pwc-archive/papers-with-abstracts huggingface dataset:
Linear Queries Estimation with Local Differential Privacy
Authors: Raef Bassily
Date: 2018-10-05 00:00:00

We study the problem of estimating a set of $d$ linear queries with respect
to some unknown distribution $\mathbf{p}$ over a domain $\mathcal{J}=[J]$ based
on a sensitive data set of $n$ individuals under the constraint of local
differential privacy. This problem subsumes a wide range of estimation tasks,
e.g., distribution estimation and $d$-dimensional mean estimation. We provide
new algorithms for both the offline (non-adaptive) and adaptive versions of
this problem.
  In the offline setting, the set of queries are fixed before the algorithm
starts. In the regime where $n\lesssim d^2/\log(J)$, our algorithms attain
$L_2$ estimation error that is independent of $d$, and is tight up to a factor
of $\tilde{O}\left(\log^{1/4}(J)\right)$. For the special case of distribution
estimation, we show that projecting the output estimate of an algorithm due to
[Acharya et al. 2018] on the probability simplex yields an $L_2$ error that
depends only sub-logarithmically on $J$ in the regime where $n\lesssim
J^2/\log(J)$. These results show the possibility of accurate estimation of
linear queries in the high-dimensional settings under the $L_2$ error
criterion.
  In the adaptive setting, the queries are generated over $d$ rounds; one query
at a time. In each round, a query can be chosen adaptively based on all the
history of previous queries and answers. We give an algorithm for this problem
with optimal $L_{\infty}$ estimation error (worst error in the estimated values
for the queries w.r.t. the data distribution). Our bound matches a lower bound
on the $L_{\infty}$ error for the offline version of this problem [Duchi et al.
2013].
POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset
Authors: Gengchen Mai, Krzysztof Janowicz, Cheng He, Sumang Liu, Ni Lao
Date: 2018-10-05 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Sentence, Sentence Embedding, Sentence-Embedding

Many services that perform information retrieval for Points of Interest (POI)
utilize a Lucene-based setup with spatial filtering. While this type of system
is easy to implement it does not make use of semantics but relies on direct
word matches between a query and reviews leading to a loss in both precision
and recall. To study the challenging task of semantically enriching POIs from
unstructured data in order to support open-domain search and question answering
(QA), we introduce a new dataset POIReviewQA. It consists of 20k questions
(e.g."is this restaurant dog friendly?") for 1022 Yelp business types. For each
question we sampled 10 reviews, and annotated each sentence in the reviews
whether it answers the question and what the corresponding answer is. To test a
system's ability to understand the text we adopt an information retrieval
evaluation by ranking all the review sentences for a question based on the
likelihood that they answer this question. We build a Lucene-based baseline
model, which achieves 77.0% AUC and 48.8% MAP. A sentence embedding-based model
achieves 79.2% AUC and 41.8% MAP, indicating that the dataset presents a
challenging problem for future research by the GIR community. The result
technology can help exploit the thematic content of web documents and social
media for characterisation of locations.
Doubly Semi-Implicit Variational Inference
Authors: Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov
Date: 2018-10-05 00:00:00
Tasks: Variational Inference

We extend the existing framework of semi-implicit variational inference
(SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way
to perform variational inference and learning when both the approximate
posterior and the prior distribution are semi-implicit. In other words, DSIVI
performs inference in models where the prior and the posterior can be expressed
as an intractable infinite mixture of some analytic density with a highly
flexible implicit mixing distribution. We provide a sandwich bound on the
evidence lower bound (ELBO) objective that can be made arbitrarily tight.
Unlike discriminator-based and kernel-based approaches to implicit variational
inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically
exact. We evaluate DSIVI on a set of problems that benefit from implicit
priors. In particular, we show that DSIVI gives rise to a simple modification
of VampPrior, the current state-of-the-art prior for variational autoencoders,
which improves its performance.
Interpretable Convolutional Neural Networks via Feedforward Design
Authors: C. -C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan, Yueru Chen
Date: 2018-10-05 00:00:00

