Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images

A variety of deep neural networks have been applied in medical image segmentation and achieve good performance. Unlike natural images, medical images of the same imaging modality are characterized by the same pattern, which indicates that same normal organs or tissues locate at similar positions in the images. Thus, in this paper we try to incorporate the prior knowledge of medical images into the structure of neural networks such that the prior knowledge can be utilized for accurate segmentation. Based on this idea, we propose a novel deep network called knowledge-based fully convolutional network (KFCN) for medical image segmentation. The segmentation function and corresponding error is analyzed. We show the existence of an asymptotically stable region for KFCN which traditional FCN doesn't possess. Experiments validate our knowledge assumption about the incorporation of prior knowledge into the convolution kernels of KFCN and show that KFCN can achieve a reasonable segmentation and a satisfactory accuracy.
Multi atlas based segmentation: should we prefer the best atlas group over the group of best atlases?

Multi atlas based segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases. Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split into two groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle selection, where atlases are chosen by the highest dice similarity coefficient (DSC) with the target, was performed. This was compared to oracle group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, convolutional neural networks were designed to predict the best group of atlases. The results were also compared with the selection strategy based on normalized mutual information (NMI). Oracle group was proven to be significantly better than classic oracle selection (p < 10−5). Atlas group selection led to a median ± interquartile DSC of 0.740 ± 0.084, 0.718 ± 0.086 and 0.670 ± 0.097 for brainstem and left/right parotid glands respectively, outperforming NMI selection 0.676 ± 0.113, 0.632 ± 0.104 and 0.606 ± 0.118 (p < 0.001) as well as classic oracle selection. The implemented methodology is a proof of principle that selecting the atlases by considering the performance of the entire group of atlases instead of each single atlas leads to higher segmentation accuracy, being even better then current oracle strategy. This finding opens a new discussion about the most appropriate atlas selection criterion for MABS.
Experimental Validation of Localization Method for Finding Magnetic Sources on IOT Devices

Recently, we proposed a method for accurately locating instruction-dependent magnetic field sources on printed circuit boards (PCBs). This method first excites the device's processor by executing an alternating pair of two instructions at a specific frequency. Using the measurements of the magnetic field taken around the edges of the PCB and an algorithm based on simplex optimization, the locations of the sources are evaluated. In this paper, we present extensive experimental verification of this method on two devices, a field-programmable gate array (FPGA) development board and an Internet of Things (IoT) device. The results illustrate that sources of instruction-dependent emanations are confined to a small area near the processor and that the positions of these sources are dependent on the specific instructions being executed.
Effect of grayscale resolution on the performance of lung nodule detection on a softcopy display

A four-alternative forced-choice experiment was carried out to examine the effect of 8-bit versus 10-bit grayscale resolution on the detection of subtle lung nodules on a medical grayscale liquid crystal display (LCD). Sets of four independent backgrounds from each of three regions were derived from a very low-noise X-ray acquisition of a chest-phantom with an amorphous selenium radiographic detector. Simulated nodules of fixed diameter (10 mm) and varying contrast were digitally added to the centers of selected background images. Subsequently, multifrequency image processing was performed to enhance the image structures, followed by a tonescaling procedure that resulted in pixel values being specified as p-values, according to DICOM Part 14: The Grayscale Display Function. To investigate the effect that grayscale resolution may have upon softcopy detectability, each set of four images in the experiment was quantized to both 8-bit and 10-bit resolution. The resulting images were displayed on a DICOM-calibrated LCD display supporting up to 10 bits of grayscale input. Twenty observers with imaging expertise performed the nodule detection task for which the signal and location were known exactly. Results from all readers, chest regions, and backgrounds were pooled, and statistical significance between fractions of correct responses between 8-bit and 10-bit resolution was tested. Experimental results do not demonstrate a statistically significant difference in the fraction of correct answers between these two input grayscale resolutions.
Heparin-induced thrombocytopenia complicating extracorporeal membrane oxygenation support in pediatric patients: review of the literature and alternative anticoagulants

