Miracle-dz/UniFAR-FTUs huggingface dataset:
A GaN Single-Chip Front-End for Active Electronically Scanned Arrays

A Single-Chip Front-End design exploiting a 250-nm gate length GaN process provided by United Monolithic Semiconductor (UMS) is proposed in this contribution. The design procedure and preliminary measurement results are provided. In Rx mode, a noise figure lower than 1.75 dB and a gain better than 30 dB, with a ripple lower than 0.5 dB, have been achieved. In Tx mode, a 45 dBm output power and 400% PAE have been achieved at the nominal operating point (11 dBm input power). The reported performance spans a 13% bandwidth in the S-band and the MMIC is targeted for Active Electronically Scanned Antenna applications. The whole chip occupies an area of 7×7 mm2.
Tucker tensor decomposition-based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos

The anomaly detection and localisation (ADL) gains remarkable interest as dealing with the complex surveillance videos for detecting the abnormal behaviour is tedious. The human effort in monitoring and classifying the abnormal object is inaccurate and time-consuming; therefore, the method is proposed using the Tucker tensor decomposition (TTD) and classification of the objects using Gaussian mixture model (GMM). Initially, the object is detected in the frames for easy recognition using simple background subtraction. The TTD decomposes the tensor as core tensor and factor matrices and the two decomposed tensors are compared using the cosine similarity measure that determines the location of the object in the frame. Finally, the features including shape and speed of the object are extracted that is used for classification using the GMM that follows the maximum posterior probability principle to detect and locate the anomaly in the video. The experimentation for anomaly detection proves that the proposed TTD and TTD-GMM method attains a higher rate of multiple object tracking precision, accuracy, sensitivity, and specificity at 0.96375, 0.975, 1, and 1, respectively.
Video Corner-Logo Recognition System and Matching Algorithm

Video corner-logo is widely used in television programs and online videos to provide video column information or advertising information in a variety of forms. Video corner-logo detection and recognition algorithm study is therefore getting more and more attention due to the practical needs. Effective detection and recognition algorithm is the key to corner-logo extraction and corner-logo information analyzation. In this paper, a whole framework and design flow of video corner-logo detection and recognition system are introduced. The design of video corner-logo recognition algorithm is discussed in details and analyzed. The special characteristics of corner-logo makes it difficult to detect, extract and recognize corner-logo. Aiming at this problem, this paper proposes an effective corner-logo recognition algorithm, SURF based color invariance and shape context algorithm. The algorithm calculates the color invariance information of the image, constructs the color descriptor, extracts the interest points of the image, at the same time, introduces shape feature descriptor of the shape context, combines the two kinds of feature descriptions into a new joint descriptor with optimal weight coefficient, and then the similarity is calculated by means of Euclidean distance to match the interest points. The experimental results show that this recognition system and matching algorithm can improve the image recognition accuracy and can recognize corner-logo effectively.
Beyond text querying and ranking list: How people are searching through faceted catalogs in two library environments

This paper reports the result of a transaction log analysis on two faceted library catalogs (University of North Carolina at Chapel Hill (UNC) Library catalog and Phoenix Public Library (PPL) catalog). The goal is to investigate people's searching behavior with the faceted catalogs in an academic library and a public library. Two large data sets (with 504,142 logs for 40 days, and 1,010,239 logs for 60 days respectively) are analyzed. Descriptive statistics are reported and cluster analysis is conducted. It is found that people do incorporate facets when they are searching through a faceted catalog for either the academic or the public library. The facet usage for the PPL catalog is higher than that of its UNC counterpart probably due to its support of facet browsing in addition to facet refining, and that they have facets that describe the content better. For the UNC data, many popular facets are the "administrative" (or circulative) metadata rather than the "real content" metadata, whereas for the PPL data, frequently used facets are the browsing titles appearing as search tabs on the search page, such as books and movies. Finally, cluster analysis reveals common search groups across the two library environments. A better understanding of people's searching behavior can help system developers to develop more responsive systems to cater to different behaviors and different patrons. Particularly, an insight into how people are using facets will guide library technical staff to make facets more effective in helping people find what they want.
A Linearization Method for Phased Array Antenna Based on DPD Technology

