pwc-archive/papers-with-abstracts huggingface dataset:
Multiplexed Neural Recording Down a Single Optical Fiber via Optical Reflectometry with Capacitive Signal Enhancement
Authors: Samuel G. Rodriques, Adam H. Marblestone, Max Mankin, Lowell Wood, Edward S. Boyden
Date: 2015-06-02 00:00:00

We introduce a fiber-optic architecture for neural recording without contrast
agents, and study its properties theoretically. Our sensor design is inspired
by electrooptic modulators, which modulate the refractive index of a waveguide
by applying an electric field across an electrooptic core material, and allows
recording of the activities of individual neurons located at points along a 10
cm length of optical fiber with 20 um axial resolution, sensitivity down to 100
uV and a dynamic range of up to 1V using commercially available optical
reflectometers as readout devices. A key concept of the design is the ability
to create an "intensified" electric field inside an optical waveguide by
applying the extracellular voltage from a neural spike over a nanoscopic
distance. Implementing this concept requires the use of ultrathin
high-dielectric capacitor layers. If suitable materials can be found --
possessing favorable properties with respect to toxicity, ohmic junctions, and
surface capacitance -- then such sensing fibers could, in principle, be scaled
down to few-micron cross-sections for minimally invasive neural interfacing.
Custom-designed multi-material optical fibers, probed using a reflectometric
readout, may therefore provide a powerful platform for neural sensing.
On the Communication of Scientific Results: The Full-Metadata Format
Authors: Moritz Riede, Rico Schueppel, Kristian O. Sylvester-Hvid, Martin Kuehne, Michael C. Roettger, Klaus Zimmermann, Andreas W. Liehr
Date: 2009-04-08 00:00:00

In this paper, we introduce a scientific format for text-based data files,
which facilitates storing and communicating tabular data sets. The so-called
Full-Metadata Format builds on the widely used INI-standard and is based on
four principles: readable self-documentation, flexible structure, fail-safe
compatibility, and searchability. As a consequence, all metadata required to
interpret the tabular data are stored in the same file, allowing for the
automated generation of publication-ready tables and graphs and the semantic
searchability of data file collections. The Full-Metadata Format is introduced
on the basis of three comprehensive examples. The complete format and syntax is
given in the appendix.
Towards Large-Scale Video Video Object Mining
Authors: Aljosa Osep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
Date: 2018-09-19 00:00:00
Tasks: Object

We propose to leverage a generic object tracker in order to perform object
mining in large-scale unlabeled videos, captured in a realistic automotive
setting. We present a dataset of more than 360'000 automatically mined object
tracks from 10+ hours of video data (560'000 frames) and propose a method for
automated novel category discovery and detector learning. In addition, we show
preliminary results on using the mined tracks for object detector adaptation.
Combinatorial and Structural Results for gamma-Psi-dimensions
Authors: Yann Guermeur
Date: 2018-09-19 00:00:00

This article deals with the generalization performance of margin multi-category classifiers, when minimal learnability hypotheses are made. In that context, the derivation of a guaranteed risk is based on the handling of capacity measures belonging to three main families: Rademacher/Gaussian complexities, metric entropies and scale-sensitive combinatorial dimensions. The scale-sensitive combinatorial dimensions dedicated to the classifiers of this kind are the gamma-Psi-dimensions. We introduce the combinatorial and structural results needed to involve them in the derivation of guaranteed risks and establish the corresponding upper bounds on the metric entropies and the Rademacher complexity. Two major conclusions can be drawn: 1. the gamma-Psi-dimensions always bring an improvement compared to the use of the fat-shattering dimension of the class of margin functions; 2. thanks to their capacity to take into account basic features of the classifier, they represent a promising alternative to performing the transition from the multi-class case to the binary one with covering numbers.
Generative Adversarial Network in Medical Imaging: A Review
Authors: Xin Yi, Ekta Walia, Paul Babyn
Date: 2018-09-19 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Generative Adversarial Network, Image Reconstruction, Image-to-Image Translation, Medical Image Generation, Translation

Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.
Interpretable Textual Neuron Representations for NLP
Authors: Nina Poerner, Benjamin Roth, Hinrich Schütze
Date: 2018-09-19 00:00:00
Proceeding: WS 2018 11

