pwc-archive/papers-with-abstracts huggingface dataset:
Enabling FAIR Research in Earth Science through Research Objects
Authors: Andres Garcia-Silva, Jose Manuel Gomez-Perez, Raul Palma, Marcin Krystek, Simone Mantovani, Federica Foglini, Valentina Grande, Francesco De Leo, Stefano Salvi, Elisa Trasati, Vito Romaniello, Mirko Albani, Cristiano Silvagni, Rosemarie Leone, Fulvio Marelli, Sergio Albani, Michele Lazzarini, Hazel J. Napier, Helen M. Glaves, Timothy Aldridge, Charles Meertens, Fran Boler, Henry W. Loescher, Christine Laney, Melissa A Genazzio, Daniel Crawl, Ilkay Altintas
Date: 2018-09-27 00:00:00
Tasks: Management, Object, Recommendation Systems

Data-intensive science communities are progressively adopting FAIR practices
that enhance the visibility of scientific breakthroughs and enable reuse. At
the core of this movement, research objects contain and describe scientific
information and resources in a way compliant with the FAIR principles and
sustain the development of key infrastructure and tools. This paper provides an
account of the challenges, experiences and solutions involved in the adoption
of FAIR around research objects over several Earth Science disciplines. During
this journey, our work has been comprehensive, with outcomes including: an
extended research object model adapted to the needs of earth scientists; the
provisioning of digital object identifiers (DOI) to enable persistent
identification and to give due credit to authors; the generation of
content-based, semantically rich, research object metadata through natural
language processing, enhancing visibility and reuse through recommendation
systems and third-party search engines; and various types of checklists that
provide a compact representation of research object quality as a key enabler of
scientific reuse. All these results have been integrated in ROHub, a platform
that provides research object management functionality to a wealth of
applications and interfaces across different scientific communities. To monitor
and quantify the community uptake of research objects, we have defined
indicators and obtained measures via ROHub that are also discussed herein.
A Successive-Elimination Approach to Adaptive Robotic Sensing
Authors: Esther Rolf, David Fridovich-Keil, Max Simchowitz, Benjamin Recht, Claire Tomlin
Date: 2018-09-27 00:00:00
Tasks: Trajectory Planning

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose AdaSearch, a general algorithm for adaptive source seeking in the face of heterogeneous background noise. AdaSearch combines global trajectory planning with principled confidence intervals in order to concentrate measurements in promising regions while guaranteeing sufficient coverage of the entire area. Theoretical analysis shows that AdaSearch confers gains over a uniform sampling strategy when the distribution of background signals is highly variable. Simulation experiments demonstrate that when applied to the problem of radioactive source seeking, AdaSearch outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We also demonstrate AdaSearch in hardware, providing further evidence of its potential for real-time implementation.
Counterfactual Fairness in Text Classification through Robustness
Authors: Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel
Date: 2018-09-27 00:00:00
Tasks: Attribute, Classification, counterfactual, Fairness, General Classification, text-classification, Text Classification

In this paper, we study counterfactual fairness in text classification, which
asks the question: How would the prediction change if the sensitive attribute
referenced in the example were different? Toxicity classifiers demonstrate a
counterfactual fairness issue by predicting that "Some people are gay" is toxic
while "Some people are straight" is nontoxic. We offer a metric, counterfactual
token fairness (CTF), for measuring this particular form of fairness in text
classifiers, and describe its relationship with group fairness. Further, we
offer three approaches, blindness, counterfactual augmentation, and
counterfactual logit pairing (CLP), for optimizing counterfactual token
fairness during training, bridging the robustness and fairness literature.
Empirically, we find that blindness and CLP address counterfactual token
fairness. The methods do not harm classifier performance, and have varying
tradeoffs with group fairness. These approaches, both for measurement and
optimization, provide a new path forward for addressing fairness concerns in
text classification.
Solving Statistical Mechanics Using Variational Autoregressive Networks
Authors: Dian Wu, Lei Wang, Pan Zhang
Date: 2018-09-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Monte Carlo

