Miracle-dz/UniFAR-FTUs huggingface dataset:
Possible impacts of mega‐El Niño/Southern Oscillation and Atlantic Multidecadal Oscillation on Eurasian heatwave frequency variability

Identifying predictability sources of heatwave variations is a scientific challenge and of practical importance. This study investigates the summertime heatwave frequency (HWF) over Eurasia for 1950–2014. The Eurasian HWF is dominated by two distinct modes: the interdecadal (ID) mode, featured by an increasing pattern overall, centred around eastern Europe–central Asia and Mongolia–southwestern China; and the interannual (IA) mode, resembling a tripole anomaly pattern with three centres over western–northern Europe, northeastern Asia and East Asia. The ID mode is found to be influenced by mega‐El Niño/Southern Oscillation (mega‐ENSO) and the Atlantic Multidecadal Oscillation (AMO), and the latter has far more effect, whereas the IA mode is connected with mega‐ENSO.
Adoptive T‐cell therapy targeting Epstein–Barr virus as a treatment for multiple sclerosis

Emergence of a definitive link between Epstein–Barr virus (EBV) and multiple sclerosis has provided an impetus to develop immune‐based therapies to target EBV‐infected B cells. Initial studies with autologous EBV‐specific T‐cell therapy demonstrated that this therapy is safe with minimal side effects and more importantly multiple patients showed both symptomatic and objective neurological improvements including improved quality of life, reduction of fatigue and reduced intrathecal IgG production. These observations have been successfully extended to an ‘off‐the‐shelf’ allogeneic EBV‐specific T‐cell therapy manufactured using peripheral blood lymphocytes of healthy seropositive individuals. This adoptive immunotherapy has also been shown to be safe with encouraging clinical responses. Allogeneic EBV T‐cell therapy overcomes some of the limitations of autologous therapy and can be rapidly delivered to patients with improved therapeutic potential.
Close-Kin Mark-Recapture

Mark-recapture (MR) methods are commonly used to study wildlife populations. Taking advantage of modern genetics one can generalize from “recapture of self” to “recapture of closely-related kin”. Abundance and other demographic parameters of adults can then be estimated using, if necessary, only samples from dead animals (live-release is optional). This greatly widens the scope of MR, e.g. to commercial fisheries where largescale tagging is impractical, and enhances the power of conventional MR studies where live release and tissue sampling is possible. We give explicit formulae for kinship (i.e., recapture) probabilities in general and specific cases. These yield a pseudo-likelihood based on pairwise comparisons of individuals in the samples. It is shown that the pseudo-likelihood approximates the full likelihood under sparse sampling of large populations. Experimental design is addressed via the principle of maximizing the Fisher information for parameters of interest. Finally, we discuss challenges related to kinship determination from genetic data, focusing on current limitations and future possibilities.
The Stochastic Coalescence Model for Cloud Droplet Growth.

Abstract The stochastic coalescence model for droplet growth in warm clouds is analyzed, with a view to clarifying the theoretical foundations and significance of the well-known stochastic coalescence equation. It is suggested that the analysis of the model is most logically carried out in terms of a function P (n, m; t which is defined as the probability that the number of cloud droplets consisting of m molecules at time t will be n. A time-evolution equation for P (n, m; t is derived, and under certain stated assumptions it is deduced that: 1) the mean value of P (n, m; t with respect to n satisfies the stochastic coalescence equation; and 2) regardless of the initial conditions, the graph of P (n, m; t vs n will approach the Poisson shape as t →∞ to with an estimable “relaxation tirne. ” The implications of these results for the stochastic fluctuations in the number of cloud droplets are examined. It is found that a distinction must be made between fluctuations in droplet concentration arising from the .
Benders decomposition algorithms for minimizing the spread of harmful contagions in networks

The COVID-19 pandemic has been a recent example for the spread of a harmful contagion in large populations. Moreover, the spread of harmful contagions is not only restricted to an infectious disease, but is also relevant to computer viruses and malware in computer networks. Furthermore, the spread of fake news and propaganda in online social networks is also of major concern. In this study, we introduce the measure-based spread minimization problem (MBSMP), which can help policy makers in minimizing the spread of harmful contagions in large networks. We develop exact solution methods based on branch-and-Benders-cut algorithms that make use of the application of Benders decomposition method to two different mixed-integer programming formulations of the MBSMP: an arc-based formulation and a path-based formulation. We show that for both formulations the Benders optimality cuts can be generated using a combinatorial procedure rather than solving the dual subproblems using linear programming. Additional improvements such as using scenario-dependent extended seed sets, initial cuts, and a starting heuristic are also incorporated into our branch-and-Benders-cut algorithms. We investigate the contribution of various components of the solution algorithms to the performance on the basis of computational results obtained on a set of instances derived from existing ones in the literature.
From clean room to machine room: commissioning of the first-generation BrainScaleS wafer-scale neuromorphic system

