Miracle-dz/UniFAR-FTUs huggingface dataset:
Nasopharyngeal Carcinoma Stem Cells

Nasopharyngeal Carcinoma (NPC) frequently occurs in the epithelial lining of the nasopharynx. It is a common cancer occurring in Southern China and South East Asia, while this disease is comparatively rare in most other parts of the world. Development of the disease is closely related to genetic, viral, environmental and dietary factors. Among these, Epstein Barr Virus infection is consistently associated with NPC. Cancer stem cells (CSC), a subset of cancer cells within a tumor having the ability to self renew and differentiate, are recently reported to be closely related with recurrence of tumours. In comparison, using the ability of exclusion of the DNA dye Hoechst 33342 as the parameter, side population (SP) cells can be isolated form the heterogeneous cell populations in tumour mass. SP cells may share characteristics of CSCs, both being enriched for tumor initiating capacity, and resistance to chemotherapy and radiotherapy. Although SP cells and CSCs were recently reported in NPC cell lines, so far no solid biomarkers were identified. NPC is often diagnosed late due to its deep location and no specificity of symptoms when the disease is in its early stage. Thus, there is a need to find sensitive biomarkers for an early diagnosis and even treatment of NPC. Screening strategies have being developed using the genomics, proteomics, immunology, miRNA and bioinformatics for discovery of molecular biomarkers.
Multi Authority Attribute-Based Encryption for Securely Sharing Personal Health Records in Cloud Computing

: Cloud Computing servers provides promising platform for storage of data. Sharing of personal medical records is an emerging patient centric model of health information exchange, which is often outsourced to store at third party, such as cloud providers. The confidentiality of the medical records is major problem when patients use commercial cloud servers to store their medical records because it can be view by everyone, to assure the patients control over access to their own medical records; it is a promising method to encrypt the files before outsourcing and access control should be enforced though cryptography instead of role based access control. There are various other issues such as risks of privacy exposure, scalability in key management, flexible access and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. To achieve fine grained and scalable data access control for medical records stored in semi trusted servers, we leverage attribute based encryption (ABE) techniques to encrypt each patient’s medical record file. In this paper, we describe a new approach which enables secure storage and controlled sharing of patient’s health data. We explore key policy attribute based encryption and multi-authority attribute based encryption to enforce patient access control policy such that everyone can download the data ,but only authorize user can view the medical records. This project also supports multiple owner scenarios and divides the users in the system into multiple security domains that greatly reduce the key management complexity for owners and users. A high degree of patient privacy is guaranteed by exploiting multi-authority ABE.
SUMMON: A Sensitivity And Uncertainty Methodology For MONte Carlo Codes

Accurate and reliable tools for sensitivity analysis and uncertainty quantification are needed to estimate the uncertainties in reactor key parameters (keff, βeff, safety coefficients, .) and to identify possible nuclear data weaknesses. Sensitivity and Uncertainty Methodology for MONte carlo codes has been developed to address the limitations of existing tools and to calculate the sensitivities of the criticality safety parameters and their uncertainties due to uncertainties in nuclear data. The methodology is currently based on the use of the KSEN card of MCNP6 code to perform the eigenvalue sensitivity calculations, although any Monte Carlo code that can produce sensitivity coefficients can be used. The sensitivity coefficients of a reactivity response are derived using the eigenvalue definition of reactivity, which is equivalent to applying Equivalent Generalized Perturbation Theory. Uncertainties are propagated using the “Sandwich Rule” of the “Propagation of Moments” method employing state-of-the-art covariance libraries. The methodology has been applied to the Advanced Lead-cooled Fast Reactor European Demonstrator and a sensitivity and uncertainty analysis has also been performed with the SCALE6.1 system. Good agreement between SUMMON and SCALE has been obtained in the sensitivity analysis and uncertainty quantification. Preliminary results for a change of coolant density scenario are also presented.
Can General Strain Theory be Used to Explain the Relationship Between Recidivism and Secure Placement?