We propose a general framework for solving statistical mechanics of systems
with finite size. The approach extends the celebrated variational mean-field
approaches using autoregressive neural networks, which support direct sampling
and exact calculation of normalized probability of configurations. It computes
variational free energy, estimates physical quantities such as entropy,
magnetizations and correlations, and generates uncorrelated samples all at
once. Training of the network employs the policy gradient approach in
reinforcement learning, which unbiasedly estimates the gradient of variational
parameters. We apply our approach to several classic systems, including 2D
Ising models, the Hopfield model, the Sherrington-Kirkpatrick model, and the
inverse Ising model, for demonstrating its advantages over existing variational
mean-field methods. Our approach sheds light on solving statistical physics
problems using modern deep generative neural networks.
AlphaGomoku: An AlphaGo-based Gomoku Artificial Intelligence using Curriculum Learning
Authors: Zheng Xie, Xingyu Fu, JinYuan Yu
Date: 2018-09-27 00:00:00
Tasks: GPU

In this project, we combine AlphaGo algorithm with Curriculum Learning to
crack the game of Gomoku. Modifications like Double Networks Mechanism and
Winning Value Decay are implemented to solve the intrinsic asymmetry and
short-sight of Gomoku. Our final AI AlphaGomoku, through two days' training on
a single GPU, has reached humans' playing level.
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Authors: Sripad Krishna Devalla, Giridhar Subramanian, Tan Hung Pham, Xiaofei Wang, Shamira Perera, Tin A. Tun, Tin Aung, Leopold Schmetterer, Alexandre H. Thiery, Michael J. A. Girard
Date: 2018-09-27 00:00:00

Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
  Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
  Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from $4.02 \pm 0.68$ dB (single-frame) to $8.14 \pm 1.03$ dB
(denoised). For all the ONH tissues, the mean CNR increased from $3.50 \pm
0.56$ (single-frame) to $7.63 \pm 1.81$ (denoised). The MSSIM increased from
$0.13 \pm 0.02$ (single frame) to $0.65 \pm 0.03$ (denoised) when compared with
the corresponding multi-frame B-scans.
  Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort.
Kernel based low-rank sparse model for single image super-resolution
Authors: Jiahe Shi, Chun Qi
Date: 2018-09-27 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Self-similarity learning has been recognized as a promising method for single
image super-resolution (SR) to produce high-resolution (HR) image in recent
years. The performance of learning based SR reconstruction, however, highly
depends on learned representation coeffcients. Due to the degradation of input
image, conventional sparse coding is prone to produce unfaithful representation
coeffcients. To this end, we propose a novel kernel based low-rank sparse model
with self-similarity learning for single image SR which incorporates
nonlocalsimilarity prior to enforce similar patches having similar
representation weights. We perform a gradual magnification scheme, using
self-examples extracted from the degraded input image and up-scaled versions.
To exploit nonlocal-similarity, we concatenate the vectorized input patch and
its nonlocal neighbors at different locations into a data matrix which consists
of similar components. Then we map the nonlocal data matrix into a
high-dimensional feature space by kernel method to capture their nonlinear
structures. Under the assumption that the sparse coeffcients for the nonlocal
data in the kernel space should be low-rank, we impose low-rank constraint on
sparse coding to share similarities among representation coeffcients and remove
outliers in order that stable weights for SR reconstruction can be obtained.
Experimental results demonstrate the advantage of our proposed method in both
visual quality and reconstruction error.
Scalar Arithmetic Multiple Data: Customizable Precision for Deep Neural Networks
Authors: Andrew Anderson, David Gregg
Date: 2018-09-27 00:00:00
Tasks: Quantization

Quantization of weights and activations in Deep Neural Networks (DNNs) is a powerful technique for network compression, and has enjoyed significant attention and success. However, much of the inference-time benefit of quantization is accessible only through the use of customized hardware accelerators or by providing an FPGA implementation of quantized arithmetic. Building on prior work, we show how to construct arbitrary bit-precise signed and unsigned integer operations using a software technique which logically \emph{embeds} a vector architecture with custom bit-width lanes in universally available fixed-width scalar arithmetic. We evaluate our approach on a high-end Intel Haswell processor, and an embedded ARM processor. Our approach yields very fast implementations of bit-precise custom DNN operations, which often match or exceed the performance of operations quantized to the sizes supported in native arithmetic. At the strongest level of quantization, our approach yields a maximum speedup of $\thicksim6\times$ on the Intel platform, and $\thicksim10\times$ on the ARM platform versus quantization to native 8-bit integers.
A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteria
Authors: Yu Zhao, Zhenhui Shi, Jingyang Zhang, Dong Chen, Lixu Gu
Date: 2018-09-27 00:00:00
Tasks: Active Learning, General Classification

