pwc-archive/papers-with-abstracts huggingface dataset:
Linguistic Reflexes of Well-Being and Happiness in Echo
Authors: Jiaqi Wu, Marilyn Walker, Pranav Anand, Steve Whittaker
Date: 2017-08-31 00:00:00
Proceeding: WS 2017 9

Different theories posit different sources for feelings of well-being and
happiness. Appraisal theory grounds our emotional responses in our goals and
desires and their fulfillment, or lack of fulfillment. Self Determination
theory posits that the basis for well-being rests on our assessment of our
competence, autonomy, and social connection. And surveys that measure happiness
empirically note that people require their basic needs to be met for food and
shelter, but beyond that tend to be happiest when socializing, eating or having
sex. We analyze a corpus of private microblogs from a well-being application
called ECHO, where users label each written post about daily events with a
happiness score between 1 and 9. Our goal is to ground the linguistic
descriptions of events that users experience in theories of well-being and
happiness, and then examine the extent to which different theoretical accounts
can explain the variance in the happiness scores. We show that recurrent event
types, such as OBLIGATION and INCOMPETENCE, which affect people's feelings of
well-being are not captured in current lexical or semantic resources.
RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs
Authors: Yingying Fan, Emre Demirkaya, Gaorong Li, Jinchi Lv
Date: 2017-08-31 00:00:00

Power and reproducibility are key to enabling refined scientific discoveries
in contemporary big data applications with general high-dimensional nonlinear
models. In this paper, we provide theoretical foundations on the power and
robustness for the model-free knockoffs procedure introduced recently in
Cand\`{e}s, Fan, Janson and Lv (2016) in high-dimensional setting when the
covariate distribution is characterized by Gaussian graphical model. We
establish that under mild regularity conditions, the power of the oracle
knockoffs procedure with known covariate distribution in high-dimensional
linear models is asymptotically one as sample size goes to infinity. When
moving away from the ideal case, we suggest the modified model-free knockoffs
method called graphical nonlinear knockoffs (RANK) to accommodate the unknown
covariate distribution. We provide theoretical justifications on the robustness
of our modified procedure by showing that the false discovery rate (FDR) is
asymptotically controlled at the target level and the power is asymptotically
one with the estimated covariate distribution. To the best of our knowledge,
this is the first formal theoretical result on the power for the knockoffs
procedure. Simulation results demonstrate that compared to existing approaches,
our method performs competitively in both FDR control and power. A real data
set is analyzed to further assess the performance of the suggested knockoffs
procedure.
Exact Blur Measure Outperforms Conventional Learned Features for Depth Finding
Authors: Akbar Saadat
Date: 2017-08-31 00:00:00
Tasks: Depth Estimation

Image analysis methods that are based on exact blur values are faced with the
computational complexities due to blur measurement error. This atmosphere
encourages scholars to look for handcrafted and learned features for finding
depth from a single image. This paper introduces a novel exact realization for
blur measures on digital images and implements it on a new measure of defocus
Gaussian blur at edge points in Depth From Defocus (DFD) methods with the
potential to change this atmosphere. The experiments on real images indicate
superiority of the proposed measure in error performance over conventional
learned features in the state-of the-art single image based depth estimation
methods.
Weather impacts expressed sentiment
Authors: Patrick Baylis, Nick Obradovich, Yury Kryvasheyeu, Haohui Chen, Lorenzo Coviello, Esteban Moro, Manuel Cebrian, James H. Fowler
Date: 2017-08-31 00:00:00

We conduct the largest ever investigation into the relationship between
meteorological conditions and the sentiment of human expressions. To do this,
we employ over three and a half billion social media posts from tens of
millions of individuals from both Facebook and Twitter between 2009 and 2016.
We find that cold temperatures, hot temperatures, precipitation, narrower daily
temperature ranges, humidity, and cloud cover are all associated with worsened
expressions of sentiment, even when excluding weather-related posts. We compare
the magnitude of our estimates with the effect sizes associated with notable
historical events occurring within our data.
Learning Inference Models for Computer Vision
Authors: Varun Jampani
Date: 2017-08-31 00:00:00
Tasks: Bayesian Inference

