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In this contribution, we summarize our results on the complete NLO predictions for the production of off-shell top quark pairs in the $\ell+j$ channel at the LHC. All NLO QCD and electroweak corrections are consistently included across the full set of LO contributions and partonic subprocesses, while retaining finite-width effects of the top quarks and the electroweak gauge bosons. Resonant and non-resonant contributions as well as interference effects are included in the complete calculation. We pay special attention to infrared singularities associated with photons and jets appearing simultaneously in the final state. Fiducial integrated and differential cross-section results are presented for $pp$ collisions at $\sqrt{s}=13.6$ TeV.

We present the calculation of the complete NLO corrections to the off-shell top-quark pair production in the $\ell+j$ decay channel, denoted as $pp \to \ell^- \barν_l\, j_b j_b \,jj + X$, where $\ell^- = e^-,\, μ^-$. The calculation consistently preserves the finite-width effects of the top quarks and massive gauge bosons, as well as takes into account all doubly-, singly-, and non-resonant contributions along with their interference effects. All Born-level contributions, at the perturbative orders from ${\cal O}(α_s^4α^2)$ to ${\cal O}(α_s^0α^6)$, are included and corrected by both NLO QCD and NLO EW effects. Furthermore, all possible partonic initial states are taken into account. Particular attention is paid to the infrared safety in the presence of photons and jets. This requires the use of the so-called parton-to-photon fragmentation function and the photon-to-jet conversion function, which makes the democratic photon-parton clustering and the $γ\to q\bar{q}$ splittings finite. We present our findings at the integrated and differential fiducial cross-section levels for the LHC Run III centre-of-mass energy of $\sqrt{s}= 13.6$ TeV. In addition, we quantify the impact of sublead

Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when multi-modal information is considered in KGE models. They are also inefficient due to their complex design. In this paper, we propose Modality-Aware Negative Sampling (MANS) for multi-modal knowledge graph embedding (MMKGE) to address the mentioned problems. MANS could align structural and visual embeddings for entities in KGs and learn meaningful embeddings to perform better in multi-modal KGE while keeping lightweight and efficient. Empirical results on two benchmarks demonstrate that MANS outperforms existing NS methods. Meanwhile, we make further explorations about MANS to confirm its effectiveness.

Gradient normalization and soft clipping are two popular techniques for tackling instability issues and improving convergence of stochastic gradient descent (SGD) with momentum. In this article, we study these types of methods through the lens of dissipative Hamiltonian systems. Gradient normalization and certain types of soft clipping algorithms can be seen as (stochastic) implicit-explicit Euler discretizations of dissipative Hamiltonian systems, where the kinetic energy function determines the type of clipping that is applied. We make use of dynamical systems theory to show in a unified way that all of these schemes converge to stationary points of the objective function, almost surely, in several different settings: a) for $L$-smooth objective functions, when the variance of the stochastic gradients is possibly infinite, b) under the $(L_0,L_1)$-smoothness assumption, for heavy-tailed noise with bounded variance, and c) for $(L_0,L_1)$-smooth functions in the empirical risk minimization setting, when the variance is possibly infinite but the expectation is finite.

We consider the classical field theory of 2+1-dimensional Yang-Mills-Chern-Simons theory on an arbitrary spatial manifold. We first define a gauge covariant transverse electric field strength, which together with the gauge covariant scalar magnetic field strength can be taken as coordinates on the classical phase space. We then determine the Poisson-Dirac bracket and find that these coordinates are canonically conjugate to each other. The Hamiltonian is non-polynomial when expressed in terms of these coordinates, but can be expanded in a power series in the coupling constant with polynomial coefficients.

The Majorana tetron is a prototypical topological qubit stemming from the ground state degeneracy of a superconducting island hosting four Majorana modes. This degeneracy manifests as an effective non-local spin degree of freedom, whose most paradigmatic signature is the topological Kondo effect. Degeneracies of states with different fermionic parities characterize also minimal Kitaev chains which have lately emerged as a platform to realize and study unprotected versions of Majorana modes, dubbed poor man's Majorana modes. Here, we introduce the ``poor man's Majorana tetron'', comprising four quantum dots coupled via a floating superconducting island. Its charging energy yields non-trivial correlations among the dots, although, unlike a standard tetron, it is not directly determined by the fermionic parity of the Majorana modes. The poor man's tetron displays parameter regions with a two-fold degenerate ground state with odd fermionic parity, that gives rise to an effective Anderson impurity model when coupled to external leads. We show that this system can approach a regime featuring the topological Kondo effect under a suitable tuning of experimental parameters. Therefore, the p

It is well established that migratory birds in general have advanced their arrival times in spring, and in this paper we investigate potential ways of enhancing the level of detail in future phenological analyses. We perform single as well as multiple species analyses, using linear models on empirical quantiles, non-parametric quantile regression and likelihood-based parametric quantile regression with asymmetric Laplace distributed error terms. We conclude that non-parametric quantile regression appears most suited for single as well as multiple species analyses.

