Miracle-dz/UniFAR-FTUs huggingface dataset:
The proposal of a Constructive Particle Swarm Classifier

The data classification task is one of the main tasks within the knowledge discovering from databases (KDD). Its goal is to allow the correct classification of new objects (records from a database), unknown to the classifier, based upon the extraction of knowledge from objects known a priori. These data already known can be used to generate a classification model, or simply to infer the class of new objects, from those whose classes are known. This paper presents a proposal for a classification algorithm, called Constructive Particle Swarm Classifier (cPSClass), which uses mechanisms from the Particles Swarm Clustering algorithm and Artificial Immune Systems to determine dynamically the number of prototypes from a database and use them to predict the correct class to which a new input object should belong. For performance evaluation the cPSClass was applied to some datasets from the literature and its performance was compared with its predecessor version, the non constructive Particle Swarm Classifier, and also the Naïve Bayes algorithm.
Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine

BACKGROUND AND PURPOSE: Current imaging assessment of high-grade brain tumors relies on the Response Assessment in Neuro-Oncology criteria, which measure gross volume of enhancing and nonenhancing lesions from conventional MRI sequences. These assessments may fail to reliably distinguish tumor and nontumor. This study aimed to classify enhancing and nonenhancing lesion areas into tumor-versus-nontumor components. MATERIALS AND METHODS: A total of 140 MRI scans obtained from 32 patients with high-grade gliomas and 6 patients with brain metastases were included. Classification of lesion areas was performed using a support vector machine classifier trained on 4 components: enhancing and nonenhancing, tumor and nontumor, based on T1-weighted, FLAIR, and dynamic-contrast-enhancing MRI parameters. Classification results were evaluated by 2-fold cross-validation analysis of the training set and MR spectroscopy. Longitudinal changes of the component volumes were compared with Response Assessment in Neuro-Oncology criteria. RESULTS: Normalized T1-weighted values, FLAIR, plasma volume, volume transfer constant, and bolus-arrival-time parameters differentiated components. High sensitivity and specificity (100%) were obtained within the enhancing and nonenhancing areas. Longitudinal changes in component volumes correlated with the Response Assessment in Neuro-Oncology criteria in 27 patients; 5 patients (16%) demonstrated an increase in tumor component volumes indicating tumor progression. These changes preceded Response Assessment in Neuro-Oncology assessments by several months. Seven patients treated with bevacizumab showed a shift to an infiltrative pattern of progression. CONCLUSIONS: This study proposes an automatic classification method: segmented Response Assessment in Neuro-Oncology criteria based on advanced imaging that reliably differentiates tumor and nontumor components in high-grade gliomas. The segmented Response Assessment in Neuro-Oncology criteria may improve therapy-response assessment and provide earlier indication of progression.
Analysis, Design and Implementation of Human Fingerprint Patterns System "Towards Age & Gender Determination, Ridge Thickness To Valley Thickness Ratio (RTVTR) & Ridge Count On Gender Detection

The aim of this research is to analyze humans fingerprint texture in order to determine their Age & Gender, and correlation of RTVTR and Ridge Count on gender detection. The study is to analyze the effectiveness of physical biometrics (thumbprint) in order to determine age and gender in humans. An application system was designed to capture the finger prints of sampled population through a fingerprint scanner device interfaced to the computer system via Universal Serial Bus (USB), and stored in Microsoft SQL Server database, while back-propagation neural network will be used to train the stored fingerprint. The specific Objectives of this research are to: Use fingerprint sensor to collect different individual fingerprint, alongside their age and gender, Formulate a model and develop a fingerprint based identification system to determine age and gender of individuals and evaluate the developed system.
Uncoordinated two-sided matching markets

Various economic interactions can be modeled as two-sided matching markets. A central solution concept to these markets are stable matchings, introduced by Gale and Shapley. It is well known that stable matchings can be computed in polynomial time, but many real-life markets lack a central authority to match agents. In those markets, matchings are formed by actions of selfinterested agents, whose behavior is often modeled by Nash dynamics such as best and better response dynamics. In this note, we summarize recent results on Nash dynamics in two-sided markets.
Observability analysis of a vision-aided inertial navigation system using planar features on the ground

