pwc-archive/papers-with-abstracts huggingface dataset:
The ISTI Rapid Response on Exploring Cloud Computing 2018
Authors: Carleton Coffrin, James Arnold, Stephan Eidenbenz, Derek Aberle, John Ambrosiano, Zachary Baker, Sara Brambilla, Michael Brown, K. Nolan Carter, Pinghan Chu, Patrick Conry, Keeley Costigan, Ariane Eberhardt, David M. Fobes, Adam Gausmann, Sean Harris, Donovan Heimer, Marlin Holmes, Bill Junor, Csaba Kiss, Steve Linger, Rodman Linn, Li-Ta Lo, Jonathan MacCarthy, Omar Marcillo, Clay McGinnis, Alexander McQuarters, Eric Michalak, Arvind Mohan, Matt Nelson, Diane Oyen, Nidhi Parikh, Donatella Pasqualini, Aaron s. Pope, Reid Porter, Chris Rawlings, Hannah Reinbolt, Reid Rivenburgh, Phil Romero, Kevin Schoonover, Alexei Skurikhin, Daniel Tauritz, Dima Tretiak, Zhehui Wang, James Wernicke, Brad Wolfe, Phillip Wolfram, Jonathan Woodring
Date: 2019-01-04 00:00:00
Tasks: Cloud Computing

This report describes eighteen projects that explored how commercial cloud
computing services can be utilized for scientific computation at national
laboratories. These demonstrations ranged from deploying proprietary software
in a cloud environment to leveraging established cloud-based analytics
workflows for processing scientific datasets. By and large, the projects were
successful and collectively they suggest that cloud computing can be a valuable
computational resource for scientific computation at national laboratories.
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning
Authors: Roi Ceren
Date: 2019-01-04 00:00:00
Tasks: Decision Making, Image Segmentation, Model-based Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Segmentation

Optimal decision making with limited or no information in stochastic
environments where multiple agents interact is a challenging topic in the realm
of artificial intelligence. Reinforcement learning (RL) is a popular approach
for arriving at optimal strategies by predicating stimuli, such as the reward
for following a strategy, on experience. RL is heavily explored in the
single-agent context, but is a nascent concept in multiagent problems. To this
end, I propose several principled model-free and partially model-based
reinforcement learning approaches for several multiagent settings. In the realm
of normative reinforcement learning, I introduce scalable extensions to Monte
Carlo exploring starts for partially observable Markov Decision Processes
(POMDP), dubbed MCES-P, where I expand the theory and algorithm to the
multiagent setting. I first examine MCES-P with probably approximately correct
(PAC) bounds in the context of multiagent setting, showing MCESP+PAC holds in
the presence of other agents. I then propose a more sample-efficient
methodology for antagonistic settings, MCESIP+PAC. For cooperative settings, I
extend MCES-P to the Multiagent POMDP, dubbed MCESMP+PAC. I then explore the
use of reinforcement learning as a methodology in searching for optima in
realistic and latent model environments. First, I explore a parameterized
Q-learning approach in modeling humans learning to reason in an uncertain,
multiagent environment. Next, I propose an implementation of MCES-P, along with
image segmentation, to create an adaptive team-based reinforcement learning
technique to positively identify the presence of phenotypically-expressed water
and pathogen stress in crop fields.
On the Utility of Model Learning in HRI
Authors: Gokul Swamy, Jens Schulz, Rohan Choudhury, Dylan Hadfield-Menell, Anca Dragan
Date: 2019-01-04 00:00:00
Tasks: Autonomous Driving, model

Fundamental to robotics is the debate between model-based and model-free learning: should the robot build an explicit model of the world, or learn a policy directly? In the context of HRI, part of the world to be modeled is the human. One option is for the robot to treat the human as a black box and learn a policy for how they act directly. But it can also model the human as an agent, and rely on a "theory of mind" to guide or bias the learning (grey box). We contribute a characterization of the performance of these methods for an autonomous driving task under the optimistic case of having an ideal theory of mind, as well as under different scenarios in which the assumptions behind the robot's theory of mind for the human are wrong, as they inevitably will be in practice.
JECL: Joint Embedding and Cluster Learning for Image-Text Pairs
Authors: Sean T. Yang, Kuan-Hao Huang, Bill Howe
Date: 2019-01-04 00:00:00
Tasks: Clustering

We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise frequently in high-value applications where structured training data is expensive to produce, but free-text descriptions are common. JECL trains by minimizing the Kullback-Leibler divergence between the distribution of the images and text to that of a combined joint target distribution and optimizing the Jensen-Shannon divergence between the soft cluster assignments of the images and text. Regularizers are also applied to JECL to prevent trivial solutions. Experiments show that JECL outperforms both single-view and multi-view methods on large benchmark image-caption datasets, and is remarkably robust to missing captions and varying data sizes.
Low-Shot Learning from Imaginary 3D Model
Authors: Frederik Pahde, Mihai Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi
Date: 2019-01-04 00:00:00
Tasks: Few-Shot Learning, model

