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Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of automatic classification, detection, and segmentation of bleeding and non-bleeding frames is currently lacking. The present work focused on development of a medically annotated WCE dataset called WCEbleedGen for automatic classification, detection, and segmentation of bleeding and non-bleeding frames. It comprises 2,618 WCE bleeding and non-bleeding frames which were collected from various internet resources and existing WCE datasets. A comprehensive benchmarking and evaluation of the developed dataset was done using nine classification-based, three detection-based, and three segmentation-based deep learning models. The dataset is of high-quality, is class-balanced and contains single and multiple bleeding sites. Overall, our standard benchmark results show that Visual Geometric Group (VGG) 19, You Only Look Once version 8 nano (YOLOv8n), and Link network (Linknet) performed best in automatic classification, detection, and segmentation-based evaluations, respectively. Automatic bleeding diagnosis is crucial for WCE video interpretations. This diverse dataset will aid in developing of real-time, multi-task learning-based innovative solutions for automatic bleeding diagnosis in WCE. The dataset and code are publicly available at https://zenodo.org/records/10156571 and https://github.com/misahub2023/Benchmarking-Codes-of-the-WCEBleedGen-dataset.

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It is being virtually organized by the Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria and Medical Imaging and Signal Analysis Hub (MISAHUB) in collaboration with the 9th International Conference on Computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document describes the overview of the challenge, its registration and rules, submission format, and the description of the utilized datasets., Comment: 6 pages

The CAGE-2 challenge is considered a standard benchmark to compare methods for autonomous cyber defense. Current state-of-the-art methods evaluated against this benchmark are based on model-free (offline) reinforcement learning, which does not provide provably optimal defender strategies. We address this limitation and present a formal (causal) model of CAGE-2 together with a method that produces a provably optimal defender strategy, which we call Causal Partially Observable Monte-Carlo Planning (C-POMCP). It has two key properties. First, it incorporates the causal structure of the target system, i.e., the causal relationships among the system variables. This structure allows for a significant reduction of the search space of defender strategies. Second, it is an online method that updates the defender strategy at each time step via tree search. Evaluations against the CAGE-2 benchmark show that C-POMCP achieves state-of-the-art performance with respect to effectiveness and is two orders of magnitude more efficient in computing time than the closest competitor method., Comment: This work has been submitted to the IEEE for possible publication

We present a comprehensive analysis of weak transition form factors, semileptonic decays, and nonleptonic decays of $B_c$ meson involving pseudoscalar ($P$) and vector ($V$) meson for bottom-conserving and bottom-changing decay modes. We employ self-consistent covariant light-front quark model (CLFQM), termed as Type-II correspondence, to calculate the $B_c$ to $P(V)$ transition form factors. The Type-II correspondence in the CLF approach gives self-consistent results associated with the $B^{(i)}_j$ functions, which vanish numerically after the replacement $M^{\prime(\prime\prime)} \to M_0^{\prime(\prime\prime)}$ in traditional Type-I correspondence, and the covariance of the matrix elements is also restored. We investigate these effects on bottom-conserving $B_c$ to $P(V)$ form factors that have not yet been studied in CLFQM Type-II correspondence. In addition, we quantify the implications of self-consistency propagating to weak decays involving both bottom-conserving and bottom-changing $B_c$ transition form factors. We use two different parameterizations, the usual three-parameter function of $q^2$ and the model-independent $z$-series expansion, to establish a clear understanding of $q^2$ dependence. Using the numerical values of the form factors, we predict the branching ratios and other physical observables, such as forward-backward asymmetries, polarization fractions, etc., of the semileptonic $B_c$ decays. Subsequently, we predict the branching ratios of two-body nonleptonic weak decays using the factorization hypothesis in self-consistent CLFQM. We also compare our results with those of other theoretical studies., Comment: 65 pages, 11 figures

