Miracle-dz/UniFAR-FTUs huggingface dataset:
Gabor Based Face Recognition Using EBGM and PCA

- Face recognition has received substantial attention in recent years due to applications in Research fields such as biometrics community and computer vision. A lot of face recognition algorithms have been developed during the past decades. These algorithms can be classified into appearance-based and model-based schemes. Principal Component Analysis (PCA) is a typical linear appearance-algorithm, and Elastic Bunch Graph Matching (EBGM) is a two-dimensional model-based approach. This paper reviews the two classical methods and a typical face image database for standard testing. In many applications of Face recognition, the best possible accuracy is required. In such circumstances, existing efficient algorithms do not guarantee good accuracy for the approximations they produce. We propose an efficient algorithm that recognizes novel faces by first localizing a set of landmark features, Approximates the landmark features, and then measuring similarity between these features. For the first approach we use feature based algorithm called as EBGM (Elastic bunch graph matching). For the second approach we use the statistical based algorithm called as PCA (Principal component analysis). The algorithms are implemented on Matlab environment. Scenarios and performance benchmarking are compared for each of the algorithms.
Spitzer IRS Spectroscopy of IRAS-discovered Debris Disks

We have obtained Spitzer Space Telescope Infrared Spectrograph (IRS) 5.5-35 μm spectra of 59 main-sequence stars that possess IRAS 60 μm excess. The spectra of five objects possess spectral features that are well-modeled using micron-sized grains and silicates with crystalline mass fractions 0%-80%, consistent with T Tauri and Herbig AeBe stars. With the exception of η Crv, these objects are young with ages ≤50 Myr. Our fits require the presence of a cool blackbody continuum, Tgr = 80-200 K, in addition to hot, amorphous, and crystalline silicates, Tgr = 290-600 K, suggesting that multiple parent body belts are present in some debris disks, analogous to the asteroid and Kuiper belts in our solar system. The spectra for the majority of objects are featureless, suggesting that the emitting grains probably have radii a > 10 μm. We have modeled the excess continua using a continuous disk with a uniform surface density distribution, expected if Poynting-Robertson and stellar wind drag are the dominant grain removal processes, and using a single-temperature blackbody, expected if the dust is located in a narrow ring around the star. The IRS spectra of many objects are better modeled with a single-temperature blackbody, suggesting that the disks possess inner holes. The distribution of grain temperatures, based on our blackbody fits, peaks at Tgr = 110-120 K. Since the timescale for ice sublimation of micron-sized grains with Tgr > 110 K is a fraction of a Myr, the lack of warmer material may be explained if the grains are icy. If planets dynamically clear the central portions of debris disks, then the frequency of planets around other stars is probably high. We estimate that the majority of debris disk systems possess parent body masses, MPB < 1 M⊕. The low inferred parent body masses suggest that planet formation is an efficient process.
PRONTO: support for real-time decision making

For public transport authorities, the most important aspects for operations are passenger satisfaction and safety. Based on surveys, passengers regard punctuality as the most important aspect, which makes it an important factor of passenger satisfaction for the operators [10]. In the City of Helsinki, the public transport vehicles' movements and status are monitored through a real-time information system, which provides authorities information about punctuality of vehicles. Furthermore, the gathered information is used for route planning and scheduling. Due to the existing information systems in Helsinki, the focus in operation planning has gradually moved from real-time timetables to other factors affecting passenger satisfaction and safety, aiming to find ways to further improve public transport operations. The PRONTO research project focuses on two demonstration cases. One is emergency rescue operations in Dortmund, Germany. The other one is public transport in Helsinki, where the research focuses on developing methods for improving passengers' travel experience through monitoring and analyzing vehicle events in real-time. Through these methods, it is possible to further improve public transport punctuality and especially the driving style that would lead to improved passenger safety and satisfaction, as well as better vehicle endurance. A variety of sensors and connections to existing systems have been implemented in order to provide the PRONTO system with valuable data about the both demonstration cases. This paper presents the activities and achievements so far of the public transport demonstration case.
Joint Linear Precoder-Decoder Optimization over MIMO-CDMA Multipath Channels

This paper addresses the problem of joint transmitter-receiver optimization in the downlink multiple input multiple output (MIMO) DS-CDMA systems over multipath fading channels. Both the transmitter and the receivers are equipped with antenna arrays and the precoding/decoding matrices can be designed to minimize the overall mean-squared-error (MSE) of the system subject to power constraint. The study is based on the spatio-temporal array (STAR) manifold concept. The performance of the proposed algorithm is supported by numerical simulations.
Evolving Fuzzy Neural Networks for Adaptive, On-line Intelligent Agents and Systems

