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While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the use of large neural architectures and the application of complex language models in Information Retrieval (IR) have reduced the transparency of retrieval methods. Consequently, Explainability and Interpretability have emerged as important research topics in IR. Several axiomatic and post-hoc explanation methods, as well as approaches that attempt to be interpretable-by-design, have been proposed. This article presents \irexplain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework. \irexplain supports the three standard categories of post-hoc explanations, namely pointwise, pairwise, and listwise explanations. The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods. To facilitate adoption, \irexplain is well-integrated with widely-used toolkits such as Pyserini and \irdatasets.

Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, applying this transition in engineering software presents challenges, including low surrogate model accuracy, the curse of dimensionality in inverse design, and rising complexity in physical simulations. We introduce an interpolating neural network (INN), grounded in interpolation theory and tensor decomposition, to realize Engineering Software 2.0 by advancing data training, partial differential equation solving, and parameter calibration. INN offers orders of magnitude fewer trainable/solvable parameters for comparable model accuracy than traditional multi-layer perceptron (MLP) or physics-informed neural networks (PINN). Demonstrated in metal additive manufacturing, INN rapidly constructs an accurate surrogate model of Laser Powder Bed Fusion (L-PBF) heat transfer simulation, achieving sub-10-micrometer resolution for a 10 mm path in under 15 minutes on a single GPU. This makes a transformative step forward across all domains essential to engineering software., Comment: 9 pages, 2 figures

Monkeypox is a rare disease that raised concern among medical specialists following the convi-19 pandemic. It's concerning since monkeypox is difficult to diagnose early on because of symptoms that are similar to chickenpox and measles. Furthermore, because this is a rare condition, there is a knowledge gap among healthcare professionals. As a result, there is an urgent need for a novel technique to combat and anticipate the disease in the early phases of individual virus infection. Multiple CNN-based pre-trained models, including VGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, MobileNetV2, Alexnet, Lenet, and majority Voting, were employed in classification in this study. For this study, multiple data sets were combined, such as monkeypox vs chickenpox, monkeypox versus measles, monkeypox versus normal, and monkeypox versus all diseases. Majority voting performed 97% in monkeypox vs chickenpox, Xception achieved 79% in monkeypox against measles, MobileNetV2 scored 96% in monkeypox vs normal, and Lenet performed 80% in monkeypox versus all.

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

Recent advances in operator learning theory have improved our knowledge about learning maps between infinite dimensional spaces. However, for large-scale engineering problems such as concurrent multiscale simulation for mechanical properties, the training cost for the current operator learning methods is very high. The article presents a thorough analysis on the mathematical underpinnings of the operator learning paradigm and proposes a kernel learning method that maps between function spaces. We first provide a survey of modern kernel and operator learning theory, as well as discuss recent results and open problems. From there, the article presents an algorithm to how we can analytically approximate the piecewise constant functions on R for operator learning. This implies the potential feasibility of success of neural operators on clustered functions. Finally, a k-means clustered domain on the basis of a mechanistic response is considered and the Lippmann-Schwinger equation for micro-mechanical homogenization is solved. The article briefly discusses the mathematics of previous kernel learning methods and some preliminary results with those methods. The proposed kernel operator learning method uses graph kernel networks to come up with a mechanistic reduced order method for multiscale homogenization.

The article proposes formulating and codifying a set of applied numerical methods, coined as Deep Learning Discrete Calculus (DLDC), that uses the knowledge from discrete numerical methods to interpret the deep learning algorithms through the lens of applied mathematics. The DLDC methods aim to leverage the flexibility and ever increasing resources of deep learning and rich literature of numerical analysis to formulate a general class of numerical method that can directly use data with uncertainty to predict the behavior of an unknown system as well as elevate the speed and accuracy of numerical solution of the governing equations for known systems. The article is structured in two major sections. In the first section, the building blocks of the DLDC methods are presented and deep learning structures analogous to traditional numerical methods such as finite difference and finite element methods are constructed with a view to incorporate these techniques in Science, Technology, Engineering, Mathematics (STEM) syllabus for K-12 students. The second section builds upon the building blocks of the previous discussion,and proposes new solution schemes for differential and integral equations pertinent to multiscale mechanics. Each section is accompanied with mathematical formulation of the numerical methods, analogous DLDC formulation, and suitable examples.