Since the advent of deep learning, neural networks have demonstrated
remarkable results in many visual recognition tasks, constantly pushing the
limits. However, the state-of-the-art approaches are largely unsuitable in
scarce data regimes. To address this shortcoming, this paper proposes employing
a 3D model, which is derived from training images. Such a model can then be
used to hallucinate novel viewpoints and poses for the scarce samples of the
few-shot learning scenario. A self-paced learning approach allows for the
selection of a diverse set of high-quality images, which facilitates the
training of a classifier. The performance of the proposed approach is showcased
on the fine-grained CUB-200-2011 dataset in a few-shot setting and
significantly improves our baseline accuracy.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
Authors: Tolga Birdal, Benjamin Busam, Nassir Navab, Slobodan Ilic, Peter Sturm
Date: 2019-01-04 00:00:00

We present a novel and effective method for detecting 3D primitives in
cluttered, unorganized point clouds, without axillary segmentation or type
specification. We consider the quadric surfaces for encapsulating the basic
building blocks of our environments - planes, spheres, ellipsoids, cones or
cylinders, in a unified fashion. Moreover, quadrics allow us to model higher
degree of freedom shapes, such as hyperboloids or paraboloids that could be
used in non-rigid settings.
  We begin by contributing two novel quadric fits targeting 3D point sets that
are endowed with tangent space information. Based upon the idea of aligning the
quadric gradients with the surface normals, our first formulation is exact and
requires as low as four oriented points. The second fit approximates the first,
and reduces the computational effort. We theoretically analyze these fits with
rigor, and give algebraic and geometric arguments. Next, by re-parameterizing
the solution, we devise a new local Hough voting scheme on the null-space
coefficients that is combined with RANSAC, reducing the complexity from
$O(N^4)$ to $O(N^3)$ (three points). To the best of our knowledge, this is the
first method capable of performing a generic cross-type multi-object primitive
detection in difficult scenes without segmentation. Our extensive qualitative
and quantitative results show that our method is efficient and flexible, as
well as being accurate.
Learning Graph Embedding with Adversarial Training Methods
Authors: Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang
Date: 2019-01-04 00:00:00
Tasks: Clustering, Decoder, Graph Clustering, Graph Embedding, Link Prediction, Node Clustering

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding distribution of the latent codes, which unfortunately may lead to inferior representation in many cases. In this paper, we present a novel adversarially regularized framework for graph embedding. By employing the graph convolutional network as an encoder, our framework embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph. The adversarial training principle is applied to enforce our latent codes to match a prior Gaussian or Uniform distribution. Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit other potential variations of ARGA and ARVGA to get a deeper understanding on our designs. Experimental results compared among twelve algorithms for link prediction and twenty algorithms for graph clustering validate our solutions.
A Computational Framework for Motor Skill Acquisition
Authors: Krishn Bera, Tejas Savalia, Bapi Raju
Date: 2019-01-03 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

There have been numerous attempts in explaining the general learning
behaviours by various cognitive models. Multiple hypotheses have been put
further to qualitatively argue the best-fit model for motor skill acquisition
task and its variations. In this context, for a discrete sequence production
(DSP) task, one of the most insightful models is Verwey's Dual Processor Model
(DPM). It largely explains the learning and behavioural phenomenon of skilled
discrete key-press sequences without providing any concrete computational basis
of reinforcement. Therefore, we propose a quantitative explanation for Verwey's
DPM hypothesis by experimentally establishing a general computational framework
for motor skill learning. We attempt combining the qualitative and quantitative
theories based on a best-fit model of the experimental simulations of
variations of dual processor models. The fundamental premise of sequential
decision making for skill learning is based on interacting model-based (MB) and
model-free (MF) reinforcement learning (RL) processes. Our unifying framework
shows the proposed idea agrees well to Verwey's DPM and Fitts' three phases of
skill learning. The accuracy of our model can further be validated by its
statistical fit with the human-generated data on simple environment tasks like
the grid-world.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
Authors: Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus
Date: 2018-12-24 00:00:00
Tasks: Program Repair

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate it on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SequenceR is able to perfectly predict the fixed line for 950/4711 testing samples, and find correct patches for 14 bugs in Defects4J. It captures a wide range of repair operators without any domain-specific top-down design.
Inference in Graded Bayesian Networks
Authors: Robert Leppert, Karl-Heinz Zimmermann
Date: 2018-12-23 00:00:00

