pwc-archive/papers-with-abstracts huggingface dataset:
TwitterMancer: Predicting Interactions on Twitter Accurately
Authors: Konstantinos Sotiropoulos, John W. Byers, Polyvios Pratikakis, Charalampos E. Tsourakakis
Date: 2019-04-25 00:00:00
Tasks: Graph Mining

This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or quote interactions between a pair of accounts? Also, how
important is time locality, and which features of interaction patterns are most
important to enable accurate prediction of specific Twitter interactions? Our
empirical study of Twitter interactions contributes initial answers to these
questions.
  We have crawled an extensive dataset of Greek-speaking Twitter accounts and
their follow, quote, retweet, reply interactions over a period of a month.
  We find we can accurately predict many interactions of Twitter users.
Interestingly, the most predictive features vary with the user profiles, and
are not the same across all users.
  For example, for a pair of users that interact with a large number of other
Twitter users, we find that certain "higher-dimensional" triads, i.e., triads
that involve multiple types of interactions, are very informative, whereas for
less active Twitter users, certain in-degrees and out-degrees play a major
role. Finally, we provide various other insights on Twitter user behavior.
  Our code and data are available at https://github.com/twittermancer/.
  Keywords: Graph mining, machine learning, social media, social networks
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Authors: Daniel Lopez-Martinez, Patrick Eschenfeldt, Sassan Ostvar, Myles Ingram, Chin Hur, Rosalind Picard
Date: 2019-04-25 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Opioids are the preferred medications for the treatment of pain in the
intensive care unit. While undertreatment leads to unrelieved pain and poor
clinical outcomes, excessive use of opioids puts patients at risk of
experiencing multiple adverse effects. In this work, we present a sequential
decision making framework for opioid dosing based on deep reinforcement
learning. It provides real-time clinically interpretable dosing
recommendations, personalized according to each patient's evolving pain and
physiological condition. We focus on morphine, one of the most commonly
prescribed opioids. To train and evaluate the model, we used retrospective data
from the publicly available MIMIC-3 database. Our results demonstrate that
reinforcement learning may be used to aid decision making in the intensive care
setting by providing personalized pain management interventions.
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
Authors: Chaoyang Wang, Simon Lucey, Federico Perazzi, Oliver Wang
Date: 2019-04-25 00:00:00
Tasks: Depth Estimation, Depth Prediction

We present a fully data-driven method to compute depth from diverse monocular
video sequences that contain large amounts of non-rigid objects, e.g., people.
In order to learn reconstruction cues for non-rigid scenes, we introduce a new
dataset consisting of stereo videos scraped in-the-wild. This dataset has a
wide variety of scene types, and features large amounts of nonrigid objects,
especially people. From this, we compute disparity maps to be used as
supervision to train our approach. We propose a loss function that allows us to
generate a depth prediction even with unknown camera intrinsics and stereo
baselines in the dataset. We validate the use of large amounts of Internet
video by evaluating our method on existing video datasets with depth
supervision, including SINTEL, and KITTI, and show that our approach
generalizes better to natural scenes.
Learning the Depths of Moving People by Watching Frozen People
Authors: Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, William T. Freeman
Date: 2019-04-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Depth Prediction

We present a method for predicting dense depth in scenarios where both a
monocular camera and people in the scene are freely moving. Existing methods
for recovering depth for dynamic, non-rigid objects from monocular video impose
strong assumptions on the objects' motion and may only recover sparse depth. In
this paper, we take a data-driven approach and learn human depth priors from a
new source of data: thousands of Internet videos of people imitating
mannequins, i.e., freezing in diverse, natural poses, while a hand-held camera
tours the scene. Because people are stationary, training data can be generated
using multi-view stereo reconstruction. At inference time, our method uses
motion parallax cues from the static areas of the scenes to guide the depth
prediction. We demonstrate our method on real-world sequences of complex human
actions captured by a moving hand-held camera, show improvement over
state-of-the-art monocular depth prediction methods, and show various 3D
effects produced using our predicted depth.
Characterization of Glue Variables in CDCL SAT Solving
Authors: Md Solimul Chowdhury, Martin Müller, Jia-Huai You
Date: 2019-04-25 00:00:00

A state-of-the-art criterion to evaluate the importance of a given learned
clause is called Literal Block Distance (LBD) score. It measures the number of
distinct decision levels in a given learned clause. The lower the LBD score of
a learned clause, the better is its quality. The learned clauses with LBD score
of 2, called glue clauses, are known to possess high pruning power which are
never deleted from the clause databases of the modern CDCL SAT solvers. In this
work, we relate glue clauses to decision variables. We call the variables that
appeared in at least one glue clause up to the current search state Glue
Variables. We first show experimentally, by running the state-of-the-art CDCL
SAT solver MapleLCMDist on benchmarks from SAT Competition-2017 and 2018, that
branching decisions with glue variables are categorically more inference and
conflict efficient than nonglue variables. Based on this observation, we
develop a structure aware CDCL variable bumping scheme, which bumps the
activity score of a glue variable based on its appearance count in the glue
clauses that are learned so far by the search. Empirical evaluation shows
effectiveness of the new method over the main track instances from SAT
Competition 2017 and 2018.
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation
Authors: Mayur J. Bency, Ahmed H. Qureshi, Michael C. Yip
Date: 2019-04-25 00:00:00
Tasks: Motion Planning

Fast and efficient path generation is critical for robots operating in
complex environments. This motion planning problem is often performed in a
robot's actuation or configuration space, where popular pathfinding methods
such as A*, RRT*, get exponentially more computationally expensive to execute
as the dimensionality increases or the spaces become more cluttered and
complex. On the other hand, if one were to save the entire set of paths
connecting all pair of locations in the configuration space a priori, one would
run out of memory very quickly. In this work, we introduce a novel way of
producing fast and optimal motion plans for static environments by using a
stepping neural network approach, called OracleNet. OracleNet uses Recurrent
Neural Networks to determine end-to-end trajectories in an iterative manner
that implicitly generates optimal motion plans with minimal loss in performance
in a compact form. The algorithm is straightforward in implementation while
consistently generating near-optimal paths in a single, iterative, end-to-end
roll-out. In practice, OracleNet generally has fixed-time execution regardless
of the configuration space complexity while outperforming popular pathfinding
algorithms in complex environments and higher dimensions
On Learning to Prove
Authors: Daniel Huang
Date: 2019-04-24 00:00:00
Tasks: Model Selection

In this paper, we consider the problem of learning a first-order theorem prover that uses a representation of beliefs in mathematical claims to construct proofs. The inspiration for doing so comes from the practices of human mathematicians where "plausible reasoning" is applied in addition to deductive reasoning to find proofs. Towards this end, we introduce a representation of beliefs that assigns probabilities to the exhaustive and mutually exclusive first-order possibilities found in Hintikka's theory of distributive normal forms. The representation supports Bayesian update, induces a distribution on statements that does not enforce that logically equivalent statements are assigned the same probability, and suggests an embedding of statements into an associated Hilbert space. We then examine conjecturing as model selection and an alternating-turn game of determining consistency. The game is amenable (in principle) to self-play training to learn beliefs and derive a prover that is complete when logical omniscience is attained and sound when beliefs are reasonable. The representation has super-exponential space requirements as a function of quantifier depth so the ideas in this paper should be taken as theoretical. We will comment on how abstractions can be used to control the space requirements at the cost of completeness.
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity
Authors: Minsu Cho, Chinmay Hegde
Date: 2019-04-24 00:00:00
Tasks: BIG-bench Machine Learning, Hyperparameter Optimization

We propose a new algorithm for hyperparameter selection in machine learning
algorithms. The algorithm is a novel modification of Harmonica, a spectral
hyperparameter selection approach using sparse recovery methods. In particular,
we show that a special encoding of hyperparameter space enables a natural
group-sparse recovery formulation, which when coupled with HyperBand (a
multi-armed bandit strategy) leads to improvement over existing hyperparameter
optimization methods such as Successive Halving and Random Search. Experimental
results on image datasets such as CIFAR-10 confirm the benefits of our
approach.
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics
Authors: Mariem Ben Fadhel, Kofi Nyarko
Date: 2019-04-24 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Anomaly detection is the process of finding data points that deviate from a
baseline. In a real-life setting, anomalies are usually unknown or extremely
rare. Moreover, the detection must be accomplished in a timely manner or the
risk of corrupting the system might grow exponentially. In this work, we
propose a two level framework for detecting anomalies in sequences of discrete
elements. First, we assess whether we can obtain enough information from the
statistics collected from the discriminator's layers to discriminate between
out of distribution and in distribution samples. We then build an unsupervised
anomaly detection module based on these statistics. As to augment the data and
keep track of classes of known data, we lean toward a semi-supervised
adversarial learning applied to discrete elements.
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Authors: Muhan Zhang, Shali Jiang, Zhicheng Cui, Roman Garnett, Yixin Chen
Date: 2019-04-24 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Neural Architecture Search, valid

Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study deep generative models for DAGs, and propose a novel DAG variational autoencoder (D-VAE). To encode DAGs into the latent space, we leverage graph neural networks. We propose an asynchronous message passing scheme that allows encoding the computations on DAGs, rather than using existing simultaneous message passing schemes to encode local graph structures. We demonstrate the effectiveness of our proposed DVAE through two tasks: neural architecture search and Bayesian network structure learning. Experiments show that our model not only generates novel and valid DAGs, but also produces a smooth latent space that facilitates searching for DAGs with better performance through Bayesian optimization.
How much do you perceive this? An analysis on perceptions of geometric features, personalities and emotions in virtual humans (Extended Version)
Authors: Victor Araujo, Rodolfo Migon Favaretto, Paulo Knob, Soraia Raupp Musse, Felipe Vilanova, Angelo Brandelli Costa
Date: 2019-04-24 00:00:00

This work aims to evaluate people's perception regarding geometric features,
personalities and emotions characteristics in virtual humans. For this, we use
as a basis, a dataset containing the tracking files of pedestrians captured
from spontaneous videos and visualized them as identical virtual humans. The
goal is to focus on their behavior and not being distracted by other features.
In addition to tracking files containing their positions, the dataset also
contains pedestrian emotions and personalities detected using Computer Vision
and Pattern Recognition techniques. We proceed with our analysis in order to
answer the question if subjects can perceive geometric features as
distances/speeds as well as emotions and personalities in video sequences when
pedestrians are represented by virtual humans. Regarding the participants, an
amount of 73 people volunteered for the experiment. The analysis was divided in
two parts: i) evaluation on perception of geometric characteristics, such as
density, angular variation, distances and speeds, and ii) evaluation on
personality and emotion perceptions. Results indicate that, even without
explaining to the participants the concepts of each personality or emotion and
how they were calculated (considering geometric characteristics), in most of
the cases, participants perceived the personality and emotion expressed by the
virtual agents, in accordance with the available ground truth.
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning
Authors: Xinlei Pan, Wei-Yao Wang, Xiaoshuai Zhang, Bo Li, Jin-Feng Yi, Dawn Song
Date: 2019-04-24 00:00:00
Tasks: Autonomous Driving, continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Navigation

Machine learning has been widely applied to various applications, some of
which involve training with privacy-sensitive data. A modest number of data
breaches have been studied, including credit card information in natural
language data and identities from face dataset. However, most of these studies
focus on supervised learning models. As deep reinforcement learning (DRL) has
been deployed in a number of real-world systems, such as indoor robot
navigation, whether trained DRL policies can leak private information requires
in-depth study. To explore such privacy breaches in general, we mainly propose
two methods: environment dynamics search via genetic algorithm and candidate
inference based on shadow policies. We conduct extensive experiments to
demonstrate such privacy vulnerabilities in DRL under various settings. We
leverage the proposed algorithms to infer floor plans from some trained Grid
World navigation DRL agents with LiDAR perception. The proposed algorithm can
correctly infer most of the floor plans and reaches an average recovery rate of
95.83% using policy gradient trained agents. In addition, we are able to
recover the robot configuration in continuous control environments and an
autonomous driving simulator with high accuracy. To the best of our knowledge,
this is the first work to investigate privacy leakage in DRL settings and we
show that DRL-based agents do potentially leak privacy-sensitive information
from the trained policies.
An Attentional Neural Network Architecture for Folk Song Classification
Authors: Aitor Arronte-Alvarez, Francisco Gomez-Martin
Date: 2019-04-24 00:00:00
Tasks: Classification, General Classification

In this paper we present an attentional neural network for folk song
classification. We introduce the concept of musical motif embedding, and show
how using melodic local context we are able to model monophonic folk song
motifs using the skipgram version of the word2vec algorithm. We use the motif
embeddings to represent folk songs from Germany, China, and Sweden, and
classify them using an attentional neural network that is able to discern
relevant motifs in a song. The results show how the network obtains state of
the art accuracy in a completely unsupervised manner, and how motif embeddings
produce high quality motif representations from folk songs. We conjecture on
the advantages of this type of representation in large symbolic music corpora,
and how it can be helpful in the musicological analysis of folk song
collections from different cultures and geographical areas.
Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
Authors: Matthew England, Dorian Florescu
Date: 2019-04-24 00:00:00
Tasks: BIG-bench Machine Learning

