Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation of enhanced long-range elliptic anisotropies inside high-multiplicity jets in pp collisions at $\sqrt{s}$ = 13 TeV

A search for partonic collective effects inside jets produced in proton-proton collisions is performed via correlation measurements of charged constituents using the CMS detector at the CERN LHC. The analysis uses data collected at a center-of-mass energy of $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Jets are reconstructed with the anti-$k_\mathrm{T}$ algorithm with a distance parameter of 0.8 and are required to have transverse momentum greater than 550 GeV and pseudorapidity $\lvert\eta\rvert$ $\lt$ 1.6. Two-particle correlations among the charged constituents within the jets are studied as functions of the particles' azimuthal angle and pseudorapidity separations ($\Delta\phi^*$ and $\Delta\eta^*$) in a jet coordinate basis, where constituents' $\eta^*$, $\phi^*$ are defined relative to the direction of the jet. The correlation functions are studied in classes of in-jet charged-particle multiplicity up to $N_\text{ch}^\mathrm{j}$ $\approx$ 100. Fourier harmonics are extracted from long-range azimuthal correlation functions to characterize azimuthal anisotropy for $\lvert\Delta\eta^*\rvert$ $\gt$ 2. For low-multiplicity jets, the long-range elliptic anisotropic harmonic, $v^*_2$, is observed to decrease with $N_\text{ch}^\mathrm{j}$. This trend is well described by Monte Carlo event generators. However, a rising trend for $v^*_2$ emerges at $N_\text{ch}^\mathrm{j}$ $\gtrsim$ 80, hinting at a possible onset of collective behavior, which is not reproduced by the models tested. This observation yields new insights into the dynamics of parton fragmentation processes in the vacuum.
Resonance based schemes for SPDEs

Resonance based numerical schemes are those in which cancellations in the oscillatory components of the equation are taken advantage of in order to reduce the regularity required of the initial data to achieve a particular order of error and convergence. We investigate the potential for the derivation of resonance based schemes in the context of nonlinear stochastic PDEs. By comparing the regularity conditions required for error analysis to traditional exponential schemes we demonstrate that at orders less than $ \mathcal{O}(t^2) $, the techniques are successful and provide a significant gain on the regularity of the initial data, while at orders greater than $ \mathcal{O}(t^2) $, that the resonance based techniques does not achieve any gain. This is due to limitations in the explicit path-wise analysis of stochastic integrals. As examples of applications of the method, we present schemes for the Schr\"odinger equation and Manakov system accompanied by local error and stability analysis as well as proof of global convergence in both the strong and path-wise sense.
Best-of-Both-Worlds Linear Contextual Bandits

This study investigates the problem of $K$-armed linear contextual bandits, an instance of the multi-armed bandit problem, under an adversarial corruption. At each round, a decision-maker observes an independent and identically distributed context and then selects an arm based on the context and past observations. After selecting an arm, the decision-maker incurs a loss corresponding to the selected arm. The decision-maker aims to minimize the cumulative loss over the trial. The goal of this study is to develop a strategy that is effective in both stochastic and adversarial environments, with theoretical guarantees. We first formulate the problem by introducing a novel setting of bandits with adversarial corruption, referred to as the contextual adversarial regime with a self-bounding constraint. We assume linear models for the relationship between the loss and the context. Then, we propose a strategy that extends the RealLinExp3 by Neu&Olkhovskaya (2020) and the Follow-The-Regularized-Leader (FTRL). The regret of our proposed algorithm is shown to be upper-bounded by $O\left(\min\left\{\frac{(\log(T))^3}{\Delta_{*}} + \sqrt{\frac{C(\log(T))^3}{\Delta_{*}}},\ \ \sqrt{T}(\log(T))^2\right\}\right)$, where $T \in\mathbb{N}$ is the number of rounds, $\Delta_{*}>0$ is the constant minimum gap between the best and suboptimal arms for any context, and $C\in[0, T] $ is an adversarial corruption parameter. This regret upper bound implies $O\left(\frac{(\log(T))^3}{\Delta_{*}}\right)$ in a stochastic environment and by $O\left( \sqrt{T}(\log(T))^2\right)$ in an adversarial environment. We refer to our strategy as the Best-of-Both-Worlds (BoBW) RealFTRL, due to its theoretical guarantees in both stochastic and adversarial regimes.
What is the Magnus Expansion?

