pwc-archive/papers-with-abstracts huggingface dataset:
Detecting cell and protein concentrations by the use of a thermal based sensor
Authors: Juul Goossens, Gilles Oudebrouckx, Seppe Bormans, Thijs Vandenryt, Ronald Thoelen
Date: 2021-02-16 00:00:00
Tasks: Cell Detection

Biosensors are frequently used nowadays for the sake of their attractive capabilities. Because of their high accuracy and precision, they are more and more used in the medical sector. Natural receptors are mostly used, but their use have some specific drawbacks. Therefore, new read-out methods are being developed where there is no need for these receptors. Via a Transient Plane Source (TPS) sensor, the thermal properties of a fluid can be determined. These sensors can detect the capability of a fluid to absorb heat i.e. the thermal effusivity. By the way of monitoring this property, many potential bioprocesses can be monitored. The use of this promising technique was further developed in this research for later use of detecting cell growth and protein concentrations. Firstly, the thermal properties of growth medium and yeast cells were determined. Here, it became clear that the thermal properties change in different concentrations. Also, measurements were performed on protein concentration. No unambiguously results were obtained from these tests. But, the overall results from the use of this sensor are very promising, especially in the cell detection compartment. However, further research will tell about the applicability and sensitivity of this type of sensor.
A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
Authors: Bryar A. Hassan, Tarik A. Rashid
Date: 2021-01-01 00:00:00
Tasks: Clustering

Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multi-disciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECAStar) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multiple-featured datasets. The ECAStar is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECAStar against five conventional approaches: K-means (KM), K-meansPlusPlus (KMPlusPlus), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clusteringPlusPlus (GENCLUSTPlusPlus).
Reinforced Contact Tracing and Epidemic Intervention
Authors: Tao Feng, Sirui Song, Tong Xia, Yong Li
Date: 2021-02-04 00:00:00

The recent outbreak of COVID-19 poses a serious threat to people's lives. Epidemic control strategies have also caused damage to the economy by cutting off humans' daily commute. In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention. IDRLECA first hires an infection probability model to calculate the current infection probability of each individual. Then, the infection probabilities together with individuals' health status and movement information are fed to a novel GNN to estimate the spread of the virus through human contacts. The estimated risks are used to further support an RL agent to select individual-level epidemic-control actions. The training of IDRLECA is guided by a specially designed reward function considering both the cost of mobility intervention and the effectiveness of epidemic control. Moreover, we design a constraint for control-action selection that eases its difficulty and further improve exploring efficiency. Extensive experimental results demonstrate that IDRLECA can suppress infections at a very low level and retain more than 95% of human mobility.
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels
Authors: Jintai Chen, Hongyun Yu, Ruiwei Feng, Danny Z. Chen, Jian Wu
Date: 2021-02-09 00:00:00
Tasks: image-classification, Image Classification, Medical Image Classification

In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, called Flow-Mixup, for multi-labeled medical image classification with corrupted labels. Flow-Mixup guides the models to capture robust features for each abnormality, thus helping handle corrupted labels effectively and making it possible to apply automatic annotation. Specifically, Flow-Mixup decouples the extracted features by adding constraints to the hidden states of the models. Also, Flow-Mixup is more stable and effective comparing to other known regularization methods, as shown by theoretical and empirical analyses. Experiments on two electrocardiogram datasets and a chest X-ray dataset containing corrupted labels verify that Flow-Mixup is effective and insensitive to corrupted labels.
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint
Authors: Ella Y. Wang, Anirudh Som, Ankita Shukla, Hongjun Choi, Pavan Turaga
Date: 2021-02-02 00:00:00
Tasks: Classification, Data Augmentation, Deep Learning, General Classification, X-ray Classification

Deep neural networks have increasingly been used as an auxiliary tool in healthcare applications, due to their ability to improve performance of several diagnosis tasks. However, these methods are not widely adopted in clinical settings due to the practical limitations in the reliability, generalizability, and interpretability of deep learning based systems. As a result, methods have been developed that impose additional constraints during network training to gain more control as well as improve interpretabilty, facilitating their acceptance in healthcare community. In this work, we investigate the benefit of using Orthogonal Spheres (OS) constraint for classification of COVID-19 cases from chest X-ray images. The OS constraint can be written as a simple orthonormality term which is used in conjunction with the standard cross-entropy loss during classification network training. Previous studies have demonstrated significant benefits in applying such constraints to deep learning models. Our findings corroborate these observations, indicating that the orthonormality loss function effectively produces improved semantic localization via GradCAM visualizations, enhanced classification performance, and reduced model calibration error. Our approach achieves an improvement in accuracy of 1.6% and 4.8% for two- and three-class classification, respectively; similar results are found for models with data augmentation applied. In addition to these findings, our work also presents a new application of the OS regularizer in healthcare, increasing the post-hoc interpretability and performance of deep learning models for COVID-19 classification to facilitate adoption of these methods in clinical settings. We also identify the limitations of our strategy that can be explored for further research in future.
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum
Authors: Dylan Bates
Date: 2021-02-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Using the policy gradient algorithm, we train a single-hidden-layer neural network to balance a physically accurate simulation of a single inverted pendulum. The trained weights and biases can then be transferred to a physical agent, where they are robust enough to to balance a real inverted pendulum. This hybrid approach of training a simulation allows thousands of trial runs to be completed orders of magnitude faster than would be possible in the real world, resulting in greatly reduced training time and more iterations, producing a more robust model. When compared with existing reinforcement learning methods, the resulting control is smoother, learned faster, and able to withstand forced disturbances.
Modeling the Hallucinating Brain: A Generative Adversarial Framework
Authors: Masoumeh Zareh, Mohammad Hossein Manshaei, Sayed Jalal Zahabi
Date: 2021-02-09 00:00:00
Tasks: Generative Adversarial Network, Hallucination

This paper looks into the modeling of hallucination in the human's brain. Hallucinations are known to be causally associated with some malfunctions within the interaction of different areas of the brain involved in perception. Focusing on visual hallucination and its underlying causes, we identify an adversarial mechanism between different parts of the brain which are responsible in the process of visual perception. We then show how the characterized adversarial interactions in the brain can be modeled by a generative adversarial network.
End-2-End COVID-19 Detection from Breath & Cough Audio
Authors: Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Alice Baird, Lyn Jones, Björn W. Schuller
Date: 2021-01-07 00:00:00

Our main contributions are as follows: (I) We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the COVID-19 Identification ResNet, (CIdeR), has potential for rapid scalability, minimal cost and improving performance as more data becomes available. This could enable regular COVID-19 testing at apopulation scale; (III) We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation; (IV) We release our four stratified folds for cross parameter optimisation and validation on a standard public corpus and details on the models for reproducibility and future reference.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
Authors: John Blitzer, Mark Dredze, Fernando Pereira
Date: 2007-06-01 00:00:00
Tasks: Domain Adaptation, General Classification, Sentiment Analysis, Sentiment Classification

Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is impractical. We investigate domain adaptation for sentiment classifiers, focusing on online reviews for different types of products. First, we extend to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline. Second, we identify a measure of domain similarity that correlates well with the potential for adaptation of a classifier from one domain to another. This measure could for instance be used to select a small set of domains to annotate whose trained classifiers would transfer well to many other domains.
Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection
Authors: Shaogang Ren, Dingcheng Li, Zhixin Zhou, Ping Li
Date: 2020-04-20 00:00:00
Tasks: Anomaly Detection

The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. A stable inverse function of the generator can be learned with the help of a variance network of the generator. The local variance of the sample distribution can be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on real-world data sets validate the proposed algorithm, which outperforms several baseline methods in these tasks. The proposed method has been further applied to anomaly detection. Experiments show that the method can achieve state-of-the-art anomaly detection performance on real-world data sets.
Robust Disambiguation of Named Entities in Text
Authors: Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, Gerhard Weikum
Date: 2011-07-01 00:00:00
Tasks: Entity Disambiguation, Entity Linking

Disambiguating named entities in naturallanguage text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO. This paper presents a robust method for collective disambiguation, by harnessing context from knowledge bases and using a new form of coherence graph. It unifies prior approaches into a comprehensive framework that combines three measures: the prior probability of an entity being mentioned, the similarity between the contexts of a mention and a candidate entity, as well as the coherence among candidate entities for all mentions together. The method builds a weighted graph of mentions and candidate entities, and computes a dense subgraph that approximates the best joint mention-entity mapping. Experiments show that the new method significantly outperforms prior methods in terms of accuracy, with robust behavior across a variety of inputs.
Client-driven Animated GIF Generation Framework Using an Acoustic Feature
Authors: Ghulam Mujtaba, Sangsoon Lee, Jaehyoun Kim, Eun-Seok Ryu
Date: 2021-02-12 00:00:00
Tasks: Animated GIF Generation, Music Genre Recognition

This paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device. The method analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch. Further, it processes a small video segment to generate the GIF instead of processing the entire video. This makes the proposed method computationally efficient, unlike baseline approaches that use entire videos to create GIFs. The proposed method retrieves and uses the audio file and video segment so that communication and storage efficiencies are improved in the GIF generation process. Experiments on a set of 16 videos show that the proposed approach is 3.76 times more computationally efficient than a baseline method on an Nvidia Jetson TX2. Additionally, in a qualitative evaluation, the GIFs generated using the proposed method received higher overall ratings compared to those generated by the baseline method. To the best of our knowledge, this is the first technique that uses an acoustic feature in the GIF generation process.
Canonicalizing Open Knowledge Bases
Authors: Luis Galárraga, Geremy Heitz
Date: 2014-11-03 00:00:00
Tasks: Clustering, Noun Phrase Canonicalization, Open Information Extraction

Open information extraction approaches have led to the creation of large knowledge bases from the Web. The problem with such methods is that their entities and relations are not canonicalized, leading to redundant and ambiguous facts. For example, they may store hBarack Obama, was born in, Honolului and hObama, place of birth, Honolului. In this paper, we present an approach based on machine learning methods that can canonicalize such Open IE triples, by clustering synonymous names and phrases.