Machine learning provides algorithms that can learn from data and make
inferences or predictions on data. Bayesian networks are a class of graphical
models that allow to represent a collection of random variables and their
condititional dependencies by directed acyclic graphs. In this paper, an
inference algorithm for the hidden random variables of a Bayesian network is
given by using the tropicalization of the marginal distribution of the observed
variables. By restricting the topological structure to graded networks, an
inference algorithm for graded Bayesian networks will be established that
evaluates the hidden random variables rank by rank and in this way yields the
most probable states of the hidden variables. This algorithm can be viewed as a
generalized version of the Viterbi algorithm for graded Bayesian networks.
Wayeb: a Tool for Complex Event Forecasting
Authors: Elias Alevizos, Alexander Artikis, Georgios Paliouras
Date: 2018-12-16 00:00:00

Complex Event Processing (CEP) systems have appeared in abundance during the
last two decades. Their purpose is to detect in real-time interesting patterns
upon a stream of events and to inform an analyst for the occurrence of such
patterns in a timely manner. However, there is a lack of methods for
forecasting when a pattern might occur before such an occurrence is actually
detected by a CEP engine. We present Wayeb, a tool that attempts to address the
issue of Complex Event Forecasting. Wayeb employs symbolic automata as a
computational model for pattern detection and Markov chains for deriving a
probabilistic description of a symbolic automaton.
Proceedings of the 2018 XCSP3 Competition
Authors: Christophe Lecoutre, Olivier Roussel
Date: 2018-12-17 00:00:00

This document represents the proceedings of the 2018 XCSP3 Competition. The
results of this competition of constraint solvers were presented at CP'18, the
24th International Conference on Principles and Practice of Constraint
Programming, held in Lille, France from 27th August 2018 to 31th August, 2018.
Multi-Fidelity Recursive Behavior Prediction
Authors: Mihir Jain, Kyle Brown, Ahmed K. Sadek
Date: 2018-12-18 00:00:00
Tasks: Prediction

Predicting the behavior of surrounding vehicles is a critical problem in
automated driving. We present a novel game theoretic behavior prediction model
that achieves state of the art prediction accuracy by explicitly reasoning
about possible future interaction between agents. We evaluate our approach on
the NGSIM vehicle trajectory data set and demonstrate lower root mean square
error than state-of-the-art methods.
"Ge Shu Zhi Zhi": Towards Deep Understanding about Worlds
Authors: Bao-Gang Hu, Wei-Ming Dong
Date: 2018-12-19 00:00:00
Tasks: Position

"Ge She Zhi Zhi" is a novel saying in Chinese, stated as "To investigate things from the underlying principle(s) and to acquire knowledge in the form of mathematical representations". The saying is adopted and modified based on the ideas from the Eastern and Western philosophers. This position paper discusses the saying in the background of artificial intelligence (AI). Some related subjects, such as the ultimate goals of AI and two levels of knowledge representations, are discussed from the perspective of machine learning. A case study on objective evaluations over multi attributes, a typical problem in the filed of social computing, is given to support the saying for wide applications. A methodology of meta rules is proposed for examining the objectiveness of the evaluations. The possible problems of the saying are also presented.
Personal Universes: A Solution to the Multi-Agent Value Alignment Problem
Authors: Roman V. Yampolskiy
Date: 2019-01-01 00:00:00

AI Safety researchers attempting to align values of highly capable
intelligent systems with those of humanity face a number of challenges
including personal value extraction, multi-agent value merger and finally
in-silico encoding. State-of-the-art research in value alignment shows
difficulties in every stage in this process, but merger of incompatible
preferences is a particularly difficult challenge to overcome. In this paper we
assume that the value extraction problem will be solved and propose a possible
way to implement an AI solution which optimally aligns with individual
preferences of each user. We conclude by analyzing benefits and limitations of
the proposed approach.
Nonlinear price impact from linear models
Authors: Felix Patzelt, Jean-Philippe Bouchaud
Date: 2017-08-08 00:00:00

The impact of trades on asset prices is a crucial aspect of market dynamics
for academics, regulators and practitioners alike. Recently, universal and
highly nonlinear master curves were observed for price impacts aggregated on
all intra-day scales [1]. Here we investigate how well these curves, their
scaling, and the underlying return dynamics are captured by linear "propagator"
models. We find that the classification of trades as price-changing versus
non-price-changing can explain the price impact nonlinearities and short-term
return dynamics to a very high degree. The explanatory power provided by the
change indicator in addition to the order sign history increases with
increasing tick size. To obtain these results, several long-standing technical
issues for model calibration and -testing are addressed. We present new
spectral estimators for two- and three-point cross-correlations, removing the
need for previously used approximations. We also show when calibration is
unbiased and how to accurately reveal previously overlooked biases. Therefore,
our results contribute significantly to understanding both recent empirical
results and the properties of a popular class of impact models.
Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis
Authors: Joao Reis, Gil Gonçalves
Date: 2018-10-16 00:00:00
Tasks: General Classification, image-classification, Image Classification, regression, Zero-Shot Learning

Zero-shot learning (ZSL) can be defined by correctly solving a task where no
training data is available, based on previous acquired knowledge from
different, but related tasks. So far, this area has mostly drawn the attention
from computer vision community where a new unseen image needs to be correctly
classified, assuming the target class was not used in the training procedure.
Apart from image classification, only a couple of generic methods were proposed
that are applicable to both classification and regression. These learn the
relation among model coefficients so new ones can be predicted according to
provided conditions. So far, up to our knowledge, no methods exist that are
applicable only to regression, and take advantage from such setting. Therefore,
the present work proposes a novel algorithm for regression problems that uses
data drawn from trained models, instead of model coefficients. In this case, a
shape analyses on the data is performed to create a statistical shape model and
generate new shapes to train new models. The proposed algorithm is tested in a
theoretical setting using the beta distribution where main problem to solve is
to estimate a function that predicts curves, based on already learned
different, but related ones.
Panoptic Feature Pyramid Networks
Authors: Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár
Date: 2019-01-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation

The recently introduced panoptic segmentation task has renewed our
community's interest in unifying the tasks of instance segmentation (for thing
classes) and semantic segmentation (for stuff classes). However, current
state-of-the-art methods for this joint task use separate and dissimilar
networks for instance and semantic segmentation, without performing any shared
computation. In this work, we aim to unify these methods at the architectural
level, designing a single network for both tasks. Our approach is to endow Mask
R-CNN, a popular instance segmentation method, with a semantic segmentation
branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly,
this simple baseline not only remains effective for instance segmentation, but
also yields a lightweight, top-performing method for semantic segmentation. In
this work, we perform a detailed study of this minimally extended version of
Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust
and accurate baseline for both tasks. Given its effectiveness and conceptual
simplicity, we hope our method can serve as a strong baseline and aid future
research in panoptic segmentation.
Unseen Object Segmentation in Videos via Transferable Representations
Authors: Yi-Wen Chen, Yi-Hsuan Tsai, Chu-Ya Yang, Yen-Yu Lin, Ming-Hsuan Yang
Date: 2019-01-08 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation

In order to learn object segmentation models in videos, conventional methods
require a large amount of pixel-wise ground truth annotations. However,
collecting such supervised data is time-consuming and labor-intensive. In this
paper, we exploit existing annotations in source images and transfer such
visual information to segment videos with unseen object categories. Without
using any annotations in the target video, we propose a method to jointly mine
useful segments and learn feature representations that better adapt to the
target frames. The entire process is decomposed into two tasks: 1) solving a
submodular function for selecting object-like segments, and 2) learning a CNN
model with a transferable module for adapting seen categories in the source
domain to the unseen target video. We present an iterative update scheme
between two tasks to self-learn the final solution for object segmentation.
Experimental results on numerous benchmark datasets show that the proposed
method performs favorably against the state-of-the-art algorithms.
Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
Authors: Joseph L. Betthauser, John T. Krall, Rahul R. Kaliki, Matthew S. Fifer, Nitish V. Thakor
Date: 2019-01-08 00:00:00
Tasks: Classification, General Classification

Transient muscle movements influence the temporal structure of myoelectric
signal patterns, often leading to unstable prediction behavior from
movement-pattern classification methods. We show that temporal convolutional
network sequential models leverage the myoelectric signal's history to discover
contextual temporal features that aid in correctly predicting movement
intentions, especially during interclass transitions. We demonstrate
myoelectric classification using temporal convolutional networks to effect 3
simultaneous hand and wrist degrees-of-freedom in an experiment involving nine
human-subjects. Temporal convolutional networks yield significant $(p<0.001)$
performance improvements over other state-of-the-art methods in terms of both
classification accuracy and stability.
Using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions
Authors: Sergij V. Goncharov
Date: 2019-01-08 00:00:00

We consider fuzzy, or continuous, bits, which take values in [0;1] and (-1;1]
instead of {0;1}, and operations on them (NOT, XOR etc.) and on their sequences
(ADD), to obtain the generalization of cryptographic hash functions, CHFs, for
the messages consisting of fuzzy bits, so that CHFs become smooth and
non-constant functions of each bit of the message. We then train the neural
networks to predict the message that has a given hash, where the loss function
for the hash of predicted message and given true hash is backpropagatable. The
results of the trainings for the standard CHFs - MD5, SHA1, SHA2-256, and
SHA3/Keccak - with small number of (optionally weakened) rounds are presented
and compared.