Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative study of retinal vessel segmentation methods on a new publicly available database

In this work we compare the performance of a number of vessel segmentation algorithms on a newly constructed retinal vessel image database. Retinal vessel segmentation is important for the detection of numerous eye diseases and plays an important role in automatic retinal disease screening systems. A large number of methods for retinal vessel segmentation have been published, yet an evaluation of these methods on a common database of screening images has not been performed. To compare the performance of retinal vessel segmentation methods we have constructed a large database of retinal images. The database contains forty images in which the vessel trees have been manually segmented. For twenty of those forty images a second independent manual segmentation is available. This allows for a comparison between the performance of automatic methods and the performance of a human observer. The database is available to the research community. Interested researchers are encouraged to upload their segmentation results to our website (http://www.isi.uu.nl/Research/Databases). The performance of five different algorithms has been compared. Four of these methods have been implemented as described in the literature. The fifth pixel classification based method was developed specifically for the segmentation of retinal vessels and is the only supervised method in this test. We define the segmentation accuracy with respect to our gold standard as the performance measure. Results show that the pixel classification method performs best, but the second observer still performs significantly better.
Spectra and Stability of Spatially Periodic Pulse Patterns II: The Critical Spectral Curve

In the stability analysis of pulse patterns in singularly perturbed reaction-diffusion systems, the scale separation is often exploited to reduce complexity. There are various methods to decompose the spectrum of the linearization about the pattern into slow and fast pieces and thus gain control over the spectrum in the asymptotic limit. These reduction methods have been developed in prototype models such as the FitzHugh--Nagumo, Gray--Scott, and Gierer--Meinhardt equations, which are of slowly linear nature; i.e., the slow dynamics away from the pulses are driven by linear equations. Recently, these methods were extended to homoclinic pulses in a general, slowly nonlinear class of reaction-diffusion systems. Yet, a straightforward extension to periodic pulse solutions is impossible due to two major obstructions. First, the reduction methods that lead to asymptotic spectral control for the homoclinic pulses fail for periodic patterns in slowly nonlinear systems. Second, there is a curve of spectrum attach.
A Reweighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction

High density implants such as metals often lead to serious artifacts in the reconstructed CT images which hampers the accuracy of image based diagnosis and treatment planning. In this paper, we propose a novel wavelet frame based CT image reconstruction model to reduce metal artifacts. This model is built on a joint spatial and Radon (projection) domain (JSR) image reconstruction framework with a built-in weighting and re-weighting mechanism in Radon domain to repair degraded projection data. The new weighting strategy used in the proposed model not only makes the regularization in Radon domain by wavelet frame transform more effective, but also makes the commonly assumed linear model for CT imaging a more accurate approximation of the nonlinear physical problem. The proposed model, which will be referred to as the re-weighted JSR model, combines the ideas of the recently proposed wavelet frame based JSR model \cite{Dong2013} and the normalized metal artifact reduction model \cite{meyer2010normalized}, and manages to achieve noticeably better CT reconstruction quality than both methods. To solve the proposed re-weighted JSR model, an efficient alternative iteration algorithm is proposed with guaranteed convergence. Numerical experiments on both simulated and real CT image data demonstrate the effectiveness of the re-weighted JSR model and its advantage over some of the state-of-the-art methods.
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow

For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating these models require parallel data between natural language (NL) and code with fine-grained alignments. StackOverflow (SO) is a promising source to create such a data set: the questions are diverse and most of them have corresponding answers with high-quality code snippets. However, existing heuristic methods (e.g. pairing the title of a post with the code in the accepted answer) are limited both in their coverage and the correctness of the NL-code pairs obtained. In this paper, we propose a novel method to mine high-quality aligned data from SO using two sets of features: hand-crafted features considering the structure of the extracted snippets, and correspondence features obtained by training a probabilistic model to capture the correlation between NL and code using neural networks. These features are fed into a classifier that determines the quality of mined NL-code pairs. Experiments using Python and Java as test beds show that the proposed method greatly expands coverage and accuracy over existing mining methods, even when using only a small number of labeled examples. Further, we find that reasonable results are achieved even when training the classifier on one language and testing on another, showing promise for scaling NL-code mining to a wide variety of programming languages beyond those for which we are able to annotate data.
Using Patch Analysis Methods to Detect Images Tampered with Seam Insertion

Retargeting images by seam carving and seam insertion is hard to identify; therefore, detection of seam tampered images has been an important but challenging research topic. Aside from existing methods, i.e., those derived from steganography attacks and those based on statistical features, we have proposed a novel detection method in our previouswork, referred to as the patch analysis method. This method divides images into 2 × 2 blocks, named as mini-squares, and then searches for one of nine patch types that is likely to recover a mini-square from seam carving. By analyzing the patch transition probability among three-connected mini-squares, we achieved currently best accuracies for detecting seam carved images. Here we extend the application of this method to detect imagestampered with seam insertion. We will present and discuss the experimental results in this paper.
Optimal Control of Partially Observable Piecewise Deterministic Markov Processes

