Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial differential equations for Asian option prices

In this article, we consider fixed and floating strike European style Asian call and put options. For such options, there is no convenient closed-form formula for the prices. Previously, Rogers and Shi, Vecer, and Dubois and Lelièvre have derived partial differential equations with one state variable, with the stock price as numeraire, for the option prices. In this paper, we derive a whole family of partial differential equations, each with one state variable with the stock price as numeraire, from which Asian options can be priced. Any one of these partial differential equations can be transformed into any other. This family includes four partial differential equations which have a particularly simple form including the three found by Rogers and Shi, Vecer, and Dubois and Lelièvre. Our analysis includes the case of a dividend yield; we also include the case of in progress Asian options with floating strike, whereby we discuss the new equation proposed by Vecer, which uses the average asset as numeraire. We perform an error analysis on the four special partial differential equations and Vecer’s new equation and find that their truncation errors are all of the same order. We also perform numerical comparisons of the five partial differential equations and conclude, as expected, that Vecer’s equations and that of Dubois and Lelièvre do better when the volatility is low but that with higher volatilities the performance of all five equations is similar. Vecer’s equations are unique in possessing a certain martingale property and as they perform numerically well or better than the others, must be considered the preferred choice.
A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems

For solving linear inverse problems, particularly of the type that appears in tomographic imaging and compressive sensing, this paper develops two new approaches. The first approach is an iterative algorithm that minimizes a regularized least squares objective function where the regularization is based on a compound Gaussian prior distribution. The compound Gaussian prior subsumes many of the commonly used priors in image reconstruction, including those of sparsity-based approaches. The developed iterative algorithm gives rise to the paper's second new approach, which is a deep neural network that corresponds to an “unrolling” or “unfolding” of the iterative algorithm. Unrolled deep neural networks have interpretable layers and outperform standard deep learning methods. This paper includes a detailed computational theory that provides insight into the construction and performance of both algorithms. The conclusion is that both algorithms outperform other state-of-the-art approaches to tomographic image formation and compressive sensing, especially in the difficult regime of low training.
Transport analysis and modelling of the evolution of hollow density profiles plasmas in JET and implication for ITER

The density evolution during the transient phase just after the L–H transition is investigated using theoretical transport models. Cases characterized by core densities which evolve in longer timescales than the edge densities, leading to hollow density profiles (R/Ln=−R∇n/n<0 ? >) are modelled. This density evolution is particularly interesting because it has been shown to be beneficial in the view of the access to burning plasma conditions in ITER (Loarte et al 2013 Nucl. Fusion 53 083031). Self-consistent simulations of the JET discharge 79676 of the density-only, and of the density and the temperatures are carried out using a quasilinear gyrokinetic code, QuaLiKiz (Bourdelle et al 2007 Phys. Plasmas 14 112501), coupled with a transport code CRONOS (Artaud et al 2010 Nucl. Fusion 50 043001). The slow evolution of the hollow density, associated with the self-consistently calculated hollow NBI particle deposition, is well reproduced in the plasma core. Indeed, QuaLiKiz is shown to reproduce nonlinear gyrokinetic heat and particle fluxes well for both positive and negative R/Ln ? >. That gives a theoretical and general basis for the persistence of the hollowness, laying the groundwork for the extrapolation to ITER.
Noise-aware Speech Separation with Contrastive Learning

Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixture, as the neural model is vulnerable to assign background noise to each speaker. In this paper, we propose a noise-aware SS (NASS) method, which aims to improve the speech quality for separated signals under noisy conditions. Specifically, NASS views background noise as an additional output and predicts it along with other speakers in a mask-based manner. To effectively denoise, we introduce patch-wise contrastive learning (PCL) between noise and speaker representations from the decoder input and encoder output. PCL loss aims to minimize the mutual information between predicted noise and other speakers at multiple-patch level to suppress the noise information in separated signals. Experimental results show that NASS achieves 1 to 2dB SI-SNRi or SDRi over DPRNN and Sepformer on WHAM! and LibriMix noisy datasets, with less than 0.1M parameter increase.
The Water Health Open Knowledge Graph

