pwc-archive/papers-with-abstracts huggingface dataset:
Full Wafer Redistribution and Wafer Embedding as Key Technologies for a Multi-Scale Neuromorphic Hardware Cluster
Authors: Kai Zoschke, Maurice Güttler, Lars Böttcher, Andreas Grübl, Dan Husmann, Johannes Schemmel, Karlheinz Meier, Oswin Ehrmann
Date: 2018-01-15 00:00:00

Together with the Kirchhoff-Institute for Physics(KIP) the Fraunhofer IZM has
developed a full wafer redistribution and embedding technology as base for a
large-scale neuromorphic hardware system. The paper will give an overview of
the neuromorphic computing platform at the KIP and the associated hardware
requirements which drove the described technological developments. In the first
phase of the project standard redistribution technologies from wafer level
packaging were adapted to enable a high density reticle-to-reticle routing on
200mm CMOS wafers. Neighboring reticles were interconnected across the scribe
lines with an 8{\mu}m pitch routing based on semi-additive copper
metallization. Passivation by photo sensitive benzocyclobutene was used to
enable a second intra-reticle routing layer. Final IO pads with flash gold were
generated on top of each reticle. With that concept neuromorphic systems based
on full wafers could be assembled and tested. The fabricated high density
inter-reticle routing revealed a very high yield of larger than 99.9%. In order
to allow an upscaling of the system size to a large number of wafers with
feasible effort a full wafer embedding concept for printed circuit boards was
developed and proven in the second phase of the project. The wafers were
thinned to 250{\mu}m and laminated with additional prepreg layers and copper
foils into a core material. After lamination of the PCB panel the reticle IOs
of the embedded wafer were accessed by micro via drilling, copper
electroplating, lithography and subtractive etching of the PCB wiring
structure. The created wiring with 50um line width enabled an access of the
reticle IOs on the embedded wafer as well as a board level routing. The panels
with the embedded wafers were subsequently stressed with up to 1000 thermal
cycles between 0C and 100C and have shown no severe failure formation over the
cycle time.
Combining Stereo Disparity and Optical Flow for Basic Scene Flow
Authors: René Schuster, Christian Bailer, Oliver Wasenmüller, Didier Stricker
Date: 2018-01-15 00:00:00
Tasks: Optical Flow Estimation, Scene Flow Estimation

Scene flow is a description of real world motion in 3D that contains more
information than optical flow. Because of its complexity there exists no
applicable variant for real-time scene flow estimation in an automotive or
commercial vehicle context that is sufficiently robust and accurate. Therefore,
many applications estimate the 2D optical flow instead. In this paper, we
examine the combination of top-performing state-of-the-art optical flow and
stereo disparity algorithms in order to achieve a basic scene flow. On the
public KITTI Scene Flow Benchmark we demonstrate the reasonable accuracy of the
combination approach and show its speed in computation.
3D-DETNet: a Single Stage Video-Based Vehicle Detector
Authors: Suichan Li
Date: 2018-01-05 00:00:00
Tasks: Object Detection, vehicle detection

Video-based vehicle detection has received considerable attention over the
last ten years and there are many deep learning based detection methods which
can be applied to it. However, these methods are devised for still images and
applying them for video vehicle detection directly always obtains poor
performance. In this work, we propose a new single-stage video-based vehicle
detector integrated with 3DCovNet and focal loss, called 3D-DETNet. Draw
support from 3D Convolution network and focal loss, our method has ability to
capture motion information and is more suitable to detect vehicle in video than
other single-stage methods devised for static images. The multiple video frames
are initially fed to 3D-DETNet to generate multiple spatial feature maps, then
sub-model 3DConvNet takes spatial feature maps as input to capture temporal
information which is fed to final fully convolution model for predicting
locations of vehicles in video frames. We evaluate our method on UA-DETAC
vehicle detection dataset and our 3D-DETNet yields best performance and keeps a
higher detection speed of 26 fps compared with other competing methods.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Authors: Ao Zhang, Nan Li, Jian Pu, Jun Wang, Junchi Yan, Hongyuan Zha
Date: 2018-01-15 00:00:00

Learning a classifier with control on the false-positive rate plays a
critical role in many machine learning applications. Existing approaches either
introduce prior knowledge dependent label cost or tune parameters based on
traditional classifiers, which lack consistency in methodology because they do
not strictly adhere to the false-positive rate constraint. In this paper, we
propose a novel scoring-thresholding approach, tau-False Positive Learning
(tau-FPL) to address this problem. We show the scoring problem which takes the
false-positive rate tolerance into accounts can be efficiently solved in linear
time, also an out-of-bootstrap thresholding method can transform the learned
ranking function into a low false-positive classifier. Both theoretical
analysis and experimental results show superior performance of the proposed
tau-FPL over existing approaches.
Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks
Authors: Bo Luo, Yannan Liu, Lingxiao Wei, Qiang Xu
Date: 2018-01-15 00:00:00