There has been extensive research conducted on recidivism among serious juvenile offenders. This study examines juvenile recidivism through the lenses of General Strain Theory (GST). GST has been used in previous studies to explain recidivism, however, secure placement and its effect on juvenile mental health, has not been studied. The purpose of this study is to test for a relationship between emotional responses like anger and hostility and secure placement, utilizing the Pathways to Desistance data. I will also examine if anger and hostility act as a mediator between secure placement and recidivism. Pathways to Desistance was a prospective study of serious juvenile offenders in Phoenix, Arizona (N = 654) and Philadelphia, Pennsylvania (N = 700). Specifically, I examined if secure placement, as measured by length of time spent in a secure facility (i.e., detention center), affects self-reported offending and criminal history. Anger and hostility were measured using the Brief Symptom Inventory (BSI; Derogatis and Melisaratos, 1983). If results suggested that assigning juveniles to a secure placement does evoke negative emotional responses which in turn increase the likelihood of recidivism, policy reflecting a more constructive deterrent and rehabilitation for juveniles would need to be created.
Machine Learning based Steady State Visual Evoked Potential Brain Computer Interfaces

Electroencephalogram (EEG)-based brain computer interfaces (BCI) are being increasingly sought out in the study of neural engineering, rehabilitation, and brain related sciences, by the research community. BCI enables people who have acute challenges in using their motor skills like hands, feet etc, in leading life that is not locked-in. This report performs a study on the topic of Brain Computer Interface, the science involved and its applications. A study of Machine Learning and its application in the area of Brain Computer Interface is also investigated. Artiﬁcial Neural Networks are studies and results from previous authors are also shared. A continued study in the area of Recurrent Neural Network(RNN) and Long and Short Term Memory (LSTM) are proposed at the end of this study.
Problem Solving Therapy-induced Amygdala Engagement Mediates Weight Loss and Lifestyle Behavior Change in Patients With Obesity and Depression: A Randomized Controlled Trial

Background: Behavioral interventions for obesity have modest and variable effectiveness. Emotion dysregulation may hinder effective weight loss interventions, especially among those with comorbid depression. Whether neural mechanisms related to emotion dysregulation mediate the effect of behavioral intervention on improving health behaviors in obesity combined with depression is unknown. Hyperactivation of the amygdala and loss of functional connectivity with the anterior cingulate regions are neural characteristics of unhealthy responses to negative emotion and accompanying emotion dysregulation. Methods: Using data from a randomized clinical trial of 108 primary care adult patients with obesity and depression, we investigated whether changes in amygdala activity and connectivity following problem solving therapy for depression over 2 months mediate weight loss, physical activity, and/or dietary outcomes at 6 and 12 months. Results: Compared with controls (n=49), intervention participants (n=59) had a significant blunting of the otherwise unhealthy increase in activity of the bilateral amygdala at 2 months (right: -0.83, 95% CI -1.55 to -0.11; left: -0.86, -1.63 to -0.10). While increases in amygdala activity significantly correlated with decreases in leisure-time physical activity levels and total energy expenditure at 12 months in the control group, this relationship was tempered or reversed among intervention participants (for physical activity, right: usual care -692.9, -1365.3 to -20.6; interaction 652.2, -115.4 to 1419.9; left: usual care -686.0, -1127.9 to -244.1; interaction 664.7, 73.1 to 1256.4; for energy expenditure, right: usual care -1.82, -3.08 to -0.56; interaction 1.94, 0.50 to 3.38; left: usual care -1.72, -2.51 to -0.93; interaction 1.89, 0.84 to 2.94). Compared with controls, intervention participants had a significant increase of the otherwise unhealthy loss of amygdala-subgenual anterior cingulate connectivity at 2 months (0.78, 0.05 to 1.52). This increase in functional connectivity significantly correlated with a decrease in BMI at 6 months among intervention participants (usual care 0.04, -0.42 to 0.50; interaction -0.61, -1.20 to -0.02) and an increase in fruit and vegetable intake at 12 months across all participants (usual care 1.42, 0.27 to 2.57; interaction -0.95, -2.36 to 0.46). Conclusions: These findings highlight potential neural mechanism for optimizing the efficacy of behavioral intervention in comorbid obesity and depression.Trial registration: ClinicalTrials.gov#NCT02246413.URL: https://clinicaltrials.gov/ct2/show/NCT02246413.
Virtual repair of ancient Chinese bronze articles