Input optimization methods, such as Google Deep Dream, create interpretable
representations of neurons for computer vision DNNs. We propose and evaluate
ways of transferring this technology to NLP. Our results suggest that gradient
ascent with a gumbel softmax layer produces n-gram representations that
outperform naive corpus search in terms of target neuron activation. The
representations highlight differences in syntax awareness between the language
and visual models of the Imaginet architecture.
Modeling Online Discourse with Coupled Distributed Topics
Authors: Nikita Srivatsan, Zachary Wojtowicz, Taylor Berg-Kirkpatrick
Date: 2018-09-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: GPU

In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
Authors: Rémi Delbouys, Romain Hennequin, Francesco Piccoli, Jimena Royo-Letelier, Manuel Moussallam
Date: 2018-09-19 00:00:00
Tasks: Multimodal Emotion Recognition, Music Emotion Recognition, Prediction

We consider the task of multimodal music mood prediction based on the audio
signal and the lyrics of a track. We reproduce the implementation of
traditional feature engineering based approaches and propose a new model based
on deep learning. We compare the performance of both approaches on a database
containing 18,000 tracks with associated valence and arousal values and show
that our approach outperforms classical models on the arousal detection task,
and that both approaches perform equally on the valence prediction task. We
also compare the a posteriori fusion with fusion of modalities optimized
simultaneously with each unimodal model, and observe a significant improvement
of valence prediction. We release part of our database for comparison purposes.
Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robots
Authors: Chandrakant Bothe, Fernando Garcia, Arturo Cruz Maya, Amit Kumar Pandey, Stefan Wermter
Date: 2018-09-19 00:00:00
Tasks: Navigate

Service robots need to show appropriate social behaviour in order to be
deployed in social environments such as healthcare, education, retail, etc.
Some of the main capabilities that robots should have are navigation and
conversational skills. If the person is impatient, the person might want a
robot to navigate faster and vice versa. Linguistic features that indicate
politeness can provide social cues about a person's patient and impatient
behaviour. The novelty presented in this paper is to dynamically incorporate
politeness in robotic dialogue systems for navigation. Understanding the
politeness in users' speech can be used to modulate the robot behaviour and
responses. Therefore, we developed a dialogue system to navigate in an indoor
environment, which produces different robot behaviours and responses based on
users' intention and degree of politeness. We deploy and test our system with
the Pepper robot that adapts to the changes in user's politeness.
Bayesian functional optimisation with shape prior
Authors: Pratibha Vellanki, Santu Rana, Sunil Gupta, David Rubin de Celis Leal, Alessandra Sutti, Murray Height, Svetha Venkatesh
Date: 2018-09-19 00:00:00
Tasks: Bayesian Optimisation

Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that changes over time. Such problems can be formulated as a functional optimisation problem. We develop a novel Bayesian optimisation framework for such functional optimisation of expensive black-box processes. We represent the control function using Bernstein polynomial basis and optimise in the coefficient space. We derive the theory and practice required to dynamically adjust the order of the polynomial degree, and show how prior information about shape can be integrated. We demonstrate the effectiveness of our approach for short polymer fibre design and optimising learning rate schedules for deep networks.
DPPy: Sampling DPPs with Python
Authors: Guillaume Gautier, Guillermo Polito, Rémi Bardenet, Michal Valko
Date: 2018-09-19 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, Point Processes

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms for both finite and continuous DPPs. The project is hosted on GitHub and equipped with an extensive documentation.
MTLE: A Multitask Learning Encoder of Visual Feature Representations for Video and Movie Description
Authors: Oliver Nina, Washington Garcia, Scott Clouse, Alper Yilmaz
Date: 2018-09-19 00:00:00
Tasks: Decoder, Video Captioning

Learning visual feature representations for video analysis is a daunting task
that requires a large amount of training samples and a proper generalization
framework. Many of the current state of the art methods for video captioning
and movie description rely on simple encoding mechanisms through recurrent
neural networks to encode temporal visual information extracted from video
data. In this paper, we introduce a novel multitask encoder-decoder framework
for automatic semantic description and captioning of video sequences. In
contrast to current approaches, our method relies on distinct decoders that
train a visual encoder in a multitask fashion. Our system does not depend
solely on multiple labels and allows for a lack of training data working even
with datasets where only one single annotation is viable per video. Our method
shows improved performance over current state of the art methods in several
metrics on multi-caption and single-caption datasets. To the best of our
knowledge, our method is the first method to use a multitask approach for
encoding video features. Our method demonstrates its robustness on the Large
Scale Movie Description Challenge (LSMDC) 2017 where our method won the movie
description task and its results were ranked among other competitors as the
most helpful for the visually impaired.
Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks
Authors: Sumant Sharma, Connor Beierle, Simone D'Amico
Date: 2018-09-19 00:00:00
Tasks: Benchmarking, Image Generation, Pose Estimation