The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons. Following a ‘physical modeling’ principle, its VLSI circuits are designed to emulate the dynamics of biological examples: analog circuits implement neurons and synapses with time constants that arise from their electronic components’ intrinsic properties. It operates in continuous time, with dynamics typically matching an acceleration factor of 10 000 compared to the biological regime. A fault-tolerant design allows it to achieve wafer-scale integration despite unavoidable analog variability and component failures. In this paper, we present the commissioning process of a BrainScaleS-1 wafer module, providing a short description of the system’s physical components, illustrating the steps taken during its assembly and the measures taken to operate it. Furthermore, we reflect on the system’s development process and the lessons learned to conclude with a demonstration of its functionality by emulating a wafer-scale synchronous firing chain, the largest spiking network emulation ran with analog components and individual synapses to date.
A two-dimensional minimum residual technique for accelerating two-step iterative solvers with applications to discrete ill-posed problems

This paper deals with speeding up the convergence of a class of two-step iterative methods for solving linear systems of equations. To implement the acceleration technique, the residual norm associated with computed approximations for each sub-iterate is minimized over a certain two-dimensional subspace. Convergence properties of the proposed method are studied in detail. The approach is further developed to solve (regularized) normal equations arising from the discretization of ill-posed problems. The results of numerical experiments are reported to illustrate the performance of exact and inexact variants of the method on several test problems from different application areas.
On Domain-Specific Pre- Training for Effective Semantic Perception in Agricultural Robotics

Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic manner. Semantic perception mostly relies on deep learning using supervised approaches, which require time and qualified workers to label fairly large amounts of data. In this paper, we look into the problem of reducing the amount of labels without compromising the final segmentation performance. For robots operating in the field, pre-training networks in a supervised way is already a popular method to reduce the number of required labeled images. We investigate the possibility of pre-training in a self-supervised fashion using data from the target domain. To better exploit this data, we propose a set of domain-specific augmentation strategies. We evaluate our pre-training on semantic segmentation and leaf instance segmentation, two important tasks in our domain. The experimental results suggest that pre-training with domain-specific data paired with our data augmentation strategy leads to superior performance compared to commonly used pre-trainings. Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
Distributed Safe Control Design and Safety Verification for Multi-Agent Systems

We propose distributed iterative algorithms for safe control design and safety verification for networked multi-agent systems. These algorithms rely on distributing a control barrier function (CBF) related quadratic programming (QP) problem. The proposed distributed algorithm addresses infeasibility issues of existing schemes by dynamically allocating auxiliary variables across iterations. The resulting control input is guaranteed to be optimal, and renders the system safe. Furthermore, a truncated algorithm is proposed to facilitate computational implementation. The performance of the truncated algorithm is evaluated using a distributed safety verification algorithm. The algorithm quantifies safety for a multi-agent system probabilistically, using a certain locally Lipschitz continuous feedback controller by means of CBFs. Both upper and lower bounds on the probability of safety are obtained using the so called scenario approach. Both the scenario sampling and safety verification procedures are fully distributed. The efficacy of our algorithms is demonstrated by an example on multi-robot collision avoidance.
Quantitative Nonclassicality of Mediated Interactions

In plethora of physical situations one can distinguish a mediator -- a system that couples other, non-interacting systems. Often the mediator itself is not directly accessible to experimentation, yet it is interesting and sometimes crucial to understand if it admits non-classical properties. An example of this sort that recently enjoys considerable attention are two quantum masses coupled via gravitational field. It has been argued that the gain of quantum entanglement between the masses indicates non-classicality of the states of the whole tripartite system. Here, we focus on non-classical properties of the involved interactions rather than the states. We derive inequalities whose violation indicates non-commutativity and non-decomposability (open system generalisation of non-commuting unitaries) of interactions through the mediators. The derivations are based on properties of general quantum formalism and make minimalistic assumptions about the studied systems, in particular the interactions can remain uncharacterised throughout the assessment. Furthermore, we also present conditions that solely use correlations between the coupled systems, excluding the need to measure the mediator. Next, we show that the amount of violation places a lower bound on suitably defined degree of non-decomposability. This makes the methods quantitative and at the same time experiment ready. We give applications of these techniques in two different fields: for detecting non-classicality of gravitational interaction and in bounding the Trotter error in quantum simulations.
Principal Points and Self-Consistent Points of Elliptical Distributions

