Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.
Maximally Spatial-Disjoint Lightpaths in Optical Networks

Lightpaths enable end-to-end all-optical transmission between network nodes. For survivable routing, traffic is often carried on a primary lightpath, and rerouted to another disjointed backup lightpath in case of the failure of the primary lightpath. Though both lightpaths can be physically disjointed, they can still fail simultaneously if a disaster affects them simultaneously on the physical plane. Hence, we propose a routing algorithm for provisioning a pair of link-disjoint lightpaths between two network nodes such that the minimum spatial distance between them (while disregarding safe regions) is maximized. Through means of simulation, we show that our algorithm can provide higher survivability against spatial-based simultaneous link failures (due to the maximized spatial distance).
Author's Accepted Manuscript Functional Connectivity Decreases in Autism in Emotion, Self, and Face Circuits Identified by Knowledge-based Enrichment Analysis Functional Connectivity Decreases in Autism in Emotion, Self, and Face Circuits Identified by Knowledge-based Enrichment Analysis

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ABSTRACT A powerful new method is described called Knowledge based functional connectivity Enrichment Analysis (KEA) for interpreting resting state functional connectivity, using circuits that are functionally identified using search terms with the Neurosynth database. The method derives its power by focusing on neural circuits, sets of brain regions that share a common biological function, instead of trying to interpret single functional connectivity links. This provides a novel way of investigating how task-or function-related related networks have resting state functional connectivity differences in different psychiatric states, provides a new way to bridge the gap between task and resting-state functional networks, and potentially 1 These authors contributed equally to this work. 2 2 helps to identify brain networks that might be treated. The method was applied to interpreting functional connectivity differences in autism. Functional connectivity decreases at the network circuit level in 394 patients with autism compared with 473 controls were found in networks involving the orbitofrontal cortex, anterior cingulate cortex, middle temporal gyrus cortex, and the precuneus, in networks that are implicated in the sense of self, face processing, and theory of mind. The decreases were correlated with symptom severity.
Hyperspectral measurements of immature Lucilia sericata (Meigen) (Diptera: Calliphoridae) raised on different food substrates

Immature Lucilia sericata (Meigen) raised on beef liver, beef heart, pork liver and pork heart at a mean temperature of 20.6°C took a minimum of 20 days to complete development. Minimum development time differences within stages were observed between the meat types (pork/beef), but not the organ types (liver/heart). Daily hyperspectral measurements were conducted and a functional regression was completed to examine the main effects of meat and organ type on daily spectral measurements. The model examined post feeding larval spectral measurements of insects raised on beef liver alone, the effect of those raised on pork compared with those raised on beef, the effect of those raised on heart compared with those raised on liver and the interactional effect of those raised on pork heart compared with those raised on beef liver. The analyses indicated that the spectral measurements of post feeding L. sericata raised on pork and beef organs (liver and heart) are affected by the meat and organ type.
Evaluating recommender systems

Recommender systems now tend to gain popularity and significance. The proliferation of many recommender systems leads to the difficulty of locating a good recommender system. The algorithms contained in the recommender system determine the efficiency of the recommender systems. The question now is to find the most appropriate algorithms to meet users' needs. So far, the research carried out has focused on improving the accuracy of recommender systems. In this paper, we propose that the recommender system should move beyond the conventional accuracy criteria and take some other criteria into account, such as coverage, diversity, serendipity, scalability, adaptability, risk, novelty and so on. Experimental results with data from VELO indicate that people with different interest degree tend to prefer different algorithms; thus the use of various evaluation criteria to judge the performance of algorithm is meaningful.
GNSS Multi-Frequency Multi-System Highly Robust Differential Positioning Based on an Autonomous Fault Detection and Exclusion Method

GNSS multi-frequency multi-system carrier phase differential positioning has become the main technology used in high-precision positioning. Until recently, the fault detection and exclusion (FDE) methods for multi-frequency multi-system carrier phase differential positioning mostly focus on procession of errors in the carrier phase domain, which cannot exclude all the faults causing a faulted baseline resolution, e.g., a fault that occurs in resolution process. Besides, the multi-fault of multi-frequency in the carrier phase domain cannot be identified due to the multi-frequency carrier phase observation errors’ high correlation. We present a method of autonomous FDE based on multi-frequency multi-system carrier phase differential positioning. It focuses on the procession of errors in the position domain, and detects and excludes the faults in different frequency baseline resolutions caused not only by measurement fault as traditional method, but also the resolution fault, which can enhance the robustness and accuracy of the differential positioning system. The experimental results show that the method can effectively detect and exclude the failure of different frequency baseline resolutions and then the accurate multi-frequency multi-system positioning results can subsequently be effectively fused. The proposed method improves the accuracy and robustness of the differential positioning system.
Improved image alignment method in application to X-ray images and biological images