A linearization method, which is easy to realize, for phased array antenna based on DPD technology is given in this paper. Amplitude unbalance factor is introduced firstly to cancel the amplitude and phase error between each transmitter channel so that the phased array antenna can be treated as a linear combination of each channel. As a result, the DPD method is used to linearize the phased array antenna. The simulation result show that this method can improve both the AM-AM and PSD characteristic, the ACPR of each transmit channel can be improved for more than 15dB, and the distortion of the pattern caused by the nonlinearity of the system can be effectively corrected. Meanwhile, the given method can also be used to cancel the mutual coupling between the transmitter channel and improve the performance of the phased array antenna.
Comparison between ropivacaine and bupivacaine in deep topical fornix nerve block anesthesia in patients undergoing cataract surgery by phacoemulsification

Purpose: In this study, we intend to analyze ropivacaine and bupivacaine in various parameters during phacoemulsification under deep topical fornix nerve block (DTFNB), a known form of nerve block for phacoemulsification. Methods: This prospective randomized study was conducted on 100 patients undergoing elective cataract surgery by phacoemulsification under DTFNB. Patients were divided into two equal groups of fifty patients each, Groups B (bupivacaine) and Group R (ropivacaine). Two sponges, approximately 2 mm × 3 mm dimensions, saturated with either 0.5% bupivacaine or 0.75% ropivacaine were placed deep in the conjunctival fornices to perform the deep topical block. Both groups were evaluated for magnitude of pain and discomfort at various stages of phacoemulsification using a simple pain scoring system. The level of surgeon satisfaction, requirement for supplementary anesthesia, and surgical complications were also evaluated. Quantitative variables between the two groups were compared using unpaired t-test. Qualitative variables were correlated using Chi-square test. Results: Overall demographic parameters of patients were similar in both groups. Similar mean pain scores were found in the ropivacaine and bupivacaine groups, with no statistical significance. Surgical satisfaction and the need for supplemental anesthesia were also statistically insignificant. Conclusion: Ropivacaine is a good alternative for deep topical anesthesia as it has a better safety margin and lesser toxic effect than other comparable local anesthetic agents.
Expression of miR-30c and miR-29b in prostate cancer and its diagnostic significance

This study aimed to investigate the expression of miR-30c and miR-29b in prostate cancer (PCa) and its clinical significance. The expression of miR-30c and miR-29b was detected by RT-qPCR in 187 cases of PCa and their adjacent tissues. Combined with clinical information, the correlation between the expression of miR-29b and miR-30c and the clinical features of PCa was analyzed, and ROC curve was plotted. The expression of miR-30c and miR-29b detected by RT-qPCR showed that the expression of miR-29b and miR-30c in PCa tissues was significantly lower than that in adjacent cancerous tissues (p<0.05). By comparing the expression and clinical data of miR-29b and miR-30c in the cancer tissues of PCa patients, it was observed that age, smoking, and TNM staging were not related to miR-29b and miR-30c expression (p>0.05), while lymph node metastasis, bone metastasis, and Gleason score were related to the expression of miR-29b and miR-30c (p<0.01). The ROC curve showed that miR-29b AUC, 0.924; 95% CI, 0.824–0.967, and miR-30c AUC, 0.944; 95% CI, 0.798–0.972. miR-30c and miR-29b are clinically relevant to PCa. In conclusion, detecting the expression of miR-30c and miR-29b not only can differentiate between PCa and paracancerous tissues, but it is also anticipated to become a new biomarker for the diagnosis of PCa.
A Recipe for Arabic-English Neural Machine Translation