This paper discusses and illustrates one paradigm of neuro-fuzzy techniques for building on-line, adaptive intelligent agents and systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data including new features, new classes, etc. The ECOS framework is presented and illustrated on a particular type of evolving neural networks - evolving fuzzy neural networks. ECOS are orders of magnitude faster than multilayer perceptrons, or fuzzy neural networks and they belong to the new generation of adaptive intelligent systems. ECOS are suitable techniques for building intelligent agents on the WWW, intelligent mobile robots and embedded systems. An ECOS based structure of an intelligent agent is proposed and discussed.
Influence-Based Network-Oblivious Community Detection

How can we detect communities when the social graphs is not available? We tackle this problem by modeling social contagion from a log of user activity, that is a dataset of tuples (u, i, t) recording the fact that user u "adopted" item i at time t. This is the only input to our problem. We propose a stochastic framework which assumes that item adoptions are governed by un underlying diffusion process over the unobserved social network, and that such diffusion model is based on community-level influence. By fitting the model parameters to the user activity log, we learn the community membership and the level of influence of each user in each community. This allows to identify for each community the "key" users, i.e., the leaders which are most likely to influence the rest of the community to adopt a certain item. The general framework can be instantiated with different diffusion models. In this paper we define two models: the extension to the community level of the classic (discrete time) Independent Cascade model, and a model that focuses on the time delay between adoptions. To the best of our knowledge, this is the first work studying community detection without the network.
Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI

Accurate identification of the most relevant brain regions linked to Alzheimer’s disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.
MISO PRECODING FOR TEMPORAL AND SPATIAL FOCUSING

Time reversal precoding accomplishes matched-filtering of the channel in wireless communication systems by precoding before every transmit antenna with the time reversed complex conjugate of the wireless channel impulse response . While time reversal maximizes the received signal energy at a single time instant (as with matched filtering) it does not eliminate intersymbol interference. This is equivalen t to saying that the combined precoder/channel response has significant temporal sidelobes and thus, not sufficiently te mporally focused. We propose a method of multiple-inputsingle-output (MISO) precoding for lowering sidelobe levels to improve temporal focusing. Through simulations, we analyze and show improved spatial focusing properties of the developed method in correlated and uncorrelated frequency selective channels. Although equalizer design is absent in this paper, we include equalizer length into the optimization, using a methodology similar to channel short ening, such that flexible communication system solutions easily follow.
Survival in patients with type 2 diabetes in a Swedish community: skaraborg hypertension and diabetes project.

OBJECTIVE To explore risk factors for all-cause mortality in patients with type 2 diabetes treated in primary care. RESEARCH DESIGN AND METHODS A prospective population-based study of 400 patients with type 2 diabetes who consecutively completed an annual checkup in primary care in Skara, Sweden, during 1992-1993. Vital status was ascertained to year 2000. Baseline characteristics as predictors for mortality were analyzed by Cox regression and expressed as relative risks (RRs), with 95% CIs. RESULTS During a mean follow-up time of 5.9 years, 131 patients died (56 deaths per 1,000 patients per year). In both sexes, all-cause mortality was predicted by HbA(1c) (by 1%; RR 1.14, 95% CI 1.01-1.27), and by LDL-to-HDL cholesterol ratios (1.15, 1.00-1.32). Increased mortality was also seen with prevalent hypertension (1.72, 1.21-2.44), microalbuminuria (1.87, 1.27-2.76), and previous cardiovascular disease (1.70, 1.15-2.50). Subanalyses revealed that increased mortality related to HbA(1c) was restricted to hypertensive patients with type 2 diabetes (1.23, 1.04-1.47). Serum triglycerides (by 1 mmol/l) predicted all-cause mortality in women (1.25, 1.06-1.47). CONCLUSIONS Poor glucose and lipid control and hypertension predicted all-cause mortality. Survival was also predicted by prevalent microalbuminuria and by previous cardiovascular disease. Confirming results from clinical trials, this population-based study has implications for primary and secondary prevention.
The Research of DDoS Attack Detecting Algorithm Based on the Feature of the Traffic

As DDoS attacks can not be detected effectively by traditional methods in time, a DDoS attack detecting algorithm based on the relation of characteristic parameters is researched according to the analysis of the essential characteristic of DDoS. The scheme can detect a DDoS attack traffic in its early stages when the attacking packet's attribute value has no distinct features. It can differentiate DDoS from normal flash crowd traffic. The simulation shows the algorithm is more effective than traditional methods.
Lutein, Zeaxanthin and Meso-zeaxanthin Supplementation Associated with Macular Pigment Optical Density

The purpose of this study was to evaluate the effects of lutein, zeaxanthin and meso-zeaxanthin on macular pigment optical density (MPOD) in randomized controlled trials (RCTs) among patients with age-related macular degeneration (AMD) and healthy subjects. Medline, Embase, Web of Science and Cochrane Library databases was searched through May 2016. Meta-analysis was conducted to obtain adjusted weighted mean differences (WMD) for intervention-versus-placebo group about the change of MPOD between baseline and terminal point. Pearson correlation analysis was used to determine the relationship between the changes in MPOD and blood xanthophyll carotenoids or baseline MPOD levels. Twenty RCTs involving 938 AMD patients and 826 healthy subjects were identified. Xanthophyll carotenoids supplementation was associated with significant increase in MPOD in AMD patients (WMD, 0.07; 95% CI, 0.03 to 0.11) and healthy subjects (WMD, 0.09; 95% CI, 0.05 to 0.14). Stratified analysis showed a greater increase in MPOD among trials supplemented and combined with meso-zeaxanthin. Additionally, the changes in MPOD were related with baseline MPOD levels (rAMD = −0.43, p = 0.06; rhealthy subjects = −0.71, p < 0.001) and blood xanthophyll carotenoids concentration (rAMD = 0.40, p = 0.07; rhealthy subjects = 0.33, p = 0.05). This meta-analysis revealed that lutein, zeaxanthin and meso-zeaxanthin supplementation improved MPOD both in AMD patients and healthy subjects with a dose-response relationship.
Space-Time Continuous Analysis of Waveform Relaxation for the Heat Equation

Waveform relaxation algorithms for partial differential equations (PDEs) are traditionally obtained by discretizing the PDE in space and then splitting the discrete operator using matrix splittings. For the semidiscrete heat equation one can show linear convergence on unbounded time intervals and superlinear convergence on bounded time intervals by this approach. However, the bounds depend in general on the mesh parameter and convergence rates deteriorate as one refines the mesh. Motivated by the original development of waveform relaxation in circuit simulation, where the circuits are split in the physical domain into subcircuits, we split the PDE by using overlapping domain decomposition. We prove linear convergence of the algorithm in the continuous case on an infinite time interval, at a rate depending on the size of the overlap. This result remains valid after discretization in space and the convergence rates are robust with respect to mesh refinement. The algorithm is in the class of waveform relaxation algorithms based on overlapping multisplittings. Our analysis quantifies the empirical observation by Jeltsch and Pohl [SIAM J. Sci. Comput. , 16 (1995), pp. 40--49] that the convergence rate of a multisplitting algorithm depends on the overlap. Numerical results are presented which support the convergence theory.
Adaptive swarm behavior acquisition by a neuro-fuzzy system and reinforcement learning algorithm

Purpose – The purpose of this paper is to present a neuro‐fuzzy system with a reinforcement learning algorithm (RL) for adaptive swarm behaviors acquisition. The basic idea is that each individual (agent) has the same internal model and the same learning procedure, and the adaptive behaviors are acquired only by the reward or punishment from the environment. The formation of the swarm is also designed by RL, e.g. temporal difference (TD)‐error learning algorithm, and it may bring out a faster exploration procedure comparing with the case of individual learning.Design/methodology/approach – The internal model of each individual composes a part of input states classification by a fuzzy net, and a part of optimal behavior learning network which adopting a kind of RL methodology named actor‐critic method. The membership functions and fuzzy rules in the fuzzy net are adaptively formed online by the change of environment states observed in the trials of agent's behaviors. The weights of connections between the .
The PBR Approach: Analysis, Performance and Perspective

Usual RWA (routing and wavelength assignment) algorithms take the lightpath decision based on the network state information existing in those nodes caring about it. Unfortunately, this information might not be accurate enough so that the routing decisions could be incorrectly performed hence driving to a significant connection blocking increment. The prediction-based routing mechanism appears as an appealing and solid contribution challenging the inaccuracy introduced in the RWA problem. A significant topic currently raising many research efforts is the influence of physical impairments on the network performance degradation, in particular on the lightpath selection process. Some parameters should be added to the routing protocol to include information related to physical impairments. Authors in this paper aim at analysing main PBR contributions also presenting main issues related to the introduction of physical parameters in the routing protocol
The day Sherlock Holmes decided to do EDA

Semiconductor design companies are in a continuous search for design tools that address the ever increasing chip design complexity coupled with strict time-to-market schedules and budgetary constraints. A fundamental aspect of the design process that remains primitive is that of debugging. It takes months to close, it introduces costs and it may jeopardize the release date of the chip. This paper reviews the debugging problem and the research behind it over the past 20 years. The case for automated RTL debug tools and methodologies is also made to help ease the manual burden and complement current industrial verification practices.
Segmentation of Cluttered Scenes through Interactive Perception

For robot to perform its tasks competently, robustly and in the right context it has to understand the course of its actions and their consequences. For example, imagine the robot being tasked with the clean up of the breakfast table. The robot is confronted with a heavily cluttered scene and has to be able to tell waste, dirty, clean and valuable objects apart. The robot shall be equipped with the knowledge that will, for instance, stop it from throwing away an expensive item. Herein proposed approach elevates robot’s perception skills in that it utilizes its capabilities to interact with the clutter of objects. This allows for better segmentation and finally also better object recognition by means of constraining the recognition to a region or regions of interest. Similar to Katz et al. [1] and Bergstrom et al. [2], we propose a system that uses a robot arm to induce motions in a scene to enable effective object segmentation. Our system employs a combination of the following techniques: i) estimation of a contact point and a push direction of the robot’s end effector by detecting the concave corners in the cluttered scene, ii) feature extraction using features proposed by Shi and Tomasi and tracking using optical flow, and iii) a novel clustering algorithm to segment the objects. Segmentation of rigid objects from a video stream of objects being moved by the robot has been addressed by Fitzpatrick [3] and Kenney et al. [4]. In contrast, our arm motion is not pre-planned but adapts to the scene, we make use of the 3D data to segment the object candidates from the background and we use a novel clustering approach for the segmentation of textured objects. Overview of the whole system is shown in Fig. 2. The system will be demostrated live during the workshop.
Virtual TMR Schemes Combining Fault Tolerance and Self Repair

Nano-electronic circuits and systems with a minimum feature size of 45 nm and below exhibit an increasing variety of defect and fault mechanisms. Their rising sensitivity to radiation and coupling induced single and multiple event upsets is one problem, new or enhanced aging processes that lead to early lifetime failures (ELF) pose another threat. The compensation of transient fault effects is a well explored area of science, while repair technologies that tackle permanent faults have so far found a broad acceptance only for embedded memories and for FPGA-based systems. In this paper we describe two alternative schemes of fault detection and on-line error correction based on virtual and time-shared triple modular redundancy (TMR). Optionally, both schemes allow for optional built- in self repair at reasonable total cost.
A traffic-efficient fronthaul for the cloud-RAN

As the mobile communication technology evolves towards the cloud RAN based 4G and 5G systems, data traffic of a BS fronthaul which is mainly packed up of sampled baseband TRx signals increases geometrically and makes it hard the existing technologies over optical link handle such a traffic. In this paper, a new fronthaul based on RRPH which integrates baseband PHY into the RRH in order to minimize the fronthaul data traffic rate is proposed. Issues come from the architecture of new fronthaul based BS and the implementation results of it to support IEEE 802.16m specification are also introduced.
The Use of Plasma and Urine Neutrophil Gelatinase Associated Lipocalin (NGAL) and Cystatin C in Early Diagnosis of Septic Acute Kidney Injury in Critically Ill Patients

Aim: To assess and compare the roles of plasma and urine concentrations of neutrophil gelatinase associated lipocalin (NGAL) and Cystatin C for early diagnosis of septic acute kidney injury (AKI) in adult critically ill patients. Methods: Patients were divided into three groups as sepsis-non AKI, sepsis-AKI and non sepsis-non AKI. Plasma samples for NGAL and Cystatin C were determined on admission and on alternate days and urinary samples were collected for every day until ICU discharge. Results: One hundred fifty one patients were studied; 66 in sepsis-non AKI, 63 in sepsis-AKI, 22 in non-sepsis-non-AKI groups. Although plasma NGAL performed less well (AUC 0.44), urinary NGAL showed significant discrimination for AKI diagnosis (AUC 0.80) with a threshold value of 29.5 ng/ml (88% sensitivity, 73% specificity). Both plasma and urine Cystatin C worked well for the diagnosis of AKI (AUC 0.82 and 0.86, thresholds 1.5 and 0.106 mg/L respectively). Conclusion: Plasma and urinary Cystatin C and urinary NGAL are useful markers in predicting AKI in septic critically ill patients. Plasma NGAL raises in patients with sepsis in the absence of AKI and should be used with caution as a marker of AKI in septic ICU patients.
Heritabilities and Genetic Correlations of Body Composition and Structural Soundness Traits in Commercial Gilts

Recommended Citation Nikkilä, Marja; Stalder, Kenneth J.; Mote, Benny E. ; Lampe, Jay; Thorn, Bridget; Rothschild, Max F. ; Johnson, Anna K. ; Karriker, Locke A. ; and Serenius, Timo (2008) "Heritabilities and Genetic Correlations of Body Composition and Structural Soundness Traits in Commercial Gilts," Animal Industry Report: AS 654, ASL R2349. DOI: https://doi.org/10.31274/ans_air-180814-147 Available at: https://lib.dr.iastate.edu/ans_air/vol654/iss1/98