We present a novel method for repairing ancient Chinese bronze articles in digitized photographs, aiming at removing corrosion stains and restoring broken portions of the bronze articles. The key idea of our approach is to separate color from textures during the re-pairing process. We utilize Perlin noise to approximate the roughness of the bronze surface and ﬁll-in the detected portions on the bronze articles in the input image, the chromatic content is added to Perlin noise with the hue and saturation components estimated by pop-ulating the grid over the detected portions with seed values taken from the exterior and interpolating in be-tween to get values at non-integer points. The lighting eﬀect on the detected portions is preserved by estimating the value component with a simple weighted sum. Our method repairs images of bronze articles fairly fast and the repaired images look plausible, as demonstrated for a variety of input images that exhibit diﬀerent kind of defects including corrosion stains and broken portions on bronze articles.
Optimal Deterministic Algorithm for the Hammock(2,2)-Poset Cover Problem

The Poset Cover Problem is a hard optimization problem that has application to problems in data mining where the goal is to come up with directed acyclic networks that characterize the ordering pattern of a given set of sequential data. There have been variations to the problem, one of which is the Hammock( 𝑘 2,2,.,2 )-Poset Cover Problem. It has been shown that the decision version of Hammock( 𝑘 2,2,.,2 )-Poset Cover Problem is NP-complete when 𝑘≥ 3 but in 𝑃 when 𝑘= 1 , the complexity of the problem when 𝑘= 2 was left open. In this study, we focus on the variation where 𝑘= 2 , that is the Hammock( 2,2 )-Poset Cover Problem, where the input is a collection of linear orders over a set and the output is a minimum set of Hammock( 2,2 )-Posets that covers or generates the given linear orders. We present an optimal deterministic algorithm for the problem that runs in
Factors That Shape Bankers' Ethical Performance

Since the banking crisis of 2007, there has been renewed interest in the constitutes of professional ethics in the banking industry as well as its imbedded culture. The public continues to be sensitive on matters relating to ethical and corporate governance failures, and the banking industry has been at the receiving end of some strong ethical criticism. Yet, a decade after the crisis, and despite the subsequent regulatory changes, ethics is still a major issue in an industry where the corporate governance systems implemented by companies have failed to control employee behaviours, even in companies branding themselves as ethical banks. In this paper, we study factors inside and around institutions in the banking industry, that impact on moral anomie in bankers' professional environment. We apply an Ordinary Least Square regression analysis, preceded by exploratory and confirmatory factor analysis, to test the hypothesised relations between anomie and the factors proposed. Our results show that long-term orientation, strategic aggressiveness and competitive intensity do have an influence on anomie. These results are compared to previous research applied in non-financial industries, and prompt the strengthening of corporate governance systems in companies with aggressive corporate cultures.
Large-scale 3D-modeling of a realistic cavern field within a salt dome – Combined application of Griddle , FLAC3D & Python

Predictions of subsidence and horizontal displacement over large cavern fields is most commonly performed using mathematical approaches correlating the underground convergence with a corresponding subsidence field on the surface (Zimmermann 2011). While conceptually simple and easy to perform, these approaches largely rely on mathematical fits and the applicability of the chosen subsidence transfer function, while inevitably not capturing the actual state of deformation in the underground.
ASSESSMENT OF NUMERICAL INTEGRATION SCHEMES APPLIED TO INERTIAL PARTICLE TRACKING IN CYCLONE SEPARATORS

