pwc-archive/papers-with-abstracts huggingface dataset:
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
Authors: Michael Kölle, Alessandro Giovagnoli, Jonas Stein, Maximilian Balthasar Mansky, Julian Hager, Claudia Linnhoff-Popien
Date: 2022-12-22 00:00:00
Tasks: Quantum Machine Learning

In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a wide range of AI tasks as massively parameterized function approximators. VQCs have already demonstrated promising results, for example, in generalization and the requirement for fewer parameters to train, by utilizing the more robust algorithmic toolbox available in quantum computing. A VQCs' trainable parameters or weights are usually used as angles in rotational gates and current gradient-based training methods do not account for that. We introduce weight re-mapping for VQCs, to unambiguously map the weights to an interval of length $2\pi$, drawing inspiration from traditional ML, where data rescaling, or normalization techniques have demonstrated tremendous benefits in many circumstances. We employ a set of five functions and evaluate them on the Iris and Wine datasets using variational classifiers as an example. Our experiments show that weight re-mapping can improve convergence in all tested settings. Additionally, we were able to demonstrate that weight re-mapping increased test accuracy for the Wine dataset by $10\%$ over using unmodified weights.
Unsupervised learning for structure detection in plastically deformed crystals
Authors: Armand Barbot, Riccardo Gatti
Date: 2022-12-22 00:00:00

Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different local parameters, these approaches could only detect already known structures.We introduce an unsupervised learning algorithm to automatically detect structures within a crystal under plastic deformation. This approach is based on a study developed for structural detection on colloidal materials. This algorithm has the advantage of being computationally fast and easy to implement. We show that by using local parameters based on bond-angle distributions, we are able to detect more structures and with a higher degree of precision than traditional hand-made criteria.
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
Authors: Katharina Jeblick, Balthasar Schachtner, Jakob Dexl, Andreas Mittermeier, Anna Theresa Stüber, Johanna Topalis, Tobias Weber, Philipp Wesp, Bastian Sabel, Jens Ricke, Michael Ingrisch
Date: 2022-12-30 00:00:00
Tasks: Language Modeling, Language Modelling

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.
Leveraging World Knowledge in Implicit Hate Speech Detection
Authors: Jessica Lin
Date: 2022-12-28 00:00:00
Tasks: Entity Linking, Hate Speech Detection, World Knowledge

While much attention has been paid to identifying explicit hate speech, implicit hateful expressions that are disguised in coded or indirect language are pervasive and remain a major challenge for existing hate speech detection systems. This paper presents the first attempt to apply Entity Linking (EL) techniques to both explicit and implicit hate speech detection, where we show that such real world knowledge about entity mentions in a text does help models better detect hate speech, and the benefit of adding it into the model is more pronounced when explicit entity triggers (e.g., rally, KKK) are present. We also discuss cases where real world knowledge does not add value to hate speech detection, which provides more insights into understanding and modeling the subtleties of hate speech.
Sequence Generation with Label Augmentation for Relation Extraction
Authors: Bo Li, Dingyao Yu, Wei Ye, Jinglei Zhang, Shikun Zhang
Date: 2022-12-29 00:00:00
Tasks: Relation, Relation Extraction

Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.
Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction
Authors: Bo Li, Wei Ye, Jinglei Zhang, Shikun Zhang
Date: 2022-12-29 00:00:00
Tasks: Prediction, Relation, Relation Extraction

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic $k$-selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe that KLG is more effective in dealing with long-tailed classes.
Error syntax aware augmentation of feedback comment generation dataset
Authors: Nikolay Babakov, Maria Lysyuk, Alexander Shvets, Lilya Kazakova, Alexander Panchenko
Date: 2022-12-29 00:00:00
Tasks: Comment Generation

This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team "nigula" obtained second place according to manual evaluation by the organizers.
Examining Political Rhetoric with Epistemic Stance Detection
Authors: Ankita Gupta, Su Lin Blodgett, Justin H Gross, Brendan O'Connor
Date: 2022-12-29 00:00:00
Tasks: Stance Detection

Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders -- respected allies and opposed bogeymen -- across U.S. political ideologies.
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?
Authors: BoWen Zhang, Daijun Ding, Liwen Jing, Genan Dai, Nan Yin
Date: 2022-12-30 00:00:00
Tasks: Language Modelling, Stance Detection, text-classification, Text Classification

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection.
Distant Reading of the German Coalition Deal: Recognizing Policy Positions with BERT-based Text Classification
Authors: Michael Zylla, Thomas Haider
Date: 2022-12-30 00:00:00
Tasks: text-classification, Text Classification

Automated text analysis has become a widely used tool in political science. In this research, we use a BERT model trained on German party manifestos to identify the individual parties' contribution to the coalition agreement of 2021.
Multi-modal deep learning system for depression and anxiety detection
Authors: Brian Diep, Marija Stanojevic, Jekaterina Novikova
Date: 2022-12-30 00:00:00
Tasks: Anxiety Detection, Deep Learning

Traditional screening practices for anxiety and depression pose an impediment to monitoring and treating these conditions effectively. However, recent advances in NLP and speech modelling allow textual, acoustic, and hand-crafted language-based features to jointly form the basis of future mental health screening and condition detection. Speech is a rich and readily available source of insight into an individual's cognitive state and by leveraging different aspects of speech, we can develop new digital biomarkers for depression and anxiety. To this end, we propose a multi-modal system for the screening of depression and anxiety from self-administered speech tasks. The proposed model integrates deep-learned features from audio and text, as well as hand-crafted features that are informed by clinically-validated domain knowledge. We find that augmenting hand-crafted features with deep-learned features improves our overall classification F1 score comparing to a baseline of hand-crafted features alone from 0.58 to 0.63 for depression and from 0.54 to 0.57 for anxiety. The findings of our work suggest that speech-based biomarkers for depression and anxiety hold significant promise in the future of digital health.
Twitter's Agenda-Setting Role: A Study of Twitter Strategy for Political Diversion
Authors: Yuyang Chen, Xiaoyu Cui, Yunjie Song, Manli Wu
Date: 2022-12-16 00:00:00
Tasks: Time Series, Time Series Analysis

This study verified the effectiveness of Donald Trump's Twitter campaign in guiding agen-da-setting and deflecting political risk and examined Trump's Twitter communication strategy and explores the communication effects of his tweet content during Covid-19 pandemic. We collected all tweets posted by Trump on the Twitter platform from January 1, 2020 to December 31, 2020.We used Ordinary Least Squares (OLS) regression analysis with a fixed effects model to analyze the existence of the Twitter strategy. The correlation between the number of con-firmed daily Covid-19 diagnoses and the number of particular thematic tweets was investigated using time series analysis. Empirical analysis revealed Twitter's strategy is used to divert public attention from negative Covid-19 reports during the epidemic, and it posts a powerful political communication effect on Twitter. However, findings suggest that Trump did not use false claims to divert political risk and shape public opinion.
Monge-Kantorovich Optimal Transport Through Constrictions and Flow-rate Constraints
Authors: Anqi Dong, Arthur Stephanovitch, Tryphon T. Georgiou
Date: 2022-12-30 00:00:00
Tasks: Scheduling

We consider the problem to transport resources/mass while abiding by constraints on the flow through constrictions along their path between specified terminal distributions. Constrictions, conceptualized as toll stations at specified points, limit the flow rate across. We quantify flow-rate constraints via a bound on a sought probability density of the times that mass-elements cross toll stations and cast the transportation scheduling in a Kantorovich-type of formalism. Recent work by our team focused on the existence of Monge maps for similarly constrained transport minimizing average kinetic energy. The present formulation in this paper, besides being substantially more general, is cast as a (generalized) multi-marginal transport problem - a problem of considerable interest in modern-day machine learning literature and motivated extensive computational analyses. An enabling feature of our formalism is the representation of an average quadratic cost on the speed of transport as a convex constraint that involves crossing times.
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Authors: Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen, Sibei Yang, Yizhou Yu
Date: 2023-01-02 00:00:00
Tasks: Brain Tumor Segmentation, Medical Image Analysis, Organ Segmentation, Segmentation, Self-Supervised Learning, Tumor Segmentation

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.
Historical Patterns and Recent Impacts of Chinese Investors in United States Real Estate
Authors: Kevin Sun
Date: 2022-12-28 00:00:00

Since supplanting Canada in 2014, Chinese investors have been the lead foreign buyers of U.S. real estate, concentrating their purchases in urban areas with higher Chinese populations like California. The reasons for investment include prestige, freedom from capital confiscation, and safe, diversified opportunities from abroad simply being more lucrative and available than in their home country, where the market is eroding. Interestingly, since 2019, Chinese investors have sold a net 23.6 billion dollars of U.S. commercial real estate, a stark contrast to past acquisitions between 2013 to 2018 where they were net buyers of almost 52 billion dollars worth of properties. A similar trend appears in the residential real estate segment too. In both 2017 and 2018, Chinese buyers purchased over 40,000 U.S. residential properties which were halved in 2019 and steadily declined to only 6,700 in the past year. This turnaround in Chinese investment can be attributed to a deteriorating relationship between the U.S. and China during the Trump Presidency, financial distress in China, and new Chinese government regulations prohibiting outbound investments. Additionally, while Chinese investment is a small share of U.S. real estate (~1.5% at its peak), it has outsized impacts on market valuations of home prices in U.S. zip codes with higher populations of foreign-born Chinese, increasing property prices and exacerbating the issue of housing affordability in these areas. This paper investigates the rapid growth and decline of Chinese investment in U.S. real estate and its effect on U.S. home prices in certain demographics.
Optimizing Readability Using Genetic Algorithms
Authors: Jorge Martinez-Gil
Date: 2023-01-01 00:00:00
Tasks: Diversity, Readability optimization

This research presents ORUGA, a method that tries to automatically optimize the readability of any text in English. The core idea behind the method is that certain factors affect the readability of a text, some of which are quantifiable (number of words, syllables, presence or absence of adverbs, and so on). The nature of these factors allows us to implement a genetic learning strategy to replace some existing words with their most suitable synonyms to facilitate optimization. In addition, this research seeks to preserve both the original text's content and form through multi-objective optimization techniques. In this way, neither the text's syntactic structure nor the semantic content of the original message is significantly distorted. An exhaustive study on a substantial number and diversity of texts confirms that our method was able to optimize the degree of readability in all cases without significantly altering their form or meaning. The source code of this approach is available at https://github.com/jorge-martinez-gil/oruga.
Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images
Authors: Kun Zhao, Qian Gao, Siyuan Hao, Jie Sun, Lijian Zhou
Date: 2023-01-02 00:00:00
Tasks: Decision Making, Scene Classification

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.
A Fault Location Method Based on Electromagnetic Transient Convolution Considering Frequency-Dependent Parameters and Lossy Ground
Authors: Guanbo Wang, Chijie Zhuang, Jun Deng, Zhicheng Xie
Date: 2022-12-29 00:00:00

As the capacity of power systems grows, the need for quick and precise short-circuit fault location becomes increasingly vital for ensuring the safe and continuous supply of power. In this paper, we propose a fault location method that utilizes electromagnetic transient convolution (EMTC). We assess the performance of a naive EMTC implementation in multi-phase power lines by using frequency-dependent parameters in real fault simulation, while using constant parameters in pre-calculation. Our results show that the location error increases as the distance between the fault location and the measurement location increases. Therefore, we adopt the aerial mode transients after phase-mode transformation to perform the convolution, which reduces the influence of frequency-dependence and ground loss. We conduct numerical experiments in a 3-phase 100-km transmission line, a radial distribution network and IEEE 9-bus system under different fault conditions. Our results show that the proposed method achieves tolerable location errors and operates efficiently through direct convolution of the real fault-generated transient signals and the pre-stored calculated transient signals.
Doppler Spoofing in OFDM Wireless Communication Systems
Authors: Antonios Argyriou, Dimitrios Kosmanos
Date: 2022-12-29 00:00:00

In this paper we present a method that prevents an unauthorized receiver (URx) from correctly estimating the Doppler shift present in an orthogonal frequency division multiplexing (OFDM) wireless signal. To prevent this estimation we propose to insert an artificial frequency variation in the transmitted signal that mimics a transmitter (Tx) movement with a spoofed/fake speed. This spoofed Doppler shift does not affect data demodulation since it can be compensated at the legitimate receiver (LRx). We evaluate our method for its efficacy through simulations and we show that it offers a reliable way to protect one key element of the privacy of a wireless source, namely the speed of the transmitter.