pwc-archive/papers-with-abstracts huggingface dataset:
A New Parameterization of Photoinhibition For Phytoplankton
Authors: Mohammad M. Amirian, Zoe V. Finkel, Emmanuel Devred, Andrew J. Irwin
Date: 2024-12-23 00:00:00

Mathematical models of photosynthesis-irradiance relationships in phytoplankton are used to compute integrated water-column photosynthetic rates and predict primary production in ecosystem models. Models typically ignore an important phenomenon observed in most experiments: photosynthetic rate is an approximately constant maximum value over a range of irradiances until photoinhibition leads to decreasing photosynthetic rate. Here we develop a new model of photoinhibition that captures this plateau. We test six new models of photoinhibition and ten more photoinhibition models from the literature against a database of 1808 photosynthesis-irradiance curves exhibiting photoinhibition. In the best model, photoinhibition is phenomenologically described by multiplication by a saturating function of the reciprocal of irradiance, simplifies to the widely-used Jassby & Platt photosynthesis-irradiance curve in the absence of photoinhibition, and only requires one new parameter. This photoinhibition parameter identifies the onset of photoinhibition and is the rate of decrease in photosynthetic rate at that irradiance. The parameters common to both of these models have a consistent interpretation, but our analysis of 3,615 experiments and 10,000 simulated datasets revealed significant discrepancies in parameter interpretation across other widely-used photoinhibition models. Deviations in parameter estimates across models of up to 40% were observed underscoring the need for consistent modeling approaches and supporting the application of our new photoinhibition model.
7 Tesla multimodal MRI dataset of ex-vivo human brain
Authors: Qinfeng Zhu, Sihui Li, Zuozhen Cao, Yao Shen, Haoan Xu, Guojun Xu, Haotian Li, Keqing Zhu, Zhiyong Zhao, Jing Zhang, Dan Wu
Date: 2024-12-06 00:00:00
Tasks: Diffusion  MRI, Diversity

Ex-vivo MRI offers invaluable insights into the complexity of the human brain, enabling high-resolution anatomical delineation and integration with histopathology, and thus, contributes to both basic and clinical studies on normal and pathological brains. However, ex-vivo MRI is challenging in sample preparation, acquisition, and data analysis, and existing ex-vivo MRI datasets are often single image modality and lack of ethnic diversity. In our study, we aimed to address these limitations by constructing a comprehensive multimodal MRI database acquired from six ex-vivo Chinese human brains. This database included structural MRI, high-angular resolution diffusion MRI, quantitative susceptibility mapping, and quantitative T1 and T2 maps, which enabled multifaceted depiction of brain microstructure and connectivity. Furthermore, we generated population-averaged multimodal templates and the segmentation labels to facilitate analysis of ex-vivo brain MRI. This public database offers a collection of high-resolution and multi-parametric ex-vivo human brain MRI and filled the gap of lacking Asian brain samples in existing databases.
A Fluid-Structure Interaction Model of the Zebrafish Aortic Valve
Authors: Alexander D. Kaiser, Jing Wang, Aaron L. Brown, Enbo Zhu, Tzung Hsiai, Alison L. Marsden
Date: 2024-12-24 00:00:00
Tasks: Anatomy

The zebrafish is a valuable model organism for studying cardiac development and diseases due to its many shared aspects of genetics and anatomy with humans and ease of experimental manipulations. Computational fluid-structure interaction (FSI) simulations are an efficient and highly controllable means to study the function of cardiac valves in development and diseases. Due to their small scales, little is known about the mechanical properties of zebrafish cardiac valves, limiting existing computational studies of zebrafish valves and their interaction with blood. To circumvent these limitations, we took a largely first-principles approach called design-based elasticity that allows us to derive valve geometry, fiber orientation and material properties. In FSI simulations of an adult zebrafish aortic valve, these models produce realistic flow rates when driven by physiological pressures and demonstrate the spatiotemporal dynamics of valvular mechanical properties. These models can be used for future studies of zebrafish cardiac hemodynamics, development, and disease.
Generalized Mean Absolute Directional Loss as a Solution to Overfitting and High Transaction Costs in Machine Learning Models Used in High-Frequency Algorithmic Investment Strategies
Authors: Jakub Michańków, Paweł Sakowski, Robert Ślepaczuk
Date: 2024-12-24 00:00:00
Tasks: Algorithmic Trading