The k principal points of a p-variate random vector X are those points ξ 1 ,. , ξ k ∈ R p which approximate the distribution of X by minimizing the expected squared distance of X from the nearest of the ξ j . Any set of k points y 1 ,. , y k partitions R p into domains of attraction D 1 ,. , D k according to minimal distance; following Hastie and Stuetzle we call y 1 ,. , y k self-consistent if E[XX ∈ D j ] = y j for j = 1,. , k. Principal points are a special case of self-consistent points. In this paper we study principal points and self-consistent points of p-variate elliptical distributions. The main results are the following: (1) If k self-consistent points of X span a subspace of dimension q < p, then this subspace is also spanned by q principal components, that is, self-consistent points of elliptical distributions exist only in principal component subspaces. (2) The subspace spanned by k principal points of X is identical with the subspace spanned by the principal components associated with the largest roots. This proves a conjecture of Flury. We also discuss implications of our results for the computation and estimation of principal points.
A General Algorithm for Solving Rank-one Matrix Sensing

Matrix sensing has many real-world applications in science and engineering, such as system control, distance embedding, and computer vision. The goal of matrix sensing is to recover a matrix $A_\star \in \mathbb{R}^{n \times n}$, based on a sequence of measurements $(u_i,b_i) \in \mathbb{R}^{n} \times \mathbb{R}$ such that $u_i^\top A_\star u_i = b_i$. Previous work [ZJD15] focused on the scenario where matrix $A_{\star}$ has a small rank, e.g. rank-$k$. Their analysis heavily relies on the RIP assumption, making it unclear how to generalize to high-rank matrices. In this paper, we relax that rank-$k$ assumption and solve a much more general matrix sensing problem. Given an accuracy parameter $\delta \in (0,1)$, we can compute $A \in \mathbb{R}^{n \times n}$ in $\widetilde{O}(m^{3/2} n^2 \delta^{-1} )$, such that $ |u_i^\top A u_i - b_i| \leq \delta$ for all $i \in [m]$. We design an efficient algorithm with provable convergence guarantees using stochastic gradient descent for this problem.
Real-time forecasting within soccer matches through a Bayesian lens

This article employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilise a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to evaluate the efficacy of our method. Different evaluation metrics establish that the proposed model outperforms potential competitors inspired by existing statistical or machine learning algorithms. Additionally, we apply robustness checks to demonstrate the model’s accuracy across various scenarios.
Old Stellar Populations. IV. Empirical Fitting Functions for Features in the Spectra of G and K Stars

Empirical fitting functions are presented that model the behavior of 11 strong atomic and molecular features in the spectra of Galactic G and K stars. The functions express the strength of spectral features as a function of V - K, surface gravity, and metallicity. The gravity calibration rests on stars in Galactic globular and open clusters, for which gravities have been derived by fitting to stellar evolutionary isochrones. The range of application of the fitting functions is set by the calibrating stars. There is good metallicity coverage from [Fe/H] = -0.75 to +0.50 dex, and cluster ages span 3.15 x 10^9 yr. However, the range in age is large only near solar metallicity, and the gravity partial derivatives may therefore be uncertain at metallicities much higher or lower than this value. In addition, the fitting functions build in whatever abundance ratio trajectories of elements relative to iron are present in local Galactic stars; these might differ from the element ratios present in external galaxies. In general, the behavior of the fitting functions matches expectations as to how these spectral features should behave as a function of basic stellar atmospheric parameters. The exception is CN (4150 A), for which we find strong evidence for rapid onset of giant-branch mixing of CNO products to the stellar surface, setting in at V - K = 2.10. This confirms other recent studies of this index. The paper also presents new Lick line strength data on 112 field dwarfs and 92 cluster stars, plus colors, metallicities, and surface gravities for all Lick G and K stars published previously. The fitting functions are inverted to produce new output gravity and metallicity estimates for these stars and new average metallicities for all clusters, with errors of ±0.15 dex in [Fe/H] and ±0.23 dex in log g per star.
VecFontSDF: Learning to Reconstruct and Synthesize High-Quality Vector Fonts via Signed Distance Functions

