pwc-archive/papers-with-abstracts huggingface dataset:
The FLUXCOM ensemble of global land-atmosphere energy fluxes
Authors: Martin Jung, Sujan Koirala, Ulrich Weber, Kazuhito Ichii, Fabian Gans, Gustau-Camps-Valls, Dario Papale, Christopher Schwalm, Gianluca Tramontana, Markus Reichstein
Date: 2018-12-11 00:00:00
Tasks: BIG-bench Machine Learning

Although a key driver of Earth's climate system, global land-atmosphere
energy fluxes are poorly constrained. Here we use machine learning to merge
energy flux measurements from FLUXNET eddy covariance towers with remote
sensing and meteorological data to estimate net radiation, latent and sensible
heat and their uncertainties. The resulting FLUXCOM database comprises 147
global gridded products in two setups: (1) 0.0833${\deg}$ resolution using
MODIS remote sensing data (RS) and (2) 0.5${\deg}$ resolution using remote
sensing and meteorological data (RS+METEO). Within each setup we use a full
factorial design across machine learning methods, forcing datasets and energy
balance closure corrections. For RS and RS+METEO setups respectively, we
estimate 2001-2013 global (${\pm}$ 1 standard deviation) net radiation as
75.8${\pm}$1.4 ${W\ m^{-2}}$ and 77.6${\pm}$2 ${W\ m^{-2}}$, sensible heat as
33${\pm}$4 ${W\ m^{-2}}$ and 36${\pm}$5 ${W\ m^{-2}}$, and evapotranspiration
as 75.6${\pm}$10 ${\times}$ 10$^3$ ${km^3\ yr^{-1}}$ and 76${\pm}$6 ${\times}$
10$^3$ ${km^3\ yr^{-1}}$. FLUXCOM products are suitable to quantify global
land-atmosphere interactions and benchmark land surface model simulations.
DCASE 2018 Challenge: Solution for Task 5
Authors: Jeremy Chew, Ying-Xiang Sun, Lahiru Jayasinghe, Chau Yuen
Date: 2018-12-11 00:00:00
Tasks: Ensemble Learning, General Classification

To address Task 5 in the Detection and Classification of Acoustic Scenes and
Events (DCASE) 2018 challenge, in this paper, we propose an ensemble learning
system. The proposed system consists of three different models, based on
convolutional neural network and long short memory recurrent neural network.
With extracted features such as spectrogram and mel-frequency cepstrum
coefficients from different channels, the proposed system can classify
different domestic activities effectively. Experimental results obtained from
the provided development dataset show that good performance with F1-score of
92.19% can be achieved. Compared with the baseline system, our proposed system
significantly improves the performance of F1-score by 7.69%.
A Tensor-based Structural Health Monitoring Approach for Aeroservoelastic Systems
Authors: Prasad Cheema, Nguyen Lu Dang Khoa, Moray Kidd, Gareth A. Vio
Date: 2018-12-11 00:00:00
Tasks: Structural Health Monitoring

Structural health monitoring is a condition-based field of study utilised to
monitor infrastructure, via sensing systems. It is therefore used in the field
of aerospace engineering to assist in monitoring the health of aerospace
structures. A difficulty however is that in structural health monitoring the
data input is usually from sensor arrays, which results in data which are
highly redundant and correlated, an area in which traditional two-way matrix
approaches have had difficulty in deconstructing and interpreting. Newer
methods involving tensor analysis allow us to analyse this multi-way structural
data in a coherent manner. In our approach, we demonstrate the usefulness of
tensor-based learning coupled with for damage detection, on a novel $N$-DoF
Lagrangian aeroservoelastic model.
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs
Authors: Sachin Kumar, Yulia Tsvetkov
Date: 2018-12-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, Text Generation, Translation, Word Embeddings

The Softmax function is used in the final layer of nearly all existing
sequence-to-sequence models for language generation. However, it is usually the
slowest layer to compute which limits the vocabulary size to a subset of most
frequent types; and it has a large memory footprint. We propose a general
technique for replacing the softmax layer with a continuous embedding layer.
Our primary innovations are a novel probabilistic loss, and a training and
inference procedure in which we generate a probability distribution over
pre-trained word embeddings, instead of a multinomial distribution over the
vocabulary obtained via softmax. We evaluate this new class of
sequence-to-sequence models with continuous outputs on the task of neural
machine translation. We show that our models obtain upto 2.5x speed-up in
training time while performing on par with the state-of-the-art models in terms
of translation quality. These models are capable of handling very large
vocabularies without compromising on translation quality. They also produce
more meaningful errors than in the softmax-based models, as these errors
typically lie in a subspace of the vector space of the reference translations.
Impact of Intervals on the Emotional Effect in Western Music
Authors: Cengiz Kaygusuz, Julian Zuluaga
Date: 2018-12-10 00:00:00