Multiple query criteria active learning (MQCAL) methods have a higher
potential performance than conventional active learning methods in which only
one criterion is deployed for sample selection. A central issue related to
MQCAL methods concerns the development of an integration criteria strategy
(ICS) that makes full use of all criteria. The conventional ICS adopted in
relevant research all facilitate the desired effects, but several limitations
still must be addressed. For instance, some of the strategies are not
sufficiently scalable during the design process, and the number and type of
criteria involved are dictated. Thus, it is challenging for the user to
integrate other criteria into the original process unless modifications are
made to the algorithm. Other strategies are too dependent on empirical
parameters, which can only be acquired by experience or cross-validation and
thus lack generality; additionally, these strategies are counter to the
intention of active learning, as samples need to be labeled in the validation
set before the active learning process can begin. To address these limitations,
we propose a novel MQCAL method for classification tasks that employs a third
strategy via weighted rank aggregation. The proposed method serves as a
heuristic means to select high-value samples of high scalability and generality
and is implemented through a three-step process: (1) the transformation of the
sample selection to sample ranking and scoring, (2) the computation of the
self-adaptive weights of each criterion, and (3) the weighted aggregation of
each sample rank list. Ultimately, the sample at the top of the aggregated
ranking list is the most comprehensively valuable and must be labeled. Several
experiments generating 257 wins, 194 ties and 49 losses against other
state-of-the-art MQCALs are conducted to verify that the proposed method can
achieve superior results.
Dropout Distillation for Efficiently Estimating Model Confidence
Authors: Corina Gurau, Alex Bewley, Ingmar Posner
Date: 2018-09-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, model, object-detection, Object Detection

We propose an efficient way to output better calibrated uncertainty scores
from neural networks. The Distilled Dropout Network (DDN) makes standard
(non-Bayesian) neural networks more introspective by adding a new training loss
which prevents them from being overconfident. Our method is more efficient than
Bayesian neural networks or model ensembles which, despite providing more
reliable uncertainty scores, are more cumbersome to train and slower to test.
We evaluate DDN on the the task of image classification on the CIFAR-10 dataset
and show that our calibration results are competitive even when compared to 100
Monte Carlo samples from a dropout network while they also increase the
classification accuracy. We also propose better calibration within the state of
the art Faster R-CNN object detection framework and show, using the COCO
dataset, that DDN helps train better calibrated object detectors.
Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors On an Autonomous Racecar
Authors: Ankit Dhall
Date: 2018-09-27 00:00:00
Tasks: 3D Pose Estimation, Object, object-detection, Object Detection, Pose Estimation

We propose a complete pipeline that allows object detection and
simultaneously estimate the pose of these multiple object instances using just
a single image. A novel "keypoint regression" scheme with a cross-ratio term is
introduced that exploits prior information about the object's shape and size to
regress and find specific feature points. Further, a priori 3D information
about the object is used to match 2D-3D correspondences and accurately estimate
object positions up to a distance of 15m. A detailed discussion of the results
and an in-depth analysis of the pipeline is presented. The pipeline runs
efficiently on a low-powered Jetson TX2 and is deployed as part of the
perception pipeline on a real-time autonomous vehicle cruising at a top speed
of 54 km/hr.
Towards a classification of Lindenmayer systems
Authors: Diego Krivochen, Douglas Saddy
Date: 2018-09-27 00:00:00
Tasks: Classification, General Classification

In this paper we will attempt to classify Lindenmayer systems based on properties of sets of rules and the kind of strings those rules generate. This classification will be referred to as a parametrization of the L-space: the L-space is the phase space in which all possible L-developments are represented. This space is infinite, because there is no halting algorithm for L-grammars; but it is also subjected to hard conditions, because there are grammars and developments which are not possible states of an L-system: a very well-known example is the space of normal grammars. Just as the space of normal grammars is parametrized into Regular, Context-Free, Context-Sensitive, and Unrestricted (with proper containment relations holding among them; see Chomsky, 1959: Theorem 1), we contend here that the L-space is a very rich landscape of grammars which cluster into kinds that are not mutually translatable.
Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery
Authors: Chao Wang, Robert Plemmons, Sudhakar Prasad, Raymond Chan, Mila Nikolova
Date: 2018-09-27 00:00:00

