Miracle-dz/UniFAR-FTUs huggingface dataset:
A 60 GHz Push-Push Voltage-Controlled Oscillator with Adaptive Gate Biasing in 28 nm Bulk CMOS Technology

This paper presents a 60 GHz push-push voltage-controlled oscillator (VCO) with adaptive gate biasing realized in 28 nm bulk CMOS technology. The circuit uses a trifilar transformer - primary coil is used in the LC resonator tank, secondary coil realizes an inductive feedback of the cross-coupled pair and the third coil extracts the fundamental signal. The proposed inductive feedback technique enables an adaptive bias without use of capacitors at the gates of the cross-coupled pair transistors. The varactor banks use standard threshold voltage (VT) NMOS devices. The VCO achieves a 30 % continuous frequency tuning range (FTR) and a mean phase noise (PN) of −86 dBc/Hz at 1 MHz offset from carrier. The total DC power dissipation including output buffers is less than 80 mW at a 0.9 V power supply. The circuit area excluding pads is 0.2 mm2. A reference VCO without gate biasing is fabricated and used for comparison.
The Box of Virtual Middleboxes Design and Implementation in Software Defined Networking

The purpose of this paper is to introduce a box of packages containing application models of virtual middleboxes based hypervisor technology and to propose a standard container (box) program with a virtual appliance. The net element of physical hardware is the virtual middleboxes applying virtual appliance of open virtualization format (OVF) package for business application service. It is software-defined networking (SDN) based on network management theory, NFV implementation, and cloud computing service concept. It addresses flexible, elastic, and on-demand self-service of the out-of-the-box service platform. Applications packaged with operating system in the virtual appliance are pre-configured software stack. They can be loaded into virtual middleboxes anywhere, enabling flexible rapid distribution and deployment to meet customized user needs. In this study, the virtual appliance container is x86-based server hardware with installed hypervisor virtualization platform.
Planning Education for Long-Term Retention: The Cognitive Science and Implementation of Retrieval Practice

Abstract Educational systems are rarely designed for long-term retention of information. Strong evidence has emerged from cognitive psychology and applied education studies that repeated retrieval of information significantly improves retention compared to repeated studying. This effect likely emerges from the processes of memory consolidation and reconsolidation. Consolidation and reconsolidation are the means by which memories are organized into associational networks or schemas that are created and recreated as memories are formed and recalled. As educators implement retrieval practice, they should consider how various test formats lead to different degrees of schema activation. Repeated acts of retrieval provide opportunities for schemas to be updated and strengthened. Spacing of retrieval allows more consolidated schemas to be reactivated. Feedback provides metacognitive monitoring to ensure retrieval accuracy and can lead to shifts from ineffective to effective retrieval strategies. By using the principles of retrieval practice, educators can improve the likelihood that learners will retain information for longer periods of time.
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets

Recently, network lasso has dawn much attention due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data (noise, missing values, etc.), and yields sub-optimal solutions. The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results. In this paper, we propose triangle lasso to avoid its disadvantage for graph datasets. In a graph dataset, each instance is represented by a vertex. If two instances have many common adjacent vertices, they tend to become similar. Although some instances are profiled by the imperfect data, it is still able to find the similar counterparts. Furthermore, we develop an efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) to obtain a moderately accurate solution. In addition, we present a dual method to obtain the accurate solution with the low additional time consumption. We demonstrate through extensive numerical experiments that triangle lasso is robust to the imperfect data. It usually yields a better performance than the state-of-the-art method when performing data analysis tasks in practical scenarios.
Metabolic Syndrome Increases Risk of Postoperative Myocardial Infarction Following Percutaneous Nephrolithotomy.

