Miracle-dz/UniFAR-FTUs huggingface dataset:
Are serum ferritin and transferrin saturation risk markers for restless legs syndrome in young adults? Longitudinal and cross‐sectional data from the Western Australian Pregnancy Cohort (Raine) Study

Restless legs syndrome has been associated with serum iron deficiency in clinical studies. However, studies investigating this relationship have had inconsistent results and there are no studies in young adults. Therefore, we investigated the relationship between serum measures of iron stores and restless legs syndrome in young adults in the community. Participants in the Western Australian Pregnancy Cohort (Raine) Study answered questions on restless legs syndrome (n = 1,100, 54% female) at age 22 years, and provided serum measures of iron stores (ferritin and transferrin saturation) at ages 17 and 22 years. Restless legs syndrome was diagnosed when four International RLS Study Group criteria were met (urge to move, dysaesthesia, relief by movement, worsening during evening/night) and these symptoms occurred ≥5 times per month. Logistic regression was used to assess associations between serum iron stores and restless legs syndrome, adjusting for potential confounders. The prevalence of restless legs syndrome at age 22 years was 3.0% (n = 33, 70% female). Among those who provided restless legs syndrome and iron data at age 22 years (n = 865), the median (interquartile range) ferritin was not different between the restless legs syndrome (55 [29.5–103.5] µg L−1) and the non‐restless legs syndrome group (65.0 [35.0–103.3] µg L−1, p = 0.2), nor were there differences in iron deficiency prevalence (p = 0.36). There was no association between restless legs syndrome (22 years) and iron stores (17, 22 years) before or after adjustment for potential confounders. There was no association between restless legs syndrome at 22 years and iron stores at 17 or 22 years in this cohort. Serum iron stores may not be a useful indicator of restless legs syndrome risk in young adults in the community.
Manzamine A Exerts Anticancer Activity against Human Colorectal Cancer Cells

Marine sponges are known to produce numerous bioactive secondary metabolites as defense strategies to avoid predation. Manzamine A is a sponge-derived β-carboline-fused pentacyclic alkaloid with various bioactivities, including recently reported anticancer activity on pancreatic cancer. However, its cytotoxicity and mode of action against other tumors remain unclear. In this study, we exhibit that manzamine A reduced cell proliferation in several colorectal cancer (CRC) cell lines. To further investigate the manzamine A triggered molecular regulation, we analyzed the gene expression with microarray and revealed that pathways including cell cycle, DNA repair, mRNA metabolism, and apoptosis were dysregulated. We verified that manzamine A induced cell cycle arrest at G0/G1 phase via inhibition of cyclin-dependent kinases by p53/p21/p27 and triggered a caspase-dependent apoptotic cell death through mitochondrial membrane potential depletion. Additionally, we performed bioinformatics analysis and demonstrated that manzamine A abolished epithelial–mesenchymal transition process. Several mesenchymal transcriptional factors, such as Snail, Slug, and Twist were suppressed and epithelial marker E-cadherin was induced simultaneously in HCT116 cells by manzamine A, leading to the epithelial-like phenotype and suppression of migration. These findings suggest that manzamine A may serve as a starting point for the development of an anticancer drug for the treatment of metastatic CRC.
Acetylation of Amaranthus viridis starch: Modeling and process parameters optimization

Abstract The optimum reaction conditions for the derivation of acetylated (esterified) starch using response surface methodology (RSM) and artificial neural network (ANN) were studied. All the independent variables (starch solids, acetic anhydride concentration, and reaction time) were of significant (p < .05) importance in achieving esterified starch of Amaranthus viridis. Optimum conditions of 152.46 g of starch, 11 ml of acetic anhydride and time of 2.92 min with corresponding acetyl content and degree of substitution (DS) of 1.74% and 0.06, respectively, were established for ANN. The RSM gave optimum conditions of 149.57 g (starch), 10.38 ml (acetic anhydride) and 3 min (time) with corresponding acetyl content and DS of 1.61% and 0.06, respectively. The order of priority of the process variables was established as acetic anhydride (42.59%), starch solids (33.90%), and reaction time (23.51%). The results provided useful information on development of economic and efficient acetylation process for modification of A. viridis starch.
Absence of electrocardiographic left ventricular hypertrophy is associated with increased mortality after transcatheter aortic valve replacement

Electrocardiographic (ECG) left ventricular hypertrophy (LVH) has been associated with increased mortality in patients with asymptomatic aortic stenosis (AS) and hypertension. However, patients with symptomatic AS undergoing transcatheter aortic valve replacement (TAVR) have higher percentages of myocardial fibrosis or amyloidosis that have been associated with decreased ECG voltage and worse outcomes.
SafeDrive-Fi: A Multimodal and Device Free Dangerous Driving Recognition System Using WiFi