Recently, an increasing interest in the management of water and health resources has been recorded. This interest is fed by the global sustainability challenges posed to the humanity that have water scarcity and quality at their core. Thus, the availability of effective, meaningful and open data is crucial to address those issues in the broader context of the Sustainable Development Goals of clean water and sanitation as targeted by the United Nations. In this paper, we present the Water Health Open Knowledge Graph (WHOW-KG) along with its design methodology and analysis on impact. WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, infectious disease rates and drug distribution. The WHOW-KG is developed in the context of the EU-funded WHOW (Water Health Open Knowledge) project and aims at supporting a wide range of applications: from knowledge discovery to decision-making, making it a valuable resource for researchers, policymakers, and practitioners in the water and health domains. The WHOW-KG consists of a network of five ontologies and related linked open data, modelled according to those ontologies.
Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model

Natural language generation models reproduce and often amplify the biases present in their training data. Previous research explored using sequence-to-sequence rewriting models to transform biased model outputs (or original texts) into more gender-fair language by creating pseudo training data through linguistic rules. However, this approach is not practical for languages with more complex morphology than English. We hypothesise that creating training data in the reverse direction, i.e. starting from gender-fair text, is easier for morphologically complex languages and show that it matches the performance of state-of-the-art rewriting models for English. To eliminate the rule-based nature of data creation, we instead propose using machine translation models to create gender-biased text from real gender-fair text via round-trip translation. Our approach allows us to train a rewriting model for German without the need for elaborate handcrafted rules. The outputs of this model increased gender-fairness as shown in a human evaluation study.
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models

We focus on the task of learning a single index model $\sigma(w^\star \cdot x)$ with respect to the isotropic Gaussian distribution in $d$ dimensions. Prior work has shown that the sample complexity of learning $w^\star$ is governed by the information exponent $k^\star$ of the link function $\sigma$, which is defined as the index of the first nonzero Hermite coefficient of $\sigma$. Ben Arous et al. (2021) showed that $n \gtrsim d^{k^\star-1}$ samples suffice for learning $w^\star$ and that this is tight for online SGD. However, the CSQ lower bound for gradient based methods only shows that $n \gtrsim d^{k^\star/2}$ samples are necessary. In this work, we close the gap between the upper and lower bounds by showing that online SGD on a smoothed loss learns $w^\star$ with $n \gtrsim d^{k^\star/2}$ samples. We also draw connections to statistical analyses of tensor PCA and to the implicit regularization effects of minibatch SGD on empirical losses.
Physics Inspired Approaches Towards Understanding Gaussian Processes

Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel. However, beyond kernel choices, the decision-making process of GP models remains poorly understood. In this work, we contribute an analysis of the loss landscape for GP models using methods from physics. We demonstrate ν -continuity for Mat ´ ern kernels and outline aspects of catastrophe theory at critical points in the loss landscape. By directly including ν in the hyperparameter optimisation for Mat ´ ern kernels, we ﬁnd that typical values of ν are far from optimal in terms of performance, yet prevail in the literature due to the increased computational speed. We also provide an a priori method for evaluating the effect of GP ensembles and discuss various voting approaches based on physical properties of the loss landscape. The utility of these approaches is demonstrated for various synthetic and real datasets. Our ﬁndings provide an enhanced understanding of the decision-making process behind GPs and offer practical guidance for improving their performance and interpretability in a range of applications.
Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races

Vector embeddings have been successfully applied in several domains to obtain effective representations of non-numeric data which can then be used in various downstream tasks. We present a novel application of vector embeddings in professional road cycling by demonstrating a method to learn representations for riders and races based on historical results. We use unsupervised learning techniques to validate that the resultant embeddings capture interesting features of riders and races. These embeddings could be used for downstream prediction tasks such as early talent identification and race outcome prediction.
Integrating Item Relevance in Training Loss for Sequential Recommender Systems

Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r. t the best baseline model.
Radar-Derived Estimates of Latent Heating in the Subtropics