The model parameters of convolutional neural networks (CNNs) are determined
by backpropagation (BP). In this work, we propose an interpretable feedforward
(FF) design without any BP as a reference. The FF design adopts a data-centric
approach. It derives network parameters of the current layer based on data
statistics from the output of the previous layer in a one-pass manner. To
construct convolutional layers, we develop a new signal transform, called the
Saab (Subspace Approximation with Adjusted Bias) transform. It is a variant of
the principal component analysis (PCA) with an added bias vector to annihilate
activation's nonlinearity. Multiple Saab transforms in cascade yield multiple
convolutional layers. As to fully-connected (FC) layers, we construct them
using a cascade of multi-stage linear least squared regressors (LSRs). The
classification and robustness (against adversarial attacks) performances of BP-
and FF-designed CNNs applied to the MNIST and the CIFAR-10 datasets are
compared. Finally, we comment on the relationship between BP and FF designs.
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Authors: Jörg Wagner, Volker Fischer, Michael Herman, Sven Behnke
Date: 2018-10-05 00:00:00

Generating a robust representation of the environment is a crucial ability of
learning agents. Deep learning based methods have greatly improved perception
systems but still fail in challenging situations. These failures are often not
solvable on the basis of a single image. In this work, we present a
parameter-efficient temporal filtering concept which extends an existing
single-frame segmentation model to work with multiple frames. The resulting
recurrent architecture temporally filters representations on all abstraction
levels in a hierarchical manner, while decoupling temporal dependencies from
scene representation. Using a synthetic dataset, we show the ability of our
model to cope with data perturbations and highlight the importance of recurrent
and hierarchical filtering.
Subset selection in sparse matrices
Authors: Alberto Del Pia, Santanu S. Dey, Robert Weismantel
Date: 2018-10-05 00:00:00

In subset selection we search for the best linear predictor that involves a small subset of variables. From a computational complexity viewpoint, subset selection is NP-hard and few classes are known to be solvable in polynomial time. Using mainly tools from discrete geometry, we show that some sparsity conditions on the original data matrix allow us to solve the problem in polynomial time.
From direct tagging to Tagging with sentences compression
Authors: Peihui Chen
Date: 2018-10-05 00:00:00
Tasks: TAG

In essence, the two tagging methods (direct tagging and tagging with
sentences compression) are to tag the information we need by using regular
expression which basing on the inherent language patterns of the natural
language. Though it has many advantages in extracting regular data, Direct
tagging is not applicable to some situations. if the data we need extract is
not regular and its surrounding words are regular is relatively regular, then
we can use information compression to cut the information we do not need before
we tagging the data we need. In this way we can increase the precision of the
data while not undermine the recall of the data.
Automatic Detection of Arousals during Sleep using Multiple Physiological Signals
Authors: Saman Parvaneh, Jonathan Rubin, Ali Samadani, Gajendra Katuwal
Date: 2018-10-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The visual scoring of arousals during sleep routinely conducted by sleep
experts is a challenging task warranting an automatic approach. This paper
presents an algorithm for automatic detection of arousals during sleep. Using
the Physionet/CinC Challenge dataset, an 80-20% subject-level split was
performed to create in-house training and test sets, respectively. The data for
each subject in the training set was split to 30-second epochs with no overlap.
A total of 428 features from EEG, EMG, EOG, airflow, and SaO2 in each epoch
were extracted and used for creating subject-specific models based on an
ensemble of bagged classification trees, resulting in 943 models. For marking
arousal and non-arousal regions in the test set, the data in the test set was
split to 30-second epochs with 50% overlaps. The average of arousal
probabilities from different patient-specific models was assigned to each
30-second epoch and then a sample-wise probability vector with the same length
as test data was created for model evaluation. Using the PhysioNet/CinC
Challenge 2018 scoring criteria, AUPRCs of 0.25 and 0.21 were achieved for the
in-house test and blind test sets, respectively.
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
Authors: Pengcheng Yin, Graham Neubig
Date: 2018-10-05 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Code Generation, Semantic Parsing

We present TRANX, a transition-based neural semantic parser that maps natural
language (NL) utterances into formal meaning representations (MRs). TRANX uses
a transition system based on the abstract syntax description language for the
target MR, which gives it two major advantages: (1) it is highly accurate,
using information from the syntax of the target MR to constrain the output
space and model the information flow, and (2) it is highly generalizable, and
can easily be applied to new types of MR by just writing a new abstract syntax
description corresponding to the allowable structures in the MR. Experiments on
four different semantic parsing and code generation tasks show that our system
is generalizable, extensible, and effective, registering strong results
compared to existing neural semantic parsers.
Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms
Authors: D. Bucur, G. Iacca, M. Gaudesi, G. Squillero, A. Tonda
Date: 2018-10-05 00:00:00
Tasks: Evolutionary Algorithms