We present the first WiFi based driver state recognition system: SafeDrive-Fi. Our proposed framework extracts fine-grain Channel State Information (CSI) of WiFi signal to accurately predict driver states through gestures and body movements. Different from vision based techniques, SafeDrive-Fi provides a simple, cost-effective and ubiquitous solution to prevent accidents and loss of lives due to reckless driving. We incorporate a unique DETECT algorithm to differentiate between normal and dangerous driving in a challenging and noisy in-vehicle conditions. Using only commercially available products, SafeDrive-Fi is compatible with 802.11n/ac and can assist drivers and law enforcement in discovering dangerous driving states. To the best of our knowledge, this is the first system that aggregates information from all the channel subcarriers and use multidomain CSI features to classify dangerous driving conditions. SafeDrive-Fi achieves an overall 98.04% recognition accuracy and 19.8% improvement over similar Received Signal Strength (RSS) based solution using an already deployed infrastructure.
An Investigation into Inner Source Software Development: Preliminary Findings from a Systematic Literature Review

Given the value and effectiveness of open source software development to date, practitioners are keen to replicate these practices inside their respective corporations. This application of open source practices inside the confines of a corporate entity has been coined inner source software development. However, while organisations have found ways to directly benefit from revenue streams as a result of leveraging open source practices internally, the current research on inner source is scattered among different areas. Thus gaining clarity on the state-of-the-art in inner source research is challenging. In particular, there is no systematic literature review of known research to date on inner source. We address this challenge by presenting a systematic literature review that identifies, critically evaluates and integrates the findings of 29 primary studies on inner source. Case study approach is the common research approach undertaken in the area. We also identified 8 frameworks/methods, models and tools proposed in the literature to support inner source, as well as a set of benefits and challenges associated with inner source. We envision future work to perform deeper analysis and synthesis on the empirical research on inner source software development.
Hyper-reduced projective dynamics

We present a method for the real-time simulation of deformable objects that combines the robustness, generality, and high performance of Projective Dynamics with the efficiency and scalability offered by model reduction techniques. The method decouples the cost for time integration from the mesh resolution and can simulate large meshes in real-time. The proposed hyper-reduction of Projective Dynamics combines a novel fast approximation method for constraint projections and a scalable construction of sparse subspace bases. The resulting system achieves real-time rates for large sub-spaces enabling rich dynamics and can resolve general user interactions, collision constraints, external forces and changes to the materials. The construction of the hyper-reduced system does not require user-interaction and refrains from using training data or modal analysis, which results in a fast preprocessing stage.
The Study and Design of Collaboration Tools for Flight Attendants

Collaboration is a core component of work activities amongst flight attendants. This is as they work to promote onboard safety and deliver a high level of customer service. Yet we know little of how flight attendants collaborate and how we can best design technology to support this collaboration. Through an interview study with flight attendants, the authors explored their collaborative practices and processes and how technology aided such practices. While technologies like interphones and flight attendant call buttons act as collaboration tools, they identified instances where the usability and functionality of these devices were barriers for maintaining efficient communication, situation awareness, and information exchange. The authors used these results to identify design suggestions for technology that can enhance communication and collaboration in aircraft settings amongst flight attendants. To illustrate these design suggestions, they designed and developed Smart Crew, a smartwatch application that allows flight attendants to maintain an awareness of each other and communicate through messaging with haptic feedback. Smart Crew is designed with an emphasis on real time information access, location updates and direct communication between flight attendants regardless of their location on the airplane.
Set Similarity Joins on MapReduce: An Experimental Survey

Set similarity joins, which compute pairs of similar sets, constitute an important operator primitive in a variety of applications, including applications that must process large amounts of data. To handle these data volumes, several distributed set similarity join algorithms have been proposed. Unfortunately, little is known about the relative performance, strengths and weaknesses of these techniques. Previous comparisons are limited to a small subset of relevant algorithms, and the large differences in the various test setups make it hard to draw overall conclusions. In this paper we survey ten recent, distributed set similarity join algorithms, all based on the MapReduce paradigm. We empirically compare the algorithms in a uniform test environment on twelve datasets that expose different characteristics and represent a broad range of applications. Our experiments yield a surprising result: All algorithms in our test fail to scale for at least one dataset and are sensitive to long sets, frequent set elements, low similarity thresholds, or a combination thereof. Interestingly, some algorithms even fail to handle the small datasets that can easily be processed in a non-distributed setting. Our analytic investigation of the algorithms pinpoints the reasons for the poor performance and targeted experiments confirm our analytic findings. Based on our investigation, we suggest directions for future research in the area.
Color Restoration of Scanned Archaeological Artifacts with Repetitive Patterns

