Related papers: Exclusive final states in the Dipole Model
We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approaches to initial condition perturbations are primarily…
Recent experiments have found that a mechanically distorted blue phase can exhibit a primary linear electro-optic (Pockels) effect [F. Castles \textit{et al}. Nature Mater. \textbf{13}, 817 (2014)]. Here it is shown that flexoelectricity…
We present some general results for the multi-critical multi-field models in d>2 recently obtained using CFT and Schwinger-Dyson methods at perturbative level without assuming any symmetry. Results in the leading non trivial order are…
In this paper we consider the influence of non-perturbative corrections on the large $ b $ (impact parameter) behavior of the BFKL amplitude. This is done in the framework of a model where such ``soft'' corrections are taken into account in…
Accurate trajectory prediction is crucial for the safe and efficient operation of autonomous vehicles. The growing popularity of deep learning has led to the development of numerous methods for trajectory prediction. While deterministic…
Due to the imbalanced nature of networked observational data, the causal effect predictions for some individuals can severely violate the positivity/overlap assumption, rendering unreliable estimations. Nevertheless, this potential risk of…
This paper establishes fundamental results for statistical inference of diagnostic classification models (DCM). The results are developed at a high level of generality, applicable to essentially all diagnostic classification models. In…
Large Language Models (LLMs) have showcased their In-Context Learning (ICL) capabilities, enabling few-shot learning without the need for gradient updates. Despite its advantages, the effectiveness of ICL heavily depends on the choice of…
The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and predefined order. This is not very realistic in federated learning environments where each client works independently…
It has been suggested that under certain approximations the Sivers effect can be described in terms of factorization of final state interactions and a spatial distortion of impact parameter space parton distribution; that is a convolution…
The production of forward jets of transverse momentum $p_T(j)\approx Q$ and large momentum fraction $x_{jet}\gg x$ probes the onset of BFKL dynamics at HERA. A full ${\cal O}(\alpha_s^2)$ calculation of the inclusive forward jet cross…
Denoising Diffusion Probabilistic Models (DDPMs) represent a contemporary class of generative models with exceptional qualities in both synthesis and maximizing the data likelihood. These models work by traversing a forward Markov Chain…
Mueller Navelet jets were proposed 25 years ago as a decisive test of BFKL dynamics at hadron colliders. We here present the first next-to-leading BFKL study of the cross section and azimuthal decorrelation of these jets. This includes both…
We present results on dijet and W+dijet production at hadron colliders obtained by supplementing the leading log BFKL resummation with energy and momentum conservation. For pure dijet production, the inclusion of the BFKL radiation in the…
Semi-hard processes in the large COM energy limit offer us an exclusive chance to test the dynamics behind strong interactions in kinematical sectors so far unexplored. In the Regge limit, fixed-order calculations in pQCD based on collinear…
We consider inhomogeneous spatial random graphs on the real line. Each vertex carries an i.i.d. weight and edges are drawn such that short edges and edges to vertices with large weights occur with higher probability. This allows the study…
Model checking is usually based on a comprehensive traversal of the state space. Causality-based model checking is a radically different approach that instead analyzes the cause-effect relationships in a program. We give an overview on a…
The bistable deployable composite boom (Bi-DCB) can achieve bistable function by storing and releasing strain energy, which has a good application prospect in space field. For example, it serves as the main support section of deployable…
Deep energy-based models (EBMs) are very flexible in distribution parametrization but computationally challenging because of the intractable partition function. They are typically trained via maximum likelihood, using contrastive divergence…
Attribute bias in federated learning (FL) typically leads local models to optimize inconsistently due to the learning of non-causal associations, resulting degraded performance. Existing methods either use data augmentation for increasing…