pwc-archive/papers-with-abstracts huggingface dataset:
Boosting propagule transport models with individual-specific data from mobile apps
Authors: Samuel M. Fischer, Pouria Ramazi, Sean Simmons, Mark S. Poesch, Mark A. Lewis
Date: 2021-05-29 00:00:00
Tasks: Management

Management of invasive species and pathogens requires information about the traffic of potential vectors. Such information is often taken from vector traffic models fitted to survey data. Here, user-specific data collected via mobile apps offer new opportunities to obtain more accurate estimates and to analyze how vectors' individual preferences affect propagule flows. However, data voluntarily reported via apps may lack some trip records, adding a significant layer of uncertainty. We show how the benefits of app-based data can be exploited despite this drawback. Based on data collected via an angler app, we built a stochastic model for angler traffic in the Canadian province Alberta. There, anglers facilitate the spread of whirling disease, a parasite-induced fish disease. The model is temporally and spatially explicit and accounts for individual preferences and repeating behaviour of anglers, helping to address the problem of missing trip records. We obtained estimates of angler traffic between all subbasins in Alberta. The model's accuracy exceeds that of direct empirical estimates even when fewer data were used to fit the model. The results indicate that anglers' local preferences and their tendency to revisit previous destinations reduce the number of long inter-waterbody trips potentially dispersing whirling disease. According to our model, anglers revisit their previous destination in 64% of their trips, making these trips irrelevant for the spread of whirling disease. Furthermore, 54% of fishing trips end in individual-specific spatially contained areas with mean radius of 54.7km. Finally, although the fraction of trips that anglers report was unknown, we were able to estimate the total yearly number of fishing trips in Alberta, matching an independent empirical estimate.
A Mathematical Model For the Spread of a Virus
Authors: Neil R. Sheeley Jr
Date: 2020-11-20 00:00:00

This paper describes a mathematical model for the spread of a virus through an isolated population of a given size. The model uses three, color-coded components, called molecules (red for infected and still contagious; green for infected, but no longer contagious; and blue for uninfected). In retrospect, the model turns out to be a digital analogue for the well-known SIR model of Kermac and McKendrick (1927). In our RGB model, the number of accumulated infections goes through three phases, beginning at a very low level, then changing to a transition ramp of rapid growth, and ending in a plateau of final values. Consequently, the differential change or growth rate begins at 0, rises to a peak corresponding to the maximum slope of the transition ramp, and then falls back to 0. The properties of these time variations, including the slope, duration, and height of the transition ramp, and the width and height of the infection rate, depend on a single parameter - the time that a red molecule is contagious divided by the average time between collisions of the molecules. Various temporal milestones, including the starting time of the transition ramp, the time that the accumulating number of infections obtains its maximum slope, and the location of the peak of the infection rate depend on the size of the population in addition to the contagious lifetime ratio. Explicit formulas for these quantities are derived and summarized. Finally, Appendix E has been added to describe the effect of vaccinations.
Network embedding unveils the hidden interactions in the mammalian virome
Authors: Timothée Poisot, Marie-Andrée Ouellet, Nardus Mollentze, Maxwell J. Farrell, Daniel J. Becker, Liam Brierly, Gregory F. Albery, Rory J. Gibb, Stephanie N. Seifert, Colin J. Carlson
Date: 2021-05-31 00:00:00
Tasks: Graph Embedding, Imputation, Network Embedding, Recommendation Systems

At most 1-2% of the global virome has been sampled to date. Recent work has shown that predicting which host-virus interactions are possible but undiscovered or unrealized is, fundamentally, a network science problem. Here, we develop a novel method that combines a coarse recommender system (Linear Filtering; LF) with an imputation algorithm based on low-rank graph embedding (Singular Value Decomposition; SVD) to infer host-virus associations. This combination of techniques results in informed initial guesses based on directly measurable network properties (density, degree distribution) that are refined through SVD (which is able to leverage emerging features). Using this method, we recovered highly plausible undiscovered interactions with a strong signal of viral coevolutionary history, and revealed a global hotspot of unusually unique but unsampled (or unrealized) host-virus interactions in the Amazon rainforest. We develop several tests for quantifying the bias and realism of these predictions, and show that the LF-SVD method is robust in each aspect. We finally show that graph embedding of the imputed network can be used to improve predictions of human infection from viral genome features, showing that the global structure of the mammal-virus network provides additional insights into human disease emergence.
Multi-step Reflection Principle and Barrier Options
Authors: Hangsuck Lee, Gaeun Lee, Seongjoo Song
Date: 2021-05-31 00:00:00

