pwc-archive/papers-with-abstracts huggingface dataset:
Application of Traditional Vaccine Development Strategies to SARS-CoV-2
Authors: Halie M. Rando, Ronan Lordan, Alexandra J. Lee, Amruta Naik, Nils Wellhausen, Elizabeth Sell, Likhitha Kolla, COVID-19 Review Consortium, Anthony Gitter, Casey S. Greene
Date: 2022-08-16 00:00:00
Tasks: Management

Over the past 150 years, vaccines have revolutionized the relationship between people and disease. During the COVID-19 pandemic, technologies such as mRNA vaccines have received attention due to their novelty and successes. However, more traditional vaccine development platforms have also yielded important tools in the worldwide fight against the SARS-CoV-2 virus. A variety of approaches have been used to develop COVID-19 vaccines that are now authorized for use in countries around the world. In this review, we highlight strategies that focus on the viral capsid and outwards, rather than on the nucleic acids inside. These approaches fall into two broad categories: whole-virus vaccines and subunit vaccines. Whole-virus vaccines use the virus itself, either in an inactivated or attenuated state. Subunit vaccines contain instead an isolated, immunogenic component of the virus. Here, we highlight vaccine candidates that apply these approaches against SARS-CoV-2 in different ways. In a companion manuscript, we review the more recent and novel development of nucleic-acid based vaccine technologies. We further consider the role that these COVID-19 vaccine development programs have played in prophylaxis at the global scale. Well-established vaccine technologies have proved especially important to making vaccines accessible in low- and middle-income countries. Vaccine development programs that use established platforms have been undertaken in a much wider range of countries than those using nucleic-acid-based technologies, which have been led by wealthy Western countries. Therefore, these vaccine platforms, though less novel from a biotechnological standpoint, have proven to be extremely important to the management of SARS-CoV-2.
No-Arbitrage Pricing, Dynamics and Forward Prices of Collateralized Derivatives
Authors: Alessio Calvelli
Date: 2022-08-18 00:00:00

This paper analyzes the pricing of collateralized derivatives, i.e. contracts where counterparties are not only subject to financial derivatives cash flows but also to collateral cash flows arising from a collateral agreement. We do this along the lines of the brilliant approach of the first part of Moreni and Pallavicini (2017): in particular we extend their framework where underlyings are continuous processes driven by a Brownian vector, to a more general setup where underlyings are semimartingales (and hence jump processes). First of all, we briefly derive from scratch the theoretical foundations of the main subsequent achievements, i.e. the extension of the classical No-Arbitrage theory to dividend paying semimartingale assets, where by dividend we mean any cash flow earned/paid from holding the asset. In this part we merge, in the same treatment and under the same notation, the principal known results with some original ones. Then we extend the approach of Moreni and Pallavicini (2017) in different directions and we derive not only the pricing formulae but also the dynamics and forward prices of collateralized derivatives (extending the achievements of the first part of Gabrielli et al. (2019)). Finally, we study some important applications (Repurchase Agreements, Securities Lending and Futures contracts) of previously established theoretical frameworks, obtaining some results that are commonly used in practitioners literature, but often not well understood.
POCS-based Clustering Algorithm
Authors: Le-Anh Tran, Henock M. Deberneh, Truong-Dong Do, Thanh-Dat Nguyen, My-Ha Le, Dong-Chul Park
Date: 2022-08-15 00:00:00
Tasks: Clustering

A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-means clustering algorithms.
Compressive Self-localization Using Relative Attribute Embedding
Authors: Ryogo Yamamoto, Kanji Tanaka
Date: 2022-08-03 00:00:00
Tasks: Attribute, Visual Place Recognition

The use of relative attribute (e.g., beautiful, safe, convenient) -based image embeddings in visual place recognition, as a domain-adaptive compact image descriptor that is orthogonal to the typical approach of absolute attribute (e.g., color, shape, texture) -based image embeddings, is explored in this paper.
Utilizing Language Models to Expand Vision-Based Commonsense Knowledge Graphs
Authors: Navid Rezaei, Marek Z. Reformat
Date: 2022-08-17 00:00:00
Proceeding: journal 2022 8
Tasks: Knowledge Graphs, Language Modelling

