pwc-archive/papers-with-abstracts huggingface dataset:
Rhythm Zone Theory: Speech Rhythms are Physical after all
Authors: Dafydd Gibbon, Xuewei Lin
Date: 2019-01-31 00:00:00
Tasks: All, Edge Detection, Rhythm

Speech rhythms have been dealt with in three main ways: from the
introspective analyses of rhythm as a correlate of syllable and foot timing in
linguistics and applied linguistics, through analyses of durations of segments
of utterances associated with consonantal and vocalic properties, syllables,
feet and words, to models of rhythms in speech production and perception as
physical oscillations. The present study avoids introspection and
human-filtered annotation methods and extends the signal processing paradigm of
amplitude envelope spectrum analysis by adding an additional analytic step of
edge detection, and postulating the co-existence of multiple speech rhythms in
rhythm zones marked by identifiable edges (Rhythm Zone Theory, RZT). An
exploratory investigation of the utility of RZT is conducted, suggesting that
native and non-native readings of the same text are distinct sub-genres of read
speech: a reading by a US native speaker and non-native readings by relatively
low-performing Cantonese adult learners of English. The study concludes by
noting that with the methods used, RZT can distinguish between the speech
rhythms of well-defined sub-genres of native speaker reading vs. non-native
learner reading, but needs further refinement in order to be applied to the
paradoxically more complex speech of low-performing language learners, whose
speech rhythms are co-determined by non-fluency and disfluency factors in
addition to well-known linguistic factors of grammar, vocabulary and discourse
constraints.
Riconoscimento ortografico per apostrofo ed espressioni polirematiche
Authors: Massimiliano Polito
Date: 2019-01-31 00:00:00

The work presents two algorithms of manipulation and comparison between
strings whose purpose is the orthographic recognition of the apostrophe and of
the compound expressions. The theory supporting general reasoning refers to the
basic concept of EditDistance, the improvements that ensure the achievement of
the objective are achieved with the aid of tools borrowed from the use of
techniques for processing large amounts of data on distributed platforms.
Model-Based Detector for SSDs in the Presence of Inter-cell Interference
Authors: Hachem Yassine, Mihai-Alin Badiu, Justin Coon
Date: 2019-01-31 00:00:00
Tasks: Decoder, Quantization

In this paper, we consider the problem of reducing the bit error rate of
flash-based solid state drives (SSDs) when cells are subject to inter-cell
interference (ICI). By observing that the outputs of adjacent victim cells can
be correlated due to common aggressors, we propose a novel channel model to
accurately represent the true flash channel. This model, equivalent to a
finite-state Markov channel model, allows the use of the sum-product algorithm
to calculate more accurate posterior distributions of individual cell inputs
given the joint outputs of victim cells. These posteriors can be easily mapped
to the log-likelihood ratios that are passed as inputs to the soft LDPC
decoder. When the output is available with high precision, our simulation
showed that a significant reduction in the bit-error rate can be obtained,
reaching $99.99\%$ reduction compared to current methods, when the diagonal
coupling is very strong. In the realistic case of low-precision output, our
scheme provides less impressive improvements due to information loss in the
process of quantization. To improve the performance of the new detector in the
quantized case, we propose a new iterative scheme that alternates multiple
times between the detector and the decoder. Our simulations showed that the
iterative scheme can significantly improve the bit error rate even in the
quantized case.
Application of image processing in optical method, Moire deflectometry for investigating the optical properties of zinc oxide nanoparticle
Authors: Fatemeh Jamal, Fatemeh Ahmadi, Mohammad Khanzadeh, Saber Malekzadeh
Date: 2019-01-02 00:00:00

Nowadays for measurement of refractive index of nanomaterials usually
spectro-photometric and mechanical methods are used which are expensive and
indirect. In this paper for measuring these parameters of zinc oxide
nanomaterial with two different stabilizers, a simple optical method, Moire
deflectometry, which is based on wave front analysis and geometric optics is
used. In the Moire deflectometry method, the beam of light from the laser diode
passes through the sample. As a result of that, a change in the sample
environment is observed as deflections of the fringes. By recording of these
deflections using CCD and image processing with MATLAB, the nanomaterials
refractive indices can be calculated. Due to the high accuracy of this method
and improved the image processing code in Matlab, the smallest changes of the
refractive index in the sample can be measured. Digital Image processing is
used for processing images in a way that features can be selected and being
shown. The results obtained in this method show a good improvement over the
other used methods.
Deconstructing Word Embeddings
Authors: Koushik Varma Kalidindi
Date: 2019-01-08 00:00:00
Tasks: Relation, Word Embeddings

A review of Word Embedding Models through a deconstructive approach reveals
their several shortcomings and inconsistencies. These include instability of
the vector representations, a distorted analogical reasoning, geometric
incompatibility with linguistic features, and the inconsistencies in the corpus
data. A new theoretical embedding model, Derridian Embedding, is proposed in
this paper. Contemporary embedding models are evaluated qualitatively in terms
of how adequate they are in relation to the capabilities of a Derridian
Embedding.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Authors: Ning Miao, Hao Zhou, Lili Mou, Rui Yan, Lei LI
Date: 2018-11-14 00:00:00
Tasks: Sentence, Text Generation

In real-world applications of natural language generation, there are often
constraints on the target sentences in addition to fluency and naturalness
requirements. Existing language generation techniques are usually based on
recurrent neural networks (RNNs). However, it is non-trivial to impose
constraints on RNNs while maintaining generation quality, since RNNs generate
sentences sequentially (or with beam search) from the first word to the last.
In this paper, we propose CGMH, a novel approach using Metropolis-Hastings
sampling for constrained sentence generation. CGMH allows complicated
constraints such as the occurrence of multiple keywords in the target
sentences, which cannot be handled in traditional RNN-based approaches.
Moreover, CGMH works in the inference stage, and does not require parallel
corpora for training. We evaluate our method on a variety of tasks, including
keywords-to-sentence generation, unsupervised sentence paraphrasing, and
unsupervised sentence error correction. CGMH achieves high performance compared
with previous supervised methods for sentence generation. Our code is released
at https://github.com/NingMiao/CGMH
Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach
Authors: Faisal I Qureshi
Date: 2018-12-20 00:00:00
Tasks: BIG-bench Machine Learning

With the proliferation of algorithmic high-frequency trading in financial
markets, the Limit Order Book has generated increased research interest.
Research is still at an early stage and there is much we do not understand
about the dynamics of Limit Order Books. In this paper, we employ a machine
learning approach to investigate Limit Order Book features and their potential
to predict short term price movements. This is an initial broad-based
investigation that results in some novel observations about LOB dynamics and
identifies several promising directions for further research. Furthermore, we
obtain prediction results that are significantly superior to a baseline
predictor.
Distribution-Dependent Analysis of Gibbs-ERM Principle
Authors: Ilja Kuzborskij, Nicolò Cesa-Bianchi, Csaba Szepesvári
Date: 2019-02-05 00:00:00
Tasks: Stochastic Optimization

Gibbs-ERM learning is a natural idealized model of learning with stochastic
optimization algorithms (such as Stochastic Gradient Langevin Dynamics and
---to some extent--- Stochastic Gradient Descent), while it also arises in
other contexts, including PAC-Bayesian theory, and sampling mechanisms. In this
work we study the excess risk suffered by a Gibbs-ERM learner that uses
non-convex, regularized empirical risk with the goal to understand the
interplay between the data-generating distribution and learning in large
hypothesis spaces. Our main results are distribution-dependent upper bounds on
several notions of excess risk. We show that, in all cases, the
distribution-dependent excess risk is essentially controlled by the effective
dimension $\mathrm{tr}\left(\boldsymbol{H}^{\star} (\boldsymbol{H}^{\star} +
\lambda \boldsymbol{I})^{-1}\right)$ of the problem, where
$\boldsymbol{H}^{\star}$ is the Hessian matrix of the risk at a local minimum.
This is a well-established notion of effective dimension appearing in several
previous works, including the analyses of SGD and ridge regression, but ours is
the first work that brings this dimension to the analysis of learning using
Gibbs densities. The distribution-dependent view we advocate here improves upon
earlier results of Raginsky et al. (2017), and can yield much tighter bounds
depending on the interplay between the data-generating distribution and the
loss function. The first part of our analysis focuses on the localized excess
risk in the vicinity of a fixed local minimizer. This result is then extended
to bounds on the global excess risk, by characterizing probabilities of local
minima (and their complement) under Gibbs densities, a results which might be
of independent interest.
Global convergence of neuron birth-death dynamics
Authors: Grant Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden
Date: 2019-02-05 00:00:00

Neural networks with a large number of parameters admit a mean-field
description, which has recently served as a theoretical explanation for the
favorable training properties of "overparameterized" models. In this regime,
gradient descent obeys a deterministic partial differential equation (PDE) that
converges to a globally optimal solution for networks with a single hidden
layer under appropriate assumptions. In this work, we propose a non-local mass
transport dynamics that leads to a modified PDE with the same minimizer. We
implement this non-local dynamics as a stochastic neuronal birth-death process
and we prove that it accelerates the rate of convergence in the mean-field
limit. We subsequently realize this PDE with two classes of numerical schemes
that converge to the mean-field equation, each of which can easily be
implemented for neural networks with finite numbers of parameters. We
illustrate our algorithms with two models to provide intuition for the
mechanism through which convergence is accelerated.
How to "DODGE" Complex Software Analytics?
Authors: Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen, Tim Menzies
Date: 2019-02-05 00:00:00
Tasks: BIG-bench Machine Learning, Hyperparameter Optimization

Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner's control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time exploring "redundant tunings"', i.e., pairs of tunings which lead to indistinguishable results. By ignoring redundant tunings, DODGE, a tuning tool, runs orders of magnitude faster, while also generating learners with more accurate predictions than seen in prior state-of-the-art approaches.
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
Authors: Roberto Valle, José M. Buenaposada, Antonio Valdés, Luis Baumela
Date: 2019-02-05 00:00:00
Tasks: Face Alignment, Face Model, Facial Landmark Detection, regression

Face alignment algorithms locate a set of landmark points in images of faces taken in unrestricted situations. State-of-the-art approaches typically fail or lose accuracy in the presence of occlusions, strong deformations, large pose variations and ambiguous configurations. In this paper we present 3DDE, a robust and efficient face alignment algorithm based on a coarse-to-fine cascade of ensembles of regression trees. It is initialized by robustly fitting a 3D face model to the probability maps produced by a convolutional neural network. With this initialization we address self-occlusions and large face rotations. Further, the regressor implicitly imposes a prior face shape on the solution, addressing occlusions and ambiguous face configurations. Its coarse-to-fine structure tackles the combinatorial explosion of parts deformation. In the experiments performed, 3DDE improves the state-of-the-art in 300W, COFW, AFLW and WFLW data sets. Finally, we perform cross-dataset experiments that reveal the existence of a significant data set bias in these benchmarks.
Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video
Authors: Süleyman Aslan, Uğur Güdükbay, B. Uğur Töreyin, A. Enis Çetin
Date: 2019-02-05 00:00:00

Real-time flame detection is crucial in video based surveillance systems. We
propose a vision-based method to detect flames using Deep Convolutional
Generative Adversarial Neural Networks (DCGANs). Many existing supervised
learning approaches using convolutional neural networks do not take temporal
information into account and require substantial amount of labeled data. In
order to have a robust representation of sequences with and without flame, we
propose a two-stage training of a DCGAN exploiting spatio-temporal flame
evolution. Our training framework includes the regular training of a DCGAN with
real spatio-temporal images, namely, temporal slice images, and noise vectors,
and training the discriminator separately using the temporal flame images
without the generator. Experimental results show that the proposed method
effectively detects flame in video with negligible false positive rates in
real-time.
Modular Block-diagonal Curvature Approximations for Feedforward Architectures
Authors: Felix Dangel, Stefan Harmeling, Philipp Hennig
Date: 2019-02-05 00:00:00
Tasks: BIG-bench Machine Learning

We propose a modular extension of backpropagation for the computation of block-diagonal approximations to various curvature matrices of the training objective (in particular, the Hessian, generalized Gauss-Newton, and positive-curvature Hessian). The approach reduces the otherwise tedious manual derivation of these matrices into local modules, and is easy to integrate into existing machine learning libraries. Moreover, we develop a compact notation derived from matrix differential calculus. We outline different strategies applicable to our method. They subsume recently-proposed block-diagonal approximations as special cases, and are extended to convolutional neural networks in this work.
Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals
Authors: Seyedroohollah Hosseini, Xuan Guo
Date: 2019-02-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sensitivity, Specificity

Mind wandering (MW) is a ubiquitous phenomenon which reflects a shift in
attention from task-related to task-unrelated thoughts. There is a need for
intelligent interfaces that can reorient attention when MW is detected due to
its detrimental effects on performance and productivity. In this paper, we
propose a deep learning model for MW detection using Electroencephalogram (EEG)
signals. Specifically, we develop a channel-wise deep convolutional neural
network (CNN) model to classify the features of focusing state and MW extracted
from EEG signals. This is the first study that employs CNN to automatically
detect MW using only EEG data. The experimental results on the collected
dataset demonstrate promising performance with 91.78% accuracy, 92.84%
sensitivity, and 90.73% specificity.
Homogeneous Linear Inequality Constraints for Neural Network Activations
Authors: Thomas Frerix, Matthias Nießner, Daniel Cremers
Date: 2019-02-05 00:00:00

We propose a method to impose homogeneous linear inequality constraints of the form $Ax\leq 0$ on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.
Unbiased Smoothing using Particle Independent Metropolis-Hastings
Authors: Lawrence Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob
Date: 2019-02-05 00:00:00

