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The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Vision-Language Models (VLMs) have recently achieved groundbreaking results. VLM-based open-vocabulary object detection extends the capabilities of traditional object detection frameworks, enabling the recognition and classification of objects beyond predefined categories. Investigating OOD robustness in recent open-vocabulary object detection is essential to increase the trustworthiness of these models. This study presents a comprehensive robustness evaluation of the zero-shot capabilities of three recent open-vocabulary (OV) foundation object detection models: OWL-ViT, YOLO World, and Grounding DINO. Experiments carried out on the robustness benchmarks COCO-O, COCO-DC, and COCO-C encompassing distribution shifts due to information loss, corruption, adversarial attacks, and geometrical deformation, highlighting the challenges of the model's robustness to foster the research for achieving robustness. Source code shall be made available to the research community on GitHub., Comment: 14 + 3 single column pages

Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating camera intrinsic and extrinsic parameters is a challenging task that prevents synthesizing perspective distortion. Non-availability of dedicated training data poses a critical barrier to developing robust computer vision methods. Additionally, distortion correction methods make other computer vision tasks a multi-step approach and lack performance. In this work, we propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of M\"obius transform to model real-world distortion without estimating camera intrinsic and extrinsic parameters and without the need for actual distorted data. Also, we present a dedicated perspectively distorted benchmark dataset, ImageNet-PD, to benchmark the robustness of deep learning models against this new dataset. The proposed method outperforms existing benchmarks, ImageNet-E and ImageNet-X. Additionally, it significantly improves performance on ImageNet-PD while consistently performing on standard data distribution. Notably, our method shows improved performance on three PD-affected real-world applications crowd counting, fisheye image recognition, and person re-identification and one PD-affected challenging CV task: object detection. The source code, dataset, and models are available on the project webpage at https://prakashchhipa.github.io/projects/mpd., Comment: Accepted to European Conference on Computer Vision(ECCV2024). project page- https://prakashchhipa.github.io/projects/mpd

Medical question answer (QA) assistants respond to lay users' health-related queries by synthesizing information from multiple sources using natural language processing and related techniques. They can serve as vital tools to alleviate issues of misinformation, information overload, and complexity of medical language, thus addressing lay users' information needs while reducing the burden on healthcare professionals. QA systems, the engines of such assistants, have typically used either language models (LMs) or knowledge graphs (KG), though the approaches could be complementary. LM-based QA systems excel at understanding complex questions and providing well-formed answers, but are prone to factual mistakes. KG-based QA systems, which represent facts well, are mostly limited to answering short-answer questions with pre-created templates. While a few studies have jointly used LM and KG approaches for text-based QA, this was done to answer multiple-choice questions. Extant QA systems also have limitations in terms of automation and performance. We address these challenges by designing a novel, automated disease QA system which effectively utilizes both LM and KG techniques through a joint-reasoning approach to answer disease-related questions appropriate for lay users. Our evaluation of the system using a range of quality metrics demonstrates its efficacy over benchmark systems, including the popular ChatGPT., Comment: 36 pages, 6 figures, submitted to TMIS on 14 July 2023 (status: under review)

Fitting's Heyting-valued logic and Heyting-valued modal logic have already been studied from an algebraic viewpoint. In addition to algebraic axiomatizations with the completeness of Fitting's Heyting-valued logic and Heyting-valued modal logic, both topological and coalgebraic dualities have also been developed for algebras of Fitting's Heyting-valued modal logic. Bitopological methods have recently been employed to investigate duality for Fitting's Heyting-valued logic. However, the concepts of bitopology and biVietoris coalgebras are conspicuously absent from the development of dualities for Fitting's many-valued modal logic. With this study, we try to bridge that gap. We develop a bitopological duality for algebras of Fitting's Heyting-valued modal logic. We construct a bi-Vietoris functor on the category $PBS_{\mathcal{L}}$ of $\mathcal{L}$-valued ($\mathcal{L}$ is a Heyting algebra) pairwise Boolean spaces. Finally, we obtain a dual equivalence between categories of biVietoris coalgebras and algebras of Fitting's Heyting-valued modal logic. As a result, we conclude that Fitting's many-valued modal logic is sound and complete with respect to the coalgebras of a biVietoris functor. We discuss the application of this coalgebraic approach to bitopological duality.