This paper examines a class of barrier options-multi-step barrier options, which can have any finite number of barriers of any level. We obtain a general, explicit expression of option prices of this type under the Black-Scholes model. Multi-step barrier options are not only useful in that they can handle barriers of different levels and time steps, but can also approximate options with arbitrary barriers. Moreover, they can be embedded in financial products such as deposit insurances based on jump models with simple barriers. Along the way, we derive multi-step reflection principle, which generalizes the reflection principle of Brownian motion.
Instance Segmentation of Microscopic Foraminifera
Authors: Thomas Haugland Johansen, Steffen Aagaard Sørensen, Kajsa Møllersen, Fred Godtliebsen
Date: 2021-05-15 00:00:00
Tasks: Instance Segmentation, Novel Object Detection, object-detection, Object Detection, Segmentation, Semantic Segmentation

Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in e.g. paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of $0.78 \pm 0.00$ on the classification and detection task, and $0.80 \pm 0.00$ on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to $0.84 \pm 0.00$ and $0.86 \pm 0.00$, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work, and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifera.
Classifying States of Cooking Objects Using Convolutional Neural Network
Authors: Qi Zheng
Date: 2021-04-30 00:00:00

Automated cooking machine is a goal for the future. The main aim is to make the cooking process easier, safer, and create human welfare. To allow robots to accurately perform the cooking activities, it is important for them to understand the cooking environment and recognize the objects, especially correctly identifying the state of the cooking objects. This will significantly improve the correctness of the following cooking recipes. In this project, several parts of the experiment were conducted to design a robust deep convolutional neural network for classifying the state of the cooking objects from scratch. The model is evaluated by using various techniques, such as adjusting architecture layers, tuning key hyperparameters, and using different optimization techniques to maximize the accuracy of state classification.
Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring
Authors: Michael J. Horry, Subrata Chakraborty, Biswajeet Pradhan, Maryam Fallahpoor, Chegeni Hossein, Manoranjan Paul
Date: 2021-04-20 00:00:00

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 severity using an independent new dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. The study shows high variability in the generalization of models trained on these datasets due to varied sample image provenances and acquisition processes amongst other factors. We show that under certain conditions, an internally consistent dataset can generalize well to an external dataset despite structural differences between these datasets with f1 scores up to 86%. Our best performing model shows high predictive accuracy for lung involvement score for an independent dataset for which expertly labelled lung involvement stratification is available. Creating an ensemble of our best model for disease positive prediction with our best model for disease negative prediction using a min-max function resulted in a superior model for lung involvement prediction with average predictive accuracy of 75% for zero lung involvement and 96% for 75-100% lung involvement with almost linear relationship between these stratifications.
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems
Authors: Krishna M. Sivalingam
Date: 2021-04-21 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Fault Diagnosis, Management, Scheduling, Traffic Classification, Traffic Prediction

This article presents a primer/overview of applications of Artificial Intelligence and Machine Learning (AI/ML) techniques to address problems in the domain of computer networking. In particular, the techniques have been used to support efficient and accurate traffic prediction, traffic classification, anomaly detection, network management, network security, network resource allocation and optimization, network scheduling algorithms, fault diagnosis and many more such applications. The article first summarizes some of the key networking concepts and a few representative machine learning techniques and algorithms. The article then presents details regarding the availability of data sets for networking applications and machine learning software and toolkits for processing these data sets. Highlights of some of the standards activities, pursued by ITU-T and ETSI, which are related to AI/ML for networking, are also presented. Finally, the article discusses a small set of representative networking problems where AI/ML techniques have been successfully applied.
RaspberryPI for mosquito neutralization by power laser
Authors: R. Ildar
Date: 2021-05-20 00:00:00

In this article for the first time, comprehensive studies of mosquito neutralization using machine vision and a 1 W power laser are considered. Developed laser installation with Raspberry Pi that changing the direction of the laser with a galvanometer. We developed a program for mosquito tracking in real. The possibility of using deep neural networks, Haar cascades, machine learning for mosquito recognition was considered. We considered in detail the classification problems of mosquitoes in images. A recommendation is given for the implementation of this device based on a microcontroller for subsequent use as part of an unmanned aerial vehicle. Any harmful insects in the fields can be used as objects for control.
A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems
Authors: Nada Ibrahim, Preeti Maurya, Omid Jafari, Parth Nagarkar
Date: 2021-05-10 00:00:00
Tasks: object-detection, Object Detection, Survey

