Abstract

Leptoquarks (LQs) offer a promising framework for exploring physics beyond the Standard Model (BSM). These particles are currently probed at the LHC, primarily through pair production (PP). Our study improves the LHC exclusion limits by including additional production modes like single productions, t-channel LQ exchange, and its interference with the Standard Model background, sometimes providing more substantial constraints than PP alone. We also account for model-independent production through mixed QCD-QED processes, which can significantly shift the exclusion limits based on the electric charge of the LQs. Furthermore, since LQs often arise alongside other BSM particles, such as vector-like fermions and right-handed neutrinos, we investigate the potential for strong production of these particles along with LQs at the High-Luminosity LHC (HL-LHC), offering new perspectives on collider search strategies.

Abstract

In this study, we propose an interesting production mechanism for Vector-Like Leptons (VLLs) through Leptoquarks (LQs). We explore the prospect of producing them at the High-Luminosity LHC around 3000 fb^-1 through various channels involving both scalar and vector LQs. Here, we have performed an analysis of third-generation VLL. Further, we combine various production channels to enhance the detection sensitivity in SM-heavy backgrounds.

Abstract

This study investigates TeV-scale vector leptoquarks (vLQs) and their contributions to the magnetic dipole moments of charged leptons. Significant parameter space for these LQs is excluded using current LHC data. Among vLQs only U1 and V2 can account for the observed positive shift in (a^mu_exp-a^mu_SM) through a lepton chirality-flipping contribution with
LQ-quark-lepton coupling. These LQs can also fits for the electron dipole moment and atomic-parity violation measurements.

Abstract

The Standard Model (SM) successfully describes particle physics phenomena but leaves several open questions, motivating extensions with new gauge interactions. One such extension is the anomaly-free Leptophobic U(1) model, which introduces a new Z' boson and right-handed neutrinos (RHNs) while avoiding stringent collider constraints. These RHNs, with GeV-scale masses and small mixing with SM neutrinos, can behave as long-lived particles (LLPs), leading to displaced vertex (DV) signatures at colliders. In this work, we analyze DV signatures arising from RHN decays and evaluate the sensitivity of various current and proposed LLP detectors, including CMS, LHCb, MATHUSLA, CODEXb, FACET, FASER, and MAPP. Our results highlight the complementary reach of these experiments, demonstrating their potential to probe new physics beyond the SM.

Abstract

We demonstrate how LHC results can be reinterpreted to establish exclusion limits on leptoquark models by incorporating multiple production mechanisms. We highlight several unexplored leptoquark signatures predicted by well-motivated models that have yet to be investigated by experimentalists.

Abstract

Safe and computationally efficient local planning for mobile robots in dense, unstructured human crowds remains a fundamental challenge. Moreover, ensuring that robot trajectories are similar to how a human moves will increase the acceptance of the robot in human environments. In this paper, we present Crowd-FM, a learning-based approach to address both safety and human-likeness challenges. Our approach has two novel components. First, we train a Conditional Flow-Matching (CFM) policy over a dataset of optimally controlled trajectories to learn a set of collision-free primitives that a robot can choose at any given scenario. The chosen optimal control solver can generate multi-modal collision-free trajectories, allowing the CFM policy to learn a diverse set of maneuvers. Secondly, we learn a score function over a dataset of human demonstration trajectories that provides a human-likeness score for the flow primitives. At inference time, computing the optimal trajectory requires selecting the one with the highest score. Our approach improves the state-of-the-art by showing that our CFM policy alone can produce collision-free navigation with a higher success rate than existing learning-based baselines. Furthermore, when augmented with inference-time refinement, our approach can outperform even expensive optimisation-based planning approaches. Finally, we validate that our scoring network can select trajectories closer to the expert data than a manually designed cost function.

Abstract

Anu Bradford (2020) argues that the EU creates a 'Brussels Effect' on the digital economy through its data regulations. In this context, understanding the evolution and lifespan of the value propositions on AI Ethics in the EU regulations becomes crucial, as it tends to have global implications.

