pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation
Authors: Martin Heßler, Tobias Wand, Oliver Kamps
Date: 2023-07-25 00:00:00

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an on-line adaptive manner in pre-crisis segments. The on-line sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint to the importance of the U.S. housing bubble as trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states and could work as a comparative impact rating of specific economic events.
Exploiting the dynamics of commodity futures curves
Authors: Robert J Bianchi, John Hua Fan, Joelle Miffre, Tingxi Zhang
Date: 2023-08-01 00:00:00

The Nelson-Siegel framework is employed to model the term structure of commodity futures prices. Exploiting the information embedded in the level, slope and curvature parameters, we develop novel investment strategies that assume short-term continuation of recent parallel, slope or butterfly movements of futures curves. Systematic strategies based on the change in the slope generate significant profits that are unrelated to previously documented risk factors and can survive reasonable transaction costs. Further analysis demonstrates that the profitability of the slope strategy increases with investor sentiment and is in part a compensation for the drawdowns incurred during economic slowdowns. The profitability can also be magnified through timing and persists under alternative specifications of the Nelson-Siegel model.
An Empirical Study on the Holiday Effect of China's Time-Honored Companies
Authors: Xianyang Li, Jiayi Xu, Haoxuan Xu, Yunxuan Ma, Yu Zhong, Lei Wang
Date: 2023-06-29 00:00:00

The stock segment of China's time-honored brand enterprises has an important position in our securities stock market. The holiday effect is one of the market anomalies that occur in the securities market, which refers to the phenomenon that the stock market has significantly different returns than other trading days around festivals. The study of the holiday effect of China's time-honored brand enterprises can provide fresh ideas for the revitalization of our time-honored brands and the revitalization of time-honored enterprises. This paper takes listed companies of China's time-honored brand enterprises as the research object and focuses on the impact of the holiday effect on listed companies of China's time-honored brands with the help of the event study, and empirically analyses the changes in the return of listed companies of China time-honored brands during the Spring Festival period from 2012 to 2021. The empirical results reveal that: the time-honored brand concept stocks have a significant post-holiday effect during the Chinese New Year period, the time-honored alcoholic beverage enterprises are more sensitive to the Chinese New Year reflection, while the holiday effect of the time-honored pharmaceutical manufacturing enterprises is not significant.
Second-Order Approximation of Limit Order Books in a Single-Scale Regime
Authors: Ulrich Horst, Dörte Kreher, Konstantins Starovoitovs
Date: 2023-08-01 00:00:00

We establish a first and second-order approximation for an infinite dimensional limit order book model (LOB) in a single (''critical'') scaling regime where market and limit orders arrive at a common time scale. With our choice of scaling we obtain non-degenerate first-order and second-order approximations for the price and volume dynamics. While the first-order approximation is given by a standard coupled ODE-PDE system, the second-order approximation is non-standard and described in terms of an infinite-dimensional stochastic evolution equation driven by a cylindrical Brownian motion. The driving noise processes exhibit a non-trivial correlation in terms of the model parameters. We prove that the evolution equation has a unique solution and that the sequence of standardized LOB models converges weakly to the solution of the evolution equation. The proof uses a non-standard martingale problem. We calibrate a simplified version of our model to market data and show that the model accurately captures correlations between price and volume fluctuations.
Incident-Specific Cyber Insurance
Authors: Wing Fung Chong, Daniel Linders, Zhiyu Quan, Linfeng Zhang
Date: 2023-08-02 00:00:00
Tasks: Decision Making

In the current market practice, many cyber insurance products offer a coverage bundle for losses arising from various types of incidents, such as data breaches and ransomware attacks, and the coverage for each incident type comes with a separate limit and deductible. Although this gives prospective cyber insurance buyers more flexibility in customizing the coverage and better manages the risk exposures of sellers, it complicates the decision-making process in determining the optimal amount of risks to retain and transfer for both parties. This paper aims to build an economic foundation for these incident-specific cyber insurance products with a focus on how incident-specific indemnities should be designed for achieving Pareto optimality for both the insurance seller and buyer. Real data on cyber incidents is used to illustrate the feasibility of this approach. Several implementation improvement methods for practicality are also discussed.
Artificial Eye for the Blind
Authors: Abhinav Benagi, Dhanyatha Narayan, Charith Rage, A Sushmitha
Date: 2023-07-07 00:00:00
Tasks: Object, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR), text-to-speech, Text to Speech