We study the charmonium ($c\bar{c}$) and bottomonium ($b\bar{b}$) spectra, using Cornell potential with additional constant potential term in a non-relativistic framework, with spin-dependent corrections corresponding to the spin-spin, spin-orbit, and tensor interactions added perturbatively. We predict the masses of low-lying and excited states of charmonium and bottomonium up to $n=6$ and $L=2$. We analyze the radial and orbital Regge trajectories of both the systems and investigate their departure from linearity. Further, we estimate the wave function at the origin and, consequently, predict the decay widths of charmonium and bottomonium states annihilating to leptons and photons. Also, we investigate the effect of scale dependence of the wave function at the origin and the strong coupling constant on the predictions of annihilation decay widths. We compare our predictions of heavy quarkonium spectra and decay widths with experimental results and other theoretical models., Comment: 54 pages, 9 figures, 9 tables, Revised version

We present a comprehensive analysis of the semileptonic weak decays of $B_c$ meson decaying to axial-vector ($A$) mesons for bottom-conserving and bottom-changing decay modes. We employ self-consistent covariant light-front quark model (CLFQM) that uses type-II correspondence to eliminate inconsistencies in the traditional type-I CLFQM. As a fresh attempt, we test the self-consistency in CLFQM through type-II correspondence for $B_c \to A$ meson transition form factors. We establish that in type-II correspondence the form factors for longitudinal and transverse polarization states are numerically equal and are free from zero-mode contributions, which confirms the self-consistency of type-II correspondence for $B_c \to A$ transition form factors. Furthermore, we ascertain that the problems of inconsistency and violation of covariance of CLFQM within the type-I correspondence are resolved in type-II correspondence for $B_c \to A$ transitions. We thoroughly investigate the effects of self-consistency between type-I and type-II schemes using a comparative analysis. We also study the $q^2$ dependence of the form factors in weak hadronic currents for the whole accessible kinematic range $0 \leqslant q^2 \leqslant q^2_{max}$ for both bottom-conserving as well as bottom-changing transitions. In addition, we extend our analysis to predict the branching ratios of the semileptonic weak decays of $B_c$ meson involving axial-vector meson in the final state to quantify the effects of self-consistency in these decays that were not studied before. We evaluate the lepton mass effect on these branching ratios and various other important physical observables, such as forward-backward asymmetries, lepton-side convexity parameter, asymmetry parameter, and longitudinal polarization asymmetries and fractions. Finally, we obtain the lepton flavor universality ratios for various decays., Comment: 68 pages, 16 figures, 8 tables. Revised manuscript

We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation ignores the causal structure, often making it inefficient and expensive. The few existing methods that consider the causal structure are myopic and do not fully accommodate the observation-intervention trade-off that emerges when estimating causal effects. In this paper, we show that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem which permits an efficient solution. We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms. Experimental results show that our formulation can enhance existing algorithms on real and synthetic benchmarks.

Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. This paper proposes a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of simulated and real-world environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches., Comment: The videos and supplementary material are available at https://sites.google.com/view/maner-supplementary

Much of the causal discovery literature prioritises guaranteeing the identifiability of causal direction in statistical models. For structures within a Markov equivalence class, this requires strong assumptions which may not hold in real-world datasets, ultimately limiting the usability of these methods. Building on previous attempts, we show how to incorporate causal assumptions within the Bayesian framework. Identifying causal direction then becomes a Bayesian model selection problem. This enables us to construct models with realistic assumptions, and consequently allows for the differentiation between Markov equivalent causal structures. We analyse why Bayesian model selection works in situations where methods based on maximum likelihood fail. To demonstrate our approach, we construct a Bayesian non-parametric model that can flexibly model the joint distribution. We then outperform previous methods on a wide range of benchmark datasets with varying data generating assumptions.

Continual learning for end-to-end automatic speech recognition has to contend with a number of difficulties. Fine-tuning strategies tend to lose performance on data already seen, a process known as catastrophic forgetting. On the other hand, strategies that freeze parameters and append tunable parameters must maintain multiple models. We suggest a strategy that maintains only a single model for inference and avoids catastrophic forgetting. Our experiments show that a simple linear interpolation of several models' parameters, each fine-tuned from the same generalist model, results in a single model that performs well on all tested data. For our experiments we selected two open-source end-to-end speech recognition models pre-trained on large datasets and fine-tuned them on 3 separate datasets: SGPISpeech, CORAAL, and DiPCo. The proposed average of domain experts model performs well on all tested data, and has almost no loss in performance on data from the domain of original training., Comment: Submitted to INTERSPEECH 2023