The Magnus expansion, introduced by Wilhelm Magnus in 1954, is an infinite Lie series employed to express solutions for first-order homogeneous linear differential equations involving a linear operator. Since its discovery it has evolved into a pivotal tool used across diverse disciplines, including physics, chemistry, and engineering. Over the past 25 years, the Magnus expansion has undergone significant mathematical developments which revealed an intricate interplay between algebra, combinatorics, and geometry. By emphasizing a modern perspectives based on the use of pre- and post-Lie algebras, we discuss the Magnus expansion from the viewpoint of the notion of crossed morphism.
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation

Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by leveraging self-supervised signals from raw data. Integration of CL with graph convolutional network (GCN)-based collaborative filterings (CFs) has been explored in recommender systems. However, current CL-based recommendation models heavily rely on low-pass filters and graph augmentations. In this paper, we propose a novel CL method for recommender systems called the reaction-diffusion graph contrastive learning model (RDGCL). We design our own GCN for CF based on both the diffusion, i.e., low-pass filter, and the reaction, i.e., high-pass filter, equations. Our proposed CL-based training occurs between reaction and diffusion-based embeddings, so there is no need for graph augmentations. Experimental evaluation on 6 benchmark datasets demonstrates that our proposed method outperforms state-of-the-art CL-based recommendation models. By enhancing recommendation accuracy and diversity, our method brings an advancement in CL for recommender systems.
BGK subgrid model for neutrino quantum kinetics

We present a new subgrid model for neutrino quantum kinetics, which is primarily designed to incorporate effects of collective neutrino oscillations into neutrino-radiation-hydrodynamic simulations for core-collapse supernovae and mergers of compact objects. We approximate the neutrino oscillation term in quantum kinetic equation by Bhatnagar-Gross-Krook (BGK) relaxation-time prescription, and the transport equation is directly applicable for classical neutrino transport schemes. The BGK model is motivated by recent theoretical indications that non-linear phases of collective neutrino oscillations settle into quasi-steady structures. We explicitly provide basic equations of the BGK subgrid model for both multi-angle and moment-based neutrino transport to facilitate the implementation of the subgrid model in the existing neutrino transport schemes. We also show the capability of our BGK subgrid model by comparing to fully quantum kinetic simulations for fast neutrino-flavor conversion. We find that the overall properties can be well reproduced in the subgrid model; the error of angular-averaged survival probability of neutrinos is within $\sim 20 \%$. By identifying the source of error, we also discuss perspectives to improve the accuracy of the subgrid model.
Preserving Ottawa’s Metropolitan Nature: How the 1970 Gatineau Park Planning Controversy Transformed the National Capital Commission and its Conservation Park

Although the National Capital Commission claimed to administer Gatineau Park according to a conservation policy, the 1960s saw numerous urban-type developments in the park and NCC planners emphasizing the park’s recreation potential. Th is paper describes how the 1970 Gatineau Park planning controversy sparked by conservationists opposed this policy reversal and forced the NCC to abandon blueprint planning. This paper then examines relations between the NCC conservationists from the time of the planning controversy to the first Gatineau Park master plan. Drawing from Arnstein’s ladder of citizen participation, it is argued that, during its transition to participatory planning, the NCC placated conservationists, receiving their input but not alwaysinfluenced by it. Thus, conservationists were disappointed by the first master plan, both its process and content. While they continue to play an important watchdog role in the park’s management, and regularly participate inNCC planning exercises, conservationists have been unable to secure protective legislation for Gatineau Park. The status quo remains, such that the NCC can alter park policy without parliamentary oversight. Meanwhile, the park’s wilderness character remains threatened by urbanization pressures.
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that supports CPU, GPU, and Ascend, and can be executed on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS. Extensive benchmarks conducted on popular environments including MuJoCo, Atari, and StarCraftII multi-agent challenge demonstrate the library's impressive performance. XuanCe is open-source and can be accessed at https://github.com/agi-brain/xuance.git.
On a finite sum of cosecants appearing in various problems

In this paper we investigate the finite sum of cosecants $\sum\csc\big(\varphi+a\pi l/n\big),$ where the index $l$ runs through 1 to $n-1$ and $\varphi$ and $a$ are arbitrary parameters, as well as several closely related sums, such as similar sums of a series of secants, of tangents and of cotangents. These trigonometric sums appear in various problems in mathematics, physics, and a variety of related disciplines. Their particular cases were fragmentarily considered in previous works, and it was noted that even a simple particular case $\sum\csc\big(\pi l/n\big)$ does not have a closed-form, i.e.~a compact summation formula. In the paper, we derive several alternative representations for the above-mentioned sums, study their properties, relate them to many other finite and infinite sums, obtain their complete asymptotic expansions for large $n$ and provide accurate upper and lower bounds (e.g. the typical relative error for the upper bound is lesser than $2\times10^{-9}$ for $n\geqslant10$ and lesser than $7\times10^{-14}$ for $n\geqslant50$, which is much better than the bounds we could find in previous works). Our researches reveal that these sums are deeply related to several special numbers and functions, especially to the digamma function (furthermore, as a by-product, we obtain several interesting summations formulae for the digamma function). Asymptotical studies show that these sums may have qualitatively different behaviour depending on the choice of $\varphi$ and $a$; in particular, as $n$ increases some of them may become sporadically large. Finally, we also provide several historical remarks related to various sums considered in the paper. We show that some results in the field either were rediscovered several times or can easily be deduced from various known formulae, including some formulae dating back to the XIIXth century.
Structure and Optimization of Parameters for Neural Network Controllers in Automatic Control Systems

The article outlines the methodology of structural and parametric synthesis of neural network controllers for controlling objects with limiters under incomplete information about the controlled object. Artificial neural networks are used to create controllers that are sequentially integrated into a control system with control objects. Reinforcement learning and pre-building a neural network imitator of the control object are used to synthesize the neural network controller. This approach is particularly effective when classical control system synthesis methods are not applicable due to significant nonlinearity and the difficulty in forming a mathematical model of the control object with the required accuracy. The proposed methods expand the class of technical systems for which direct synthesis of near-optimal control laws is possible. The robustness, adaptability and technical feasibility of neural network controllers make them interesting for practical applications. The main attention in the article is paid to the choice of neural network structure in the imitator and controller, formation of training samples taking into account the limitations of the control object.
Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning

Person re-identification (PRe-ID) is a computer vision issue, that has been a fertile research area in the last few years. It aims to identify persons across different non-overlapping camera views. In this paper, We propose a novel PRe-ID system that combines tensor feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Convolutional Neural Networks (CNNs) as a strong deep feature extractor, along with two complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis (TXQDA) is used to learn a discriminative subspace that enhances the separability between different individuals. Mahalanobis distance is used to match and similarity computation between query and gallery samples. Finally, we evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s.
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation

Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Internet are automatically harvested for training. However, it inevitably includes mismatched pairs, i.e., noisy correspondences, undermining supervision reliability and degrading performance. Current methods leverage deep neural networks' memorization effect to address noisy correspondences, which overconfidently focus on similarity-guided training with hard negatives and suffer from self-reinforcing errors. In light of above, we introduce a novel noisy correspondence learning framework, namely Self-Reinforcing Errors Mitigation (SREM). Specifically, by viewing sample matching as classification tasks within the batch, we generate classification logits for the given sample. Instead of a single similarity score, we refine sample filtration through energy uncertainty and estimate model's sensitivity of selected clean samples using swapped classification entropy, in view of the overall prediction distribution. Additionally, we propose cross-modal biased complementary learning to leverage negative matches overlooked in hard-negative training, further improving model optimization stability and curbing self-reinforcing errors. Extensive experiments on challenging benchmarks affirm the efficacy and efficiency of SREM.
Local Magnitude Estimation in Greece, Based on Recordings of the Hellenic Unified Seismic Network (HUSN)

Προτeίνeται μια νέα σχέση για τον υπολογισμό τοπικών μeγeθών (ML) στην Eλλάδα. Για το σκοπό αυτό χρησιμοποιούνται συνθeτικές καταγραφές Wood-Anderson (SWA) που προέρχονται από 98 ψηφιακούς σeισμολογικούς σταθμούς eυρέος φάσματος και καλύπτουν το χρονικό διάστημα 2007-2011. Οι σeισμολογικοί αυτοί σταθμοί ανήκουν σe τέσσeρα σeισμολογικά δίκτυα που eλέγχονται από: α) το Γeωδυναμικό Ινστιτούτο του Eθνικού Αστeροσκοπeίου Αθηνών (HL), β) το Eργαστήριο Γeωφυσικής του Αριστοτέλeιου Πανeπιστημίου Θeσσαλονίκης (HT), γ) το Eργαστήριο Σeισμολογίας του Eθνικού Καποδιστριακού Πανeπιστημίου Αθηνών (HA) και δ) το Eργαστήριο Σeισμολογίας του Πανeπιστημίου Πατρών (HP). Τα δίκτυα αυτά συνιστούν από το 2004 το Eνιαίο Eθνικό Δίκτυο Σeισμογράφων (E.E.Δ.Σ.). Οι καταγραφές των 98 αυτών σταθμών χρησιμοποιούνται για τον καθορισμό συντeλeστών γeωμeτρικής διασποράς και ανeλαστικής απόσβeσης, αντιπροσωπeυτικών για την Eλλάδα και τις γύρω πeριοχές, κατάλληλων για τον υπολογισμό τοπικών μeγeθών στις πeριοχές αυτές. Υπολογίζονται, eπίσης, σταθeρές διορθώσeις, που σχeτίζονται μe τις τοπικές συνθήκeς της θέσης κάθe σταθμού, για την πeραιτέρω βeλτίωση της ακρίβeιας στον καθορισμό του μeγέθους. Τέλος eπιχeιρeίται συσχέτιση του υπολογιζόμeνου μeγέθους μe αυθeντικά μeγέθη σeισμικής ροπής, η οποία αναδeικνύeι ισοδυναμία των δύο αυτών κλιμάκων μeγeθών για ένα eυρύ φάσμα τιμών (2.9≤MW≤6.4).
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications

Detecting anomalies has become an increasingly critical function in the financial service industry. Anomaly detection is frequently used in key compliance and risk functions such as financial crime detection fraud and cybersecurity. The dynamic nature of the underlying data patterns especially in adversarial environments like fraud detection poses serious challenges to the machine learning models. Keeping up with the rapid changes by retraining the models with the latest data patterns introduces pressures in balancing the historical and current patterns while managing the training data size. Furthermore the model retraining times raise problems in time-sensitive and high-volume deployment systems where the retraining period directly impacts the models ability to respond to ongoing attacks in a timely manner. In this study we propose a temporal knowledge distillation-based label augmentation approach (TKD) which utilizes the learning from older models to rapidly boost the latest model and effectively reduces the model retraining times to achieve improved agility. Experimental results show that the proposed approach provides advantages in retraining times while improving the model performance.
Models of cosmological black holes

We study various aspects of modeling astrophysical black holes using the recently introduced semiclassical formalism of physical black holes (PBHs). This approach is based on the minimal requirements of observability and regularity of the horizons. We demonstrate that PBHs do not directly couple to the cosmological background in the current epoch, and their equation of state renders them unsuitable for describing dark energy. Utilizing their properties for analysis of more exotic models, we present a consistent semiclassical scenario for a black-to-white hole bounce and identify obstacles to the transformation from a black hole horizon to a wormhole mouth.
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs

Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent family of SSL that extract similar representations of two augmented views of an image while pushing away others in the representation space as negatives. However, the state-of-the-art contrastive methods require large batch sizes and augmentations designed for natural images that are impractical for 3D medical images. To address these limitations, we propose a new longitudinal SSL method, 3DTINC, based on non-contrastive learning. It is designed to learn perturbation-invariant features for 3D optical coherence tomography (OCT) volumes, using augmentations specifically designed for OCT. We introduce a new non-contrastive similarity loss term that learns temporal information implicitly from intra-patient scans acquired at different times. Our experiments show that this temporal information is crucial for predicting progression of retinal diseases, such as age-related macular degeneration (AMD). After pretraining with 3DTINC, we evaluated the learned representations and the prognostic models on two large-scale longitudinal datasets of retinal OCTs where we predict the conversion to wet-AMD within a six months interval. Our results demonstrate that each component of our contributions is crucial for learning meaningful representations useful in predicting disease progression from longitudinal volumetric scans.
Hierarchical variable clustering based on the predictive strength between random vectors

A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive information about the behaviour of the variables in the other group and/or vice versa. The method presented here is model-free, dependence-based and does not require any distributional assumptions. Various general invariance and continuity properties are investigated, with special attention to those that are beneficial for the agglomerative hierarchical clustering procedure. A fully non-parametric estimator is considered whose excellent performance is demonstrated in several simulation studies and by means of real-data examples.
Representation functions in the set of natural numbers

Let $\mathbb{N}$ be the set of all nonnegative integers. For $S\subseteq \mathbb{N}$ and $n\in \mathbb{N}$, let $R_S(n)$ denote the number of solutions of the equation $n=s+s'$, $s, s'\in S$, $s<s'$. In this paper, we determine the structure of all sets $A$ and $B$ such that $A\cup B=\mathbb{N}\setminus\{r+mk:k\in\mathbb{N}\}$, $A\cap B=\emptyset$ and $R_{A}(n)=R_{B}(n)$ for every positive integer $n$, where $m$ and $r$ are two integers with $m\ge 2$ and $r\ge 0$.