Video analytics systems perform automatic events, movements, and actions recognition in a video and make it possible to execute queries on the video. As a result of a large number of video data that need to be processed, optimizing the performance of video analytics systems has become an important research topic. Neural networks are the state-of-the-art for performing video analytics tasks such as video annotation and object detection. Prior survey papers consider application-specific video analytics techniques that improve accuracy of the results; however, in this survey paper, we provide a review of the techniques that focus on optimizing the performance of Neural Network-Based Video Analytics Systems.
Anchor Nodes Positioning for Self-localization in Wireless Sensor Networks using Belief Propagation and Evolutionary Algorithms
Authors: Saeed Ghadiri
Date: 2021-04-19 00:00:00
Tasks: Evolutionary Algorithms

Locating each node in a wireless sensor network is essential for starting the monitoring job and sending information about the area. One method that has been used in hard and inaccessible environments is randomly scattering each node in the area. In order to reduce the cost of using GPS at each node, some nodes should be equipped with GPS (anchors), Then using the belief propagation algorithm, locate other nodes. The number of anchor nodes must be reduced since they are expensive. Furthermore, the location of these nodes affects the algorithm's performance. Using multi-objective optimization, an algorithm is introduced in this paper that minimizes the estimated location error and the number of anchor nodes. According to simulation results, This algorithm proposes a set of solutions with less energy consumption and less error than similar algorithms.
Completion of Missing Labels for Multi-Label Annotation by a Unified Graph Laplacian Regularization
Authors: Jonathan MOJOO†a), Yu ZHAO†, Muthu Subash KAVITHA†, Nonmembers, Junichi MIYAO†, Member, and Takio KURITA
Date: 2020-10-01 00:00:00
Tasks: Image Retrieval, Missing Labels, Retrieval

The task of image annotation is becoming enormously important for efficient image retrieval from the web and other large databases.
However, huge semantic information and complex dependency of labels
on an image make the task challenging. Hence determining the semantic similarity between multiple labels on an image is useful to understand
any incomplete label assignment for image retrieval. This work proposes
a novel method to solve the problem of multi-label image annotation by
unifying two different types of Laplacian regularization terms in deep convolutional neural network (CNN) for robust annotation performance. The
unified Laplacian regularization model is implemented to address the missing labels efficiently by generating the contextual similarity between labels
both internally and externally through their semantic similarities, which
is the main contribution of this study. Specifically, we generate similarity matrices between labels internally by using Hayashi’s quantification
method-type III and externally by using the word2vec method. The generated similarity matrices from the two different methods are then combined
as a Laplacian regularization term, which is used as the new objective function of the deep CNN. The Regularization term implemented in this study is
able to address the multi-label annotation problem, enabling a more effectively trained neural network. Experimental results on public benchmark
datasets reveal that the proposed unified regularization model with deep
CNN produces significantly better results than the baseline CNN without
regularization and other state-of-the-art methods for predicting missing labels.
Deep Neural Networks for YouTube Recommendations
Authors: Paul Covington, Jay Adams, Emre Sargin
Date: 2016-09-07 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.
[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Authors: Maja Schneider, Marco Körner
Date: 2020-12-06 00:00:00
Proceeding: RC 2020
Tasks: General Classification, Time Series, Time Series Analysis, Time Series Classification

Scope of Reproducibility 

The evaluated paper presents a method to classify crop types from multispectral satellite image time series with a newly developed pixel-set encoder and an adaption of the Transformer [2], called temporal attention encoder. 

Methodology 

In order to assess both the architecture and the performance of the approach, we first attempted to implement the method from scratch, followed by a study of the authorsʼ openly provided code. Additionally, we also compiled an alternative dataset similar to the one presented in the paper and evaluated the methodology on it. 

Results 

During the study, we were not able to reproduce the method due to a conceptual misinterpretation of ours regarding the authorsʼ adaption of the Transformer [2]. However, the publicly available implementation helped us answering our questions and proved its validity during our experiments on different datasets. Additionally, we compared the papersʼ temporal attention encoder to our adaption of it, which we came across while we were trying to reimplement and grasp the authorsʼ ideas. 