In this paper, we present a recipe for building a good Arabic-English neural machine translation. We compare neural systems with traditional phrase-based systems using various parallel corpora including UN, ISI and Ummah. We also investigate the importance of special preprocessing of the Arabic script. The presented results are based on test sets from NIST MT 2005 and 2012. The best neural system produces a gain of +13 BLEU points compared to an equivalent simple phrase-based system in NIST MT12 test set. Unexpectedly, we find that tuning a model trained on the whole data using a small high quality corpus like Ummah gives a substantial improvement (+3 BLEU points). We also find that training a neural system with a small Arabic-English corpus is competitive to a traditional phrase-based system.
Goal conflict in chronic pain: day reconstruction method

Background When suffering from chronic pain, attempts to control or avoid pain often compete with other daily activities. Engaging in one activity excludes engaging in another, equally valued activity, which is referred to as “goal conflict. ” As yet, the presence and effects of goal conflicts in patients with chronic pain remain poorly understood. Methods This study systematically mapped the presence and experience of goal conflicts in patients with fibromyalgia compared to healthy controls. A total of 40 patients and 37 controls completed a semi-structured interview in which they first reconstructed the previous day, identified conflicts experienced during that day, and classified each of the conflicting goals in one of nine goal categories. Additionally, they assessed how they experienced the previous day and the reported conflicts. Results Results showed that patients did not experience more goal conflicts than healthy controls, but that they did differ in the type of conflicts experienced. Compared to controls, patients reported more conflicts related to pain, and fewer conflicts involving work-related, social or pleasure-related goals. Moreover, patients experienced conflicts as more aversive and more difficult to resolve than control participants. Discussion This study provides more insight in the dynamics of goal conflict in daily life, and indicates that patients experience conflict as more aversive than controls, and that conflict between pain control (and avoidance) and other valued activities is part of the life of patients.
Glaucoma Diagnosis over Eye Fundus Image through Deep Features

Glaucoma is an ocular disease that causes damage to the eye's optic nerve and successive narrowing of the visual field in affected patients, causing an increase of intra-ocular pressure, which can lead the patient, in advanced stage, to blindness. This work presents a study on the use of Convolutional Neural Networks (CNNs) for the automatic diagnosis through eye fundus images. Therefore, a comparison was made among the main CNNs architectures for feature extraction. The features extracted were compared using different classifiers and tested on RIM-ONE datasets. The results are promising for the combination of ResNet and Logistic Regression, on the RIM-ONE-r2, with AUC of 0.957 and through InceptionResNet with the same classifier with AUC of 0.860 on the RIM-ONE-r3.
On Geometric Analysis of Affine Sparse Subspace Clustering

Sparse subspace clustering (SSC) is a state-of-the-art method for segmenting a set of data points drawn from a union of subspaces into their respective subspaces. It is now well understood that SSC produces subspace-preserving data affinity under broad geometric conditions but suffers from a connectivity issue. In this paper, we develop a novel geometric analysis for a variant of SSC, named affine SSC (ASSC), for the problem of clustering data from a union of affine subspaces. Our contributions include a new concept called affine independence for capturing the arrangement of a collection of affine subspaces. Under the affine independence assumption, we show that ASSC is guaranteed to produce subspace-preserving affinity. Moreover, inspired by the phenomenon that the $\ell _1$ regularization no longer induces sparsity when the solution is nonnegative, we further show that subspace-preserving recovery can be achieved under much weaker conditions for all data points other than the extreme points of samples from each subspace. In addition, we confirm a curious observation that the affinity produced by ASSC may be subspace-dense—which could guarantee the subspace-preserving affinity of ASSC to produce correct clustering under rather weak conditions. We validate the theoretical findings on carefully designed synthetic data and evaluate the performance of ASSC on several real datasets.
Spatio-Temporal Wireless Traffic Prediction With Recurrent Neural Network

