pwc-archive/papers-with-abstracts huggingface dataset:
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
Authors: Salman Razzaki, Adam Baker, Yura Perov, Katherine Middleton, Janie Baxter, Daniel Mullarkey, Davinder Sangar, Michael Taliercio, Mobasher Butt, Azeem Majeed, Arnold DoRosario, Megan Mahoney, Saurabh Johri
Date: 2018-06-27 00:00:00
Tasks: Diagnostic

Online symptom checkers have significant potential to improve patient care,
however their reliability and accuracy remain variable. We hypothesised that an
artificial intelligence (AI) powered triage and diagnostic system would compare
favourably with human doctors with respect to triage and diagnostic accuracy.
We performed a prospective validation study of the accuracy and safety of an AI
powered triage and diagnostic system. Identical cases were evaluated by both an
AI system and human doctors. Differential diagnoses and triage outcomes were
evaluated by an independent judge, who was blinded from knowing the source (AI
system or human doctor) of the outcomes. Independently of these cases,
vignettes from publicly available resources were also assessed to provide a
benchmark to previous studies and the diagnostic component of the MRCGP exam.
Overall we found that the Babylon AI powered Triage and Diagnostic System was
able to identify the condition modelled by a clinical vignette with accuracy
comparable to human doctors (in terms of precision and recall). In addition, we
found that the triage advice recommended by the AI System was, on average,
safer than that of human doctors, when compared to the ranges of acceptable
triage provided by independent expert judges, with only a minimal reduction in
appropriateness.
A Hybrid Framework for Tumor Saliency Estimation
Authors: Fei Xu, Min Xian, Yingtao Zhang, Kuan Huang, H. D. Cheng, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
Date: 2018-06-27 00:00:00
Tasks: Saliency Prediction, Segmentation, Tumor Segmentation

Automatic tumor segmentation of breast ultrasound (BUS) image is quite
challenging due to the complicated anatomic structure of breast and poor image
quality. Most tumor segmentation approaches achieve good performance on BUS
images collected in controlled settings; however, the performance degrades
greatly with BUS images from different sources. Tumor saliency estimation (TSE)
has attracted increasing attention to solving the problem by modeling
radiologists' attention mechanism. In this paper, we propose a novel hybrid
framework for TSE, which integrates both high-level domain-knowledge and robust
low-level saliency assumptions and can overcome drawbacks caused by direct
mapping in traditional TSE approaches. The new framework integrated the
Neutro-Connectedness (NC) map, the adaptive-center, the correlation and the
layer structure-based weighted map. The experimental results demonstrate that
the proposed approach outperforms state-of-the-art TSE methods.
Dynamic texture analysis with diffusion in networks
Authors: Lucas C. Ribas, Wesley N. Goncalves, Odemir M. Bruno
Date: 2018-06-27 00:00:00
Tasks: Fire Detection, General Classification, Texture Classification

Dynamic texture is a field of research that has gained considerable interest
from computer vision community due to the explosive growth of multimedia
databases. In addition, dynamic texture is present in a wide range of videos,
which makes it very important in expert systems based on videos such as medical
systems, traffic monitoring systems, forest fire detection system, among
others. In this paper, a new method for dynamic texture characterization based
on diffusion in directed networks is proposed. The dynamic texture is modeled
as a directed network. The method consists in the analysis of the dynamic of
this network after a series of graph cut transformations based on the edge
weights. For each network transformation, the activity for each vertex is
estimated. The activity is the relative frequency that one vertex is visited by
random walks in balance. Then, texture descriptor is constructed by
concatenating the activity histograms. The main contributions of this paper are
the use of directed network modeling and diffusion in network to dynamic
texture characterization. These tend to provide better performance in dynamic
textures classification. Experiments with rotation and interference of the
motion pattern were conducted in order to demonstrate the robustness of the
method. The proposed approach is compared to other dynamic texture methods on
two very well know dynamic texture database and on traffic condition
classification, and outperform in most of the cases.
Generalized chart constraints for efficient PCFG and TAG parsing
Authors: Stefan Grünewald, Sophie Henning, Alexander Koller
Date: 2018-06-27 00:00:00
Proceeding: ACL 2018 7
Tasks: TAG