What was easy 

Running the provided code and obtaining the presented dataset turned out to be easily possible. Even adapting the method to our own ideas did not cause issues, due to a well documented and clear implementation.

What was difficult 

Reimplementing the approach from scratch turned out to be harder than expected, especially because we had a certain type of architecture in mind that did not fit the dimensions of the layers mentioned in the paper. Furthermore, knowing how the dataset was exactly assembled would have been beneficial for us, as we tried to retrace these steps, and therefore would have made the results on our dataset easier to compare to the ones from the paper. 

Communication with original authors 

While working on the challenge, we stood in E-mail contact with the first and second author, had two online meetings and got feedback to our implementation on GITHUB. Additionally, one of the authors of the Transformer paper [2] provided us with further answers regarding their modelsʼ architecture.
[Re] Reimplementation of FixMatch and Investigation on Noisy (Pseudo) Labels and Confirmation Errors of FixMatch
Authors: Ci Li, Ruibo Tu, HUI ZHANG
Date: 2020-12-06 00:00:00
Proceeding: RC 2020
Tasks: Semi-Supervised Image Classification

FixMatch is a semi-supervised learning method, which achieves comparable results with fully supervised learning by leveraging a limited number of labeled data (pseudo labelling technique) and taking a good use of the unlabeled data (consistency regularization ). In this work, we reimplement FixMatch and achieve reasonably comparable performance with the official implementation, which supports that FixMatch outperforms semi-superivesed learning benchmarks and demonstrates that the authorʼs choices with respect to those ablations were experimentally sound. Next, we investigate the existence of a major problem of FixMatch, confirmation errors, by reconstructing the batch structure during the training process. It reveals existing confirmation errors, especially the ones caused by asymmetric noise in pseudo labels. To deal with the problem, we apply equal-frequency and confidence entropy regularization to the labeled data and add them in the loss function. Our experimental results on CIFAR-10 show that using either of the entropy regularization in the loss function can reduce the asymmetric noise in pseudo labels and improve the performance of FixMatch in the presence of (pseudo) labels containing (asymmetric) noise. Our code is available at the url: https://github.com/Celiali/FixMatch.
[Re] Can gradient clipping mitigate label noise?
Authors: David Mizrahi, Oğuz Kaan Yüksel, Aiday Marlen Kyzy
Date: 2021-01-31 00:00:00
Proceeding: RC 2020
Tasks: CPU, GPU, Image Classification, Learning with noisy labels

Scope of Reproducibility 

The original paper proposes partially Huberised losses, which possess label noise robustness. The authors claim that there exist label noise scenarios that defeat Huberised but not partially Huberised losses, and that partially Huberised versions of existing losses perform well on real-world datasets subject to symmetric label noise. 

Methodology 

All the experiments described in the paper were fully re-implemented using NumPy, SciPy and PyTorch. The experiments on synthetic data were run on a CPU, while the deep learning experiments were run using a Nvidia RTX 2080 Ti GPU. Running the experimentation necessary to gain some insight on some of the network architectures used and reproducing the real-world experiments required over 550 GPU hours. 

Results 

Overall, our results mostly support the claims of the original paper. For the synthetic experiments, our results differ when using the exact values described in the paper, although they still support the main claim. After slightly modifying some of the experiment settings, our reproduced figures are nearly identical to the figures from the original paper. For the deep learning experiments, our results differ, with some of the baselines reaching a much higher accuracy on MNIST, CIFAR-10 and CIFAR-100. Nonetheless, with the help of an additional experiment, our results support the authorsʼ claim that partially Huberised losses perform well on real-world datasets subject to label noise. 

What was easy 

The original paper is well written and insightful, which made it fairly easy to implement the partially Huberised version of standard losses based on the information given. In addition, recreating the synthetic datasets used in two of the original paperʼs experiments was relatively straightforward.

What was difficult 

Even though the authors were very detailed in their feedback, finding the exact hyperparameters used in the real-world experiments required many iterations of inquiry and experimentation. In addition, the CIFAR-10 and CIFAR-100 experiments can be difficult to reproduce due to the high number of experiment configurations, resulting in many training runs and a relatively high computational cost of over 550 GPU hours. 

Communication with original authors 

We contacted the authors on multiple occasions regarding some of the hyperparameters used in their experiments, to which they promptly replied with very detailed explanations.