pwc-archive/papers-with-abstracts huggingface dataset:
Determining the effects of storage on cotton and soybean leaf samples for hyperspectral analysis
Authors: Lee, M.A.; Huang, Y.; Yao, H.; Thomson, S.J.
Date: 2014-07-01 00:00:00
Proceeding: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014 7

This paper studies the effect of storage techniques for
transporting collected plant leaves from the field to the laboratory
for hyperspectral analysis. The strategy of collecting leaf samples in
the field for laboratory analysis is typically used when ground
truthing is needed in remote sensing studies. Results indicate that
the accuracy of hyperspectral measurements depends on a combination of storage technique (in a cooler or outside a cooler), time
elapsed between collecting leaf samples in the field and measuring in
the laboratory, and the plant species. A nonlinear model fitting
method is proposed to estimate the spectrum of decaying plant
leaves. This revealed that the reflectance of soybean leaves remained
within the normal range for 45 min when the leaves were stored in a
cooler, while soybean leaves stored outside a cooler remained within
the normal range for 30 min. However, cotton leaves stored in a
cooler decayed faster initially. Regardless of storage technique,
results indicate that up to a maximum of 30 min can elapse between
plant leaf sampling in the field and hyperspectral measurements in
the laboratory. This study focused on cotton and soybean leaves, but
the implication that time elapsing between sampling leaves and
measuring their spectrum should be limited as much as possible can
be applied to any study on other crop leaves. Results of the study also
provide a guideline for crop storage limits when analyzing by
laboratory hyperspectral sensing setting to improve the quality
and reliability of data for precision agriculture.
Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems
Authors: Isabelle Leang, Ganesh Sistu, Fabian Burger, Andrei Bursuc, Senthil Yogamani
Date: 2020-01-07 00:00:00
Tasks: Autonomous Driving, Meta-Learning

Deep multi-task networks are of particular interest for autonomous driving systems. They can potentially strike an excellent trade-off between predictive performance, hardware constraints and efficient use of information from multiple types of annotations and modalities. However, training such models is non-trivial and requires balancing learning over all tasks as their respective losses display different scales, ranges and dynamics across training. Multiple task weighting methods that adjust the losses in an adaptive way have been proposed recently on different datasets and combinations of tasks, making it difficult to compare them. In this work, we review and systematically evaluate nine task weighting strategies on common grounds on three automotive datasets (KITTI, Cityscapes and WoodScape). We then propose a novel method combining evolutionary meta-learning and task-based selective backpropagation, for computing task weights leading to reliable network training. Our method outperforms state-of-the-art methods by a significant margin on a two-task application.
Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation
Authors: Di You, Nguyen Vo, Kyumin Lee, Qiang Liu
Date: 2020-01-07 00:00:00
Tasks: Fact Checking, Graph Attention, Recommendation Systems

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted via social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this paper we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms eight state-of-the-art recommendation models, achieving at least 3~5.3% improvement.
Minimal entropy production due to constraints on rate matrix dependencies in multipartite processes
Authors: David H. Wolpert
Date: 2020-01-07 00:00:00
Tasks: counterfactual

I consider multipartite processes in which there are constraints on each subsystem's rate matrix, restricting which other subsystems can directly affect its dynamics. I derive a strictly nonzero lower bound on the minimal achievable entropy production rate of the process in terms of these constraints on the rate matrices of its subsystems. The bound is based on constructing counterfactual rate matrices, in which some subsystems are held fixed while the others are allowed to evolve. This bound is related to the "learning rate" of stationary bipartite systems, and more generally to the "information flow" in bipartite systems.
On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning
Authors: Ahmadreza Ghanbarpour, Amr H. Mahmoud, Markus A. Lill
Date: 2020-01-07 00:00:00