Computer vision can be understood as the ability to perform inference on
image data. Breakthroughs in computer vision technology are often marked by
advances in inference techniques. This thesis proposes novel inference schemes
and demonstrates applications in computer vision. We propose inference
techniques for both generative and discriminative vision models. The use of
generative models in vision is often hampered by the difficulty of posterior
inference. We propose techniques for improving inference in MCMC sampling and
message-passing inference. Our inference strategy is to learn separate
discriminative models that assist Bayesian inference in a generative model.
Experiments on a range of generative models show that the proposed techniques
accelerate the inference process and/or converge to better solutions. A main
complication in the design of discriminative models is the inclusion of prior
knowledge. We concentrate on CNN models and propose a generalization of
standard spatial convolutions to bilateral convolutions. We generalize the
existing use of bilateral filters and then propose new neural network
architectures with learnable bilateral filters, which we call `Bilateral Neural
Networks'. Experiments demonstrate the use of the bilateral networks on a wide
range of image and video tasks and datasets. In summary, we propose techniques
for better inference in several vision models ranging from inverse graphics to
freely parameterized neural networks. In generative models, our inference
techniques alleviate some of the crucial hurdles in Bayesian posterior
inference, paving new ways for the use of model based machine learning in
vision. In discriminative CNN models, the proposed filter generalizations aid
in the design of new neural network architectures that can handle sparse
high-dimensional data as well as provide a way to incorporate prior knowledge
into CNNs.
On Security and Sparsity of Linear Classifiers for Adversarial Settings
Authors: Ambra Demontis, Paolo Russu, Battista Biggio, Giorgio Fumera, Fabio Roli
Date: 2017-08-31 00:00:00
Tasks: Malware Detection

Machine-learning techniques are widely used in security-related applications,
like spam and malware detection. However, in such settings, they have been
shown to be vulnerable to adversarial attacks, including the deliberate
manipulation of data at test time to evade detection. In this work, we focus on
the vulnerability of linear classifiers to evasion attacks. This can be
considered a relevant problem, as linear classifiers have been increasingly
used in embedded systems and mobile devices for their low processing time and
memory requirements. We exploit recent findings in robust optimization to
investigate the link between regularization and security of linear classifiers,
depending on the type of attack. We also analyze the relationship between the
sparsity of feature weights, which is desirable for reducing processing cost,
and the security of linear classifiers. We further propose a novel octagonal
regularizer that allows us to achieve a proper trade-off between them. Finally,
we empirically show how this regularizer can improve classifier security and
sparsity in real-world application examples including spam and malware
detection.
Multi-task Dictionary Learning based Convolutional Neural Network for Computer aided Diagnosis with Longitudinal Images
Authors: Jie Zhang, Qingyang Li, Richard J. Caselli, Jieping Ye, Yalin Wang
Date: 2017-08-31 00:00:00
Tasks: Dictionary Learning, image-classification, Image Classification, Prognosis, Transfer Learning

