Miracle-dz/UniFAR-FTUs huggingface dataset:
Mask Convolution for Filtering on Irregular-Shaped Image

In this paper, we proposed the concept of mask convolution, which operates on irregular shaped foreground, and could gain improvement on the margin of the object. In particular, we prove that this operation could be done in an efficient way. The extra calculation than normal convolution only lies on mask image convolution, and one time elements-wise multiply and division. To verify the necessity of mask convolution, we use mean filtering on retina images to eliminate spots. Compared to normal convolution, the proposed method could obtain clearer image on the margin of foreground, which is beneficial for subsequent CNN-based vessel segmentation model. We also proved that this method can be extended to other convolution kernels such as Gaussian and sharpen filters.
Predicting Wikipedia Infobox Type Information using Word Embeddings on Categories

Wikipedia has emerged as the largest multilingual, web based general reference work on the Internet. A huge amount of human resources have been invested in the creation and update of Wikipedia articles which are ideally complemented by so-called infobox templates defining the type of the underlying article. It has been observed that the Wikipedia infobox type information is often incomplete and inconsistent due to various reasons. However, the Wikipedia infobox type information plays a fundamental role for the RDF type information of Wikipedia based Knowledge Graphs such as DBpedia. This stimulates the need of always having the correct and complete infobox type information. In this work, we propose an approach to predict Wikipedia infobox types by using word embeddings on categories of Wikipedia articles, and analyze the impact of using minimal information from the Wikipedia articles in the prediction process.
ICNLoWPAN – named-data networking for low power IoT networks

Information Centric Networking is considered a promising communication technology for the constrained IoT, but NDN was designed only for standard network infrastructure. In this paper, we design and evaluate an NDN convergence layer for low power lossy links that (1) augments the NDN stateful forwarding with a highly efficient name eliding, (2) devises stateless compression schemes for standard NDN use cases, (3) adapts NDN packets to the small MTU size of IEEE 802.15. 4, and (4) generates compatibility with 6LoWPAN so that IPv6 and NDN can coexist on the same LoWPAN links. Our findings indicate that stateful compression can reduce the size of NDN data packets by more than 70 % in realistic examples. Our experiments show that for common use cases ICNLoWPAN saves 33 % of transmission resources over NDN, and about 20 % over 6LoWPAN.
The development of a web application for the automatic analysis of the tonality of texts based on machine learning methods

The work is devoted to the study of the existing methods of analyzing the sentimentality of the text, development and realization of web application for determining the key of the text in commentaries and paragraphs in social networks. Experiment is based on data sets from the social media portals like «Tengrinews», «Nur.kz» and «Zakon.kz», which analyzes the tone of message entered by user. We developed a software to determine the tone of text data, according to the problem of the system for analyzing the emotional coloring of messages. For sampling data, we applied the data collecting system, called “Crawler”, which collects text data from our web portals. After assembling data from our portals, we apply several models of the algorithm for analyzing text data. In this study, models of the machine learning method are applied; the tone of the user input is analyzed, with the help of which we can get the social mood of the people. Thereby, the advantages of working with the above-mentioned methods are shown. A focus is to put on showing where and how naive Bayesian classifiers can provide valuable analysis in the text analysis process.
Dual-Polarized Omnidirectional Antenna With High Isolation Based on the Theory of Characteristic Modes

A dual-polarized antenna with omnidirectional radiation and high isolation performance is proposed in this paper. The proposed antenna consists of courtyard-shaped metal frame, feeding network, and metal background. Working mechanism of the proposed antenna is analyzed by the theory of characteristic modes. Two orthogonal polarizations can be achieved by utilizing the orthogonal current distributions on the courtyard-shaped metal frame. In order to obtain the horizontal-polarized mode, eight slots etched on the walls of the courtyard-shaped metal frame are excited. A feeding network is placed at the inner part of courtyard-shaped metal frame for a compact design. Measured results show that the proposed antenna can cover the impedance bandwidth of 1.80–2.81 GHz for both polarizations with their corresponding isolations better than 35 dB. A peak gain of 7 dBi and a gain ripple less than 3 dB in the horizontal radiation plane are achieved. The proposed antenna can be applied in the indoor wireless communication.
Dysfunctional approach behavior triggered by alcohol‐unrelated Pavlovian cues predicts long‐term relapse in alcohol dependence

