Obywatelska inicjatywa uchwałodawcza mieszkańców jednostek samorządu terytorialnego w Polsce

One of the consequences of the political changes in Poland after 1989 was granting citizens the right to initiate legal acts. It has activated society’s participation in public life and contributed to building a civil society. In 1994, a citizens’ constitutional initiative was established, and, in 1997, a citizens’ legislative initiative was undertaken. The aim of the study was to present the citizens’ initiative of resolution of the inhabitants of local government units in Poland. Originally, it functioned without a statutory basis and was established by the local government units themselves. This raised doubts as to its legitimacy, which was also reflected in the judgments of voivodeship administrative courts. The practice was in favor of its universal establishment, and it also became increasingly popular, especially in communes. In 2018, all local government laws were amended to grant the residents of all local government units in Poland the option of submitting a citizens’ initiative, and it is only up to their activity whether they will exercise this right.Jedną z konsekwencji przemian ustrojowych w Polsce po 1989 r. było przyznanie obywatelom prawa do inicjowania aktów prawnych. Zaktywizowało to udział społeczeństwa w życiu publicznym i przyczyniło się do budowy społeczeństwa obywatelskiego. W 1994 r. ustanowiono obywatelską inicjatywę konstytucyjną, a w 1997 r. – obywatelską inicjatywę ustawodawczą. Celem opracowania było przedstawienie obywatelskiej inicjatywy uchwałodawczej mieszkańców jednostek samorządu terytorialnego w Polsce. Pierwotnie funkcjonowała ona bez podstawy ustawowej i została wprowadzona przez same jednostki samorządu terytorialnego. Budziło to wątpliwości co do jej zasadności, co znalazło odzwierciedlenie również w orzecznictwie wojewódzkich sądów administracyjnych. Praktyka sprzyjała jej powszechnemu ustanowieniu, a także stawała się coraz bardziej popularna, zwłaszcza w gminach. W 2018 r. wszystkie ustawy samorządowe zostały znowelizowane tak, aby dać mieszkańcom wszystkich jednostek samorządu terytorialnego w Polsce możliwość zgłoszenia inicjatywy obywatelskiej i tylko od ich aktywności zależy, czy z tego prawa skorzystają

Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays

Recent advancements in computer vision promise to automate medical image analysis. Rheumatoid arthritis is an autoimmune disease that would profit from computer-based diagnosis, as there are no direct markers known, and doctors have to rely on manual inspection of X-ray images. In this work, we present a multi-task deep learning model that simultaneously learns to localize joints on X-ray images and diagnose two kinds of joint damage: narrowing and erosion. Additionally, we propose a modification of label smoothing, which combines classification and regression cues into a single loss and achieves 5% relative error reduction compared to standard loss functions. Our final model obtained 4th place in joint space narrowing and 5th place in joint erosion in the global RA2 DREAM challenge.Comment: Presented at the Workshop on AI for Public Health at ICLR 202

Various preconcentrator structures for determination of acetone in a wide range of concentration

In this paper, the investigation results on preconcentration of acetone at various initial concentrations are presented. The structures were made of conventional materials, such as stainless steel, quartz tube as well as fabricated in MEMS technology - micropreconcentrators. All structures have the same ‘active’ area to obtain more suitable comparison. The adsorbent materials were selected from commercial available Sigma-Aldrich Carbon Adsorbent Sampler Kit, consisting of 8 various adsorbents. The highest concentration factors were obtained by utilization of micropreconcentrator filled with Carboxen-1018, which is recommended for adsorption of C2-C3 compounds. The preconcentrators were placed into microsystem, and semiconductor gas sensor array was used as a detector unit. The microsystem was previously tested and designed for exhaled breath acetone analysis. The obtained results show that micropreconcentrator can be a useful tool for an increasing sensor sensitivity

Retro-fallback: retrosynthetic planning in an uncertain world

Retrosynthesis is the task of proposing a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by the algorithm may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms.Comment: 39 pages (including appendices). Currently undergoing peer revie

Holistic Multi-View Building Analysis in the Wild with Projection Pooling

We address six different classification tasks related to fine-grained building attributes: construction type, number of floors, pitch and geometry of the roof, facade material, and occupancy class. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. To this end, we introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases various real-world challenges, such as occlusions, blur, partially visible objects, and a broad spectrum of buildings. We propose a new projection pooling layer, creating a unified, top-view representation of the top-view and the side views in a high-dimensional space. It allows us to utilize the building and imagery metadata seamlessly. Introducing this layer improves classification accuracy -- compared to highly tuned baseline models -- indicating its suitability for building analysis.Comment: Accepted for publication at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Re-evaluating Retrosynthesis Algorithms with Syntheseus

The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques. To remedy this, we present a benchmarking library called syntheseus which promotes best practice by default, enabling consistent meaningful evaluation of single-step and multi-step retrosynthesis algorithms. We use syntheseus to re-evaluate a number of previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes when evaluated carefully. We end with guidance for future works in this area

Retrosynthetic Planning with Dual Value Networks

Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design. Recently, the combination of ML-based single-step reaction predictors with multi-step planners has led to promising results. However, the single-step predictors are mostly trained offline to optimize the single-step accuracy, without considering complete routes. Here, we leverage reinforcement learning (RL) to improve the single-step predictor, by using a tree-shaped MDP to optimize complete routes. Specifically, we propose a novel online training algorithm, called Planning with Dual Value Networks (PDVN), which alternates between the planning phase and updating phase. In PDVN, we construct two separate value networks to predict the synthesizability and cost of molecules, respectively. To maintain the single-step accuracy, we design a two-branch network structure for the single-step predictor. On the widely-used USPTO dataset, our PDVN algorithm improves the search success rate of existing multi-step planners (e.g., increasing the success rate from 85.79% to 98.95% for Retro*, and reducing the number of model calls by half while solving 99.47% molecules for RetroGraph). Additionally, PDVN helps find shorter synthesis routes (e.g., reducing the average route length from 5.76 to 4.83 for Retro*, and from 5.63 to 4.78 for RetroGraph).Comment: Accepted to ICML 202