Gas-solid flow is extremely important in many industrial branches, since it is found in various operations, which include pneumatic transport, fluidized beds and several industrial pollution control devices, such as cyclone separators. From a numerical point of view there are basically two treatments for gas-solid flow, namely the Eulerian-Eulerian and the EulerianLagrangian approaches. In the former, both the continuous phase (gas flow field) and the particulate phase (solid particles) are treated in an Eulerian reference frame, i.e., it is assumed that the particulate phase can also be treated as a continuum, being more recommended for dense flows. In the second approach, the particulate phase is treated in a Lagrangian reference frame. The individual particles trajectories are computed as ordinary differential equations based on Newton’s second law. In order to compute particles velocity and position, these ODEs have to be integrated in time. In the present work, a comparative study between different numerical schemes for integrating these ODEs, namely the Euler scheme, the trapezoidal scheme and an analytical scheme, was performed. The assessment of these integration schemes was performed in an LES simulation (with the dynamic turbulence model) of the flow within a small laboratory cyclone separator, operating at a moderate Reynolds number. The grade efficiency curves are compared to experimental data for each scheme. The numerical code utilized is an in-house tool, UNSCYFL3D, based on the finite volume approach on unstructured grids with the SIMPLE algorithm for pressure-velocity coupling. The results shows that, regardless of the integration scheme, the usage of sub-time steps are mandatory for the integration of particle motion in order to improve results and reduce the cost of the simulations. Also, both the trapezoidal and analytical schemes provide quite similar results, whereas the Euler scheme requires a smaller particle substep to ensure accurate solutions.
Discussion on the paper BOOTSTRAP FOR DEPENDENT DATA : A REVIEW by Jens-Peter Kreiss and Efstathios Paparoditis

Congratulations to the authors, this is a very clear and very informative overview on bootstrap for dependent data. Over the past 30 years, research on resampling methods and bootstrap procedures in time series analysis and for dependent data has become a dark thick forest of a variety of approaches. It is very helpful to have this clear and detailed review paper that shed some light on this field of research.
Cost-Sensitive learning on classification

Real-world predictive data mining (classification or regression) problems are often cost sensitive, meaning that different types of prediction errors are not equally costly. In this paper we propose a new algorithm for cost-sensitive classification in a multiple time series prediction problems. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. The proposed model is evaluated in a real world application based on a network of satellite network map distributed in land spatial pattern evolution in Cheng d u Plain. These satellite networks generate multiple time series data representing land spatial pattern. This study presents a new algorithm for cost-sensitive classification that deal with class imbalance using both recompiling and CSL. The method combines and compares several sampling methods with CSL using support vector machines (SVM). We build our cost-benefit model for the prediction process as a function of satellite network in a distributed land spatial and measured the optimum number of satellite network that will balance the expenses of the system with the prediction accuracy.
FOUNDATIONS IN FACTORS THE QUEST TO CAPTURE EFFICIENT ALPHA MEANS CHALLENGING MODERN PORTFOLIO

Although the efficacy of style factors conflicts with modern financial theory, they have been successfully employed for more than 40 years to improve upon passive capitalization weighted equity portfolios. Empirical studies have repeatedly shown style factors outperform capitalization weighted benchmarks across most global markets. These results are considered anomalous because they are inconsistent with the concept that expected return is determined solely by an investment’s sensitivity to the broader market, traditionally referred to as market beta.
On the Nature of Instabilities of Traffic Flow – What Traffic Data and Mathematics can tell us