Algorithmic image-based diagnosis and prognosis of neurodegenerative diseases
on longitudinal data has drawn great interest from computer vision researchers.
The current state-of-the-art models for many image classification tasks are
based on the Convolutional Neural Networks (CNN). However, a key challenge in
applying CNN to biological problems is that the available labeled training
samples are very limited. Another issue for CNN to be applied in computer aided
diagnosis applications is that to achieve better diagnosis and prognosis
accuracy, one usually has to deal with the longitudinal dataset, i.e., the
dataset of images scanned at different time points. Here we argue that an
enhanced CNN model with transfer learning for the joint analysis of tasks from
multiple time points or regions of interests may have a potential to improve
the accuracy of computer aided diagnosis. To reach this goal, we innovate a CNN
based deep learning multi-task dictionary learning framework to address the
above challenges. Firstly, we pre-train CNN on the ImageNet dataset and
transfer the knowledge from the pre-trained model to the medical imaging
progression representation, generating the features for different tasks. Then,
we propose a novel unsupervised learning method, termed Multi-task Stochastic
Coordinate Coding (MSCC), for learning different tasks by using shared and
individual dictionaries and generating the sparse features required to predict
the future cognitive clinical scores. We apply our new model in a publicly
available neuroimaging cohort to predict clinical measures with two different
feature sets and compare them with seven other state-of-the-art methods. The
experimental results show our proposed method achieved superior results.
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Authors: Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth
Date: 2017-08-31 00:00:00
Tasks: Earth Observation, General Classification, Land Cover Classification

In this paper, we address the challenge of land use and land cover
classification using Sentinel-2 satellite images. The Sentinel-2 satellite
images are openly and freely accessible provided in the Earth observation
program Copernicus. We present a novel dataset based on Sentinel-2 satellite
images covering 13 spectral bands and consisting out of 10 classes with in
total 27,000 labeled and geo-referenced images. We provide benchmarks for this
novel dataset with its spectral bands using state-of-the-art deep Convolutional
Neural Network (CNNs). With the proposed novel dataset, we achieved an overall
classification accuracy of 98.57%. The resulting classification system opens a
gate towards a number of Earth observation applications. We demonstrate how
this classification system can be used for detecting land use and land cover
changes and how it can assist in improving geographical maps. The
geo-referenced dataset EuroSAT is made publicly available at
https://github.com/phelber/eurosat.
Glyph-aware Embedding of Chinese Characters
Authors: Falcon Z. Dai, Zheng Cai
Date: 2017-08-31 00:00:00
Proceeding: WS 2017 9
Tasks: Language Modeling, Language Modelling

Given the advantage and recent success of English character-level and
subword-unit models in several NLP tasks, we consider the equivalent modeling
problem for Chinese. Chinese script is logographic and many Chinese logograms
are composed of common substructures that provide semantic, phonetic and
syntactic hints. In this work, we propose to explicitly incorporate the visual
appearance of a character's glyph in its representation, resulting in a novel
glyph-aware embedding of Chinese characters. Being inspired by the success of
convolutional neural networks in computer vision, we use them to incorporate
the spatio-structural patterns of Chinese glyphs as rendered in raw pixels. In
the context of two basic Chinese NLP tasks of language modeling and word
segmentation, the model learns to represent each character's task-relevant
semantic and syntactic information in the character-level embedding.
A State-Space Approach to Dynamic Nonnegative Matrix Factorization
Authors: Nasser Mohammadiha, Paris Smaragdis, Ghazaleh Panahandeh, Simon Doclo
Date: 2017-08-31 00:00:00
Tasks: Time Series, Time Series Analysis

Nonnegative matrix factorization (NMF) has been actively investigated and
used in a wide range of problems in the past decade. A significant amount of
attention has been given to develop NMF algorithms that are suitable to model
time series with strong temporal dependencies. In this paper, we propose a
novel state-space approach to perform dynamic NMF (D-NMF). In the proposed
probabilistic framework, the NMF coefficients act as the state variables and
their dynamics are modeled using a multi-lag nonnegative vector autoregressive
(N-VAR) model within the process equation. We use expectation maximization and
propose a maximum-likelihood estimation framework to estimate the basis matrix
and the N-VAR model parameters. Interestingly, the N-VAR model parameters are
obtained by simply applying NMF. Moreover, we derive a maximum a posteriori
estimate of the state variables (i.e., the NMF coefficients) that is based on a
prediction step and an update step, similarly to the Kalman filter. We
illustrate the benefits of the proposed approach using different numerical
simulations where D-NMF significantly outperforms its static counterpart.
Experimental results for three different applications show that the proposed
approach outperforms two state-of-the-art NMF approaches that exploit temporal
dependencies, namely a nonnegative hidden Markov model and a frame stacking
approach, while it requires less memory and computational power.
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Authors: Christian Häne, Lionel Heng, Gim Hee Lee, Friedrich Fraundorfer, Paul Furgale, Torsten Sattler, Marc Pollefeys
Date: 2017-08-31 00:00:00
Tasks: Camera Calibration, Self-Driving Cars, Visual Localization, Visual Navigation

Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction.
The duality structure gradient descent algorithm: analysis and applications to neural networks
Authors: Thomas Flynn
Date: 2017-08-01 00:00:00

The training of machine learning models is typically carried out using some form of gradient descent, often with great success. However, non-asymptotic analyses of first-order optimization algorithms typically employ a gradient smoothness assumption (formally, Lipschitz continuity of the gradient) that is too strong to be applicable in the case of deep neural networks. To address this, we propose an algorithm named duality structure gradient descent (DSGD) that is amenable to non-asymptotic performance analysis, under mild assumptions on the training set and network architecture. The algorithm can be viewed as a form of layer-wise coordinate descent, where at each iteration the algorithm chooses one layer of the network to update. The decision of what layer to update is done in a greedy fashion, based on a rigorous lower bound on the improvement of the objective function for each choice of layer. In the analysis, we bound the time required to reach approximate stationary points, in both the deterministic and stochastic settings. The convergence is measured in terms of a parameter-dependent family of norms that is derived from the network architecture and designed to confirm a smoothness-like property on the gradient of the training loss function. We empirically demonstrate the behavior of DSGD in several neural network training scenarios.
Inferring Human Activities Using Robust Privileged Probabilistic Learning
Authors: Michalis Vrigkas, Evangelos Kazakos, Christophoros Nikou, Ioannis A. Kakadiaris
Date: 2017-08-31 00:00:00
Tasks: General Classification

Classification models may often suffer from "structure imbalance" between
training and testing data that may occur due to the deficient data collection
process. This imbalance can be represented by the learning using privileged
information (LUPI) paradigm. In this paper, we present a supervised
probabilistic classification approach that integrates LUPI into a hidden
conditional random field (HCRF) model. The proposed model is called LUPI-HCRF
and is able to cope with additional information that is only available during
training. Moreover, the proposed method employes Student's t-distribution to
provide robustness to outliers by modeling the conditional distribution of the
privileged information. Experimental results in three publicly available
datasets demonstrate the effectiveness of the proposed approach and improve the
state-of-the-art in the LUPI framework for recognizing human activities.
Correlation between the Hurst exponent and the maximal Lyapunov exponent: examining some low-dimensional conservative maps
Authors: Mariusz Tarnopolski
Date: 2015-01-15 00:00:00

The Chirikov standard map and the 2D Froeschl\'e map are investigated. A few
thousand values of the Hurst exponent (HE) and the maximal Lyapunov exponent
(mLE) are plotted in a mixed space of the nonlinear parameter versus the
initial condition. Both characteristic exponents reveal remarkably similar
structures in this space. A tight correlation between the HEs and mLEs is
found, with the Spearman rank $\rho=0.83$ and $\rho=0.75$ for the Chirikov and
2D Froeschl\'e maps, respectively. Based on this relation, a machine learning
(ML) procedure, using the nearest neighbor algorithm, is performed to reproduce
the HE distribution based on the mLE distribution alone. A few thousand HE and
mLE values from the mixed spaces were used for training, and then using
$2-2.4\times 10^5$ mLEs, the HEs were retrieved. The ML procedure allowed to
reproduce the structure of the mixed spaces in great detail.
Efficient tracking of a growing number of experts
Authors: Jaouad Mourtada, Odalric-Ambrym Maillard
Date: 2017-08-31 00:00:00