In this paper, we present an observability analysis of a vision-aided inertial navigation system (VINS) in which the camera is downward looking and observes a single point feature on the ground. In our analysis, the full INS parameter vector (including position, velocity, rotation, and inertial sensor biases) as well as the 3D position of the observed point feature are considered as state variables. In particular, we prove that the system has only three unobservable directions corresponding to global translations along the x and y axes, and rotations around the gravity vector. Hence, compared to general VINS, an advantage of using only ground features is that the vertical translation becomes observable. The findings of the theoretical analysis are validated through real-world experiments.
Implementation of data mining techniques in upcoding fraud detection in the monetary domains

Fraud detection is a scenario applicable to many industries such as banking and financial sectors, insurance, healthcare, government agencies and law enforcement and more. There has been a drastic increase in recent years, pushing fraud detection more important than ever. Hundreds of millions of dollars are lost to fraud every year. Upcoding fraud is one such fraud in which a service provider acquires additional financial gain by coding a service by upgrading it even though the lesser service has been performed. Incorporating artificial intelligence with data mining and statistics help to anticipate and detect these frauds and minimize costs. Using sophisticated data mining tools, millions of transcations can be searched to spot patterns and detect fraudulent transactions. This paper gives an insight into the various datamining tools which are efficient in detecting upcoding frauds especially in the healthcare insurance sector in India.
Two Dimensional Silicon Micromachined Optical Scanner Integrated With Photo Detector And Piezoresistor

The silicon micromachined optical scanner integrated with photo detectors and piezoresistor has been developed for highly miniaturized scanning type of optical sensors. The scanner is fabricated with silicon micromachining technologies including IC manufacturing methods. It is capable of scanning a light beam spot two dimensionally and photo detection with the information of where the light beam is being scanned. The scanning angle was over 40 deg. x 30 deg. and the photo sensitivity was 0.5 A/W at 680 nm wavelength.
Mild strain cross protection of tristeza: a review of research to protect against decline on sour orange in Florida

Tristeza, caused by Citrus tristeza virus (CTV), has long been present in Florida but outbreaks of decline on sour orange rootstock were occasional events until the late 1970s. Sour orange rootstock was valued for the high quality of fruit produced and was widely used because of its tolerance of citrus blight, a disease of unknown etiology. Research was directed towards the selection and screening of mild strains of CTV which could protect against sour orange decline strains. Following the introduction of Toxoptera citricida (also known as the brown citrus aphid) in 1995 there was a greater concern for maintaining production of existing blocks of citrus on sour orange rootstock. Availability of the CTV genome sequence around the same time as well as molecular characterization of in planta CTV populations led to the selection of mild CTV isolates which when inoculated into existing field trees, extended the productive life of the groves and enabled a more graduate replanting of trees on CTV-tolerant rootstocks. The history of CTV in Florida and the methods developed to select mild isolates for use for mild strain cross protection will be reviewed.
Leflunomide for the treatment of rheumatoid arthritis: a systematic review and metaanalysis.

