Deep learning approach to describe and classify fungi microscopic images

Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species by microbiologist due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and Fisher Vector (advanced bag-of-words method) to classify microscopic images of various fungi species. Our approach has the potential to make the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis

ICICLE: Interpretable Class Incremental Continual Learning

Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models. We make the code available.Comment: Under review, code will be shared after the acceptanc

Token Recycling for Efficient Sequential Inference with Vision Transformers

Vision Transformers (ViTs) overpass Convolutional Neural Networks in processing incomplete inputs because they do not require the imputation of missing values. Therefore, ViTs are well suited for sequential decision-making, e.g. in the Active Visual Exploration problem. However, they are computationally inefficient because they perform a full forward pass each time a piece of new sequential information arrives. To reduce this computational inefficiency, we introduce the TOken REcycling (TORE) modification for the ViT inference, which can be used with any architecture. TORE divides ViT into two parts, iterator and aggregator. An iterator processes sequential information separately into midway tokens, which are cached. The aggregator processes midway tokens jointly to obtain the prediction. This way, we can reuse the results of computations made by iterator. Except for efficient sequential inference, we propose a complementary training policy, which significantly reduces the computational burden associated with sequential decision-making while achieving state-of-the-art accuracy.Comment: The code will be released upon acceptanc

Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations

Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations. However, their similarity maps are calculated in the penultimate network layer. Therefore, the receptive field of the prototype activation region often depends on parts of the image outside this region, which can lead to misleading interpretations. We name this undesired behavior a spatial explanation misalignment and introduce an interpretability benchmark with a set of dedicated metrics for quantifying this phenomenon. In addition, we propose a method for misalignment compensation and apply it to existing state-of-the-art models. We show the expressiveness of our benchmark and the effectiveness of the proposed compensation methodology through extensive empirical studies.Comment: Under review. Code will be release upon acceptanc

Studies on magnetic properties of unique molecular magnet {[FeII(pyrazole)4]2[NbIV(CN)8]⋅4H2O}n\{[Fe^{II}(pyrazole)_4]_2[Nb^{IV}(CN)_8]\cdot4H_2O\}_n

In this paper magnetic properties of hybrid inorganic-organic compound {[FeII(pyrazole)4]2[NbIV(CN)8]∙4H2O}n are presented. This is a three dimensional molecular magnet with well localized magnetic moments, which make it a suitable candidate for testing magnetic models. In order to characterize the magnetic properties of the above compound we performed the AC/DC magnetometry in the range 0-5 T. The special attention was paid to the phase transition at 7.9 K. The study in magnetic field supports magnetic ordering below 7.9 K

USE OF A BALANCED EFFLUENT FROM THE ANAEROBIC REACTOR FOR ALGAE CHLORELLA VULGARIS GROWTH FOR BIOMASS PRODUCTION

The aim of the study was to determine the possible use of the effluent, produced in the process of the anaerobic decomposition of organic substances, as a medium in the cultivation of microalgae Chlorella vulgaris. The characteristics of efficiency and kinetic of algae growth rate was determined. The scope included balancing of the effluent so as to provide an adequate level of nutrients required for algae growth. The effluent dilutions of 25%, 50%, 75% and 100% was tested. The effluent was supplemented with nutrients to create the same conditions as in the synthetic medium. The tested effluent can be used in the intensive cultivation of biomass of microalgae Chlorella vulgaris. The best results were obtained with the effluent dilution of 75%