Abstract Atmospheric warming from cloud heating has a major affect on worldwide atmospheric circulations and climate. Studies have shown that the dominant source for cloud heating is the phase change of water. The location and magnitude of cloud heating has a substantial impact on atmospheric circulations. Therefore, identifying the location of phase changes provides information necessary for accurate modeling of atmospheric circulations and climate. Radar reflectivity is a signature predominantly produced from rain formed from condensation, the primary process that produces heating. Through the application of principal component analysis on a nonhydrostatic cloud model, heating, and derived reflectivity data, a technique to illustrate a future heating algorithm capable of estimating cloud heating from reflectivity data is examined. Formative, intensifying, and mature stages of a Convection and Precipitation Electrification Experiment squall-type convective system were used to demonstrate these results. T.
Dissecting the RELICS cluster SPT-CLJ0615-5746 through intracluster light: Confirmation of the multiple merging state of the cluster formation

The intracluster light (ICL) fraction, measured at certain specific wavelengths, has been shown to provide a good marker for determining the dynamical stage of galaxy clusters, i.e., merging versus relaxed, for small to intermediate redshifts. Here, we apply it for the first time to a high-redshift system, SPT-CLJ0615-5746 at z=0.97, using its RELICS (Reionization Lensing Cluster Survey) observations in the optical and infrared. We find the ICL fraction signature of merging, with values ranging from 16 to 37%. A careful re-analysis of the X-ray data available for this cluster points to the presence of at least one current merger, and plausibly a second merger. These two results are in contradiction with previous works based on X-ray data, which claimed the relaxed state of SPT-CLJ0615-5746, and confirmed the evidences presented by kinematic analyses. We also found an abnormally high ICL fraction in the rest-frame near ultraviolet wavelengths, which may be attributed to the combination of several phenomena such as an ICL injection during recent mergers of stars with average early-type spectra, the reversed star formation-density relation found at this high redshift in comparison with lower-redshift clusters, and projection effects.
Multilingual Event Extraction from Historical Newspaper Adverts

NLP methods can aid historians in analyzing textual materials in greater volumes than manually feasible. Developing such methods poses substantial challenges though. First, acquiring large, annotated historical datasets is difficult, as only domain experts can reliably label them. Second, most available off-the-shelf NLP models are trained on modern language texts, rendering them significantly less effective when applied to historical corpora. This is particularly problematic for less well studied tasks, and for languages other than English. This paper addresses these challenges while focusing on the under-explored task of event extraction from a novel domain of historical texts. We introduce a new multilingual dataset in English, French, and Dutch composed of newspaper ads from the early modern colonial period reporting on enslaved people who liberated themselves from enslavement. We find that: 1) even with scarce annotated data, it is possible to achieve surprisingly good results by formulating the problem as an extractive QA task and leveraging existing datasets and models for modern languages; and 2) cross-lingual low-resource learning for historical languages is highly challenging, and machine translation of the historical datasets to the considered target languages is, in practice, often the best-performing solution.
Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs

Causal effect estimation from observational data is a fundamental task in empirical sciences. It becomes particularly challenging when unobserved confounders are involved in a system. This paper focuses on front-door adjustment – a classic technique which, using observed mediators allows to identify causal effects even in the presence of unobserved confounding. While the statistical properties of the front-door estimation are quite well understood, its algorithmic aspects remained unexplored for a long time. In 2022, Jeong, Tian, and Bareinboim presented the first polynomial-time algorithm for finding sets satisfying the front-door criterion in a given directed acyclic graph (DAG), with an O(n³(n+m)) run time, where n denotes the number of variables and m the number of edges of the causal graph. In our work, we give the first linear-time, i.e., O(n+m), algorithm for this task, which thus reaches the asymptotically optimal time complexity. This result implies an O(n(n+m)) delay enumeration algorithm of all front-door adjustment sets, again improving previous work by a factor of n³. Moreover, we provide the first linear-time algorithm for finding a minimal front-door adjustment set. We offer implementations of our algorithms in multiple programming languages to facilitate practical usage and empirically validate their feasibility, even for large graphs.
Communication-Efficient Triangle Counting