OBJECTIVE To systematically review the evidence from clinical trials on the efficacy and toxicity of leflunomide for the treatment of active rheumatoid arthritis (RA). METHODS We searched Medline, Embase, Current Contents, and the Cochrane Controlled Trial Register for human randomized controlled trials (RCT) and controlled clinical trials up to December 2001. We also hand-searched reference lists and conference proceedings and consulted content experts. Relative benefit (RB), and weighted mean differences or standardized mean differences with their 95% confidence interval (95% CI) were calculated. RESULTS Six RCT totaling 2044 patients with RA were included in this review. Using specific criteria, all trials were considered of high methodological quality. Leflunomide improved the ACR20 response rate roughly 2 times over placebo both at 6 months (RB = 1.93, 95% CI 1.51, 2.47) and at 12 months (RB = 1.99, 95% CI 1.42, 2.77). Other clinical outcomes of disease activity and function and radiological scores were also significantly better for leflunomide patients than those taking placebo. No significant differences for most of the outcomes were observed between leflunomide and sulfasalazine (SSZ) or methotrexate (MTX). Adverse events were more common in the leflunomide group, but withdrawal rates were fewer than for placebo. Overall, withdrawal rates and adverse events in the leflunomide group were not different from SSZ or MTX. CONCLUSION Leflunomide improves all clinical outcomes and delays radiographic progression at 6 and 12 months of RA treatment compared to placebo. Its efficacy and adverse events at 2 years of treatment are comparable to SSZ and MTX. Longterm efficacy and toxicity remain to be established.
On the relation between the WoS impact factor, the Eigenfactor, the SCImago Journal Rank, the Article Influence Score and the journal h-index

Four alternatives to the journal Impact Factor (IF) indicator are compared to find out their similarities. Together with the IF, the SCImago Journal Rank indicator (SJR), the EigenfactorTM score, the Article InfluenceTM score and the journal h-index of 77 journals from more than ten fields were collected. Results show that although those indicators are calculated with different methods and even use different databases, they are strongly correlated with the WoS IF and among each other. These findings corroborate results published by several colleagues and show the feasibility of using free alternatives to the Web of Science for evaluating scientific journals.
Chaos Monkey: Increasing SDN Reliability through Systematic Network Destruction

As modern networking applications become increasingly dynamic and high-bandwidth, software defined networking (SDN) has emerged as an agile, cost effective architecture with widespread adoption across industry. In SDN, the control-plane program runs on a logically-centralized controller which directly configures the packet-handling mechanisms in the underlying switches using an open API (e.g., OpenFlow). While the controller makes it exceptionally convenient for a network operator to control and manage a network, the controller requires complex logic and becomes a single point of failure within the network. As a result, configuration errors by the controller could be extremely costly for the network provider. Several SDN controllers have been developed since the conception of SDN, and network operators have utilized very traditional means of identifying bugs in the controller, such as unit testing and model checking [1]. However, it has become apparent that these methods cannot practically handle the inherent complexity of the controller platform that manages large networks. Ultimately, one major source of this complexity are network failures, as they trigger execution of unexplored portions of code; these network failures are inevitable, costly, and considering all possible interleaving of bugs is simply unfeasible. To address this problem, we propose “Chaos Monkey” a real-time post-deployment failure injection tool. Inspired by industry practices in the cloud [2], Chaos Monkey is intended to systematically introduce failure (e.g., link failure, network failure) into a network. Chaos Monkey is guided by the following design principles:
Modelling the Full MIMO Matrix Using the Richness Function

A richness function depending only on normalized eigenvalues of the MIMO channel matrix is introduced. The function is a good measure for comparing environments illuminated by the same antennas. A new channel model incorporating the effective number of scatterers and propagation between the antenna environments s introduced, and the richness function is introduced to extract channel parameters. Examples of indoor environments consisting of offices on the same and on different floors are given.
The Case for the Precision Timed (PRET) Machine

It is time for a new era of processors whose temporal behavior is as easily controlled as their logical function. We call them precision timed (PRET) machines. Our basic argument is that real-time systems, in which temporal behavior is as important as logical function, are an important and growing application; processor architecture needs to follow suit.
Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation

The brain contains a complex network of axons rapidly communicating information between billions of synaptically connected neurons. The morphology of individual axons, therefore, defines the course of information flow within the brain. More than a century ago, Ramón y Cajal proposed that conservation laws to save material (wire) length and limit conduction delay regulate the design of individual axon arbors in cerebral cortex. Yet the spatial and temporal communication costs of single neocortical axons remain undefined. Here, using reconstructions of in vivo labelled excitatory spiny cell and inhibitory basket cell intracortical axons combined with a variety of graph optimization algorithms, we empirically investigated Cajal's conservation laws in cerebral cortex for whole three-dimensional (3D) axon arbors, to our knowledge the first study of its kind. We found intracortical axons were significantly longer than optimal. The temporal cost of cortical axons was also suboptimal though far superior to wire-minimized arbors. We discovered that cortical axon branching appears to promote a low temporal dispersion of axonal latencies and a tight relationship between cortical distance and axonal latency. In addition, inhibitory basket cell axonal latencies may occur within a much narrower temporal window than excitatory spiny cell axons, which may help boost signal detection. Thus, to optimize neuronal network communication we find that a modest excess of axonal wire is traded-off to enhance arbor temporal economy and precision. Our results offer insight into the principles of brain organization and communication in and development of grey matter, where temporal precision is a crucial prerequisite for coincidence detection, synchronization and rapid network oscillations.
Self-healing Effect of Boron Nitride Precipitation on Creep Cavitation in Austenitic Stainless Steel

In this study, the precipitation of boron nitride (BN) on creep cavity surfaces during creep and its beneficial effect on the creep rupture properties are reported for a type 304 austenitic stainless steel. In the conventional 304 stainless steel, the trace of soluble S segregates strongly on the creep cavity surfaces and promotes its growth during creep. In the modified 304 stainless steel added with B, Ce, and Ti, the trace of soluble S is removed effectively by the formation of Ce and Ti sulfides, and the segregation of S on the creep cavity surface is replaced by the precipitation of the BN compound. As BN is very stable at high temperatures, its precipitation on the cavity surface is expected to suppress the creep cavity growth rate. It is suggested that the precipitation of BN on the creep cavity surface provides austenitic stainless steel with the function of self-healing for creep cavitation with an associated increase in the creep rupture strength and ductility.
L-glutamine decreases the severity of mucositis induced by chemoradiotherapy in patients with locally advanced head and neck cancer: A double-blind, randomized, placebo-controlled trial

The incidence of severe mucositis in the oral cavity, pharynx and larynx is high among patients with head and neck cancer (HNC) receiving chemoradiotherapy (CRT), resulting in significant pain and impairment of quality of life. The present study investigated whether L-glutamine (glutamine) decreases the severity of mucositis in the oral cavity, pharynx and larynx induced by CRT. This double-blind, randomized, placebo-controlled trial included 40 untreated patients with squamous cell carcinoma of the nasopharynx, oropharynx, hypopharynx or larynx. Patients received 66 or 70 Gy of total radiation at the rate of 2 Gy/fraction daily and 5 fractions/week. Cisplatin (20 mg/m2) and docetaxel (10 mg/m2) were intravenously co-administered once a week for 6 weeks. Patients were randomized to orally receive either glutamine (group G) or placebo (group P) at a dose of 10 g 3 times a day throughout the CRT course. Mucositis was assessed using the National Cancer Institute Common Terminology Criteria for Adverse Events version 3.0. The primary end point was mucositis severity. Mucositis developed in all patients. A maximal mucositis grade of G4 was observed in 0 and 25% group G and P patients, respectively, while that of G2 was observed in 10 and 0% group G and P patients, respectively (p=0.023). Glutamine significantly decreased the maximal mucositis grade (group G, 2.9±0.3; group P, 3.3±0.4; p=0.005) and pain score at weeks 4, 5 and 6. Glutamine significantly decreased mucositis severity in the oral cavity, pharynx and larynx induced by CRT in patients with HNC.
Intelligent Vision Systems for Robotic Applications

Vision-based sensors are a key component for robot-systems, where many tasks depend on image data. Realtime control constraints bind a lot of processing power for only a single sensor modality. Dedicated and distributed processing resources are the "natural" solution to overcome this limitation. This paper presents experiments, using embedded processors as well as dedicated hardware, to execute various image (pre)processing tasks. Architectural concepts and requirements for intelligent vision systems have been acquired.
Adaptive scalable video streaming in wireless networks