Electrical current rectification is an interesting electronic feature, popularly known as a diode. Achieving a high rectification ratio in a molecular junction has been a long-standing goal in molecular electronics. The present work describes mimicking electrical current rectification with pi-stacked rhenium(I) compound sandwiched between two electrical contacts. Among the two mononuclear rhenium compounds studied here, [Re(CO)4(PPh3){(N)-saccharinate}] (1) and [Re(CO)3(phen){(N)-saccharinate}] (2), the latter show strong pi-pi interactions-induced high rectification ratio of ~ 4000 at 2.0 V at room temperature. Alternating current (AC)-based electrical measurements ensuring AC to DC electrical signal conversion at a frequency f of 1 KHz showing 2 can act as an excellent half-wave rectifier. Asymmetric charge injection barrier height at the electrode/Re(I) interfaces of the devices with a stacking configuration of p++-Si/Re compound31nm(2)/ITO originates the flow of electrical current unidirectionally. The charge transport mechanism governed by thermally activated hopping phenomena, and charge carrier propagation is explained through an energy profile considering the Fermi levels of two electrodes, and the energy of frontier molecular orbitals, HOMO, and LUMO, confirming rectification is of a molecular origin. The present work paves the way to combine different organometallic compounds as circuit elements in nanoelectronic devices to achieve numerous exciting electronic features., Comment: 16 pages, 5 figures

This article contributes to the new summation of Sz\'asz operators with the help of Appell polynomials of class $A^{2}$. We verified Bohman-Korovkin's theorem and prove the convergence results like Lipschitz-type space, Voronvaskaja-type asymptotic formula, and modulus of continuity using the given operators. Furthermore, we have shown the weighted modulus of continuity and the derivative of bounded variation.

Self-supervised learning in computer vision aims to leverage the inherent structure and relationships within data to learn meaningful representations without explicit human annotation, enabling a holistic understanding of visual scenes. Robustness in vision machine learning ensures reliable and consistent performance, enhancing generalization, adaptability, and resistance to noise, variations, and adversarial attacks. Self-supervised paradigms, namely contrastive learning, knowledge distillation, mutual information maximization, and clustering, have been considered to have shown advances in invariant learning representations. This work investigates the robustness of learned representations of self-supervised learning approaches focusing on distribution shifts and image corruptions in computer vision. Detailed experiments have been conducted to study the robustness of self-supervised learning methods on distribution shifts and image corruptions. The empirical analysis demonstrates a clear relationship between the performance of learned representations within self-supervised paradigms and the severity of distribution shifts and corruptions. Notably, higher levels of shifts and corruptions are found to significantly diminish the robustness of the learned representations. These findings highlight the critical impact of distribution shifts and image corruptions on the performance and resilience of self-supervised learning methods, emphasizing the need for effective strategies to mitigate their adverse effects. The study strongly advocates for future research in the field of self-supervised representation learning to prioritize the key aspects of safety and robustness in order to ensure practical applicability. The source code and results are available on GitHub., Comment: Accepted at 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). Corresponding author - prakash.chandra.chhipa@ltu.se

Bio-spinterfaces present numerous opportunities to study spintronics across the biomolecules attached to (ferro)magnetic electrodes. While it offers various exciting phenomena to investigate, it's simultaneously challenging to make stable bio-spinterfaces, as biomolecules are sensitive to many factors that it encounters during thin-film growth to device fabrication. The chirality-induced spin-selectivity (CISS) effect is an exciting discovery demonstrating an understanding that a specific electron's spin (either UP or DOWN) passes through a chiral molecule. The present work utilizes Ustilago maydis Rvb2 protein, an ATP-dependent DNA helicase (also known as Reptin) for the fabrication of bio-spintronic devices to investigate spin-selective electron transport through protein. Ferromagnetic materials are well-known for showing spin-polarization, which many chiral and biomolecules can mimic. We report spin-selective electron transmission through Rvb2 that exhibits 30% spin polarization at a low bias (+ 0.5 V) in a device configuration, Ni/Rvb2 protein/ITO measured under two different magnetic configurations. Our findings demonstrate that biomolecules can be put in circuit components without any expensive vacuum deposition for the top contact. Thus, it holds a remarkable potential to advance spin-selective electron transport in other biomolecules such as proteins, and peptides for biomedical applications., Comment: 14 pages, 5 figures