The calculation of thermodynamic properties of biochemical systems typically requires the use of resource-intensive molecular simulation methods. One example thereof is the thermodynamic profiling of hydration sites, i.e. high-probability locations for water molecules on the protein surface, which play an essential role in protein-ligand associations and must therefore be incorporated in the prediction of binding poses and affinities. To replace time-consuming simulations in hydration site predictions, we developed two different types of deep neural-network models aiming to predict hydration site data. In the first approach, meshed 3D images are generated representing the interactions between certain molecular probes placed on regular 3D grids, encompassing the binding pocket, with the static protein. These molecular interaction fields are mapped to the corresponding 3D image of hydration occupancy using a neural network based on an U-Net architecture. In a second approach, hydration occupancy and thermodynamics were predicted point-wise using a neural network based on fully-connected layers. In addition to direct protein interaction fields, the environment of each grid point was represented using moments of a spherical harmonics expansion of the interaction properties of nearby grid points. Application to structure-activity relationship analysis and protein-ligand pose scoring demonstrates the utility of the predicted hydration information.
An Exploration of Embodied Visual Exploration
Authors: Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman
Date: 2020-01-07 00:00:00
Tasks: Benchmarking

Embodied computer vision considers perception for robots in novel, unstructured environments. Of particular importance is the embodied visual exploration problem: how might a robot equipped with a camera scope out a new environment? Despite the progress thus far, many basic questions pertinent to this problem remain unanswered: (i) What does it mean for an agent to explore its environment well? (ii) Which methods work well, and under which assumptions and environmental settings? (iii) Where do current approaches fall short, and where might future work seek to improve? Seeking answers to these questions, we first present a taxonomy for existing visual exploration algorithms and create a standard framework for benchmarking them. We then perform a thorough empirical study of the four state-of-the-art paradigms using the proposed framework with two photorealistic simulated 3D environments, a state-of-the-art exploration architecture, and diverse evaluation metrics. Our experimental results offer insights and suggest new performance metrics and baselines for future work in visual exploration. Code, models and data are publicly available: https://github.com/facebookresearch/exploring_exploration
Heaps' law and Heaps functions in tagged texts: Evidences of their linguistic relevance
Authors: Andrés Chacoma, Damián H. Zanette
Date: 2020-01-07 00:00:00
Tasks: TAG

We study the relationship between vocabulary size and text length in a corpus of $75$ literary works in English, authored by six writers, distinguishing between the contributions of three grammatical classes (or ``tags,'' namely, {\it nouns}, {\it verbs}, and {\it others}), and analyze the progressive appearance of new words of each tag along each individual text. While the power-law relation prescribed by Heaps' law is satisfactorily fulfilled by total vocabulary sizes and text lengths, the appearance of new words in each text is on the whole well described by the average of random shufflings of the text, which does not obey a power law. Deviations from this average, however, are statistically significant and show a systematic trend across the corpus. Specifically, they reveal that the appearance of new words along each text is predominantly retarded with respect to the average of random shufflings. Moreover, different tags are shown to add systematically distinct contributions to this tendency, with {\it verbs} and {\it others} being respectively more and less retarded than the mean trend, and {\it nouns} following instead this overall mean. These statistical systematicities are likely to point to the existence of linguistically relevant information stored in the different variants of Heaps' law, a feature that is still in need of extensive assessment.
Generalized mean shift with triangular kernel profile
Authors: Sébastien Razakarivony, Axel Barrau
Date: 2020-01-07 00:00:00
Tasks: Clustering, Visual Tracking

The mean shift algorithm is a popular way to find modes of some probability density functions taking a specific kernel-based shape, used for clustering or visual tracking. Since its introduction, it underwent several practical improvements and generalizations, as well as deep theoretical analysis mainly focused on its convergence properties. In spite of encouraging results, this question has not received a clear general answer yet. In this paper we focus on a specific class of kernels, adapted in particular to the distributions clustering applications which motivated this work. We show that a novel Mean Shift variant adapted to them can be derived, and proved to converge after a finite number of iterations. In order to situate this new class of methods in the general picture of the Mean Shift theory, we alo give a synthetic exposure of existing results of this field.
Trained Trajectory based Automated Parking System using Visual SLAM on Surround View Cameras
Authors: Nivedita Tripathi, Senthil Yogamani
Date: 2020-01-07 00:00:00

