pwc-archive/papers-with-abstracts huggingface dataset:
MIST-CF: Chemical formula inference from tandem mass spectra
Authors: Samuel Goldman, Jiayi Xin, Joules Provenzano, Connor W. Coley
Date: 2023-07-17 00:00:00
Tasks: Learning-To-Rank

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parameterized fragmentation tree construction and scoring. In this work we extend our previous spectrum Transformer methodology into an energy based modeling framework, MIST-CF, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge dataset, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or post-processing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formula with data driven learning.
Approaches for benchmarking single-cell gene regulatory network inference methods
Authors: Yasin Uzun
Date: 2023-07-17 00:00:00
Tasks: Benchmarking

Gene regulatory networks are powerful tools for modeling interactions among genes to regulate their expression for homeostasis and differentiation. Single-cell sequencing offers a unique opportunity to build these networks with high-resolution data. There are many proposed computational methods to build these networks using single-cell data and different approaches are followed to benchmark these methods. In this review, we lay the basic terminology in the field and define the success metrics. Next, we present an overview of approaches for benchmarking computational gene regulatory network approaches for building gene regulatory networks and point out gaps and future directions in this regard.
ProSt: computing, storing and visualizing attributes of prokaryotic genomes
Authors: Giorgio Gonnella
Date: 2023-07-17 00:00:00
Tasks: Attribute

Prokaryotic organisms usually possess compact genomes, which are particularly suitable to complete sequencing with existing technologies, which led to an escalating accumulation of available genome data. In response to this ever-expanding repository of information, we introduce ProSt, a computational system designed for the batch computation, storage, and interactive visualization of the values of attributes of prokaryotic genomes. The system allows for parallel attribute value batch computation, dynamically designed to incrementally integrate new attribute values as additional genomes become available. ProSt is flexible permitting the definition of attributes by implementing attribute value computation plugins, supporting several languages (Python, Nim, Rust and Bash). This allows the system to continually evolve in accordance with changing research needs and developments. Additionally, our computation and storage systems maintain comprehensive metadata, thereby enabling data provenance tracking for the computed attribute values.
Aligned and oblique dynamics in recurrent neural networks
Authors: Friedrich Schuessler, Francesca Mastrogiuseppe, Srdjan Ostojic, Omri Barak
Date: 2023-07-14 00:00:00
Tasks: Relation

The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. There is increasing evidence, however, for partial dissociations between activity in an area and relevant external variables. While many explanations have been proposed, a theoretical framework for the relationship between external and internal variables is lacking. Here, we utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view. We find that training RNNs can lead to two dynamical regimes: dynamics can either be aligned with the directions that generate output variables, or oblique to them. We show that the choice of readout weight magnitude before training can serve as a control knob between the regimes, similar to recent findings in feedforward networks. These regimes are functionally distinct. Oblique networks are more heterogeneous and suppress noise in their output directions. They are furthermore more robust to perturbations along the output directions. Crucially, the oblique regime is specific to recurrent (but not feedforward) networks, arising from dynamical stability considerations. Finally, we show that tendencies towards the aligned or the oblique regime can be dissociated in neural recordings. Altogether, our results open a new perspective for interpreting neural activity by relating network dynamics and their output.
Extinction time distributions of populations and genotypes
Authors: David Kessler, Nadav M. Shnerb
Date: 2023-07-17 00:00:00

In the long run, the eventual extinction of any biological population is an inevitable outcome. While extensive research has focused on the average time it takes for a population to go extinct under various circumstances, there has been limited exploration of the distributions of extinction times and the likelihood of significant fluctuations. Recently, Hathcock and Strogatz [PRL 128, 218301 (2022)] identified Gumbel statistics as a universal asymptotic distribution for extinction-prone dynamics in a stable environment. In this study, we aim to provide a comprehensive survey of this problem by examining a range of plausible scenarios, including extinction-prone, marginal (neutral), and stable dynamics. We consider the influence of demographic stochasticity, which arises from the inherent randomness of the birth-death process, as well as cases where stochasticity originates from the more pronounced effect of random environmental variations. Our work proposes several generic criteria that can be used for the classification of experimental and empirical systems, thereby enhancing our ability to discern the mechanisms governing extinction dynamics. By employing these criteria, we can improve our understanding of the underlying mechanisms driving extinction processes.
Adjusting the nuclear reactor's neutron transport and diffusion theory for an alternative description and modelling of postage or supplies delivery processes
Authors: Nick P. Petropoulos
Date: 2023-07-15 00:00:00
Tasks: Unity

