pwc-archive/papers-with-abstracts huggingface dataset:
Investor Sentiment in Asset Pricing Models: A Review of Empirical Evidence
Authors: Szymon Lis
Date: 2024-11-20 00:00:00

This study conducted a comprehensive review of 71 papers published between 2000 and 2021 that employed various measures of investor sentiment to model returns. The analysis indicates that higher complexity of sentiment measures and models improves the coefficient of determination. However, there was insufficient evidence to support that models incorporating more complex sentiment measures have better predictive power than those employing simpler proxies. Additionally, the significance of sentiment varies based on the asset and time period being analyzed, suggesting that the consensus relying on the BW index as a sentiment measure may be subject to change.
On multivariate contribution measures of systemic risk with applications in cryptocurrency market
Authors: Limin Wen, Junxue Li, Tong Pu, Yiying Zhang
Date: 2024-11-20 00:00:00

Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk interactions. This paper introduces various types of contribution ratio measures based on the MCoVaR, MCoES, and MMME studied in Ortega-Jim\'enez et al. (2021) and Das & Fasen-Hartmann (2018) to assess the relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and statistically dependent notions. Numerical examples are presented to validate these conditions. Finally, a real dataset from the cryptocurrency market is used to analyze the spillover effects through our proposed contribution measures.
Robust Graph Neural Networks for Stability Analysis in Dynamic Networks
Authors: Xin Zhang, Zhen Xu, Yue Liu, Mengfang Sun, Tong Zhou, Wenying Sun
Date: 2024-10-29 00:00:00
Tasks: Graph Neural Network, Representation Learning

In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system.
Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data
Authors: Bartosz Bieganowski, Robert Ślepaczuk
Date: 2024-11-06 00:00:00
Tasks: Time Series, Time Series Forecasting

This paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders (SAE), to improve investment strategy performance. Using the Sharpe and Information Ratios, it specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns. The study focuses on Bitcoin, Litecoin, and Ethereum as the traded assets from January 1, 2016, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance.
FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics
Authors: Mabsur Fatin Bin Hossain, Lubna Zahan Lamia, Md Mahmudur Rahman, Md Mosaddek Khan
Date: 2024-11-02 00:00:00
Tasks: Sentiment Analysis, Time Series, Time Series Forecasting

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.
Deep Learning-Driven Anomaly Detection for Green IoT Edge Networks
Authors: Ahmad Shahnejat Bushehri, Ashkan Amirnia, Adel Belkhiri, Samira Keivanpour, Felipe Gohring de Magalhães, Gabriela Nicolescu
Date: 2024-03-01 00:00:00
Proceeding: https://ieeexplore.ieee.org/document/10325633 2024 3
Tasks: Anomaly Detection, Deep Learning

The widespread use of sensor devices in IoT networks imposes a significant burden on energy consumption at the network’s edge. To address energy concerns, a prompt anomaly detection strategy is required on demand for troubleshooting resource-constrained IoT devices. It enables devices to adapt their configuration according to the dynamic signal quality and transmission settings. However, obtaining accurate energy data from IoT nodes without external devices is unfeasible. This paper proposes a framework for energy anomaly detection of IoT nodes using data transmission analysis. We use a public dataset that contains peer-to-peer IoT communication energy and link quality data. Our framework first utilizes linear regression to analyze and identify the dominant features of data communication for IoT transceivers. Later, a deep neural network modifies the gradient flow to focus on the dominant features. This modification improves the detection accuracy of anomalies by minimizing the associated reconstruction error. Finally, the energy stabilization feedback provides nodes with insight to change their transmission configuration for future communication. The experimental results show that the proposed approach outperforms other unsupervised models in anomalous energy detection. It also proves that redesigning the conventional loss function by enhancing the impact of our dominant features can dramatically improve the reliability of the anomaly detection method.
Assessing Writing in Computer Science Bachelor of Science Degree Programs
Authors: Sherri Harms, John Hastings, Marilyn Jussel
Date: 2013-04-19 00:00:00
Proceeding: Midwest Instruction and Computing Symposium 2013 4

Program assessment has become increasingly important to maintaining and enhancing quality within computer science undergraduate degree programs. In addition, the assessment process is designed to measure the degree to which graduates meet industry requirements for entry-level positions.