Heparin-induced thrombocytopenia (HIT) is a prothrombotic, immune-mediated complication of unfractionated heparin (UFH) and low molecular weight heparin therapy. HIT is characterized by moderate thrombocytopenia 5-10 days after initial heparin exposure, detection of platelet-activating anti-platelet factor 4/heparin antibodies and an increased risk of venous and arterial thrombosis. Extracorporeal membrane oxygenation (ECMO) is a form of mechanical circulatory support used in critically ill patients with respiratory or cardiac failure. Systemic anticoagulation is used to alleviate the thrombotic complications that may occur when blood is exposed to artificial surfaces within the ECMO circuit. Therefore, when HIT complicates patients on ECMO support, it is associated with a high thrombotic morbidity and mortality. The following article reviews the current knowledge in pediatric HIT, especially in ECMO patients, and the alternative anticoagulation options in the presence of HIT.
Learning Over Multitask Graphs—Part I: Stability Analysis

This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. The smoothness condition softens the transition in the tasks among adjacent nodes and allows incorporating information about the graph structure into the solution of the inference problem. A diffusion strategy is devised that responds to streaming data and employs stochastic approximations in place of actual gradient vectors, which are generally unavailable. The approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that promotes smoothness. We show in this Part I of the work, under conditions on the step-size parameter, that the adaptive strategy induces a contraction mapping and leads to small estimation errors on the order of the small step-size. The results in the accompanying Part II will reveal explicitly the influence of the network topology and the regularization strength on the network performance and will provide insights into the design of effective multitask strategies for distributed inference over networks.
An Environment for Submillisecond-Latency Audio and Sensor Processing on BeagleBone Black

This paper presents a new environment for ultra-low-latency processing of audio and sensor data on embedded hardware. The platform, which is targeted at digital musical instruments and audio eﬀects, is based on the low-cost BeagleBone Black single-board computer. A custom expansion board features stereo audio and 8 channels each of 16-bit ADC and 16-bit DAC for sensors and actuators. In contrast to typical embedded Linux approaches, the platform uses the Xenomai real-time kernel extensions to achieve latency as low as 80 microseconds, making the platform suitable for the most demanding of low-latency audio tasks. The paper presents the hardware, software, evaluation and applications of the system
The influence of non-stationary teleconnections on palaeoclimate reconstructions of ENSO variance using a pseudoproxy framework

Abstract. Reconstructions of the El Nino–Southern Oscillation (ENSO) ideally require high-quality, annually resolved and long-running palaeoclimate proxy records in the eastern tropical Pacific Ocean, located in ENSO's centre of action. However, to date, the palaeoclimate records that have been extracted in the region are short or temporally and spatially sporadic, limiting the information that can be provided by these reconstructions. Consequently, most ENSO reconstructions exploit the downstream influences of ENSO on remote locations, known as teleconnections, where longer records from palaeoclimate proxies exist. However, using teleconnections to reconstruct ENSO relies on the assumption that the relationship between ENSO and the remote location is stationary in time. Increasing evidence from observations and climate models suggests that some teleconnections are, in fact, non-stationary, potentially threatening the validity of those palaeoclimate reconstructions that exploit teleconnections. This study examines the implications of non-stationary teleconnections on modern multi-proxy reconstructions of ENSO variance. The sensitivity of the reconstructions to non-stationary teleconnections were tested using a suite of idealised pseudoproxy experiments that employed output from a fully coupled global climate model. Reconstructions of the variance in the Nino 3.4 index representing ENSO variability were generated using four different methods. Surface temperature data from the GFDL CM2.1 were used as pseudoproxies for these reconstruction methods. As well as sensitivity of the reconstruction to the method, the experiments tested the sensitivity of the reconstruction to the number of non-stationary pseudoproxies and the location of these proxies. We find that non-stationarities can act to degrade the skill of ENSO variance reconstructions. However, when global, randomly spaced networks (assuming a minimum of approximately 20 proxies) were employed, the resulting pseudoproxy ENSO reconstructions were not sensitive to non-stationary teleconnections. Neglecting proxies from ENSO's centre of action still produced skilful reconstructions, but with a lower likelihood. Different reconstruction methods exhibited varying sensitivities to non-stationary pseudoproxies, which affected the robustness of the resulting reconstructions. The results suggest that caution should be taken when developing reconstructions using proxies from a single teleconnected region, or those that use less than 20 source proxies.
Optimization of spray coating for the fabrication of sequentially deposited planar perovskite solar cells