There seems to exist significant similarities between a reactor system and a supply chain from collection to delivery. In the reactor case, neutrons are continuously produced and absorbed in nuclear fuel. In a supply system case, items are continuously collected and continuously delivered to destinations. Stable reactor operation is ensured by keeping the ratio of neutrons produced to neutrons absorbed in the reactor equal to one. Profitable and qualitative supply operation is ensured by keeping the ratio of items delivered to items collected as close to unity as possible. The analogy between the two systems is obvious. This text, which is provided as is and has not undergone any peer review process, proposes transferring parts of the nuclear reactor's neutron transport and diffusion theory to deterministically model supply processes. To this end a set of assumptions and definitions are provided as needed along with the introduction of reactions or interactions like collections, deliveries, and losses occurring into the supply chain. The interaction rates are calculated with the method used in reactors employing analogy factors and interactors with which the items in the chain interact. The main aim is to describe losses escape in steady state and in parallel estimate the analogy factors and optimize for the correct selection of the interactors pool. The model as proposed seems to be a tool for a different insight method into supply problems. The model, if proven and applied, is discussed to be a strong optimization tool, which could deterministically pinpoint flaws in existing supply systems or stochastically efficiently organize proposed supplied chains.
Exploring Implied Certainty Equivalent Rates in Financial Markets: Empirical Analysis and Application to the Electric Vehicle Industry
Authors: Yifan He, Svetlozar Rachev
Date: 2023-06-30 00:00:00

In this paper, we mainly study the impact of the implied certainty equivalent rate on investment in financial markets. First, we derived the mathematical expression of the implied certainty equivalent rate by using put-call parity, and then we selected some company stocks and options; we considered the best-performing and worst-performing company stocks and options from the beginning of 2023 to the present for empirical research. By visualizing the relationship between the time to maturity, moneyness, and implied certainty equivalent rate of these options, we have obtained a universal conclusion -- a positive implied certainty equivalent rate is more suitable for investment than a negative implied certainty equivalent rate, but for a positive implied certainty equivalent rate, a larger value also means a higher investment risk. Next, we applied these results to the electric vehicle industry, and by comparing several well-known US electric vehicle production companies, we further strengthened our conclusions. Finally, we give a warning concerning risk, that is, investment in the financial market should not focus solely on the implied certainty equivalent rate, because investment is not an easy task, and many factors need to be considered, including some factors that are difficult to predict with models.
Generalized Families of Fractional Stochastic Dominance
Authors: Ehsan Azmoodeh, Ozan Hür
Date: 2023-07-03 00:00:00

Introduced by M\"uller et al. in their seminal paper \cite{muller}, fractional stochastic dominance (SD) offers a nuanced approach to ordering distributions. In this paper, we propose a fundamentally new framework by replacing the fixed parameter $\gamma \in [0,1]$ in fractional SD with a function $\boldsymbol{\gamma}: \mathbb{R} \to [0,1]$. This yields two novel families, multi-fractional stochastic dominance (MFSD) and functional fractional stochastic dominance (FFSD). They enable the ranking of a broader range of distributions and incorporate a more informative utility class, including those with local non-concavities whose steepness varies depending on the location. Furthermore, our framework introduces the concept of partial greediness, which dynamically captures how behaviour of decision makers adapts to changes in wealth. We also extend this framework to encompass almost stochastic dominance. We provide the mathematical foundations of our generalized framework and study how it offers a novel tool for ordering distributions across various settings.
Bayesian Forecasting of Stock Returns on the JSE using Simultaneous Graphical Dynamic Linear Models
Authors: Nelson Kyakutwika, Bruce Bartlett
Date: 2023-07-07 00:00:00
Tasks: Time Series

Cross-series dependencies are crucial in obtaining accurate forecasts when forecasting a multivariate time series. Simultaneous Graphical Dynamic Linear Models (SGDLMs) are Bayesian models that elegantly capture cross-series dependencies. This study forecasts returns of a 40-dimensional time series of stock data from the Johannesburg Stock Exchange (JSE) using SGDLMs. The SGDLM approach involves constructing a customised dynamic linear model (DLM) for each univariate time series. At each time point, the DLMs are recoupled using importance sampling and decoupled using mean-field variational Bayes. Our results suggest that SGDLMs forecast stock data on the JSE accurately and respond to market gyrations effectively.
Shannon entropy to quantify complexity in the financial market
Authors: Alexis Rodriguez Carranza, José Luis Ponte Bejarano, Juan Carlos Ponte Bejarano, Segundo Eloy Soto Abanto
Date: 2023-07-05 00:00:00