Chart constraints, which specify at which string positions a constituent may
begin or end, have been shown to speed up chart parsers for PCFGs. We
generalize chart constraints to more expressive grammar formalisms and describe
a neural tagger which predicts chart constraints at very high precision. Our
constraints accelerate both PCFG and TAG parsing, and combine effectively with
other pruning techniques (coarse-to-fine and supertagging) for an overall
speedup of two orders of magnitude, while improving accuracy.
Uncoupled isotonic regression via minimum Wasserstein deconvolution
Authors: Philippe Rigollet, Jonathan Weed
Date: 2018-06-27 00:00:00
Tasks: regression

Isotonic regression is a standard problem in shape-constrained estimation
where the goal is to estimate an unknown nondecreasing regression function $f$
from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots
n$. While this problem is well understood both statistically and
computationally, much less is known about its uncoupled counterpart where one
is given only the unordered sets $\{x_1, \ldots, x_n\}$ and $\{y_1, \ldots,
y_n\}$. In this work, we leverage tools from optimal transport theory to derive
minimax rates under weak moments conditions on $y_i$ and to give an efficient
algorithm achieving optimal rates. Both upper and lower bounds employ
moment-matching arguments that are also pertinent to learning mixtures of
distributions and deconvolution.
A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update
Authors: Yingyu Zhang, Bing Zeng
Date: 2018-06-27 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Existing studies have shown that the conventional multi-objective
evolutionary algorithms (MOEAs) based on decomposition may lose the population
diversity when solving some many-objective optimization problems. In this
paper, a simple decomposition-based MOEA with local iterative update (LIU) is
proposed. The LIU strategy has two features that are expected to drive the
population to approximate the Pareto Front with good distribution. One is that
only the worst solution in the current neighborhood is swapped out by the newly
generated offspring, preventing the population from being occupied by copies of
a few individuals. The other is that its iterative process helps to assign
better solutions to subproblems, which is beneficial to make full use of the
similarity of solutions to neighboring subproblems and explore local areas in
the search space. In addition, the time complexity of the proposed algorithm is
the same as that of MOEA/D, and lower than that of other known MOEAs, since it
considers only individuals within the current neighborhood at each update. The
algorithm is compared with several of the best MOEAs on problems chosen from
two famous test suites DTLZ and WFG. Experimental results demonstrate that only
a handful of running instances of the algorithm on DTLZ4 lose their population
diversity. What's more, the algorithm wins in most of the test instances in
terms of both running time and solution quality, indicating that it is very
effective in solving MaOPs.
Record Linkage to Match Customer Names: A Probabilistic Approach
Authors: Bahare Fatemi, Seyed Mehran Kazemi, David Poole
Date: 2018-06-26 00:00:00

Consider the following problem: given a database of records indexed by names
(e.g., name of companies, restaurants, businesses, or universities) and a new
name, determine whether the new name is in the database, and if so, which
record it refers to. This problem is an instance of record linkage problem and
is a challenging problem because people do not consistently use the official
name, but use abbreviations, synonyms, different order of terms, different
spelling of terms, short form of terms, and the name can contain typos or
spacing issues. We provide a probabilistic model using relational logistic
regression to find the probability of each record in the database being the
desired record for a given query and find the best record(s) with respect to
the probabilities. Building on term-matching and translational approaches for
search, our model addresses many of the aforementioned challenges and provides
good results when existing baselines fail. Using the probabilities outputted by
the model, we can automate the search process for a portion of queries whose
desired documents get a probability higher than a trust threshold. We evaluate
our model on a large real-world dataset from a telecommunications company and
compare it to several state-of-the-art baselines. The obtained results show
that our model is a promising probabilistic model for record linkage for names.
We also test if the knowledge learned by our model on one domain can be
effectively transferred to a new domain. For this purpose, we test our model on
an unseen test set from the business names of the secondString dataset.
Promising results show that our model can be effectively applied to unseen
datasets. Finally, we study the sensitivity of our model to the statistics of
datasets.
Multimodal Image Denoising based on Coupled Dictionary Learning
Authors: Pingfan Song, Miguel R. D. Rodrigues
Date: 2018-06-26 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