We analyze detector data from several hundred traffic jams on freeways in Germany, Holland, England, and the USA with respect to the nature of traffic instabilities. By applying dedicated analyzing tools, we summarize the qualitative aspects in terms of the stylized facts of jam propagation. In the overwhelming majority of all cases, the data are compatible with linear string instabilities of the convective type, i.e., perturbations grow but eventually propagate out of the congested region. We quantify the instability in terms of linear growth rate, wavelength of the oscillations, and propagation velocity as a function of the empirically determined bottleneck strength. On the theoretical side, we derive analytical criteria for linear and convective stability applicable to a wide range of microscopic and macroscopic models and divide these models into five stability classes which uniquely determine the set of observable spatiotemporal patterns in real systems. Finally, we show, by means of approximate analytic solutions to systems with sustained localized noise (at the bottleneck) that controversial phenomena such as the “pinch effect” can be quantitatively explained by the phenomenon of convective instability using stability quantities that can be measured but also analytically derived from the corresponding microscopic or macroscopic model.
A Joint Financial Event Entity Extracting Method Based on Integrating of Sequence Labeling and Question Answering

Event detection is one of the important tasks in the field of public opinion monitoring and financial risk control. The core of event detection lies in the entity extraction of a particular type of event. The CCKS committee sets up a task for event entity detection in the field of finance, which requires the extraction of the entity involved in a specific type of events in the text. To solve this problem, we develop a model which combines event entity extraction model(BERT - QA) based on question answering and named entity recognition model(BERT-CRF-NER) based on sequence labeling. Our best fusion model on the official test dataset achieves the F1-score of 0.8390 which ranks the first place in the competition.
A study of characteristics extraction of dynamic pressure signals in pipeline based on EMD

Characteristic selection is a key to accurate signal recognition. Specific to the signal recognition of dynamic pressure waves in pipelines, this article proposes to use EMD method to decompose dynamic pressure signals into a series of IMFs, adopt the correlation theories of signals to determine and eliminate noise, retain valid IMFs from 4th to 8th level and extract kurtosis, energy and statistical characteristics. The experiment analyses and verifies that the energy or statistical characteristics (mean variance, mean standard deviation, mean range and mean quartile range) can effectively represent signals.
MULTIVARIATE VOLATILITY AND SPILLOVER EFFECTS IN FINANCIAL MARKETS CASE STUDY USA AND MAJOR EUROPEAN STOCK MARKETS

This Paper Examine the Spillover Effect between USA and Major European Stock Markets . This issue is carried out through DCC form of EGARCH model by Nelson 1991. The empirical evidence suggests that there is spillover effect from London market to New York, Pairs and Frankfurt stock markets, Within the Europe Stock Markets there are unidirectional volatility spillover effect from Frankfurt to Paris and from Paris to London. Volatility increases induced by bad news are transmitted more strongly than volatility declines.
Accuracy of the HTC Vive Tracker for Indoor Localization

—The area of indoor localization has been extensively explored by researchers in recent years. Many of the approaches either require prior knowledge of the local environment, or require a complex setup of instruments to achieve the desired degree of localization. In this research, we examine an approach that makes use of readily available equipment from HTC. The Vive Tracker uses IR on a local projector (lighthouse) to achieve a localized position that is relative to the local projector. We examine an implementation of the HTC Vive Tracker in a robotic swarm test scenario and determine the degree of accuracy that can be obtained in this approach. In this testing, we show that there is radial drift that affects the accuracy, and must be accounted for in deployment solutions. Finally, we present and demonstrate a leader-follower algorithm using the HTC Vive Tracker and discuss methods of addressing the issues discovered by this research.
IMAGE PROCESSING APPLICATIONS IN LIFE SAVER DRONES

Many people residing in hilly regions have a serious problem with their health that needs to be monitored. Due to the insufficient medical support provided in the rural areas, people living there face difficulties during medical emergencies. So we have designed a system that detects human pulse and heart rate (BPM) using Image Processing without the body contact. It can also be used in cases of national security force in mountainous regions at adverse climatic conditions. In such cases the human body may not support the climatic condition and might require medical support at any instance of time. In such cases an immediate medical assistance and regular monitoring of the person’s health condition is required. So, Doctor who lives nearby the location will understand the state of an injured person before he arrives to the hospital. The main principle involved in our system, it helps to calculate the heart rate (BPM) through the face detection by using Deep Learning and the results are forwarded to the nearby physician using which the required medical assistance is provided.