@article{14408, abstract = {We prove that the mesoscopic linear statistics ∑if(na(σi−z0)) of the eigenvalues {σi}i of large n×n non-Hermitian random matrices with complex centred i.i.d. entries are asymptotically Gaussian for any H20-functions f around any point z0 in the bulk of the spectrum on any mesoscopic scale 00 we have P[N≥(1+δ)EN]=exp(−Θ(n4/3logn)). Finally, as an application of some new general tools for studying substructures in random Latin squares, we show that in almost all order- n Latin squares, the number of cuboctahedra (i.e., the number of pairs of possibly degenerate 2×2 submatrices with the same arrangement of symbols) is of order n4, which is the minimum possible. As observed by Gowers and Long, this number can be interpreted as measuring ``how associative'' the quasigroup associated with the Latin square is.}, author = {Kwan, Matthew Alan and Sah, Ashwin and Sawhney, Mehtaab and Simkin, Michael}, issn = {1565-8511}, journal = {Israel Journal of Mathematics}, number = {2}, pages = {363--416}, publisher = {Springer Nature}, title = {{Substructures in Latin squares}}, doi = {10.1007/s11856-023-2513-9}, volume = {256}, year = {2023}, } @article{14445, abstract = {We prove the following quantitative Borsuk–Ulam-type result (an equivariant analogue of Gromov’s Topological Overlap Theorem): Let X be a free ℤ/2-complex of dimension d with coboundary expansion at least ηk in dimension 0 ≤ k < d. Then for every equivariant map F: X →ℤ/2 ℝd, the fraction of d-simplices σ of X with 0 ∈ F (σ) is at least 2−d Π d−1k=0ηk. As an application, we show that for every sufficiently thick d-dimensional spherical building Y and every map f: Y → ℝ2d, we have f(σ) ∩ f(τ) ≠ ∅ for a constant fraction μd > 0 of pairs {σ, τ} of d-simplices of Y. In particular, such complexes are non-embeddable into ℝ2d, which proves a conjecture of Tancer and Vorwerk for sufficiently thick spherical buildings. We complement these results by upper bounds on the coboundary expansion of two families of simplicial complexes; this indicates some limitations to the bounds one can obtain by straighforward applications of the quantitative Borsuk–Ulam theorem. Specifically, we prove • an upper bound of (d + 1)/2d on the normalized (d − 1)-th coboundary expansion constant of complete (d + 1)-partite d-dimensional complexes (under a mild divisibility assumption on the sizes of the parts); and • an upper bound of (d + 1)/2d + ε on the normalized (d − 1)-th coboundary expansion of the d-dimensional spherical building associated with GLd+2(Fq) for any ε > 0 and sufficiently large q. This disproves, in a rather strong sense, a conjecture of Lubotzky, Meshulam and Mozes.}, author = {Wagner, Uli and Wild, Pascal}, issn = {1565-8511}, journal = {Israel Journal of Mathematics}, number = {2}, pages = {675--717}, publisher = {Springer Nature}, title = {{Coboundary expansion, equivariant overlap, and crossing numbers of simplicial complexes}}, doi = {10.1007/s11856-023-2521-9}, volume = {256}, year = {2023}, } @article{14446, abstract = {Recent work has paid close attention to the first principle of Granger causality, according to which cause precedes effect. In this context, the question may arise whether the detected direction of causality also reverses after the time reversal of unidirectionally coupled data. Recently, it has been shown that for unidirectionally causally connected autoregressive (AR) processes X → Y, after time reversal of data, the opposite causal direction Y → X is indeed detected, although typically as part of the bidirectional X↔ Y link. As we argue here, the answer is different when the measured data are not from AR processes but from linked deterministic systems. When the goal is the usual forward data analysis, cross-mapping-like approaches correctly detect X → Y, while Granger causality-like approaches, which should not be used for deterministic time series, detect causal independence X → Y. The results of backward causal analysis depend on the predictability of the reversed data. Unlike AR processes, observables from deterministic dynamical systems, even complex nonlinear ones, can be predicted well forward, while backward predictions can be difficult (notably when the time reversal of a function leads to one-to-many relations). To address this problem, we propose an approach based on models that provide multiple candidate predictions for the target, combined with a loss function that consideres only the best candidate. The resulting good forward and backward predictability supports the view that unidirectionally causally linked deterministic dynamical systems X → Y can be expected to detect the same link both before and after time reversal.