A key learning objective for computer science students is communication in writing to both non-technical and technical audiences. Computer scientists must follow guidelines in the traditional sense of writing, as well as writing as it applies to the field of computer science, especially in the use of programming languages.

This paper describes a writing assessment process designed and implemented for an undergraduate program in computer science. Direct assessment measures at the course-level are used to assess student writing in both the software development processes as well as writing in the traditional sense. This paper includes an explanation of how the assessment measures are used to evaluate course and program-level outcomes.
Artemis: Towards Referential Understanding in Complex Videos
Authors: Jihao Qiu, Yuan Zhang, Xi Tang, Lingxi Xie, Tianren Ma, Pengyu Yan, David Doermann, Qixiang Ye, Yunjie Tian
Date: 2024-06-01 00:00:00
Tasks: Text Summarization, Video Grounding

Videos carry rich visual information including object description, action, interaction, etc., but the existing multimodal large language models (MLLMs) fell short in referential understanding scenarios such as video-based referring. In this paper, we present Artemis, an MLLM that pushes video-based referential understanding to a finer level. Given a video, Artemis receives a natural-language question with a bounding box in any video frame and describes the referred target in the entire video. The key to achieving this goal lies in extracting compact, target-specific video features, where we set a solid baseline by tracking and selecting spatiotemporal features from the video. We train Artemis on the newly established VideoRef45K dataset with 45K video-QA pairs and design a computationally efficient, three-stage training procedure. Results are promising both quantitatively and qualitatively. Additionally, we show that \model can be integrated with video grounding and text summarization tools to understand more complex scenarios. Code and data are available at https://github.com/qiujihao19/Artemis.
WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusion
Authors: Teysir Baoueb, Xiaoyu Bie, Hicham Janati, Gael Richard
Date: 2024-09-06 00:00:00
Tasks: Denoising, Scheduling

As diffusion-based deep generative models gain prevalence, researchers are actively investigating their potential applications across various domains, including music synthesis and style alteration. Within this work, we are interested in timbre transfer, a process that involves seamlessly altering the instrumental characteristics of musical pieces while preserving essential musical elements. This paper introduces WaveTransfer, an end-to-end diffusion model designed for timbre transfer. We specifically employ the bilateral denoising diffusion model (BDDM) for noise scheduling search. Our model is capable of conducting timbre transfer between audio mixtures as well as individual instruments. Notably, it exhibits versatility in that it accommodates multiple types of timbre transfer between unique instrument pairs in a single model, eliminating the need for separate model training for each pairing. Furthermore, unlike recent works limited to 16 kHz, WaveTransfer can be trained at various sampling rates, including the industry-standard 44.1 kHz, a feature of particular interest to the music community.
VioPose: Violin Performance 4D Pose Estimation by Hierarchical Audiovisual Inference
Authors: Seong Jong Yoo, Snehesh Shrestha, Irina Muresanu, Cornelia Fermüller
Date: 2024-11-19 00:00:00
Tasks: Human-Object Interaction Detection, Pose Estimation

Musicians delicately control their bodies to generate music. Sometimes, their motions are too subtle to be captured by the human eye. To analyze how they move to produce the music, we need to estimate precise 4D human pose (3D pose over time). However, current state-of-the-art (SoTA) visual pose estimation algorithms struggle to produce accurate monocular 4D poses because of occlusions, partial views, and human-object interactions. They are limited by the viewing angle, pixel density, and sampling rate of the cameras and fail to estimate fast and subtle movements, such as in the musical effect of vibrato. We leverage the direct causal relationship between the music produced and the human motions creating them to address these challenges. We propose VioPose: a novel multimodal network that hierarchically estimates dynamics. High-level features are cascaded to low-level features and integrated into Bayesian updates. Our architecture is shown to produce accurate pose sequences, facilitating precise motion analysis, and outperforms SoTA. As part of this work, we collected the largest and the most diverse calibrated violin-playing dataset, including video, sound, and 3D motion capture poses. Code and dataset can be found in our project page \url{https://sj-yoo.info/viopose/}.
Regional Attention for Shadow Removal
Authors: Hengxing Liu, Mingjia Li, Xiaojie Guo
Date: 2024-11-21 00:00:00
Tasks: Shadow Removal

