pwc-archive/papers-with-abstracts huggingface dataset:
The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States
Authors: Roberto Ammendola, Andrea Biagioni, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Pier Stanislao Paolucci, Elena Pastorelli, Luca Pontisso, Francesco Simula, Piero Vicini
Date: 2018-04-10 00:00:00

Efficient brain simulation is a scientific grand challenge, a
parallel/distributed coding challenge and a source of requirements and
suggestions for future computing architectures. Indeed, the human brain
includes about 10^15 synapses and 10^11 neurons activated at a mean rate of
several Hz. Full brain simulation poses Exascale challenges even if simulated
at the highest abstraction level. The WaveScalES experiment in the Human Brain
Project (HBP) has the goal of matching experimental measures and simulations of
slow waves during deep-sleep and anesthesia and the transition to other brain
states. The focus is the development of dedicated large-scale
parallel/distributed simulation technologies. The ExaNeSt project designs an
ARM-based, low-power HPC architecture scalable to million of cores, developing
a dedicated scalable interconnect system, and SWA/AW simulations are included
among the driving benchmarks. At the joint between both projects is the INFN
proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation
engine. DPSNN can be configured to stress either the networking or the
computation features available on the execution platforms. The simulation
stresses the networking component when the neural net - composed by a
relatively low number of neurons, each one projecting thousands of synapses -
is distributed over a large number of hardware cores. When growing the number
of neurons per core, the computation starts to be the dominating component for
short range connections. This paper reports about preliminary performance
results obtained on an ARM-based HPC prototype developed in the framework of
the ExaNeSt project. Furthermore, a comparison is given of instantaneous power,
total energy consumption, execution time and energetic cost per synaptic event
of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server
platforms.
Evaluating Actuators in a Purely Information-Theory Based Reward Model
Authors: Wojciech Skaba
Date: 2018-04-10 00:00:00

AGINAO builds its cognitive engine by applying self-programming techniques to
create a hierarchy of interconnected codelets - the tiny pieces of code
executed on a virtual machine. These basic processing units are evaluated for
their applicability and fitness with a notion of reward calculated from
self-information gain of binary partitioning of the codelet's input
state-space. This approach, however, is useless for the evaluation of
actuators. Instead, a model is proposed in which actuators are evaluated by
measuring the impact that an activation of an effector, and consequently the
feedback from the robot sensors, has on average reward received by the
processing units.
The AGINAO Self-Programming Engine
Authors: Wojciech Skaba
Date: 2018-04-10 00:00:00

The AGINAO is a project to create a human-level artificial general
intelligence system (HL AGI) embodied in the Aldebaran Robotics' NAO humanoid
robot. The dynamical and open-ended cognitive engine of the robot is
represented by an embedded and multi-threaded control program, that is
self-crafted rather than hand-crafted, and is executed on a simulated Universal
Turing Machine (UTM). The actual structure of the cognitive engine emerges as a
result of placing the robot in a natural preschool-like environment and running
a core start-up system that executes self-programming of the cognitive layer on
top of the core layer. The data from the robot's sensory devices supplies the
training samples for the machine learning methods, while the commands sent to
actuators enable testing hypotheses and getting a feedback. The individual
self-created subroutines are supposed to reflect the patterns and concepts of
the real world, while the overall program structure reflects the spatial and
temporal hierarchy of the world dependencies. This paper focuses on the details
of the self-programming approach, limiting the discussion of the applied
cognitive architecture to a necessary minimum.
Who framed Roger Reindeer? De-censorship of Facebook posts by snippet classification
Authors: Fabio Del Vigna, Marinella Petrocchi, Alessandro Tommasi, Cesare Zavattari, Maurizio Tesconi
Date: 2018-04-10 00:00:00
Tasks: General Classification

This paper considers online news censorship and it concentrates on censorship
of identities. Obfuscating identities may occur for disparate reasons, from
military to judiciary ones. In the majority of cases, this happens to protect
individuals from being identified and persecuted by hostile people. However,
being the collaborative web characterised by a redundancy of information, it is
not unusual that the same fact is reported by multiple sources, which may not
apply the same restriction policies in terms of censorship. Also, the proven
aptitude of social network users to disclose personal information leads to the
phenomenon that comments to news can reveal the data withheld in the news
itself. This gives us a mean to figure out who the subject of the censored news
is. We propose an adaptation of a text analysis approach to unveil censored
identities. The approach is tested on a synthesised scenario, which however
resembles a real use case. Leveraging a text analysis based on a context
classifier trained over snippets from posts and comments of Facebook pages, we
achieve promising results. Despite the quite constrained settings in which we
operate -- such as considering only snippets of very short length -- our system
successfully detects the censored name, choosing among 10 different candidate
names, in more than 50\% of the investigated cases. This outperforms the
results of two reference baselines. The findings reported in this paper, other
than being supported by a thorough experimental methodology and interesting on
their own, also pave the way for further investigation on the insidious issues
of censorship on the web.
Graphical Generative Adversarial Networks
Authors: Chongxuan Li, Max Welling, Jun Zhu, Bo Zhang
Date: 2018-04-10 00:00:00
Proceeding: NeurIPS 2018 12

