Optimization of XNOR Convolution for Binary Convolutional Neural Networks on GPU

Binary convolutional networks have lower computational load and lower memory foot-print compared to their full-precision counterparts. So, they are a feasible alternative for the deployment of computer vision applications on limited capacity embedded devices. Once trained on less resource-constrained computational environments, they can be deployed for real-time inference on such devices. In this study, we propose an implementation of binary convolutional network inference on GPU by focusing on optimization of XNOR convolution. Experimental results show that using GPU can provide a speed-up of up to $42.61\times$ with a kernel size of $3\times3$. The implementation is publicly available at https://github.com/metcan/Binary-Convolutional-Neural-Network-Inference-on-GP

Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation

Endoscopic imaging is commonly used to diagnose Ulcerative Colitis (UC) and classify its severity. It has been shown that deep learning based methods are effective in automated analysis of these images and can potentially be used to aid medical doctors. Unleashing the full potential of these methods depends on the availability of large amount of labeled images; however, obtaining and labeling these images are quite challenging. In this paper, we propose a active learning based generative augmentation method. The method involves generating a large number of synthetic samples by training using a small dataset consisting of real endoscopic images. The resulting data pool is narrowed down by using active learning methods to select the most informative samples, which are then used to train a classifier. We demonstrate the effectiveness of our method through experiments on a publicly available endoscopic image dataset. The results show that using synthesized samples in conjunction with active learning leads to improved classification performance compared to using only the original labeled examples and the baseline classification performance of 68.1% increases to 74.5% in terms of Quadratic Weighted Kappa (QWK) Score. Another observation is that, attaining equivalent performance using only real data necessitated three times higher number of images.Comment: 6 pages, 3 figures, to be published in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 202

Assessment of Water Quality in Brackish Lake Bafa (Muğla, Turkey) by Using Multivariate Statistical Techniques

Lake Bafa is one of the biggest lake in the western part of Turkey. It has a great importance in terms of both historical and biodiversity. Lake Bafa which was fed by Büyük Menderes River has become an area where the pollutant factors carried by the river accumulated over time. In this context, the complex physicochemical characteristics of Lake Bafa were evaluated and water quality classes were determined in order to constitute a monitoring pattern. In this research, the water samples were taken monthly from eight different stations located in the Lake Bafa during the two years study period (2015-2017). Total of 22 water quality parameters including atmospheric pressure, temperature, pH, electrical conductivity, total dissolution solids, salinity, turbidity, dissolved oxygen, oxygen saturation, biological oxygen demand, chemical oxygen demand, ammonium, nitrate, nitrite, calcium, sodium, magnesium, potassium, chloride, sulphate total phosphorus and total nitrogen were investigated in water samples. The data obtained were statistically evaluated by using Principal Component Analysis (PCA), Cluster Analysis (CA) and Pearson Correlation Analysis and compared with the limit values reported by various national and international organizations. According to the results PCA, three factors (PCA 1, PCA 2 and PCA 3) explained 79.05% of the total variance while CA results exhibited three statistically significant clusters. Overall the results suggested that Lake Bafa has been exposed to high amount of pollution and it is generally classified in “Class III-IV” water quality level based on both Klee’s method and Turkish Water Pollution Control Regulation

The determination of Water Quality in Balaban Lake (West Anatolia of Turkey)with Trophic State Indices

In this study, which was carried out between October 2019- August 2020, totally five stations were chosen from the research area. Water samples (for the analysis of total phosphorus and chlorophyll-a) were taken seasonally and also Secchi disc depth and chlorophyll-a were measured from these stations. Carlson’s Trophic State Index, OECD criteria and value range of the Turkish Water Quality Regulation were used for the determination of the trophic status of the study area. According to obtained data, the studied area of Balaban Lake has mesotrophic character according to chlorophyll a, total phosphorus, and Secchi disc depth. At the end of the study, it was determined that the Balaban Lake was at the mesotrophic level according to the Carlson trophic status index, the average±1SD interval of the OECD criteria, and Turkish Water Quality Regulation

Comparative analysis of benthic macroinvertebrate-based biotic and diversity indices used to evaluate the water quality of Kozluoluk Stream (West Anatolia of Turkey)

