pwc-archive/papers-with-abstracts huggingface dataset:
Combined Image- and World-Space Tracking in Traffic Scenes
Authors: Aljosa Osep, Wolfgang Mehner, Markus Mathias, Bastian Leibe
Date: 2018-09-19 00:00:00
Tasks: Multiple Object Tracking, Self-Driving Cars

Tracking in urban street scenes plays a central role in autonomous systems
such as self-driving cars. Most of the current vision-based tracking methods
perform tracking in the image domain. Other approaches, eg based on LIDAR and
radar, track purely in 3D. While some vision-based tracking methods invoke 3D
information in parts of their pipeline, and some 3D-based methods utilize
image-based information in components of their approach, we propose to use
image- and world-space information jointly throughout our method. We present
our tracking pipeline as a 3D extension of image-based tracking. From enhancing
the detections with 3D measurements to the reported positions of every tracked
object, we use world-space 3D information at every stage of processing. We
accomplish this by our novel coupled 2D-3D Kalman filter, combined with a
conceptually clean and extendable hypothesize-and-select framework. Our
approach matches the current state-of-the-art on the official KITTI benchmark,
which performs evaluation in the 2D image domain only. Further experiments show
significant improvements in 3D localization precision by enabling our coupled
2D-3D tracking.
A Generalized Representer Theorem for Hilbert Space - Valued Functions
Authors: Sanket Diwale, Colin Jones
Date: 2018-09-19 00:00:00
Tasks: Decision Making, regression

The necessary and sufficient conditions for existence of a generalized
representer theorem are presented for learning Hilbert space-valued functions.
Representer theorems involving explicit basis functions and Reproducing Kernels
are a common occurrence in various machine learning algorithms like generalized
least squares, support vector machines, Gaussian process regression and kernel
based deep neural networks to name a few. Due to the more general structure of
the underlying variational problems, the theory is also relevant to other
application areas like optimal control, signal processing and decision making.
We present the generalized representer as a unified view for supervised and
semi-supervised learning methods, using the theory of linear operators and
subspace valued maps. The implications of the theorem are presented with
examples of multi input-multi output regression, kernel based deep neural
networks, stochastic regression and sparsity learning problems as being special
cases in this unified view.
Autonomous Driving System Design for Formula Student Driverless Racecar
Authors: Hanqing Tian, Jun Ni, Jibin Hu
Date: 2018-09-19 00:00:00
Tasks: Autonomous Driving

This paper summarizes the work of building the autonomous system including
detection system and path tracking controller for a formula student autonomous
racecar. A LIDAR-vision cooperating method of detecting traffic cone which is
used as track mark is proposed. Detection algorithm of the racecar also
implements a precise and high rate localization method which combines the
GPS-INS data and LIDAR odometry. Besides, a track map including the location
and color information of the cones is built simultaneously. Finally, the system
and vehicle performance on a closed loop track is tested. This paper also
briefly introduces the Formula Student Autonomous Competition (FSAC) in 2017.
Deep Hybrid Scattering Image Learning
Authors: Mu Yang, Zheng-Hao Liu, Ze-Di Cheng, Jin-Shi Xu, Chuan-Feng Li, Guang-Can Guo
Date: 2018-09-19 00:00:00

A well-trained deep neural network is shown to gain capability of
simultaneously restoring two kinds of images, which are completely destroyed by
two distinct scattering medias respectively. The network, based on the U-net
architecture, can be trained by blended dataset of speckles-reference images
pairs. We experimentally demonstrate the power of the network in reconstructing
images which are strongly diffused by glass diffuser or multi-mode fiber. The
learning model further shows good generalization ability to reconstruct images
that are distinguished from the training dataset. Our work facilitates the
study of optical transmission and expands machine learning's application in
optics.
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Authors: Shang-Yu Su, Yun-Nung Chen
Date: 2018-09-19 00:00:00
Tasks: Decoder, Sentence, Text Generation