In this paper, we propose a new multimodal image denoising approach to
attenuate white Gaussian additive noise in a given image modality under the aid
of a guidance image modality. The proposed coupled image denoising approach
consists of two stages: coupled sparse coding and reconstruction. The first
stage performs joint sparse transform for multimodal images with respect to a
group of learned coupled dictionaries, followed by a shrinkage operation on the
sparse representations. Then, in the second stage, the shrunken
representations, together with coupled dictionaries, contribute to the
reconstruction of the denoised image via an inverse transform. The proposed
denoising scheme demonstrates the capability to capture both the common and
distinct features of different data modalities. This capability makes our
approach more robust to inconsistencies between the guidance and the target
images, thereby overcoming drawbacks such as the texture copying artifacts.
Experiments on real multimodal images demonstrate that the proposed approach is
able to better employ guidance information to bring notable benefits in the
image denoising task with respect to the state-of-the-art.
ProbFuse: A Probabilistic Approach to Data Fusion
Authors: David Lillis, Fergus Toolan, Rem Collier, John Dunnion
Date: 2014-09-30 00:00:00
Tasks: Retrieval

Data fusion is the combination of the results of independent searches on a
document collection into one single output result set. It has been shown in the
past that this can greatly improve retrieval effectiveness over that of the
individual results.
  This paper presents probFuse, a probabilistic approach to data fusion.
ProbFuse assumes that the performance of the individual input systems on a
number of training queries is indicative of their future performance. The fused
result set is based on probabilities of relevance calculated during this
training process. Retrieval experiments using data from the TREC ad hoc
collection demonstrate that probFuse achieves results superior to that of the
popular CombMNZ fusion algorithm.
A Software-Defined Channel Sounder for Industrial Environments with Fast Time Variance
Authors: Niels Hendrik Fliedner, Dimitri Block, Uwe Meier
Date: 2018-05-03 00:00:00

Novel industrial wireless applications require wideband, real-time channel
characterization due to complex multipath propagation. Rapid machine motion
leads to fast time variance of the channel's reflective behavior, which must be
captured for radio channel characterization. Additionally, inhomogeneous radio
channels demand highly flexible measurements. Existing approaches for radio
channel measurements either lack flexibility or wide-band, real-time
performance with fast time variance. In this paper, we propose a correlative
channel sounding approach utilizing a software-defined architecture. The
approach enables real-time, wide-band measurements with fast time variance
immune to active interference. The desired performance is validated with a
demanding industrial application example.
Nonparametric learning from Bayesian models with randomized objective functions
Authors: S. P. Lyddon, S. G. Walker, C. C. Holmes
Date: 2018-06-29 00:00:00
Proceeding: NeurIPS 2018

Bayesian learning is built on an assumption that the model space contains a
true reflection of the data generating mechanism. This assumption is
problematic, particularly in complex data environments. Here we present a
Bayesian nonparametric approach to learning that makes use of statistical
models, but does not assume that the model is true. Our approach has provably
better properties than using a parametric model and admits a Monte Carlo
sampling scheme that can afford massive scalability on modern computer
architectures. The model-based aspect of learning is particularly attractive
for regularizing nonparametric inference when the sample size is small, and
also for correcting approximate approaches such as variational Bayes (VB). We
demonstrate the approach on a number of examples including VB classifiers and
Bayesian random forests.
Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation
Authors: Yikang Li, Wanli Ouyang, Bolei Zhou, Jianping Shi, Chao Zhang, Xiaogang Wang
Date: 2018-06-29 00:00:00
Proceeding: ECCV 2018 9
Tasks: Clustering, Graph Generation, Relationship Detection, Scene Graph Generation, Visual Relationship Detection