Regardless of the selected asset class and the level of model complexity (Transformer versus LSTM versus Perceptron/RNN), the GMADL loss function produces superior results than standard MSE-type loss functions and has better numerical properties in the context of optimization than MADL. Better results mean the possibility of achieving a higher risk-weighted return based on buy and sell signals built on forecasts generated by a given theoretical model estimated using the GMADL versus MSE or MADL function. In practice, GMADL solves the problem of selecting the most preferable feature in both classification and regression problems, improving the performance of each estimation. What is important is that, through additional parameterization, GMADL also solves the problem of optimizing investment systems on high-frequency data in such a way that they focus on strategy variants that contain fewer transactions so that transaction costs do not reduce the effectiveness of a given strategy to zero. Moreover, the implementation leverages state-of-the-art machine learning tools, including frameworks for hyperparameter tuning, architecture testing, and walk-forward optimization, ensuring robust and scalable solutions for real-world algorithmic trading.
A mathematical framework for modelling CLMM dynamics in continuous time
Authors: Shen-Ning Tung, Tai-Ho Wang
Date: 2024-12-24 00:00:00

This paper develops a rigorous mathematical framework for analyzing Concentrated Liquidity Market Makers (CLMMs) in Decentralized Finance (DeFi) within a continuous-time setting. We model the evolution of liquidity profiles as measure-valued processes and characterize their dynamics under continuous trading. Our analysis encompasses two critical aspects of CLMMs: the mechanics of concentrated liquidity provision and the strategic behavior of arbitrageurs. We examine three distinct arbitrage models -- myopic, finite-horizon, and infinite-horizon with discounted and ergodic controls -- and derive closed-form solutions for optimal arbitrage strategies under each scenario. Importantly, we demonstrate that the presence of trading fees fundamentally constrains the admissible price processes, as the inclusion of fees precludes the existence of diffusion terms in the price process to avoid infinite fee generation. This finding has significant implications for CLMM design and market efficiency.
Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks
Authors: Abhiroop Bhattacharya, Nandinee Haq
Date: 2024-12-19 00:00:00
Tasks: Domain Adaptation, Kolmogorov-Arnold Networks, Time Series, Time Series Forecasting

Accurate energy price forecasting is crucial for participants in day-ahead energy markets, as it significantly influences their decision-making processes. While machine learning-based approaches have shown promise in enhancing these forecasts, they often remain confined to the specific markets on which they are trained, thereby limiting their adaptability to new or unseen markets. In this paper, we introduce a cross-domain adaptation model designed to forecast energy prices by learning market-invariant representations across different markets during the training phase. We propose a doubly residual N-BEATS network with Kolmogorov Arnold networks at its core for time series forecasting. These networks, grounded in the Kolmogorov-Arnold representation theorem, offer a powerful way to approximate multivariate continuous functions. The cross domain adaptation model was generated with an adversarial framework. The model's effectiveness was tested in predicting day-ahead electricity prices in a zero shot fashion. In comparison with baseline models, our proposed framework shows promising results. By leveraging the Kolmogorov-Arnold networks, our model can potentially enhance its ability to capture complex patterns in energy price data, thus improving forecast accuracy across diverse market conditions. This addition not only enriches the model's representational capacity but also contributes to a more robust and flexible forecasting tool adaptable to various energy markets.
Graph Structure Refinement with Energy-based Contrastive Learning
Authors: Xianlin Zeng, Yufeng Wang, Yuqi Sun, Guodong Guo, Baochang Zhang, Wenrui Ding
Date: 2024-12-20 00:00:00
Tasks: Contrastive Learning, Node Classification

Graph Neural Networks (GNNs) have recently gained widespread attention as a successful tool for analyzing graph-structured data. However, imperfect graph structure with noisy links lacks enough robustness and may damage graph representations, therefore limiting the GNNs' performance in practical tasks. Moreover, existing generative architectures fail to fit discriminative graph-related tasks. To tackle these issues, we introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation, aiming to improve the discriminative performance of generative models. We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR. To our knowledge, this is the first work to combine energy-based models with contrastive learning for GSR. Specifically, we leverage ECL to approximate the joint distribution of sample pairs, which increases the similarity between representations of positive pairs while reducing the similarity between negative ones. Refined structure is produced by augmenting and removing edges according to the similarity metrics among node representations. Extensive experiments demonstrate that ECL-GSR outperforms the state-of-the-art on eight benchmark datasets in node classification. ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
Inductive Linguistic Reasoning with Large Language Models
Authors: Raghav Ramji, Keshav Ramji
Date: 2024-12-09 00:00:00