This work presents a novel label-efficient selfsupervised representation learning-based approach for classifying diabetic retinopathy (DR) images in cross-domain settings. Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for training. The proposed approach uses the prior learning from the source DR image dataset to classify images drawn from the target datasets. The image representations learned from the unlabeled source domain dataset through contrastive learning are used to classify DR images from the target domain dataset. Moreover, the proposed approach requires a few labeled images to perform successfully on DR image classification tasks in cross-domain settings. The proposed work experiments with four publicly available datasets: EyePACS, APTOS 2019, MESSIDOR-I, and Fundus Images for self-supervised representation learning-based DR image classification in cross-domain settings. The proposed method achieves state-of-the-art results on binary and multiclassification of DR images, even in cross-domain settings. The proposed method outperforms the existing DR image binary and multi-class classification methods proposed in the literature. The proposed method is also validated qualitatively using class activation maps, revealing that the method can learn explainable image representations. The source code and trained models are published on GitHub., Comment: Accepted to International Joint Conference on Neural Networks (IJCNN) 2023

This work presents a novel domain adaption paradigm for studying contrastive self-supervised representation learning and knowledge transfer using remote sensing satellite data. Major state-of-the-art remote sensing visual domain efforts primarily focus on fully supervised learning approaches that rely entirely on human annotations. On the other hand, human annotations in remote sensing satellite imagery are always subject to limited quantity due to high costs and domain expertise, making transfer learning a viable alternative. The proposed approach investigates the knowledge transfer of selfsupervised representations across the distinct source and target data distributions in depth in the remote sensing data domain. In this arrangement, self-supervised contrastive learning-based pretraining is performed on the source dataset, and downstream tasks are performed on the target datasets in a round-robin fashion. Experiments are conducted on three publicly available datasets, UC Merced Landuse (UCMD), SIRI-WHU, and MLRSNet, for different downstream classification tasks versus label efficiency. In self-supervised knowledge transfer, the proposed approach achieves state-of-the-art performance with label efficiency labels and outperforms a fully supervised setting. A more in-depth qualitative examination reveals consistent evidence for explainable representation learning. The source code and trained models are published on GitHub., Comment: Accepted in International Joint Conference on Neural Networks (IJCNN) 2023. First three authors shares equal contribution!

Nanoscale molecular junctions are celebrated nanoelectronic devices for mimicking several electronic functions including rectifiers, sensors, wires, switches, transistors, and memory but capacitive behavior is nearly unexplored. Capacitors are crucial energy storage devices that store energy in the form of electrical charges. A capacitor utilizes two electrical conductors separated by a dielectric material. However, many oxides-based dielectrics are well-studied for integrating capacitors, however, capacitors comprised of thin-film molecular layers are not well-studied. The present work describes electrochemically grafted thin films of benzimidazole (BENZ) grown on patterned ITO electrodes on which a 50 nm Al is deposited to fabricate large-scale (500 x 500 micron2) molecular junctions. The nitrogen and sulfur-containing molecular junctions, ITO/BENZ/Al act as a parallel-plate capacitor with a maximum capacitance of ~59.6 to 4.79 microFcm-2. The present system can be an excellent platform for molecular charge storage for future energy applications., Comment: 12 Pages, 5 Figures