Generating scene graph to describe all the relations inside an image gains
increasing interests these years. However, most of the previous methods use
complicated structures with slow inference speed or rely on the external data,
which limits the usage of the model in real-life scenarios. To improve the
efficiency of scene graph generation, we propose a subgraph-based connection
graph to concisely represent the scene graph during the inference. A bottom-up
clustering method is first used to factorize the entire scene graph into
subgraphs, where each subgraph contains several objects and a subset of their
relationships. By replacing the numerous relationship representations of the
scene graph with fewer subgraph and object features, the computation in the
intermediate stage is significantly reduced. In addition, spatial information
is maintained by the subgraph features, which is leveraged by our proposed
Spatial-weighted Message Passing~(SMP) structure and Spatial-sensitive Relation
Inference~(SRI) module to facilitate the relationship recognition. On the
recent Visual Relationship Detection and Visual Genome datasets, our method
outperforms the state-of-the-art method in both accuracy and speed.
An Exact Quantized Decentralized Gradient Descent Algorithm
Authors: Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Date: 2018-06-29 00:00:00
Tasks: Distributed Optimization, Quantization

We consider the problem of decentralized consensus optimization, where the sum of $n$ smooth and strongly convex functions are minimized over $n$ distributed agents that form a connected network. In particular, we consider the case that the communicated local decision variables among nodes are quantized in order to alleviate the communication bottleneck in distributed optimization. We propose the Quantized Decentralized Gradient Descent (QDGD) algorithm, in which nodes update their local decision variables by combining the quantized information received from their neighbors with their local information. We prove that under standard strong convexity and smoothness assumptions for the objective function, QDGD achieves a vanishing mean solution error under customary conditions for quantizers. To the best of our knowledge, this is the first algorithm that achieves vanishing consensus error in the presence of quantization noise. Moreover, we provide simulation results that show tight agreement between our derived theoretical convergence rate and the numerical results.
End-to-end Learning of Multi-sensor 3D Tracking by Detection
Authors: Davi Frossard, Raquel Urtasun
Date: 2018-06-29 00:00:00
Tasks: Multiple Object Tracking

In this paper we propose a novel approach to tracking by detection that can
exploit both cameras as well as LIDAR data to produce very accurate 3D
trajectories. Towards this goal, we formulate the problem as a linear program
that can be solved exactly, and learn convolutional networks for detection as
well as matching in an end-to-end manner. We evaluate our model in the
challenging KITTI dataset and show very competitive results.
TextWorld: A Learning Environment for Text-based Games
Authors: Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan, Ben Kybartas, Tavian Barnes, Emery Fine, James Moore, Ruo Yu Tao, Matthew Hausknecht, Layla El Asri, Mahmoud Adada, Wendy Tay, Adam Trischler
Date: 2018-06-29 00:00:00
Tasks: Reinforcement Learning, text-based games, Transfer Learning

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.
Visual Attention and its Intimate Links to Spatial Cognition
Authors: John K. Tsotsos, Iuliia Kotseruba, Amir Rasouli, Markus D. Solbach
Date: 2018-06-29 00:00:00

