pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning
Authors: Gregory S. Ledva, Laura Balzano, Johanna L. Mathieu
Date: 2017-01-16 00:00:00

Though distribution system operators have been adding more sensors to their
networks, they still often lack an accurate real-time picture of the behavior
of distributed energy resources such as demand responsive electric loads and
residential solar generation. Such information could improve system
reliability, economic efficiency, and environmental impact. Rather than
installing additional, costly sensing and communication infrastructure to
obtain additional real-time information, it may be possible to use existing
sensing capabilities and leverage knowledge about the system to reduce the need
for new infrastructure. In this paper, we disaggregate a distribution feeder's
demand measurements into: 1) the demand of a population of air conditioners,
and 2) the demand of the remaining loads connected to the feeder. We use an
online learning algorithm, Dynamic Fixed Share (DFS), that uses the real-time
distribution feeder measurements as well as models generated from historical
building- and device-level data. We develop two implementations of the
algorithm and conduct case studies using real demand data from households and
commercial buildings to investigate the effectiveness of the algorithm. The
case studies demonstrate that DFS can effectively perform online disaggregation
and the choice and construction of models included in the algorithm affects its
accuracy, which is comparable to that of a set of Kalman filters.
Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images
Authors: Denton Bobeldyk, Arun Ross
Date: 2018-05-04 00:00:00
Tasks: Attribute, Iris Recognition, Prediction

Recent research has explored the possibility of automatically deducing
information such as gender, age and race of an individual from their biometric
data. While the face modality has been extensively studied in this regard, the
iris modality less so. In this paper, we first review the medical literature to
establish a biological basis for extracting gender and race cues from the iris.
Then, we demonstrate that it is possible to use simple texture descriptors,
like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary
Patterns), to extract gender and race attributes from an NIR ocular image used
in a typical iris recognition system. The proposed method predicts gender and
race from a single eye image with an accuracy of 86% and 90%, respectively. In
addition, the following analysis are conducted: (a) the role of different parts
of the ocular region on attribute prediction; (b) the influence of gender on
race prediction, and vice-versa; (c) the impact of eye color on gender and race
prediction; (d) the impact of image blur on gender and race prediction; (e) the
generalizability of the method across different datasets; and (f) the
consistency of prediction performance across the left and right eyes.
Exploration by Distributional Reinforcement Learning
Authors: Yunhao Tang, Shipra Agrawal
Date: 2018-05-04 00:00:00
Tasks: Deep Reinforcement Learning, Distributional Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a framework based on distributional reinforcement learning and
recent attempts to combine Bayesian parameter updates with deep reinforcement
learning. We show that our proposed framework conceptually unifies multiple
previous methods in exploration. We also derive a practical algorithm that
achieves efficient exploration on challenging control tasks.
Switchable Temporal Propagation Network
Authors: Sifei Liu, Guangyu Zhong, Shalini De Mello, Jinwei Gu, Varun Jampani, Ming-Hsuan Yang, Jan Kautz
Date: 2018-04-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Video Compression

Videos contain highly redundant information between frames. Such redundancy
has been extensively studied in video compression and encoding, but is less
explored for more advanced video processing. In this paper, we propose a
learnable unified framework for propagating a variety of visual properties of
video images, including but not limited to color, high dynamic range (HDR), and
segmentation information, where the properties are available for only a few
key-frames. Our approach is based on a temporal propagation network (TPN),
which models the transition-related affinity between a pair of frames in a
purely data-driven manner. We theoretically prove two essential factors for
TPN: (a) by regularizing the global transformation matrix as orthogonal, the
"style energy" of the property can be well preserved during propagation; (b)
such regularization can be achieved by the proposed switchable TPN with
bi-directional training on pairs of frames. We apply the switchable TPN to
three tasks: colorizing a gray-scale video based on a few color key-frames,
generating an HDR video from a low dynamic range (LDR) video and a few HDR
frames, and propagating a segmentation mask from the first frame in videos.
Experimental results show that our approach is significantly more accurate and
efficient than the state-of-the-art methods.
Automatic Estimation of Modulation Transfer Functions
Authors: Matthias Bauer, Valentin Volchkov, Michael Hirsch, Bernhard Schölkopf
Date: 2018-05-04 00:00:00