Automated Parking is becoming a standard feature in modern vehicles. Existing parking systems build a local map to be able to plan for maneuvering towards a detected slot. Next generation parking systems have an use case where they build a persistent map of the environment where the car is frequently parked, say for example, home parking or office parking. The pre-built map helps in re-localizing the vehicle better when its trying to park the next time. This is achieved by augmenting the parking system with a Visual SLAM pipeline and the feature is called trained trajectory parking in the automotive industry. In this paper, we discuss the use cases, design and implementation of a trained trajectory automated parking system. The proposed system is deployed on commercial vehicles and the consumer application is illustrated in \url{https://youtu.be/nRWF5KhyJZU}. The focus of this paper is on the application and the details of vision algorithms are kept at high level.
Inferring Convolutional Neural Networks' accuracies from their architectural characterizations
Authors: Duc Hoang, Jesse Hamer, Gabriel N. Perdue, Steven R. Young, Jonathan Miller, Anushree Ghosh
Date: 2020-01-07 00:00:00
Tasks: Model Selection

Convolutional Neural Networks (CNNs) have shown strong promise for analyzing scientific data from many domains including particle imaging detectors. However, the challenge of choosing the appropriate network architecture (depth, kernel shapes, activation functions, etc.) for specific applications and different data sets is still poorly understood. In this paper, we study the relationships between a CNN's architecture and its performance by proposing a systematic language that is useful for comparison between different CNN's architectures before training time. We characterize CNN's architecture by different attributes, and demonstrate that the attributes can be predictive of the networks' performance in two specific computer vision-based physics problems -- event vertex finding and hadron multiplicity classification in the MINERvA experiment at Fermi National Accelerator Laboratory. In doing so, we extract several architectural attributes from optimized networks' architecture for the physics problems, which are outputs of a model selection algorithm called Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL). We use machine learning models to predict whether a network can perform better than a certain threshold accuracy before training. The models perform 16-20% better than random guessing. Additionally, we found an coefficient of determination of 0.966 for an Ordinary Least Squares model in a regression on accuracy over a large population of networks.
PaRoT: A Practical Framework for Robust Deep Neural Network Training
Authors: Edward Ayers, Francisco Eiras, Majd Hawasly, Iain Whiteside
Date: 2020-01-07 00:00:00
Tasks: Adversarial Defense, Autonomous Vehicles

Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation. Raising unique challenges for assurance due to their black-box nature, DNNs pose a fundamental problem for regulatory acceptance of these types of systems. Robust training --- training to minimize excessive sensitivity to small changes in input --- has emerged as one promising technique to address this challenge. However, existing robust training tools are inconvenient to use or apply to existing codebases and models: they typically only support a small subset of model elements and require users to extensively rewrite the training code. In this paper we introduce a novel framework, PaRoT, developed on the popular TensorFlow platform, that greatly reduces the barrier to entry. Our framework enables robust training to be performed on arbitrary DNNs without any rewrites to the model. We demonstrate that our framework's performance is comparable to prior art, and exemplify its ease of use on off-the-shelf, trained models and its testing capabilities on a real-world industrial application: a traffic light detection network.
General 3D Room Layout from a Single View by Render-and-Compare
Authors: Sinisa Stekovic, Shreyas Hampali, Mahdi Rad, Sayan Deb Sarkar, Friedrich Fraundorfer, Vincent Lepetit
Date: 2020-01-07 00:00:00
Proceeding: ECCV 2020 8

We present a novel method to reconstruct the 3D layout of a room (walls, floors, ceilings) from a single perspective view in challenging conditions, by contrast with previous single-view methods restricted to cuboid-shaped layouts. This input view can consist of a color image only, but considering a depth map results in a more accurate reconstruction. Our approach is formalized as solving a constrained discrete optimization problem to find the set of 3D polygons that constitute the layout. In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate. As no dataset was available to evaluate our method quantitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It offers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts.
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
Authors: Yunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy
Date: 2020-01-07 00:00:00
Tasks: Decision Making

Today, AI is being increasingly used to help human experts make decisions in high-stakes scenarios. In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success. We refer to these scenarios as AI-assisted decision making, where the individual strengths of the human and the AI come together to optimize the joint decision outcome. A key to their success is to appropriately \textit{calibrate} human trust in the AI on a case-by-case basis; knowing when to trust or distrust the AI allows the human expert to appropriately apply their knowledge, improving decision outcomes in cases where the model is likely to perform poorly. This research conducts a case study of AI-assisted decision making in which humans and AI have comparable performance alone, and explores whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. Specifically, we study the effect of showing confidence score and local explanation for a particular prediction. Through two human experiments, we show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making, which may also depend on whether the human can bring in enough unique knowledge to complement the AI's errors. We also highlight the problems in using local explanation for AI-assisted decision making scenarios and invite the research community to explore new approaches to explainability for calibrating human trust in AI.
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning
Authors: Roula Nassif, Stefan Vlaski, Cedric Richard, Jie Chen, Ali H. Sayed
Date: 2020-01-07 00:00:00
Tasks: BIG-bench Machine Learning, Inductive Bias, Transfer Learning