INTRODUCTION Percutaneous nephrolithotomy (PCNL) is the gold standard treatment for upper tract stone burdens greater than 2 cm. Metabolic syndrome (MetS) is a constellation of conditions (diabetes mellitus, hypertension, dyslipidemia, and obesity) and is a risk factor for nephrolithiasis. Our objective was to investigate adverse cardiovascular outcomes of PCNL in patients with comorbid MetS diagnoses. MATERIALS AND METHODS Data from the Healthcare Cost and Utilization Project State Inpatient Database for Florida and California were used to identify PCNL patients (ICD9: 55.03, 55.04) between 2007 and 2011. Patients were categorized having 0, 1-2, or 3-4 components of MetS. Postoperative myocardial infarction (MI) and inhospital mortality rate outcomes were identified. Multivariate logistic regression was used to control for patient characteristics (age, race, and primary insurance provider) and medical comorbidities. RESULTS PCNL was performed on 39,868 patients, of whom 17,932 (45.0%) had no MetS conditions, 19,268 (48.3%) had 1-2 MetS conditions, and 2668 (6.7%) had 3-4 MetS conditions. With increasing MetS conditions, patients had increased incidence of postoperative MI (0: 0.6%; 1-2: 1.0%; 3-4: 1.8%, p < 0.001). On multivariate analysis, the presence of 3-4 MetS comorbidities increased the odds of a postoperative MI (1-2: odds ratio [OR] 1.2, 95% confidence interval [CI] 0.94-1.53, p = 0.147; 3-4: OR 2.2, 95% CI 1.54-3.15, p < 0.001). CONCLUSIONS MetS patients have an increased risk of MI following PCNL given their pre-existing comorbidities. Routine preoperative cardiac testing may benefit this population before PCNL.
Variation in the Protein Composition of Human Milk during Extended Lactation: A Narrative Review

The aim of this review is to evaluate changes in protein parameters in the second year postpartum. There is considerable agreement among authors about the declining trend of human milk protein concentrations, but most research on protein content in breast milk focuses on the first year of life and comes from developed countries. Whereas this is the case for exclusive breastfeeding or for breastfeeding into the first year of life, the opposite applies to weaning or extended breastfeeding. This review is predominantly based on observational epidemiological evidence and on comparative research linking breast milk composition with cutting down on breastfeeding. Studies dating back several decades have shown an increase in the proportion of immunoglobulins, lactoferrin, and serum albumin during weaning. According to the limited data available, it seems likely that the regulation of milk protein composition during involution can be ascribed to alterations in tight junctions. In studies on humans and other mammalian species, offspring suckle more from mothers that produce more dilute milk and the increase in milk protein concentration is positively correlated to a decrease in suckling frequency during weaning. High milk protein contents were first reported in nonindustrial communities where breastfeeding is sustained the longest, but recent papers from urbanized communities have taken credit for rediscovering the increase in protein content of human milk that becomes evident with prolonged breastfeeding. This review presents an overview of the changes in breast milk protein parameters in the second year postpartum to enable milk banks’ practitioners to make informed nutritional decisions on preterm infants.
Prevalence and distribution of left ventricular diastolic dysfunction in treated patients with long-lasting hypertension