Freshwater ecosystems are vitally important which supports biological diversity. With this aim, a total of eight biotic and three species diversity indices were used to determine water quality of Kozluoluk Stream in West Anatolia of Turkey. The biotic indices were: Saprobi (SI), Biological Monitoring Working Party (BMWP-O, BMWP-S and BMWP-G), Average Score per Taxon (ASPT), Family Biotic Index (FBI), Belgian Biotic Index (BBI), EPT-Taxa [%], and species diversity indices consisted of: Shannon–Weaver (SWDI), Simpsons (SDI) and Margalef (MDI). Principal component analysis (PCA) was applied to the physicochemical and biotic dataset. Similarities between the sampling stations were clustered by using cluster analysis (CLUS). Pearson-based correlations were used to determine which index is more suitable in determining water quality of the stream. The nine taxonomic groups were found in Kozluoluk Stream consisting of Amphipoda, Oligochaeta, Gastropoda, Ephemeroptera, Plecoptera, Trichoptera, Odonata, Coleoptera and Diptera. The 1st and 2nd stations (90%) were the most similar stations in terms of benthic macroinvertebrate species distribution. The results indicate that the ASPT, BBI, BMWP-O, BMWP-S, BMWP-G and EPT-Taxa [%] are more proper than FBI and SI indices to determine the water quality of Kozluoluk Stream. The water quality along the stream varied from good class in upstream stations, to moderate in downstream stations. This study clearly showed that the specific biotic index according to the ecological characteristics of Turkey should be developed

Endoscopic artefact detection with ensemble of deep neural networks and false positive elimination

Video frames obtained through endoscopic examination can be corrupted by many artefacts. These artefacts adversely affect the diagnosis process and make the examination of the underlying tissue difficult for the professionals. In addition, detection of these artefacts is essential for further automated analysis of the images and high-quality frame restoration. In this study, we propose an endoscopic artefact detection framework based on an ensemble of deep neural networks, classagnostic non-maximum suppression, and false-positive elimination. We have used different ensemble techniques and combined both one-stage and two-stage networks to have a heterogeneous solution exploiting the distinctive properties of different approaches. Faster R-CNN, Cascade R-CNN, which are two-stage detector, and RetinaNet, which is single-stage detector, have been used as base models. The best results have been obtained using the consensus of their predictions, which were passed through class-agnostic non-maximum suppression, and false-positive elimination

Assessment of the ecological and trophic status of Lake Bafa (Turkey) based on phytoplankton

Phytoplankton groups are one of the major quality element to be used in the evaluation of the trophic and ecological state of freshwater ecosystems according to the EU Water Framework Directive. This research was made to assess the trophic and ecological status of Lake Bafa in Turkey, on the basis of phytoplankton communities. Buyuk Menderes River is one of the most important factor that carries pollutants to Lake Bafa. The eight sampling station were assigned to evaluate the ecological and trophic state of the lake. Phytoplankton species were collected monthly for 2 years study period. Most commonly used phtoplankton indices Q index and Carlson's Trophic State Index (TSI), and different versions of diversity indices were used to estimate trophic and ecological state of the lake. Similarities between the sampling stations were clustured by using the unweighted pair group method using arithmetic average (UPGMA), based on phytoplankton communities. Correlations between the applied indices were determined by using Pearson Correlation. After the identification of collected phytoplanktons, total of 63 taxa which belong to classis of Cyanophyceae (11.2%), Bacillariophyceae (49.2%), Chlorophyceae (23.8%), Xanthophyceae (1.5%), Euglenophyceae (11.2%) and Dinophyceae (3.1%) were detected. The 1st and 2nd stations were the most similar stations to each other (88%) according to phytoplankton communities. Secchi disc depth (SD) and TP played an important role in the distribution of phytoplankton species in Lake Bafa. The highest significant positive correlation was determined between Q and TSI (r = 0.987, p0.01). Considering the TDI values in the phytoplankton composition of the lake, it can be said that although the productivity status of the studied lake is still mesotrophic, it has a tendency towards eutrophic state. According to the Q values, the first five stations reflect the moderate ecological state, while the 6th, 7th and 8th stations represent the poor ecological state.This research was supported by Scientific and Technological Research Council of Turkey (TUBITAK, Project no: 114Y249)Scientific and Technological Research Council of Turkey (TUBITAK) [114Y249

A Combination of Heart Rate-Corrected QT Interval and GRACE Risk Score Better Predict Early Mortality in Patients with Non-ST Segment Elevation Acute Coronary Syndrome

Objective: This study aimed to evaluate whether the addition of heart rate-corrected QT interval prolongation to the Global Registry of Acute Coronary Events risk score improves the predictive value for early mortality in patients with non-ST segment elevation acute coronary syndrome