and is split into a
training set(254 cases including 210 preoperative and 44 postoperative scans )
and a test set(20 cases).Based on STENT,we develop a series of methods
including automated TBAD segmentation and automated measurement of TBAD
parameters that facilitate personalized and precise management of the disease.
In this work, the database and the proposed methods are thoroughly introduced
and evaluated and the results of our study shows the feasibility and
effectiveness of our approach to easing the decision-making process for
vascular surgeons during personalized TBAD management.