Abstract Background: Although the presence of sub-clinical left ventricular diastolic dysfunction (LVDD) increases cardiovascular risk, the current ESH/ESC guidelines do not include the presence of this condition in the list of target organ damage or cardiovascular risk charts dedicated to the hypertensive population. Several conditions may predict the LVDD occurrence, however, clustering of these factors with hypertension makes the relationship less clear. Therefore, the aim of this study was to evaluate both the occurrence and the severity of diastolic dysfunction in a large cohort of treated hypertensives. Methods: We retrospectively analyzed records of 610 hypertensive participants of the CARE NORTH Study who consented to echocardiography and were free of overt cardiovascular disease. Mean age was 54.0 ± 13.9 years (mean ± SD), BMI 29.7 ± 4.8 kg/m2. The exclusion criteria were: established heart failure, LVEF <45%, coronary revascularization, valvular defect, atrial fibrillation, or stroke. The staging of LVDD was based on comprehensive transthoracic echocardiographic measurements. Results: 49.7% percent of the patients had normal diastolic function (38.8% vs. 59.0%, females (F) vs. males (M), respectively; p < . 001). Grade 1 LVDD was documented in 24.4% (27.8% and 21.6%; F and M; p = .08) and grade 2 LVDD in 19.3% (24.9% and 14.6%; F and M; p = .001) of the patients. None were diagnosed with grade 3 LVDD. In the logistic regression model, female sex, advancing age, obesity status, established diabetes mellitus, higher 24-hour SBP, and increasing LVMI were identified as the independent variables increasing the odds for the presence of LVDD, whereas blood-lowering therapy attenuated the risk. Conclusions: There is an unexpectedly high prevalence of different forms of diastolic dysfunction in treated hypertensive patients who are free of overt cardiovascular disease.
Docosahexaenoic acid attenuates the detrimental effect of palmitic acid on human endothelial cells by modulating genes from the atherosclerosis signaling pathway

The formation of atherosclerotic changes leads to dysfunction in numerous cell types, especially endothelial cells. In the current experiment, we aimed to show the therapeutic effect of Docosahexaenoic acid on palmitic‐induced atherosclerotic changes in the human endothelial lineage. Human Umbilical Vein Endothelial cells were incubated with 1 mM palmitic acid for 48 hours and then exposed to 40 µM docosahexaenoic acid for next 24 hours. Cellular atherosclerosis and lipid removal were confirmed by the application of Oil red O solution. The cell survival rate was studied by using MTT assay and flow cytometry analysis of Annexin V. We also measured the protein level of tumor necrosis factor‐α and granulocyte‐macrophage colony‐stimulating factor by immunofluorescence imaging. The transcription level of genes participating in the atherosclerosis signaling pathway was monitored in atherosclerotic endothelial cells before and after treatment with docosahexaenoic acid. The viability of the cells was reduced after 48 hours incubation with palmitic acid. It is noteworthy that the number of viable endothelial cells was increased after exposure to docosahexaenoic acid. Compared with the cells that received palmitic acid, Oil red O staining showed a decrease in the cellular content of fatty acid after incubation with docosahexaenoic acid (P < 0.05). PCR array indicated that the modulation of key genes played a role in atherosclerosis and reached near‐control levels. These data support the notion that incubation of atherosclerotic human endothelial cells with docosahexaenoic acid could return the detrimental effects of palmitic acid by modulation of the atherosclerosis signaling pathway.
Image region annotation based on segmentation and semantic correlation analysis

The authors propose an image region annotation framework by exploring syntactic and semantic correlations among segmented regions in an image. A texture-enhanced image segmentation JSEG algorithm is first used to improve the pixel consistency in a segmented image region. Next, each region is represented by a set of image codewords, also known as visual alphabets, with each of them used to characterise certain low-level image features. A visual lexicon, with its vocabulary items defined as either a codeword or a co-occurrence of multiple alphabets, is formed and used to model middle-level semantic concepts. The concept classification models are trained by a maximal figure-of-merit algorithm with a collection of training images with multiple correlations, including spatial, syntactic and semantic relationship, between regions and their corresponding concepts. In addition, a region-semantic correlation model constructed with latent semantic analysis is used to correct the potentially wrong annotations by analysing the relationship between image region positions and labels. When evaluated on the Corel 5K dataset, the proposed image region annotation framework achieves accurate results on image region concept tagging as well as whole image based annotations.
Deep Learned Full-3D Object Completion from Single View