In this paper we consider a control problem for a Partially Observable Piecewise Deterministic Markov Process of the following type: After the jump of the process the controller receives a noisy signal about the state and the aim is to control the process continuously in time in such a way that the expected discounted cost of the system is minimized. We solve this optimization problem by reducing it to a discrete-time Markov Decision Process. This includes the derivation of a filter for the unobservable state. Imposing sufficient continuity and compactness assumptions we are able to prove the existence of optimal policies and show that the value function satisfies a fixed point equation. A generic application is given to illustrate the results.
Physiological and Comparative Genomic Analysis of Arthrobacter sp. SRS-W-1-2016 Provides Insights on Niche Adaptation for Survival in Uraniferous Soils

Arthrobacter sp. strain SRS-W-1-2016 was isolated on high concentrations of uranium (U) from the Savannah River Site (SRS) that remains co-contaminated by radionuclides, heavy metals, and organics. SRS is located on the northeast bank of the Savannah River (South Carolina, USA), which is a U.S. Department of Energy (DOE) managed ecosystem left historically contaminated from decades of nuclear weapons production activities. Predominant contaminants within the impacted SRS environment include U and Nickel (Ni), both of which can be transformed microbially into less toxic forms via metal complexation mechanisms. Strain SRS-W-1-2016 was isolated from the uraniferous SRS soils on high concentrations of U (4200 μM) and Ni (8500 μM), but rapid growth was observed at much lower concentrations of 500 μM U and 1000 μM Ni, respectively. Microcosm studies established with strain SRS-W-1-2016 revealed a rapid decline in the concentration of spiked U such that it was almost undetectable in the supernatant by 72 h of incubation. Conversely, Ni concentrations remained unchanged, suggesting that the strain removed U but not Ni under the tested conditions. To obtain a deeper understanding of the metabolic potential, a draft genome sequence of strain SRS-W-1-2016 was obtained at a coverage of 90×, assembling into 93 contigs with an N50 contig length of 92,788 bases. The genomic size of strain SRS-W-1-2016 was found to be 4,564,701 bases with a total number of 4327 putative genes. An in-depth, genome-wide comparison between strain SRS-W-1-2016 and its four closest taxonomic relatives revealed 1159 distinct genes, representing 26.7% of its total genome; many associating with metal resistance proteins (e.g., for cadmium, cobalt, and zinc), transporter proteins, stress proteins, cytochromes, and drug resistance functions. Additionally, several gene homologues coding for resistance to metals were identified in the strain, such as outer membrane efflux pump proteins, peptide/nickel transport substrate and ATP-binding proteins, a high-affinity nickel-transport protein, and the spoT gene, which was recently implicated in bacterial resistance towards U. Detailed genome mining analysis of strain SRS-W-1-2016 also revealed the presence of a plethora of secondary metabolite biosynthetic gene clusters likely facilitating resistance to antibiotics, biocides, and metals. Additionally, several gene homologous for the well-known oxygenase enzyme system were also identified, potentially functioning to generate energy via the breakdown of organic compounds and thus enabling the successful colonization and natural attenuation of contaminants by Arthrobacter sp. SRS-W-1-2016 at the SRS site.
Approximate Newton-based statistical inference using only stochastic gradients

We present a novel inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion of finite differences and allows the approximation of a Hessian-vector product from first-order information. In theory, our method efficiently computes the statistical error covariance in $M$-estimation, both for unregularized convex learning problems and high-dimensional LASSO regression, without using exact second order information, or resampling the entire data set. In practice, we demonstrate the effectiveness of our framework on large-scale machine learning problems, that go even beyond convexity: as a highlight, our work can be used to detect certain adversarial attacks on neural networks.
Improvement in automated detection of pulmonary nodules on helical x-ray CT images

We previously developed a scheme to automatically detect pulmonary nodules on CT images, as a part of computer-aided diagnosis (CAD) system. The proposed method consisted of two template-matching approaches based on simple models that simulate real nodules. One was a new template-matching technique based on a genetic algorithm (GA) template matching (GATM) for detecting nodules within the lung area. The other one was a conventional template matching along the lung wall [lung wall template matching (LWTM)] for detecting nodules on the lung wall. After the two template matchings, thirteen feature values were calculated and used for eliminating false positives. Twenty clinical cases involving a total of 557 sectional images were applied; 71 nodules out of 98 were correctly detected with the number of false positives at approximately 30.8/case by applying two template matchings (GATM and LWTM) and elimination process of false positives. In this study, five features were newly added, and threshold-values of our previous features were reconsidered for further eliminating false positives. As the result, the number of false positives was decreased to 5.5/case without elimination of true positives.
City-wide building height determination using light detection and ranging data

