pwc-archive/papers-with-abstracts huggingface dataset:
A new approach to improve the performance of OFDM signal for 6G communication
Authors: Usha S. M, Mahesh H. B
Date: 2022-06-04 00:00:00

The orthogonal frequency division multiplexing is a very efficient modulation technique that can achieve very high throughput by transmitting many carriers simultaneously and it is spectrally efficient because of the proximity of the subcarriers. OFDM is used in 5G communication for higher data transmission. 6th generation communication also demands OFDM, since it is more spectrally efficient and suitable for high data transmission. The drawback of the OFDM includes peak to average power ratio and sensitivity to carrier offsets and drifts. The usage of a non-linear power amplifier causes the signal spreading and leads to inter-modulation and signal constellation distortion. These two distortions have an impact on the signal-to-noise ratio and hence reduce the efficiency. The methods used to reduce PAPR are clipping and filtering, selective mapping, partial transmit sequence, tone reservation, and injection and non-linear commanding. The drawbacks of the above methods are computational complexity, spectrum inefficiency, increase in bit error rate and PAPR rate. In this work, three effective methods are discussed and compared to improve the performance parameters. These are adaptive peak window method based on harmonize clipping, harmonics kernel adaptive filter and Slepian-based flat-top window techniques are presented to reduce the BER, PAPR, and CCDF to improve the signal-to-noise of the system. This window technique averages out the noise spread out in the spectrum and thus reduces the signal loss by minimizing peak to average power ratio. The results are analyzed and compared with the existing conventional methods. Finally, the reductions in PAPR, BER and CCDF obtained are discussed in the results and comparison section. The proposed work has a higher signal-to-noise ratio than the conventional methods.
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations
Authors: Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski
Date: 2022-06-04 00:00:00
Tasks: Federated Learning, Scheduling

Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered. Based on a federated learning (FL) algorithm specifically targeted at the unique challenges of the satellite scenario, we design a scheduler that exploits the predictability of visiting times between ground stations (GS) and satellites to reduce model staleness. Numerical experiments show that this can improve the convergence speed by a factor three.
8D Parameters Estimation for Bistatic EMVS-MIMO Radar via the nested PARAFAC
Authors: Qianpeng Xie, He Wang, Yihang Du, Xiaoyi Pan, Feng Zhao
Date: 2022-06-04 00:00:00

In this letter, a novel nested PARAFAC algorithm was proposed to improve the 8D parameters estimation performance for the bistatic EMVS-MIMO radar. Firstly, the outer part PARAFAC algorithm was carried out to estimate the receive spatial response matrix and its first way factor matrix. For the estimated first way factor matrix, a theory is given to rearrange its data into an new matrix, which is the mode-1 unfolding matrix of a three-way tensor. Then, the inner part PARAFAC algorithm was used to estimate the transmit steering vector matrix, the transmit spatial response matrix and the receive steering vector matrix. Thus, the transmit 4D parameters and receive 4D parameters can be accurately located via the abovementioned process. Compared with the original PARAFAC algorithm, the proposed nested PARAFAC algorithm can avoid additional reconstruction process when estimating the transmit/receive spatial response matrix. Moreover, the proposed algorithm can offer a highly-accurate 8D parameters estimaiton than that of the original PARAFAC algorithm. Simulated results verify the effectiveness of the proposed algorithm.
Persistent Homology of Coarse Grained State Space Networks
Authors: Audun D. Myers, Max M. Chumley, Firas A. Khasawneh, Elizabeth Munch
Date: 2022-05-20 00:00:00
Tasks: Time Series, Time Series Analysis, Topological Data Analysis

This work is dedicated to the topological analysis of complex transitional networks for dynamic state detection. Transitional networks are formed from time series data and they leverage graph theory tools to reveal information about the underlying dynamic system. However, traditional tools can fail to summarize the complex topology present in such graphs. In this work, we leverage persistent homology from topological data analysis to study the structure of these networks. We contrast dynamic state detection from time series using a coarse-grained state-space network (CGSSN) and topological data analysis (TDA) to two state of the art approaches: ordinal partition networks (OPNs) combined with TDA and the standard application of persistent homology to the time-delay embedding of the signal. We show that the CGSSN captures rich information about the dynamic state of the underlying dynamical system as evidenced by a significant improvement in dynamic state detection and noise robustness in comparison to OPNs. We also show that because the computational time of CGSSN is not linearly dependent on the signal's length, it is more computationally efficient than applying TDA to the time-delay embedding of the time series.
The fractional volatility model and rough volatility
Authors: R. Vilela Mendes
Date: 2022-06-05 00:00:00
Tasks: model

The question of the volatility roughness is interpreted in the framework of a data-reconstructed fractional volatility model, where volatility is driven by fractional noise. Some examples are worked out and also, using Malliavin calculus for fractional processes, an option pricing equation and its solution are obtained.
Body schema or the body as its own best model
Authors: Matej Hoffmann
Date: 2022-06-06 00:00:00

Rodney Brooks (1991) put forth the idea that during an agent's interaction with its environment, representations of the world often stand in the way. Instead, using the world as its own best model, i.e. interacting with it directly without making models, often leads to better and more natural behavior. The same perspective can be applied to representations of the agent's body. I analyze different examples from biology -- octopus and humans in particular -- and compare them with robots and their body models. At one end of the spectrum, the octopus, a highly intelligent animal, largely relies on the mechanical properties of its arms and peripheral nervous system. No central representations or maps of its body were found in its central nervous system. Primate brains do contain areas dedicated to processing body-related information and different body maps were found. Yet, these representations are still largely implicit and distributed and some functionality is also offloaded to the periphery. Robots, on the other hand, rely almost exclusively on their body models when planning and executing behaviors. I analyze the pros and cons of these different approaches and propose what may be the best solution for robots of the future.
Only what exists can cause: An intrinsic view of free will
Authors: Giulio Tononi, Larissa Albantakis, Melanie Boly, Chiara Cirelli, Christof Koch
Date: 2022-06-04 00:00:00

This essay addresses the implications of integrated information theory (IIT) for free will. IIT is a theory of what consciousness is and what it takes to have it. According to IIT, the presence of consciousness is accounted for by a maximum of cause-effect power in the brain. Moreover, the way specific experiences feel is accounted for by how that cause-effect power is structured. If IIT is right, we do have free will in the fundamental sense: we have true alternatives, we make true decisions, and we - not our neurons or atoms - are the true cause of our willed actions and bear true responsibility for them. IIT's argument for true free will hinges on the proper understanding of consciousness as true existence, as captured by its intrinsic powers ontology: what truly exists, in physical terms, are intrinsic entities, and only what truly exists can cause.
Online FDR Control for RNAseq Data
Authors: Lathan Liou, Milena Hornburg, David S. Robertson
Date: 2022-06-05 00:00:00

Motivation: While the analysis of a single RNA sequencing (RNAseq) dataset has been well described in the literature, modern research workflows often have additional complexity in that related RNAseq experiments are performed sequentially over time. The simplest and most widely used analysis strategy ignores the temporal aspects and analyses each dataset separately. However, this can lead to substantial inflation of the overall false discovery rate (FDR). We propose applying recently developed methodology for online hypothesis testing to analyse sequential RNAseq experiments in a principled way, guaranteeing FDR control at all times while never changing past decisions. Results: We show that standard offline approaches have variable control of FDR of related RNAseq experiments over time and a naively composed approach may improperly change historic decisions. We demonstrate that the online FDR algorithms are a principled way to guarantee control of FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to offline approaches. Availability and Implementation: The onlineFDR package is freely available at http: //www.bioconductor.org/packages/onlineFDR. Additional code used for the simulation studies can be found at https://github.com/latlio/onlinefdr_rnaseq_simulation
Comparison of aerosol emissions during specific speech tasks
Authors: Miriam van Mersbergen, Jeffrey Marchetta, Daniel Foti, Eric Pillow, Apartim Dasgupta, Chandler Cain, Stephen Morvant
Date: 2022-05-18 00:00:00

The study of aerosols and droplets emitted from the oral cavity has become increasingly important throughout the COVID-19 pandemic. Studies show particulates emitted while speaking were generally much smaller compared to coughing or sneezing. However, recent investigations revealed that they are large enough to carry respiratory contagions. Although studies have shown that particulate emissions do indeed occur during speech, to date, there is little information about the relative contribution of different speech sounds in producing particle emissions. This study compares airborne aerosol generation in participants producing isolated speech sounds: fricative consonants, plosive consonants, and vowel sounds. While participants produced isolated speech tasks, a planar beam of laser light, a high-speed camera, and image software calculated the number of particulates detected overtime. This study compares airborne aerosols emitted by human participants at a distance of 2.54 cm between the laser sheet and the mouth and reveals statistically significant increases in particulate counts over ambient dust distribution for all speech sounds. Vowel sounds were statistically greater than consonants, suggesting that mouth opening, as opposed to place of vocal tract constriction or manner of sound production, might be the primary influence in the degree to which particulates become aerosolized during speech. Results of this research will inform boundary conditions for computation models of aerosolized particulates during speech.
Medical Coding with Biomedical Transformer Ensembles and Zero/Few-shot Learning
Authors: Angelo Ziletti, Alan Akbik, Christoph Berns, Thomas Herold, Marion Legler, Martina Viell
Date: 2022-05-01 00:00:00
Proceeding: NAACL (ACL) 2022 7
Tasks: Few-Shot Learning, Retrieval

Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. Given a free-text reported term (RT) such as "pain of right thigh to the knee", the task is to identify the matching lowest-level term (LLT) - in this case "unilateral leg pain" - from a very large and continuously growing repository of standardized medical terms. However, automating this task is challenging due to a large number of LLT codes (as of writing over 80,000), limited availability of training data for long tail/emerging classes, and the general high accuracy demands of the medical domain. With this paper, we introduce the MC task, discuss its challenges, and present a novel approach called xTARS that combines traditional BERT-based classification with a recent zero/few-shot learning approach (TARS). We present extensive experiments that show that our combined approach outperforms strong baselines, especially in the few-shot regime. The approach is developed and deployed at Bayer, live since November 2021. As we believe our approach potentially promising beyond MC, and to ensure reproducibility, we release the code to the research community.
Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild
Authors: Stephanie Brandl, Daniel Hershcovich, Anders Søgaard
Date: 2022-05-03 00:00:00
Tasks: Decision Making

We argue that we need to evaluate model interpretability methods 'in the wild', i.e., in situations where professionals make critical decisions, and models can potentially assist them. We present an in-the-wild evaluation of token attribution based on Deep Taylor Decomposition, with professional journalists performing reliability assessments. We find that using this method in conjunction with RoBERTa-Large, fine-tuned on the Gossip Corpus, led to faster and better human decision-making, as well as a more critical attitude toward news sources among the journalists. We present a comparison of human and model rationales, as well as a qualitative analysis of the journalists' experiences with machine-in-the-loop decision making.
A Survey on Sentence Embedding Models Performance for Patent Analysis
Authors: Hamid Bekamiri, Daniel S. Hain, Roman Jurowetzki
Date: 2022-04-28 00:00:00
Tasks: Patent classification, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Survey, Word Embeddings

Patent data is an important source of knowledge for innovation research, while the technological similarity between pairs of patents is a key enabling indicator for patent analysis. Recently researchers have been using patent vector space models based on different NLP embeddings models to calculate the technological similarity between pairs of patents to help better understand innovations, patent landscaping, technology mapping, and patent quality evaluation. More often than not, Text Embedding is a vital precursor to patent analysis tasks. A pertinent question then arises: How should we measure and evaluate the accuracy of these embeddings? To the best of our knowledge, there is no comprehensive survey that builds a clear delineation of embedding models' performance for calculating patent similarity indicators. Therefore, in this study, we provide an overview of the accuracy of these algorithms based on patent classification performance and propose a standard library and dataset for assessing the accuracy of embeddings models based on PatentSBERTa approach. In a detailed discussion, we report the performance of the top 3 algorithms at section, class, and subclass levels. The results based on the first claim of patents show that PatentSBERTa, Bert-for-patents, and TF-IDF Weighted Word Embeddings have the best accuracy for computing sentence embeddings at the subclass level. According to the first results, the performance of the models in different classes varies, which shows researchers in patent analysis can utilize the results of this study to choose the best proper model based on the specific section of patent data they used.
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data
Authors: Hang Yuan, Shing Chan, Andrew P. Creagh, Catherine Tong, Aidan Acquah, David A. Clifton, Aiden Doherty
Date: 2022-06-06 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Self-Supervised Learning

Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset--the largest of its kind to date--containing more than 700,000 person-days of unlabelled wearable sensor data. Our resulting activity recognition model consistently outperformed strong baselines across seven benchmark datasets, with an F1 relative improvement of 2.5%-100% (median 18.4%), the largest improvements occurring in the smaller datasets. In contrast to previous studies, our results generalise across external datasets, devices, and environments. Our open-source model will help researchers and developers to build customisable and generalisable activity classifiers with high performance.
Improving Students' Academic Performance with AI and Semantic Technologies
Authors: Yixin Cheng
Date: 2022-05-02 00:00:00

Artificial intelligence and semantic technologies are evolving and have been applied in various research areas, including the education domain. Higher Education institutions strive to improve students' academic performance. Early intervention to at-risk students and a reasonable curriculum is vital for students' success. Prior research opted for deploying traditional machine learning models to predict students' performance. In terms of curriculum semantic analysis, after conducting a comprehensive systematic review regarding the use of semantic technologies in the Computer Science curriculum, a major finding of the study is that technologies used to measure similarity have limitations in terms of accuracy and ambiguity in the representation of concepts, courses, etc. To fill these gaps, in this study, three implementations were developed, that is, to predict students' performance using marks from the previous semester, to model a course representation in a semantic way and compute the similarity, and to identify the prerequisite between two similar courses. Regarding performance prediction, we used the combination of Genetic Algorithm and Long-Short Term Memory (LSTM) on a dataset from a Brazilian university containing 248730 records. As for similarity measurement, we deployed BERT to encode the sentences and used cosine similarity to obtain the distance between courses. With respect to prerequisite identification, TextRazor was applied to extract concepts from course description, followed by employing SemRefD to measure the degree of prerequisite between two concepts. The outcomes of this study can be summarized as: (i) a breakthrough result improves Manrique's work by 2.5% in terms of accuracy in dropout prediction; (ii) uncover the similarity between courses based on course description; (iii) identify the prerequisite over three compulsory courses of School of Computing at ANU.
Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering
Authors: Kenneth Ooi, Bhan Lam, Joo Young Hong, Karn N. Watcharasupat, Zhen-Ting Ong, Woon-Seng Gan
Date: 2022-06-07 00:00:00
Tasks: Selection bias, Unsupervised Spatial Clustering

The ecological validity of soundscape studies usually rests on a choice of soundscapes that are representative of the perceptual space under investigation. For example, a soundscape pleasantness study might investigate locations with soundscapes ranging from "pleasant" to "annoying". The choice of soundscapes is typically researcher-led, but a participant-led process can reduce selection bias and improve result reliability. Hence, we propose a robust participant-led method to pinpoint characteristic soundscapes possessing arbitrary perceptual attributes. We validate our method by identifying Singaporean soundscapes spanning the perceptual quadrants generated from the "Pleasantness" and "Eventfulness" axes of the ISO 12913-2 circumplex model of soundscape perception, as perceived by local experts. From memory and experience, 67 participants first selected locations corresponding to each perceptual quadrant in each major planning region of Singapore. We then performed weighted k-means clustering on the selected locations, with weights for each location derived from previous frequencies and durations spent in each location by each participant. Weights hence acted as proxies for participant confidence. In total, 62 locations were thereby identified as suitable locations with characteristic soundscapes for further research utilizing the ISO 12913-2 perceptual quadrants. Audio-visual recordings and acoustic characterization of the soundscapes will be made in a future study.
Review on Multiple Plagiarism: A Performance Comparison Study
Authors: Jabir Al Nahian, Abu Kaisar Mohammad Masum
Date: 2022-06-07 00:00:00
Tasks: Sentence

Plagiarism is the practice of claiming to be someone else content, thoughts or ideas as one own without any proper credit and citations. This paper is a survey paper that, represent the some of the great research paper and its comparison that is work done on plagiarism. Now a days, plagiarism became one of the most interesting and crucial research points in Natural Language Processing area. We review some old research paper based on different types of plagiarism detection and their models and algorithm, and comparison of the accuracy of those papers. There are many several ways which are available for plagiarism detection in different language. There are a few algorithms to detecting plagiarism. Like, corpus, CL-CNG, LSI, Levenshtein Distance etc. We analysis those papers, and learn that they used different types of algorithms for detecting plagiarism. After experiment those papers, we got that some of the algorithms give a better output and accuracy for detecting plagiarism. We are going to give a review on some papers about Plagiarism and will discuss about the pros and cons of their models. And we also show a propose method for plagiarism detection method which based on sentience separation, word separation and make sentence based on synonym and compare with any sources.
Graph Rationalization with Environment-based Augmentations
Authors: Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang
Date: 2022-06-06 00:00:00
Tasks: Graph Regression, Property Prediction, Representation Learning

Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizability and interpretability of neural networks on vision and language data. In graph applications such as molecule and polymer property prediction, identifying representative subgraph structures named as graph rationales plays an essential role in the performance of graph neural networks. Existing graph pooling and/or distribution intervention methods suffer from lack of examples to learn to identify optimal graph rationales. In this work, we introduce a new augmentation operation called environment replacement that automatically creates virtual data examples to improve rationale identification. We propose an efficient framework that performs rationale-environment separation and representation learning on the real and augmented examples in latent spaces to avoid the high complexity of explicit graph decoding and encoding. Comparing against recent techniques, experiments on seven molecular and four polymer real datasets demonstrate the effectiveness and efficiency of the proposed augmentation-based graph rationalization framework.