The modulation transfer function (MTF) is widely used to characterise the
performance of optical systems. Measuring it is costly and it is thus rarely
available for a given lens specimen. Instead, MTFs based on simulations or, at
best, MTFs measured on other specimens of the same lens are used. Fortunately,
images recorded through an optical system contain ample information about its
MTF, only that it is confounded with the statistics of the images. This work
presents a method to estimate the MTF of camera lens systems directly from
photographs, without the need for expensive equipment. We use a custom grid
display to accurately measure the point response of lenses to acquire ground
truth training data. We then use the same lenses to record natural images and
employ a data-driven supervised learning approach using a convolutional neural
network to estimate the MTF on small image patches, aggregating the information
into MTF charts over the entire field of view. It generalises to unseen lenses
and can be applied for single photographs, with the performance improving if
multiple photographs are available.
Bayesian active learning for choice models with deep Gaussian processes
Authors: Jie Yang, Diego Klabjan
Date: 2018-05-04 00:00:00
Tasks: Active Learning, Gaussian Processes

In this paper, we propose an active learning algorithm and models which can
gradually learn individual's preference through pairwise comparisons. The
active learning scheme aims at finding individual's most preferred choice with
minimized number of pairwise comparisons. The pairwise comparisons are encoded
into probabilistic models based on assumptions of choice models and deep
Gaussian processes. The next-to-compare decision is determined by a novel
acquisition function. We benchmark the proposed algorithm and models using
functions with multiple local optima and one public airline itinerary dataset.
The experiments indicate the effectiveness of our active learning algorithm and
models.
High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel
Authors: Brian L. DeCost, Bo Lei, Toby Francis, Elizabeth A. Holm
Date: 2018-05-04 00:00:00
Tasks: Segmentation

We apply a deep convolutional neural network segmentation model to enable
novel automated microstructure segmentation applications for complex
microstructures typically evaluated manually and subjectively. We explore two
microstructure segmentation tasks in an openly-available ultrahigh carbon steel
microstructure dataset: segmenting cementite particles in the spheroidized
matrix, and segmenting larger fields of view featuring grain boundary carbide,
spheroidized particle matrix, particle-free grain boundary denuded zone, and
Widmanst\"atten cementite. We also demonstrate how to combine these data-driven
microstructure segmentation models to obtain empirical cementite particle size
and denuded zone width distributions from more complex micrographs containing
multiple microconstituents. The full annotated dataset is available on
materialsdata.nist.gov (https://materialsdata.nist.gov/handle/11256/964).
Spatial Morphing Kernel Regression For Feature Interpolation
Authors: Xueqing Deng, Yi Zhu, Shawn Newsam
Date: 2018-02-21 00:00:00
Tasks: Action Detection, Activity Detection, regression

In recent years, geotagged social media has become popular as a novel source
for geographic knowledge discovery. Ground-level images and videos provide a
different perspective than overhead imagery and can be applied to a range of
applications such as land use mapping, activity detection, pollution mapping,
etc. The sparse and uneven distribution of this data presents a problem,
however, for generating dense maps. We therefore investigate the problem of
spatially interpolating the high-dimensional features extracted from sparse
social media to enable dense labeling using standard classifiers. Further, we
show how prior knowledge about region boundaries can be used to improve the
interpolation through spatial morphing kernel regression. We show that an
interpolate-then-classify framework can produce dense maps from sparse
observations but that care must be taken in choosing the interpolation method.
We also show that the spatial morphing kernel improves the results.
Machine learning for neural decoding
Authors: Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich, Lee E. Miller, Konrad P. Kording
Date: 2017-08-02 00:00:00
Tasks: BIG-bench Machine Learning, Hippocampus

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help advance engineering applications such as brain machine interfaces.
Modeling Dengue Vector Population Using Remotely Sensed Data and Machine Learning
Authors: J. M. Scavuzzo, F. Trucco, M. Espinosa, C. B. Tauro, M. Abril, C. M. Scavuzzo, A. C. Frery
Date: 2018-05-04 00:00:00
Tasks: BIG-bench Machine Learning, Earth Observation, Time Series Analysis