Pavlovian conditioned cues (CSs) can drive instrumental behavior in alcohol‐dependent patients. However, it remains unclear if the influence of Pavlovian CSs might also promote maladaptive decisions that can increase the risk of relapse. We studied 109 abstinent alcohol‐dependent patients and 93 controls who completed a Pavlovian‐to‐instrumental transfer (PIT) paradigm, and assessed patients' subsequent relapse status during a 1‐year follow‐up. In our PIT task, participants had to collect “good shells” (instrumental approach) or leave “bad shells” (instrumental inhibition) during the presence of money‐related Pavlovian CSs or drink‐related pictures in the background. Pavlovian CSs indicated either a monetary gain (ie, 1€, 2€), a monetary loss (ie, ‐1€, −2€) or a neutral stimulus (0€). Drink‐related background pictures were either pictures of participants' favorite alcoholic drink or pictures of water. We found that the influence of money‐related Pavlovian CSs on instrumental behavior (ie, the PIT effect) was more pronounced in future relapsers compared with abstainers and controls. Relapsers particularly failed to correctly perform in trials where the instrumental stimulus required inhibition while a Pavlovian background CS indicated a monetary gain. Under that condition, relapsers approached the instrumental stimulus, independent of the expected punishment. In contrast, we found no difference in PIT between relapsers and abstainers when drink‐related background pictures were presented. The failure of inhibiting an aversive stimulus in favor of approaching an appetitive non‐alcohol‐related context cue might reflect dysfunctional altered learning mechanisms in relapsers. A possible relation to maladaptive decision making that can lead to high‐risk situations for relapse is discussed.
3D printing with silk: considerations and applications

ABSTRACT Silk is a natural polymer sourced mainly from spiders and silkworms. Due to its biocompatibility, biodegradability, and mechanical properties, it has been heavily investigated for biomedical applications. It can be processed into a number of formats, such as scaffolds, films, and nanoparticles. Common methods of production create constructs with limited complexity. 3D printing allows silk to be printed into more intricate designs, increasing its potential applications. Extrusion and inkjet printing are the primary ways silk has been 3D printed, though other methods are beginning to be investigated. Silk has been integrated into bioink with other polymers, both natural and synthetic. The addition of silk is primarily done to offer more desirable viscosity characteristics and mechanical properties for printing. Silk-based bioinks have been used to fabricate medical devices and tissues. This article discusses recent research and printing parameters important for 3D printing with silk.
Missing Image Data Estimation via Iterative Convolutional Sparse Coding

A novel method for missing image data estimation is presented in this paper. The proposed method realizes accurate estimation of missing image data by iterating dictionary learning and Convolutional Sparse Coding (CSC). Specifically, our method iterates estimation of missing image data via CSC by using a dictionary that is constructed from a target image, and reconstruction of the dictionary by using the obtained estimation results. As the main contribution of our paper, the proposed method enables the missing image data estimation by using more suitable dictionaries obtained by this iterative scheme. Experimental results show high missing image data estimation performance by the proposed method.
Indoor People Counting Method Based on Fingerprinting Localization with Kernel Fuzzy C-Means Clustering

Efficient and accurate people counting has become significant as it is essential in many applications such as smart guide and intelligent building. In this paper, a people counting approach based on fingerprinting localization is proposed for multiple regions in indoor environment. We obtain the number of people in each subarea by localizing each target in the environment. The approach is composed of the offline training phase and online counting phase. In offline phase, we construct the fingerprint database which is also called radiomap, and transform it through DIFF method to solve the problem of device diversity. Then we divide the whole region into different subareas utilizing Kernel Fuzzy C-Means (KFCM) Clustering algorithm to reduce the computational complexity. In online phase, we first select the nearest subarea of the target and then estimate its location. The location is compared with the boundaries of all subareas to determine the subarea it belongs to. When applied to multiple targets, the number of people in each region will be counted. Experimental results show that our proposed method is effective and the average people counting accuracy can achieve 90.24%.
Recent beam development for nuclear science at the MIT-bates linear accelerator center

