We provide finite-particle convergence rates for the Stein Variational Gradient Descent (SVGD) algorithm in the Kernelized Stein Discrepancy ($\KSD$) and Wasserstein-2 metrics. Our key insight is that the time derivative of the relative entropy between the joint density of $N$ particle locations and the $N$-fold product target measure, starting from a regular initial distribution, splits into a dominant 'negative part' proportional to $N$ times the expected $\KSD^2$ and a smaller 'positive part'. This observation leads to $\KSD$ rates of order $1/\sqrt{N}$, in both continuous and discrete time, providing a near optimal (in the sense of matching the corresponding i.i.d. rates) double exponential improvement over the recent result by~\cite{shi2024finite}. Under mild assumptions on the kernel and potential, these bounds also grow polynomially in the dimension $d$. By adding a bilinear component to the kernel, the above approach is used to further obtain Wasserstein-2 convergence in continuous time. For the case of `bilinear + Mat\'ern' kernels, we derive Wasserstein-2 rates that exhibit a curse-of-dimensionality similar to the i.i.d. setting. We also obtain marginal convergence and long-time propagation of chaos results for the time-averaged particle laws.

This study explores the use of digital technologies by food supply chain firms to enhance circular practices, aiming to boost the social, economic, and environmental sustainability of food supply chains. The literature on digital technologies in the food supply chain has experienced significant growth in the last few years. Given the critical importance of these technologies, there is an urgent need for a thorough systematic literature review to integrate and reconcile the findings. This research uses SPAR-4 SLR with the theories, context and methods (TCM) framework to synthesize the body of literature on digital technologies for circular and sustainable food supply chain. The study integrates existing knowledge to propose future research directions, while also addressing identified gaps in theories, methods and contexts.

In this paper, we propose a novel framework for multi-image co-segmentation using class agnostic meta-learning strategy by generalizing to new classes given only a small number of training samples for each new class. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed DVICE network and a novel few-shot learning approach to tackle the small sample size problem encountered in co-segmentation with small datasets like iCoseg and MSRC. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation over multiple datasets using only a small volume of training data, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.

In recent years, fingerprint recognition systems have made remarkable advancements in the field of biometric security as it plays an important role in personal, national and global security. In spite of all these notable advancements, the fingerprint recognition technology is still susceptible to spoof attacks which can significantly jeopardize the user security. The cross sensor and cross material spoof detection still pose a challenge with a myriad of spoof materials emerging every day, compromising sensor interoperability and robustness. This paper proposes a novel method for fingerprint spoof detection using both global and local fingerprint feature descriptors. These descriptors are extracted using DenseNet which significantly improves cross-sensor, cross-material and cross-dataset performance. A novel patch attention network is used for finding the most discriminative patches and also for network fusion. We evaluate our method on four publicly available datasets: LivDet 2011, 2013, 2015 and 2017. A set of comprehensive experiments are carried out to evaluate cross-sensor, cross-material and cross-dataset performance over these datasets. The proposed approach achieves an average accuracy of 99.52%, 99.16% and 99.72% on LivDet 2017, 2015 and 2011 respectively outperforming the current state-of-the-art results by 3% and 4% for LivDet 2015 and 2011 respectively.

The objective in image co-segmentation is to jointly segment unknown common objects from a given set of images. In this paper, we propose a novel deep convolution neural network based end-to-end co-segmentation model. It is composed of a metric learning and decision network leading to a novel conditional siamese encoder-decoder network for estimating a co-segmentation mask. The role of the metric learning network is to find an optimum latent feature space where objects of the same class are closer and that of different classes are separated by a certain margin. Depending on the extracted features, the decision network decides whether input images have common objects or not and the encoder-decoder network produces a cosegmentation mask accordingly. Key aspects of the architecture are as follows. First, it is completely class agnostic and does not require any semantic information. Second, in addition to producing masks, the decoder network also learns similarity across image pairs that improves co-segmentation significantly. Experimental results reflect an excellent performance of our method compared to state of-the-art methods on challenging co-segmentation datasets.