Natural language generation (NLG) is a critical component in spoken dialogue
system, which can be divided into two phases: (1) sentence planning: deciding
the overall sentence structure, (2) surface realization: determining specific
word forms and flattening the sentence structure into a string. With the rise
of deep learning, most modern NLG models are based on a sequence-to-sequence
(seq2seq) model, which basically contains an encoder-decoder structure; these
NLG models generate sentences from scratch by jointly optimizing sentence
planning and surface realization. However, such simple encoder-decoder
architecture usually fail to generate complex and long sentences, because the
decoder has difficulty learning all grammar and diction knowledge well. This
paper introduces an NLG model with a hierarchical attentional decoder, where
the hierarchy focuses on leveraging linguistic knowledge in a specific order.
The experiments show that the proposed method significantly outperforms the
traditional seq2seq model with a smaller model size, and the design of the
hierarchical attentional decoder can be applied to various NLG systems.
Furthermore, different generation strategies based on linguistic patterns are
investigated and analyzed in order to guide future NLG research work.
Fighting Redundancy and Model Decay with Embeddings
Authors: Dan Shiebler, Luca Belli, Jay Baxter, Hanchen Xiong, Abhishek Tayal
Date: 2018-09-18 00:00:00
Tasks: model

Every day, hundreds of millions of new Tweets containing over 40 languages of
ever-shifting vernacular flow through Twitter. Models that attempt to extract
insight from this firehose of information must face the torrential covariate
shift that is endemic to the Twitter platform. While regularly-retrained
algorithms can maintain performance in the face of this shift, fixed model
features that fail to represent new trends and tokens can quickly become stale,
resulting in performance degradation. To mitigate this problem we employ
learned features, or embedding models, that can efficiently represent the most
relevant aspects of a data distribution. Sharing these embedding models across
teams can also reduce redundancy and multiplicatively increase cross-team
modeling productivity. In this paper, we detail the commoditized tools,
algorithms and pipelines that we have developed and are developing at Twitter
to regularly generate high quality, up-to-date embeddings and share them
broadly across the company.
An Efficient Approximation Algorithm for Multi-criteria Indoor Route Planning Queries
Authors: Chaluka Salgado, Muhammad Aamir Cheema, David Taniar
Date: 2018-09-18 00:00:00

A route planning query has many real-world applications and has been studied
extensively in outdoor spaces such as road networks or Euclidean space. Despite
its many applications in indoor venues (e.g., shopping centres, libraries,
airports), almost all existing studies are specifically designed for outdoor
spaces and do not take into account unique properties of the indoor spaces such
as hallways, stairs, escalators, rooms etc. We identify this research gap and
formally define the problem of category aware multi-criteria route planning
query, denoted by CAM, which returns the optimal route from an indoor source
point to an indoor target point that passes through at least one indoor point
from each given category while minimizing the total cost of the route in terms
of travel distance and other relevant attributes. We show that CAM query is
NP-hard. Based on a novel dominance-based pruning, we propose an efficient
algorithm which generates high-quality results. We provide an extensive
experimental study conducted on the largest shopping centre in Australia and
compare our algorithm with alternative approaches. The experiments demonstrate
that our algorithm is highly efficient and produces quality results.
Alchemical Response Parameters from an Analytical Model of Molecular Binding
Authors: Emilio Gallicchio
Date: 2017-07-03 00:00:00

We present a parameterized analytical model of alchemical molecular binding.
The model describes accurately the free energy profiles of linear
single-decoupling alchemical binding free energy calculations. The parameters
of the model, which are physically motivated, are obtained by fitting model
predictions to numerical simulations. The validity of the model has been
assessed on a set of host-guest complexes. The model faithfully reproduces both
the binding free energy profiles and the probability densities of the
perturbation energy as a function of the alchemical progress parameter
$\lambda$. The model offers a rationalization for the characteristic shape of
the free energy profiles. The parameters obtained from the model are
potentially useful descriptors of the association equilibrium of molecular
complexes.
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network
Authors: Rohit Pardasani, Utkarsh Shreemali
Date: 2018-09-21 00:00:00
Tasks: Deep Learning, Denoising, Image Denoising, Image Super-Resolution, Super-Resolution