The problem of learning simultaneously several related tasks has received considerable attention in several domains, especially in machine learning with the so-called multitask learning problem or learning to learn problem [1], [2]. Multitask learning is an approach to inductive transfer learning (using what is learned for one problem to assist in another problem) and helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias. Several strategies have been derived within this community under the assumption that all data are available beforehand at a fusion center. However, recent years have witnessed an increasing ability to collect data in a distributed and streaming manner. This requires the design of new strategies for learning jointly multiple tasks from streaming data over distributed (or networked) systems. This article provides an overview of multitask strategies for learning and adaptation over networks. The working hypothesis for these strategies is that agents are allowed to cooperate with each other in order to learn distinct, though related tasks. The article shows how cooperation steers the network limiting point and how different cooperation rules allow to promote different task relatedness models. It also explains how and when cooperation over multitask networks outperforms non-cooperative strategies.
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
Authors: Huiwei Zhou, Zhuang Liu, Shixian Ning, Yunlong Yang, Chengkun Lang, Yingyu Lin, Kun Ma
Date: 2020-01-07 00:00:00
Tasks: Entity Embeddings

Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leverages prior knowledge about protein-protein pairs with memory networks. The proposed MNM captures important context clues related to knowledge representations learned from knowledge bases. Both entity embeddings and relation embeddings of prior knowledge are effective in improving the PPI extraction model, leading to a new state-of-the-art performance on the BioCreative VI PPI dataset. The paper also shows that multiple computational layers over an external memory are superior to long short-term memory networks with the local memories.
State Transition Modeling of the Smoking Behavior using LSTM Recurrent Neural Networks
Authors: Chrisogonas O. Odhiambo, Casey A. Cole, Alaleh Torkjazi, Homayoun Valafar
Date: 2020-01-07 00:00:00

The use of sensors has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch sensors to recognize smoking activity. More specifically, we have reformulated the previous work in detection of smoking to include in-context recognition of smoking. Our presented reformulation of the smoking gesture as a state-transition model that consists of the mini-gestures hand-to-lip, hand-on-lip, and hand-off-lip, has demonstrated improvement in detection rates nearing 100% using conventional neural networks. In addition, we have begun the utilization of Long-Short-Term Memory (LSTM) neural networks to allow for in-context detection of gestures with accuracy nearing 97%.
Knowledge-aware Attention Network for Protein-Protein Interaction Extraction
Authors: Huiwei Zhou, Zhuang Liu1, Shixian Ning, Chengkun Lang, Yingyu Lin, Lei Du
Date: 2020-01-07 00:00:00
Tasks: Feature Engineering

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KB). KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in PPI extraction. This paper proposes a knowledge-aware attention network (KAN) to fuse prior knowledge about protein-protein pairs and context information for PPI extraction. The proposed model first adopts a diagonal-disabled multi-head attention mechanism to encode context sequence along with knowledge representations learned from KB. Then a novel multi-dimensional attention mechanism is used to select the features that can best describe the encoded context. Experiment results on the BioCreative VI PPI dataset show that the proposed approach could acquire knowledge-aware dependencies between different words in a sequence and lead to a new state-of-the-art performance.
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
Authors: Joosung Lee, Sangwon Hwang, Kyungjae Lee, Woo Jin Kim, Junhyeop Lee, Tae-young Chung, Sangyoun Lee
Date: 2020-01-07 00:00:00
Tasks: Monocular Visual Odometry, Optical Flow Estimation, Visual Odometry

Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we propose a learning-based approach for frame-to-frame monocular visual odometry estimation. The proposed network is only learned by disparity maps for not only covering the environment changes but also solving the scale problem. Furthermore, attention block and skip-ordering scheme are introduced to achieve robust performance in various driving environment. Our network is compared with the conventional methods which use common domain such as color or optical flow. Experimental results confirm that the proposed network shows better performance than other approaches with higher and more stable results.