pwc-archive/papers-with-abstracts huggingface dataset:
OmiEmbed: a unified multi-task deep learning framework for multi-omics data
Authors: XiaoYu Zhang, Yuting Xing, Kai Sun, Yike Guo
Date: 2021-02-03 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Dimensionality Reduction, Survival Prediction

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making.
Multimodal-Aware Weakly Supervised Metric Learning with Self-weighting Triplet Loss
Authors: Huiyuan Deng, Xiangzhu Meng, Lin Feng
Date: 2021-02-03 00:00:00
Tasks: Metric Learning, Triplet, valid

In recent years, we have witnessed a surge of interests in learning a suitable distance metric from weakly supervised data. Most existing methods aim to pull all the similar samples closer while push the dissimilar ones as far as possible. However, when some classes of the dataset exhibit multimodal distribution, these goals conflict and thus can hardly be concurrently satisfied. Additionally, to ensure a valid metric, many methods require a repeated eigenvalue decomposition process, which is expensive and numerically unstable. Therefore, how to learn an appropriate distance metric from weakly supervised data remains an open but challenging problem. To address this issue, in this paper, we propose a novel weakly supervised metric learning algorithm, named MultimoDal Aware weakly supervised Metric Learning (MDaML). MDaML partitions the data space into several clusters and allocates the local cluster centers and weight for each sample. Then, combining it with the weighted triplet loss can further enhance the local separability, which encourages the local dissimilar samples to keep a large distance from the local similar samples. Meanwhile, MDaML casts the metric learning problem into an unconstrained optimization on the SPD manifold, which can be efficiently solved by Riemannian Conjugate Gradient Descent (RCGD). Extensive experiments conducted on 13 datasets validate the superiority of the proposed MDaML.
Disease Prediction with a Maximum Entropy Method
Authors: Michael Shub, Qing Xu, Xiaohua, Xuan
Date: 2021-02-03 00:00:00
Tasks: Disease Prediction, Prediction

In this paper, we propose a maximum entropy method for predicting disease risks. It is based on a patient's medical history with diseases coded in ICD-10 which can be used in various cases. The complete algorithm with strict mathematical derivation is given. We also present experimental results on a medical dataset, demonstrating that our method performs well in predicting future disease risks and achieves an accuracy rate twice that of the traditional method. We also perform a comorbidity analysis to reveal the intrinsic relation of diseases.
Directive Explanations for Actionable Explainability in Machine Learning Applications
Authors: Ronal Singh, Paul Dourish, Piers Howe, Tim Miller, Liz Sonenberg, Eduardo Velloso, Frank Vetere
Date: 2021-02-03 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual

This paper investigates the prospects of using directive explanations to assist people in achieving recourse of machine learning decisions. Directive explanations list which specific actions an individual needs to take to achieve their desired outcome. If a machine learning model makes a decision that is detrimental to an individual (e.g. denying a loan application), then it needs to both explain why it made that decision and also explain how the individual could obtain their desired outcome (if possible). At present, this is often done using counterfactual explanations, but such explanations generally do not tell individuals how to act. We assert that counterfactual explanations can be improved by explicitly providing people with actions they could use to achieve their desired goal. This paper makes two contributions. First, we present the results of an online study investigating people's perception of directive explanations. Second, we propose a conceptual model to generate such explanations. Our online study showed a significant preference for directive explanations ($p<0.001$). However, the participants' preferred explanation type was affected by multiple factors, such as individual preferences, social factors, and the feasibility of the directives. Our findings highlight the need for a human-centred and context-specific approach for creating directive explanations.
A step toward a reinforcement learning de novo genome assembler
Authors: Kleber Padovani, Roberto Xavier, Rafael Cabral Borges, Andre Carvalho, Anna Reali, Annie Chateau, Ronnie Alves
Date: 2021-02-02 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

De novo genome assembly is a relevant but computationally complex task in genomics. Although de novo assemblers have been used successfully in several genomics projects, there is still no 'best assembler', and the choice and setup of assemblers still rely on bioinformatics experts. Thus, as with other computationally complex problems, machine learning may emerge as an alternative (or complementary) way for developing more accurate and automated assemblers. Reinforcement learning has proven promising for solving complex activities without supervision - such games - and there is a pressing need to understand the limits of this approach to 'real' problems, such as the DFA problem. This study aimed to shed light on the application of machine learning, using reinforcement learning (RL), in genome assembly. We expanded upon the sole previous approach found in the literature to solve this problem by carefully exploring the learning aspects of the proposed intelligent agent, which uses the Q-learning algorithm, and we provided insights for the next steps of automated genome assembly development. We improved the reward system and optimized the exploration of the state space based on pruning and in collaboration with evolutionary computing. We tested the new approaches on 23 new larger environments, which are all available on the internet. Our results suggest consistent performance progress; however, we also found limitations, especially concerning the high dimensionality of state and action spaces. Finally, we discuss paths for achieving efficient and automated genome assembly in real scenarios considering successful RL applications - including deep reinforcement learning.
Parametrized Quantum Circuits of Synonymous Sentences in Quantum Natural Language Processing
Authors: Mina Abbaszadeh, S. Shahin Mousavi, Vahid Salari
Date: 2021-02-02 00:00:00
Tasks: Sentence

In this paper, we develop a compositional vector-based semantics of positive transitive sentences in quantum natural language processing for a non-English language, i.e. Persian, to compare the parametrized quantum circuits of two synonymous sentences in two languages, English and Persian. By considering grammar+meaning of a transitive sentence, we translate DisCoCat diagram via ZX-calculus into quantum circuit form. Also, we use a bigraph method to rewrite DisCoCat diagram and turn into quantum circuit in the semantic side.
Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning
Authors: Letian Zhang, Lixing Chen, Jie Xu
Date: 2021-02-02 00:00:00
Tasks: Decision Making, object-detection, Object Detection

Recent breakthroughs in deep learning (DL) have led to the emergence of many intelligent mobile applications and services, but in the meanwhile also pose unprecedented computing challenges on resource-constrained mobile devices. This paper builds a collaborative deep inference system between a resource-constrained mobile device and a powerful edge server, aiming at joining the power of both on-device processing and computation offloading. The basic idea of this system is to partition a deep neural network (DNN) into a front-end part running on the mobile device and a back-end part running on the edge server, with the key challenge being how to locate the optimal partition point to minimize the end-to-end inference delay. Unlike existing efforts on DNN partitioning that rely heavily on a dedicated offline profiling stage to search for the optimal partition point, our system has a built-in online learning module, called Autodidactic Neurosurgeon (ANS), to automatically learn the optimal partition point on-the-fly. Therefore, ANS is able to closely follow the changes of the system environment by generating new knowledge for adaptive decision making. The core of ANS is a novel contextual bandit learning algorithm, called $\mu$LinUCB, which not only has provable theoretical learning performance guarantee but also is ultra-lightweight for easy real-world implementation. We implement our system on a video stream object detection testbed to validate the design of ANS and evaluate its performance. The experiments show that ANS significantly outperforms state-of-the-art benchmarks in terms of tracking system changes and reducing the end-to-end inference delay.
Single-Shell NODDI Using Dictionary Learner Estimated Isotropic Volume Fraction
Authors: Abrar Faiyaz, Marvin Doyley, Giovanni Schifitto, Jianhui Zhong, Md Nasir Uddin
Date: 2021-02-02 00:00:00
Tasks: Dictionary Learning, Diffusion  MRI

Neurite orientation dispersion and density imaging (NODDI) enables the assessment of intracellular, extracellular and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous studies caused by failure when fitting, especially for the neurite density index (NDI). Here, we investigated the possibility of creating robust NODDI parameter maps with single-shell data, using the isotropic volume fraction (fISO) as prior. Prior estimation was made independent of the NODDI model constraint using a dictionary learning approach. First, we used a stochastic sparse dictionary-based network (DictNet) in predicting fISO which is trained with data obtained from in vivo and simulated diffusion MRI data. In single-shell cases, the mean diffusivity (MD) and raw T2 signal with no diffusion weighting (S0) was incorporated in the dictionary for the fISO estimation. Then, the NODDI framework was used with the known fISO to estimate the NDI and orientation dispersion index (ODI). The fISO estimated by our model was compared with other fISO estimators in the simulation. Further, using both synthetic data simulation and human data collected on a 3T scanner, we compared the performance of our dictionary-based learning prior NODDI (DLpN) with the original NODDI for both single-shell and multi-shell data. Our results suggest that DLpN derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and protocol with b=2000 s/mm2 performs the best (error ~5% in white and grey matter). This may allow NODDI evaluation of studies on single-shell data by multi-shell scanning of two subjects for DictNet fISO training.
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
Authors: Matthew E. Taylor, Nicholas Nissen, YuAn Wang, Neda Navidi
Date: 2021-02-02 00:00:00
Tasks: OpenAI Gym, Reinforcement Learning (RL)

Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. However, RL agents often have long learning times with high data requirements because they begin by acting randomly. In order to better learn in complex tasks, this article argues that an external teacher can often significantly help the RL agent learn. OpenAI Gym is a common framework for RL research, including a large number of standard environments and agents, making RL research significantly more accessible. This article introduces our new open-source RL framework, the Human Input Parsing Platform for Openai Gym (HIPPO Gym), and the design decisions that went into its creation. The goal of this platform is to facilitate human-RL research, again lowering the bar so that more researchers can quickly investigate different ways that human teachers could assist RL agents, including learning from demonstrations, learning from feedback, or curriculum learning.
Deep Autoencoder-based Fuzzy C-Means for Topic Detection
Authors: Hendri Murfi, Natasha Rosaline, Nora Hariadi
Date: 2021-02-02 00:00:00
Tasks: Clustering, Decoder, Representation Learning

Topic detection is a process for determining topics from a collection of textual data. One of the topic detection methods is a clustering-based method, which assumes that the centroids are topics. The clustering method has the advantage that it can process data with negative representations. Therefore, the clustering method allows a combination with a broader representation learning method. In this paper, we adopt deep learning for topic detection by using a deep autoencoder and fuzzy c-means called deep autoencoder-based fuzzy c-means (DFCM). The encoder of the autoencoder performs a lower-dimensional representation learning. Fuzzy c-means groups the lower-dimensional representation to identify the centroids. The autoencoder's decoder transforms back the centroids into the original representation to be interpreted as the topics. Our simulation shows that DFCM improves the coherence score of eigenspace-based fuzzy c-means (EFCM) and is comparable to the leading standard methods, i.e., nonnegative matrix factorization (NMF) or latent Dirichlet allocation (LDA).
Pick the Right Edge Device: Towards Power and Performance Estimation of CUDA-based CNNs on GPGPUs
Authors: Christopher A. Metz, Mehran Goli, Rolf Drechsler
Date: 2021-02-02 00:00:00
Tasks: Decision Making

The emergence of Machine Learning (ML) as a powerful technique has been helping nearly all fields of business to increase operational efficiency or to develop new value propositions. Besides the challenges of deploying and maintaining ML models, picking the right edge device (e.g., GPGPUs) to run these models (e.g., CNN with the massive computational process) is one of the most pressing challenges faced by organizations today. As the cost of renting (on Cloud) or purchasing an edge device is directly connected to the cost of final products or services, choosing the most efficient device is essential. However, this decision making requires deep knowledge about performance and power consumption of the ML models running on edge devices that must be identified at the early stage of ML workflow. In this paper, we present a novel ML-based approach that provides ML engineers with the early estimation of both power consumption and performance of CUDA-based CNNs on GPGPUs. The proposed approach empowers ML engineers to pick the most efficient GPGPU for a given CNN model at the early stage of development.
Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms
Authors: Swarajya Lakshmi V Papineni, Snigdha Yarlagadda, Harita Akkineni, A. Mallikarjuna Reddy
Date: 2021-02-02 00:00:00
Tasks: Cloud Computing, Decision Making, Deep Learning

Data is evolving with the rapid progress of population and communication for various types of devices such as networks, cloud computing, Internet of Things (IoT), actuators, and sensors. The increment of data and communication content goes with the equivalence of velocity, speed, size, and value to provide the useful and meaningful knowledge that helps to solve the future challenging tasks and latest issues. Besides, multicriteria based decision making is one of the key issues to solve for various issues related to the alternative effects in big data analysis. It tends to find a solution based on the latest machine learning techniques that include algorithms like decision making and deep learning mechanism based on multicriteria in providing insights to big data. On the other hand, the derivations are made for it to go with the approximations to increase the duality of runtime and improve the entire system's potentiality and efficacy. In essence, several fields, including business, agriculture, information technology, and computer science, use deep learning and multicriteria-based decision-making problems. This paper aims to provide various applications that involve the concepts of deep learning techniques and exploiting the multicriteria approaches for issues that are facing in big data analytics by proposing new studies with the fusion approaches of data-driven techniques.
A Living Review of Machine Learning for Particle Physics
Authors: Matthew Feickert, Benjamin Nachman
Date: 2021-02-02 00:00:00
Tasks: BIG-bench Machine Learning

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest developments. A list of proper (unchanging) reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible. Suggestions and contributions are most welcome, and we provide instructions for participating.
A Systematic Review on Software Architectures for IoT Systems and Future Direction to the Adoption of Microservices Architecture
Authors: Dr.razzaq@zju.edu.cn; abdull.razzaq786@gmail.com 1 Marine Information Science and Engineering, Zhejiang University, Zhoushan 316000, China
Date: 2020-10-20 00:00:00
Tasks: Articles, Decision Making

The Internet of Things-based systems and software allow computations anywhere at any time by interconnecting individuals,
networks, services, computers and artefacts that allow autonomous systems to form digitized communities. As the blueprint
for software-intensive applications, and software architecture that precise the complexity of a network’s planning, development, and changing phases to effectively and efficiently build complex IoT-driven applications. In any case, there exists no
comprehensive analysis in the state of the research on the adoption of MSA for IoT systems. This study effort is needed to
explore architectural concepts and practices for designing and developing IoT software to excel state-of-the-art for IoTs along
with suggestions and recommendations for IoT software to the adoption of MSA to fulfil the identified gaps. A systematic
analysis was coordinated, covering up the literature on existing IoT solutions by studying 140 qualitatively selected articles
performed between 2005 and Jan 2020. One hundred forty articles were comprised in this SLR. The findings of this study
demonstrated different research topics including software architectural styles, patterns, and models to build IoT software.
This research presents cloud-based computing environments, autonomous, software-defined networking, and responsive
applications, and IoT-driven agent-based systems, (1) thirteen MSA architectural and design patterns for IoTs and classification of patterns, (2) classification of software architectures for IoTs into nine main categories and their sub-categories, (3)
twenty-three most investigated IoT challenges, and (4) mapping of IoT challenges with software architectural solutions. The
study revealed the innovative work on IoT software architecture and trends that help in the creation and dynamic adaptation
of IoT software for reusability, automation and human decision-making. The outputs of this SLR are useful in revealing
many recommendations to the software industry, software engineering community, and computer sciences community with
over the past 15 years of research into the adoption of MSA. This study reflects a distilled awareness of architectural practices and principles to assist researchers and practitioners in promoting information sharing software architectural roles and
responsibilities for the Internet of Things software.
Safety Case Templates for Autonomous Systems
Authors: Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf, Luke Hinde, Philippa Ryan
Date: 2021-01-29 00:00:00

This report documents safety assurance argument templates to support the deployment and operation of autonomous systems that include machine learning (ML) components. The document presents example safety argument templates covering: the development of safety requirements, hazard analysis, a safety monitor architecture for an autonomous system including at least one ML element, a component with ML and the adaptation and change of the system over time. The report also presents generic templates for argument defeaters and evidence confidence that can be used to strengthen, review, and adapt the templates as necessary. This report is made available to get feedback on the approach and on the templates. This work was sponsored by the UK Dstl under the R-cloud framework.
Federated mmWave Beam Selection Utilizing LIDAR Data
Authors: Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Tze-Yang Tung, Szymon Kobus, Deniz Gunduz
Date: 2021-02-04 00:00:00

Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infrastructure (V2I) networks, side information from LIDAR sensors mounted on the vehicles has been leveraged to reduce the beam search overhead. In this letter, we propose a federated LIDAR aided beam selection method for V2I mmWave communication systems. In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system. We also propose a reduced-complexity convolutional NN (CNN) classifier architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.