Our work addresses the problem of virtually restoring archaeological artifacts. Virtual restoration is the process of creating a noise-free model of a degraded object, to visualize its original appearance. Our work focuses on restoring the coloring of the object. We considered both 2D and 3D objects, including scans of ancient texts and 3D models of decorated pottery. Our denoising method exploits typical characteristics of archaeological artifacts, such as repetitive decoration motifs and a limited palette of colors. Our classification method is based on minimization of an energy function, which includes a correspondence term, to encourage consistent labeling of similar regions. The energy function is minimized using the Graph-Cuts algorithm.
TIM: A Trapdoor Hash Function-based Authentication Mechanism for Streaming Applications

Achieving efficient authentication is a crucial issue for stream data commonly seen in content delivery, peer-to-peer, and multicast/broadcast networks. Stream authentication mechanisms need to be operated efficiently at both sender-side and receiver-side at the same time because of the properties of stream data such as real-time and delay-sensitivity . Until now, many stream authentication mechanisms have been proposed, but they are not efficient enough to be used in stream applications where the efficiency for sender and receiver sides are required simultaneously since most of them could achieve one of either sender-side and receiver-side efficiency. In this paper, we propose an efficient stream authentication mechanism, so called TIM , by integrating Trapdoor Hash Function and Merkle Hash Tree. Our construction can support efficient streaming data processing at both sender-side and receiver-side at the same time differently from previously proposed other schemes. Through theoretical and experimental analysis, we show that TIM can provide enhanced performance at both sender and receiver sides compared with existing mechanisms. Furthermore, TIM provides an important feature for streaming authentication, the resilience against transmission loss, since each data block can be verified with authentication information contained in itself.
Decoupled Rate-Optimal User Association for Full-Duplex Heterogeneous Networks

Full-duplex (FD) transmission in a point-to-point (P2P) link, wherein bidirectional traffic flows simultaneously share the same spectrum, has the capability of doubling the link throughput by completely removing self-interferences. However, the throughput gain of FD transmission in an interference-limited network is not as clear as a P2P link due to the interferences induced by complex FD and half- duplex (HD) transmission behaviors in the network. To thoroughly investigate the link throughputs of users and base stations (BSs) in a heterogeneous network (HetNet) with decoupled user association, a general approach is proposed to model a HetNet in which all BSs and users can perform HD or FD transmission depending on their traffic patterns. We first characterize the decoupled rate-optimal user association scheme and use it to define the downlink and uplink throughputs in the HetNet. The tight lower bounds on the link throughputs of the FD users and BSs are found in a neat form that characterizes general channel fading, user and BS intensities and imperfect self- interference cancellation. Numerical results validate that our analytical results are fairly accurate and the proposed decoupled rate-optimal user association indeed significantly improves the link throughputs.
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora

Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval. However, NED quality drops when inputs span long time periods like in archives or historic corpora. This paper presents the first time-aware method for NED that resolves ambiguities even when mention contexts give only few cues. The method is based on computing temporal signatures for entities and comparing these to the temporal contexts of input mentions. Our experiments show superior quality on a newly created diachronic corpus.
Analysis of spatiotemporal trajectories for stops along taxi paths

ABSTRACT Stops along taxi trajectories, such as picking up and dropping off passengers, are spatially clustered and related to certain attributes of places where stops are made. To detect the hidden knowledge regarding these places, this article examines the semantics of massive taxi stops in a large city. Each taxi trajectory is modeled as a series of sequential semantic stops labeled by street names. All the trajectories can be examined as a document corpus, from which the hidden themes of the stops are identified through Latent Dirichlet Allocation model. Conventional GIS tools are coupled with topic modeling toolkit to visualize and analyze potential information of stop topics for understanding intra-city dynamics. The effectiveness of this approach is illustrated by a case study using a large dataset of taxi trajectories including approximately 4,000 taxis in Wuhan, China.
Power Distribution Monitoring Using LoRa: Coverage Analysis in Suburban Areas

LoRa is an increasingly hot technology for LPWAN scenarios. In this paper, a low-cost, real-time monitoring system for power distribution grids based on LoRa and 3G/4G networks, is presented. Analysis of early experimental results of LoRa network coverage in suburban areas shows how communication range is affected by different parameters: LoS, vegetation, buildings and communication mode. Besides, the use of simulation tools to estimate LoRa coverage is evaluated.
Autonomous UAV Flight Control for GPS-Based Navigation