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model
structured data. Graphical-GAN conjoins the power of Bayesian networks on
compactly representing the dependency structures among random variables and
that of generative adversarial networks on learning expressive dependency
functions. We introduce a structured recognition model to infer the posterior
distribution of latent variables given observations. We generalize the
Expectation Propagation (EP) algorithm to learn the generative model and
recognition model jointly. Finally, we present two important instances of
Graphical-GAN, i.e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN),
which can successfully learn the discrete and temporal structures on visual
datasets, respectively.
Evaluating Scoped Meaning Representations
Authors: Rik van Noord, Lasha Abzianidze, Hessel Haagsma, Johan Bos
Date: 2018-02-23 00:00:00
Proceeding: LREC 2018 5
Tasks: Natural Language Understanding, Negation, Semantic Parsing

Semantic parsing offers many opportunities to improve natural language
understanding. We present a semantically annotated parallel corpus for English,
German, Italian, and Dutch where sentences are aligned with scoped meaning
representations in order to capture the semantics of negation, modals,
quantification, and presupposition triggers. The semantic formalism is based on
Discourse Representation Theory, but concepts are represented by WordNet
synsets and thematic roles by VerbNet relations. Translating scoped meaning
representations to sets of clauses enables us to compare them for the purpose
of semantic parser evaluation and checking translations. This is done by
computing precision and recall on matching clauses, in a similar way as is done
for Abstract Meaning Representations. We show that our matching tool for
evaluating scoped meaning representations is both accurate and efficient.
Applying this matching tool to three baseline semantic parsers yields F-scores
between 43% and 54%. A pilot study is performed to automatically find changes
in meaning by comparing meaning representations of translations. This
comparison turns out to be an additional way of (i) finding annotation mistakes
and (ii) finding instances where our semantic analysis needs to be improved.
Deep learning for affective computing: text-based emotion recognition in decision support
Authors: Bernhard Kratzwald, Suzana Ilic, Mathias Kraus, Stefan Feuerriegel, Helmut Prendinger
Date: 2018-03-16 00:00:00
Tasks: Decision Making, Emotion Recognition, Sentiment Analysis, Transfer Learning

Emotions widely affect human decision-making. This fact is taken into account
by affective computing with the goal of tailoring decision support to the
emotional states of individuals. However, the accurate recognition of emotions
within narrative documents presents a challenging undertaking due to the
complexity and ambiguity of language. Performance improvements can be achieved
through deep learning; yet, as demonstrated in this paper, the specific nature
of this task requires the customization of recurrent neural networks with
regard to bidirectional processing, dropout layers as a means of
regularization, and weighted loss functions. In addition, we propose
sent2affect, a tailored form of transfer learning for affective computing: here
the network is pre-trained for a different task (i.e. sentiment analysis),
while the output layer is subsequently tuned to the task of emotion
recognition. The resulting performance is evaluated in a holistic setting
across 6 benchmark datasets, where we find that both recurrent neural networks
and transfer learning consistently outperform traditional machine learning.
Altogether, the findings have considerable implications for the use of
affective computing.
3D Fluid Flow Estimation with Integrated Particle Reconstruction
Authors: Katrin Lasinger, Christoph Vogel, Thomas Pock, Konrad Schindler
Date: 2018-04-09 00:00:00
Tasks: 3D Reconstruction, Motion Estimation

The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the acquired multi-view video in two separate steps, utilizing either a pure Eulerian or pure Lagrangian approach. Eulerian methods perform a voxel-based reconstruction of particles per time step, followed by 3D motion estimation, with some form of dense matching between the precomputed voxel grids from different time steps. In this sequential procedure, the first step cannot use temporal consistency considerations to support the reconstruction, while the second step has no access to the original, high-resolution image data. Alternatively, Lagrangian methods reconstruct an explicit, sparse set of particles and track the individual particles over time. Physical constraints can only be incorporated in a post-processing step when interpolating the particle tracks to a dense motion field. We show, for the first time, how to jointly reconstruct both the individual tracer particles and a dense 3D fluid motion field from the image data, using an integrated energy minimization. Our hybrid Lagrangian/Eulerian model reconstructs individual particles, and at the same time recovers a dense 3D motion field in the entire domain. Making particles explicit greatly reduces the memory consumption and allows one to use the high-res input images for matching. Whereas the dense motion field makes it possible to include physical a-priori constraints and account for the incompressibility and viscosity of the fluid. The method exhibits greatly (~70%) improved results over our recently published baseline with two separate steps for 3D reconstruction and motion estimation. Our results with only two time steps are comparable to those of sota tracking-based methods that require much longer sequences.
A Fast Hierarchically Preconditioned Eigensolver Based On Multiresolution Matrix Decomposition
Authors: Thomas Y. Hou, De Huang, Ka Chun Lam, Ziyun Zhang
Date: 2018-04-10 00:00:00

In this paper we propose a new iterative method to hierarchically compute a
relatively large number of leftmost eigenpairs of a sparse symmetric positive
matrix under the multiresolution operator compression framework. We exploit the
well-conditioned property of every decomposition components by integrating the
multiresolution framework into the Implicitly restarted Lanczos method. We
achieve this combination by proposing an extension-refinement iterative scheme,
in which the intrinsic idea is to decompose the target spectrum into several
segments such that the corresponding eigenproblem in each segment is
well-conditioned. Theoretical analysis and numerical illustration are also
reported to illustrate the efficiency and effectiveness of this algorithm.
Automatic Salient Object Detection for Panoramic Images Using Region Growing and Fixation Prediction Model
Authors: Chunbiao Zhu, Kan Huang, Ge Li
Date: 2017-10-10 00:00:00
Tasks: Density Estimation, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Almost all previous works on saliency detection have been dedicated to
conventional images, however, with the outbreak of panoramic images due to the
rapid development of VR or AR technology, it is becoming more challenging,
meanwhile valuable for extracting salient contents in panoramic images.
  In this paper, we propose a novel bottom-up salient object detection
framework for panoramic images. First, we employ a spatial density estimation
method to roughly extract object proposal regions, with the help of region
growing algorithm. Meanwhile, an eye fixation model is utilized to predict
visually attractive parts in the image from the perspective of the human visual
search mechanism. Then, the previous results are combined by the maxima
normalization to get the coarse saliency map. Finally, a refinement step based
on geodesic distance is utilized for post-processing to derive the final
saliency map.
  To fairly evaluate the performance of the proposed approach, we propose a
high-quality dataset of panoramic images (SalPan). Extensive evaluations
demonstrate the effectiveness of our proposed method on panoramic images and
the superiority of the proposed method against other methods.
QA4IE: A Question Answering based Framework for Information Extraction
Authors: Lin Qiu, Hao Zhou, Yanru Qu, Wei-Nan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu, Yong Yu
Date: 2018-04-10 00:00:00
Tasks: Question Answering, Relation, Relation Extraction, Sentence

Information Extraction (IE) refers to automatically extracting structured
relation tuples from unstructured texts. Common IE solutions, including
Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence
tuples, and are severely restricted by limited relation types as well as
informal relation specifications (e.g., free-text based relation tuples). In
order to overcome these weaknesses, we propose a novel IE framework named
QA4IE, which leverages the flexible question answering (QA) approaches to
produce high quality relation triples across sentences. Based on the framework,
we develop a large IE benchmark with high quality human evaluation. This
benchmark contains 293K documents, 2M golden relation triples, and 636 relation
types. We compare our system with some IE baselines on our benchmark and the
results show that our system achieves great improvements.
Higher Order and Long-Range Synchronization Effects for Classification and Computing in Oscillator-Based Spiking Neural Networks
Authors: Andrei Velichko, Vadim Putrolaynen, Maksim Belyaev
Date: 2018-04-10 00:00:00
Tasks: General Classification

In the circuit of two thermally coupled VO2 oscillators, we studied a higher order synchronization effect, which can be used in object classification techniques to increase the number of possible synchronous states of the oscillator system. We developed the phase-locking estimation method to determine the values of subharmonic ratio and synchronization effectiveness. In our experiment, the number of possible synchronous states of the oscillator system was twelve, and subharmonic ratio distributions were shaped as Arnold's tongues. In the model, the number of states may reach the maximum value of 150 at certain levels of coupling strength and noise. The long-range synchronization effect in a one-dimensional chain of oscillators occurs even at low values of synchronization effectiveness for intermediate links. We demonstrate a technique for storing and recognizing vector images, which can used for reservoir computing. In addition, we present the implementation of analog operation of multiplication, the synchronization based logic for binary computations, and the possibility to develop the interface between spike neural network and a computer. Based on the universal physical effects, the high order synchronization can be applied to any spiking oscillators with any coupling type, enhancing the practical value of the presented results to expand spike neural network capabilities.
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets
Authors: Dario Azzimonti, David Ginsbourger, Clément Chevalier, Julien Bect, Yann Richet
Date: 2016-11-22 00:00:00

We consider the problem of estimating the set of all inputs that leads a system to some particular behavior. The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, i.e. the set of points where the function takes values above or below some prescribed threshold. The objective function is emulated with a Gaussian Process (GP) model based on an initial design of experiments enriched with evaluation results at (batch-)sequentially determined input points. The GP model provides conservative estimates for the excursion set, which control false positives while minimizing false negatives. We introduce adaptive strategies that sequentially select new evaluations of the function by reducing the uncertainty on conservative estimates. Following the Stepwise Uncertainty Reduction approach we obtain new evaluations by minimizing adapted criteria. Tractable formulae for the conservative criteria are derived, which allow more convenient optimization. The method is benchmarked on random functions generated under the model assumptions in different scenarios of noise and batch size. We then apply it to a reliability engineering test case. Overall, the proposed strategy of minimizing false negatives in conservative estimation achieves competitive performance both in terms of model-based and model-free indicators.
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Authors: Hao Yu, Zhaoning Zhang, Zheng Qin, Hao Wu, Dongsheng Li, Jun Zhao, Xicheng Lu
Date: 2018-04-10 00:00:00

Modern object detectors usually suffer from low accuracy issues, as
foregrounds always drown in tons of backgrounds and become hard examples during
training. Compared with those proposal-based ones, real-time detectors are in
far more serious trouble since they renounce the use of region-proposing stage
which is used to filter a majority of backgrounds for achieving real-time
rates. Though foregrounds as hard examples are in urgent need of being mined
from tons of backgrounds, a considerable number of state-of-the-art real-time
detectors, like YOLO series, have yet to profit from existing hard example
mining methods, as using these methods need detectors fit series of
prerequisites. In this paper, we propose a general hard example mining method
named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for
real-time detectors, as it utilizes the final feature map which exists in all
real-time detectors to mine hard examples. By using LRM, some elements
representing easy examples in final feature map are filtered and detectors are
forced to concentrate on hard examples during training. Extensive experiments
validate the effectiveness of our method. With our method, the improvements of
YOLOv2 detector on auto-driving related dataset KITTI and more general dataset
PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first
hard example mining strategy which could fit YOLOv2 perfectly and make it
better applied in series of real scenarios where both real-time rates and
accurate detection are strongly demanded.
The block-Poisson estimator for optimally tuned exact subsampling MCMC
Authors: Matias Quiroz, Minh-Ngoc Tran, Mattias Villani, Robert Kohn, Khue-Dung Dang
Date: 2016-03-27 00:00:00

Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators, allowing us to update a single block of subsample indicators in each MCMC iteration so that a desired correlation is achieved between the logs of successive likelihood estimates. This is important since pseudo-marginal MCMC with positively correlated likelihood estimates can use substantially smaller subsamples without adversely affecting the sampling efficiency. The block-Poisson estimator is unbiased but not necessarily positive, so the algorithm runs the MCMC on the absolute value of the likelihood estimator and uses an importance sampling correction to obtain consistent estimates of the posterior mean of any function of the parameters. Our article derives guidelines to select the optimal tuning parameters for our method and shows that it compares very favourably to regular MCMC without subsampling, and to two other recently proposed exact subsampling approaches in the literature.
Discovery and usage of joint attention in images
Authors: Daniel Harari, Joshua B. Tenenbaum, Shimon Ullman
Date: 2018-04-10 00:00:00
Tasks: Image Captioning

Joint visual attention is characterized by two or more individuals looking at
a common target at the same time. The ability to identify joint attention in
scenes, the people involved, and their common target, is fundamental to the
understanding of social interactions, including others' intentions and goals.
In this work we deal with the extraction of joint attention events, and the use
of such events for image descriptions. The work makes two novel contributions.
First, our extraction algorithm is the first which identifies joint visual
attention in single static images. It computes 3D gaze direction, identifies
the gaze target by combining gaze direction with a 3D depth map computed for
the image, and identifies the common gaze target. Second, we use a human study
to demonstrate the sensitivity of humans to joint attention, suggesting that
the detection of such a configuration in an image can be useful for
understanding the image, including the goals of the agents and their joint
activity, and therefore can contribute to image captioning and related tasks.