Every art form ultimately aims to invoke an emotional response over the
audience, and music is no different. While the precise perception of music is a
highly subjective topic, there is an agreement in the "feeling" of a piece of
music in broad terms. Based on this observation, in this study, we aimed to
determine the emotional feeling associated with short passages of music;
specifically by analyzing the melodic aspects. We have used the dataset put
together by Eerola et. al. which is comprised of labeled short passages of film
music. Our initial survey of the dataset indicated that other than "happy" and
"sad" labels do not possess a melodic structure. We transcribed the main melody
of the happy and sad tracks and used the intervals between the notes to
classify them. Our experiments have shown that treating a melody as a
bag-of-intervals do not possess any predictive power whatsoever, whereas
counting intervals with respect to the key of the melody yielded a classifier
with 85% accuracy.
TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control
Authors: Srishti Dhamija, Pradeep Varakantham
Date: 2018-12-13 00:00:00
Tasks: Scheduling, Traffic Signal Control, vehicle detection

Real-time traffic signal control is a challenging problem owing to constantly
changing traffic demand patterns, limited planning time and various sources of
uncertainty (e.g., turn movements, vehicle detection) in the real world.
SURTRAC (Scalable URban TRAffic Control) is a recently developed traffic signal
control approach which computes delay-minimizing and coordinated (across
neighbouring traffic lights) schedules of oncoming vehicle clusters in real
time. To ensure real-time responsiveness in the presence of turn-induced
uncertainty, SURTRAC computes schedules which minimize the delay for the
expected turn movements as opposed to minimizing the expected delay under
turn-induced uncertainty. This approximation ensures real-time tractability,
but degrades solution quality in the presence of turn-induced uncertainty. To
address this limitation, we introduce TuSeRACT (Turn Sample based Real-time
trAffic signal ConTrol), a distributed sample-based scheduling approach to
traffic signal control. Unlike SURTRAC, TuSeRACT computes schedules that
minimize expected delay over sampled turn movements of observed traffic, and
communicates samples of traffic outflows to neighbouring intersections. We
formulate this sample-based scheduling problem as a constraint program and
empirically evaluate our approach on synthetic traffic networks. Our approach
provides substantially lower mean vehicular waiting times relative to SURTRAC.
FA-RPN: Floating Region Proposals for Face Detection
Authors: Mahyar Najibi, Bharat Singh, Larry S. Davis
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Detection, Region Proposal

We propose a novel approach for generating region proposals for performing
face-detection. Instead of classifying anchor boxes using features from a pixel
in the convolutional feature map, we adopt a pooling-based approach for
generating region proposals. However, pooling hundreds of thousands of anchors
which are evaluated for generating proposals becomes a computational bottleneck
during inference. To this end, an efficient anchor placement strategy for
reducing the number of anchor-boxes is proposed. We then show that proposals
generated by our network (Floating Anchor Region Proposal Network, FA-RPN) are
better than RPN for generating region proposals for face detection. We discuss
several beneficial features of FA-RPN proposals like iterative refinement,
placement of fractional anchors and changing anchors which can be enabled
without making any changes to the trained model. Our face detector based on
FA-RPN obtains 89.4% mAP with a ResNet-50 backbone on the WIDER dataset.
Scene Recomposition by Learning-based ICP
Authors: Hamid Izadinia, Steven M. Seitz
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2020 6
Tasks: Deep Reinforcement Learning, Object, Reinforcement Learning

By moving a depth sensor around a room, we compute a 3D CAD model of the environment, capturing the room shape and contents such as chairs, desks, sofas, and tables. Rather than reconstructing geometry, we match, place, and align each object in the scene to thousands of CAD models of objects. In addition to the fully automatic system, the key technical contribution is a novel approach for aligning CAD models to 3D scans, based on deep reinforcement learning. This approach, which we call Learning-based ICP, outperforms prior ICP methods in the literature, by learning the best points to match and conditioning on object viewpoint. LICP learns to align using only synthetic data and does not require ground truth annotation of object pose or keypoint pair matching in real scene scans. While LICP is trained on synthetic data and without 3D real scene annotations, it outperforms both learned local deep feature matching and geometric based alignment methods in real scenes. The proposed method is evaluated on real scenes datasets of SceneNN and ScanNet as well as synthetic scenes of SUNCG. High quality results are demonstrated on a range of real world scenes, with robustness to clutter, viewpoint, and occlusion.
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
Authors: Sean Andrew Chen, Andrew Escay, Christopher Haberland, Tessa Schneider, Valentina Staneva, Youngjun Choe
Date: 2018-12-13 00:00:00
Tasks: Damaged Building Detection, object-detection, Object Detection