Evaluating large language models (LLMs) on their linguistic reasoning capabilities is an important task to understand the gaps in their skills that may surface during large-scale adoption. In this work, we investigate the abilities of such models to perform abstract multilingual reasoning through the lens of linguistic puzzles on extremely low-resource languages. As these translation tasks involve inductive and deductive reasoning from reference instances, we examine whether diverse auxiliary demonstrations can be automatically induced from seed exemplars, through analogical prompting. We employ a two-stage procedure, first generating analogical exemplars with a language model, and then applying them in-context along with provided target language exemplars. Our results on the modeLing dataset show that analogical prompting is effective in eliciting models' knowledge of language grammar similarities, boosting the performance of GPT-4o by as much as 8.1% and Llama-3.1-405B-Instruct by 5.9% over chain-of-thought approaches. These gains are attributable to the analogical demonstrations, both when self-generated as well as when produced by weaker multilingual models. Furthermore, we demonstrate that our method generalizes to other tasks present in Linguistics Olympiad competitions, achieving sizable improvements across all problem types and difficulty levels included in the LINGOLY dataset with GPT-4o. We also report several findings about interesting phenomena which drive linguistic reasoning performance, suggesting that such puzzles are a valuable benchmark for new reasoning methods.
The Rosetta Paradox: Domain-Specific Performance Inversions in Large Language Models
Authors: Basab Jha, Ujjwal Puri
Date: 2024-12-09 00:00:00
Tasks: Common Sense Reasoning, Specificity

While large language models, such as GPT and BERT, have already demonstrated unprecedented skills in everything from natural language processing to domain-specific applications, there came an unexplored phenomenon we term the Rosetta Paradox. The Rosetta Paradox characterizes the counterintuitive performance inversions across domains of knowledge. This paradox captures how such LLMs can excel in highly specialized fields but do poorly on tasks which require general, everyday knowledge. This paper formalizes the definition of the Rosetta Paradox and introduces a panoramic analysis framework that includes both a Domain Specificity Index (DSI) and a Performance Inversion Metric (PIM) for consistent quantification of domain-specific behavior in LLMs. We adopt this paradox and conduct a series of investigations through extensive experiments across diverse models and knowledge domains, ranging from rich technical areas to common-sense reasoning. Our findings indicate that the Rosetta Paradox is likely not a mere artifact of data distribution but an intrinsic architectural and emergent property of deep neural networks. We present comparative analyses across different model architectures, sizes, and training methodologies that shed light into the peculiar ways this paradox manifests itself and challenge the standard evaluation metrics.
Leveraging Sentiment for Offensive Text Classification
Authors: Khondoker Ittehadul Islam
Date: 2024-12-09 00:00:00
Tasks: Classification, text-classification, Text Classification

In this paper, we conduct experiment to analyze whether models can classify offensive texts better with the help of sentiment. We conduct this experiment on the SemEval 2019 task 6, OLID, dataset. First, we utilize pre-trained language models to predict the sentiment of each instance. Later we pick the model that achieved the best performance on the OLID test set, and train it on the augmented OLID set to analyze the performance. Results show that utilizing sentiment increases the overall performance of the model.
Look Ahead Text Understanding and LLM Stitching
Authors: Junlin Julian Jiang, Xin Li
Date: 2024-12-16 00:00:00
Tasks: Sentiment Analysis, Sentiment Classification

This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand the direction of a developing text or conversation. We tackle the problem using transformer-based LLMs. We show that LASI is more challenging than classic section identification (SI). We argue that both bidirectional contextual information (e.g., BERT) and unidirectional predictive ability (e.g., GPT) will benefit the task. We propose two approaches to stitch together BERT and GPT. Experiments show that our approach outperforms the established models, especially when there is noise in the text (which is often the case for developing text in generative AI). Our paper sheds light on other look ahead text understanding tasks that are important to social media, such as look ahead sentiment classification, and points out the opportunities to leverage pre-trained LLMs through stitching.
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding
Authors: Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, Grigori Sidorov, Dietrich Klakow, Philipp Slusallek, Olga Kolesnikova, Seid Muhie Yimam
Date: 2024-12-17 00:00:00
Tasks: Decoder, Emotion Classification

Large Language Models (LLMs) show promising learning and reasoning abilities. Compared to other NLP tasks, multilingual and multi-label emotion evaluation tasks are under-explored in LLMs. In this paper, we present EthioEmo, a multi-label emotion classification dataset for four Ethiopian languages, namely, Amharic (amh), Afan Oromo (orm), Somali (som), and Tigrinya (tir). We perform extensive experiments with an additional English multi-label emotion dataset from SemEval 2018 Task 1. Our evaluation includes encoder-only, encoder-decoder, and decoder-only language models. We compare zero and few-shot approaches of LLMs to fine-tuning smaller language models. The results show that accurate multi-label emotion classification is still insufficient even for high-resource languages such as English, and there is a large gap between the performance of high-resource and low-resource languages. The results also show varying performance levels depending on the language and model type. EthioEmo is available publicly to further improve the understanding of emotions in language models and how people convey emotions through various languages.
Enhancing Knowledge Distillation for LLMs with Response-Priming Prompting
Authors: Vijay Goyal, Mustafa Khan, Aprameya Tirupati, Harveer Saini, Michael Lam, Kevin Zhu
Date: 2024-12-18 00:00:00
Tasks: GSM8K, Knowledge Distillation, Transfer Learning