This paper proposes an unmanned aerial vehicle (UAV) flight control method where a graph-based path is generated after the collected UAV flight data by a pilot are analyzed. UAV flights are planned by using hierarchical A* search algorithms based on graph-based generated flight paths to take images at multiple surveillance points. Generating a graph-based path makes it possible for UAVs to fly autonomously along paths shorter than that of the pilot collecting UAV flight data given that the shorter paths can be derived by connecting partially flied paths. A* search algorithms can be applied hierarchically to a graph-based path that contains circulation paths. The proposed method was experimentally verified through an analysis of the collected UAV flight data to generate graph-based and planned paths. The pilot flew the UAV six times and obtained 8115 UAV flight data points. The generated graph-based path included 17 monitoring points for taking surveillance images and 90 intermediate flight points. The length of the flight paths collected by six time flights was 1364.32 m, and the length of the flight paths by the proposed method was 764.27 m. Given that 8115 flight points were collected and 109 flight points were selected by the proposed method, the complexity of the generated graph-based path consisted of flight points was reduced to 1.34% by hierarchical A* search algorithms.
Proactive In-Network Caching for Mobile On-Demand Video Streaming

Mobile video streaming has become an essential application in mobile wireless networks, making up most of the mobile data of today's Internet trafﬁc. Studies have shown that mobile video data is projected to make up about 78 percent of the global mobile data trafﬁc, and that global mobile data trafﬁc is expected to increase sevenfold by 2021. Massive small cell base station (SBS) deployments have emerged as a potential solution promising to fulﬁll these unprecedented mobile data demands, by offering great coverage enhancements and maintaining high quality of video streaming. However, due to relatively small cell sizes and high user mobility, mobile video streaming in dense SBS networks faces fundamental challenges such as intermittent connectivity and frequent handoffs, causing degradation in video streaming quality. In this paper, we tackle this issue by introducing a hybrid proactive in-network caching framework that stores some popular videos at the edge of the network, namely at the SBSs, while also pre-caching video contents in advance to better service mobile users. The proposed framework essentially reduces the need for bringing every requested video from the core (original) network, which results in alleviating network congestion by reducing back-haul trafﬁc and in improving mobile video streaming experience by avoiding service discontinuity during handoffs. We develop a simulation framework using MATLAB to study the performance of the proposed caching technique and show that the proposed technique can effectively improve video quality of experience and reduce back-haul trafﬁc.
Numerical Analysis of a Nonlinear Free-Energy Diminishing Discrete Duality Finite Volume Scheme for Convection Diffusion Equations

Abstract We propose a nonlinear Discrete Duality Finite Volume scheme to approximate the solutions of drift diffusion equations. The scheme is built to preserve at the discrete level even on severely distorted meshes the energy/energy dissipation relation. This relation is of paramount importance to capture the long-time behavior of the problem in an accurate way. To enforce it, the linear convection diffusion equation is rewritten in a nonlinear form before being discretized. We establish the existence of positive solutions to the scheme. Based on compactness arguments, the convergence of the approximate solution towards a weak solution is established. Finally, we provide numerical evidences of the good behavior of the scheme when the discretization parameters tend to 0 and when time goes to infinity.
Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery

Harmonic/percussive source separation (HPSS) consists in separating the pitched instruments from the percussive parts in a music mixture. In this paper, we propose to apply the recently introduced Masker-Denoiser with twin networks (MaD TwinNet) system to this task. MaD TwinNet is a deep learning architecture that has reached state-of-the-art results in monaural singing voice separation. Herein, we propose to apply it to HPSS by using it to estimate the magnitude spectrogram of the percussive source. Then, we retrieve the complex-valued short-time Fourier transform of the sources by means of a phase recovery algorithm, which minimizes the reconstruction error and enforces the phase of the harmonic part to follow a sinusoidal phase model. Experiments conducted on realistic music mixtures show that this novel separation system outperforms the previous state-of-the art kernel additive model approach.
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base

Humans can learn multiple languages. If they know a fact in one language, they can answer a question in another language they understand. They can also answer Code-mix (CM) questions: questions which contain both languages. This behavior is attributed to the unique learning ability of humans. Our task aims to study if machines can achieve this. We demonstrate how effectively a machine can answer CM questions. In this work, we adopt a two phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. We show experiments on the SimpleQuestions dataset. Our network is trained only on English questions provided in this dataset and noisy Hindi translations of these questions and can answer English-Hindi CM questions effectively without the need of translation into English. Back-transliterated CM questions outperform their lexical and sentence level translated counterparts by 5% & 35% in accuracy respectively, highlighting the efficacy of our approach in a resource constrained setting.