Font design is of vital importance in the digital content design and modern printing industry. Developing algorithms capable of automatically synthesizing vector fonts can significantly facilitate the font design process. However, existing methods mainly concentrate on raster image generation, and only a few approaches can directly synthesize vector fonts. This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts using signed distance functions (SDFs). Specifically, based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves, which can be precisely converted to quadratic Bézier curves that are widely used in vector font products. In this manner, most image generation methods can be easily extended to synthesize vector fonts. Qualitative and quantitative experiments conducted on a publicly-available dataset demonstrate that our method obtains high-quality results on several tasks, including vector font reconstruction, interpolation, and few-shot vector font synthesis, markedly outperforming the state of the art.
An mCherry biolaser based on microbubble cavity with ultra-low threshold

Biolasers show considerable potential in the biomedical field. Fluorescent protein (FP) is a type of biomaterial with good luminescence efficiency that can be used as the luminescent gain medium in biolasers. Due to the higher cell/tissue permeability, lower cell phototoxicity, and relatively less background fluorescence than other fluorescent proteins, the red fluorescent protein is more suitable in biological applications. MCherry is the most extensively used high-quality red fluorescent protein because of its short maturation time and stable luminescence properties. In this study, using mCherry and microbubble cavity, we realize a highly stable mCherry fluorescent protein laser. The laser resonator achieves a quality factor of 108, which is the highest Q factor among the currently available FP lasers. Moreover, this laser exhibits a low threshold of 1.15 μJ/mm2, which can effectively protect the luminescent material from being damaged by pump light. The prepared laser shows excellent stability in a wide pH range with good photobleaching resistance and can be stored at 4 °C for 30 days. Also, the laser can serve as a high-sensitivity molecular concentration detector with mCherry as biomarker, owing to its lasing threshold behavior.
The Fundamental theorem of tropical differential algebra over nontrivially valued fields and the radius of convergence of nonarchimedean differential equations

We prove a fundamental theorem for tropical partial differential equations analogue of the fundamental theorem of tropical geometry in this context. We extend results from Aroca et al., Falkensteiner et al. and from Fink and Toghani, which work only in the case of trivial valuation as introduced by Grigoriev, to differential equations with power series coefficients over any valued field. To do so, a crucial ingredient is the framework for tropical partial differential equations introduced by Giansiracusa and Mereta. Using this framework we also add a fourth statement to the fundamental theorem, seeing the tropicalization as the set of evaluations of points of the differential Berkovich analytification on the generators of a differential algebra for a given presentation. Lastly, as a corollary of the fundamental theorem, we have that the radius of convergence of solutions of an ordinary differential equation over a nontrivially valued field can be computed tropically.
The calculation of the distribution function of a strictly stable law at large X

The paper considers the problem of calculating the distribution function of a strictly stable law at $x\to\infty$. To solve this problem, an expansion of the distribution function in a power series was obtained, and an estimate of the remainder term was also obtained. It was shown that in the case $\alpha<1$ this series was convergent for any $x$, in the case $\alpha=1$ the series was convergent at $N\to\infty$ in the domain $|x|>1$, and in the case $\alpha>1$ the series was asymptotic at $x\to\infty$. The case $\alpha=1$ was considered separately and it was demonstrated that in that case the series converges to the generalized Cauchy distribution. An estimate for the threshold coordinate $x_\varepsilon^N$ was obtained which determined the area of applicability of the obtained expansion. It was shown that in the domain $|x|\geqslant x_\varepsilon^N$ this power series could be used to calculate the distribution function, which completely solved the problem of calculating the distribution function at large $x$.
Can we trust the evaluation on ChatGPT?

ChatGPT, the first large language model with mass adoption, has demonstrated remarkableperformance in numerous natural language tasks. Despite its evident usefulness, evaluatingChatGPT’s performance in diverse problem domains remains challenging due to the closednature of the model and its continuous updates via Reinforcement Learning from HumanFeedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models.
Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense

Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, their wider utilization without a requirement for technical expertise, made in large part possible through conversational fine-tuning, revealed the extent of their true capabilities in a real-world environment. This has garnered both public excitement for their potential applications and concerns about their capabilities and potential malicious uses. This review aims to provide a brief overview of the history, state of the art, and implications of Generative Language Models in terms of their principles, abilities, limitations, and future prospects -- especially in the context of cyber-defense, with a focus on the Swiss operational environment.