On-board estimation of the pose of an uncooperative target spacecraft is an
essential task for future on-orbit servicing and close-proximity formation
flying missions. However, two issues hinder reliable on-board monocular vision
based pose estimation: robustness to illumination conditions due to a lack of
reliable visual features and scarcity of image datasets required for training
and benchmarking. To address these two issues, this work details the design and
validation of a monocular vision based pose determination architecture for
spaceborne applications. The primary contribution to the state-of-the-art of
this work is the introduction of a novel pose determination method based on
Convolutional Neural Networks (CNN) to provide an initial guess of the pose in
real-time on-board. The method involves discretizing the pose space and
training the CNN with images corresponding to the resulting pose labels. Since
reliable training of the CNN requires massive image datasets and computational
resources, the parameters of the CNN must be determined prior to the mission
with synthetic imagery. Moreover, reliable training of the CNN requires
datasets that appropriately account for noise, color, and illumination
characteristics expected in orbit. Therefore, the secondary contribution of
this work is the introduction of an image synthesis pipeline, which is tailored
to generate high fidelity images of any spacecraft 3D model. The proposed
technique is scalable to spacecraft of different structural and physical
properties as well as robust to the dynamic illumination conditions of space.
Through metrics measuring classification and pose accuracy, it is shown that
the presented architecture has desirable robustness and scalable properties.
Unsupervised cross-lingual matching of product classifications
Authors: Denis Gordeev, Alexey Rey, Dmitry Shagarov
Date: 2018-09-19 00:00:00
Tasks: Translation

Unsupervised cross-lingual embeddings mapping has provided a unique tool for
completely unsupervised translation even for languages with different scripts.
In this work we use this method for the task of unsupervised cross-lingual
matching of product classifications. Our work also investigates limitations of
unsupervised vector alignment and we also suggest two other techniques for
aligning product classifications based on their descriptions: using
hierarchical information and translations.
Noise Statistics Oblivious GARD For Robust Regression With Sparse Outliers
Authors: Sreejith Kallummil, Sheetal Kalyani
Date: 2018-09-19 00:00:00
Tasks: regression

Linear regression models contaminated by Gaussian noise (inlier) and possibly
unbounded sparse outliers are common in many signal processing applications.
Sparse recovery inspired robust regression (SRIRR) techniques are shown to
deliver high quality estimation performance in such regression models.
Unfortunately, most SRIRR techniques assume \textit{a priori} knowledge of
noise statistics like inlier noise variance or outlier statistics like number
of outliers. Both inlier and outlier noise statistics are rarely known
\textit{a priori} and this limits the efficient operation of many SRIRR
algorithms. This article proposes a novel noise statistics oblivious algorithm
called residual ratio thresholding GARD (RRT-GARD) for robust regression in the
presence of sparse outliers. RRT-GARD is developed by modifying the recently
proposed noise statistics dependent greedy algorithm for robust de-noising
(GARD). Both finite sample and asymptotic analytical results indicate that
RRT-GARD performs nearly similar to GARD with \textit{a priori} knowledge of
noise statistics. Numerical simulations in real and synthetic data sets also
point to the highly competitive performance of RRT-GARD.
3D Human Pose Estimation with Siamese Equivariant Embedding
Authors: Márton Véges, Viktor Varga, András Lőrincz
Date: 2018-09-19 00:00:00
Tasks: 3D Human Pose Estimation, Data Augmentation, Monocular 3D Human Pose Estimation, Pose Estimation

In monocular 3D human pose estimation a common setup is to first detect 2D
positions and then lift the detection into 3D coordinates. Many algorithms
suffer from overfitting to camera positions in the training set. We propose a
siamese architecture that learns a rotation equivariant hidden representation
to reduce the need for data augmentation. Our method is evaluated on multiple
databases with different base networks and shows a consistent improvement of
error metrics. It achieves state-of-the-art cross-camera error rate among
algorithms that use estimated 2D joint coordinates only.
Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
Authors: Jack Turner, José Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey
Date: 2018-09-19 00:00:00
Tasks: CPU, GPU, Neural Network Compression