The study employs computational tools to systematically analyze the emergence and disappearance of AI-related concepts in the EU legal-policy discourse. Preliminary findings indicate that AI-related policy discourse within the EU is highly dynamic, and many concepts such as Green AI, Safe AI, Trustworthy AI, etc., are emerging and disappearing from discussions over time. It also indicates that several AI Ethics propositions, such as Fair AI or Explainable AI, have a short-lived presence. At the same time, the presence of Green AI and Sustainable AI has increased in the documents in recent years, indicating a shift in political priorities.The study also provides comparative insights on the weightage of these propositions in legal acts vs preparatory documents within the EU regulatory landscape.

Furthermore, through a case study of Green AI the study examines the institutional authorship of these propositions over time to understand how different EU institutions shape regulatory priorities. In this context the paper aims to offer insights on how various concepts on AI Ethics are introduced, discussed, and ultimately fade from the discourse within the EU's official preparatory documents and regulatory framework.

Abstract

Binary tree network, being a subclass of Cayley tree network, is a significant topological structure used for information transfer in a hierarchical sense. In this article, we consider four types of binary tree repeater networks (directed and undirected, asymmetric and symmetric) and obtain the analytical expressions of the average of the maximum teleportation fidelities for each of these binary tree networks. We contribute a methodology for the analytical calculation of path lengths in all considered graph types. Based on these, we have used simple Werner state-based models and are able to identify the parameter ranges for which these networks can show quantum advantage. We also explore the role of maximally entangled states in the network to enhance the quantum advantage. We provide a detailed examination of the large-scale behaviour of these networks, obtaining the limiting value of the average maximum teleportation fidelity as the number of nodes, N, approaches infinity, same as fractal tree. Our findings reveal that the directed symmetric binary tree represents the most advantageous topology for quantum teleportation within this context. From the context of quantum repeater networks, this work makes a significant advancement in the process of identifying resourceful tree networks for distributed quantum teleportation, i.e. teleportation between all possible sources and targets.

Abstract

We present CURIO, an OCR system for low-resource
historical manuscripts. In many challenging cases,
manuscripts feature curved text lines, unsegmented lines
with lack of spacing between words, and highly vari-
able line lengths -- conditions under which existing
OCR methods fail. To tackle this challenge, we first
extract lines and corresponding curvature profiles from
manuscripts, then straighten them using a rectification
procedure to reduce redundant background within each
line. Because data is scarce, we compliment real data
with synthetic data. To bridge the synthetic-real gap,
we generate line images by warping rendered straight
text along the rectified profiles, ensuring both real and
synthetic lines align in their curvature characteristics.
Our recognizer is a lightweight CNN-Transformer with
padding-aware null activations, sparse attention and
optimized with CTC loss for efficient training. We
evaluate our method on challenging manuscript collec-
tions written in Sharada, a rare and endangered Indic
script. CURIO outperforms strong CNN+RNN and
Transformer baselines, with the largest gains on high-
curvature lines and long lines. CURIO further transfers
zero-shot to printed Sharada text, indicating robustness
beyond manuscript domain.

Abstract

Foundation models for time series forecasting are highly sensitive to configuration parameters such as context length and patch size, which substantially influence predictive performance. These parameters are typically chosen via static defaults or per-series hyperparameter search using AutoML, the latter requiring repeated evaluations of a large model. We reinterpret configuration selection as an amortized learning problem: instead of optimizing configurations independently for each new series, we learn a lightweight learning-to-rank model that predicts high-performing configurations. Using the Moirai 1.1-R-small foundation model on the Uber TLC and Electricity benchmarks, Tune-as-Inference achieves accuracy within 3-5% of 20-trial Bayesian optimization while reducing per-series configuration time by approximately 20 times. These results suggest that configuration adaptation for time series foundation models are better treated as inference rather than iterative search.