Can Top Down Participatory Budgeting Work? The Case of Polish Community Fund

The article addresses the participatory budgeting (PB), which is one of the most recognised governance innovations of recent decades. This global phenomenon represents in practice a shift towards participatory and collaborative management of public resources at the local level. The purpose of this article is to determine when top down approach to PB might be welcomed, taking into account the characteristics of PB schemes all around the world that they emerged as local initiatives, instigated either by civil society groups or local governments. The analysis is based on the description of the PB example as introduced via country-wide legislation, exhaustively regulating PB procedure. The article examines Polish experience in the field of functioning top down approach to PB. It demonstrates that top down PB can effectively work, if it is accompanied with significant incentives and grants, as well as the extensive autonomy and flexibility of local communities. Polish experience suggests that such an initiative might be relatively successful, yet there is a number of conditions that has to be met in order to ensure the dissemination of legislative model of participatory budgeting. The results have practical implications to central government institutions that consider introduction of some legislative framework for participatory budgeting at the local level. The originality of the research is in the analysis of one of successful stories of the PB introduced via country-wide legislation, and determining when this approach can work, also in other countries

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

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

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