}, author = {Jakubík, Jozef and Bui Thi Mai, Phuong and Chvosteková, Martina and Krakovská, Anna}, issn = {1335-8871}, journal = {Measurement Science Review}, number = {4}, pages = {175--183}, publisher = {Sciendo}, title = {{Against the flow of time with multi-output models}}, doi = {10.2478/msr-2023-0023}, volume = {23}, year = {2023}, } @article{14447, abstract = {Auxin belongs among major phytohormones and governs multiple aspects of plant growth and development. The establishment of auxin concentration gradients, determines, among other processes, plant organ positioning and growth responses to environmental stimuli. Herein we report the synthesis of new NBD- or DNS-labelled IAA derivatives and the elucidation of their biological activity, fluorescence properties and subcellular accumulation patterns in planta. These novel compounds did not show auxin-like activity, but instead antagonized physiological auxin effects. The DNS-labelled derivatives FL5 and FL6 showed strong anti-auxin activity in roots and hypocotyls, which also occurred at the level of gene transcription as confirmed by quantitative PCR analysis. The auxin antagonism of our derivatives was further demonstrated in vitro using an SPR-based binding assay. The NBD-labelled compound FL4 with the best fluorescence properties proved to be unsuitable to study auxin accumulation patterns in planta. On the other hand, the strongest anti-auxin activity possessing compounds FL5 and FL6 could be useful to study binding mechanisms to auxin receptors and for manipulations of auxin-regulated processes.}, author = {Bieleszová, Kristýna and Hladík, Pavel and Kubala, Martin and Napier, Richard and Brunoni, Federica and Gelová, Zuzana and Fiedler, Lukas and Kulich, Ivan and Strnad, Miroslav and Doležal, Karel and Novák, Ondřej and Friml, Jiří and Žukauskaitė, Asta}, issn = {1573-5087}, journal = {Plant Growth Regulation}, publisher = {Springer Nature}, title = {{New fluorescent auxin derivatives: anti-auxin activity and accumulation patterns in Arabidopsis thaliana}}, doi = {10.1007/s10725-023-01083-0}, year = {2023}, } @inproceedings{14448, abstract = {We consider the problem of solving LP relaxations of MAP-MRF inference problems, and in particular the method proposed recently in [16], [35]. As a key computational subroutine, it uses a variant of the Frank-Wolfe (FW) method to minimize a smooth convex function over a combinatorial polytope. We propose an efficient implementation of this subroutine based on in-face Frank-Wolfe directions, introduced in [4] in a different context. More generally, we define an abstract data structure for a combinatorial subproblem that enables in-face FW directions, and describe its specialization for tree-structured MAP-MRF inference subproblems. Experimental results indicate that the resulting method is the current state-of-art LP solver for some classes of problems. Our code is available at pub.ist.ac.at/~vnk/papers/IN-FACE-FW.html.}, author = {Kolmogorov, Vladimir}, booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, isbn = {9798350301298}, issn = {1063-6919}, location = {Vancouver, Canada}, pages = {11980--11989}, publisher = {IEEE}, title = {{Solving relaxations of MAP-MRF problems: Combinatorial in-face Frank-Wolfe directions}}, doi = {10.1109/CVPR52729.2023.01153}, volume = {2023}, year = {2023}, } @article{14449, abstract = {The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.}, author = {D’Elia, Domenica and Truu, Jaak and Lahti, Leo and Berland, Magali and Papoutsoglou, Georgios and Ceci, Michelangelo and Zomer, Aldert and Lopes, Marta B. and Ibrahimi, Eliana and Gruca, Aleksandra and Nechyporenko, Alina and Frohme, Marcus and Klammsteiner, Thomas and Pau, Enrique Carrillo De Santa and Marcos-Zambrano, Laura Judith and Hron, Karel and Pio, Gianvito and Simeon, Andrea and Suharoschi, Ramona and Moreno-Indias, Isabel and Temko, Andriy and Nedyalkova, Miroslava and Apostol, Elena Simona and Truică, Ciprian Octavian and Shigdel, Rajesh and Telalović, Jasminka Hasić and Bongcam-Rudloff, Erik and Przymus, Piotr and Jordamović, Naida Babić and Falquet, Laurent and Tarazona, Sonia and Sampri, Alexia and Isola, Gaetano and Pérez-Serrano, David and Trajkovik, Vladimir and Klucar, Lubos and Loncar-Turukalo, Tatjana and Havulinna, Aki S. and Jansen, Christian and Bertelsen, Randi J. and Claesson, Marcus Joakim}, issn = {1664-302X}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, title = {{Advancing microbiome research with machine learning: Key findings from the ML4Microbiome COST action}}, doi = {10.3389/fmicb.2023.1257002}, volume = {14}, year = {2023}, } @article{14451, abstract = {We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency single-asset trading: in particular, we consider a Multi-Objective algorithm which generalizes the reward functions and discount factor (i.e., these components are not specified a priori, but incorporated in the learning process). Firstly, using several important assets (BTCUSD, ETHUSDT, XRPUSDT, AAPL, SPY, NIFTY50), we verify the reward generalization property of the proposed Multi-Objective algorithm, and provide preliminary statistical evidence showing increased predictive stability over the corresponding Single-Objective strategy. Secondly, we show that the Multi-Objective algorithm has a clear edge over the corresponding Single-Objective strategy when the reward mechanism is sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss the generalization properties with respect to the discount factor. The entirety of our code is provided in open-source format.