MOTIVATION Alignment of medical images is a vital component of a large number of applications throughout the clinical track of events; not only within clinical diagnostic settings, but prominently so in the area of planning, consummation and evaluation of surgical and radiotherapeutical procedures. However, image registration of medical images is challenging because of variations on data appearance, imaging artifacts and complex data deformation problems. Hence, the aim of this study is to develop a robust image alignment method for medical images. RESULTS An improved image registration method is proposed, and the method is evaluated with two types of medical data, including biological microscopic tissue images and dental X-ray images and compared with five state-of-the-art image registration techniques. The experimental results show that the presented method consistently performs well on both types of medical images, achieving 88.44 and 88.93% averaged registration accuracies for biological tissue images and X-ray images, respectively, and outperforms the benchmark methods. Based on the Tukey's honestly significant difference test and Fisher's least square difference test tests, the presented method performs significantly better than all existing methods (P ≤ 0.001) for tissue image alignment, and for the X-ray image registration, the proposed method performs significantly better than the two benchmark b-spline approaches (P < 0.001). AVAILABILITY The software implementation of the presented method and the data used in this study are made publicly available for scientific communities to use (http://www-o.ntust.edu.tw/∼cweiwang/ImprovedImageRegistration/). CONTACT cweiwang@mail.ntust. edu.tw.
Learning Asynchronous Typestates for Android Classes

In event-driven programming frameworks, such as Android, the client and the framework interact using callins (framework methods that the client invokes) and callbacks (client methods that the framework invokes). The protocols for interacting with these frameworks can often be described by finite-state machines we dub *asynchronous typestates*. Asynchronous typestates are akin to classical typestates, with the key difference that their outputs (callbacks) are produced asynchronously. We present an algorithm to infer asynchronous typestates for Android framework classes. It is based on the L* algorithm that uses membership and equivalence queries. We show how to implement these queries for Android classes. Membership queries are implemented using testing. Under realistic assumptions, equivalence queries can be implemented using membership queries. We provide an improved algorithm for equivalence queries that is better suited for our application than the algorithms from literature. Instead of using a bound on the size of the typestate to be learned, our algorithm uses a *distinguisher bound*. The distinguisher bound quantifies how two states in the typestate are locally different. We implement our approach and evaluate it empirically. We use our tool, Starling, to learn asynchronous typestates for Android classes both for cases where one is already provided by the documentation, and for cases where the documentation is unclear. The results show that Starling learns asynchronous typestates accurately and efficiently. Additionally, in several cases, the synthesized asynchronous typestates uncovered surprising and undocumented behaviors.
The lysine requirement and ratio of total sulfur amino acids to lysine for chicks fed adequate or inadequate lysine.

Experiments (Exp.) were conducted with Cornish Rock males (4 to 14 or 15 d of age) to determine the Lys requirement (Exp. 1) and the optimum ratio of TSAA:Lys for chicks fed adequate or inadequate Lys (Exp. 2). In Exp. 1, 180 chicks were allotted on the basis of BW to six treatments with six replications of five chicks each in a completely randomized design (CRD). Average initial and final BW were 73.5 and 415.5 g. The Lys levels fed were: 0.8, 0.9, 1.0, 1.1, 1.2, and 1.3% digestible Lys. In Exp. 2, 240 chicks were allotted on the basis of BW to 12 treatments with four replications of five chicks each in a CRD. Average initial and final BW were 68.5 and 336.3 g. Chicks were fed either 0.82 or 1.0% digestible Lys and within each Lys level, a ratio of TSAA:Lys of: 0.55, 0.63, 0.72, 0.80, 0.88, and 0.96, resulting in a 2 x 6 factorial arrangement of treatments. At the end of each trial, all chicks were weighed and pen feed consumption was measured. In Exp. 1, average daily gain (ADG) and gain:feed (GF) increased (linear, P < 0.01; quadratic, P < 0.02) as dietary Lys increased. A cubic (P < 0.04) effect of Lys for average daily feed intake (ADFI) was observed. One-slope, broken-line regression models estimated Lys requirements of 1.0, 0.9, and 1.1% for ADG, ADFI, and GF, respectively. In Exp. 2, chicks fed 1.0% Lys had higher (P < 0.01) ADG, ADFI, and GF than chicks fed 0.82% Lys. Daily gain, ADFI, and GF increased (linear, P < 0.01; quadratic, P < 0.01) as TSAA:Lys increased. For ADG, ADFI, and GF, one-slope, broken-line regression models estimated required ratios of TSAA:Lys of 0.66, 0.71, and 0.63 for chicks fed 1.0% Lys and 0.66, 0.67, and 0.63 for chicks fed 0.82% Lys. There were no differences (P > 0.05) in the estimated ratios of TSAA:Lys required to maximize ADG, ADFI, and GF for chicks fed 0.82 and 1.0% Lys. Thus, similar ratios of an indispensable amino acid to Lys can be obtained when chicks are fed at or slightly below their Lys requirement.
Oxidative Stress–Related Molecular Biomarker Candidates for Glaucoma