We also provide a detailed discussion about the different signals, features and design choices that influence the quality of synonym resolution for noun phrases in Open IE KBs, thus shedding light on the middle ground between “open” and “closed” information extraction systems.
Robust multi-rate predictive control using multi-step prediction models learned from data
Authors: Enrico Terzi, Lorenzo Fagiano, Marcello Farina, Riccardo Scattolini
Date: 2021-02-16 00:00:00

This note extends a recently proposed algorithm for model identification and robust MPC of asymptotically stable, linear time-invariant systems subject to process and measurement disturbances. Independent output predictors for different steps ahead are estimated with Set Membership methods. It is here shown that the corresponding prediction error bounds are the least conservative in the considered model class. Then, a new multi-rate robust MPC algorithm is developed, employing said multi-step predictors to robustly enforce constraints and stability against disturbances and model uncertainty, and to reduce conservativeness. A simulation example illustrates the effectiveness of the approach.
On-Demand Transit User Preference Analysis using Hybrid Choice Models
Authors: Nael Alsaleh, Bilal Farooq, Yixue Zhang, Steven Farber
Date: 2021-02-16 00:00:00

In light of the increasing interest to transform the fixed-route public transit (FRT) services into on-demand transit (ODT) services, there exists a strong need for a comprehensive evaluation of the effects of this shift on the users. Such an analysis can help the municipalities and service providers to design and operate more convenient, attractive, and sustainable transit solutions. To understand the user preferences, we developed three hybrid choice models: integrated choice and latent variable (ICLV), latent class (LC), and latent class integrated choice and latent variable (LC-ICLV) models. We used these models to analyze the public transit user's preferences in Belleville, Ontario, Canada. Hybrid choice models were estimated using a rich dataset that combined the actual level of service attributes obtained from Belleville's ODT service and self-reported usage behaviour obtained from a revealed preference survey of the ODT users. The latent class models divided the users into two groups with different travel behaviour and preferences. The results showed that the captive user's preference for ODT service was significantly affected by the number of unassigned trips, in-vehicle time, and main travel mode before the ODT service started. On the other hand, the non-captive user's service preference was significantly affected by the Time Sensitivity and the Online Service Satisfaction latent variables, as well as the performance of the ODT service and trip purpose. This study attaches importance to improving the reliability and performance of the ODT service and outlines directions for reducing operational costs by updating the required fleet size and assigning more vehicles for work-related trips.
Usability Aware Secret Protection with Minimum Cost
Authors: Shoma Matsui, Kai Cai
Date: 2021-02-16 00:00:00

In this paper we study a cybersecurity problem of protecting system's secrets with multiple protections and a required security level, while minimizing the associated cost due to implementation/maintenance of these protections as well as the affected system usability. The target system is modeled as a discrete-event system (DES) in which there are a subset of marker states denoting the services/functions provided to regular users, a subset of secret states, and multiple subsets of protectable events with different security levels. We first introduce usability-aware cost levels for the protectable events, and then formulate the security problem as to ensure that every system trajectory that reaches a secret state contains a specified number of protectable events with at least a certain security level, and the highest usability-aware cost level of these events is minimum. We first provide a necessary and sufficient condition under which this security problem is solvable, and when this condition holds we propose an algorithm to solve the problem based on the supervisory control theory of DES. Moreover, we extend the problem to the case of heterogeneous secrets with different levels of importance, and develop an algorithm to solve this extended problem. Finally, we demonstrate the effectiveness of our solutions with a network security example.
A Novel Stochastic Epidemic Model with Application to COVID-19
Authors: Edilson F. Arruda, Rodrigo e Alvim Alexandre, Marcelo D. Fragoso, João B. R. do val, Sinnu S. Thomas
Date: 2021-02-16 00:00:00

In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated.
Learned Fitting of Spatially Varying BRDFs
Authors: Sebastian Merzbach, Max Hermann, Martin Rump, and Reinhard Klein
Date: 2019-07-30 00:00:00

The use of spatially varying reflectance models (SVBRDF) is the state of the art in physically based rendering and the ultimate goal is to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such models from a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While the achieved results are already very impressive, the reconstruction accuracy that is achieved by these approaches is still far from that of specialized devices. On the other hand, fitting SVBRDF parameter maps to the gibabytes of calibrated HDR images per material acquired by state of the art high quality material scanners takes on the order of several hours for realistic spatial resolutions. In this paper, we present a first deep learning approach that is capable of producing SVBRDF parameter maps more than two orders of magnitude faster than state of the art approaches, while still providing results ef equal quality and generalizing to new materials unseen during the training. This is made possible by training our network on a large-scale database of material scans that we have gathered with a commercially available SVBRDF scanner. In particular, we train a convolutional neural network to map calibrated input images to the 13 parameter maps of an anisotropic Ward BRDF, modified to account for Fresnel reflections, and evaluate the results by comparing the measured images against re-renderings from our SVBRDF predictions. The novel approach is extensively validated on real world data taken from our material database, which we make publicly available under https://cg.cs.uni-bonn.de/svbrdfs/.