We consider the approximation of expectations with respect to the
distribution of a latent Markov process given noisy measurements. This is known
as the smoothing problem and is often approached with particle and Markov chain
Monte Carlo (MCMC) methods. These methods provide consistent but biased
estimators when run for a finite time. We propose a simple way of coupling two
MCMC chains built using Particle Independent Metropolis-Hastings (PIMH) to
produce unbiased smoothing estimators. Unbiased estimators are appealing in the
context of parallel computing, and facilitate the construction of confidence
intervals. The proposed scheme only requires access to off-the-shelf Particle
Filters (PF) and is thus easier to implement than recently proposed unbiased
smoothers. The approach is demonstrated on a L\'evy-driven stochastic
volatility model and a stochastic kinetic model.
Learning Decision Trees Recurrently Through Communication
Authors: Stephan Alaniz, Diego Marcos, Bernt Schiele, Zeynep Akata
Date: 2019-02-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: Decision Making, image-classification, Image Classification

Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our model is its ability to build a decision tree whose structure is encoded into the memory representation of a Recurrent Neural Network jointly learned by two models communicating through message passing. In addition, our model assigns a semantic meaning to each decision in the form of binary attributes, providing concise, semantic and relevant rationalizations to the user. On three benchmark image classification datasets, including the large-scale ImageNet, our model generates human interpretable binary decision sequences explaining the predictions of the network while maintaining state-of-the-art accuracy.
Dynamical Component Analysis (DyCA) and its application on epileptic EEG
Authors: Katharina Korn, Bastian Seifert, Christian Uhl
Date: 2019-02-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection, Specificity

Dynamical Component Analysis (DyCA) is a recently-proposed method to detect
projection vectors to reduce the dimensionality of multi-variate deterministic
datasets. It is based on the solution of a generalized eigenvalue problem and
therefore straight forward to implement. DyCA is introduced and applied to EEG
data of epileptic seizures. The obtained eigenvectors are used to project the
signal and the corresponding trajectories in phase space are compared with PCA
and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection
and the obtained results in terms of specificity, false discovery rate and miss
rate are compared to other seizure detection algorithms.
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence
Authors: Dustin Dannenhauer, Michael W. Floyd, Jonathan Decker, David W. Aha
Date: 2019-02-05 00:00:00
Tasks: Starcraft, Starcraft II

Dungeon Crawl Stone Soup is a popular, single-player, free and open-source
rogue-like video game with a sufficiently complex decision space that makes it
an ideal testbed for research in cognitive systems and, more generally,
artificial intelligence. This paper describes the properties of Dungeon Crawl
Stone Soup that are conducive to evaluating new approaches of AI systems. We
also highlight an ongoing effort to build an API for AI researchers in the
spirit of recent game APIs such as MALMO, ELF, and the Starcraft II API.
Dungeon Crawl Stone Soup's complexity offers significant opportunities for
evaluating AI and cognitive systems, including human user studies. In this
paper we provide (1) a description of the state space of Dungeon Crawl Stone
Soup, (2) a description of the components for our API, and (3) the potential
benefits of evaluating AI agents in the Dungeon Crawl Stone Soup video game.
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression
Authors: Ji Xu, Arian Maleki, Kamiar Rahnama Rad, Daniel Hsu
Date: 2019-02-05 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalized cross validation, and Bootstrap. The theoretical properties of such estimates have been extensively studied in the low-dimensional settings, where the number of predictors $p$ is much smaller than the number of observations $n$. However, a unifying methodology accompanied with a rigorous theory is lacking in high-dimensional settings. This paper studies the problem of risk estimation under the moderately high-dimensional asymptotic setting $n,p \rightarrow \infty$ and $n/p \rightarrow \delta>1$ ($\delta$ is a fixed number), and proves the consistency of three risk estimates that have been successful in numerical studies, i.e., leave-one-out cross validation (LOOCV), approximate leave-one-out (ALO), and approximate message passing (AMP)-based techniques. A corner stone of our analysis is a bound that we obtain on the discrepancy of the `residuals' obtained from AMP and LOOCV. This connection not only enables us to obtain a more refined information on the estimates of AMP, ALO, and LOOCV, but also offers an upper bound on the convergence rate of each estimate.
An RNN-based IMM Filter Surrogate
Authors: Stefan Becker, Ronny Hug, Wolfgang Hübner, Michael Arens
Date: 2019-02-05 00:00:00

The problem of varying dynamics of tracked objects, such as pedestrians, is
traditionally tackled with approaches like the Interacting Multiple Model (IMM)
filter using a Bayesian formulation. By following the current trend towards
using deep neural networks, in this paper an RNN-based IMM filter surrogate is
presented. Similar to an IMM filter solution, the presented RNN-based model
assigns a probability value to a performed dynamic and, based on them, puts out
a multi-modal distribution over future pedestrian trajectories. The evaluation
is done on synthetic data, reflecting prototypical pedestrian maneuvers.