The introduction and ever-growing size of the transformer deep-learning architecture have had a tremendous impact not only in the field of natural language processing but also in other fields. The transformer-based language models have contributed to a renewed interest in commonsense knowledge due to the abilities of deep learning models. Recent literature has focused on analyzing commonsense embedded within the pre-trained parameters of these models and embedding missing commonsense using knowledge graphs and fine-tuning. We base our current work on the empirically proven language understanding of very large transformer-based language models to expand a limited commonsense knowledge graph, initially generated only on visual data. The few-shot-prompted pre-trained language models can learn the context of an initial knowledge graph with less bias than language models fine-tuned on a large initial corpus. It is also shown that these models can offer new concepts that are added to the vision-based knowledge graph. This two-step approach of vision mining and language model prompts results in the auto-generation of a commonsense knowledge graph well equipped with physical commonsense, which is human commonsense gained by interacting with the physical world. To prompt the language models, we adapted the chain-of-thought method of prompting. To the best of our knowledge, it is a novel contribution to the domain of the generation of commonsense knowledge, which can result in a five-fold cost reduction compared to the state-of-the-art. Another contribution is assigning fuzzy linguistic terms to the generated triples. The process is end to end in the context of knowledge graphs. It means the triples are verbalized to natural language, and after being processed, the results are converted back to triples and added to the commonsense knowledge graph.
SemEval-2010 Task 13: TempEval-2
Authors: Marc Verhagen, Roser Saurí, Tommaso Caselli, James Pustejovsky
Date: 2010-07-01 00:00:00
Proceeding: Proceedings of the 5th International Workshop on Semantic Evaluation 2010 7
Tasks: Event Extraction, Relation, Relation Extraction, Temporal Relation Classification, Temporal Relation Extraction, Temporal Tagging

Tempeval-2 comprises evaluation tasks for time expressions, events and temporal relations, the latter of which was split up in four sub tasks, motivated by the notion that smaller subtasks would make both data preparation and temporal relation extraction easier. Manually annotated data were provided for six languages: Chinese, English, French, Italian, Korean and Spanish.
Spanish TimeBank 1.0
Authors: Roser Saurí, Toni Badia
Date: 2012-08-16 00:00:00
Proceeding: - 2012 8
Tasks: Event Detection, Temporal Relation Classification, Temporal Relation Extraction, Temporal Tagging

The Spanish TimeBank Corpus contains 210 documents, which have been annotated with time and eventuality information according to the TimeML scheme, now accepted as an international cross-language ISO standard
(ISO WD 24617-1:200).
The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
Authors: David Pearce, Hans-Günter Hirsch
Date: 2000-10-16 00:00:00
Proceeding: Interspeech 2000 10
Tasks: speech-recognition, Speech Recognition

This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used for the evaluation of front-end feature extraction algorithms using a defined HMM recognition back-end or complete recognition systems. The source speech for this database is the TIdigits, consisting of connected digits task spoken by American English talkers (downsampled to 8kHz) . A selection of 8 different real-world noises have been added to the speech over a range of signal to noise ratios and special care has been taken to control the filtering of both the speech and noise. The framework was prepared as a contribution to the ETSI STQ-AURORA DSR Working Group [1]. Aurora is developing standards for Distributed Speech Recognition (DSR) where the speech analysis is done in the telecommunication terminal and the recognition at a central location in the telecom network. The framework is currently being used to evaluate alternative proposals for front-end feature extraction. The database has been made publicly available through ELRA so that other speech researchers can evaluate and compare the performance of noise robust algorithms. Recognition results are presented for the first standard DSR feature extraction scheme that is based on a cepstral analysis.
CID: Cow Images Dataset for Regression and Classification
Authors: Mobasshir Bhuiya Shagor, Md Zahidul Haque Alvi, Khandaker Tabin Hasan
Date: 2022-03-10 00:00:00
Proceeding: Proceedings of the 2nd International Conference on Computing Advancements 2022 3
Tasks: Classification, regression

Cattle livestock trading is scaling with exponential growth and managing this scaled production and procurement has become a huge challenge for the industry. With a vision to solve these challenges with emerging technologies and state-of-the-art techniques from computer vision, we tried to develop a large possible dataset that is labeled and accurate so that our research community can build their novel models or finetune existing ones without the hassle of data collection. We developed our cow images dataset of 17,899 images with vitals (sex, color, breed, feed, age, teeth, height, weight, price, size) which can be used for both classification and regression. We are also contributing baseline models demonstrating how this dataset can be used for regression and classification. These baseline models consist of multi input-output network (1 input - 2 outputs; 3 outputs; 4 outputs) to classify and regress cattle livestock vitals among them (1 input - 2 outputs) have the best accuracy of 75% and 67% respectively for age and breed with the minimum loss. Estimating or predicting cattle livestock vitals is an open research area and our cow images dataset and baseline models are going to play a vital role towards further research opportunities. The repository link for this paper is attached here https://github.com/bhuiyanmobasshir94/CID
French TimeBank: An ISO-TimeML Annotated Reference Corpus
Authors: André Bittar, Pascal Amsili, Pascal Denis, Laurence Danlos
Date: 2011-06-01 00:00:00
Proceeding: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies 2011 6

This article presents the main points in the creation of the French TimeBank (Bittar, 2010), a reference corpus annotated according to the ISO-TimeML standard for temporal annotation. A number of improvements were made to the markup language to deal with linguistic phenomena not yet covered by ISO-TimeML, including cross-language modifications and others specific to French. An automatic pre-annotation system was used to speed up the annotation process. A preliminary evaluation of the methodology adopted for this project yields positive results in terms of data quality and annotation time.
Catalan TimeBank 1.0
Authors: Roser Saurí, Toni Badia
Date: 2012-07-18 00:00:00
Proceeding: Linguistic Data Consortium 2012 7

The Catalan TimeBank Corpus contains 210 documents (mostly news reports), which have
been annotated with time and eventuality information according to the TimeML scheme
(Pustejovsky et al., 2005), now accepted as an international cross-language ISO standard
(ISO WD 24617-1:200).
Conversion Efficiency of Strained Wurtzite ZnSnN2/InxGa1-xN Cylindrical Quantum Dot Solar Cell Under Influence of Built-in Electric Field
Authors: ＣＨＥＮ Ｑｉ￣ｙａｎｇ, ＳＨＩ Ｌｅｉ, ＹＡＮ Ｚｕ￣ｗｅｉ
Date: 2021-04-01 00:00:00
Proceeding: Chinese Journal of Luminescence 2021 4
Tasks: Band Gap

Within the effective mass approximation, the band gap of strained wurtzite ZnSnN2/InxGa1-xN cylindrical quantum dot is calculated theoretically by a variational method. Furthermore, based on the detailed balance theory, the relationship between the conversion efficiency of cylindrical quantum dot solar cell and the radius, height and In component of quantum dot under the influence of built-in electric field and multiple exciton generation are studied. The results show that the conversion efficiency of quantum dot solar cell increases monotonously with the increase of quantum dot radius, height and In component. The multiple exciton generation can obviously improve the conversion efficiency of solar cells, but the built-in electric field can reduce the conversion efficiency of solar cells significantly.
Multi-modal Siamese Network for Entity Alignment
Authors: Liyi Chen, Zhi Li, Tong Xu, Han Wu, Zhefeng Wang, Nicholas Jing Yuan, Enhong Chen
Date: 2022-08-14 00:00:00
Proceeding: KDD 2022 8
Tasks: Attribute, Contrastive Learning, Entity Alignment, Knowledge Graphs, Multi-modal Entity Alignment, Multi-modal Knowledge Graph

The booming of multi-modal knowledge graphs (MMKGs) has raised the imperative demand for multi-modal entity alignment techniques, which facilitate the integration of multiple MMKGs from separate data sources. Unfortunately, prior arts harness multi-modal knowledge only via the heuristic merging of uni-modal feature embeddings. Therefore, inter-modal cues concealed in multi-modal knowledge could be largely ignored. To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect. Specifically, we first devise a multi-modal knowledge embedding module to extract visual, relational, and attribute features of entities to generate holistic entity representations for distinct MMKGs. During this procedure, we employ inter-modal enhancement mechanisms to integrate visual features to guide relational feature learning and adaptively assign attention weights to capture valuable attributes for alignment. Afterwards, we design a multi-modal contrastive learning module to achieve inter-modal enhancement fusion with avoiding the overwhelming impact of weak modalities. Experimental results on two public datasets demonstrate that our proposed MSNEA provides state-of-the-art performance with a large margin compared with competitive baselines.
Discovering Faint and High Apparent Motion Rate Near-Earth Asteroids Using A Deep Learning Program
Authors: Franklin Wang, Jian Ge, Kevin Willis
Date: 2022-08-19 00:00:00

Although many near-Earth objects have been found by ground-based telescopes, some fast-moving ones, especially those near detection limits, have been missed by observatories. We developed a convolutional neural network for detecting faint fast-moving near-Earth objects. It was trained with artificial streaks generated from simulations and was able to find these asteroid streaks with an accuracy of 98.7% and a false positive rate of 0.02% on simulated data. This program was used to search image data from the Zwicky Transient Facility (ZTF) in four nights in 2019, and it identified six previously undiscovered asteroids. The visual magnitudes of our detections range from ~19.0 - 20.3 and motion rates range from ~6.8 - 24 deg/day, which is very faint compared to other ZTF detections moving at similar motion rates. Our asteroids are also ~1 - 51 m diameter in size and ~5 - 60 lunar distances away at close approach, assuming their albedo values follow the albedo distribution function of known asteroids. The use of a purely simulated dataset to train our model enables the program to gain sensitivity in detecting faint and fast-moving objects while still being able to recover nearly all discoveries made by previously designed neural networks which used real detections to train neural networks. Our approach can be adopted by any observatory for detecting fast-moving asteroid streaks.
DCNNV-19: A Deep Convolutional Neural Network for COVID-19 Detection in Chest Computed Tomographies
Authors: Victor Felipe Reis-Silva
Date: 2022-08-18 00:00:00
Tasks: Diagnostic

This technical report proposes the use of a deep convolutional neural network as a preliminary diagnostic method in the analysis of chest computed tomography images from patients with symptoms of Severe Acute Respiratory Syndrome (SARS) and suspected COVID-19 disease, especially on occasions when the delay of the RT-PCR result and the absence of urgent care could result in serious temporary, long-term, or permanent health damage. The model was trained on 83,391 images, validated on 15,297, and tested on 22,185 figures, achieving an F1-Score of 98%, 97.59% in Cohen's Kappa, 98.4% in Accuracy, and 5.09% in Loss. Attesting a highly accurate automated classification and providing results in less time than the current gold-standard exam, Real-Time reverse-transcriptase Polymerase Chain Reaction (RT-PCR).
Feature Selection for Fault Detection and Prediction based on Event Log Analysis
Authors: Zhong Li, Matthijs van Leeuwen
Date: 2022-08-19 00:00:00
Tasks: Anomaly Detection, Fault Detection, feature selection, Log Parsing, Prediction

Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction, largely improving the effectiveness and efficiency.
Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing
Authors: Naresh Kumar Gurulingan, Elahe Arani, Bahram Zonooz
Date: 2022-08-19 00:00:00
Tasks: Computational Efficiency, Decoder, Inductive Bias, Multi-Task Learning

Multi-task learning of dense prediction tasks, by sharing both the encoder and decoder, as opposed to sharing only the encoder, provides an attractive front to increase both accuracy and computational efficiency. When the tasks are similar, sharing the decoder serves as an additional inductive bias providing more room for tasks to share complementary information among themselves. However, increased sharing exposes more parameters to task interference which likely hinders both generalization and robustness. Effective ways to curb this interference while exploiting the inductive bias of sharing the decoder remains an open challenge. To address this challenge, we propose Progressive Decoder Fusion (PDF) to progressively combine task decoders based on inter-task representation similarity. We show that this procedure leads to a multi-task network with better generalization to in-distribution and out-of-distribution data and improved robustness to adversarial attacks. Additionally, we observe that the predictions of different tasks of this multi-task network are more consistent with each other.
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Authors: Wei Huang, Xingyu Zhao, Gaojie Jin, Xiaowei Huang
Date: 2022-08-19 00:00:00
Proceeding: ICCV 2023 1
Tasks: Explainable Artificial Intelligence (XAI)

Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Authors: Yufeng Huang, Zhuo Chen, Jiaoyan Chen, Jeff Z. Pan, Zhen Yao, Wen Zhang
Date: 2022-08-19 00:00:00
Tasks: Decoder, Image Captioning, Sentence, Sentiment Analysis, Sentiment Classification

Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image. However, previous methods failed to account for the fine-grained semantic association between the image and the text, which resulted in limited identification of fine-grained image aspects and opinions. To address these limitations, in this paper we propose a new approach called SeqCSG, which enhances the encoder-decoder sentiment classification framework using sequential cross-modal semantic graphs. SeqCSG utilizes image captions and scene graphs to extract both global and local fine-grained image information and considers them as elements of the cross-modal semantic graph along with tokens from tweets. The sequential cross-modal semantic graph is represented as a sequence with a multi-modal adjacency matrix indicating relationships between elements. Experimental results show that the approach outperforms existing methods and achieves state-of-the-art performance on two standard datasets. Further analysis has demonstrated that the model can implicitly learn the correlation between fine-grained information of the image and the text with the given target. Our code is available at https://github.com/zjukg/SeqCSG.