We study set-valued classification for a Bayesian model where data originates from one of a finite number $N$ of possible hypotheses. Thus we consider the scenario where the size of the classified set of categories ranges from 0 to $N$. Empty sets corresponds to an outlier, size 1 represents a firm decision that singles out one hypotheses, size $N$ corresponds to a rejection to classify, whereas sizes $2\ldots,N-1$ represent a partial rejection, where some hypotheses are excluded from further analysis. We introduce a general framework of reward functions with a set-valued argument and derive the corresponding optimal Bayes classifiers, for a homogeneous block of hypotheses and for when hypotheses are partitioned into blocks, where ambiguity within and between blocks are of different severity. We illustrate classification using an ornithological dataset, with taxa partitioned into blocks and parameters estimated using MCMC. The associated reward function's tuning parameters are chosen through cross-validation.

The James Webb Space Telescope (JWST) is ushering in a new era in remote sensing of exoplanetary atmospheres. Atmospheric retrievals of exoplanets can be highly sensitive to high-precision JWST data. It is, therefore, imperative to characterise the instruments and noise sources using early observations to enable robust characterisation of exoplanetary atmospheres using JWST-quality spectra. The present work is a step in that direction, focusing on the NIRISS SOSS instrument mode, with a wavelength coverage of 0.6 - 2.8 μm and R ~ 700. Using a custom-built pipeline, JExoRes, we investigate key diagnostics of NIRISS SOSS with observations of two giant exoplanets, WASP-39 b and WASP-96 b, as case studies. We conduct a detailed evaluation of the different aspects of the data reduction and analysis, including sources of contamination, 1/f noise, and system properties such as limb darkening. The slitless nature of NIRISS SOSS makes it susceptible to contamination due to background sources. We present a method to model and correct for dispersed field stars which can significantly improve the accuracy of the observed spectra. In doing so, we also report an empirically determined throughput

Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Different AI models can today generate very realistic synthetic images, which can potentially be openly shared as they do not belong to specific persons. However, recent work has shown that using synthetic images for training deep networks often leads to worse performance compared to using real images. Here we demonstrate that using synthetic images and annotations from an ensemble of 20 GANs, instead of from a single GAN, increases the Dice score on real test images with 4.7 % to 14.0 % on specific classes.

In this paper we survey and unify a large class or $L$-functionals of the conditional distribution of the response variable in regression models. This includes robust measures of location, scale, skewness, and heavytailedness of the response, conditionally on covariates. We generalize the concepts of $L$-moments (Sittinen, 1969), $L$-skewness, and $L$-kurtosis (Hosking, 1990) and introduce order numbers for a large class of $L$-functionals through orthogonal series expansions of quantile functions. In particular, we motivate why location, scale, skewness, and heavytailedness have order numbers 1, 2, (3,2), and (4,2) respectively and describe how a family of $L$-functionals, with different order numbers, is constructed from Legendre, Hermite, Laguerre or other types of polynomials. Our framework is applied to models where the relationship between quantiles of the response and the covariates follow a transformed linear model, with a link function that determines the appropriate class of $L$-functionals. In this setting, the distribution of the response is treated parametrically or nonparametrically, and the response variable is either censored/truncated or not. We also provide a unif

Learning control policies with large discrete action spaces is a challenging problem in the field of reinforcement learning due to present inefficiencies in exploration. With high dimensional action spaces, there are a large number of potential actions in each individual dimension over which policies would be learned. In this work, we introduce a Deep Reinforcement Learning (DRL) algorithm call Multi-Action Networks (MAN) Learning that addresses the challenge of high-dimensional large discrete action spaces. We propose factorizing the N-dimension action space into N 1-dimensional components, known as sub-actions, creating a Value Neural Network for each sub-action. Then, MAN uses temporal-difference learning to train the networks synchronously, which is simpler than training a single network with a large action output directly. To evaluate the proposed method, we test MAN on three scenarios: an n-dimension maze task, a block stacking task, and then extend MAN to handle 12 games from the Atari Arcade Learning environment with 18 action spaces. Our results indicate that MAN learns faster than both Deep Q-Learning and Double Deep Q-Learning, implying our method is a better performing

Modern humans carry DNA from two mysterious extinct lineages in addition to Neanderthals and Denisovans。 Their hidden genetic legacy shows that our evolutionary past was shaped by repeated mixing among many different human groups

New Mexico judge orders $567M fund to help address youth mental health crisis

A new chemical process can transform three of the most common plastics into high-purity hydrogen without sorting them first。 The technique operates at much lower temperatures than traditional gasification and captures most of the plastic’s carbon in solid or liquid forms instead of releasing it as carbon dioxide

Russian attack。 Explosive drone targeted parked aircraft at Leipzig airport

We expected them to be there, but they had previously been too small to see