The main backbone of our Artificial Eye model is the Raspberry pi3 which is connected to the webcam ,ultrasonic proximity sensor, speaker and we also run all our software models i.e object detection, Optical Character recognition, google text to speech conversion and the Mycroft voice assistance model. At first the ultrasonic proximity sensor will be measuring the distance between itself and any obstacle in front of it .When the Proximity sensor detects any obstacle in front within its specified range, the blind person will hear an audio prompt about an obstacle in his way at a certain distance. At this time the Webcam will capture an image in front of it and the Object detection model and the Optical Character Recognition model will begin to run on the Raspberry pi. The imat of the blind person. The text and the object detected are conveyed to the blind pege captured is first sent through the Tesseract OCR module to detect any texts in the image and then through the Object detection model to detect the objects in fronrson by converting the texts to speech by using the gTTS module. Along with the above mentioned process going on there will be an active MYCROFT voice assistant model which can be used to interact with the blind person. The blind person can ask about the weather , daily news , any information on the internet ,etc
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Authors: Suruchi Kumari, Pravendra Singh
Date: 2023-07-18 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.
Model-contrastive explanations through symbolic reasoning
Authors: Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Andrea Seveso
Date: 2023-06-26 00:00:00
Proceeding: Decision Support Systems 2023 6
Tasks: Counterfactual Explanation, Explainable artificial intelligence, model

Explaining how two machine learning classification models differ in their behaviour is gaining significance in eXplainable AI, given the increasing diffusion of learning-based decision support systems. Human decision-makers deal with more than one machine learning model in several practical situations. Consequently, the importance of understanding how two machine learning models work beyond their prediction performances is key to understanding their behaviour, differences, and likeness.

Some attempts have been made to address these problems, for instance, by explaining text classifiers in a time-contrastive fashion. In this paper, we present MERLIN, a novel eXplainable AI approach that provides contrastive explanations of two machine learning models, introducing the concept of model-contrastive explanations. We propose an encoding that allows MERLIN to work with both text and tabular data and with mixed continuous and discrete features. To show the effectiveness of our approach, we evaluate it on an extensive set of benchmark datasets. MERLIN is also implemented as a python-pip package.
Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models
Authors: Sarah J. Zhang, Samuel Florin, Ariel N. Lee, Eamon Niknafs, Andrei Marginean, Annie Wang, Keith Tyser, Zad Chin, Yann Hicke, Nikhil Singh, Madeleine Udell, Yoon Kim, Tonio Buonassisi, Armando Solar-Lezama, Iddo Drori
Date: 2023-06-15 00:00:00
Tasks: Electrical Engineering, Few-Shot Learning, Language Modeling, Language Modelling, Large Language Model, Prompt Engineering

We curate a comprehensive dataset of 4,550 questions and solutions from problem sets, midterm exams, and final exams across all MIT Mathematics and Electrical Engineering and Computer Science (EECS) courses required for obtaining a degree. We evaluate the ability of large language models to fulfill the graduation requirements for any MIT major in Mathematics and EECS. Our results demonstrate that GPT-3.5 successfully solves a third of the entire MIT curriculum, while GPT-4, with prompt engineering, achieves a perfect solve rate on a test set excluding questions based on images. We fine-tune an open-source large language model on this dataset. We employ GPT-4 to automatically grade model responses, providing a detailed performance breakdown by course, question, and answer type. By embedding questions in a low-dimensional space, we explore the relationships between questions, topics, and classes and discover which questions and classes are required for solving other questions and classes through few-shot learning. Our analysis offers valuable insights into course prerequisites and curriculum design, highlighting language models' potential for learning and improving Mathematics and EECS education.
Unsupervised feature selection method based on iterative similarity graph factorization and clustering by modularity
Authors: Marcos de Souza Oliveira, Sergio Queiroz, Francisco Tenório
Date: 2022-07-15 00:00:00
Proceeding: Expert Systems with Applications 2022 7
Tasks: Clustering, feature selection, Graph Learning, Representation Learning

Feature selection is an important research area aimed at eliminating unwanted features from high-dimensional datasets. Feature selection methods are categorized according to the availability of labels. Supervised methods usually select features that have high correlations with the data labels, however, this cannot be done by unsupervised methods, since the labels are not available, making the development of them even more challenging. Some unsupervised methods proposed in the literature get around this problem by generating “pseudo-labels”, using clustering techniques, and then making feature selection as in the supervised scenario. There are also methods in which the selection is not guided by a clustering criterion, generally performing some kind of reconstruction of the original dataset in low dimensionality. However, in both approaches a local structure is generated, usually starting from a similarity graph built by using the entire set of features. This may compromise the results of the methods when there are many irrelevant and noisy features, which makes it difficult to reveal patterns or an initial representation of the data. Another drawback of some current methods is the large amount of hyper-parameters, or the computational time required for a single execution, which can render such methods unfeasible for some large datasets. In order to address these problems, it is proposed in this work a new unsupervised feature selection method, called KNMFS, which performs the scoring of the features in low computational time using a three-step procedure: (1) a similarity graph learning step based on non-negative iterative matrix factorization together with a Gaussian filter to mitigate the noise effects caused by irrelevant features; (2) a clustering step from the learned graph using a modularity optimization strategy and (3) a random forest algorithm is applied to compute the scores based on the labels generated by the clustering step. To verify the effectiveness of the proposed method, experiments in real and synthetic datasets were conducted. In both cases, the obtained results showed that the KNMFS method, compared to other state-of-the-art methods, obtained good results according to the metrics of Accuracy, ARI and NMI. Friedman’s statistical tests were also performed to give stronger evidence to the reported results.
Reasoning in Large Language Models Through Symbolic Math Word Problems
Authors: Vedant Gaur, Nikunj Saunshi
Date: 2023-08-03 00:00:00
Tasks: Math