It is almost universal to regard attention as the facility that permits an
agent, human or machine, to give priority processing resources to relevant
stimuli while ignoring the irrelevant. The reality of how this might manifest
itself throughout all the forms of perceptual and cognitive processes possessed
by humans, however, is not as clear. Here we examine this reality with a broad
perspective in order to highlight the myriad ways that attentional processes
impact both perception and cognition. The paper concludes by showing two real
world problems that exhibit sufficient complexity to illustrate the ways in
which attention and cognition connect. These then point to new avenues of
research that might illuminate the overall cognitive architecture of spatial
cognition.
Counting to Explore and Generalize in Text-based Games
Authors: Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, Adam Trischler
Date: 2018-06-29 00:00:00
Tasks: text-based games

We propose a recurrent RL agent with an episodic exploration mechanism that
helps discovering good policies in text-based game environments. We show
promising results on a set of generated text-based games of varying difficulty
where the goal is to collect a coin located at the end of a chain of rooms. In
contrast to previous text-based RL approaches, we observe that our agent learns
policies that generalize to unseen games of greater difficulty.
Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade
Authors: Melanie F. Pradier, Viktor Stojkoski, Zoran Utkovski, Ljupco Kocarev, Fernando Perez-Cruz
Date: 2018-06-29 00:00:00

This paper presents a Bayesian nonparametric latent feature model specially
suitable for exploratory analysis of high-dimensional count data. We perform a
non-negative doubly sparse matrix factorization that has two main advantages:
not only we are able to better approximate the row input distributions, but the
inferred topics are also easier to interpret. By combining the three-parameter
and restricted Indian buffet processes into a single prior, we increase the
model flexibility, allowing for a full spectrum of sparse solutions in the
latent space. We demonstrate the usefulness of our approach in the analysis of
countries' economic structure. Compared to other approaches, empirical results
show our model's ability to give easy-to-interpret information and better
capture the underlying sparsity structure of data.
Recognition of Offline Handwritten Devanagari Numerals using Regional Weighted Run Length Features
Authors: Pawan Kumar Singh, Supratim Das, Ram Sarkar, Mita Nasipuri
Date: 2018-06-29 00:00:00

Recognition of handwritten Roman characters and numerals has been extensively
studied in the last few decades and its accuracy reached to a satisfactory
state. But the same cannot be said while talking about the Devanagari script
which is one of most popular script in India. This paper proposes an efficient
digit recognition system for handwritten Devanagari script. The system uses a
novel 196-element Mask Oriented Directional (MOD) features for the recognition
purpose. The methodology is tested using five conventional classifiers on 6000
handwritten digit samples. On applying 3-fold cross-validation scheme, the
proposed system yields the highest recognition accuracy of 95.02% using Support
Vector Machine (SVM) classifier.
Bayesian Counterfactual Risk Minimization
Authors: Ben London, Ted Sandler
Date: 2018-06-29 00:00:00
Tasks: counterfactual

We present a Bayesian view of counterfactual risk minimization (CRM) for offline learning from logged bandit feedback. Using PAC-Bayesian analysis, we derive a new generalization bound for the truncated inverse propensity score estimator. We apply the bound to a class of Bayesian policies, which motivates a novel, potentially data-dependent, regularization technique for CRM. Experimental results indicate that this technique outperforms standard $L_2$ regularization, and that it is competitive with variance regularization while being both simpler to implement and more computationally efficient.
Mammographic Image Enhancement using Digital Image Processing Technique
Authors: Ardymulya Iswardani, Wahyu Hidayat
Date: 2018-06-29 00:00:00
Tasks: Image Enhancement

Abstract PURPOSES this study aims to perform microcalsification detection by
performing image enhancement in mammography image by using transformation of
negative image and histogram equalization. image mammography with .pgm format
changed to. jpg format then processed into negative image result then processed
again using histogram equalization. the results of the image enhancement
process using negative image techniques and equalization histograms are
compared and validated with MSE and PSNR on each mammographic image.
CONCLUSION: Image enhancement process on mammography image can be done, however
there are only some image that have improved quality, this affected by
threshold usage, which have important role to get better visualization on
mammographic image. Keywords-component; Image enhancement, image negative,
histogram equalization, mammographic, breast cancer