Accurate prediction of user traffic in cellular networks is crucial to improve the system performance in terms of energy efficiency and resource utilization. However, existing work mainly considers the temporal traffic correlation within each cell while neglecting the spatial correlation across neighboring cells. In this letter, machine learning models that jointly explore the spatio-temporal correlations are proposed. Specifically, several recurrent neural network structures are utilized. Furthermore, a multi-task learning approach is adopted to explore the commonalities and differences across cells in improving the prediction performance. Base on real data, we demonstrate the benefits of joint learning over spatial and temporal dimensions.
Monic acid A: a biomarker in clinical intra-nasal mupirocin medication for MRSA decolonisation

Abstract Context: Mupirocin (BactrobanR) is widely prescribed for intra-nasal decolonisation of MRSA for in-patients awaiting surgery or self-medicated for out-patients although adherence for the latter is not monitored. Non-adherence is a widespread pharmaceutical problem but could encourage selection of antibiotic resistance. Mupirocin is only a topical antibiotic because it decomposes in stomach acidity to monic acid A, but this has not previously been exploited as a biomarker for clinical intra-nasal medication. Materials and methods: Urine from three catheterised patients in two London hospitals during and after mupirocin medication, was passed through Waters Oasis cartridges to isolate organic acids. Sensitive LC-MS-MS analysis for monic acid A in methanolic eluate has been developed to identify ∼10 pg. Results: Monic acid A was quantified in all samples from one patient, translating into 6–46 ng from 12 mg mupirocin, assuming 1 L daily urine output. However, no urinary monic acid A was detected for two other patients. Discussion and conclusions: Consistent occurrence of monic acid A in urine of one mupirocin patient shows for the first time that antibiotic distribution across nasal mucous membranes had generally been maintained during medication. In contrast, consistent absence in the two other patients requires wider study in hospital.
Dissecting the Pre-Handoff chart biopsy: Information seeking in the electronic health record

Pre-handoff "chart biopsy" involves the brief reviewing by a clinician of a patient's record prior to accepting responsibility for the care of that patient. It is an information seeking activity enabled by electronic health records. This paper reports on a qualitative study of chart biopsies conducted by General Medicine physicians in a large quaternary teaching hospital prior to discussing possible admissions of patients from the Emergency Department (ED). The paper makes three contributions. First, this research provides an account of an information practice not previously documented and characterizes that practice as a social constructionist activity. Second, the study demonstrates that new information technologies can enable new information practices which in turn can alter the balance of participation in organizational coordination efforts. Third, the study shows that the understandings constructed from information seeking can have consequences for organizational orders.
Investigation of the Impact of Illumination on the Polarization-Type Potential-Induced Degradation of Crystalline Silicon Photovoltaic Modules

Accelerated potential-induced degradation (PID) testing of photovoltaic modules is conventionally conducted in the dark and at high temperature and humidity levels without considering the influence of illumination. This study investigates the impact of illumination on the polarization-type PID (PID-p) on two different types of encapsulated (glass/backsheet) crystalline silicon solar cells: 1) n-type bifacial passivated emitter rear totally diffused (the front side is facing glass and PID-stressed); and 2) p-type bifacial passivated emitter and rear cell (the rear side is facing glass and PID-stressed). The samples are stressed under the conditions of −1000 V, 40 °C, and 40% relative humidity and at different irradiance levels (xenon lamps). While the type-A modules show no reduction in PID-p sensitivity under illumination up to 800 W/m2, PID-p in the type-B modules is arrested by the light at an irradiance level as low as 10 W/m2. Furthermore, PID-degraded type-B modules (degradation induced in the dark) exhibit a rapid recovery (full recovery in 20 min) upon exposure to light (40 W/m2). External quantum efficiency measurements on the type-B modules show that ultraviolet from 300 to 400 nm is mainly responsible for the fast recovery.
Exploring the effect of E76K mutation on SHP2 cause gain‐of‐function activity by a molecular dynamics study