Abstract. We use facile coating techniques including spray coating and drop casting to fabricate methylammonium lead iodide perovskite solar cells through a two-step sequential deposition approach. In the first step, for the deposition of the lead iodide, spray coating substitutes for the commonly used lab-scale spin coating, while the operating parameters of the former process are optimized to achieve a fully covered and uniform film of lead iodide. In the second step, to deposit methylammonium iodide atop the lead iodide layer to form methylammonium lead iodide perovskite, dip-coating process is replaced by the touch-free drop casting and scalable pulsed-spray coating. It is found that the performance of the perovskite films and devices made by pulsed-spray coating and drop casting is similar to those prepared by dip coating, while the large-scale production capabilities of such methods beside the low material consumptions of drop casting prove their potential to replace dip coating in large-scale manufacturing of perovskite solar cells. The champion devices fabricated by spray-drop and spin-drop techniques demonstrated power conversion efficiencies of 6.92% and 9.48%, respectively. It is expected that device fabrication in a low-humidity environment using the optimized parameters and optimization of other layers will result in higher efficiencies.
Requirements Engineering Improvement Today - A Systematic Mapping Study

Context Requirements engineering (RE) research has been receiving much attention from industry for many years. As the complexity of the software projects has vastly increased, project managers find it imperative that their RE processes are streamlined in order to extract maximum value. There is a need for specialized requirements engineering process improvement (REPI). However, there are no state-of-the-art studies done in the field of REPI that talk about the established concepts and their feasibility. Objective We aim to get an overview of the current state of reported research in RE process improvement, in terms of existing solutions, evaluations, experience reports, and opinion papers. A state-of-the-art view of process improvement with special attention on the particularities of RE will help drive efforts towards targeted gaps in research. Method To get an overview of the state of research in REPI, we conduct a systematic mapping study of the publication space. The collection of papers is first filtered based on the inclusion and exclusion criteria. The list of relevant papers are arranged according to their respective years of publication and then classified according to our research questions like on the research type facet, RE phase discussion or contribution paradigm. Results Our review of 58 publications shows that until 2002, only a few solutions were proposed to improve RE processes. The research gathered steam after 2002 when more improvement solutions were presented and experiences in their industrial applications could be discussed. Most of these publications dealt with REPI with a focus on activity based improvement in RE. Conclusion We see from our results that much effort has been spent on activity-based RE improvement based on existing norms, i.e. REPI is mostly done by adapting the activities provided in the norms. However, there is still little known about the problemdriven improvement of RE w.r. t. individual goals and problems while focusing on the quality of the created RE artefacts.
A Level Set Method to Image Segmentation Based on Local Direction Gradient

For image segmentation with intensity inhomogeneity, many region-based level set methods have been proposed. Some of them however can’t get the relatively ideal segmentation results under the severe intensity inhomogeneity and weak edges, and without use of the image gradient information. To improve that, we propose a new level set method combined with local direction gradient in this paper. Firstly, based on two assumptions on intensity inhomogeneity to images, the relationships between segmentation objects and image gradients to local minimum and maximum around a pixel are presented, from which a new pixel classification method based on weight of Euclidian distance is introduced. Secondly, to implement the model, variational level set method combined with image spatial neighborhood information is used, which enhances the anti-noise capacity of the proposed gradient information based model. Thirdly, a new diffusion process with an edge indicator function is incorporated into the level set function to classify the pixels in homogeneous regions of the same segmentation object, and also to make the proposed method more insensitive to initial contours and stable numerical implementation. To verify our proposed method, different testing images including synthetic images, magnetic resonance imaging (MRI) and real-world images are introduced. The image segmentation results demonstrate that our method can deal with the relatively severe intensity inhomogeneity and obtain the comparatively ideal segmentation results efficiently.
A Survey on Malicious Domains Detection through DNS Data Analysis

Malicious domains are one of the major resources required for adversaries to run attacks over the Internet. Due to the important role of the Domain Name System (DNS), extensive research has been conducted to identify malicious domains based on their unique behavior reflected in different phases of the life cycle of DNS queries and responses. Existing approaches differ significantly in terms of intuitions, data analysis methods as well as evaluation methodologies. This warrants a thorough systematization of the approaches and a careful review of the advantages and limitations of every group. In this article, we perform such an analysis. To achieve this goal, we present the necessary background knowledge on DNS and malicious activities leveraging DNS. We describe a general framework of malicious domain detection techniques using DNS data. Applying this framework, we categorize existing approaches using several orthogonal viewpoints, namely (1) sources of DNS data and their enrichment, (2) data analysis methods, and (3) evaluation strategies and metrics. In each aspect, we discuss the important challenges that the research community should address in order to fully realize the power of DNS data analysis to fight against attacks leveraging malicious domains.
University of Florida DSR Lab System for KBP Slot Filler Validation 2015