There has been recent interest in the use of machine learning (ML) approaches within mathematical software to make choices that impact on the computing performance without affecting the mathematical correctness of the result. We address the problem of selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm in Symbolic Computation. Prior work to apply ML on this problem implemented a Support Vector Machine (SVM) to select between three existing human-made heuristics, which did better than anyone heuristic alone. The present work extends to have ML select the variable ordering directly, and to try a wider variety of ML techniques. We experimented with the NLSAT dataset and the Regular Chains Library CAD function for Maple 2018. For each problem, the variable ordering leading to the shortest computing time was selected as the target class for ML. Features were generated from the polynomial input and used to train the following ML models: k-nearest neighbours (KNN) classifier, multi-layer perceptron (MLP), decision tree (DT) and SVM, as implemented in the Python scikit-learn package. We also compared these with the two leading human constructed heuristics for the problem: Brown's heuristic and sotd. On this dataset all of the ML approaches outperformed the human made heuristics, some by a large margin.
Bridging the Domain Gap for Ground-to-Aerial Image Matching
Authors: Krishna Regmi, Mubarak Shah
Date: 2019-04-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Retrieval

The visual entities in cross-view images exhibit drastic domain changes due to the difference in viewpoints each set of images is captured from. Existing state-of-the-art methods address the problem by learning view-invariant descriptors for the images. We propose a novel method for solving this task by exploiting the generative powers of conditional GANs to synthesize an aerial representation of a ground level panorama and use it to minimize the domain gap between the two views. The synthesized image being from the same view as the target image helps the network to preserve important cues in aerial images following our Joint Feature Learning approach. Our Feature Fusion method combines the complementary features from a synthesized aerial image with the corresponding ground features to obtain a robust query representation. In addition, multi-scale feature aggregation preserves image representations at different feature scales useful for solving this complex task. Experimental results show that our proposed approach performs significantly better than the state-of-the-art methods on the challenging CVUSA dataset in terms of top-1 and top-1% retrieval accuracies. Furthermore, to evaluate the generalization of our method on urban landscapes, we collected a new cross-view localization dataset with geo-reference information.
Physical Adversarial Textures that Fool Visual Object Tracking
Authors: Rey Reza Wiyatno, Anqi Xu
Date: 2019-04-24 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, Object Tracking, Visual Object Tracking

We present a system for generating inconspicuous-looking textures that, when displayed in the physical world as digital or printed posters, cause visual object tracking systems to become confused. For instance, as a target being tracked by a robot's camera moves in front of such a poster, our generated texture makes the tracker lock onto it and allows the target to evade. This work aims to fool seldom-targeted regression tasks, and in particular compares diverse optimization strategies: non-targeted, targeted, and a new family of guided adversarial losses. While we use the Expectation Over Transformation (EOT) algorithm to generate physical adversaries that fool tracking models when imaged under diverse conditions, we compare the impacts of different conditioning variables, including viewpoint, lighting, and appearances, to find practical attack setups with high resulting adversarial strength and convergence speed. We further showcase textures optimized solely using simulated scenes can confuse real-world tracking systems.
Multi-Scale Body-Part Mask Guided Attention for Person Re-identification
Authors: Honglong Cai, Zhiguan Wang, Jinxing Cheng
Date: 2019-04-24 00:00:00
Tasks: Person Re-Identification

Person re-identification becomes a more and more important task due to its
wide applications. In practice, person re-identification still remains
challenging due to the variation of person pose, different lighting, occlusion,
misalignment, background clutter, etc. In this paper, we propose a multi-scale
body-part mask guided attention network (MMGA), which jointly learns whole-body
and part body attention to help extract global and local features
simultaneously. In MMGA, body-part masks are used to guide the training of
corresponding attention. Experiments show that our proposed method can reduce
the negative influence of variation of person pose, misalignment and background
clutter. Our method achieves rank-1/mAP of 95.0%/87.2% on the Market1501
dataset, 89.5%/78.1% on the DukeMTMC-reID dataset, outperforming current
state-of-the-art methods.
Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation
Authors: Nicholas Ruiz, Marcello Federico
Date: 2019-04-24 00:00:00
Tasks: speech-recognition, Speech Recognition, Translation, Word Alignment

We propose a variation to the commonly used Word Error Rate (WER) metric for
speech recognition evaluation which incorporates the alignment of phonemes, in
the absence of time boundary information. After computing the Levenshtein
alignment on words in the reference and hypothesis transcripts, spans of
adjacent errors are converted into phonemes with word and syllable boundaries
and a phonetic Levenshtein alignment is performed. The aligned phonemes are
recombined into aligned words that adjust the word alignment labels in each
error region. We demonstrate that our Phonetically-Oriented Word Error Rate
(POWER) yields similar scores to WER with the added advantages of better word
alignments and the ability to capture one-to-many word alignments corresponding
to homophonic errors in speech recognition hypotheses. These improved
alignments allow us to better trace the impact of Levenshtein error types on
downstream tasks such as speech translation.