Juvenile myelomonocytic leukemia (JMML), an invasive myeloproliferative neoplasm, is a childhood disease with very high clinical lethality. Somatic mutation E76K in SHP2 is the most commonly identified mutation found in up to 35% of patients with JMML. To investigate the effect of gain‐of‐function mutation‐E76K on SHP2 activity, molecular dynamic simulations on the wild‐type SHP2 (SHP2‐WT) system and the mutated E76K (SHP2‐E76K) system were performed. The evaluation of stability of these two systems indicated that the simulated trajectories were stable after simulation for 3 nanoseconds. The root mean square fluctuation and the per‐residue root mean square deviation illustrated that there were two regions (residues Tyr 81‐Glu 83 and Glu 258‐Leu 261) in the wild‐type system and the mutated system, which had large differences. The principal component analysis, dynamic cross correlation maps analysis, as well as secondary structure analysis suggested that the mutated E76K impacted the movement of these two regions in SHP2 protein. Furthermore, residue interaction network analysis, hydrogen bond occupancy, and binding free energies analysis were used to explain how the two regions were specifically affected by the mutant. The results indicated that the primary variances between SHP2‐WT and SHP2‐E76K were the different interactions between Glu/Lys 76 and Arg 265, Tyr 80 and Leu 77, Leu 77 and Tyr 81, Thr 73 and Glu 258, Ala 75 and Cys 259, Phe 71 and Tyr 81, Ala 75 and Glu 258, and Tyr 73 and Glu/Lys 76. Consequently, these findings here might provide insights into the increased activity in SHP2‐E76K.
Screening and identification of key biomarkers in bladder carcinoma: Evidence from bioinformatics analysis

Bladder cancer (BC) is one of the most common urogenital malignancies. However, present studies of its multiple gene interaction and cellular pathways remain unable to accurately verify the genesis and the development of BC. The aim of the present study was to investigate the genetic signatures of BC and identify its potential molecular mechanisms. The gene expression profiles of GSE31189 were downloaded from the Gene Expression Omnibus database. The GSE31189 dataset contained 92 samples, including 52 BC and 40 non-cancerous urothelial cells. To further examine the biological functions of the identified differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein-protein interaction (PPI) network was mapped using Cytoscape software. In total, 976 DEGs were identified in BC, including 457 upregulated genes and 519 downregulated genes. GO and KEGG pathway enrichment analyses indicated that upregulated genes were significantly enriched in the cell cycle and the negative regulation of the apoptotic process, while the downregulated genes were mainly involved in cell proliferation, cell adhesion molecules and oxidative phosphorylation pathways (P<0.05). From the PPI network, the 12 nodes with the highest degrees were screened as hub genes; these genes were involved in certain pathways, including the chemokine-mediated signaling pathway, fever generation, inflammatory response and the immune response nucleotide oligomerization domain-like receptor signaling pathway. The present study used bioinformatics analysis of gene profile datasets and identified potential therapeutic targets for BC.
Collab-SAR: A Collaborative Avalanche Search-and-Rescue Missions Exploiting Hostile Alpine Networks

Every year, Alpine experiences a considerable number of avalanches causing danger to visitor and saviors, where most of the existing techniques to mitigate the number of fatalities in such hostile environments are based on a non-collaborative approach and is time- and effort-inefficient. A recently completed European project on Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments (SHERPA) has proposed a novel collaborative approach to improve the rescuing activities. To be an integral part of the SHERPA framework, this paper considers deployment of an air-ground collaborative wireless network (AGCWN) to support search and rescue (SAR) missions in hostile alpine environments. We propose a network infrastructure for such challenging environments by considering the available network components, hostility of the environments, scenarios, and requirements. The proposed infrastructure also considers two degrees of quality of service, in terms of high throughput and long coverage range, to enable timely delivery of videos and images of the long patrolled area, which is the key in any searching and rescuing mission. We also incorporate a probabilistic search technique, which is suitable for collaborative search assuming AGCWN infrastructure for sharing information. The effectiveness of the proposed infrastructure and collaborative search technique, referred to as Collab-SAR, is demonstrated via a series of computer simulations. The results confirm the effectiveness of the proposal.