Rapid damage assessment is of crucial importance to emergency responders
during hurricane events, however, the evaluation process is often slow,
labor-intensive, costly, and error-prone. New advances in computer vision and
remote sensing open possibilities to observe the Earth at a different scale.
However, substantial pre-processing work is still required in order to apply
state-of-the-art methodology for emergency response. To enable the comparison
of methods for automatic detection of damaged buildings from post-hurricane
remote sensing imagery taken from both airborne and satellite sensors, this
paper presents the development of benchmark datasets from publicly available
data. The major contributions of this work include (1) a scalable framework for
creating benchmark datasets of hurricane-damaged buildings and (2) public
sharing of the resulting benchmark datasets for Greater Houston area after
Hurricane Harvey in 2017. The proposed approach can be used to build other
hurricane-damaged building datasets on which researchers can train and test
object detection models to automatically identify damaged buildings.
Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines using the Theory of Functional Connections
Authors: Carl Leake, Hunter Johnston, Lidia Smith, Daniele Mortari
Date: 2018-12-13 00:00:00

Differential equations (DEs) are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. While there are many other techniques for finding approximate solutions to these equations, this paper looks to compare the application of the Theory of Functional Connections (TFC) with one based on least-squares support vector machines (LS-SVM). The TFC method uses a constrained expression, an expression that always satisfies the DE constraints, which transforms the process of solving a DE into solving an unconstrained optimization problem that is ultimately solved via least-squares (LS). In addition to individual analysis, the two methods are merged into a new methodology, called constrained SVMs (CSVM), by incorporating the LS-SVM method into the TFC framework to solve unconstrained problems. Numerical tests are conducted on four sample problems: One first order linear ordinary differential equation (ODE), one first order nonlinear ODE, one second order linear ODE, and one two-dimensional linear partial differential equation (PDE). Using the LS-SVM method as a benchmark, a speed comparison is made for all the problems by timing the training period, and an accuracy comparison is made using the maximum error and mean squared error on the training and test sets. In general, TFC is shown to be slightly faster (by an order of magnitude or less) and more accurate (by multiple orders of magnitude) than the LS-SVM and CSVM approaches.
Exploration Conscious Reinforcement Learning Revisited
Authors: Lior Shani, Yonathan Efroni, Shie Mannor
Date: 2018-12-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as $\epsilon$-greedy exploration or by adding Gaussian noise, while still trying to learn an optimal policy. In this work, we take a different approach and study exploration-conscious criteria, that result in optimal policies with respect to the exploration mechanism. Solving these criteria, as we establish, amounts to solving a surrogate Markov Decision Process. We continue and analyze properties of exploration-conscious optimal policies and characterize two general approaches to solve such criteria. Building on the approaches, we apply simple changes in existing tabular and deep Reinforcement Learning algorithms and empirically demonstrate superior performance relatively to their non-exploration-conscious counterparts, both for discrete and continuous action spaces.
The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos
Authors: Hazel Doughty, Walterio Mayol-Cuevas, Dima Damen
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6

We present a new model to determine relative skill from long videos, through
learnable temporal attention modules. Skill determination is formulated as a
ranking problem, making it suitable for common and generic tasks. However, for
long videos, parts of the video are irrelevant for assessing skill, and there
may be variability in the skill exhibited throughout a video. We therefore
propose a method which assesses the relative overall level of skill in a long
video by attending to its skill-relevant parts. Our approach trains temporal
attention modules, learned with only video-level supervision, using a novel
rank-aware loss function. In addition to attending to task relevant video
parts, our proposed loss jointly trains two attention modules to separately
attend to video parts which are indicative of higher (pros) and lower (cons)
skill. We evaluate our approach on the EPIC-Skills dataset and additionally
annotate a larger dataset from YouTube videos for skill determination with five
previously unexplored tasks. Our method outperforms previous approaches and
classic softmax attention on both datasets by over 4% pairwise accuracy, and as
much as 12% on individual tasks. We also demonstrate our model's ability to
attend to rank-aware parts of the video.
Stochastic Image Deformation in Frequency Domain and Parameter Estimation using Moment Evolutions
Authors: Line Kühnel, Alexis Arnaudon, Tom Fletcher, Stefan Sommer
Date: 2018-12-13 00:00:00
Tasks: Anatomy, parameter estimation