The present era has seen tremendous demands for low-cost electrochromic materials for visible-region multicolor display technology, paper-based, flexible, and wearable electronic devices, smart windows, and optoelectronic applications. Towards this goal, we report large-scale polyelectrochromic devices fabricated on rigid to flexible ITO substrates comprising novel anthracene containing viologen, (1,1'-bis(anthracen-9-ylmethyl)-[4,4'-bipyridine]-1,1'-diium bromide, abbreviated as AnV2+), and polythiophene (P3HT). Inter-estingly, the devices show three states of reversible visible color in response to the applied bias, sub-second to second switching time (0.7 s/1.6 s), and high coloration efficiency (484 cm2/C), longer cycling stability up to 3000 s (103 switching cycles). Thanks to the anthracenes moieties introduced to viologen that inhibit formation of undesired dimer of cation radicals formed in response to the applied bias, other-wise it would hamper the devices reconfiguration. The devices are fully characterized, and electrochromic performances are ensured by bias-dependent UV-Vis, and Raman spectroscopy. The fabricated electro-chromic devices are tested with the commercially available low-cost cells to perform, which is highly de-sired for practical applications. The computational study facilitates the understanding of experimental re-sults. The alternating current (AC)-based electrical impedance spectroscopy reveals that P3HT facilitates reducing charge transfer resistance of the devices. Our work shows CMOS compatibility and one of the best-performing devices that could pave the way for developing cost-effective flexible, and wearable electrochromic devices., Comment: 13 Pages, 7 Figures

Due to the shorter channel length allowing faster ion/charge movement, nanoscale molecular thin films can be attractive electronic components for next-generation high-performing energy storage devices. However, controlling chemical functionalization and achieving stable electrode-molecule interfaces at the nanoscale via covalent functionalization for low-voltage operational, ultrafast charging/discharging remains a challenge. Herein, we present a simple, controllable, scalable, low-cost, and versatile electrochemical grafting approach to modulate chemical and electronic properties of graphite rods (GRs) that are extracted from low-cost EVEREADY cells (1.5 US $ for 10 cells of 1.5 V). On the ANT-modified GR (ANT/GR), the total capacitance unveils 350-fold enhancement as compared to an unmodified GR tested with 0.1 M H2SO4 electrolyte ensured by both potentiostatic and galvanostatic measurements. Such enhancement in capacitance is attributed to the contribution from the electrical double layer and Faradaic charge transfer. Due to higher conductivity, anthracene molecular layers possess more azo groups (-N=N-) over pyrene, and naphthalene molecular films during the electrochemical grafting, which is key to capacitance improvements. The ultra-low-loading nanofilms expose high surface area leading to extremely high energy density. The nanoscale molecular films (~ 23 nm thickness) show exceptional galvanostatic charge-discharge cycling stability (10,000) that operates at low potential. Electrochemical impedance spectroscopy was performed along with the DC measurements to unravel in-depth charge storage performances. Electrochemically grafted molecular films on GR show excellent balance in capacitance and electrical conductivity, high diffusion coefficient toward ferrocene, and can easily be synthesized in good yield on rigid to flexible electrodes., Comment: 18 pages, 6 Figures

This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization of self-supervised learning pseudo task and supervised learning task, improving supervised learning task performance. Recent progress in self-supervised learning uses a large volume of data, which becomes a constraint for its applications on small-scale datasets. This work shares a simple yet effective joint training framework that reinforces human-supervised task learning by learning self-supervised representations just-in-time and vice versa. Experiments on three public datasets from different visual domains, Intel Image, CIFAR, and APTOS, reveal a consistent track of performance improvements on classification tasks during joint optimization. Qualitative analysis also supports the robustness of learnt representations. Source code and trained models are available on GitHub., Comment: Accepted at IEEE International Conference on Image Processing (ICIP 2023)

In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation, and autoencoding. Deep perceptual loss is a type of loss function for images that computes the error between two images as the distance between deep features extracted from a neural network. Most applications of the loss use pretrained networks called loss networks for deep feature extraction. However, despite increasingly widespread use, the effects of loss network implementation on the trained models have not been studied. This work rectifies this through a systematic evaluation of the effect of different pretrained loss networks on four different application areas. Specifically, the work evaluates 14 different pretrained architectures with four different feature extraction layers. The evaluation reveals that VGG networks without batch normalization have the best performance and that the choice of feature extraction layer is at least as important as the choice of architecture. The analysis also reveals that deep perceptual loss does not adhere to the transfer learning conventions that better ImageNet accuracy implies better downstream performance and that feature extraction from the later layers provides better performance.