The research presented in this paper addresses a current gap in the availability of building geometry data and provides estimates of individual building characteristics at city scale. Such data are crucial for a wide range of subjects such as modelling building energy consumption as well as regional housing market studies. However, such data are currently not available in the UK. In this work, a new approach was developed to automatically estimate the geometric characteristics of buildings, including height and floor count. A wide range of datasets have been brought together including high-resolution light detection and ranging data to accurately estimate building elevation and to obtain the external dimension of buildings. In the UK, most of the datasets required for this model are available for urban areas, allowing the model to be widely applied both in cities and beyond. The paper presents the results of building height and floor count determined from this model and compares these with the actual data obtained from a survey of 108 representative buildings in the city of Southampton. The results show good accuracy of the model with 97% of the estimates having an error under ±1 floor and an absolute mean error of 0.3 floors. These results provide confidence in utilising this model for future building studies at a city scale.
Helical cone beam reconstruction using rotated 1D ramp filtering

With the evolution from multi-detector-row CT to cone beam volumetric CT, to maintain reconstruction accuracy becomes more challenging. To combat the severe artifacts caused by a large cone angle, three-dimensional reconstruction algorithms have to be used in CB volumetric CT. In practice, the filtered backprojection (FBP) reconstruction algorithm is the most desirable due to its pipe-line computational structure and image generation efficiency. The first CB-FBP reconstruction algorithm is the well-known FDK algorithm that was originally derived for a circular x-ray source trajectory by heuristically extending its two-dimensional counterpart. Later on, a general CB-FBP reconstruction algorithm was derived for non-circular, such as helical, source trajectories. It has been recognized that, a filtering operation in the projection data along the tangential direction of a helical x-ray source trajectory can significantly improve the reconstruction accuracy of helical CB volumetric CT. However, the tangential filtering encounters latitudinal data truncation, resulting in degraded noise characteristics or data manipulation inefficiency. A CB-FBP reconstruction algorithm using one-dimensional rotational filtering across detector rows (namely CB-RFBP) is proposed in this manuscript. In principal, the proposed CB-RFBP reconstruction algorithm is approximate, but approaches the reconstruction accuracy that can be achieved by exact helical CB-FBP reconstruction algorithms because of the rotational filtering. Unlike exact CB-FBP reconstruction algorithms in which all redundant data are usually discarded to maintain data redundancy, the proposed CB-RFBP reconstruction algorithm makes use of all available projection data, resulting in significantly improved noise characteristics and dose efficiency. Moreover, the rotational filtering across detector rows not only survives the so-called long object problem, but also avoids latitudinal data truncation existing in other helical CB-FBP reconstruction algorithm in which a tangential filtering is carried out, providing improved noise characteristics, dose efficiency or data manipulation efficiency.
Deep Learning Driven Plates Recognition System

Optical character recognition (OCR) represents one of well-studied areas of computer vision and machine learning. Due to the complexity of the problem there is always room for improvement. Usually, there are specialized solutions for certain types of character recognition tasks in many areas of industry: banking, legal, government, education and etc. Authors of this paper propose a new approach of developing the deep learning based method for plate recognition system. Proposed method consists of combining the state-of-the-art deep neural networks for both of the essential tasks: plate detection and character recognition which were trained and validated on artificially generated vehicle plates’ dataset. Complete development process is described in details and a test system is implemented, as well. The proposed system is tested in the real-time environment and achieved overall accuracy is 98% for plate detection, 97% for letters recognition and 94.5% for digit recognition. The proposed system has almost real-time response and it is suitable for image and video based
Cypermethrin Residues in Fresh Vegetables: Detection by HPLC and LC- ESIMS and their Effect on Antioxidant Activity

Cypermethrin, a synthetic pyrethroid, is used as pesticide in large-scale commercial agricultural applications as well as in consumer products for domestic purposes. Fresh vegetables are of the focal points of this study, since they are a significant source of phenolic antioxidants. In this study, we applied the HPLC and LC-ESIMS techniques to detect cypermethrin residues in five fresh vegetables, when applicable with illegal doses by some farmers. For this purpose, samples of fresh bio-vegetables were bought and divided into three groups, one of which was treated with an illegal dose of cypermethrin. All samples (blank, treated with legal and illegal doses) were then cut into small pieces, frozen and lyophilized. Freeze- dried samples were extracted with ethyl acetate, filtered and evaporated till dryness. Interestingly, comparison of antioxidant activity of organic fennel, cabbage, and celery samples which were not exposed to pesticide treatment with treated samples (propylgallate positive control having 100% antioxidant activity) showing a reduction of antioxidant activity for the treated samples which was very obvious in the cabbage sample but slighter in fennel and celery samples. These results indicate a lower nutritional quality of the treated vegetables with illegal doses.
A Clustering with Slope Algorithm based on MapReduce