Modelling deformation of anatomical objects observed in medical images can
help describe disease progression patterns and variations in anatomy across
populations. We apply a stochastic generalisation of the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) framework to model differences in the
evolution of anatomical objects detected in populations of image data. The
computational challenges that are prevalent even in the deterministic LDDMM
setting are handled by extending the FLASH LDDMM representation to the
stochastic setting keeping a finite discretisation of the infinite dimensional
space of image deformations. In this computationally efficient setting, we
perform estimation to infer parameters for noise correlations and local
variability in datasets of images. Fundamental for the optimisation procedure
is using the finite dimensional Fourier representation to derive approximations
of the evolution of moments for the stochastic warps. Particularly, the first
moment allows us to infer deformation mean trajectories. The second moment
encodes variation around the mean, and thus provides information on the noise
correlation. We show on simulated datasets of 2D MR brain images that the
estimation algorithm can successfully recover parameters of the stochastic
model.
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification
Authors: Dino Ienco, Raffaele Gaetano, Roberto Interdonato Kenji Ose, Dinh Ho Tong Minh
Date: 2018-12-13 00:00:00
Tasks: General Classification, Land Cover Classification, Management, Time Series, Time Series Analysis

Radar and Optical Satellite Image Time Series (SITS) are sources of
information that are commonly employed to monitor earth surfaces for tasks
related to ecology, agriculture, mobility, land management planning and land
cover monitoring. Many studies have been conducted using one of the two
sources, but how to smartly combine the complementary information provided by
radar and optical SITS is still an open challenge. In this context, we propose
a new neural architecture for the combination of Sentinel-1 (S1) and Sentinel-2
(S2) imagery at object level, applied to a real-world land cover classification
task. Experiments carried out on the Reunion Island, a overseas department of
France in the Indian Ocean, demonstrate the significance of our proposal.
Impact of Data Normalization on Deep Neural Network for Time Series Forecasting
Authors: Samit Bhanja, Abhishek Das
Date: 2018-12-13 00:00:00
Tasks: image-classification, Image Classification, speech-recognition, Speech Recognition, Time Series, Time Series Analysis, Time Series Forecasting

For the last few years it has been observed that the Deep Neural Networks
(DNNs) has achieved an excellent success in image classification, speech
recognition. But DNNs are suffer great deal of challenges for time series
forecasting because most of the time series data are nonlinear in nature and
highly dynamic in behaviour. The time series forecasting has a great impact on
our socio-economic environment. Hence, to deal with these challenges its need
to be redefined the DNN model and keeping this in mind, data pre-processing,
network architecture and network parameters are need to be consider before
feeding the data into DNN models. Data normalization is the basic data
pre-processing technique form which learning is to be done. The effectiveness
of time series forecasting is heavily depend on the data normalization
technique. In this paper, different normalization methods are used on time
series data before feeding the data into the DNN model and we try to find out
the impact of each normalization technique on DNN to forecast the time series.
Here the Deep Recurrent Neural Network (DRNN) is used to predict the closing
index of Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE) by
using BSE and NYSE time series data.
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
Authors: Kim P. Wabersich, Melanie N. Zeilinger
Date: 2018-12-13 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration

The transfer of reinforcement learning (RL) techniques into real-world applications is challenged by safety requirements in the presence of physical limitations. Most RL methods, in particular the most popular algorithms, do not support explicit consideration of state and input constraints. In this paper, we address this problem for nonlinear systems with continuous state and input spaces by introducing a predictive safety filter, which is able to turn a constrained dynamical system into an unconstrained safe system and to which any RL algorithm can be applied `out-of-the-box'. The predictive safety filter receives the proposed control input and decides, based on the current system state, if it can be safely applied to the real system, or if it has to be modified otherwise. Safety is thereby established by a continuously updated safety policy, which is based on a model predictive control formulation using a data-driven system model and considering state and input dependent uncertainties.
Deep Face Image Retrieval: a Comparative Study with Dictionary Learning
Authors: Ahmad S. Tarawneh, Ahmad B. A. Hassanat, Ceyhun Celik, Dmitry Chetverikov, M. Sohel Rahman, Chaman Verma
Date: 2018-12-13 00:00:00
Tasks: Deep Learning, Dictionary Learning, Face Image Retrieval, Image Retrieval, Retrieval

Facial image retrieval is a challenging task since faces have many similar
features (areas), which makes it difficult for the retrieval systems to
distinguish faces of different people. With the advent of deep learning, deep
networks are often applied to extract powerful features that are used in many
areas of computer vision. This paper investigates the application of different
deep learning models for face image retrieval, namely, Alexlayer6, Alexlayer7,
VGG16layer6, VGG16layer7, VGG19layer6, and VGG19layer7, with two types of
dictionary learning techniques, namely $K$-means and $K$-SVD. We also
investigate some coefficient learning techniques such as the Homotopy, Lasso,
Elastic Net and SSF and their effect on the face retrieval system. The
comparative results of the experiments conducted on three standard face image
datasets show that the best performers for face image retrieval are Alexlayer7
with $K$-means and SSF, Alexlayer6 with $K$-SVD and SSF, and Alexlayer6 with
$K$-means and SSF. The APR and ARR of these methods were further compared to
some of the state of the art methods based on local descriptors. The
experimental results show that deep learning outperforms most of those methods
and therefore can be recommended for use in practice of face image retrieval