This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt. Human annotations for material categories on sensor-generated data are scarce and cost-intensive. Currently, representation learning without human annotations remains unexplored for mining materials and does not leverage on utilization of sensor-generated data. The proposed method, Depth Contrast, enables self-supervised learning of representations without labels on the 3DPM dataset by exploiting depth maps and inductive transfer. The proposed method outperforms material classification over ImageNet transfer learning performance in fully supervised learning settings and achieves an F1 score of 0.73. Further, The proposed method yields an F1 score of 0.65 with an 11% improvement over ImageNet transfer learning performance in a semi-supervised setting when only 20% of labels are used in fine-tuning. Finally, the Proposed method showcases improved performance generalization on linear evaluation. The implementation of proposed method is available on GitHub., Comment: Accepted to CVF European Conference on Computer Vision Workshop(ECCVW 2022)

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper., Comment: 9 pages, 4 figures, to appear in AAAI-23 ([IAAI-23 track] Deployed Highly Innovative Applications of AI)

This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks, a quality of meta learning. It is important to highlight that we focus on heterogeneous tasks, which are of distinct kind, in contrast to typically considered homogeneous tasks (e.g., if all tasks are classification or if all tasks are regression tasks). The fundamental idea is to train a multi-task model, such that when an unseen task is introduced, it can learn in fewer steps whilst offering a performance at least as good as conventional single task learning on the new task or inclusion within the MTL. By conducting various experiments, we demonstrate this paradigm on two datasets and four tasks: NYU-v2 and the taskonomy dataset for which we perform semantic segmentation, depth estimation, surface normal estimation, and edge detection. MTML achieves state-of-the-art results for three out of four tasks for the NYU-v2 dataset and two out of four for the taskonomy dataset. In the taskonomy dataset, it was discovered that many pseudo-labeled segmentation masks lacked classes that were expected to be present in the ground truth; however, our MTML approach was found to be effective in detecting these missing classes, delivering good qualitative results. While, quantitatively its performance was affected due to the presence of incorrect ground truth labels. The the source code for reproducibility can be found at https://github.com/ricupa/MTML-learn-how-to-adapt-to-unseen-tasks.

Metal-organic frameworks (MOFs) symbolize the particular class of hybrid crystalline, nano-porous materials made of either discrete metal ions or clusters with organic linkers. Past studies on MOFs-based materials largely focused on porosity, chemical and structural diversity, gas sorption, sensing, drug delivery, catalysis, and separation applications. However, initial efforts either neglected or have not gained much attention to refine the electrical conductivity of MOFs materials. MOFs reported earlier with poor electrical conductivity impeded to employ in electronics, optoelectronics, and renewable energy storage applications. To overcome this issue, the MOFs community has been engaged in improving electrical conductivity by adopting several intriguing strategies. We shed light on the charge transport mechanisms which are mainly two processes, either through a bond or through space. This review aims to showcase the current scenario on creatively designed MOF materials followed by fabrication advancement of high-quality molecular thin films, and semiconductor device fabrication for stimuli-responsive current-voltage (I-V) studies. Overall, the review addresses the pros and cons of the MOFs-based electronics, followed by our prediction on improvement MOFs composition, mechanically stable interfaces, device stacking, further relevant experiments which can be of great interest to the MOFs researchers in improving further devices performances.

Ferrocene, since its discovery in 1951, has been extensively exploited as a redox probe in a variety of processes ranging from solution chemistry, medicinal chemistry, supramolecular chemistry, surface chemistry to solid-state molecular electronic and spintronic circuit elements to unravel electrochemical charge-transfer dynamics. Ferrocene represents an extremely chemically and thermally stable, and highly reproducible redox probe that undergoes reversible one-electron oxidation and reduction occurring at the interfaces of electrode/ferrocene solution in response to applied anodic and cathodic potentials, respectively. It has been almost 70 years after its discovery and has become one of the most widely studied and model organometallic compounds not only for probing electrochemical charge-transfer process but also as molecular building blocks for the synthesis of chiral organometallic catalysts, potential drug candidates, polymeric compounds, electrochemical sensors, to name a few. Ferrocene and its derivatives have been a breakthrough in many aspects due to its versatile reactivity, fascinating chemical structures, unconventional metal-ligand coordination, and the magic number of electrons (18 e-). The present review discusses the recent progress made towards ferrocene-containing molecular systems exploited for redox reactions, surface attachment, spin-dependent electrochemical process to probe spin polarization, photo-electrochemistry, and integration into prototype molecular electronic devices. Overall, the present reviews demonstrate a piece of collective information about the recent advancements made towards the ferrocene and its derivatives that have been utilized as iconic redox markers.