Convolutional Neural Networks (CNNs) are extremely computationally demanding,
presenting a large barrier to their deployment on resource-constrained devices.
Since such systems are where some of their most useful applications lie (e.g.
obstacle detection for mobile robots, vision-based medical assistive
technology), significant bodies of work from both machine learning and systems
communities have attempted to provide optimisations that will make CNNs
available to edge devices. In this paper we unify the two viewpoints in a Deep
Learning Inference Stack and take an across-stack approach by implementing and
evaluating the most common neural network compression techniques (weight
pruning, channel pruning, and quantisation) and optimising their parallel
execution with a range of programming approaches (OpenMP, OpenCL) and hardware
architectures (CPU, GPU). We provide comprehensive Pareto curves to instruct
trade-offs under constraints of accuracy, execution time, and memory space.
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Authors: Peng Sun, Xinghai Sun, Lei Han, Jiechao Xiong, Qing Wang, Bo Li, Yang Zheng, Ji Liu, Yongsheng Liu, Han Liu, Tong Zhang
Date: 2018-09-19 00:00:00
Tasks: AI Agent, Decision Making, Deep Reinforcement Learning, Real-Time Strategy Games, Reinforcement Learning, Starcraft, Starcraft II

Starcraft II (SC2) is widely considered as the most challenging Real Time
Strategy (RTS) game. The underlying challenges include a large observation
space, a huge (continuous and infinite) action space, partial observations,
simultaneous move for all players, and long horizon delayed rewards for local
decisions. To push the frontier of AI research, Deepmind and Blizzard jointly
developed the StarCraft II Learning Environment (SC2LE) as a testbench of
complex decision making systems. SC2LE provides a few mini games such as
MoveToBeacon, CollectMineralShards, and DefeatRoaches, where some AI agents
have achieved the performance level of human professional players. However, for
full games, the current AI agents are still far from achieving human
professional level performance. To bridge this gap, we present two full game AI
agents in this paper - the AI agent TStarBot1 is based on deep reinforcement
learning over a flat action structure, and the AI agent TStarBot2 is based on
hard-coded rules over a hierarchical action structure. Both TStarBot1 and
TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in
a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level
8, level 9, and level 10 are cheating agents with unfair advantages such as
full vision on the whole map and resource harvest boosting. To the best of our
knowledge, this is the first public work to investigate AI agents that can
defeat the built-in AI in the StarCraft II full game.
String Transduction with Target Language Models and Insertion Handling
Authors: Garrett Nicolai, Saeed Najafi, Grzegorz Kondrak
Date: 2018-09-19 00:00:00
Proceeding: WS 2018 10

Many character-level tasks can be framed as sequence-to-sequence
transduction, where the target is a word from a natural language. We show that
leveraging target language models derived from unannotated target corpora,
combined with a precise alignment of the training data, yields state-of-the art
results on cognate projection, inflection generation, and phoneme-to-grapheme
conversion.
A survey of advances in epistemic logic program solvers
Authors: Anthony P. Leclerc, Patrick Thor Kahl
Date: 2018-09-19 00:00:00
Tasks: Survey

Recent research in extensions of Answer Set Programming has included a
renewed interest in the language of Epistemic Specifications, which adds modal
operators K ("known") and M ("may be true") to provide for more powerful
introspective reasoning and enhanced capability, particularly when reasoning
with incomplete information. An epistemic logic program is a set of rules in
this language. Infused with the research has been the desire for an efficient
solver to enable the practical use of such programs for problem solving. In
this paper, we report on the current state of development of epistemic logic
program solvers.
Extending Modular Semantics for Bipolar Weighted Argumentation (Technical Report)
Authors: Nico Potyka
Date: 2018-09-19 00:00:00

Weighted bipolar argumentation frameworks offer a tool for decision support
and social media analysis. Arguments are evaluated by an iterative procedure
that takes initial weights and attack and support relations into account. Until
recently, convergence of these iterative procedures was not very well
understood in cyclic graphs. Mossakowski and Neuhaus recently introduced a
unification of different approaches and proved first convergence and divergence
results. We build up on this work, simplify and generalize convergence results
and complement them with runtime guarantees. As it turns out, there is a
tradeoff between semantics' convergence guarantees and their ability to move
strength values away from the initial weights. We demonstrate that divergence
problems can be avoided without this tradeoff by continuizing semantics.
Semantically, we extend the framework with a Duality property that assures a
symmetric impact of attack and support relations. We also present a Java
implementation of modular semantics and explain the practical usefulness of the
theoretical ideas.