Image super-resolution and denoising are two important tasks in image
processing that can lead to improvement in image quality. Image
super-resolution is the task of mapping a low resolution image to a high
resolution image whereas denoising is the task of learning a clean image from a
noisy input. We propose and train a single deep learning network that we term
as SuRDCNN (super-resolution and denoising convolutional neural network), to
perform these two tasks simultaneously . Our model nearly replicates the
architecture of existing state-of-the-art deep learning models for
super-resolution and denoising. We use the proven strategy of residual
learning, as supported by state-of-the-art networks in this domain. Our trained
SuRDCNN is capable of super-resolving image in the presence of Gaussian noise,
Poisson noise or any random combination of both of these noises.
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies
Authors: Syed Yusha Kareem, Luca Buoncompagni, Fulvio Mastrogiovanni
Date: 2018-09-21 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Aging population ratios are rising significantly. Meanwhile, smart home based
health monitoring services are evolving rapidly to become a viable alternative
to traditional healthcare solutions. Such services can augment qualitative
analyses done by gerontologists with quantitative data. Hence, the recognition
of Activities of Daily Living (ADL) has become an active domain of research in
recent times. For a system to perform human activity recognition in a
real-world environment, multiple requirements exist, such as scalability,
robustness, ability to deal with uncertainty (e.g., missing sensor data), to
operate with multi-occupants and to take into account their privacy and
security. This paper attempts to address the requirements of scalability and
robustness, by describing a reasoning mechanism based on modular spatial and/or
temporal context models as a network of ontologies. The reasoning mechanism has
been implemented in a smart home system referred to as Arianna+. The paper
presents and discusses a use case, and experiments are performed on a simulated
dataset, to showcase Arianna+'s modularity feature, internal working, and
computational performance. Results indicate scalability and robustness for
human activity recognition processes.
Towards Exploiting Background Knowledge for Building Conversation Systems
Authors: Nikita Moghe, Siddhartha Arora, Suman Banerjee, Mitesh M. Khapra
Date: 2018-09-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles

Existing dialog datasets contain a sequence of utterances and responses
without any explicit background knowledge associated with them. This has
resulted in the development of models which treat conversation as a
sequence-to-sequence generation task i.e, given a sequence of utterances
generate the response sequence). This is not only an overly simplistic view of
conversation but it is also emphatically different from the way humans converse
by heavily relying on their background knowledge about the topic (as opposed to
simply relying on the previous sequence of utterances). For example, it is
common for humans to (involuntarily) produce utterances which are copied or
suitably modified from background articles they have read about the topic. To
facilitate the development of such natural conversation models which mimic the
human process of conversing, we create a new dataset containing movie chats
wherein each response is explicitly generated by copying and/or modifying
sentences from unstructured background knowledge such as plots, comments and
reviews about the movie. We establish baseline results on this dataset (90K
utterances from 9K conversations) using three different models: (i) pure
generation based models which ignore the background knowledge (ii) generation
based models which learn to copy information from the background knowledge when
required and (iii) span prediction based models which predict the appropriate
response span in the background knowledge.
High-Temperature Structure Detection in Ferromagnets
Authors: Yuan Cao, Matey Neykov, Han Liu
Date: 2018-09-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper studies structure detection problems in high temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph is empty, i.e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure. We give matching upper and lower minimax bounds under which testing this problem is possible/impossible respectively. Our results reveal that a key quantity called graph arboricity drives the testability of the problem. On the computational front, under a conjecture of the computational hardness of sparse principal component analysis, we prove that, unless the signal is strong enough, there are no polynomial time tests which are capable of testing this problem. In order to prove this result we exhibit a way to give sharp inequalities for the even moments of sums of i.i.d. Rademacher random variables which may be of independent interest.
Low rank methods for multiple network alignment
Authors: Huda Nassar, Georgios Kollias, Ananth Grama, David F. Gleich
Date: 2018-09-21 00:00:00

Multiple network alignment is the problem of identifying similar and related
regions in a given set of networks. While there are a large number of effective
techniques for pairwise problems with two networks that scale in terms of
edges, these cannot be readily extended to align multiple networks as the
computational complexity will tend to grow exponentially with the number of
networks.In this paper we introduce a new multiple network alignment algorithm
and framework that is effective at aligning thousands of networks with
thousands of nodes. The key enabling technique of our algorithm is identifying
an exact and easy to compute low-rank tensor structure inside of a principled
heuristic procedure for pairwise network alignment called IsoRank. This can be
combined with a new algorithm for $k$-dimensional matching problems on low-rank
tensors to produce the alignment. We demonstrate results on synthetic and
real-world problems that show our technique (i) is as good or better in terms
of quality as existing methods, when they work on small problems, while running
considerably faster and (ii) is able to scale to aligning a number of networks
unreachable by current methods. We show in this paper that our method is the
realistic choice for aligning multiple networks when no prior information is
present.
Analysis of Irregular Spatial Data with Machine Learning: Classification of Building Patterns with a Graph Convolutional Neural Network
Authors: Xiongfeng Yan, Tinghua Ai
Date: 2018-09-21 00:00:00
Tasks: General Classification