In this paper, we investigate the optimal streaming strategy for dynamic adaptive streaming over HTTP (DASH). Specifically, we focus on the rate adaptation algorithm for streaming scalable video (H.264/SVC) in wireless networks. We model the rate adaptation problem as a Markov Decision Process (MDP), aiming to find an optimal streaming strategy in terms of user-perceived quality of experience (QoE) such as playback interruption, average playback quality and playback smoothness. We then obtain the optimal MDP solution using dynamic programming. We further define a reward parameter in our proposed streaming strategy, which can be adjusted to make a good trade-off between the average playback quality and playback smoothness. We also use a simple testbed to validate our solution. Experiment results show the feasibility of the proposed solution and its advantage over the existing work.
Effects of Adhesion Dynamics and Substrate Compliance on the Shape and Motility of Crawling Cells

Computational modeling of eukaryotic cells moving on substrates is an extraordinarily complex task: many physical processes, such as actin polymerization, action of motors, formation of adhesive contacts concomitant with both substrate deformation and recruitment of actin etc., as well as regulatory pathways are intertwined. Moreover, highly nontrivial cell responses emerge when the substrate becomes deformable and/or heterogeneous. Here we extended a computational model for motile cell fragments, based on an earlier developed phase field approach, to account for explicit dynamics of adhesion site formation, as well as for substrate compliance via an effective elastic spring. Our model displays steady motion vs. stick-slip transitions with concomitant shape oscillations as a function of the actin protrusion rate, the substrate stiffness, and the rates of adhesion. Implementing a step in the substrate’s elastic modulus, as well as periodic patterned surfaces exemplified by alternating stripes of high and low adhesiveness, we were able to reproduce the correct motility modes and shape phenomenology found experimentally. We also predict the following nontrivial behavior: the direction of motion of cells can switch from parallel to perpendicular to the stripes as a function of both the adhesion strength and the width ratio of adhesive to non-adhesive stripes.
Romidepsin alone or in combination with anti-CD20 chimeric antigen receptor expanded natural killer cells targeting Burkitt lymphoma in vitro and in immunodeficient mice

ABSTRACT Facilitating the development of alternative targeted therapeutic strategies is urgently required to improve outcome or circumvent chemotherapy resistance in children, adolescents, and adults with recurrent/refractory de novo mature B-cell (CD20) non-Hodgkin lymphoma, including Burkitt lymphoma (BL). Romidepsin, a histone deacetylase inhibitor (HDACi), has been used to treat cutaneous T-cell lymphoma. We have demonstrated the significant anti-tumor effect of anti-CD20 chimeric antigen receptor (CAR) modified expanded peripheral blood natural killer (exPBNK) against rituximab-sensitive and -resistant BL. This study examined the anti-tumor activity of romidepsin alone and in combination with anti-CD20 CAR exPBNKs against rituximab-sensitive and -resistant BL in vitro and in vivo. We found that romidepsin significantly inhibited both rituximab-sensitive and -resistant BL cell proliferation in vitro (P < 0.001) and induced cell death in rituximab-sensitive Raji (P < 0.001) and cell cycle arrest in rituximab-resistant Raji-2R and Raji-4RH (P < 0.001). Consistent with in vitro observations, we also found romidepsin significantly inhibited the growth of rituximab-sensitive and -resistant BL in BL xenografted NSG mice. We also demonstrated that romidpesin significantly induced the expression of Natural Killer Group 2, Member D (NKG2D) ligands MICA/B in both rituximab-sensitive and -resistant BL cells (P < 0.001) resulting in enhancement of exPBNK in vitro cytotoxicity through NKG2D. Finally, we observed the combination of romidepsin and anti-CD20 CAR exPBNK significantly induced cell death in BL cells in vitro, reduced tumor burden and enhanced survival in humanized BL xenografted NSG mice (p < 0.05). Our data suggests that romidepsin is an active HDAC inhibitor that also potentiates expanded NK and anti-CD20 CAR exPBNK activity against rituximab-sensitive and -resistant BL.