Purpose Glaucoma-related molecular biomarkers can improve clinical testing to diagnose the disease early, predict its prognosis, and monitor treatment responses. Based on the evidence of increased oxidative stress in glaucomatous tissues, this study analyzed oxidative stress–related biomarker candidates in blood and aqueous humor samples with or without glaucoma. Methods The blood and aqueous humor samples collected from carefully selected groups of 96 patients with glaucoma and 64 healthy subjects without glaucoma were included in the study. The samples were analyzed for protein carbonyls and advanced glycation end products (AGEs) through ELISA-based quantification assays. To allow proper comparisons, the Goldmann-Witmer coefficient that reflects the ratio of aqueous humor to blood values corrected to total protein concentration in individual samples was calculated. Results Blood and aqueous humor levels of protein carbonyls and AGEs were found significantly higher in glaucomatous samples compared with age-matched nonglaucomatous controls (P < 0.001). The glaucoma-related increase in protein carbonyls and AGEs was more prominent in aqueous humor samples than blood samples (2.6-fold versus 1.9-fold for protein carbonyls, and 3.1-fold versus 1.9-fold for AGEs; P < 0.001). Comparison of the Goldmann-Witmer coefficients indicated greater values for protein carbonyls (1.37 ± 0.3 vs. 3.07 ± 0.8) and AGEs (1.2 ± 0.3 vs. 3.2 ± 1.1) in the glaucoma group (P < 0.001). Conclusions Findings of this study encourage further validation studies of oxidative stress–related biomarkers in glaucoma. Analysis of protein carbonyls and AGEs in longitudinal studies of larger and heterogeneous patient cohorts should better assess the value of these promising candidates as molecular biomarkers of glaucoma for clinical predictions.
5-Azacytidine prevents relapse and produces long-term complete remissions in leukemia xenografts treated with Moxetumomab pasudotox

Significance Moxetumomab pasudotox is a fusion protein of an anti-CD22 Fv and Pseudomonas exotoxin. It is highly active against leukemia in vitro but acute lymphoblastic leukemia (ALL) patients often are resistant. Studies with cultured cells showed resistance is caused by reduced diphthamide, the intracellular target of Pseudomonas exotoxin, but diphthamide is not reduced in most cells from most ALL patients. To study how resistance develops in animals, we injected ALL cells into mice and found that resistant cells occur in discrete bone marrow niches and contain major chromosomal and transcriptional changes. Mice pretreated with 5-azacytidine show greatly improved responses, supporting a trial of the combination in leukemia patients. Moxetumomab pasudotox (Moxe) is a chimeric protein composed of an anti-CD22 Fv fused to a portion of Pseudomonas exotoxin A and kills CD22-expressing leukemia cells. It is very active in hairy-cell leukemia, but many children with relapsed/refractory acute lymphoblastic leukemia (ALL) either respond transiently or are initially resistant. Resistance to Moxe in cultured cells is due to low expression of diphthamide genes (DPH), but only two of six ALL blast samples from resistant patients had low DPH expression. To develop a more clinically relevant model of resistance, we treated NSG mice bearing KOPN-8 or Reh cells with Moxe. More than 99.9% of the cancer cells were killed by Moxe, but relapse occurred from discrete bone marrow sites. The resistant cells would no longer grow in cell culture and showed major chromosomal changes and changes in phenotype with greatly decreased CD22. RNA deep sequencing of resistant KOPN-8 blasts revealed global changes in gene expression, indicating dedifferentiation toward less-mature B cell precursors, and showed an up-regulation of myeloid genes. When Moxe was combined with 5-azacytidine, resistance was prevented and survival increased to over 5 months in the KOPN-8 model and greatly improved in the Reh model. We conclude that Moxe resistance in mice is due to a new mechanism that could not be observed using cultured cells and is prevented by treatment with 5-azacytidine.
Secure Detection of Image Manipulation by Means of Random Feature Selection

We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector. Specifically, we assume that the attacker knows the architecture of the detector, the training data, and the class of features $\mathcal V$ the detector can rely on. In order to get an advantage in his race of arms with the attacker, the analyst designs the detector by relying on a subset of features chosen at random in $\mathcal V$ . Given its ignorance about the exact feature set, the adversary attacks a version of the detector based on the entire feature set. In this way, the effectiveness of the attack diminishes since there is no guarantee that attacking a detector working in the full feature space will result in a successful attack against the reduced-feature detector. We theoretically prove that, thanks to random feature selection, the security of the detector significantly increases at the expense of a negligible loss of performance in the absence of attacks. We also provide an experimental validation of the proposed procedure by focusing on the detection of two specific kinds of image manipulations, namely adaptive histogram equalization and median filtering. The experiments confirm the gain in security at the expense of a negligible loss of performance in the absence of attacks.
Integrated analysis of copy number alterations and gene expression: a bivariate assessment of equally directed abnormalities

MOTIVATION The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material). RESULTS In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in GE and CN data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches. AVAILABILITY Code and data available as R packages edira and ediraAMLdata from http://www.statistik. tu-dortmund.de/~schaefer/ CONTACT martin.schaefer@udo.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach

Background Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance. Methods/Results 137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI<25 with absence of NAFLD (G1), n = 82; BMI>30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size <0.2), but did predict obesity phenotype. Conclusion Family history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD.