pwc-archive/papers-with-abstracts huggingface dataset:
DeepQMLP: A Scalable Quantum-Classical Hybrid DeepNeural Network Architecture for Classification
Authors: Mahabubul Alam, Swaroop Ghosh
Date: 2022-02-02 00:00:00
Tasks: Quantum Machine Learning

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.
Artificial Intelligence Powered Material Search Engine
Authors: Mohendra Roy
Date: 2022-01-19 00:00:00
Tasks: Graph Neural Network

Many data-driven applications in material science have been made possible because of recent breakthroughs in artificial intelligence(AI). The use of AI in material engineering is becoming more viable as the number of material data such as X-Ray diffraction, various spectroscopy, and microscope data grows. In this work, we have reported a material search engine that uses the interatomic space (d value) from X-ray diffraction to provide material information. We have investigated various techniques for predicting prospective material using X-ray diffraction data. We used the Random Forest, Naive Bayes (Gaussian), and Neural Network algorithms to achieve this. These algorithms have an average accuracy of 88.50\%, 100.0\%, and 88.89\%, respectively. Finally, we combined all these techniques into an ensemble approach to make the prediction more generic. This ensemble method has a ~100\% accuracy rate. Furthermore, we are designing a graph neural network (GNN)-based architecture to improve interpretability and accuracy. Thus, we want to solve the computational and time complexity of traditional dictionary-based and metadata-based material search engines and to provide a more generic prediction.
Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm
Authors: Wei Huang, Tianfu Qi, Yundi Guan, Qihang Peng, Jun Wang
Date: 2022-01-24 00:00:00
Tasks: Collaborative Filtering, Intelligent Communication, Representation Learning

Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis, which is usually laborious and time-consuming. In this paper, we investigate the waveform design from a novel perspective and propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system. The proposed paradigm aims to improve the design efficiency by structural characterization and representations of existing waveforms and intelligently utilizing the knowledge learned from them. To achieve this goal, we first build a communication waveform knowledge graph (CWKG) with a first-order neighbor node, for which both structured semantic knowledge and numerical parameters of a waveform are integrated by representation learning. Based on the developed CWKG, we further propose an intelligent communication waveform recommendation system (CWRS) to generate waveform candidates. In the CWRS, an improved involution1D operator, which is channel-agnostic and space-specific, is introduced according to the characteristics of KG-based waveform representation for feature extraction, and the multi-head self-attention is adopted to weigh the influence of various components for feature fusion. Meanwhile, multilayer perceptron-based collaborative filtering is used to evaluate the matching degree between the requirement and the waveform candidate. Simulation results show that the proposed CWKG-based CWRS can automatically recommend waveform candidates with high reliability.
Identifying stimulus-driven neural activity patterns in multi-patient intracranial recordings
Authors: Jeremy R. Manning
Date: 2022-02-04 00:00:00

Identifying stimulus-driven neural activity patterns is critical for studying the neural basis of cognition. This can be particularly challenging in intracranial datasets, where electrode locations typically vary across patients. This chapter first presents an overview of the major challenges to identifying stimulus-driven neural activity patterns in the general case. Next, we will review several modality-specific considerations and approaches, along with a discussion of several issues that are particular to intracranial recordings. Against this backdrop, we will consider a variety of within-subject and across-subject approaches to identifying and modeling stimulus-driven neural activity patterns in multi-patient intracranial recordings. These approaches include generalized linear models, multivariate pattern analysis, representational similarity analysis, joint stimulus-activity models, hierarchical matrix factorization models, Gaussian process models, geometric alignment models, inter-subject correlations, and inter-subject functional correlations. Examples from the recent literature serve to illustrate the major concepts and provide the conceptual intuitions for each approach.
Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase Shifters for MU-MIMO Systems
Authors: Chia-Ho Kuo, Hsin-Yuan Chang, Ronald Y. Chang, Wei-Ho Chung
Date: 2022-02-04 00:00:00

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
Authors: Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi
Date: 2022-02-04 00:00:00
Tasks: Multi-Task Learning

We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.
Predictive Closed-Loop Service Automation in O-RAN based Network Slicing
Authors: Joseph Thaliath, Solmaz Niknam, Sukhdeep Singh, Rahul Banerji, Navrati Saxena, Harpreet S. Dhillon, Jeffrey H. Reed, Ali Kashif Bashir, Avinash Bhat, Abhishek Roy
Date: 2022-02-04 00:00:00
Tasks: Management

Network slicing provides introduces customized and agile network deployment for managing different service types for various verticals under the same infrastructure. To cater to the dynamic service requirements of these verticals and meet the required quality-of-service (QoS) mentioned in the service-level agreement (SLA), network slices need to be isolated through dedicated elements and resources. Additionally, allocated resources to these slices need to be continuously monitored and intelligently managed. This enables immediate detection and correction of any SLA violation to support automated service assurance in a closed-loop fashion. By reducing human intervention, intelligent and closed-loop resource management reduces the cost of offering flexible services. Resource management in a network shared among verticals (potentially administered by different providers), would be further facilitated through open and standardized interfaces. Open radio access network (O-RAN) is perhaps the most promising RAN architecture that inherits all the aforementioned features, namely intelligence, open and standard interfaces, and closed control loop. Inspired by this, in this article we provide a closed-loop and intelligent resource provisioning scheme for O-RAN slicing to prevent SLA violations. In order to maintain realism, a real-world dataset of a large operator is used to train a learning solution for optimizing resource utilization in the proposed closed-loop service automation process. Moreover, the deployment architecture and the corresponding flow that are cognizant of the O-RAN requirements are also discussed.
Hybrid Neural Coded Modulation: Design and Training Methods
Authors: Sung Hoon Lim, Jiyong Han, Wonjong Noh, Yujae Song, Sang-Woon Jeon
Date: 2022-02-04 00:00:00

We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). The inner code is designed using a deep neural network (DNN) which takes the channel coded bits and outputs modulated symbols. For training the DNN, we propose to use a loss function that is inspired by the generalized mutual information. The resulting constellations are shown to outperform the conventional quadrature amplitude modulation (QAM) based coding scheme for modulation order 16 and 64 with 5G standard LDPC codes.
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
Authors: Juan lu, Rebecca Hutchens, Joseph Hung, Mohammed Bennamoun, Brendan McQuillan, Tom Briffa, Ferdous Sohel, Kevin Murray, Jonathon Stewart, Benjamin Chow, Frank Sanfilippo, Girish Dwivedi
Date: 2022-02-02 00:00:00
Tasks: Decision Making

Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks. However, risk stratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictive capacity for patients with AF. Machine learning (ML) techniques may improve predictive performance and support decision-making for appropriate antithrombotic therapy. We compared the performance of multilabel ML models with the currently used risk scores for predicting outcomes in AF patients. Materials and Methods This was a retrospective cohort study of 9670 patients, mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, and had 1-year follow-up. The primary outcome was ischemic stroke and major bleeding admission. The secondary outcomes were all-cause death and event-free survival. The discriminant power of ML models was compared with clinical risk scores by the area under the curve (AUC). Risk stratification was assessed using the net reclassification index. Results Multilabel gradient boosting machine provided the best discriminant power for stroke, major bleeding, and death (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models. It provided modest performance improvement for stroke compared to CHA2DS2-VASc (AUC = 0.652), but significantly improved major bleeding prediction compared to HAS-BLED (AUC = 0.522). It also had a much greater discriminant power for death compared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additional risk features (such as hemoglobin level, renal function, etc.) for each outcome. Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.
Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization
Authors: Zhe Tang, Sihao Li, Kyeong Soo Kim, Jeremy Smith
Date: 2022-02-04 00:00:00
Tasks: Data Augmentation, Indoor Localization

Location fingerprinting based on RSSI becomes a mainstream indoor localization technique due to its advantage of not requiring the installation of new infrastructure and the modification of existing devices, especially given the prevalence of Wi-Fi-enabled devices and the ubiquitous Wi-Fi access in modern buildings. The use of AI/ML technologies like DNNs makes location fingerprinting more accurate and reliable, especially for large-scale multi-building and multi-floor indoor localization. The application of DNNs for indoor localization, however, depends on a large amount of preprocessed and deliberately-labeled data for their training. Considering the difficulty of the data collection in an indoor environment, especially under the current epidemic situation of COVID-19, we investigate three different methods of RSSI data augmentation based on Multi-Output Gaussian Process (MOGP), i.e., by a single floor, by neighboring floors, and by a single building; unlike Single-Output Gaussian Process (SOGP), MOGP can take into account the correlation among RSSI observations from multiple Access Points (APs) deployed closely to each other (e.g., APs on the same floor of a building) by collectively handling them. The feasibility of the MOGP-based RSSI data augmentation is demonstrated through experiments based on the state-of-the-art RNN indoor localization model and the UJIIndoorLoc, i.e., the most popular publicly-available multi-building and multi-floor indoor localization database, where the RNN model trained with the UJIIndoorLoc database augmented by using the whole RSSI data of a building in fitting an MOGP model (i.e., by a single building) outperforms the other two augmentation methods as well as the RNN model trained with the original UJIIndoorLoc database, resulting in the mean three-dimensional positioning error of 8.42 m.
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
Authors: Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn
Date: 2022-02-04 00:00:00
Tasks: Imitation Learning

In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach the challenge from an imitation learning perspective, aiming to study how scaling and broadening the data collected can facilitate such generalization. To that end, we develop an interactive and flexible imitation learning system that can learn from both demonstrations and interventions and can be conditioned on different forms of information that convey the task, including pre-trained embeddings of natural language or videos of humans performing the task. When scaling data collection on a real robot to more than 100 distinct tasks, we find that this system can perform 24 unseen manipulation tasks with an average success rate of 44%, without any robot demonstrations for those tasks.
Unsupervised Behaviour Analysis of News Consumption in Turkish Media
Authors: Didem Makaroglu, Altan Cakir, Behcet Ugur Toreyin
Date: 2022-02-04 00:00:00

Clickstream data, which come with a massive volume generated by human activities on websites, have become a prominent feature for identifying readers' characteristics by newsrooms after the digitization of news outlets. Although the nature of clickstream data has a similar logic within websites, it has inherent limitations in recognizing human behaviours when looking from a broad perspective, which brings the need to limit the problem in niche areas. This study investigates the anonymized readers' click activities on the organizations' websites to identify news consumption patterns following referrals from Twitter,who incidentally reach but propensity is mainly routed news content. Methodologies for ensemble cluster analysis with mixed-type embedding strategies are applied and compared to find similar reader groups and interests independent of time. Various internal validation perspectives are used to determine the optimality of the quality of clusters, where the Calinski Harabasz Index (CHI) is found to give a generalizable result. Our findings demonstrate that clustering a mixed-type dataset approaches the optimal internal validation scores, which we define to discriminate the clusters and algorithms considering applied strategies when embedded by Uniform Manifold Approximation and Projection (UMAP) and using a consensus function as a key to access the most applicable hyperparameter configurations in the given ensemble rather than using consensus function results directly. Evaluation of the resulting clusters highlights specific clusters repeatedly present in the separated monthly samples by Adjusted Mutual Information scores greater than 0.5, which provide insights to the news organizations and overcome the degradation of the modeling behaviours due to the change in the interest over time.
Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop
Authors: Akshay J. Dave, Haoyu Wang, Roberto Ponciroli, Richard B. Vilim
Date: 2022-02-04 00:00:00
Tasks: BIG-bench Machine Learning

To advance the paradigm of autonomous operation for nuclear power plants, a data-driven machine learning approach to control is sought. Autonomous operation for next-generation reactor designs is anticipated to bolster safety and improve economics. However, any algorithms that are utilized need to be interpretable, adaptable, and robust. In this work, we focus on the specific problem of optimal control during autonomous operation. We will demonstrate an interpretable and adaptable data-driven machine learning approach to autonomous control of a molten salt loop. To address interpretability, we utilize a data-driven algorithm to identify system dynamics in state-space representation. To address adaptability, a control algorithm will be utilized to modify actuator setpoints while enforcing constant, and time-dependent constraints. Robustness is not addressed in this work, and is part of future work. To demonstrate the approach, we designed a numerical experiment requiring intervention to enforce constraints during a load-follow type transient.
Musical Audio Similarity with Self-supervised Convolutional Neural Networks
Authors: Carl Thomé, Sebastian Piwell, Oscar Utterbäck
Date: 2022-02-04 00:00:00
Tasks: Triplet

We have built a music similarity search engine that lets video producers search by listenable music excerpts, as a complement to traditional full-text search. Our system suggests similar sounding track segments in a large music catalog by training a self-supervised convolutional neural network with triplet loss terms and musical transformations. Semi-structured user interviews demonstrate that we can successfully impress professional video producers with the quality of the search experience, and perceived similarities to query tracks averaged 7.8/10 in user testing. We believe this search tool will make for a more natural search experience that is easier to find music to soundtrack videos with.
Polyphonic pitch detection with convolutional recurrent neural networks
Authors: Carl Thomé, Sven Ahlbäck
Date: 2022-02-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, Language Modelling, Music Transcription, speech-recognition, Speech Recognition

Recent directions in automatic speech recognition (ASR) research have shown that applying deep learning models from image recognition challenges in computer vision is beneficial. As automatic music transcription (AMT) is superficially similar to ASR, in the sense that methods often rely on transforming spectrograms to symbolic sequences of events (e.g. words or notes), deep learning should benefit AMT as well. In this work, we outline an online polyphonic pitch detection system that streams audio to MIDI by ConvLSTMs. Our system achieves state-of-the-art results on the 2007 MIREX multi-F0 development set, with an F-measure of 83\% on the bassoon, clarinet, flute, horn and oboe ensemble recording without requiring any musical language modelling or assumptions of instrument timbre.