Miracle-dz/UniFAR-FTUs huggingface dataset:
SPACOMM 2014 Proceedings

In the fields of wireless communications, networking and signal processing, systems can be often modeled through a linear relationship involving a random Vandermonde matrix V, and their performance can be characterized through the eigenvalue distribution of the Gram matrix VV. In spite of its key role, little is known about the eigenvalue distribution of such a matrix and only few of its moments are known in closed form. In this work, we obtain a lower and an upper bound to the eigenvalue distribution of VV, as well as an excellent approximation based on entropy maximization. As an application, we consider the case of a wireless sensor network sampling a physical phenomenon to be estimated. We characterize the quality of the estimate through the eigenvalue distribution of VV by adopting an asymptotic approach, which well suites medium-large scale networks. The proposed method is particularly efficient when dealing with physical phenomena defined over a d-dimensional support, with d > 2. Keywords—sensor networks; Signal estimation; Vandermonde matrices.
Arthroscopic Management of Slap Lesion

Superior glenoid labrum tears as a source of shoulder pain were first described by Andrews et al. [1] in 1985 in the context of the high demand throwing athlete (professional baseball pitchers). The pathology correlated with the immense stress placed on the shoulder and the biceps brachial is muscle during the throwing motion. In 1990, Snyder [2] coined the term Superior Labrum Anterior and Posterior (SLAP) tear to describe the pathology of the labrum in overhead throwing athletes. Four unique types of superior labrum pathology were classified after inspection via arthroscopy.
Clinical prediction models in the precision medicine era: old and new algorithms

Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China; HealSci Technology Co., Ltd., Beijing 100176, China These authors contributed equally to this work. Correspondence to: Guo-Wei Tu, MD. Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China. Email: tu.guowei@zs-hospital.sh. cn. Provenance and Peer Review: This article was commissioned by the Editorial Office, Annals of Translational Medicine. The article did not undergo external peer review. Comment on: Zhou ZR, Wang WW, Li Y, et al. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med 2019;7:796.
RAMCIP Deliverable DX.X

Deliverable No. D3.2 Workpackage No. WP3 Workpackage Title and task type Monitoring and Modelling Human Activity at Home Task No. T3.1 T3.2 Task Title Modelling and monitoring the home environment Human activity monitoring Lead beneficiary CERTH Dissemination level PU – Public Nature of Deliverable Other Delivery date 10 November 2016 Status F: final File Name: RAMCIP Deliverable 3.2. doc Project start date, duration 01 January 2015, 36 Months
Dimension Reduction using Orthogonal Local Preserving Projection in Big data

— Big Data is unstructured data that overcome the processing complexity of conventional database systems. The dimensionality reduction approach, which is a fundamental technique for the large-scale data-processing, try to maintain the performance of the classifier while reduce the number of required features. The pedestrian data includes a number of features compare to the other data, so pedestrian detection is the complex task. The accuracy of detection and location directly affect the performance of the entire system. Moreover, the pedestrian based approaches mainly suffer from huge training samples and increase the computation complexity. In this paper, an efficient dimensionality reduction model and pedestrian data classification approach has been proposed. The proposed model has three steps Histogram of Oriented Gradients (HOG) descriptor used for feature extraction, Orthogonal Locality Preserving Projection (OLPP) approach for feature dimensionality reduction. Finally, the relevant features are forwarded to the Support Vector Machine (SVM) to classify the pedestrian data and non-pedestrian data. The proposed HOG+OLPP+SVM model performance was measured using evaluation metrics such as precision, accuracy, recall and f-measure. The proposed model used the Penn-Fudan Database and compare to the existing research the proposed model improved approximately 6% of pedestrian data classification accuracy.
Effect on Procoagulant State after Phase-1 Periodontal Therapy in Chronic Periodontitis Patients: A Clinical and Haematological Study