Machine learning systems based on deep neural networks, being able to produce
state-of-the-art results on various perception tasks, have gained mainstream
adoption in many applications. However, they are shown to be vulnerable to
adversarial example attack, which generates malicious output by adding slight
perturbations to the input. Previous adversarial example crafting methods,
however, use simple metrics to evaluate the distances between the original
examples and the adversarial ones, which could be easily detected by human
eyes. In addition, these attacks are often not robust due to the inevitable
noises and deviation in the physical world. In this work, we present a new
adversarial example attack crafting method, which takes the human perceptual
system into consideration and maximizes the noise tolerance of the crafted
adversarial example. Experimental results demonstrate the efficacy of the
proposed technique.
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution
Authors: Mu Li, Shuhang Gu, David Zhang, WangMeng Zuo
Date: 2018-01-15 00:00:00
Tasks: Computational Efficiency, Image Compression

Arithmetic coding is an essential class of coding techniques. One key issue
of arithmetic encoding method is to predict the probability of the current
coding symbol from its context, i.e., the preceding encoded symbols, which
usually can be executed by building a look-up table (LUT). However, the
complexity of LUT increases exponentially with the length of context. Thus,
such solutions are limited to modeling large context, which inevitably
restricts the compression performance. Several recent deep neural network-based
solutions have been developed to account for large context, but are still
costly in computation. The inefficiency of the existing methods are mainly
attributed to that probability prediction is performed independently for the
neighboring symbols, which actually can be efficiently conducted by shared
computation. To this end, we propose a trimmed convolutional network for
arithmetic encoding (TCAE) to model large context while maintaining
computational efficiency. As for trimmed convolution, the convolutional kernels
are specially trimmed to respect the compression order and context dependency
of the input symbols. Benefited from trimmed convolution, the probability
prediction of all symbols can be efficiently performed in one single forward
pass via a fully convolutional network. Furthermore, to speed up the decoding
process, a slope TCAE model is presented to divide the codes from a 3D code map
into several blocks and remove the dependency between the codes inner one block
for parallel decoding, which can 60x speed up the decoding process. Experiments
show that our TCAE and slope TCAE attain better compression ratio in lossless
gray image compression, and can be adopted in CNN-based lossy image compression
to achieve state-of-the-art rate-distortion performance with real-time encoding
speed.
Viewpoint Invariant Action Recognition using RGB-D Videos
Authors: Jian Liu, Naveed Akhtar, Ajmal Mian
Date: 2017-09-15 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Transfer Learning

In video-based action recognition, viewpoint variations often pose major
challenges because the same actions can appear different from different views.
We use the complementary RGB and Depth information from the RGB-D cameras to
address this problem. The proposed technique capitalizes on the spatio-temporal
information available in the two data streams to the extract action features
that are largely insensitive to the viewpoint variations. We use the RGB data
to compute dense trajectories that are translated to viewpoint insensitive deep
features under a non-linear knowledge transfer model. Similarly, the Depth
stream is used to extract CNN-based view invariant features on which Fourier
Temporal Pyramid is computed to incorporate the temporal information. The
heterogeneous features from the two streams are combined and used as a
dictionary to predict the label of the test samples. To that end, we propose a
sparse-dense collaborative representation classification scheme that strikes a
balance between the discriminative abilities of the dense and the sparse
representations of the samples over the extracted heterogeneous dictionary.
Hyperspectral recovery from RGB images using Gaussian Processes
Authors: Naveed Akhtar, Ajmal Mian
Date: 2018-01-15 00:00:00
Tasks: Gaussian Processes, Quantization

We propose to recover spectral details from RGB images of known spectral
quantization by modeling natural spectra under Gaussian Processes and combining
them with the RGB images. Our technique exploits Process Kernels to model the
relative smoothness of reflectance spectra, and encourages non-negativity in
the resulting signals for better estimation of the reflectance values. The
Gaussian Processes are inferred in sets using clusters of spatio-spectrally
correlated hyperspectral training patches. Each set is transformed to match the
spectral quantization of the test RGB image. We extract overlapping patches
from the RGB image and match them to the hyperspectral training patches by
spectrally transforming the latter. The RGB patches are encoded over the
transformed Gaussian Processes related to those hyperspectral patches and the
resulting image is constructed by combining the codes with the original
Processes. Our approach infers the desired Gaussian Processes under a fully
Bayesian model inspired by Beta-Bernoulli Process, for which we also present
the inference procedure. A thorough evaluation using three hyperspectral
datasets demonstrates the effective extraction of spectral details from RGB
images by the proposed technique.
Leapfrogging for parallelism in deep neural networks
Authors: Yatin Saraiya
Date: 2018-01-15 00:00:00