Counting and finding triangles in graphs is often used in real-world analytics to characterize cohesiveness and identify communities in graphs. In this paper, we present novel sequential and parallel tri-angle counting algorithms based on identifying horizontal-edges in a breadth-first search (BFS) traversal of the graph. Identifying horizontal-edges allows our algorithm to drastically reduce the number of edges examined for set intersections. Our new approach is a communication-efficient parallel algorithm that asymptotically reduces communication on massive graphs such as from real social networks and synthetic graphs from the Graph500 Benchmark. In our estimate from massive-scale Graph500 graphs, our new algo-rithm reduces the communication by 22.1x on a scale 36 and by 181x on a scale 42. Because communication is known to be the dominant cost of distributed parallel triangle counting, our new parallel algorithm can significantly reduce the practical execution time for counting triangles in large graphs.
Domain-Agnostic Molecular Generation with Self-feedback

The generation of molecules with desired properties has gained tremendous popularity, revolutionizing the way scientists design molecular structures and providing valuable support for chemical and drug design. However, despite the potential of language models in molecule generation, they face numerous challenges such as the generation of syntactically or chemically ﬂawed molecules, narrow domain focus, and limitations in creating diverse and directionally feasible molecules due to a dearth of annotated data or external molecular databases. To this end, we introduce M OL G EN , a pre-trained molecular language model tailored speciﬁcally for molecule generation. M OL G EN acquires intrinsic structural and grammatical insights by reconstructing over 100 million molecular SELFIES, while facilitating knowledge transfer between different domains through domain-agnostic molecular preﬁx tuning. Moreover, we present a self-feedback paradigm that inspires the pre-trained model to align with the ultimate goal of producing molecules with desirable properties. Extensive experiments demonstrate that M OL G EN achieves superior performance on well-known molecule generation benchmarks. Further analysis shows that M OL G EN can accurately capture molecule distributions, implicitly learn their structural characteristics, and efﬁciently explore chemical space. The pre-trained model, codes, and datasets are publicly available for future research 2 .
Dynamic Matrix Recovery

Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion and compressed sensing models as special cases. In this work we propose a general framework for dynamic matrix recovery of low-rank matrices that evolve smoothly over time. We start from the setting that the observations are independent across time, then extend to the setting that both the design matrix and noise possess certain temporal correlation via modified concentration inequalities. By pooling neighboring observations, we obtain sharp estimation error bounds of both settings, showing the influence of the underlying smoothness, the dependence and effective samples. We propose a dynamic fast iterative shrinkage thresholding algorithm that is computationally efficient, and characterize the interplay between algorithmic and statistical convergence. Simulated and real data examples are provided to support such findings.
Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation

Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. However, for the effective separation of time series, new entropy estimation methods are required to characterize the chaotic dynamic of the system. Our concept of Neural Network Entropy (NNetEn) is based on the classification of special datasets in relation to the entropy of the time series recorded in the reservoir of the neural network. NNetEn estimates the chaotic dynamics of time series in an original way and does not take into account probability distribution functions. We propose two new classification metrics: R2 Efficiency and Pearson Efficiency. The efficiency of NNetEn is verified on separation of two chaotic time series of sine mapping using dispersion analysis. For two close dynamic time series (r = 1.1918 and r = 1.2243), the F-ratio has reached the value of 124 and reflects high efficiency of the introduced method in classification problems. The electroencephalography signal classification for healthy persons and patients with Alzheimer disease illustrates the practical application of the NNetEn features. Our computations demonstrate the synergistic effect of increasing classification accuracy when applying traditional entropy measures and the NNetEn concept conjointly. An implementation of the algorithms in Python is presented.
Fairness in Medical Image Analysis and Healthcare: A Literature Survey

Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system. However, machine learning, especially deep learning models have been shown to demonstrate a systematic bias towards certain subgroups of people. For instance, they yield a preferential predictive performance to males over females, which is unfair and potentially harmful especially in healthcare scenarios. In this literature survey, we give a comprehensive review of the current progress of fairness studies in medical image analysis (MedIA) and healthcare. Specifically, we first discuss the definitions of fairness, the source of unfairness and potential solutions. Then, we discuss current research on fairness for MedIA categorized by fairness evaluation and unfairness mitigation. Furthermore, we conduct extensive experiments to evaluate the fairness of different medical imaging tasks. Finally, we discuss the challenges and future directions in developing fair MedIA and healthcare applications