3D geometry is a very informative cue when interacting with and navigating an environment. This writing proposes a new approach to 3D reconstruction and scene understanding, which implicitly learns 3D geometry from depth maps pairing a deep convolutional neural network architecture with an auto-encoder. A data set of synthetic depth views and voxelized 3D representations is built based on ModelNet, a large-scale collection of CAD models, to train networks. The proposed method offers a significant advantage over current, explicit reconstruction methods in that it learns key geometric features offline and makes use of those to predict the most probable reconstruction of an unseen object. The relatively small network, consisting of roughly 4 million weights, achieves a 92.9% reconstruction accuracy at a 30x30x30 resolution through the use of a pre-trained decompression layer. This is roughly 1/4 the weights of the current leading network. The fast execution time of the model makes it suitable for real-time applications.
Functions and regulatory mechanisms of metastasis‐associated lung adenocarcinoma transcript 1

Metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) is a long noncoding RNA whose transcript is around 8 kb in length. As an important stress response molecule, MALAT1 can be expressed differently under stress conditions, such as hypoxia, high glucose, hydrogen peroxide, ultraviolet irradiation, infection, and chemical stimulation. MALAT1 is involved in regulating multiple cell behaviors, such as proliferation, apoptosis, differentiation, migration, epithelial–mesenchymal transition, autophagy, and morphological maintenance. Extensive evidence show that MALAT1 plays critical roles in the physiopathological process of embryo implantation, angiogenesis, tissue inflammation, tumor progression, liver fibrosis, cardiovascular remodeling, and diabetes progression by regulating gene transcription, forming RNA–protein complexes with proteins as a structural component, regulating protein activity, assisting protein localization, mediating epigenetic changes, or by acting as a competing endogenous RNA. Furthermore, MALAT1 can affect the sensitivity of chemotherapy and radiotherapy; therefore, it could be used as a potential drug target for chemotherapy and radiotherapy sensitization. The levels of MALAT1 are reported to be overexpressed in most tumor tissues or sera, and the expression levels of MALAT1 often affect the tumor size, stage, lymph node metastasis, and distant invasion. Therefore, MALAT1 can be used as a biomarker for early diagnosis, severity assessment, or prognostic assessment. This review outlines the current understanding of the biological role and function of MALAT1. In the meantime, we have summarized the mechanisms involved in the reulation of MALAT1 expression and the mechanisms by which MALAT1 regulates the physiological and pathological processes.
Storage Protocol for Securing Blockchain Transparency

There are several studies that propose identity management that utilizes blockchain technology. In Ethereum, the main programmable blockchain system, data on a blockchain are saved as encoded binaries for executing automatic verification. To decode a binary, users need to know the application binary interfaces (ABI) that describes the data structure of the registered information. However, the manner by which ABI are shared in the current blockchain protocol is opaque. To resolve this problem, we describe a new protocol for embedding the ABI on a blockchain transaction when a registrant registers information. Our method enables users to read registered data with information on the blockchain alone, and it guarantees transparency without requiring users to trust third parties.
Segmentation of Microscopy Data for finding Nuclei in Divergent Images

Every year millions of people die due to disease of Cancer. Due to its invasive nature it is very complex to cure even in primary stages. Hence, only method to survive this disease completely is via forecasting by analyzing the early mutation in cells of the patient biopsy. Cell Segmentation can be used to find cell which have left their nuclei. This enables faster cure and high rate of survival. Cell counting is a hard, yet tedious task that would greatly benefit from automation. To accomplish this task, segmentation of cells need to be accurate. In this paper, we have improved the learning of training data by our network. It can annotate precise masks on test data. we examine the strength of activation functions in medical image segmentation task by improving learning rates by our proposed Carving Technique. Identifying the cells nuclei is the starting point for most analyses, identifying nuclei allows researchers to identify each individual cell in a sample, and by measuring how cells react to various treatments, the researcher can understand the underlying biological processes at work. Experimental results shows the efficiency of the proposed work.
A study on mycological profile of corneal ulcers in a tertiary care hospital