}, author = {Cornalba, Federico and Disselkamp, Constantin and Scassola, Davide and Helf, Christopher}, issn = {1433-3058}, journal = {Neural Computing and Applications}, publisher = {Springer Nature}, title = {{Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading}}, doi = {10.1007/s00521-023-09033-7}, year = {2023}, } @article{14452, abstract = {The classical infinitesimal model is a simple and robust model for the inheritance of quantitative traits. In this model, a quantitative trait is expressed as the sum of a genetic and an environmental component, and the genetic component of offspring traits within a family follows a normal distribution around the average of the parents’ trait values, and has a variance that is independent of the parental traits. In previous work, we showed that when trait values are determined by the sum of a large number of additive Mendelian factors, each of small effect, one can justify the infinitesimal model as a limit of Mendelian inheritance. In this paper, we show that this result extends to include dominance. We define the model in terms of classical quantities of quantitative genetics, before justifying it as a limit of Mendelian inheritance as the number, M, of underlying loci tends to infinity. As in the additive case, the multivariate normal distribution of trait values across the pedigree can be expressed in terms of variance components in an ancestral population and probabilities of identity by descent determined by the pedigree. Now, with just first-order dominance effects, we require two-, three-, and four-way identities. We also show that, even if we condition on parental trait values, the “shared” and “residual” components of trait values within each family will be asymptotically normally distributed as the number of loci tends to infinity, with an error of order 1/M−−√⁠. We illustrate our results with some numerical examples.}, author = {Barton, Nicholas H and Etheridge, Alison M. and Véber, Amandine}, issn = {1943-2631}, journal = {Genetics}, number = {2}, publisher = {Oxford Academic}, title = {{The infinitesimal model with dominance}}, doi = {10.1093/genetics/iyad133}, volume = {225}, year = {2023}, } @article{14453, abstract = {Squall lines are substantially influenced by the interaction of low-level shear with cold pools associated with convective downdrafts. Beyond an optimal shear amplitude, squall lines tend to orient themselves at an angle with respect to the low-level shear. While the mechanisms behind squall line orientation seem to be increasingly well understood, uncertainties remain on the implications of this orientation. Roca and Fiolleau (2020, https://doi.org/10.1038/s43247-020-00015-4) show that long lived mesoscale convective systems, including squall lines, are disproportionately involved in rainfall extremes in the tropics. This article investigates the influence of the interaction between low-level shear and squall line outflow on squall line generated precipitation extrema in the tropics. Using a cloud resolving model, simulated squall lines in radiative convective equilibrium amid a shear-dominated regime (super optimal), a balanced regime (optimal), and an outflow dominated regime (suboptimal). Our results show that precipitation extremes in squall lines are 40% more intense in the case of optimal shear and remain 30% superior in the superoptimal regime relative to a disorganized case. With a theoretical scaling of precipitation extremes (C. Muller & Takayabu, 2020, https://doi.org/10.1088/1748-9326/ab7130), we show that the condensation rates control the amplification of precipitation extremes in tropical squall lines, mainly due to its change in vertical mass flux (dynamic component). The reduction of dilution by entrainment explains half of this change, consistent with Mulholland et al. (2021, https://doi.org/10.1175/jas-d-20-0299.1). The other half is explained by increased cloud-base velocity intensity in optimal and superoptimal squall lines.}, author = {Abramian, Sophie and Muller, Caroline J and Risi, Camille}, issn = {1942-2466}, journal = {Journal of Advances in Modeling Earth Systems}, number = {10}, publisher = {Wiley}, title = {{Extreme precipitation in tropical squall lines}}, doi = {10.1029/2022MS003477}, volume = {15}, year = {2023}, } @inproceedings{14454, abstract = {As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complement design-time bias mitigation measures, runtime verification techniques have been introduced recently to monitor the algorithmic fairness of deployed systems. Previous monitoring techniques assume full observability of the states of the (unknown) monitored system. Moreover, they can monitor only fairness properties that are specified as arithmetic expressions over the probabilities of different events. In this work, we extend fairness monitoring to systems modeled as partially observed Markov chains (POMC), and to specifications containing arithmetic expressions over the expected values of numerical functions on event sequences. The only assumptions we make are that the underlying POMC is aperiodic and starts in the stationary distribution, with a bound on its mixing time being known. These assumptions enable us to estimate a given property for the entire distribution of possible executions of the monitored POMC, by observing only a single execution. Our monitors observe a long run of the system and, after each new observation, output updated PAC-estimates of how fair or biased the system is. The monitors are computationally lightweight and, using a prototype implementation, we demonstrate their effectiveness on several real-world examples.}, author = {Henzinger, Thomas A and Kueffner, Konstantin and Mallik, Kaushik}, booktitle = {23rd International Conference on Runtime Verification}, isbn = {9783031442667}, issn = {1611-3349}, location = {Thessaloniki, Greece}, pages = {291--311}, publisher = {Springer Nature}, title = {{Monitoring algorithmic fairness under partial observations}}, doi = {10.1007/978-3-031-44267-4_15}, volume = {14245}, year = {2023}, }