361 research outputs found
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
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