Mosquitoes are vectors of many human diseases. In particular, Aedes \ae gypti
(Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in
Latin America and it represents a global threat. Public health policies that
aim at combating this vector require dependable and timely information, which
is usually expensive to obtain with field campaigns. For this reason, several
efforts have been done to use remote sensing due to its reduced cost. The
present work includes the temporal modeling of the oviposition activity
(measured weekly on 50 ovitraps in a north Argentinean city) of Aedes \ae gypti
(Linnaeus), based on time series of data extracted from operational earth
observation satellite images. We use are NDVI, NDWI, LST night, LST day and
TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to
previous works which use linear models, we employ Machine Learning techniques
using completely accessible open source toolkits. These models have the
advantages of being non-parametric and capable of describing nonlinear
relationships between variables. Specifically, in addition to two linear
approaches, we assess a Support Vector Machine, an Artificial Neural Networks,
a K-nearest neighbors and a Decision Tree Regressor. Considerations are made on
parameter tuning and the validation and training approach. The results are
compared to linear models used in previous works with similar data sets for
generating temporal predictive models. These new tools perform better than
linear approaches, in particular Nearest Neighbor Regression (KNNR) performs
the best. These results provide better alternatives to be implemented
operatively on the Argentine geospatial Risk system that is running since 2012.
Towards a Spectrum of Graph Convolutional Networks
Authors: Mathias Niepert, Alberto Garcia-Duran
Date: 2018-05-04 00:00:00
Tasks: Attribute

We present our ongoing work on understanding the limitations of graph
convolutional networks (GCNs) as well as our work on generalizations of graph
convolutions for representing more complex node attribute dependencies. Based
on an analysis of GCNs with the help of the corresponding computation graphs,
we propose a generalization of existing GCNs where the aggregation operations
are (a) determined by structural properties of the local neighborhood graphs
and (b) not restricted to weighted averages. We show that the proposed approach
is strictly more expressive while requiring only a modest increase in the
number of parameters and computations. We also show that the proposed
generalization is identical to standard convolutional layers when applied to
regular grid graphs.
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Authors: Daniele Palossi, Antonio Loquercio, Francesco Conti, Eric Flamand, Davide Scaramuzza, Luca Benini
Date: 2018-05-04 00:00:00
Tasks: Autonomous Navigation, Visual Navigation

Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm${}^\mathrm{2}$. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.
LIME: Live Intrinsic Material Estimation
Authors: Abhimitra Meka, Maxim Maximov, Michael Zollhoefer, Avishek Chatterjee, Hans-Peter Seidel, Christian Richardt, Christian Theobalt
Date: 2018-01-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Foreground Segmentation, Image-to-Image Translation, Inverse Rendering, Mixed Reality, Translation

We present the first end to end approach for real time material estimation
for general object shapes with uniform material that only requires a single
color image as input. In addition to Lambertian surface properties, our
approach fully automatically computes the specular albedo, material shininess,
and a foreground segmentation. We tackle this challenging and ill posed inverse
rendering problem using recent advances in image to image translation
techniques based on deep convolutional encoder decoder architectures. The
underlying core representations of our approach are specular shading, diffuse
shading and mirror images, which allow to learn the effective and accurate
separation of diffuse and specular albedo. In addition, we propose a novel
highly efficient perceptual rendering loss that mimics real world image
formation and obtains intermediate results even during run time. The estimation
of material parameters at real time frame rates enables exciting mixed reality
applications, such as seamless illumination consistent integration of virtual
objects into real world scenes, and virtual material cloning. We demonstrate
our approach in a live setup, compare it to the state of the art, and
demonstrate its effectiveness through quantitative and qualitative evaluation.
Understanding Reuse, Performance, and Hardware Cost of DNN Dataflows: A Data-Centric Approach Using MAESTRO
Authors: Hyoukjun Kwon, Prasanth Chatarasi, Michael Pellauer, Angshuman Parashar, Vivek Sarkar, Tushar Krishna
Date: 2018-05-04 00:00:00
Tasks: Scheduling, valid