Large language models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing (NLP) tasks. However, these models are often difficult to deploy due to significant computational requirements and resource constraints. Knowledge distillation (KD) is an effective technique for transferring the performance of larger LLMs to smaller models. Traditional KD methods primarily focus on the direct output of the teacher model, with little emphasis on the role of prompting during knowledge transfer. In this paper, we propose a set of novel response-priming prompting strategies applied in the knowledge distillation pipeline to enhance the performance of student models. Our approach fine-tunes a smaller Llama 3.1 8B Instruct model by distilling knowledge from a quantized Llama 3.1 405B Instruct teacher model. We apply LoRA optimization and evaluate on the GSM8K benchmark. Experimental results demonstrate that integrating reasoning-eliciting prompting into the proposed KD pipeline significantly improves student model performance, offering an efficient way to deploy powerful models in resource-constrained environments. We find that Ground Truth prompting results in a 55\% performance increase on GSM8K for a distilled Llama 3.1 8B Instruct compared to the same model distilled without prompting. A thorough investigation into the self-attention layers of the student models indicates that the more successful prompted models tend to exhibit certain positive behaviors inside their attention heads which can be tied to their increased accuracy. Our implementation can be found at https://github.com/alonso130r/knowledge-distillation.
Overview of the 2024 ALTA Shared Task: Detect Automatic AI-Generated Sentences for Human-AI Hybrid Articles
Authors: Diego Mollá, Qiongkai Xu, Zijie Zeng, Zhuang Li
Date: 2024-12-19 00:00:00
Tasks: Articles

The ALTA shared tasks have been running annually since 2010. In 2024, the purpose of the task is to detect machine-generated text in a hybrid setting where the text may contain portions of human text and portions machine-generated. In this paper, we present the task, the evaluation criteria, and the results of the systems participating in the shared task.
Optimal High Frequency Trading with limit and market orders
Authors: Fabien Guilbaud, Huyen Pham
Date: 2011-06-24 00:00:00

We propose a framework for studying optimal market making policies in a limit order book (LOB). The bid-ask spread of the LOB is modelled by a Markov chain with finite values, multiple of the tick size, and subordinated by the Poisson process of the tick-time clock. We consider a small agent who continuously submits limit buy/sell orders and submits market orders at discrete dates. The objective of the market maker is to maximize her expected utility from revenue over a short term horizon by a tradeoff between limit and market orders, while controlling her inventory position. This is formulated as a mixed regime switching regular/ impulse control problem that we characterize in terms of quasi-variational system by dynamic programming methods. In the case of a mean-variance criterion with martingale reference price or when the asset price follows a Levy process and with exponential utility criterion, the dynamic programming system can be reduced to a system of simple equations involving only the inventory and spread variables. Calibration procedures are derived for estimating the transition matrix and intensity parameters for the spread and for Cox processes modelling the execution of limit orders. Several computational tests are performed both on simulated and real data, and illustrate the impact and profit when considering execution priority in limit orders and market orders
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
Authors: Alexander Denker, Zeljko Kereta, Imraj Singh, Tom Freudenberg, Tobias Kluth, Peter Maass, Simon Arridge
Date: 2024-05-06 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Electrical impedance tomography (EIT) plays a crucial role in non-invasive imaging, with both medical and industrial applications. In this paper, we present three data-driven reconstruction methods for EIT imaging. These three approaches were originally submitted to the Kuopio tomography challenge 2023 (KTC2023). First, we introduce a post-processing approach, which achieved first place at KTC2023. Further, we present a fully learned and a conditional diffusion approach. All three methods are based on a similar neural network as a backbone and were trained using a synthetically generated data set, providing with an opportunity for a fair comparison of these different data-driven reconstruction methods.
Approximate numerical solutions of fractional integral equations using Laguerre and Touchard polynomials
Authors: Supriya Kumar Paul, Lakshmi Narayan Mishra∗ and Vishnu Narayan Mishra
Date: 2023-03-12 00:00:00
Proceeding: Palestine Journal of Mathematics 2023 3
Tasks: 2k

Two numerical methods based on Laguerre and Touchard polynomials are de
scribed in this paper to solve both the fractional integral equations of the first kind and the
 second kind (FIEs-1K and FIEs-2K, respectively). The fractional integrals are described in the
 Erd´ elyi-Kober sense. Both the integral equations are transformed into an algebraic system of
 linear equations using the Laguerre and Touchard matrices. All the steps of the algorithm are
 given to find the solutions. Also, the accuracy of the solutions has been demonstrated. Five
 examples are provided to interpret the methods. The accuracy of solutions is compared for both
 methods. MATLAB program is used to perform all computations and graphs.