In this paper we present a Slot ﬁller Validation (SFV) system that uses a semi-supervised ensemble learning approach to aggregate the results from multiple slot ﬁllers from the Cold Start track. We apply Bipartite Graph-based Consensus Maximization (BGCM) to combine the output of supervised stacked ensemble methods with the output of slot ﬁlling runs that can’t be trained. By using BGCM we are also able to leverage a small set of assessed ﬁllers to increase the performance of the sys-tem. The ensemble results outperformed the best cold start run, the best ﬁltered runs, and other ensemble systems.
Road Detection and Recognition from Monocular Images Using Neural Networks

Road recognition is one of the important aspects in Autonomous Navigation Systems. These systems help to navigate the autonomous vehicle and robot on the ground. Further, road detection is useful in related sub-tasks such as finding valid road path where the robot/vehicle can go, for supportive driverless vehicles, preventing the collision with the obstacle, object detection on the road, and others. The goal of this thesis is to examine existing road detection and recognition techniques and propose an alternative solution for road classification and detection task. Our contribution consists of several parts. Firstly, we released the road images dataset with approximately 5,300 unlabeled road images. Secondly, we summarized the information about the existing road images datasets. Thirdly, we proposed the convolutional LeNet-5based neural network for the road image classification for various environments. Finally, our FCN-8-based model for pixel-wise image recognition has been presented.
Impaired serotonin communication during juvenile development in rats diminishes adult sperm quality

ABSTRACT Spermatogenesis and steroidogenesis are testicular functions regulated by gonadotrophins as well as other factors, including serotonin. Testicular serotonin acts as an autocrine regulator of testosterone secretion, but studies on its role in spermatogenesis and sperm quality are scarce. Here, we analyzed the effects of intratesticular inhibition of serotonin synthesis on gonadotrophins, testosterone, and sperm quality. Both testicles of 30-day-old rats were injected once with saline solution (SS) or distinct concentrations of p-chloroamphetamine (PCA) (0.03, 0.06, or 0.12 mg). At 65 days of age, rats were euthanized and sperm density, motility, membrane integrity, mitochondrial function, and abnormalities were evaluated in gametes from the vas deferens. Inhibition of synthesis of intratesticular serotonin by PCA diminished the concentrations of testosterone and follicle-stimulating hormone (FSH) but luteinizing hormone (LH) levels were unaltered. Sperm density was not modified in animals injected with the different concentrations of PCA. In contrast, the percentage of sperm with abnormalities increased and the sperm membrane integrity decreased in animals injected at higher PCA concentrations. The functionality of sperm mitochondria in PCA-injected animals decreased only at the highest PCA dose. Our results indicate that testicular serotonin plays a role in testosterone synthesis and in the normal development of sperm, and blocking its effects disrupts the hormonal communication between the testis and hypophysis. Abbreviations: SS: saline solution; PCA: p-chloroamphetamine; FSH: follicle-stimulating hormone; LH: luteinizing hormone; TPH: tryptophan hydroxylase; MAO: monoamine oxidase; AC: absolute control group; PI: propidium iodide; FLICA: fluorescence inhibitor of caspase; 3β-HSD: 3β-hydroxysteroid dehydrogenase; 17-KSR: 17-ketosteroid reductase; DHT: 5-dihydrotestosterone
Automatic Payload Signature Generation for Accurate Identification of Internet Applications and Application Services

The diversity and fast growth of Internet traffic volume are highly influenced by mobile and computer applications being developed. Moreover, the developed applications are too dynamic to be identified and monitored by network administrators. Several approaches have been proposed to identify network applications, however, are still not robust enough to identify modern applications. This paper proposes both, TSA (Traffic collection, Signature generation and Applications identification) system and a derived algorithm so called CSP (Contiguous Sequential Patterns) to identify applications for management and security in IP networks. The major focus of this paper is the CSP algorithm which is automated in two modules (Signature generation and Applications identification) of the proposed system. The proposed CSP algorithm generates DNA-like unique signatures capable of identifying applications and their individual services. In this paper, we show that the algorithm is suitable for generating efficient signatures to identify applications and application services in high accuracy.