Periodontitis is a chronic inflammatory disease characterized by the destruction of alveolar bone, and the loss of the connective tissue supporting the teeth. Increase in the number of white blood corpuscles (WBCs) in response to LPS and bacteria are considered a strong independent predictor of future coronary heart diseases. It has been hypothesized that platelets and leukocytes being more sensitive to stimulation by periodontal pathogens, might contribute to procoagulant state of blood. AIM: The aim of this study was to assess the effect of non-surgical periodontal therapy on procoagulant state in chronic periodontitis patients. Material and method: Total number of 55 systemically healthy male patients with chronic periodontitis were selected. On first visit blood samples had been taken for total leucocyte count (TLC), differential leucocyte count (DLC) and platelet count, bleeding time and clotting time. Scaling and root planing was completed within 24 hours. Probing pocket depth (PPD), clinical attachment loss (CAL) and bleeding on probing (BOP) gingival index and plaque index was recorded by using the UNC-15 periodontal probe at baseline. Patients were recalled after 2 weeks post-therapy to resample venous blood and to record clinical measures. Results: Significant reduction in Total leucocyte count (TLC) and platelet, whereas differential leucocyte count (DLC), bleeding time and clotting time didn’t show any statistical significance. Conclusion : Decreasing periodontal inflammation may be a successful key to decrease procoagulant state which may further decrease the risk of CVD. Key-words : procoagulant ,periodontitis , total leucocyte count , coronary heart disease ------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 15-08-2019 Date of Acceptance: 30-08-2019 -------------------------------------------------------------------------------------------------------------------------------------
Monetary Policy and Regional Inequality

We study the impact of monetary policy on regional inequality using granular data on economic activity at the city- and county-level in Europe. We document pronounced heterogeneity in the regional patterns of monetary policy transmission. The output response to monetary policy shocks is stronger and more persistent in poorer regions, with the difference becoming particularly pronounced in the extreme tails of the distribution. Regions in the lower parts of the distribution exhibit hysteresis, consisting of long-lived adjustments in employment and labor productivity in response to the shocks. As a consequence, policy tightening aggravates regional inequality and policy easing mitigates it. JEL Classification: C32, E32, E52
Revisiting Financial Development And Economic Growth In Sub-Saharan African Low-Income Countries: A Panel Data Approach

The aim of this paper is to re-assess the relationship between financial development and economic growth in17 SubSaharan African Low-Income Countries(SSALIC) over the period of 2000 to 2015.Both static and dynamic panel estimation techniques are used to re-assess relationship. On the static model, although, liquid liability and commercial bank central bank are statistically insignificant, the private sector credit and openness are positive and highly statistically significant, consistent with many of theoretical and empirical studies while, the dynamic panel analysis provides no clear indication on the relationship between financial development and economic growth. Consequently, this empirical difference provides some insight and a good basis for reflection on stipulated relation between indicators of financial development and economic growth.
The Long-Run Component of Idiosyncratic Volatility and Expected Stock Returns

This paper argues there exists a strong and robust negative relationship between expected long-run component of idiosyncratic volatility and expected stock returns. I present empirical evidence that idiosyncratic volatility of stock returns displays long memory properties. To capture this feature, I develop and estimate a parsimonious model for idiosyncratic volatility consisting of a long-run and short-run component. The long-run component is modeled to be more persistent and could contain a unit root. The short-run component is less persistent and has a zero mean. I find that there exists a robust negative relationship between expected long-run idiosyncratic volatility and expected stock returns. The negative relationship could be stronger and statistically more significant than using other measures of idiosyncratic volatility such as lagged realized idiosyncratic volatility used by Ang et al. (2006). The relationship is also robust after controlling for return reversals, unexpected idiosyncratic volatility.
Improving Aquaculture Production in Haliotis Species through the Development of a Genomic Toolkit

Commercial abalone aquaculture has greatly expanded over the past decade, becoming a thriving global industry valued at over $100 million USD. Abalone is one of the few species where culture production dominates the global market as a result of increasing demand and declining natural stocks from overexploitation and disease. U.S. abalone production is also growing due to high market value and demand. Most farms operating in California utilize three native west coast species: red abalone (Haliotis rufescens), green abalone (H. fulgens), and pink abalone (H. corrugata). These species differ in commercially important traits that are key to culture expansion in California and improved production efficiency (e.g., growth rate, disease resistance, thermal tolerance). Next generation sequencing has opened the door for extensive and explorative genetic research on abalone, and several studies were recently published examining transcriptomic data for species including the domestically important red abalone, H. rufescens. Recently, researchers working with the Southwest Fisheries Science Center (SWFSC) used restriction site associated DNA sequencing (RAD-Seq) methods to identify genome-wide SNP markers in H. fulgens and to examine population structure in wild populations. However, transcriptomic and RAD-mapping analyses were also limited by the paucity of genomic information available for abalone; without knowledge of the genomic structure, it is very difficult to ascertain coverage depth in these studies. We are working to create the first de novo abalone genome assembly using sexed H. rufescens samples, generate tissue specific transcriptomes for H. rufescens, and conduct comparative genomic analyses with other commercially important California abalone. Comparative analyses will include H. fulgens, H. corrugata, and endangered white (H. sorenseni) and black (H. cracherodii) abalone. Genomic, transcriptomic, and comparative analyses will improve our understanding of sex-determination, thermal/environmental preference, disease resistance, hybridization outcomes, and local adaptation, especially for commercially important California abalone. This will enable identification of candidate genes of interest and marker development for marker assisted selection to improve aquaculture practices in the U.S. abalone industry and elsewhere. This research will also help guide restoration and wild stock enhancement along the west coast for these species, including for the endangered white and black abalone.
Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this article, we develop such a physical human–robot interaction system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model and the Gaussian mixture regression. More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach.
Ontology-Based Security Specification Tools for SOA

Service Oriented Architecture (SOA) is an emerging paradigm for distributed computing. Web services and SOA aim to provide unobtrusive access to resources and data. However, risks and threats on the Internet compel us to protect and limit access to these same resources through security policies and mechanisms. Therefore, the success of SOA relies heavily on the ability to communicate the relevant security information required to access these resources in a machine-understandable manner. In this paper, we introduce a set of (prototype) tools that enable the specification of ontology-based security information for each layer of the SOA. They build upon existing technology standards and enable dynamic discovery of Web services. Every well-thought out security requirement has some reasons/justifications behind it. However, oftentimes the justification or context is not well documented or disconnected from the requirement. Our tools will present an applica-tion-centric view of the SOA and capture the context information from which security requirements are derived.
Multi-Glance Attention Models For Image Classiﬁcation

We present a multi-glance attention model for performing image classiﬁcation tasks. The proposed model is a combination of two convolutional networks trained via backpropagation, one looking at the entire image and the other looking at individual patches of the image. The goal is to combine the information from multiple glances to obtain better performance on different classiﬁcation tasks. This also reduces the amount of computations required at test time while giving the same accuracy as a conventional single glance network of similar depth.
JTRF2014: Analysis, Results and Comparisons to ITRF2014 and DTRF2014

We review the main steps that led to the construction of JTRF2014, the frame solution the Jet Propulsion Laboratory (JPL) submitted for the realization of ITRF2014. In the first part of this technical note, we recall the main concepts underlying the determination of terrestrial reference frames through Kalman Filtering and Rauch-Tung-Striebel smoothing, currently adopted at JPL to compute sub-secular frame products, and we discuss the main results associated with JTRF2014. In the second part, we adopt a comparative approach and, elaborating on metrics associated with Earth Orientation and Helmert transformation parameters, we analyze JTRF2014 in relation to ITRF2014, the official combined frame computed at the Laboratoire de Recherche en Géodésie of the Institut National de l’Information Géographique et Forestière (IGN) and DTRF2014, produced at the Deutsches Geodätisches Forschungsinstitut of the Technische Universität München. Introduction We present and discuss JTRF2014, the Terrestrial Reference Frame (TRF) the Jet Propulsion Laboratory constructed by combining spacegeodetic inputs from Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), Global Navigation Satellite Systems (GNSS) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) submitted for the realization of ITRF2014. Determined through a Kalman filter and RTS smoother assimilating position observations, Earth Orientation Parameters (EOPs), and local ties, JTRF2014 is a sub-secular, time series-based TRF whose origin is at the quasi instantaneous Center of Mass (CM) as sensed by SLR and whose scale is determined by the quasi instantaneous VLBI and SLR scales. JTRF2014 is rotationally aligned to ITRF2008 through the implementation of a No-Net-Rotation (NNR) condition applied at each step of the filter. The kinematic evolution of the station positions is described by superimposing a secular motion, annual, and semi-annual periodic modes. Site-dependent variances based on the analysis of loading displacements induced by mass redistributions of terrestrial fluids have been used to control the extent of random walk adopted in the combination. This technical note is conceptually articulated in two parts: The first recalls the main concepts underlying the JPL approach to TRF combination and illustrates the main products associated with JTRF2014;
Optimization of Secondary Surveillance Radar Data Processing

— Secondary surveillance radar (SSR) performs one of the main functions of information service for consumers of the airspace control system. To improve the quality the SSR information is processed using modern information technology. The use of a consistent procedure for processing surveillance system data, due to the functionally completed stages of processing, made it possible to formalize the data processing procedure. However, this significantly limited, and in some cases excluded, the opportunities for inter-stage optimization of data processing. The SSR data processing structure synthesis and analysis are considered in this paper making it possible to perform a joint optimization of signal processing and primary processing of data, as well as to improve the quality of data processing.
Methylation associated transcriptional repression of lipogenesis in novel colorectal cancer cell lines

Genetic and epigenetic alterations mark colorectal cancer (CRC). Global hypomethylation is observed in nearly all CRC, but a distinct subset of CRC show the CpG Island Methylator Phenotype (CIMP). These tumors show DNA hypermethylation of a specific subset of CpG islands, resulting in transcriptional downregulation of nearby genes. Recently we reported the establishment of 20 novel CRC cell lines derived from primary and metastatic CRC tissues. In this study we describe the DNA methylation profiling of these low passage CRC cell lines. We generated global DNA methylation profiles with Infinium HumanMethylation450 BeadChips and analysed them in conjunction with matching gene expression profiles. Multidimensional scaling of the DNA methylation and gene expression datasets showed that BRAF mutated cell lines form a distinct group. In this group we investigated the 706 loci which we have previously identified to be hypermethylated in BRAF mutant CRC. We validated the significant findings in the The Cancer Genome Atlas colon adenocarcinoma dataset. Our analysis identified ELOVL5, FAM127B, MTERF1, ZNF606 to be subject to transcriptional downregulation through DNA hypermethylation in CRC. These genes are involved in important cellular processes such as apoptosis, lipogenesis and the downstream transcriptional effect of the MAPK-pathway. These results show the BRAF mutation associated CIMP profile regulated key cellular processes, and result in a growth advantage to the tumor cells.
Media Network Based Investors’ Attention: A Powerful Predictor of Market Premium

Studies on stock market equity premium predictability mostly examine information-based predictors, such as the traditional fundamental economic variables (hard information) and the recent news tones (soft information). However, investors’ attention is largely ignored in the equity premium forecasting literature despite investor attention is crucial on how information is incorporated into stock prices. In this study, we propose a novel predictor, news network triggered attention (NNTA) index, to proxy the abnormal non-shareholders attention by analysing news co-occurrence phenomenon. We show that the NNTA index can forecast the market premium signiﬁcantly and outperform existing state-of-the-art attention proxies and various news-related or information-based predictors.
Intelligent Audio Analysis for Continuous Rainforest Occupancy Monitoring

Auditory data is used by ecologists for a variety of purposes, including identifying species ranges, estimating population sizes, and studying behaviour. Autonomous recording units (ARUs) enable auditory data collection over a wider area and time frame, and can provide improved consistency over traditional sampling methods. Moreover, in enabling passive acoustic monitoring, they mitigate the impacts of field surveys on sensitive landscapes and species. The result is an abundance of audio data – much more than can be analyzed continuously by scientists with the appropriate taxonomic skills. In recent years, ecologists have begun collaborating with computer scientists to develop machine learning tools to better utilize this data. Deep learning methods are effective for species classification, but still face a number of challenges in processing ARU data, such as handing changing environmental conditions, differentiating between co-occurring animal sounds, and detecting vocalizations in long streams of audio. In this project, I examine and address the divide between academic machine learning research on animal vocalization classifiers, and their application to conservation efforts. As a unique case study, I build a Bornean gibbon (Hylobates muelleri) call detection system for the Stability of Altered Forest Ecosystems Project’s solarpowered, mobile-connected ARU network. First, I design a GUI-based method to rapidly annotate continuous audio for supervised learning. Next, I experiment with three fundamentally different forms of feature extraction and learning: end-to-end modeling, LLD extraction with Bag-of-Audio-Words modeling, and deep learning using 2D spectral representations. Last, I examine ways to adapt the models, and their outputs, for ecological occupancy monitoring. With a top ROC-AUC score of 0.9848, the resulting models perform highly on independent test data, and showcase the potential for machine learning to transform ecological monitoring, even in dynamic and biodiverse environments. More immediately, the project provides a new tool for endangered primate conservation and a foundation for extension to other rainforest species.