In this paper we study the complexity in the information traffic that occurs in the peruvian financial market, using the Shannon entropy. Different series of prices of shares traded on the Lima stock exchange are used to reconstruct the unknown dynamics. We present numerical simulations on the reconstructed dynamics and we calculate the Shannon entropy to measure its complexity
Trend patterns statistics for assessing irreversibility in cryptocurrencies: time-asymmetry versus inefficiency
Authors: Jessica Morales Herrera, Raúl Salgado-García
Date: 2023-06-28 00:00:00
Tasks: Time Series

In this paper, we present a measure of time irreversibility using trend pattern statistics. We define the irreversibility index as the Kullback-Leibler divergence between the distribution of uptrends subsequences (increasing trends) and the corresponding downtrends subsequences distribution (decreasing trends) in a time series. We use this index to analyze the degree of irreversibility in log return series over time, specifically focusing on five cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. Our analysis reveals a strong indication of irreversibility in all these cryptocurrencies and the characteristic evolves over time. We additionally evaluate the market efficiency for these cryptocurrencies based on a recently proposed information-theoretic measure. By comparing inefficiency and irreversibility, we explore the relationship between these statistical features. This comparison provides insight into the non-trivial relationship between inefficiency and irreversibility.
The Chebyshev Polynomials Of The First Kind For Analysis Rates Shares Of Enterprises
Authors: Sergey Yekimov
Date: 2023-06-16 00:00:00
Tasks: Time Series

Chebyshev polynomials of the first kind have long been used to approximate experimental data in solving various technical problems. Within the framework of this study, the dynamics of shares of eight Czech enterprises was analyzed by the Chebyshev polynomial decomposition: CEZ A.S. (CEZP), Colt CZ Group SE (CZG), Erste Bank (ERST), Komercni Banka (BKOM), Moneta Money Bank A.S. (MONET), Photon (PENP), Vienna insurance group (VIGR) in 2021. An investor, when making a decision to purchase a security , is guided largely by an heuristic approach . And variance and correlation are not observed by human senses. The vectors of decomposition of time series of exchange values of securities allow analyzing the dynamics of exchange values of securities more effectively if their dynamics does not correspond to the normal distribution law. The proposed model allows analyzing the dynamics of the exchange value of a securities portfolio without calculating variance and correlation. This model can be useful if the dynamics of the exchange values of securities does not obey, due to certain circumstances, the normal law of distribution.
Joint Latent Topic Discovery and Expectation Modeling for Financial Markets
Authors: Lili Wang, Chenghan Huang, Chongyang Gao, Weicheng Ma, Soroush Vosoughi
Date: 2023-06-01 00:00:00

In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks. However, current relational stock methods are limited by their reliance on predefined stock relationships and the exclusive consideration of immediate effects. To address these limitations, we present a groundbreaking framework for financial market analysis. This approach, to our knowledge, is the first to jointly model investor expectations and automatically mine latent stock relationships. Comprehensive experiments conducted on China's CSI 300, one of the world's largest markets, demonstrate that our model consistently achieves an annual return exceeding 10%. This performance surpasses existing benchmarks, setting a new state-of-the-art standard in stock return prediction and multiyear trading simulations (i.e., backtesting).
Thailand Asset Value Estimation Using Aerial or Satellite Imagery
Authors: Supawich Puengdang, Worawate Ausawalaithong, Phiratath Nopratanawong, Narongdech Keeratipranon, Chayut Wongkamthong
Date: 2023-05-26 00:00:00

