Miracle-dz/UniFAR-FTUs huggingface dataset:
The neural network multi-user detection based on MMSE

The problem about multiuser detection eventually is a combinatorial optimization problem. Hopfield neural network can get the near-optimal combinatorial optimization solution instantly by dynamic evolution of itself; and it has a fast convergence time. This is necessary for real-time multi-user detection. We remove the constraints because MMSE is a free minimization problem, and let the linear transfer matrix corresponds to the neural network connected matrix and bias current corresponds to spread sequences We get the HNN linear multiuser detection algorithm based on MMSE criteria, called the new MHNN. Simulation result shows that the error bit ratio (BER) decreases compared with the former MHNN and HNN algorithm and it increases system capacity. This is because the MHNN algorithm solves the local optimization problem of original neural network and using the optimal objective function based on MMSE.
An optimal two-dimensional orthogonal range search algorithm in VLSI, design automation

It is well known that an optimal algorithm for logarithm query time and linear storage for two dimensional orthogonal range searches is nonexistent except for very few cases in which certain computational models have been assumed. A new algorithm called sliding kd tree is presented in this article where the length of the query box is fixed in any direction. The new algorithm is optimal in handling two-dimensional problems, and can be applied to VLSI Design Automations presented to show the performance of the SKD algorithm.
Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices.

AIMS Atrial tachyarrhythmias (ATs) detected by implanted devices are often atrial fibrillation or flutter (AF) associated with stroke. We hypothesized that introduction and termination of anticoagulation based upon AT monitoring would reduce both stroke and bleeding. METHODS AND RESULTS We randomized 2718 patients with dual-chamber and biventricular defibrillators to start and stop anticoagulation based on remote rhythm monitoring vs. usual office-based follow-up with anticoagulation determined by standard clinical criteria. The primary analysis compared the composite endpoint of stroke, systemic embolism, and major bleeding with the two strategies. The trial was stopped after 2 years median follow-up based on futility of finding a difference in primary endpoints between groups. A total of 945 patients (34.8%) developed AT, 264 meeting study anticoagulation criteria. Adjudicated atrial electrograms confirmed AF in 91%; median time to initiate anticoagulation was 3 vs. 54 days in the intervention and control groups, respectively (P < 0.001). Primary events (2.4 vs. 2.3 per 100 patient-years) did not differ between groups (HR 1.06; 95% CI 0.75-1.51; P = 0.732). Major bleeding occurred at 1.6 vs. 1.2 per 100 patient-years (HR 1.39; 95% CI 0.89-2.17; P = 0.145). In patients with AT, thromboembolism rates were 1.0 vs. 1.6 per 100 patient-years (relative risk -35.3%; 95% CI -70.8 to 35.3%; P = 0.251). Although AT burden was associated with thromboembolism, there was no temporal relationship between AT and stroke. CONCLUSION In patients with implanted defibrillators, the strategy of early initiation and interruption of anticoagulation based on remotely detected AT did not prevent thromboembolism and bleeding. CLINICAL TRIAL REGISTRATION IMPACT ClinicalTrials.gov identifier: NCT00559988 ( http://clinicaltrials.gov/ct2/show/NCT00559988? term=NCT00559988&rank=1 ).
Vitamin Deficiencies in Humans: Can Plant Science Help?[W]

The term vitamin describes a small group of organic compounds that are absolutely required in the human diet. Although for the most part, dependency criteria are met in developed countries through balanced diets, this is not the case for the five billion people in developing countries who depend predominantly on a single staple crop for survival. Thus, providing a more balanced vitamin intake from high-quality food remains one of the grandest challenges for global human nutrition in the coming decade(s). Here, we describe the known importance of vitamins in human health and current knowledge on their metabolism in plants. