Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic geometry modeling and performance evaluation of mmWave cellular communications

In this paper, a new mathematical framework to the analysis of millimeter wave cellular networks is introduced. Its peculiarity lies in considering realistic path-loss and blockage models, which are derived from experimental data recently reported in the literature. The path-loss model accounts for different distributions for line-of-sight and non-line-of-sight propagation conditions and the blockage model includes an outage state that provides a better representation of the outage possibilities of millimeter wave communications. By modeling the locations of the base stations as points of a Poisson point process and by relying upon a noise-limited approximation for typical millimeter wave network deployments, exact integral expressions for computing the coverage probability and the average rate are obtained. With the aid of Monte Carlo simulations, the noise-limited approximation is shown to be sufficiently accurate for typical network densities. Furthermore, it is shown that sufficiently dense millimeter wave cellular networks are capable of outperforming micro wave cellular networks, both in terms of coverage probability and average rate.
A Hurricane Wind Speed Retrieval Model for C-Band RADARSAT-2 Cross-Polarization ScanSAR Images

A hybrid backscattering model is built to provide a consistent description for C-band VH- and VV-polarized normalized radar cross sections (NRCSs). Ocean surface co- and cross-polarized NRCS are both treated as a sum of Bragg and non-Bragg scattering components. To better understand the synthetic aperture radar (SAR) observed NRCS signals under high-wind conditions, five C-band RADARSAT-2 dual-polarization SAR hurricane images and the collocated wind vectors measured by the airborne stepped-frequency microwave radiometer (SFMR) are collected. Based on the match-up data, we add a non-Bragg term in the composite Bragg theory to explain the discrepancy between the measurements in the cross-polarization channel and the existing theory results. The non-Bragg scattering to Bragg scattering ratio ( $B_{r}$ ) is found to be a constant. We build the hybrid backscattering model with $B_{r}$ and establish a relationship between the cross-polarization NRCS and the radar incidence angle under different wind conditions. The NRCS dependence on incidence angle is simulated by the hybrid backscattering model. Finally, a C-band Cross-Polarization Coupled-Parameters Ocean (C-3PO) model is developed to retrieve hurricane winds using VH-polarized ScanSAR by including the radar incidence angle. The collocated SAR and SFMR data sets are separated into two parts: data set-A, for hybrid backscattering model derivation and C-3PO model coefficients tuning, and data set-B, for hurricane wind validation. C-3PO model validation results show that the model is suitable for ocean surface wind mapping from RADARSAT-2 cross-polarization ScanSAR images. The retrieval has a root-mean-square error less than 3 m/s for wind speed up to 40 m/s.
ACM SIGACT News Distributed Computing Column 31: quantum computers meet distributed computing

We present a brief survey of results where quantum information processing is useful to solve distributed computation tasks. We describe problems that are impossible to solve using classical resources but that become feasible with the help of quantum mechanics. We also give examples where the use of quantum information significantly reduces the need for communication. The main focus of the survey is on communication complexity but we also address other distributed tasks.
ACCEPTED AUTHOR VERSION OF THE MANUSCRIPT: The effect of different concentrations of caffeine, pentoxifylline and 2’-deoxyadenosine on the biological properties of frozen-thawed canine semen

effect of different of and 2’deoxyadenosine on the biological properties of frozen-thawed canine Abstract Artificial insemination (AI) and semen cryopreservation are the most accessible and commonly used techniques for breeding domestic animals. Among many parameters, such as plasma membrane integrity and acrosome structure, one of key factor that determine the quality of frozen-thawed samples for artificial insemination is sperm motility. Sperm motility is one of the key parameters that determine the quality of frozen-thawed samples for AI. The total number of progressively motile spermatozoa in thawed canine semen is correlated with fertility. A variety of substances were used to compare sperm motility with the control. The aim of this study was to determine the effect of semen extender supplementation with motility stimulants, pentoxifylline (PTX), caffeine (CAF) and 2’-deoxyadenosine (DX), after different post-thaw incubation times (30, 60, 120 min) on the motility, selected kinematic parameters, plasma membrane integrity and mitochondrial membrane potential of cryopreserved canine spermatozoa. During attempts to improve the quality of cryopreserved semen, the applied substances exerted beneficial effects at a concentration of 10 mM. We demonstrated that both phosphodiesterase inhibitors, caffeine and pentoxifylline, as well as 2'-deoxyadenosine increased the motility and selected kinematic parameters of thawed canine spermatozoa. determine the effect of semen extender with pentoxifylline (PTX) and 2’-deoxyadenosine (DX), after different post-thaw incubation times (30, 60, 120 min) on the motility, selected kinematic parameters, plasma membrane integrity and mitochondrial membrane potential
Behavior enhanced deep bot detection in social media

Social bots are regarded as the most common kind of malwares in social platform. They can produce fake messages, spread rumours, and even manipulate public opinions. Recently, massive social bots are created and widely spread in social platform, they bring negative effects to public and netizen security. Bot detection aims to distinguish bots from human and it catches more and more attentions in recent years. In this paper, we propose a behavior enhanced deep model (BeDM) for bot detection. The proposed model regards user content as temporal text data instead of plain text to extract latent temporal patterns. Moreover, BeDM fuses content information and behavior information using deep learning method. To the best of our knowledge, this is the first trial that applies deep neural network in bot detection. Experiments on real world dataset collected from Twitter also demonstrate the effectiveness of our proposed model.
Incorporating Illumination Constraints in Deformable Models for Shape from Shading and Light Direction Estimation

We present a method for the integration of nonlinear holonomic constraints in deformable models and its application to the problems of shape and illuminant direction estimation from shading. Experimental results demonstrate that our method performs better than previous Shape from Shading algorithms applied to images of Lambertian objects under known illumination. It is also more general as it can be applied to non-Lambertian surfaces and it does not require knowledge of the illuminant direction. In this paper, (1) we first develop a theory for the numerically robust integration of nonlinear holonomic constraints within a deformable model framework. In this formulation, we use Lagrange multipliers and a Baumgarte stabilization approach (1972). (2) We also describe a fast new method for the computation of constraint based forces, in the case of high numbers of local parameters. (3) We demonstrate how any type of illumination constraint, from the simple Lambertian model to more complex highly nonlinear models can be incorporated in a deformable model framework. (4) We extend our method to work when the direction of the light source is not known. We couple our shape estimation method with a method for light estimation, in an iterative process, where improved shape estimation results in improved light estimation and vice versa. (5) We perform a series of experiments.
A method to track cortical surface deformations using a laser range scanner

This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surface and two-dimensional (2-D) nonrigid image registration, we developed a method to track surface motion during neurosurgical procedures. A series of experiments devised to evaluate the performance of the developed shift-tracking protocol are reported. In a controlled, quantitative phantom experiment, the results demonstrate that the surface shift-tracking protocol is capable of resolving shift to an accuracy of approximately 1.6 mm given initial shifts on the order of 15 mm. Furthermore, in a preliminary in vivo case using the tracked LRS and an independent optical measurement system, the automatic protocol was able to reconstruct 50% of the brain shift with an accuracy of 3.7 mm while the manual measurement was able to reconstruct 77% with an accuracy of 2.1 mm. The results suggest that a LRS is an effective tool for tracking brain surface shift during neurosurgery.
STARR: A Speech Test for Evaluation of the Effectiveness of Auditory Prostheses Under Realistic Conditions

Objectives: The objective was to develop and evaluate a new sentence test, the Sentence Test with Adaptive Randomized Roving levels, intended to emulate everyday listening experience, using both normal-hearing (NH) and cochlear implant (CI) groups, examining practicality, learning, test–retest variability, and interlist variability. Design: In experiment 1, each of 25 NH adults was tested using five lists, each comprising 30 sentences. One male and one female speaker each spoke 15 sentences. Ten sentences were presented at each of three presentation levels: 50, 65, and 80 dB SPL. The relative level of a speech-shaped noise was varied adaptively to estimate the speech reception threshold (SRT). Counterbalance for list order was achieved by staggering the allocation of lists to participants. To allow assessment of learning effects, no practice was given. The variability of mean SRTs across lists was small, but correction factors were derived for each list so that, after correction, all lists gave the same mean SRT. Test–retest variability was estimated by examining the corrected SRTs for each subject’s five lists. In experiment 2, 25 CI users each received one test list after a small amount of practice. Experiment 3 examined the effect of speech rate using time-compressed speech, for age-matched NH and CI users. Results: The mean SRT for the NH participants was approximately −6 dB and was similar for the male and female speakers. There was a small but significant improvement in SRTs between the first and later lists administered, but no further improvement for subsequent lists. On the basis of the variability of the corrected SRTs within each participant, a 2.2 dB difference in SRT is meaningful for comparisons using one test list per condition, for a single participant. The percentage of key words correct varied with presentation level over a 13% range, being best at 65 dB SPL. Only 40% of the CI group achieved an SRT lower than 20 dB for both speakers. There was large individual variability in the SRTs, and SRTs were higher for the female than for the male speaker. For the CI participants, the percentage of key words correct varied markedly with level, from 19% at the lowest level to 57% at the medium level. Time compression had a small effect for NH participants but a very large effect for CI participants. Conclusions: The Sentence Test with Adaptive Randomized Roving levels seems practical to administer and is reasonably sensitive. For NH participants, a 2.2 dB difference in SRT is meaningful for a single list per condition and a single participant. Although learning effects were small for NH participants, it seems prudent to provide some practice sentences when testing hearing-impaired or CI participants. The very large effect of time compression for the CI group has implications for live voice testing of children, because speech rate is only poorly controlled in such testing.
Computing bounding volume hierarchies using model simplification

This paper presents a framework that uses the outputs of model simplification to guide the construction of bounding volume hierarchies for use in, for example, collision detection. Simplified models, besides their application to multiresolution rendering, can provide clues to the object’s shape. These clues help in the partitioning of the object’s model into components that may be more tightly bounded by simple bounding volumes. The framework naturally employs both the bottom-up and the topdown approaches of hierarchy building, and thus can have the advantages of both approaches. Experimental results show that our method built on top of the framework can indeed improve the bounding volume hierarchy, and as a result, significantly speedup the collision detection.
Revealing Comprehensive Genotype-Phenotype Associations through Logic Relationships

A novel approach which employs principles of higher order logic analyses was developed to systematically correlate phylogenetic data with phenotype profiles by identification of phenotype specific patterns of presence of multiple proteins. For example, for most genomes expressing trait A, the presence of protein C presumes the presence of protein B, while for other genomes (not expressing the trait) the presence of protein C presumes the absence of protein B. We demonstrate that the phenotype specific patterns reflect fundamental structural changes in the genotype of microorganisms in relation to conditions provided by presence/absence of a trait. We discover many previously unidentified genotype– phenotype associations on the level of fundamental biochemical processes.
The Role of Metastables in the Formation of an Argon Discharge in a Two-Pin Geometry

The breakdown process in gases is a versatile research topic. Numerous processes play more or less important roles in discharge formation, strongly depending on the gas mixture, the electrode configuration, the applied electric field, the size of the geometry, and even on the structures surrounding the active volume where the breakdown takes place. We focus our research on the breakdown process in argon at 700 mbar, in a pin-pin (point-to-point) electrode geometry, with increasing positive voltage at the active electrode. The voltage rises by 100 V/ns. We use a 2-D fluid model to examine the formation of a charged channel between the electrodes under given conditions. We find that the results describe previous experiments reasonably well. We also explore the role of excited argon atoms at (4s) metastable levels in the breakdown process, and we conclude that the ionization path with an intermediate step containing the metastables does indeed make a notable difference in the breakdown process.
Development of real time internet of things (IoT) based air quality monitoring system

The atmospheric air pollution is a major concern in modern cities, especially in developing countries like Malaysia. In this paper, we have reported an effective implementation for Internet of Things used for monitoring the level of air pollution based on Malaysia Air Pollution Index (API). The low-cost and real time system would be able to monitor regular air quality pollutants including Particulate Matter (PM) of PM2.5, PM10 and Carbon Monoxide (CO) gas as well as the temperatures and humidity of the surroundings. The system has capability to detect Good, Moderate, Unhealthy, Very Unhealthy and Hazardous API status. Based on 5 weeks of experimental API monitoring result on specified test location, the system was able to demonstrate promising results in providing a reliable real time monitoring of the air quality condition.
Adaptive output feedback tracking control of nonlinear systems with dynamic uncertainties and prescribed performance

In this paper, adaptive prescribed performance output feedback dynamic surface control is proposed by introducing a performance function and an output error transformation for a class of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign. Radial basis function neural networks are used to approximate the unknown nonlinear functions. K-filters are employed to estimate the unmeasured states. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results illustrate the effectiveness of the proposed approach.
An Alternatively Spliced Variant of CXCR3 Mediates the Inhibition of Endothelial Cell Growth Induced by IP-10, Mig, and I-TAC, and Acts as Functional Receptor for Platelet Factor 4

The chemokines CXCL9/Mig, CXCL10/IP-10, and CXCL11/I-TAC regulate lymphocyte chemotaxis, mediate vascular pericyte proliferation, and act as angiostatic agents, thus inhibiting tumor growth. These multiple activities are apparently mediated by a unique G protein–coupled receptor, termed CXCR3. The chemokine CXCL4/PF4 shares several activities with CXCL9, CXCL10, and CXCL11, including a powerful angiostatic effect, but its specific receptor is still unknown. Here, we describe a distinct, previously unrecognized receptor named CXCR3-B, derived from an alternative splicing of the CXCR3 gene that mediates the angiostatic activity of CXCR3 ligands and also acts as functional receptor for CXCL4. Human microvascular endothelial cell line-1 (HMEC-1), transfected with either the known CXCR3 (renamed CXCR3-A) or CXCR3-B, bound CXCL9, CXCL10, and CXCL11, whereas CXCL4 showed high affinity only for CXCR3-B. Overexpression of CXCR3-A induced an increase of survival, whereas overexpression of CXCR3-B dramatically reduced DNA synthesis and up-regulated apoptotic HMEC-1 death through activation of distinct signal transduction pathways. Remarkably, primary cultures of human microvascular endothelial cells, whose growth is inhibited by CXCL9, CXCL10, CXCL11, and CXCL4, expressed CXCR3-B, but not CXCR3-A. Finally, monoclonal antibodies raised to selectively recognize CXCR3-B reacted with endothelial cells from neoplastic tissues, providing evidence that CXCR3-B is also expressed in vivo and may account for the angiostatic effects of CXC chemokines.
Cerebrovascular Disease, Amyloid Plaques, and Dementia

Amyloid plaques are depositions of amyloid β (Aβ) protein in the cerebral parenchyma and considered as one of the neuropathological diagnostic hallmarks of Alzheimer’s disease (AD). 1 Cerebrovascular disease (CVD) is also highly prevalent among the elderly and is commonly found in patients with AD.2 Autopsy studies conducted during the past 2 decades show that amyloid plaques frequently coexist with CVD, and the 2 may interact to modulate the risks and expression of clinical dementia.2 Neuropathological examinations show that between 6% and 47% individuals with dementia had coexisting AD and CVD pathologies (Table). 3–20 Indeed, mixed pathologies may account for the majority of dementia cases (38%). 11 View this table: Table. Pathology Findings in Dementia Although autopsy study is considered the gold standard for determining the underlying pathology of dementia, it has inherent limitations with correlating pathological and clinical findings. In particular, uncertainties exist with regard to the temporal relationship between the development of brain lesions and dementia syndrome because of the time lag between dementia onset and autopsy. Moreover, the exact severity or extent of certain manifestations of CVD, in particular, white matter changes (WMC), is difficult to quantify in autopsy studies.21 Also, heterogeneity in the definition of CVD lesions (eg, lacunar infarct) might contribute to inconsistency in assessments between neuropathologists.21 Clinical in-vivo study investigating the relationship between amyloid plaques and cognitive impairment has only been made possible a decade ago with the advent of amyloid imaging, such as Carbon-11-labeled Pittsburgh Compound B (PiB) positron emission tomography (PET). Other amyloid ligands, such as flutemetamol (GE-067), florbetaben (BAY-94–9172, AV-1), and florbetapir (AV-45) have also become available to the market in recent years.22 Amyloid PET can detect comorbid amyloid deposition in patients with high vascular burden.23 We reviewed the potential interactions between CVD and amyloid plaques …
Monitoring Online Tests through Data Visualization

We present an approach and a system to let tutors monitor several important aspects related to online tests, such as learner behavior and test quality. The approach includes the logging of important data related to learner interaction with the system during the execution of online tests and exploits data visualization to highlight information useful to let tutors review and improve the whole assessment process. We have focused on the discovery of behavioral patterns of learners and conceptual relationships among test items. Furthermore, we have led several experiments in our faculty in order to assess the whole approach. In particular, by analyzing the data visualization charts, we have detected several previously unknown test strategies used by the learners. Last, we have detected several correlations among questions, which gave us useful feedbacks on the test quality.
Approximating the Wisdom of the Crowd

The problem of “approximating the crowd” is that of estimating the crowd’s majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, “CrowdSense,” that works in an online fashion to dynamically sample subsets of labelers based on an exploration/exploitation criterion. The algorithm produces a weighted combination of the labelers’ votes that approximates the crowd’s opinion.
Communicating Personalized Risk Factors for Lifestyle Coaching

Chronic non-communicable diseases such as diabetes, chronic cardiorenal and respiratory disease and cancer, are serious, burdensome and costly conditions that share a common characteristic: they heavily depend on common behavioural risk factors, such as physical activity, diet, stress, and substance abuse. Despite concerted efforts it has been remarkably difficult to change such lifestyle related disease determinants, as behavioural change is a complex process requiring significant personal responsibility. In this paper we propose a personal mobile eHealth application to communicate personalized lifestyle related health risks and understand their individual impact on personal health condition and disease progression.
A framework for the efficient production of Web applications

To date developers can exploit a wealth of existing languages and tools when designing and implementing Web applications; however, comprehensive and automated tools and techniques for producing these applications are still conspicuously lacking. Here we describe a framework for efficient production of Web applications, which synergistically integrates techniques for automatic code generation with some of the most recent technologies for Web development, such as the J2EE application server and XML. We also describe a general-purpose architecture for Web applications and our mockup-driven fast-prototyping methodology (MODFM), which is based on well-tried techniques such as rapid prototyping and mockup-driven software design. Finally, we discuss an example of a Web application developed with our methodology, a comprehensive university administration system that was recently deployed at a research university. Our initial experience with this system indicates that MODFM and related tools can provide dramatic improvements in developer productivity, while also enhancing the reliability of the resulting applications.