Factorization of Joint Probability Mass Functions into Parity Check Interactions

We show that any joint probability mass function (PMF) can be expressed as a product of parity check factors and factors of degree one with the help of some auxiliary variables, if the alphabet size is appropriate for defining a parity check equation. In other words, marginalization of a joint PMF is equivalent to a soft decoding task as long as a finite field can be constructed over the alphabet of the PMF. In factor graph terminology this claim means that a factor graph representing such a joint PMF always has an equivalent Tanner graph. We provide a systematic method based on the Hilbert space of PMFs and orthogonal projections for obtaining this factorization.Comment: 5 pages, 1 figures, appeared in the proceedings of ISIT 2009; Changed content, more recent version than as appeared in the proceeding

Detecting and Classifying Nuclei on a Budget

The benefits of deep neural networks can be hard to realise in medical imaging tasks because training sample sizes are often modest. Pre-training on large data sets and subsequent transfer learning to specific tasks with limited labelled training data has proved a successful strategy in other domains. Here, we implement and test this idea for detecting and classifying nuclei in histology, important tasks that enable quantifiable characterisation of prostate cancer. We pre-train a convolutional neural network for nucleus detection on a large colon histology dataset, and examine the effects of fine-tuning this network with different amounts of prostate histology data. Results show promise for clinical translation. However, we find that transfer learning is not always a viable option when training deep neural networks for nucleus classification. As such, we also demonstrate that semi-supervised ladder networks are a suitable alternative for learning a nucleus classifier with limited data

Matematičko modeliranje u svrhu predviđanja i optimiranja zavarivačke kupke kod TIG zavarivanja

In this work, nonlinear and multi-objective mathematical models were developed to determine the process parameters corresponding to optimum weld pool geometry. The objectives of the developed mathematical models are to maximize tensile load (TL), penetration (P), area of penetration (AP) and/or minimize heat affected zone (HAZ), upper width (UW) and upper height (UH) depending upon the requirements.Razvijeni su nelinearni i multi-objektni matematički modeli da bi odredili parametre s optimalnom geometrijom zavarivačke kupke. Cilj razvijanja matematičkih modela je postići maksimalna vlačna čvrstoća, penetracija, područje pretaljivanja i/ili minimalna zona utjecaja topline, širina i nadvišenje zavara ovisno o postavljenim zahtjevima

In situ functionalization of a cellulosic-based activated carbon with magnetic iron oxides for the removal of carbamazepine from wastewater

The main goal of this work was to produce an easily recoverable waste-based magnetic activated carbon (MAC) for an efficient removal of the antiepileptic pharmaceutical carbamazepine (CBZ) from wastewater. For this purpose, the synthesis procedure was optimized and a material (MAC4) providing immediate recuperation from solution, remarkable adsorptive performance and relevant properties (specific surface area of 551 m2 g-1 and saturation magnetization of 39.84 emu g-1) was selected for further CBZ kinetic and equilibrium adsorption studies. MAC4 presented fast CBZ adsorption rates and short equilibrium times (< 30-45 min) in both ultrapure water and wastewater. Equilibrium studies showed that MAC4 attained maximum adsorption capacities (qm) of 68 ± 4 mg g-1 in ultrapure water and 60 ± 3 mg g-1 in wastewater, suggesting no significant interference of the aqueous matrix in the adsorption process. Overall, this work provides evidence of potential application of a waste-based MAC in the tertiary treatment of wastewaters.publishe

The KNee OsteoArthritis Prediction (KNOAP2020) challenge:An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.</p

Kinetic, Isotherm and Thermodynamic Analysis on Adsorption of Cr(VI) Ions from Aqueous Solutions by Synthesis and Characterization of Magnetic-Poly(divinylbenzene-vinylimidazole) Microbeads

The magnetic-poly(divinylbenzene-1-vinylimidazole) [m-poly(DVB-VIM)] microbeads (average diameter 53–212 μm) were synthesized and characterized; their use as adsorbent in removal of Cr(VI) ions from aqueous solutions was investigated. The m-poly(DVB-VIM) microbeads were prepared by copolymerizing of divinylbenzene (DVB) with 1-vinylimidazole (VIM). The m-poly(DVB-VIM) microbeads were characterized by N2 adsorption/desorption isotherms, ESR, elemental analysis, scanning electron microscope (SEM) and swelling studies. At fixed solid/solution ratio the various factors affecting adsorption of Cr(VI) ions from aqueous solutions such as pH, initial concentration, contact time and temperature were analyzed. Langmuir, Freundlich and Dubinin–Radushkvich isotherms were used as the model adsorption equilibrium data. Langmuir isotherm model was the most adequate. The pseudo-first-order, pseudo-second-order, Ritch-second-order and intraparticle diffusion models were used to describe the adsorption kinetics. The apparent activation energy was found to be 5.024 kJ mol−1, which is characteristic of a chemically controlled reaction. The experimental data fitted to pseudo-second-order kinetic. The study of temperature effect was quantified by calculating various thermodynamic parameters such as Gibbs free energy, enthalpy and entropy changes. The thermodynamic parameters obtained indicated the endothermic nature of adsorption of Cr(VI) ions. Morever, after the use in adsorption, the m-poly(DVB-VIM) microbeads with paramagnetic property were separeted via the applied magnetic force. The magnetic beads could be desorbed up to about 97% by treating with 1.0 M NaOH. These features make the m-poly(DVB-VIM) microbeads a potential candidate for support of Cr(VI) ions removal under magnetic field