Large language models (LLMs) have revolutionized NLP by solving downstream tasks with little to no labeled data. Despite their versatile abilities, the larger question of their ability to reason remains ill-understood. This paper addresses reasoning in math word problems (MWPs) by studying symbolic versions of the numeric problems, since a symbolic expression is a "concise explanation" of the numeric answer. We create and use a symbolic version of the SVAMP dataset and find that GPT-3's davinci-002 model also has good zero-shot accuracy on symbolic MWPs. To evaluate the faithfulness of the model's reasoning, we go beyond accuracy and additionally evaluate the alignment between the final answer and the outputted reasoning, which correspond to numeric and symbolic answers respectively for MWPs. We explore a self-prompting approach to encourage the symbolic reasoning to align with the numeric answer, thus equipping the LLM with the ability to provide a concise and verifiable reasoning and making it more interpretable. Surprisingly, self-prompting also improves the symbolic accuracy to be higher than both the numeric and symbolic accuracies, thus providing an ensembling effect. The SVAMP_Sym dataset will be released for future research on symbolic math problems.
Revisiting Deformable Convolution for Depth Completion
Authors: Xinglong Sun, Jean Ponce, Yu-Xiong Wang
Date: 2023-08-03 00:00:00
Tasks: Depth Completion

Depth completion, which aims to generate high-quality dense depth maps from sparse depth maps, has attracted increasing attention in recent years. Previous work usually employs RGB images as guidance, and introduces iterative spatial propagation to refine estimated coarse depth maps. However, most of the propagation refinement methods require several iterations and suffer from a fixed receptive field, which may contain irrelevant and useless information with very sparse input. In this paper, we address these two challenges simultaneously by revisiting the idea of deformable convolution. We propose an effective architecture that leverages deformable kernel convolution as a single-pass refinement module, and empirically demonstrate its superiority. To better understand the function of deformable convolution and exploit it for depth completion, we further systematically investigate a variety of representative strategies. Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance. We evaluate our model on the large-scale KITTI dataset and achieve state-of-the-art level performance in both accuracy and inference speed. Our code is available at https://github.com/AlexSunNik/ReDC.
How many preprints have actually been printed and why: a case study of computer science preprints on arXiv
Authors: Jialiang Lin, Yao Yu, Yu Zhou, Zhiyang Zhou, Xiaodong Shi
Date: 2023-08-03 00:00:00

Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.
Improving Replay Sample Selection and Storage for Less Forgetting in Continual Learning
Authors: Daniel Brignac, Niels Lobo, Abhijit Mahalanobis
Date: 2023-08-03 00:00:00
Tasks: Continual Learning

Continual learning seeks to enable deep learners to train on a series of tasks of unknown length without suffering from the catastrophic forgetting of previous tasks. One effective solution is replay, which involves storing few previous experiences in memory and replaying them when learning the current task. However, there is still room for improvement when it comes to selecting the most informative samples for storage and determining the optimal number of samples to be stored. This study aims to address these issues with a novel comparison of the commonly used reservoir sampling to various alternative population strategies and providing a novel detailed analysis of how to find the optimal number of stored samples.
Thespian: Multi-Character Text Role-Playing Game Agents
Authors: Christopher Cui, Xiangyu Peng, Mark Riedl
Date: 2023-08-03 00:00:00
Tasks: Few-Shot Learning

Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents. Text role-playing games are open-ended environments where an agent must faithfully play a particular character. We consider the distinction between characters and actors, where an actor agent has the ability to play multiple characters. We present a framework we call a thespian agent that can learn to emulate multiple characters along with a soft prompt that can be used to direct it as to which character to play at any time. We further describe an attention mechanism that allows the agent to learn new characters that are based on previously learned characters in a few-shot fashion. We show that our agent outperforms the state of the art agent framework in multi-character learning and few-shot learning.
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
Authors: Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, Yiling Lou
Date: 2023-08-03 00:00:00
Tasks: Class-level Code Generation, Code Generation, HumanEval

In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python code generation tasks with approximately 500 person-hours. Based on it, we then perform the first study of 11 state-of-the-art LLMs on class-level code generation. Based on our results, we have the following main findings. First, we find that all existing LLMs show much worse performance on class-level code generation compared to on standalone method-level code generation benchmarks like HumanEval; and the method-level coding ability cannot equivalently reflect the class-level coding ability among LLMs. Second, we find that GPT-4 and GPT-3.5 still exhibit dominate superior than other LLMs on class-level code generation, and the second-tier models includes Instruct-Starcoder, Instruct-Codegen, and Wizardcoder with very similar performance. Third, we find that generating the entire class all at once (i.e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3.5, while method-by-method generation (i.e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information. Lastly, we find the limited model ability of generating method-dependent code and discuss the frequent error types in generated classes. Our benchmark is available at https://github.com/FudanSELab/ClassEval.