The data partitioning and scheduling strategies used by DNN accelerators to leverage reuse and perform staging are known as dataflow, and they directly impact the performance and energy efficiency of DNN accelerator designs. An accelerator microarchitecture dictates the dataflow(s) that can be employed to execute a layer or network. Selecting an optimal dataflow for a layer shape can have a large impact on utilization and energy efficiency, but there is a lack of understanding on the choices and consequences of dataflows, and of tools and methodologies to help architects explore the co-optimization design space. In this work, we first introduce a set of data-centric directives to concisely specify the space of DNN dataflows in a compilerfriendly form. We then show how these directives can be analyzed to infer various forms of reuse and to exploit them using hardware capabilities. We codify this analysis into an analytical cost model, MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Reuse and Occupancy), that estimates various cost-benefit tradeoffs of a dataflow including execution time and energy efficiency for a DNN model and hardware configuration. We demonstrate the use of MAESTRO to drive a hardware design space exploration (DSE) experiment, which searches across 480M designs to identify 2.5M valid designs at an average rate of 0.17M designs per second, including Pareto-optimal throughput- and energy-optimized design points.
Object and Text-guided Semantics for CNN-based Activity Recognition
Authors: Sungmin Eum, Christopher Reale, Heesung Kwon, Claire Bonial, Clare Voss
Date: 2018-05-04 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Object Recognition

Many previous methods have demonstrated the importance of considering
semantically relevant objects for carrying out video-based human activity
recognition, yet none of the methods have harvested the power of large text
corpora to relate the objects and the activities to be transferred into
learning a unified deep convolutional neural network. We present a novel
activity recognition CNN which co-learns the object recognition task in an
end-to-end multitask learning scheme to improve upon the baseline activity
recognition performance. We further improve upon the multitask learning
approach by exploiting a text-guided semantic space to select the most relevant
objects with respect to the target activities. To the best of our knowledge, we
are the first to investigate this approach.
Assessing a mobile-based deep learning model for plant disease surveillance
Authors: Amanda Ramcharan, Peter McCloskey, Kelsee Baranowski, Neema Mbilinyi, Latifa Mrisho, Mathias Ndalahwa, James Legg, David Hughes
Date: 2018-05-04 00:00:00
Tasks: object-detection, Object Detection

Convolutional neural network models (CNNs) have made major advances in
computer vision tasks in the last five years. Given the challenge in collecting
real world datasets, most studies report performance metrics based on available
research datasets. In scenarios where CNNs are to be deployed on images or
videos from mobile devices, models are presented with new challenges due to
lighting, angle, and camera specifications, which are not accounted for in
research datasets. It is essential for assessment to also be conducted on real
world datasets if such models are to be reliably integrated with products and
services in society. Plant disease datasets can be used to test CNNs in real
time and gain insight into real world performance. We train a CNN object
detection model to identify foliar symptoms of diseases (or lack thereof) in
cassava (Manihot esculenta Crantz). We then deploy the model on a mobile app
and test its performance on mobile images and video of 720 diseased leaflets in
an agricultural field in Tanzania. Within each disease category we test two
levels of severity of symptoms - mild and pronounced, to assess the model
performance for early detection of symptoms. In both severities we see a
decrease in the F-1 score for real world images and video. The F-1 score
dropped by 32% for pronounced symptoms in real world images (the closest data
to the training data) due to a drop in model recall. If the potential of
smartphone CNNs are to be realized our data suggest it is crucial to consider
tuning precision and recall performance in order to achieve the desired
performance in real world settings. In addition, the varied performance related
to different input data (image or video) is an important consideration for the
design of CNNs in real world applications.
Extreme Adaptation for Personalized Neural Machine Translation
Authors: Paul Michel, Graham Neubig
Date: 2018-05-04 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Translation

Every person speaks or writes their own flavor of their native language,
influenced by a number of factors: the content they tend to talk about, their
gender, their social status, or their geographical origin.
  When attempting to perform Machine Translation (MT), these variations have a
significant effect on how the system should perform translation, but this is
not captured well by standard one-size-fits-all models.
  In this paper, we propose a simple and parameter-efficient adaptation
technique that only requires adapting the bias of the output softmax to each
particular user of the MT system, either directly or through a factored
approximation.
  Experiments on TED talks in three languages demonstrate improvements in
translation accuracy, and better reflection of speaker traits in the target
text.