This paper presents a framework to automate the labelling process for gestures in musical performance videos with a 3D Convolutional Neural Network (CNN). While this idea was proposed in a previous study, this paper introduces several novelties: (i) Presents a novel method to overcome the class imbalance challenge and make learning possible for co-existent gestures by batch balancing approach and spatial-temporal representations of gestures. (ii) Performs a detailed study on 7 and 18 categories of gestures generated during the performance (guitar play) of musical pieces that have been video-recorded. (iii) Investigates the possibility to use audio features. (iv) Extends the analysis to multiple videos. The novel methods significantly improve the performance of gesture identification by 12 %, when compared to the previous work (51 % in this study over 39 % in previous work). We successfully validate the proposed methods on 7 super classes (72 %), an ensemble of the 18 gestures/classes, and additional videos (75 %).

The paper aims to develop a framework for coalgebraic fuzzy geometric logic by adding modalities to the language of fuzzy geometric logic. Using the methods of coalgebra, the modal operators are introduced in the language of fuzzy geometric logic. To define the modal operators, we introduce a notion of fuzzy-open predicate lifting. Based on coalgebras for an endofunctor $T$ on the category $\textbf{Fuzzy-Top}$ of fuzzy topological spaces and fuzzy continuous maps, we build models for the coalgebraic fuzzy geometric logic. Bisimulations for the defined models are discussed in this work.

Memory has always been a building block element for information technology. Emerging technologies such as artificial intelligence, big data, the internet of things, etc., require a novel kind of memory technology that can be energy efficient and have an exception data retention period. Among several existing memory technologies, resistive random-access memory (RRAM) is an answer to the above question as it is necessary to possess the combination of speed of RAM and nonvolatility, thus proving to be one of the most promising candidates to replace flash memory in next-generation non-volatile RAM applications. This review discusses the existing challenges and technological advancements made with RRAM, including switching mechanism, device structure, endurance, fatigue resistance, data retention period, and mechanism of resistive switching in inorganic oxides material used as a dielectric layer. Finally, a summary and a perspective on future research are presented.

The concept of utilizing a molecule bridged between two electrodes as a stable rectifying device with the possibility of commercialization is a "holy grail" of molecular electronics. Molecular rectifiers do not only exploit the electronic function of the molecules but also offer the possibility of their direct integration into specific nano-electronic circuits. However, even after nearly three decades of extensive experimental and theoretical work, the concept of molecular rectifiers still has many unresolved aspects concerning both the fundamental understanding of the underlying phenomena and the practical realization. At the same time, recent advancements in molecular systems with rectification ratios exceeding 105 are highly promising and competitive to the existing silicon-based devices. Here, we provide an overview and critical analysis of the current state and recent progress in molecular rectification relying on the different design concepts and material platforms such as single molecules, self-assembled monolayers, molecular multilayers, heterostructures, and metal-organic frameworks and coordination polymers. The involvement of crucial parameters such as the energy of molecular orbitals, electrode-molecule coupling, and asymmetric shifting of the energy levels will be discussed. Finally, we conclude by critically addressing the challenges and prospects for progress in the field and perspectives for the commercialization of molecular rectifiers., Comment: 32 pages, 7 figures

This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus on fully supervised learning approaches that rely heavily on human annotations. However, the scarcity of labeled and unlabeled data is a long-standing challenge in histopathology. Currently, representation learning without labels remains unexplored for the histopathology domain. The proposed method, Magnification Prior Contrastive Similarity (MPCS), enables self-supervised learning of representations without labels on small-scale breast cancer dataset BreakHis by exploiting magnification factor, inductive transfer, and reducing human prior. The proposed method matches fully supervised learning state-of-the-art performance in malignancy classification when only 20% of labels are used in fine-tuning and outperform previous works in fully supervised learning settings. It formulates a hypothesis and provides empirical evidence to support that reducing human-prior leads to efficient representation learning in self-supervision. The implementation of this work is available online on GitHub - https://github.com/prakashchhipa/Magnification-Prior-Self-Supervised-Method, Comment: Accepted to IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023)