In novel forms of the Social Internet of Things, any mobile user within
communication range may help routing messages for another user in the network.
The resulting message delivery rate depends both on the users' mobility
patterns and the message load in the network. This new type of configuration,
however, poses new challenges to security, amongst them, assessing the effect
that a group of colluding malicious participants can have on the global message
delivery rate in such a network is far from trivial. In this work, after
modeling such a question as an optimization problem, we are able to find quite
interesting results by coupling a network simulator with an evolutionary
algorithm. The chosen algorithm is specifically designed to solve problems
whose solutions can be decomposed into parts sharing the same structure. We
demonstrate the effectiveness of the proposed approach on two medium-sized
Delay-Tolerant Networks, realistically simulated in the urban contexts of two
cities with very different route topology: Venice and San Francisco. In all
experiments, our methodology produces attack patterns that greatly lower
network performance with respect to previous studies on the subject, as the
evolutionary core is able to exploit the specific weaknesses of each target
configuration.
Memetic Viability Evolution for Constrained Optimization
Authors: A. Maesani, G. Iacca, D. Floreano
Date: 2018-10-05 00:00:00
Tasks: Evolutionary Algorithms

The performance of evolutionary algorithms can be heavily undermined when
constraints limit the feasible areas of the search space. For instance, while
Covariance Matrix Adaptation Evolution Strategy is one of the most efficient
algorithms for unconstrained optimization problems, it cannot be readily
applied to constrained ones. Here, we used concepts from Memetic Computing,
i.e. the harmonious combination of multiple units of algorithmic information,
and Viability Evolution, an alternative abstraction of artificial evolution, to
devise a novel approach for solving optimization problems with inequality
constraints. Viability Evolution emphasizes elimination of solutions not
satisfying viability criteria, defined as boundaries on objectives and
constraints. These boundaries are adapted during the search to drive a
population of local search units, based on Covariance Matrix Adaptation
Evolution Strategy, towards feasible regions. These units can be recombined by
means of Differential Evolution operators. Of crucial importance for the
performance of our method, an adaptive scheduler toggles between exploitation
and exploration by selecting to advance one of the local search units and/or
recombine them. The proposed algorithm can outperform several state-of-the-art
methods on a diverse set of benchmark and engineering problems, both for
quality of solutions and computational resources needed.
Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks
Authors: Xuan Gu, Hans Knutsson, Markus Nilsson, Anders Eklund
Date: 2018-10-05 00:00:00
Tasks: Diffusion  MRI

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive
microstructure assessment technique. Scalar measures, such as FA (fractional
anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue
properties can be obtained using diffusion models and data processing
pipelines. However, it is costly and time consuming to collect high quality
diffusion data. Here, we therefore demonstrate how Generative Adversarial
Networks (GANs) can be used to generate synthetic diffusion scalar measures
from structural T1-weighted images in a single optimized step. Specifically, we
train the popular CycleGAN model to learn to map a T1 image to FA or MD, and
vice versa. As an application, we show that synthetic FA images can be used as
a target for non-linear registration, to correct for geometric distortions
common in diffusion MRI.
Distributed optimization in wireless sensor networks: an island-model framework
Authors: Giovanni Iacca
Date: 2018-10-05 00:00:00
Tasks: Anomaly Detection, CPU, Distributed Optimization

Wireless Sensor Networks (WSNs) is an emerging technology in several
application domains, ranging from urban surveillance to environmental and
structural monitoring. Computational Intelligence (CI) techniques are
particularly suitable for enhancing these systems. However, when embedding CI
into wireless sensors, severe hardware limitations must be taken into account.
In this paper we investigate the possibility to perform an online, distributed
optimization process within a WSN. Such a system might be used, for example, to
implement advanced network features like distributed modelling, self-optimizing
protocols, and anomaly detection, to name a few. The proposed approach, called
DOWSN (Distributed Optimization for WSN) is an island-model infrastructure in
which each node executes a simple, computationally cheap (both in terms of CPU
and memory) optimization algorithm, and shares promising solutions with its
neighbors. We perform extensive tests of different DOWSN configurations on a
benchmark made up of continuous optimization problems; we analyze the influence
of the network parameters (number of nodes, inter-node communication period and
probability of accepting incoming solutions) on the optimization performance.
Finally, we profile energy and memory consumption of DOWSN to show the
efficient usage of the limited hardware resources available on the sensor
nodes.
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections
Authors: Tomi Peltola
Date: 2018-10-05 00:00:00
Tasks: Variable Selection