Real estate is a critical sector in Thailand's economy, which has led to a growing demand for a more accurate land price prediction approach. Traditional methods of land price prediction, such as the weighted quality score (WQS), are limited due to their reliance on subjective criteria and their lack of consideration for spatial variables. In this study, we utilize aerial or satellite imageries from Google Map API to enhance land price prediction models from the dataset provided by Kasikorn Business Technology Group (KBTG). We propose a similarity-based asset valuation model that uses a Siamese-inspired Neural Network with pretrained EfficientNet architecture to assess the similarity between pairs of lands. By ensembling deep learning and tree-based models, we achieve an area under the ROC curve (AUC) of approximately 0.81, outperforming the baseline model that used only tabular data. The appraisal prices of nearby lands with similarity scores higher than a predefined threshold were used for weighted averaging to predict the reasonable price of the land in question. At 20\% mean absolute percentage error (MAPE), we improve the recall from 59.26\% to 69.55\%, indicating a more accurate and reliable approach to predicting land prices. Our model, which is empowered by a more comprehensive view of land use and environmental factors from aerial or satellite imageries, provides a more precise, data-driven, and adaptive approach for land valuation in Thailand.
An empirical study of using radiology reports and images to improve ICU mortality prediction
Authors: Mingquan Lin, Song Wang, Ying Ding, Lihui Zhao, Fei Wang, Yifan Peng
Date: 2023-06-20 00:00:00
Tasks: ICU Mortality, Management, Mortality Prediction, Survival Prediction

Background: The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management because it predicts important outcomes, especially mortality. Many scoring systems have been developed and used in the ICU. These scoring systems are primarily based on the structured clinical data in the electronic health record (EHR), which may suffer the loss of important clinical information in the narratives and images. Methods: In this work, we build a deep learning based survival prediction model with multi-modality data to predict ICU mortality. Four sets of features are investigated: (1) physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2) common thorax diseases pre-defined by radiologists, (3) BERT-based text representations, and (4) chest X-ray image features. We use the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset to evaluate the proposed model. Results: Our model achieves the average C-index of 0.7829 (95% confidence interval, 0.7620-0.8038), which substantially exceeds that of the baseline with SAPS-II features (0.7470 (0.7263-0.7676)). Ablation studies further demonstrate the contributions of pre-defined labels (2.00%), text features (2.44%), and image features (2.82%).
Explainability is NOT a Game
Authors: Joao Marques-Silva, Xuanxiang Huang
Date: 2023-06-27 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Feature Importance

Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding complex machine learning (ML) models. One of the hallmarks of XAI are measures of relative feature importance, which are theoretically justified through the use of Shapley values. This paper builds on recent work and offers a simple argument for why Shapley values can provide misleading measures of relative feature importance, by assigning more importance to features that are irrelevant for a prediction, and assigning less importance to features that are relevant for a prediction. The significance of these results is that they effectively challenge the many proposed uses of measures of relative feature importance in a fast-growing range of high-stakes application domains.
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Authors: Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans, Hiroaki Kitano, Ross King
Date: 2023-07-09 00:00:00
Tasks: Model Discovery, scientific discovery

Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access to large training data sets and clear performance evaluation criteria, ranging from pattern recognition and classification to generative models. Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Generative AI, in general, and Large Language Models in particular, may represent an opportunity to augment and accelerate the scientific discovery of fundamental deep science with quantitative models. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery, including self-driven hypothesis generation and open-ended autonomous exploration of the hypothesis space. Integrating AI-driven automation into the practice of science would mitigate current problems, including the replication of findings, systematic production of data, and ultimately democratisation of the scientific process. Realising these possibilities requires a vision for augmented AI coupled with a diversity of AI approaches able to deal with fundamental aspects of causality analysis and model discovery while enabling unbiased search across the space of putative explanations. These advances hold the promise to unleash AI's potential for searching and discovering the fundamental structure of our world beyond what human scientists have been able to achieve. Such a vision would push the boundaries of new fundamental science rather than automatize current workflows and instead open doors for technological innovation to tackle some of the greatest challenges facing humanity today.
FedDefender: Backdoor Attack Defense in Federated Learning
Authors: Waris Gill, Ali Anwar, Muhammad Ali Gulzar
Date: 2023-07-02 00:00:00
Tasks: Backdoor Attack, Data Poisoning, Federated Learning, Image Classification, Knowledge Distillation, Privacy Preserving

Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively. In this work, we propose FedDefender, a defense mechanism against targeted poisoning attacks in FL by leveraging differential testing. Our proposed method fingerprints the neuron activations of clients' models on the same input and uses differential testing to identify a potentially malicious client containing a backdoor. We evaluate FedDefender using MNIST and FashionMNIST datasets with 20 and 30 clients, and our results demonstrate that FedDefender effectively mitigates such attacks, reducing the attack success rate (ASR) to 10\% without deteriorating the global model performance.