We present a technique, which we term leapfrogging, to parallelize back-
propagation in deep neural networks. We show that this technique yields a
savings of $1-1/k$ of a dominant term in backpropagation, where k is the number
of threads (or gpus).
Top k Memory Candidates in Memory Networks for Common Sense Reasoning
Authors: Vatsal Mahajan
Date: 2018-01-14 00:00:00
Tasks: Common Sense Reasoning

Successful completion of reasoning task requires the agent to have relevant prior knowledge or some given context of the world dynamics. Usually, the information provided to the system for a reasoning task is just the query or some supporting story, which is often not enough for common reasoning tasks. The goal here is that, if the information provided along the question is not sufficient to correctly answer the question, the model should choose k most relevant documents that can aid its inference process. In this work, the model dynamically selects top k most relevant memory candidates that can be used to successfully solve reasoning tasks. Experiments were conducted on a subset of Winograd Schema Challenge (WSC) problems to show that the proposed model has the potential for commonsense reasoning. The WSC is a test of machine intelligence, designed to be an improvement on the Turing test.
Personal Names in Modern Turkey
Authors: Amaç Herdağdelen
Date: 2017-12-29 00:00:00
Tasks: Diversity

We analyzed the most common 5000 male and 5000 female Turkish names based on
their etymological, morphological, and semantic attributes. The name statistics
are based on all Turkish citizens who were alive in 2014 and they cover 90% of
all population. To the best of our knowledge, this study is the most
comprehensive data-driven analysis of Turkish personal names. Female names have
a greater diversity than male names (e.g., top 15 male names cover 25% of the
male population, whereas top 28 female names cover 25% of the female
population). Despite their diversity, female names exhibit predictable
patterns. For example, certain roots such as g\"ul and nar (rose and
pomegranate/red, respectively) are used to generate hundreds of unique female
names. Turkish personal names have their origins mainly in Arabic, followed by
Turkish and Persian. We computed overall frequencies of names according to
broad semantic themes that were identified in previous studies. We found that
foreign-origin names such as olga and khaled, pastoral names such as ya\u{g}mur
and deniz (rain and sea, respectively), and names based on fruits and plants
such as filiz and menek\c{s}e (sprout and violet, respectively) are more
frequently observed among females. Among males, names based on animals such as
arslan and yunus (lion and dolphin, respectively) and names based on famous
and/or historical figures such as mustafa kemal and o\u{g}uz ka\u{g}an (founder
of the Turkish Republic and the founder of the Turks in Turkish mythology,
respectively) are observed more frequently.
Optimal Generalized Decision Trees via Integer Programming
Authors: Oktay Gunluk, Jayant Kalagnanam, Minhan Li, Matt Menickelly, Katya Scheinberg
Date: 2016-12-10 00:00:00

Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this paper, we present a mixed integer programming formulation to construct optimal decision trees of a prespecified size. We take the special structure of categorical features into account and allow combinatorial decisions (based on subsets of values of features) at each node. Our approach can also handle numerical features via thresholding. We show that very good accuracy can be achieved with small trees using moderately-sized training sets. The optimization problems we solve are tractable with modern solvers.
Deep Reinforcement Fuzzing
Authors: Konstantin Böttinger, Patrice Godefroid, Rishabh Singh
Date: 2018-01-14 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Fuzzing is the process of finding security vulnerabilities in
input-processing code by repeatedly testing the code with modified inputs. In
this paper, we formalize fuzzing as a reinforcement learning problem using the
concept of Markov decision processes. This in turn allows us to apply
state-of-the-art deep Q-learning algorithms that optimize rewards, which we
define from runtime properties of the program under test. By observing the
rewards caused by mutating with a specific set of actions performed on an
initial program input, the fuzzing agent learns a policy that can next generate
new higher-reward inputs. We have implemented this new approach, and
preliminary empirical evidence shows that reinforcement fuzzing can outperform
baseline random fuzzing.
Deep Portfolio Theory
Authors: J. B. Heaton, N. G. Polson, J. H. Witte
Date: 2016-05-23 00:00:00