The clustering with slope (CLOPE) algorithm is widely used to analyze transactional data because of its excellent performance, lower memory cost, and better quality of results compared with other clustering algorithms. However, the running time of the CLOPE algorithm in large datasets may take more than several days, which is unacceptable. To solve the time issue caused by the algorithm’s serial running mode, a new parallel running mode needs to be introduced to the CLOPE algorithm to improve its efficiency. A CLOPE algorithm based on MapReduce is presented in this paper. The new algorithm was run in parallel on a Hadoop cluster with multiple nodes. The Hadoop platform split the large dataset into multiple small data blocks, and the CLOPE algorithm was run on each block to obtain small clusters. The modified cluster-oriented CLOPE algorithm then merged these small clusters to the expected number of clusters. Experiments show that CLOPE based on the MapReduce algorithm runs faster and more efficiently than the CLOPE algorithm and demonstrates the same quality of clustering. Time remained constant against data volume, and time complexity was only affected by the size of the Hadoop cluster. Thus, the proposed algorithm solves the time issue in clustering large datasets and can be utilized to cluster transactional trade data, website logs, DNS query logs in limited time, and even transactional data with high dimension. Subject Categories and Descriptors I.2. 7 [Natural Language Processing]: Text Analysis; 1.5. 4 [Pattern Recognition]: Applications – Information Extraction General Terms: Data Mining, Information Extraction, Knowledge Management
Image Captioning

This paper discusses and demonstrates the outcomes from our experimentation on Image Captioning. Image captioning is a much more involved task than image recognition or classification, because of the additional challenge of recognizing the interdependence between the objects/concepts in the image and the creation of a succinct sentential narration. Experiments on several labeled datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. As a toy application, we apply image captioning to create video captions, and we advance a few hypotheses on the challenges we encountered.
Experimental culture of black tiger shrimp Penaeus monodon Fabricius, 1798 in open sea floating cage

An experiment was designed and conducted to assess the feasibility for culture of Penaeus monodon in 6 m dia HDPE circular floating cage, installed at 10-12 m depth off Visakhapatnam in the Bay of Bengal. The cage was fixed with three cylindrical nets; inner net of 2 mm mesh (6 m dia×5.5 m height) to rear post-larvae from stock to 80th day; middle net of 10 mm mesh (6 m×5.5 m) to rear juveniles from day 81 onwards and an outer net of 40 mm mesh (8 m dia×4.5 m height) to prevent entry of predators. Post-larvae (PL23) of P. monodon (mean total length 16.1±3.8 mm), were stocked in the cage at a density of 1179 PL m-3). Bottom water parameters viz., salinity, temperature, dissolved oxygen, pH and ammonia (NH3-N) recorded in the cage site during the culture period were 34-35 ppt, 26-32°C, 3.9-4.6 ml l-1, 8.1-8.3 and 0.05-0.07 mg l-1, respectively. Shrimp feed varying in size suitable to different growth stages, was used during the trial. Feed was given twice during the first 20 days and thrice during 21-99 days. Feeding rate per day ranged from 133.3% of the biomass of shrimp at stocking time to 3.1% of the biomass of shrimp at preharvest. P. monodon registered a growth rate of 0.87 mm (TL)/0.018 g (wt) per day during first 30 days; 0.89 mm/0.069 g per day during 31-60 days and 0.37 mm/0.051 g per day during 61-90 days. On day 100, about 207 kg of shrimp (production rate, 1.62 kg m-3) was harvested and the mean size of harvested shrimp was 81.54±15.20 mm TL/4.50±2.80 g wt. Survival recorded was 31.4% and the feed conversion ratio (FCR) was 3.97. Present study demonstrated the possibility for culture of P. monodon in open sea floating cage. The possible causes for low survival and high FCR are discussed.
Deanonymisation in Ethereum Using Existing Methods for Bitcoin February 7 , 2018

This paper aims to apply two existing deanonymisation techniques developed for the Bitcoin network to Ethereum. Ethereum is a blockchain based platform like Bitcoin, but there are some significant differences with regard to their peer-to-peer network and blockchain structure. The first attack evaluated in this paper is an attempt to reveal IP addresses of Bitcoin users by monitoring the peer-to-peer network. The second attack aims to cluster groups of addresses belonging to the same user by analysing the Bitcoin blockchain. It proves difficult to apply these methods to Ethereum due to the differences between both networks in the volatility of entry nodes and the way transactions are handled. However, by exploiting some of the specifics of the Ethereum network, such as the peer selection protocol or the bootnodes, similar attacks are potentially feasible.