We introduce a method, KL-LIME, for explaining predictions of Bayesian
predictive models by projecting the information in the predictive distribution
locally to a simpler, interpretable explanation model. The proposed approach
combines the recent Local Interpretable Model-agnostic Explanations (LIME)
method with ideas from Bayesian projection predictive variable selection
methods. The information theoretic basis helps in navigating the trade-off
between explanation fidelity and complexity. We demonstrate the method in
explaining MNIST digit classifications made by a Bayesian deep convolutional
neural network.
Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives
Authors: Hadrien Hendrikx, Francis Bach, Laurent Massoulié
Date: 2018-10-05 00:00:00

In this paper, we study the problem of minimizing a sum of smooth and
strongly convex functions split over the nodes of a network in a decentralized
fashion. We propose the algorithm $ESDACD$, a decentralized accelerated
algorithm that only requires local synchrony. Its rate depends on the condition
number $\kappa$ of the local functions as well as the network topology and
delays. Under mild assumptions on the topology of the graph, $ESDACD$ takes a
time $O((\tau_{\max} +
\Delta_{\max})\sqrt{{\kappa}/{\gamma}}\ln(\epsilon^{-1}))$ to reach a precision
$\epsilon$ where $\gamma$ is the spectral gap of the graph, $\tau_{\max}$ the
maximum communication delay and $\Delta_{\max}$ the maximum computation time.
Therefore, it matches the rate of $SSDA$, which is optimal when $\tau_{\max} =
\Omega\left(\Delta_{\max}\right)$. Applying $ESDACD$ to quadratic local
functions leads to an accelerated randomized gossip algorithm of rate $O(
\sqrt{\theta_{\rm gossip}/n})$ where $\theta_{\rm gossip}$ is the rate of the
standard randomized gossip. To the best of our knowledge, it is the first
asynchronous gossip algorithm with a provably improved rate of convergence of
the second moment of the error. We illustrate these results with experiments in
idealized settings.
IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms
Authors: Ruzhang Zhao, Pengyu Hong, Jun S. Liu
Date: 2018-10-05 00:00:00
Tasks: feature selection

Relief based algorithms have often been claimed to uncover feature interactions. However, it is still unclear whether and how interaction terms will be differentiated from marginal effects. In this paper, we propose IMMIGRATE algorithm by including and training weights for interaction terms. Besides applying the large margin principle, we focus on the robustness of the contributors of margin and consider local and global information simultaneously. Moreover, IMMIGRATE has been shown to enjoy attractive properties, such as robustness and combination with Boosting. We evaluate our proposed method on several tasks, which achieves state-of-the-art results significantly.
FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network
Authors: Yazhan Zhang, Zicheng Kan, Yu Alexander Tse, Yang Yang, Michael Yu Wang
Date: 2018-10-05 00:00:00
Tasks: General Classification

Tactile sensing is essential to the human perception system, so as to robot.
In this paper, we develop a novel optical-based tactile sensor "FingerVision"
with effective signal processing algorithms. This sensor is composed of soft
skin with embedded marker array bonded to rigid frame, and a web camera with a
fisheye lens. While being excited with contact force, the camera tracks the
movements of markers and deformation field is obtained. Compared to existing
tactile sensors, our sensor features compact footprint, high resolution, and
ease of fabrication. Besides, utilizing the deformation field estimation, we
propose a slip classification framework based on convolution Long Short Term
Memory (convolutional LSTM) networks. The data collection process takes
advantage of the human sense of slip, during which human hand holds 12 daily
objects, interacts with sensor skin and labels data with a slip or non-slip
identity based on human feeling of slip. Our slip classification framework
performs high accuracy of 97.62% on the test dataset. It is expected to be
capable of enhancing the stability of robot grasping significantly, leading to
better contact force control, finer object interaction and more active sensing
manipulation.
LiveCap: Real-time Human Performance Capture from Monocular Video
Authors: Marc Habermann, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, Christian Theobalt
Date: 2018-10-05 00:00:00
Tasks: CPU

We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster.