pwc-archive/papers-with-abstracts huggingface dataset:
Dark Matter in (Volatility and) Equity Option Risk Premiums
Authors: Gurdip Bakshi, John Crosby, Xiaohui Gao
Date: 2023-03-29 00:00:00

Emphasizing the statistics of jumps crossing the strike and local time, we develop a decomposition of equity option risk premiums. Operationalizing this theoretical treatment, we equip the pricing kernel process with unspanned risks, embed (unspanned) jump risks, and allow equity return volatility to contain unspanned risks. Unspanned risks are consistent with negative risk premiums for jumps crossing the strike and local time and imply negative risk premiums for out-of-the-money call options and straddles. The empirical evidence from weekly and farther-dated index options is supportive of our theory of economically relevant unspanned risks and reveals ``dark matter" in option risk premiums.
The inverse Black-Scholes problem in Radon measures space revisited: towards a new measure of market uncertainty
Authors: Nizar Riane
Date: 2023-03-29 00:00:00

In this paper, we revisit the inverse Black-Scholes model, the existence of the solution is proved in more rigorous way, and the empirical study is done using different approach based on finite element method. The article leads to a measure of incertitude in the option market.
A multifractional option pricing formula
Authors: Axel A. Araneda
Date: 2023-03-28 00:00:00
Tasks: Time Series

Fractional Brownian motion has become a standard tool to address long-range dependence in financial time series. However, a constant memory parameter is too restrictive to address different market conditions. Here we model the price fluctuations using a multifractional Brownian motion assuming that the Hurst exponent is a time-deterministic function. Through the multifractional Ito calculus, both the related transition density function and the analytical European Call option pricing formula are obtained. The empirical performance of the multifractional Black-Scholes model is tested by calibration of option market quotes for the SPX index and offers best fit than its counterparts based on standard and fractional Brownian motions.
Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis
Authors: Brianna M White, Chad A Melton, Parya Zareie, Robert L Davis, Robert A Bednarczyk, Arash Shaban-Nejad
Date: 2023-02-23 00:00:00
Tasks: Sentiment Analysis

The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper we examine the role of social messaging shared by Persons in the Public Eye (i.e. athletes, politicians, news personnel) in determining overall public discourse direction. We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first two years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light.
Multi-task learning improves synthetic speech detection
Authors: Yichuan Mo, Shilin Wang
Date: 2022-04-27 00:00:00
Proceeding: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022) 2022 4
Tasks: Multi-Task Learning, Speaker Verification, Synthetic Speech Detection, Voice Conversion

With the development of deep learning, synthetic speech has become more and more realistic and easier to spoof Automatic Speaker Verification (ASV) devices. Based on mining more effective hand-crafted features and proposing more powerful networks, many algorithms have been proposed to detect this malicious attack. In this paper, by observing that deepening the network impairs the performance of the network in detecting unknown attacks, we propose that the synthetic speech detection problem is an out-of-distribution (OOD) generalization problem and we enhance the robustness of networks by using multi-task learning. In our system, three auxiliary tasks are used to assist synthetic speech detection: bonafide speech reconstruction, spoofing voice conversion and speaker classification. Experimental results show that our approach can be applied to multiple architectures and can significantly improve the performance on both known attacks (development set) and unknown attacks (evaluation set). In addition, our best-performing network is quite competitive to recent state-of-the-art (SOTA) systems. It demonstrates the potential application of multi-task learning in synthetic speech detection.
Retrospective analysis of SARS-CoV-2 omicron invasion over delta in French regions in 2021-22: a status-based multi-variant model
Authors: Thomas Haschka, Elisabeta Vergu, Benjamin Roche, Chiara Poletto, Lulla Opatowski
Date: 2022-11-03 00:00:00
Proceeding: BMC Infectious Diseases 2022 11
Tasks: Epidemiology

Background: SARS-CoV-2 is a rapidly spreading disease affecting human life and the economy on a global scale. The disease has caused so far more then 5.5 million deaths. The omicron outbreak that emerged in Botswana in the south of Africa spread around the globe at further increased rates, and caused unprecedented SARS-CoV-2 infection incidences in several countries. At the start of December 2021 the first omicron cases were reported in France.

Methods: In this paper we investigate the spreading potential of this novel variant relatively to the delta variant that was also in circulation in France at that time. Using a dynamic multi-variant model accounting for cross-immunity through a status-based approach, we analyze screening data reported by Santé Publique France over 13 metropolitan French regions between 1st of December 2021 and the 30th of January 2022. During the investigated period, the delta variant was replaced by omicron in all metropolitan regions in approximately three weeks. The analysis conducted retrospectively allows us to consider the whole replacement time window and compare regions with different times of omicron introduction and baseline levels of variants' transmission potential. As large uncertainties regarding cross-immunity among variants persist, uncertainty analyses were carried out to assess its impact on our estimations.

Results: Assuming that 80% of the population was immunized against delta, a cross delta/omicron cross-immunity of 25% and an omicron generation time of 3.5 days, the relative strength of omicron to delta, expressed as the ratio of their respective reproduction rates, [Formula: see text], was found to range between 1.51 and 1.86 across regions. Uncertainty analysis on epidemiological parameters led to [Formula: see text] ranging from 1.57 to 2.34 on average over the metropolitan French regions, weighted by population size.

Conclusions: Upon introduction, omicron spread rapidly through the French territory and showed a high fitness relative to delta. We documented considerable geographical heterogeneities on the spreading dynamics. The historical reconstruction of variant emergence dynamics provide valuable ground knowledge to face future variant emergence events.
MNHN-Tree-Tools: a toolbox for tree inference using multi-scale clustering of a set of sequences
Authors: Thomas Haschka, Loic Ponger, Christophe Escudé, Julien Mozziconacci
Date: 2021-06-08 00:00:00
Proceeding: Bioinformatics 2021 6
Tasks: Multiple Sequence Alignment

Abstract
Summary

Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on multiple sequence alignment and can thus be used on large datasets to infer a sequence tree. Herein, we outline two applications: a human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences.
Availability and implementation

Open source with a Zlib License via the Git protocol: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools.
Manual

A detailed users guide and tutorial: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools-manual/-/raw/master/manual.pdf.
Website and FAQ

http://treetools.haschka.net.
Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods
Authors: Tomé Albuquerque, Luís Rosado, Ricardo Cruz, Maria João M. Vasconcelos, Tiago Oliveira, Jaime S. Cardoso
Date: 2023-02-01 00:00:00
Proceeding: Intelligent Systems with Applications 2023 2
Tasks: Benchmarking, Image Deblurring, Image Enhancement, Multi-Focus Microscopical Images Fusion

Microscopic techniques in low-to-middle income countries are constrained by the lack of adequate equipment and trained operators. Since light microscopy delivers crucial methods for the diagnosis and screening of numerous diseases, several efforts have been made by the scientific community to develop low-cost devices such as 3D-printed portable microscopes. Nevertheless, these devices present some drawbacks that directly affect image quality: the capture of the samples is done via mobile phones; more affordable lenses are usually used, leading to poorer physical properties and images with lower depth of field; misalignments in the microscopic set-up regarding optical, mechanical, and illumination components are frequent, causing image distortions such as chromatic aberrations. This work investigates several pre-processing methods to tackle the presented issues and proposed a new workflow for low-cost microscopy. Additionally, two new deep learning models based on Convolutional Neural Networks are also proposed (EDoF-CNN-Fast and EDoF-CNN-Pairwise) to generate Extended Depth of Field (EDoF) images, and compared against state-of-the-art approaches. The models were tested using two different datasets of cytology microscopic images: public Cervix93 and a new dataset that has been made publicly available containing images captured with μSmartScope. Experimental results demonstrate that the proposed workflow can achieve state-of-the-art performance when generating EDoF images from low-cost microscopes.
Human from Blur: Human Pose Tracking from Blurry Images
Authors: Yiming Zhao, Denys Rozumnyi, Jie Song, Otmar Hilliges, Marc Pollefeys, Martin R. Oswald
Date: 2023-03-30 00:00:00
Proceeding: ICCV 2023 1
Tasks: Deblurring, Image Deblurring, Image Reconstruction, Pose Tracking

We propose a method to estimate 3D human poses from substantially blurred images. The key idea is to tackle the inverse problem of image deblurring by modeling the forward problem with a 3D human model, a texture map, and a sequence of poses to describe human motion. The blurring process is then modeled by a temporal image aggregation step. Using a differentiable renderer, we can solve the inverse problem by backpropagating the pixel-wise reprojection error to recover the best human motion representation that explains a single or multiple input images. Since the image reconstruction loss alone is insufficient, we present additional regularization terms. To the best of our knowledge, we present the first method to tackle this problem. Our method consistently outperforms other methods on significantly blurry inputs since they lack one or multiple key functionalities that our method unifies, i.e. image deblurring with sub-frame accuracy and explicit 3D modeling of non-rigid human motion.
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
Authors: Lingdong Kong, Youquan Liu, Xin Li, Runnan Chen, Wenwei Zhang, Jiawei Ren, Liang Pan, Kai Chen, Ziwei Liu
Date: 2023-03-30 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, 3D Semantic Segmentation, Robust 3D Object Detection, Robust 3D Semantic Segmentation

The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets often contain data that are meticulously cleaned. Such configurations, however, cannot reflect the reliability of perception models during the deployment stage. In this work, we present Robo3D, the first comprehensive benchmark heading toward probing the robustness of 3D detectors and segmentors under out-of-distribution scenarios against natural corruptions that occur in real-world environments. Specifically, we consider eight corruption types stemming from severe weather conditions, external disturbances, and internal sensor failure. We uncover that, although promising results have been progressively achieved on standard benchmarks, state-of-the-art 3D perception models are at risk of being vulnerable to corruptions. We draw key observations on the use of data representations, augmentation schemes, and training strategies, that could severely affect the model's performance. To pursue better robustness, we propose a density-insensitive training framework along with a simple flexible voxelization strategy to enhance the model resiliency. We hope our benchmark and approach could inspire future research in designing more robust and reliable 3D perception models. Our robustness benchmark suite is publicly available.
Token Merging for Fast Stable Diffusion
Authors: Daniel Bolya, Judy Hoffman
Date: 2023-03-30 00:00:00
Tasks: Image Generation

The landscape of image generation has been forever changed by open vocabulary diffusion models. However, at their core these models use transformers, which makes generation slow. Better implementations to increase the throughput of these transformers have emerged, but they still evaluate the entire model. In this paper, we instead speed up diffusion models by exploiting natural redundancy in generated images by merging redundant tokens. After making some diffusion-specific improvements to Token Merging (ToMe), our ToMe for Stable Diffusion can reduce the number of tokens in an existing Stable Diffusion model by up to 60% while still producing high quality images without any extra training. In the process, we speed up image generation by up to 2x and reduce memory consumption by up to 5.6x. Furthermore, this speed-up stacks with efficient implementations such as xFormers, minimally impacting quality while being up to 5.4x faster for large images. Code is available at https://github.com/dbolya/tomesd.
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models
Authors: Eric Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, Humphrey Shi
Date: 2023-03-30 00:00:00
Tasks: Disentanglement, Image Generation, Memorization, Text to Image Generation, Text-to-Image Generation

The unlearning problem of deep learning models, once primarily an academic concern, has become a prevalent issue in the industry. The significant advances in text-to-image generation techniques have prompted global discussions on privacy, copyright, and safety, as numerous unauthorized personal IDs, content, artistic creations, and potentially harmful materials have been learned by these models and later utilized to generate and distribute uncontrolled content. To address this challenge, we propose \textbf{Forget-Me-Not}, an efficient and low-cost solution designed to safely remove specified IDs, objects, or styles from a well-configured text-to-image model in as little as 30 seconds, without impairing its ability to generate other content. Alongside our method, we introduce the \textbf{Memorization Score (M-Score)} and \textbf{ConceptBench} to measure the models' capacity to generate general concepts, grouped into three primary categories: ID, object, and style. Using M-Score and ConceptBench, we demonstrate that Forget-Me-Not can effectively eliminate targeted concepts while maintaining the model's performance on other concepts. Furthermore, Forget-Me-Not offers two practical extensions: a) removal of potentially harmful or NSFW content, and b) enhancement of model accuracy, inclusion and diversity through \textbf{concept correction and disentanglement}. It can also be adapted as a lightweight model patch for Stable Diffusion, allowing for concept manipulation and convenient distribution. To encourage future research in this critical area and promote the development of safe and inclusive generative models, we will open-source our code and ConceptBench at \href{https://github.com/SHI-Labs/Forget-Me-Not}{https://github.com/SHI-Labs/Forget-Me-Not}.
Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
Authors: Nico Daheim, Nouha Dziri, Mrinmaya Sachan, Iryna Gurevych, Edoardo M. Ponti
Date: 2023-03-30 00:00:00
Tasks: Dialogue Generation, Language Modelling, Reranking

Ideally, dialogue systems should generate responses that are faithful to the knowledge contained in relevant documents. However, many models generate hallucinated responses instead that contradict it or contain unverifiable information. To mitigate such undesirable behaviour, it has been proposed to fine-tune a `negative expert' on negative examples and subtract its parameters from those of a pre-trained model. However, intuitively, this does not take into account that some parameters are more responsible than others in causing hallucinations. Thus, we propose to weigh their individual importance via (an approximation of) the Fisher Information matrix, which measures the uncertainty of their estimate. We call this method Elastic Weight Removal (EWR). We evaluate our method -- using different variants of Flan-T5 as a backbone language model -- on multiple datasets for information-seeking dialogue generation and compare our method with state-of-the-art techniques for faithfulness, such as CTRL, Quark, DExperts, and Noisy Channel reranking. Extensive automatic and human evaluation shows that EWR systematically increases faithfulness at minor costs in terms of other metrics. However, we notice that only discouraging hallucinations may increase extractiveness, i.e. shallow copy-pasting of document spans, which can be undesirable. Hence, as a second main contribution, we show that our method can be extended to simultaneously discourage hallucinations and extractive responses. We publicly release the code for reproducing EWR and all baselines.
Using AI to Measure Parkinson's Disease Severity at Home
Authors: Md Saiful Islam, Wasifur Rahman, Abdelrahman Abdelkader, Phillip T. Yang, Sangwu Lee, Jamie L. Adams, Ruth B. Schneider, E. Ray Dorsey, Ehsan Hoque
Date: 2023-03-30 00:00:00

We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD). Participants performed a motor task (i.e., tapping fingers) in front of a webcam, and data from 250 global participants were rated by three expert neurologists following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The neurologists' ratings were highly reliable, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed an MDS-UPDRS certified rater, with a mean absolute error (MAE) of 0.59 compared to the rater's MAE of 0.79. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
Authors: Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang
Date: 2023-03-30 00:00:00
Tasks: Benchmarking, Code Generation, HumanEval

Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer. In this paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters for code generation. CodeGeeX is pre-trained on 850 billion tokens of 23 programming languages as of June 2022. Our extensive experiments suggest that CodeGeeX outperforms multilingual code models of similar scale for both the tasks of code generation and translation on HumanEval-X. Building upon HumanEval (Python only), we develop the HumanEval-X benchmark for evaluating multilingual models by hand-writing the solutions in C++, Java, JavaScript, and Go. In addition, we build CodeGeeX-based extensions on Visual Studio Code, JetBrains, and Cloud Studio, generating 4.7 billion tokens for tens of thousands of active users per week. Our user study demonstrates that CodeGeeX can help to increase coding efficiency for 83.4% of its users. Finally, CodeGeeX is publicly accessible and in Sep. 2022, we open-sourced its code, model weights (the version of 850B tokens), API, extensions, and HumanEval-X at https://github.com/THUDM/CodeGeeX.
BloombergGPT: A Large Language Model for Finance
Authors: Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, Gideon Mann
Date: 2023-03-30 00:00:00
Tasks: Causal Judgment, Common Sense Reasoning, Date Understanding, Disambiguation QA, Formal Fallacies Syllogisms Negation, Hyperbaton, Language Modeling, Language Modelling, Large Language Model, Logical Reasoning, model, Movie Recommendation, Multiple Choice Question Answering (MCQA), Multi-task Language Understanding, named-entity-recognition, Named Entity Recognition, Natural Language Inference, Navigate, Penguins In A Table, Question Answering, Reading Comprehension, Reasoning About Colored Objects, Ruin Names, Sarcasm Detection, Sentence Completion, Sentiment Analysis, SNARKS, Sports Understanding, Temporal Sequences

The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg's extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345 billion tokens from general purpose datasets. We validate BloombergGPT on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most accurately reflect our intended usage. Our mixed dataset training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general LLM benchmarks. Additionally, we explain our modeling choices, training process, and evaluation methodology. We release Training Chronicles (Appendix C) detailing our experience in training BloombergGPT.