We consider a variation on the problem of prediction with expert advice,
where new forecasters that were unknown until then may appear at each round. As
often in prediction with expert advice, designing an algorithm that achieves
near-optimal regret guarantees is straightforward, using aggregation of
experts. However, when the comparison class is sufficiently rich, for instance
when the best expert and the set of experts itself changes over time, such
strategies naively require to maintain a prohibitive number of weights
(typically exponential with the time horizon). By contrast, designing
strategies that both achieve a near-optimal regret and maintain a reasonable
number of weights is highly non-trivial. We consider three increasingly
challenging objectives (simple regret, shifting regret and sparse shifting
regret) that extend existing notions defined for a fixed expert ensemble; in
each case, we design strategies that achieve tight regret bounds, adaptive to
the parameters of the comparison class, while being computationally
inexpensive. Moreover, our algorithms are anytime, agnostic to the number of
incoming experts and completely parameter-free. Such remarkable results are
made possible thanks to two simple but highly effective recipes: first the
"abstention trick" that comes from the specialist framework and enables to
handle the least challenging notions of regret, but is limited when addressing
more sophisticated objectives. Second, the "muting trick" that we introduce to
give more flexibility. We show how to combine these two tricks in order to
handle the most challenging class of comparison strategies.
Learning Lexico-Functional Patterns for First-Person Affect
Authors: Lena Reed, Jiaqi Wu, Shereen Oraby, Pranav Anand, Marilyn Walker
Date: 2017-08-31 00:00:00
Proceeding: ACL 2017 7

Informal first-person narratives are a unique resource for computational
models of everyday events and people's affective reactions to them. People
blogging about their day tend not to explicitly say I am happy. Instead they
describe situations from which other humans can readily infer their affective
reactions. However current sentiment dictionaries are missing much of the
information needed to make similar inferences. We build on recent work that
models affect in terms of lexical predicate functions and affect on the
predicate's arguments. We present a method to learn proxies for these functions
from first-person narratives. We construct a novel fine-grained test set, and
show that the patterns we learn improve our ability to predict first-person
affective reactions to everyday events, from a Stanford sentiment baseline of
.67F to .75F.
Machine Learned Learning Machines
Authors: Leigh Sheneman, Arend Hintze
Date: 2017-05-29 00:00:00
Tasks: BIG-bench Machine Learning

There are two common approaches for optimizing the performance of a machine:
genetic algorithms and machine learning. A genetic algorithm is applied over
many generations whereas machine learning works by applying feedback until the
system meets a performance threshold. Though these are methods that typically
operate separately, we combine evolutionary adaptation and machine learning
into one approach. Our focus is on machines that can learn during their
lifetime, but instead of equipping them with a machine learning algorithm we
aim to let them evolve their ability to learn by themselves. We use evolvable
networks of probabilistic and deterministic logic gates, known as Markov
Brains, as our computational model organism. The ability of Markov Brains to
learn is augmented by a novel adaptive component that can change its
computational behavior based on feedback. We show that Markov Brains can indeed
evolve to incorporate these feedback gates to improve their adaptability to
variable environments. By combining these two methods, we now also implemented
a computational model that can be used to study the evolution of learning.
The TUM LapChole dataset for the M2CAI 2016 workflow challenge
Authors: Ralf Stauder, Daniel Ostler, Michael Kranzfelder, Sebastian Koller, Hubertus Feußner, Nassir Navab
Date: 2016-10-28 00:00:00

In this technical report we present our collected dataset of laparoscopic
cholecystectomies (LapChole). Laparoscopic videos of a total of 20 surgeries
were recorded and annotated with surgical phase labels, of which 15 were
randomly pre-determined as training data, while the remaining 5 videos are
selected as test data. This dataset was later included as part of the M2CAI
2016 workflow detection challenge during MICCAI 2016 in Athens.