During the past two years, two sets of high priority nuclear physics experiments, SAMPLE, and part of the Out of Plane Spectrometer (OOPS) program have been completed at the MIT-Bates Laboratory. Good progress is also being made on the next major program, the Bates Large Acceptance Spectrometer Toroid (BLAST). Research and technical innovations pursued on the accelerator complex to accomplish these experiments have established new beam delivery capabilities for nuclear science. The centerpiece of the accelerator facility is the 190 m stretcher and storage South Hall Ring (SHR). The significant new achievements are the availability of highly polarized stored or extracted electron beams, and spin manipulation capability. With a 180° Siberian Snake spin rotator, over 120 mA of polarized beam has been stored at 670 MeV. There was no evidence of depolarization during a beam storage time of one hour. A prototype of a fast spin flipper, to achieve polarization reversal of the stored beam, was tested, with about .
Anomaly Detection in POSTFIX mail log using Principal Component Analysis

Anomalies in a dataset are the abnormal observations of the data, and they often represent the problems or defectives coming from some changes in the system operation. Detecting the anomalies in a data set is the first step from which further analysis can be done to locate the actual root cause in the data. In this paper, we propose a general method to pinpoint the anomalies from a set of Postfix mail log. This method utilizes the Elastic Stack to collect and process log data, then applies Principal Component Analysis to separate the abnormal data from the normal one. To demonstrate the feasibility of the solution, we test it against a collection of a million Postfix log messages. The result is promising under experimentation as it can detect an anomaly at the time a spam mail attack occurs.
Effects of Organophosphate Ester Flame Retardants on Endochondral Ossification in Ex Vivo Murine Limb Bud Cultures

Phasing out the usage of polybrominated diphenyl ether (PBDE) flame retardants (FRs) in consumer products led to their widespread replacement with organophosphate ester (OPE) FRs, despite scarce safety data. PBDE exposures were associated with the suppression of endochondral ossification but little is known about the effects of OPEs on bones. Here, we used a novel ex vivo murine limb bud culture system to compare the effects of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) with those of several OPEs. Gestation day 13 embryos were collected from transgenic CD1 mice expressing fluorescent markers for the major stages of endochondral ossification: COL2A1-ECFP (chondrogenesis), COL10A1-mCherry (early osteogenesis), and COL1A1-YFP (late osteogenesis). Limbs were excised and cultured for 6 days in the presence of vehicle, BDE-47, or an OPE FR: triphenyl phosphate (TPHP), tert-butylphenyl diphenyl phosphate (BPDP), tris(methylphenyl) phosphate (TMPP), or isopropylated triphenyl phosphate (IPPP). BDE-47 (50 μM) decreased the extent of chondrogenesis in the digits and COL1A1-YFP expression in the radius and ulna relative to control. In comparison, concentrations of ≥1 μM of all 4 OPEs limited chondrogenesis; osteogenesis (both COL10A1-mCherry and COL1A1-YFP fluorescence) was markedly inhibited at concentrations ≥3 μM. The expression of Sox9, the master regulator of chondrogenesis, was altered by BDE-47, TPHP, and BPDP. BDE-47 exposure had minimal impact on the expression of Runx2 and Sp7, which drive osteogenesis, whereas TPHP and BPDP both suppressed the expression of these transcription factors. These data suggest that OPE FRs may be more detrimental to bone formation than their brominated predecessors.
Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing

Distributed denial-of-service (DDoS) has caused major damage to cloud computing, and the false- and missing-alarm rates of existing DDoS attack-detection methods are relatively high in cloud environment. In this paper, we propose a DDoS attack-detection method with enhanced random forest (RF) optimized by genetic algorithm based on flow correlation degree (FCD) feature. We define the FCD feature according to the asymmetric and semidirectivity interaction characteristics and use the two-tuples FCD feature consisting of packet-statistical degree (PSD) and semidirectivity interaction abnormality (SDIA) to describe the features of attack flow and normal flow. Then we use a genetic algorithm based on the FCD feature sequences to optimize two key parameters of the decision tree in the RF: the maximum number of decision trees and the maximum depth of every single decision tree. We apply the trained RF model with optimized parameters to generate the classifier to be used for DDoS attack-detection. The experiment shows that the proposed method can effectively detect DDoS attacks in cloud environment with a higher accuracy rate and lower false- and missing-alarm rates compared to existing DDoS attack-detection methods.
Data Lifecycle Challenges in Production Machine Learning

Machine learning has become an essential tool for gleaning knowledge from data and tackling a diverse set of computationally hard tasks. However, the accuracy of a machine learned model is deeply tied to the data that it is trained on. Designing and building robust processes and tools that make it easier to analyze, validate, and transform data that is fed into large-scale machine learning systems poses data management challenges. Drawn from our experience in developing data-centric infrastructure for a production machine learning platform at Google, we summarize some of the interesting research challenges that we encountered, and survey some of the relevant literature from the data management and machine learning communities. Specifically, we explore challenges in three main areas of focus - data understanding, data validation and cleaning, and data preparation. In each of these areas, we try to explore how different constraints are imposed on the solutions depending on where in the lifecycle of a model the problems are encountered and who encounters them.
Simultaneous multislice cardiac magnetic resonance fingerprinting using low rank reconstruction

This study introduces a technique for simultaneous multislice (SMS) cardiac magnetic resonance fingerprinting (cMRF), which improves the slice coverage when quantifying myocardial T1, T2, and M0. The single‐slice cMRF pulse sequence was modified to use multiband (MB) RF pulses for SMS imaging. Different RF phase schedules were used to excite each slice, similar to POMP or CAIPIRINHA, which imparts tissues with a distinguishable and slice‐specific magnetization evolution over time. Because of the high net acceleration factor (R = 48 in plane combined with the slice acceleration), images were first reconstructed with a low rank technique before matching data to a dictionary of signal timecourses generated by a Bloch equation simulation. The proposed method was tested in simulations with a numerical relaxation phantom. Phantom and in vivo cardiac scans of 10 healthy volunteers were also performed at 3 T. With single‐slice acquisitions, the mean relaxation times obtained using the low rank cMRF reconstruction agree with reference values. The low rank method improves the precision in T1 and T2 for both single‐slice and SMS cMRF, and it enables the acquisition of maps with fewer artifacts when using SMS cMRF at higher MB factors. With this technique, in vivo cardiac maps were acquired from three slices simultaneously during a breathhold lasting 16 heartbeats. SMS cMRF improves the efficiency and slice coverage of myocardial T1 and T2 mapping compared with both single‐slice cMRF and conventional cardiac mapping sequences. Thus, this technique is a first step toward whole‐heart simultaneous T1 and T2 quantification with cMRF.
An Effective Two-Stage Feature Selection Method with Parameters Optimized by Simulated Annealing Algorithm

With the increase of the dimension of the collected data, the research of feature selection has gained more and more attention in recent years. As an essential preprocessing method, feature selection can not only reduce the cost of modeling by deleting those unnecessary features, but also improve the precision of the model in some degree. In this paper, an effective feature selection method is proposed, which is based on Mutual Information and Cosine Distance with parameters optimized by Simulated Annealing algorithm (MICD-SA). The proposed method achieves the goal of dimension reduction through two-stage feature selection operations. First, features are selected based on the mutual information between features and labels. Then, cosine distance between each feature is introduced to further reduce the redundancy of the selected features in stage one. Finally, the simulated annealing algorithm is adopted to automatically optimize the two thresholds used in the previous two-stage feature selection process. In addition, the proposed method is also applied to four high dimensional datasets to test its feasibility and effectiveness. Three feature selection methods combined with four different classifiers (KNN, CART, SVM, NB)are tested and their results are compared. The proposed method's effectiveness both in accuracy and feature number are well proved by the comparison.
Role of serum levels of intraoperative brain natriuretic peptide for predicting acute kidney injury in living donor liver transplantation

Background Patients with end-stage liver disease frequently experience acute kidney injury (AKI) after living donor liver transplantation (LDLT). Serum levels of brain natriuretic peptide (BNP) have increasingly been accepted as a predictor of AKI in clinical settings. This study investigated the predictive role of intraoperative BNP levels in the early development of AKI after LDLT. Patients and methods Adult patients (≥19 years old) who had undergone elective LDLT from January 2011 to December 2017 were classified into the non-AKI and AKI groups according to the Kidney Disease: Improving Global Outcomes criteria. Serum levels of BNP were measured three times in the preanhepatic, anhepatic, and neohepatic phases. Perioperative data in recipients and donors were analyzed retrospectively. Results Sixty-one patients (22.4%) suffered from AKI immediately after LDLT. Severity according to AKI stage was as follows: 28 patients in stage 1 (10.3%), 18 patients in stage 2 (6.6%), and 15 patients in stage 3 (5.5%). In the neohepatic phase, both BNP levels and proportions of patients with high BNP levels (≥100 pg/mL) were higher in the AKI group than in the non-AKI group. Only BNP levels in the non-AKI and AKI stage 1 groups significantly decreased from the preanhepatic phase to the neohepatic phase; those in AKI stages 2 and 3 groups did not. In particular, BNP levels of all AKI stage 3 patients increased to more than 100 pg/mL, and the proportion of patients with high levels also increased significantly through the surgical phases in the AKI stage 3 group. In multivariate analyses, BNP levels in the neohepatic phase were significantly associated with early development of AKI after LDLT, as well as the total amount of packed red blood cells in transfusions and total duration of graft ischemia. Conclusions Monitoring serum levels of BNP is useful for predicting the early development of AKI after LDLT.
FM antenna with soft visual impact made from micrometric mesh metal technology

An optically transparent and electrically small FM radio receiving antenna has been designed and fabricated from a micrometric mesh silver layer printed onto a glass substrate. The studied FM antenna consists of a transparent meandering monopole associated with a micrometric size MESFET transistor. A sheet resistance lower than $0.22 \Omega/$sq and an optical transparency of 87 % in the visible light spectrum are obtained. The Signal to Noise Ratio (SNR) and the Received Signal Strength Indicator (RSSI) are both measured, compared with those of a commercial monopole antenna, and discussed.
The Effect of Cold Treatment of Parboiled Rice with Lowered Glycaemic Potency on Consumer Liking and Acceptability †

A significant reduction in rice starch digestibility and subsequent postprandial blood glucose responses following extended cold treatment (at 4 °C for 24 h) have been demonstrated in both in vitro and in vivo studies, respectively. The impact of cold treatment was more significant for parboiled rice compared to other rice varieties. This study aimed to investigate consumer liking of sensory characteristics that may influence consumer acceptability of three available rice products in the Auckland region (medium grain white, medium grain brown and parboiled rice, which were either freshly boiled or cold-treated and reheated). The consumer liking of sensory characteristics (colour, taste, flavour, and texture) of each rice sample were accessed using visual analogue scales (VAS) in a randomized single blind setting. In the second stage, the participants evaluated their acceptability on VAS after the nutritional value and the characteristics of the rice samples were revealed. Sixty-four rice consumers reported higher likings of sensory characteristics of cold-treated parboiled rice and medium grain brown rice. The effect of cold treatment on the liking of sensory characteristics was more significant for parboiled rice (p < 0.05). Participants who are between 36 and 55 years old and consume rice domestically more than 10 times per month preferred cold-treated brown rice (73.8% of the participants’ population (67.4%, 80.2%)) and parboiled rice (74.3% of the participants’ population (67.9%, 80.7%)) (p < 0.001). As a result, cold-treated reheated parboiled rice received higher likings and acceptability and could be recommended and accepted as a healthier replacement of the daily staple meal.