pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical-Absolute Reciprocity Calibration for Millimeter-wave Hybrid Beamforming Systems
Authors: Li Chen, Rongjiang Nie, Yunfei Chen, Weidong Wang
Date: 2022-04-14 00:00:00

In time-division duplexing (TDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the reciprocity mismatch severely degrades the performance of the hybrid beamforming (HBF). In this work, to mitigate the detrimental effect of the reciprocity mismatch, we investigate reciprocity calibration for the mmWave-HBF system with a fully-connected phase shifter network. To reduce the overhead and computational complexity of reciprocity calibration, we first decouple digital radio frequency (RF) chains and analog RF chains with beamforming design. Then, the entire calibration problem of the HBF system is equivalently decomposed into two subproblems corresponding to the digital-chain calibration and analog-chain calibration. To solve the calibration problems efficiently, a closed-form solution to the digital-chain calibration problem is derived, while an iterative-alternating optimization algorithm for the analog-chain calibration problem is proposed. To measure the performance of the proposed algorithm, we derive the Cram\'er-Rao lower bound on the errors in estimating mismatch coefficients. The results reveal that the estimation errors of mismatch coefficients of digital and analog chains are uncorrelated, and that the mismatch coefficients of receive digital chains can be estimated perfectly. Simulation results are presented to validate the analytical results and to show the performance of the proposed calibration approach.
Adaptive Modulation for Wobbling UAV Air-to-Ground Links in Millimeter-wave Bands
Authors: Songjiang Yang, Zitian Zhang, Jiliang Zhang, Xiaoli Chu, Jie Zhang
Date: 2022-04-13 00:00:00

The emerging millimeter-wave (mm-wave) unmanned aerial vehicle (UAV) air-to-ground (A2G) communications are facing the Doppler effect problem that arises from the inevitable wobbling of the UAV. The fast time-varying channel for UAV A2G communications may lead to the outdated channel state information (CSI) from the channel estimation. In this paper, we introduce two detectors to demodulate the received signal and get the instantaneous bit error probability (BEP) of a mm-wave UAV A2G link under imperfect CSI. Based on the designed detectors, we propose an adaptive modulation scheme to maximize the average transmission rate under imperfect CSI by optimizing the data transmission time subject to the maximum tolerable BEP. A power control policy is in conjunction with adaptive modulation to minimize the transmission power while maintaining both the BEP under the threshold and the maximized average transmission rate. Numerical results show that the proposed adaptive modulation scheme in conjunction with the power control policy could maximize the temporally averaged transmission rate, while saves as much as 50\% energy.
Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing
Authors: Shukai Ma, Thomas M. Antonsen, Steven M. Anlage, Edward Ott
Date: 2022-04-13 00:00:00
Tasks: Sensitivity

Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed execution, physical robustness, and low energy cost. Here, we experimentally demonstrate an approach that uses the high sensitivity of reverberant short wavelength waves for physical realization and enhancement of computational power of a type of ML known as reservoir computing (RC). The potential computation power of RC systems increases with their effective size. We here exploit the intrinsic property of short wavelength reverberant wave sensitivity to perturbations to expand the effective size of the RC system by means of spatial and spectral perturbations. Working in the microwave regime, this scheme is tested experimentally on different ML tasks. Our results indicate the general applicability of reverberant wave-based implementations of RC and of our effective reservoir size expansion technique
Ensemble learning using individual neonatal data for seizure detection
Authors: Ana Borovac, Steinn Gudmundsson, Gardar Thorvardsson, Saeed M. Moghadam, Päivi Nevalainen, Nathan Stevenson, Sampsa Vanhatalo, Thomas P. Runarsson
Date: 2022-04-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Ensemble Learning, Seizure Detection

Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid--Skene method when local detectors approach performance of a single detector trained on all available data.
graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS Results with Integration of Functional Annotation Data
Authors: Qiaolan Deng, Jin Hyun Nam, Ayse Selen Yilmaz, Won Chang, Maciej Pietrzak, Lang Li, Hang J. Kim, Dongjun Chung
Date: 2022-04-14 00:00:00
Tasks: Literature Mining

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a novel statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. We conducted simulation studies to evaluate GGPA 2.0. The results indicate that incorporating functional annotation data using GGPA 2.0 does not only improve detection of disease-associated variants, but also allows to identify more accurate relationships among diseases. We analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus and the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood, especially B cells and regulatory T cells across multiple diseases. Psychiatric disorders were enriched for brain, especially prefrontal cortex and inferior temporal lobe for bipolar disorder (BIP) and schizophrenia (SCZ), respectively. Finally, GGPA 2.0 successfully identified the pleiotropy between BIP and SCZ. These results demonstrate that GGPA 2.0 can be a powerful tool to identify associated variants associated with each phenotype or those shared across multiple phenotypes, while also promoting understanding of functional mechanisms underlying the associated variants.
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations
Authors: Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar, Shiranee Pereira, James A. Hendler, Jonathan S. Dordick, Payel Das
Date: 2022-04-13 00:00:00
Tasks: Transfer Learning

Explainable ML for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experimental cost and time while mitigating ethical concerns by significantly reducing animal and clinical testing. Herein, we use a deep learning framework for simultaneously modeling in vitro, in vivo, and clinical toxicity data. Two different molecular input representations are used: Morgan fingerprints and pre-training SMILES embeddings. A multi-task deep learning model accurately predicts toxicity for all endpoints, including clinical, as indicated by AUROC and balanced accuracy. In particular, SMILES embeddings as input to the multi-task model improved clinical toxicity predictions compared to existing models in MoleculeNet benchmark. Additionally, our multi-task approach is comprehensive in the sense that it is comparable to state-of-the-art approaches for specific endpoints in in vitro, in vivo and clinical platforms. Through both the multi-task model and transfer learning, we were able to indicate the minimal need of in vivo data for clinical toxicity predictions. To provide confidence and explain the model's predictions, we adapt a post-hoc contrastive explanation method that returns pertinent positive and pertinent negative features, which correspond well to known mutagenic and reactive toxicophores, such as unsubstituted bonded heteroatoms, aromatic amines, and Michael receptors. Furthermore, toxicophore recovery by pertinent feature analysis captures more of the in vitro (53%) and in vivo (56%), rather than of the clinical (8%), endpoints, and indeed uncovers a preference in known toxicophore data towards in vitro and in vivo experimental data. To our knowledge, this is the first contrastive explanation, using both present and absent substructures, for predictions of clinical and in vivo molecular toxicity.
Risk measures under model uncertainty: a Bayesian viewpoint
Authors: Christa Cuchiero, Guido Gazzani, Irene Klein
Date: 2022-04-14 00:00:00
Tasks: model

We introduce two kinds of risk measures with respect to some reference probability measure, which both allow for a certain order structure and domination property. Analyzing their relation to each other leads to the question when a certain minimax inequality is actually an equality. We then provide conditions under which the corresponding robust risk measures, being defined as the supremum over all risk measures induced by a set of probability measures, can be represented classically in terms of one single probability measure. We focus in particular on the mixture probability measure obtained via mixing over a set of probability measures using some prior, which represents for instance the regulator's beliefs. The classical representation in terms of the mixture probability measure can then be interpreted as a Bayesian approach to robust risk measures.
A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data
Authors: Christopher Meaney, Wali Hakimpour, Sumeet Kalia, Rahim Moineddin
Date: 2022-03-25 00:00:00
Tasks: De-identification, Transfer Learning

Objective: To comparatively evaluate several transformer model architectures at identifying protected health information (PHI) in the i2b2/UTHealth 2014 clinical text de-identification challenge corpus. Methods: The i2b2/UTHealth 2014 corpus contains N=1304 clinical notes obtained from N=296 patients. Using a transfer learning framework, we fine-tune several transformer model architectures on the corpus, including: BERT-base, BERT-large, ROBERTA-base, ROBERTA-large, ALBERT-base and ALBERT-xxlarge. During fine-tuning we vary the following model hyper-parameters: batch size, number training epochs, learning rate and weight decay. We fine tune models on a training data set, we evaluate and select optimally performing models on an independent validation dataset, and lastly assess generalization performance on a held-out test dataset. We assess model performance in terms of accuracy, precision (positive predictive value), recall (sensitivity) and F1 score (harmonic mean of precision and recall). We are interested in overall model performance (PHI identified vs. PHI not identified), as well as PHI-specific model performance. Results: We observe that the ROBERTA-large models perform best at identifying PHI in the i2b2/UTHealth 2014 corpus, achieving >99% overall accuracy and 96.7% recall/precision on the heldout test corpus. Performance was good across many PHI classes; however, accuracy/precision/recall decreased for identification of the following entity classes: professions, organizations, ages, and certain locations. Conclusions: Transformers are a promising model class/architecture for clinical text de-identification. With minimal hyper-parameter tuning transformers afford researchers/clinicians the opportunity to obtain (near) state-of-the-art performance.
Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction
Authors: Lianxin Song, Xiaohui Han, Guangqi Liu, Wentong Wang, Chaoran Cui, Yilong Yin
Date: 2022-03-09 00:00:00
Tasks: Articles, Multi-Task Learning, Prediction

Legal Judgment Prediction (LJP) aims to automatically predict judgment results, such as charges, relevant law articles, and the term of penalty. It plays a vital role in legal assistant systems and has become a popular research topic in recent years. This paper concerns a worthwhile but not well-studied LJP task, Appeal judgment Prediction (AJP), which predicts the judgment of an appellate court on an appeal case based on the textual description of case facts and grounds of appeal. There are two significant challenges in practice to solve the AJP task. One is how to model the appeal judgment procedure appropriately. The other is how to improve the interpretability of the prediction results. We propose a Sequential Multi-task Learning Framework with Task Dependency for Appeal Judgement Prediction (SMAJudge) to address these challenges. SMAJudge utilizes two sequential components to model the complete proceeding from the lower court to the appellate court and employs an attention mechanism to make the prediction more explainable, which handles the challenges of AJP effectively. Experimental results obtained with a dataset consisting of more than 30K appeal judgment documents have revealed the effectiveness and superiority of SMAJudge.
State of the Art in Artificial Intelligence applied to the Legal Domain
Authors: João Dias, Pedro A. Santos, Nuno Cordeiro, Ana Antunes, Bruno Martins, Jorge Baptista, Carlos Gonçalves
Date: 2022-03-10 00:00:00

While Artificial Intelligence applied to the legal domain is a topic with origins in the last century, recent advances in Artificial Intelligence are posed to revolutionize it. This work presents an overview and contextualizes the main advances on the field of Natural Language Processing and how these advances have been used to further the state of the art in legal text analysis.
Hate Speech Classification Using SVM and Naive BAYES
Authors: D. C Asogwa, C. I Chukwuneke, C. C Ngene, G. N Anigbogu
Date: 2022-03-21 00:00:00
Tasks: Classification

The spread of hatred that was formerly limited to verbal communications has rapidly moved over the Internet. Social media and community forums that allow people to discuss and express their opinions are becoming platforms for the spreading of hate messages. Many countries have developed laws to avoid online hate speech. They hold the companies that run the social media responsible for their failure to eliminate hate speech. But as online content continues to grow, so does the spread of hate speech However, manual analysis of hate speech on online platforms is infeasible due to the huge amount of data as it is expensive and time consuming. Thus, it is important to automatically process the online user contents to detect and remove hate speech from online media. Many recent approaches suffer from interpretability problem which means that it can be difficult to understand why the systems make the decisions they do. Through this work, some solutions for the problem of automatic detection of hate messages were proposed using Support Vector Machine (SVM) and Na\"ive Bayes algorithms. This achieved near state-of-the-art performance while being simpler and producing more easily interpretable decisions than other methods. Empirical evaluation of this technique has resulted in a classification accuracy of approximately 99% and 50% for SVM and NB respectively over the test set. Keywords: classification; hate speech; feature extraction, algorithm, supervised learning
Recent Advances and New Frontiers in Spiking Neural Networks
Authors: Duzhen Zhang, Shuncheng Jia, Qingyu Wang
Date: 2022-03-12 00:00:00

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware. With the development of SNNs, brain-inspired intelligence, an emerging research field inspired by brain science achievements and aiming at artificial general intelligence, is becoming hot. This paper reviews recent advances and discusses new frontiers in SNNs from five major research topics, including essential elements (i.e., spiking neuron models, encoding methods, and topology structures), neuromorphic datasets, optimization algorithms, software, and hardware frameworks. We hope our survey can help researchers understand SNNs better and inspire new works to advance this field.
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Authors: Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang
Date: 2022-03-17 00:00:00
Tasks: Functional Connectivity

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.
Multi-View Approach to Suggest Moderation Actions in Community Question Answering Sites
Authors: Issa Annamoradnejad, Jafar Habibi, Mohammadamin Fazli
Date: 2022-04-04 00:00:00
Proceeding: Information Sciences 2022 4
Tasks: Community Question Answering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Question Quality Assessment

With thousands of new questions posted every day on popular Q&A websites, there is a need for automated and accurate software solutions to replace manual moderation. In this paper, we address the critical drawbacks of crowdsourcing moderation actions in Q&A communities and demonstrate the ability to automate moderation using the latest machine learning models. From a technical point, we propose a multi-view approach that generates three distinct feature groups that examine a question from three different perspectives: 1) question-related features extracted using a BERT-based regression model; 2) context-related features extracted using a named-entity-recognition model; and 3) general lexical features derived using statistical and analytical methods. As a last step, we train a gradient boosting classifier to predict a moderation action. For evaluation purposes, we created a new dataset consisting of 60,000 Stack Overflow questions classified into three choices of moderation actions. Based on cross-validation on the novel dataset, our approach reaches 95.6% accuracy as a multiclass task and outperforms all state-of-the-art and previously-published models. Our results clearly demonstrate the high influence of our feature generation components on the overall success of the classifier.
Scene Text Image Super-Resolution via Parallelly Contextual Attention Network
Authors: Zhao Cairong, Feng Shuyang, Zhao Brian Nlong, Ding Zhijun, Wu Jun, Shen Fumin, Shen Heng Tao
Date: 2021-10-17 00:00:00
Proceeding: ACM International Conference on Multimedia 2021 10
Tasks: Image Reconstruction, Image Super-Resolution, Scene Text Recognition, Super-Resolution

Optical degradation makes text shapes and edges blurred, so the existing scene text recognition methods are difficult to achieve desirable results on low-resolution (LR) scene text images acquired in natural scenes. Therefore, efficiently extracting the sequential information for reconstructing super-resolution (SR) text images is challenging. In this paper, we propose a Parallel Contextual Attention Network (PCAN), which can effectively learn sequence-dependent features and focus more on high-frequency information of the reconstruction in text images. First, we explore the importance of sequence-dependent features in horizontal and vertical directions \emph{parallelly} for text SR, and design a parallel contextual attention block to adaptively select the key information in the text sequence that contributes to image reconstruction. Secondly, we propose a Hierarchically orthogonal texture-aware attention module and an edge guidance loss function, which can help to reconstruct high-frequency information in text images. Finally, we conduct extensive experiments on different test sets of TextZoom, and the results can easily incorporate into mainstream text algorithms to further improve their performance in LR image recognition. Compared with the SR images obtained by BICUBIC up-sampling, our method can respectively improve the recognition accuracy of ASTER, MORAN, and CRNN by 14.29%, 14.35%, and 20.60%. Besides, our PCAN outperforms 8 state-of-the-art (SOTA) SR methods in improving the recognition performance of LR images. Most importantly, it outperforms the current optimal text-orient SR method TSRN by 3.19%, 3.65%, and 6.0% on the recognition accuracy of ASTER, MORAN, and CRNN respectively.
Learning-Based Joint User-AP Association and Resource Allocation in Ultra Dense Network
Authors: Zhipeng Cheng†, Minghui LiWang†*, Ning Chen†, Hongyue Lin†, Zhibin Gao†, and Lianfen Huang†
Date: 2020-05-28 00:00:00
Proceeding: IEEE 91st Vehicular Technology Conference (VTC2020-Spring) 2020 5
Tasks: Q-Learning

With the advantages of Millimeter wave in wireless
communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN)
becomes the mainstream of future networks. The main challenge
faced by UDN is the serious inter-site interference, which needs
to be carefully addressed by joint user association and resource
allocation methods. In this paper, we propose a multi-agent Qlearning based method to jointly optimize the user association
and resource allocation in UDN. The deep Q-network is applied
to guarantee the convergence of the proposed method. Simulation
results reveal the effectiveness of the proposed method and
different performances under different simulation parameters are
evaluated.
Index Terms—User Association, Resource Allocation, Ultra
Dense Network, Multi-agent Q-learning.