Shadow, as a natural consequence of light interacting with objects, plays a crucial role in shaping the aesthetics of an image, which however also impairs the content visibility and overall visual quality. Recent shadow removal approaches employ the mechanism of attention, due to its effectiveness, as a key component. However, they often suffer from two issues including large model size and high computational complexity for practical use. To address these shortcomings, this work devises a lightweight yet accurate shadow removal framework. First, we analyze the characteristics of the shadow removal task to seek the key information required for reconstructing shadow regions and designing a novel regional attention mechanism to effectively capture such information. Then, we customize a Regional Attention Shadow Removal Model (RASM, in short), which leverages non-shadow areas to assist in restoring shadow ones. Unlike existing attention-based models, our regional attention strategy allows each shadow region to interact more rationally with its surrounding non-shadow areas, for seeking the regional contextual correlation between shadow and non-shadow areas. Extensive experiments are conducted to demonstrate that our proposed method delivers superior performance over other state-of-the-art models in terms of accuracy and efficiency, making it appealing for practical applications.
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
Authors: Advik Raj Basani, Xiao Zhang
Date: 2024-11-21 00:00:00
Tasks: Bayesian Optimization, Red Teaming

LLMs have shown impressive capabilities across various natural language processing tasks, yet remain vulnerable to input prompts, known as jailbreak attacks, carefully designed to bypass safety guardrails and elicit harmful responses. Traditional methods rely on manual heuristics but suffer from limited generalizability. Despite being automatic, optimization-based attacks often produce unnatural prompts that can be easily detected by safety filters or require high computational costs due to discrete token optimization. In this paper, we introduce Generative Adversarial Suffix Prompter (GASP), a novel automated framework that can efficiently generate human-readable jailbreak prompts in a fully black-box setting. In particular, GASP leverages latent Bayesian optimization to craft adversarial suffixes by efficiently exploring continuous latent embedding spaces, gradually optimizing the suffix prompter to improve attack efficacy while balancing prompt coherence via a targeted iterative refinement procedure. Through comprehensive experiments, we show that GASP can produce natural adversarial prompts, significantly improving jailbreak success over baselines, reducing training times, and accelerating inference speed, thus making it an efficient and scalable solution for red-teaming LLMs.
Persistent Homology for Structural Characterization in Disordered Systems
Authors: An Wang, Li Zou
Date: 2024-11-21 00:00:00

We propose a unified framework based on persistent homology (PH) to characterize both local and global structures in disordered systems. It can simultaneously generate local and global descriptors using the same algorithm and data structure, and has shown to be highly effective and interpretable in predicting particle rearrangements and classifying global phases. We also demonstrated that using a single variable enables a linear SVM to achieve nearly perfect three-phase classification. Inspired by this discovery, we define a non-parametric metric, the Separation Index (SI), which not only achieves this classification without sacrificing significant performance but also establishes a connection between particle environments and the global phase structure. Our methods provide an effective framework for understanding and analyzing the properties of disordered materials, with broad potential applications in materials science and even wider studies of complex systems.
DrugGen: Advancing Drug Discovery with Large Language Models and Reinforcement Learning Feedback
Authors: Mahsa Sheikholeslami, Navid Mazrouei, Yousof Gheisari, Afshin Fasihi, Matin Irajpour, Ali Motahharynia
Date: 2024-11-20 00:00:00
Tasks: Drug Design, Drug Discovery

Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in clinical trials. Deep learning advancements, particularly generative models, offer potential solutions to these challenges. One promising algorithm is DrugGPT, a transformer-based model, that generates small molecules for input protein sequences. Although promising, it generates both chemically valid and invalid structures and does not incorporate the features of approved drugs, resulting in time-consuming and inefficient drug discovery. To address these issues, we introduce DrugGen, an enhanced model based on the DrugGPT structure. DrugGen is fine-tuned on approved drug-target interactions and optimized with proximal policy optimization. By giving reward feedback from protein-ligand binding affinity prediction using pre-trained transformers (PLAPT) and a customized invalid structure assessor, DrugGen significantly improves performance. Evaluation across multiple targets demonstrated that DrugGen achieves 100% valid structure generation compared to 95.5% with DrugGPT and produced molecules with higher predicted binding affinities (7.22 [6.30-8.07]) compared to DrugGPT (5.81 [4.97-6.63]) while maintaining diversity and novelty. Docking simulations further validate its ability to generate molecules targeting binding sites effectively. For example, in the case of fatty acid-binding protein 5 (FABP5), DrugGen generated molecules with superior docking scores (FABP5/11, -9.537 and FABP5/5, -8.399) compared to the reference molecule (Palmitic acid, -6.177). Beyond lead compound generation, DrugGen also shows potential for drug repositioning and creating novel pharmacophores for existing targets. By producing high-quality small molecules, DrugGen provides a high-performance medium for advancing pharmaceutical research and drug discovery.
CPa-WAC: Constellation Partitioning-based Scalable Weighted Aggregation Composition for Knowledge Graph Embedding
Authors: S. Modak, Aakarsh Malhotra, Sarthak Malik, Anil Surisetty, Esam Abdel-Raheem
Date: 2024-08-01 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence 2024 8
Tasks: Graph Embedding, Graph Neural Network, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graphs

Scalability and training time are crucial for any graph neural network model processing a knowledge graph (KG). While partitioning knowledge graphs helps reduce the training time, the prediction accuracy reduces signifcantly compared to
training the model on the whole graph. In this paper, we propose CPa-WAC: a lightweight architecture that incorporates graph convolutional networks and modularity maximization-based constellation partitioning to harness the power of local graph
topology. The proposed CPa-WAC method reduces the training time and memory cost of knowledge graph embedding, making the learning model scalable. The results from our experiments on standard databases, such as Wordnet and Freebase, show that by achieving meaningful partitioning, any knowledge graph can be broken down into subgraphs and processed separately to learn embeddings. Furthermore, these learned embeddings can be used for knowledge graph completion, retaining similar performance to training a GCN on the whole KG, while speeding up the training process by uptofve times. Additionally, the proposed CPa-WAC method outperforms several other state-of-the-art
KG in terms of prediction accuracy.
Separable Mixture of Low-Rank Adaptation for Continual Visual Instruction Tuning
Authors: Ziqi Wang, Chang Che, Qi Wang, Yangyang Li, Zenglin Shi, Meng Wang
Date: 2024-11-21 00:00:00
Tasks: Continual Learning, Instruction Following

Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to effectively handle a wide range of vision tasks by framing them as language-based instructions. Building on this, continual visual instruction tuning (CVIT) extends the capability of MLLMs to incrementally learn new tasks, accommodating evolving functionalities. While prior work has advanced CVIT through the development of new benchmarks and approaches to mitigate catastrophic forgetting, these efforts largely follow traditional continual learning paradigms, neglecting the unique challenges specific to CVIT. We identify a dual form of catastrophic forgetting in CVIT, where MLLMs not only forget previously learned visual understanding but also experience a decline in instruction following abilities as they acquire new tasks. To address this, we introduce the Separable Mixture of Low-Rank Adaptation (SMoLoRA) framework, which employs separable routing through two distinct modules - one for visual understanding and another for instruction following. This dual-routing design enables specialized adaptation in both domains, preventing forgetting while improving performance. Furthermore, we propose a novel CVIT benchmark that goes beyond existing benchmarks by additionally evaluating a model's ability to generalize to unseen tasks and handle diverse instructions across various tasks. Extensive experiments demonstrate that SMoLoRA outperforms existing methods in mitigating dual forgetting, improving generalization to unseen tasks, and ensuring robustness in following diverse instructions.