Automatic Image Captioning is the never-ending effort of creating syntactically and validating the accuracy of textual descriptions of an image in natural language with context. The encoder-decoder structure used throughout existing Bengali Image Captioning (BIC) research utilized abstract image feature vectors as the encoder's input. We propose a novel transformer-based architecture with an attention mechanism with a pre-trained ResNet-101 model image encoder for feature extraction from images. Experiments demonstrate that the language decoder in our technique captures fine-grained information in the caption and, then paired with image features, produces accurate and diverse captions on the BanglaLekhaImageCaptions dataset. Our approach outperforms all existing Bengali Image Captioning work and sets a new benchmark by scoring 0.694 on BLEU-1, 0.630 on BLEU-2, 0.582 on BLEU-3, and 0.337 on METEOR., Comment: 15 pages, 6 figures, 1 table, 6 equations

Gold-Silver (Au-Ag) core-shell nanostructures are gaining importance in stretchable electronics where high tensile and fatigue resistance is of paramount importance. This work proposes the parameterization of a modified embedded atomic model (MEAM) interatomic potential through density functional theory (DFT) calculations for investigating the role of dislocations and defect interaction governing the mechanical behavior of Au-Ag and Ag-Au Core-shell nanostructures under tensile and fatigue loading using molecular dynamics (MD) simulations. A comparative analysis between the Core-shell structures and their pristine counterparts is also conducted. Throughout this work, pseudo-potential and all-electron full potential DFT schemes are used for parameterizing MEAM by calculating cohesive energy, lattice parameter, and bulk modulus of pure Au, Ag and their alloy. Using the new force-field for MD simulations, the tensile behavior of pristine and core-shell nanowires is explored for temperatures between 300K to 600K. The fatigue properties of two pristine and two core-shell nanowires in a strain range of -15% to 15% for 10 cycles is also conducted. Our results suggest that Ag-Au Core-shell nanowire shows the best reversibility under fatigue loading among the structures examined. Moreover, Ag-Au exhibit the highest dislocation formation and complete annihilation of defects consistently. While, Au-Ag present improved fatigue properties than its pristine counterparts but have some residual defects leading to lower reversibility when compared to Ag-Au. For tensile loading, all four structures exhibited deterioration in strength with increasing temperature. Thermal softening is seen to be more prominent in Au-Ag core-shell nanowires compared to Ag-Au.

Ni-based superalloy Inconel-718 is ubiquitous in metal 3D printing where high cooling rate and thermal gradient are present. These manufacturing conditions are conducive to high initial dislocation density and porosity or void in the material. This work proposes a molecular dynamics (MD) analysis method that can examine the role of dislocations, cooling rates, void, and their interactions governing the material properties and failure mechanism in Inconel-718 using Embedded Atom Method (EAM) potential. Three different structures: nanowire (NW), nanopillar, and nanoplate are used throughout this work. Initially, strain rates are varied from 10^8s^-1 to 10^10s^-1 keeping the NW diameter and temperature constant at 3.17 nm and 300K respectively. Compressive loading is applied to a 7.04 nm nanopillar by applying a constant strain rate of 109 s^-1 while temperature is varied from 100K to 700K. Different cooling rates ranging from 0.5x10^10 K/s to 1 x 10^14 K/s are applied to nanoplates (with and without a central void). The size of the central void is kept fixed at 2.12 nm. Increasing strain rates in tension not only results in strain hardening but also increase in dislocation density. Our computational method is successful to capture extensive sliding on {111} shear plane, which leads to significant necking of the alloy before fracture due to dislocation. The high cooling rates creating non-equilibrium structure leads to high strength and ductile behavior. On the other hand, the low cooling rate forming well defined crystalline structure causes low strength and brittle behavior. This brittle to ductile transition is observed solely due to cooling rate. Cooling rate may diminish the void by healing the structure during solidifications process. Subsequent mechanical properties by varying temperature and size are also presented in detail.

The mechanical properties of Cadmium telluride (CdTe) nanowire have become focus of interest now-a-days due to its promising application in opto-electro-mechanical nanodevices. Inthis study, molecular dynamics simulations have been used to investigate the mechanical behavior of Zinc Blende (ZB) crystal structured CdTe nanowires (NWs) by varying size, temperature, crystal orientation and strain rate under tension and compression. Results show that the fracture strength of the [111]-oriented CdTe NWs is always higher than that of the [110]-oriented CdTe NWs under tension whereas in compression, the fracture strength of the [111]-oriented CdTe NWs is significantly lower than that of the [110]-oriented CdTe NWs. Moreover, under tensile load, void in ZB [111]-oriented CdTe NWs has been observed which is a new failure mechanism found in this study. It has also been observed that size has negligible effect on the tensile behavior but in compression the behavior is clearly size dependent. Both tensile and compressive strengths show an inverse relation with temperature. When tensile load is applied along NWs growth direction, the [111]-oriented CdTe NWs fail by creating void in [10-1] direction regardless of temperature and NW size. Under compression, the [111]-oriented nanowire show buckling and plasticity. Finally, the impact of strain rate on [111]-oriented ZB CdTe NWs is also studied where higher fracture strengths and strains at a higher strain rates have been found under both tension and compression. With increasing the strain rate, the number of voids is also increased in the NWs.This study will help to design CdTe NWs based devices efficiently by presenting in-depth understanding of failure behavior of the [111]-oriented CdTe NWs.