Introduction: Keratitis may be due to infectious or non-infectious etiology. Breach in the corneal epithelial barrier due to trauma or surgery predisposes to infectious keratitis which can result in scarring and blindness. The most common bacteria implicated is S.aureus. Among the fungi, Fusarium, Aspergillus and Candida spp. , are common agents. The fungi are usually inoculated into the cornea by trauma involving plant or vegetable matter. This study was conducted to know the prevalence of fungal keratitis and the different fungal species implicated in patients attending our tertiary care hospital. Materials and Methods: Corneal scrapings collected under slit-lamp examination with aseptic precautions were subjected to KOH mount, Gram stain, inoculated on Sabouraud dextrose agar and sent to Microbiology lab for further processing using standard techniques. Result: Out of the 18 clinically suspected fungal keratitis cases studied, 7 (38.89%) were farmers and most common risk factor associated was trauma 66.66% (12/18) of which vegetative trauma accounted for 27.78% (5/18) cases. 10 (55.56%) cases were both KOH and culture positive. The prevalence of fungal keratitis in our study was 1.54%. Out of the 10 positive cultures, most common isolate was Candida albicans (40%). Conclusion: Fungal keratitis is an important cause of monocular blindness that occurs after trivial trauma to the eyes. Most of the patients do not come to the ophthalmologist immediately after such trauma due to lack of knowledge. Early diagnosis and prompt treatment if initiated in the early phase of the infection can prevent the occurrence of any complications. Keywords: Corneal ulcer, Mycotic Keratitis, Fusarium spp, Candida spp.
An empirical evaluation on textual results clustering for web search

Clustering web search results into dynamic clusters and cluster hierarchies has been shown to be promising in reducing the information overload typically found in the ranked list search engines. The study compared sixteen participants' search performance and subjective satisfaction level in using textual clustering and ranked list search interfaces towards conducting assigned and self-designated search tasks. The results show participants searched slightly faster, better, and were more satisfied using the ranked list interface. However, it is worth noting that participants performed slightly well in easy type of questions with the clustering interface, and obtained non-repetitive relevant results not found from using the ranked list interface. The study shows the clustering interface provides the values of highlighting prominent concepts and offering richer context for exploring, learning and discovering related concepts; yet it also induces certain degree of information uncertainty, lost, and anxiety. Discussions on the contrast view of clustering search and suggestions for future studies are also provided.
Detecting Emerging Large-Scale Vulnerability Scanning Activities by Correlating Low-Interaction Honeypots with Darknet

Cyberattacks such as scanning by botnet worms, falsification of web pages, and security breaches happen on the Internet every day. To minimize damage caused by such attacks, early discovery of new attack trends and quick response to incidents are essential since detection delays and slow responses to incidents will cause further damage. Typical methods to detect new large-scale attacks are: (1) analyzing data collected by the darknet, (2) analyzing data collected by honeypots, and (3) summarizing alerts made by intrusion detection systems (IDSs). A darknet is a reachable and unused address space on the Internet, and we can figure out coarse-grained attack trends, such as volume of scans to each TCP/UDP port, by analyzing packets arrived at the darknet. However, darknet traffic usually cannot provide enough payloads to analyze attacks in detail although there are various scans to applications running on one TCP/UDP port, such as Web applications. A honeypot system can intentionally be attacked so that the attack codes and attacker behaviors can be observed after they are attracted to it. A drawback is that honeypots cannot be deployed so large because attackers are very likely to become aware of honeypots whey they are deployed on a network scale like darknet. IDS alerts provide information about attacks, but in recent years attacks the are resistant to be detected by IDS are increasing. In this paper, we present a system that automatically detects new scan activities and estimates the scale of each attack by correlating the data obtained by both low-interaction honeypots and the darknet. A low-interaction honeypot collects payload in TCP stream to find attacks without depending on a specific protocol and classify attack codes in the context of applications. By analyzing the cooccurrence of attacks observed at honeypots and darknet by various features, the system estimates the scale of attacks per each attack. The evaluation result suggests that many attacks can be observed at both honeypots and darknet, so it may be useful to correlate both data by observed time.