We construct a deep portfolio theory. By building on Markowitz's classic
risk-return trade-off, we develop a self-contained four-step routine of encode,
calibrate, validate and verify to formulate an automated and general portfolio
selection process. At the heart of our algorithm are deep hierarchical
compositions of portfolios constructed in the encoding step. The calibration
step then provides multivariate payouts in the form of deep hierarchical
portfolios that are designed to target a variety of objective functions. The
validate step trades-off the amount of regularization used in the encode and
calibrate steps. The verification step uses a cross validation approach to
trace out an ex post deep portfolio efficient frontier. We demonstrate all four
steps of our portfolio theory numerically.
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
Authors: Peng Su, Xiao-Rong Ding, Yuan-Ting Zhang, Jing Liu, Fen Miao, Ni Zhao
Date: 2017-05-12 00:00:00
Tasks: Blood pressure estimation, Electrocardiography (ECG), Photoplethysmography (PPG), Prediction, Temporal Sequences

Existing methods for arterial blood pressure (BP) estimation directly map the
input physiological signals to output BP values without explicitly modeling the
underlying temporal dependencies in BP dynamics. As a result, these models
suffer from accuracy decay over a long time and thus require frequent
calibration. In this work, we address this issue by formulating BP estimation
as a sequence prediction problem in which both the input and target are
temporal sequences. We propose a novel deep recurrent neural network (RNN)
consisting of multilayered Long Short-Term Memory (LSTM) networks, which are
incorporated with (1) a bidirectional structure to access larger-scale context
information of input sequence, and (2) residual connections to allow gradients
in deep RNN to propagate more effectively. The proposed deep RNN model was
tested on a static BP dataset, and it achieved root mean square error (RMSE) of
3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction
respectively, surpassing the accuracy of traditional BP prediction models. On a
multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81
mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP
prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction,
respectively, which outperforms all previous models with notable improvement.
The experimental results suggest that modeling the temporal dependencies in BP
dynamics significantly improves the long-term BP prediction accuracy.
DCDistance: A Supervised Text Document Feature extraction based on class labels
Authors: Charles Henrique Porto Ferreira, Debora Maria Rossi de Medeiros, Fabricio Olivetti de França
Date: 2018-01-14 00:00:00

Text Mining is a field that aims at extracting information from textual data.
One of the challenges of such field of study comes from the pre-processing
stage in which a vector (and structured) representation should be extracted
from unstructured data. The common extraction creates large and sparse vectors
representing the importance of each term to a document. As such, this usually
leads to the curse-of-dimensionality that plagues most machine learning
algorithms. To cope with this issue, in this paper we propose a new supervised
feature extraction and reduction algorithm, named DCDistance, that creates
features based on the distance between a document to a representative of each
class label. As such, the proposed technique can reduce the features set in
more than 99% of the original set. Additionally, this algorithm was also
capable of improving the classification accuracy over a set of benchmark
datasets when compared to traditional and state-of-the-art features selection
algorithms.
Deep Learning in Finance
Authors: J. B. Heaton, N. G. Polson, J. H. Witte
Date: 2016-02-21 00:00:00
Tasks: Deep Learning, General Classification, Management, Prediction

We explore the use of deep learning hierarchical models for problems in
financial prediction and classification. Financial prediction problems -- such
as those presented in designing and pricing securities, constructing
portfolios, and risk management -- often involve large data sets with complex
data interactions that currently are difficult or impossible to specify in a
full economic model. Applying deep learning methods to these problems can
produce more useful results than standard methods in finance. In particular,
deep learning can detect and exploit interactions in the data that are, at
least currently, invisible to any existing financial economic theory.
Counterfactual equivalence for POMDPs, and underlying deterministic environments
Authors: Stuart Armstrong
Date: 2018-01-11 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual

Partially Observable Markov Decision Processes (POMDPs) are rich environments
often used in machine learning. But the issue of information and causal
structures in POMDPs has been relatively little studied. This paper presents
the concepts of equivalent and counterfactually equivalent POMDPs, where agents
cannot distinguish which environment they are in though any observations and
actions. It shows that any POMDP is counterfactually equivalent, for any finite
number of turns, to a deterministic POMDP with all uncertainty concentrated
into the initial state. This allows a better understanding of POMDP
uncertainty, information, and learning.
Evaluation of Machine Learning Fameworks on Finis Terrae II
Authors: Andres Gomez Tato
Date: 2018-01-14 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning

Machine Learning (ML) and Deep Learning (DL) are two technologies used to
extract representations of the data for a specific purpose. ML algorithms take
a set of data as input to generate one or several predictions. To define the
final version of one model, usually there is an initial step devoted to train
the algorithm (get the right final values of the parameters of the model).
There are several techniques, from supervised learning to reinforcement
learning, which have different requirements. On the market, there are some
frameworks or APIs that reduce the effort for designing a new ML model. In this
report, using the benchmark DLBENCH, we will analyse the performance and the
execution modes of some well-known ML frameworks on the Finis Terrae II
supercomputer when supervised learning is used. The report will show that
placement of data and allocated hardware can have a large influence on the
final timeto-solution.