An optical imager that exploits off-center image rotation to encode both the
lateral and depth coordinates of point sources in a single snapshot can perform
3D localization and tracking of space debris. When actively illuminated,
unresolved space debris, which can be regarded as a swarm of point sources, can
scatter a fraction of laser irradiance back into the imaging sensor.
Determining the source locations and fluxes is a large-scale sparse 3D inverse
problem, for which we have developed efficient and effective algorithms based
on sparse recovery using non-convex optimization. Numerical simulations
illustrate the efficiency and stability of the algorithms.
Physics Informed Topology Learning in Networks of Linear Dynamical Systems
Authors: Saurav Talukdar, Deepjyoti Deka, Harish Doddi, Donatello Materassi, Misha Chertkov, Murti V. Salapaka
Date: 2018-09-27 00:00:00

Learning influence pathways of a network of dynamically related processes
from observations is of considerable importance in many disciplines. In this
article, influence networks of agents which interact dynamically via linear
dependencies are considered. An algorithm for the reconstruction of the
topology of interaction based on multivariate Wiener filtering is analyzed. It
is shown that for a vast and important class of interactions, that respect flow
conservation, the topology of the interactions can be exactly recovered. The
class of problems where reconstruction is guaranteed to be exact includes power
distribution networks, dynamic thermal networks and consensus networks. The
efficacy of the approach is illustrated through simulation and experiments on
consensus networks, IEEE power distribution networks and thermal dynamics of
buildings.
Consistency and Variation in Kernel Neural Ranking Model
Authors: Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi, Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu
Date: 2018-09-27 00:00:00
Tasks: model, Word Embeddings

This paper studies the consistency of the kernel-based neural ranking model
K-NRM, a recent state-of-the-art neural IR model, which is important for
reproducible research and deployment in the industry. We find that K-NRM has
low variance on relevance-based metrics across experimental trials. In spite of
this low variance in overall performance, different trials produce different
document rankings for individual queries. The main source of variance in our
experiments was found to be different latent matching patterns captured by
K-NRM. In the IR-customized word embeddings learned by K-NRM, the
query-document word pairs follow two different matching patterns that are
equally effective, but align word pairs differently in the embedding space. The
different latent matching patterns enable a simple yet effective approach to
construct ensemble rankers, which improve K-NRM's effectiveness and
generalization abilities.
The Convergence of Sparsified Gradient Methods
Authors: Dan Alistarh, Torsten Hoefler, Mikael Johansson, Sarit Khirirat, Nikola Konstantinov, Cédric Renggli
Date: 2018-09-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Quantization

Distributed training of massive machine learning models, in particular deep
neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace.
Several families of communication-reduction methods, such as quantization,
large-batch methods, and gradient sparsification, have been proposed. To date,
gradient sparsification methods - where each node sorts gradients by magnitude,
and only communicates a subset of the components, accumulating the rest locally
- are known to yield some of the largest practical gains. Such methods can
reduce the amount of communication per step by up to three orders of magnitude,
while preserving model accuracy. Yet, this family of methods currently has no
theoretical justification.
  This is the question we address in this paper. We prove that, under analytic
assumptions, sparsifying gradients by magnitude with local error correction
provides convergence guarantees, for both convex and non-convex smooth
objectives, for data-parallel SGD. The main insight is that sparsification
methods implicitly maintain bounds on the maximum impact of stale updates,
thanks to selection by magnitude. Our analysis and empirical validation also
reveal that these methods do require analytical conditions to converge well,
justifying existing heuristics.
A rotation-equivariant convolutional neural network model of primary visual cortex
Authors: Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Andreas S. Tolias, Matthias Bethge
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5

Classical models describe primary visual cortex (V1) as a filter bank of
orientation-selective linear-nonlinear (LN) or energy models, but these models
fail to predict neural responses to natural stimuli accurately. Recent work
shows that models based on convolutional neural networks (CNNs) lead to much
more accurate predictions, but it remains unclear which features are extracted
by V1 neurons beyond orientation selectivity and phase invariance. Here we work
towards systematically studying V1 computations by categorizing neurons into
groups that perform similar computations. We present a framework to identify
common features independent of individual neurons' orientation selectivity by
using a rotation-equivariant convolutional neural network, which automatically
extracts every feature at multiple different orientations. We fit this model to
responses of a population of 6000 neurons to natural images recorded in mouse
primary visual cortex using two-photon imaging. We show that our
rotation-equivariant network not only outperforms a regular CNN with the same
number of feature maps, but also reveals a number of common features shared by
many V1 neurons, which deviate from the typical textbook idea of V1 as a bank
of Gabor filters. Our findings are a first step towards a powerful new tool to
study the nonlinear computations in V1.