Machine learning methods such as convolutional neural networks (CNNs) are
becoming an integral part of scientific research in many disciplines, spatial
vector data often fail to be analyzed using these powerful learning methods
because of its irregularities. With the aid of graph Fourier transform and
convolution theorem, it is possible to convert the convolution as a point-wise
product in Fourier domain and construct a learning architecture of CNN on graph
for the analysis task of irregular spatial data. In this study, we used the
classification task of building patterns as a case study to test this method,
and experiments showed that this method has achieved outstanding results in
identifying regular and irregular patterns, and has significantly improved in
comparing with other methods.
Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection
Authors: Lev Konstantinovskiy, Oliver Price, Mevan Babakar, Arkaitz Zubiaga
Date: 2018-09-21 00:00:00
Tasks: Sentence

In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.
Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation
Authors: Seyed Raein Hashemi, Sanjay P. Prabhu, Simon K. Warfield, Ali Gholipour
Date: 2018-09-21 00:00:00
Tasks: Image Segmentation, Infant Brain Mri Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

The most recent fast and accurate image segmentation methods are built upon
fully convolutional deep neural networks. In this paper, we propose new deep
learning strategies for DenseNets to improve segmenting images with subtle
differences in intensity values and features. We aim to segment brain tissue on
infant brain MRI at about 6 months of age where white matter and gray matter of
the developing brain show similar T1 and T2 relaxation times, thus appear to
have similar intensity values on both T1- and T2-weighted MRI scans. Brain
tissue segmentation at this age is, therefore, very challenging. To this end,
we propose an exclusive multi-label training strategy to segment the mutually
exclusive brain tissues with similarity loss functions that automatically
balance the training based on class prevalence. Using our proposed training
strategy based on similarity loss functions and patch prediction fusion we
decrease the number of parameters in the network, reduce the complexity of the
training process focusing the attention on less number of tasks, while
mitigating the effects of data imbalance between labels and inaccuracies near
patch borders. By taking advantage of these strategies we were able to perform
fast image segmentation (90 seconds per 3D volume), using a network with less
parameters than many state-of-the-art networks, overcoming issues such as
3Dvs2D training and large vs small patch size selection, while achieving the
top performance in segmenting brain tissue among all methods tested in first
and second round submissions of the isointense infant brain MRI segmentation
(iSeg) challenge according to the official challenge test results. Our proposed
strategy improves the training process through balanced training and by
reducing its complexity while providing a trained model that works for any size
input image and is fast and more accurate than many state-of-the-art methods.
Simulator Calibration under Covariate Shift with Kernels
Authors: Keiichi Kisamori, Motonobu Kanagawa, Keisuke Yamazaki
Date: 2018-09-21 00:00:00
Tasks: Bayesian Inference

We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift. Covariate shift is the situation where input distributions for training and test are different, and ubiquitous in applications of simulations. Our approach is based on Bayesian inference with kernel mean embedding of distributions, and on the use of an importance-weighted reproducing kernel for covariate shift adaptation. We provide a theoretical analysis for the proposed method, including a novel theoretical result for conditional mean embedding, as well as empirical investigations suggesting its effectiveness in practice. The experiments include calibration of a widely used simulator for industrial manufacturing processes, where we also demonstrate how the proposed method may be useful for sensitivity analysis of model parameters.
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Authors: Etienne Boursier, Vianney Perchet
Date: 2018-09-21 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-Armed Bandits

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centralized one, contradicting the existing lower bounds for that problem. This is possible by "hacking" the standard model by constructing a communication protocol between players that deliberately enforces collisions, allowing them to share their information at a negligible cost. This motivates the introduction of a more appropriate dynamic setting without sensing, where similar communication protocols are no longer possible. However, we show that the logarithmic growth of the regret is still achievable for this model with a new algorithm.
Predicting the Usefulness of Amazon Reviews Using Off-The-Shelf Argumentation Mining
Authors: Marco Passon, Marco Lippi, Giuseppe Serra, Carlo Tasso
Date: 2018-09-21 00:00:00
Proceeding: WS 2018 11

Internet users generate content at unprecedented rates. Building intelligent
systems capable of discriminating useful content within this ocean of
information is thus becoming a urgent need. In this paper, we aim to predict
the usefulness of Amazon reviews, and to do this we exploit features coming
from an off-the-shelf argumentation mining system. We argue that the usefulness
of a review, in fact, is strictly related to its argumentative content, whereas
the use of an already trained system avoids the costly need of relabeling a
novel dataset. Results obtained on a large publicly available corpus support
this hypothesis.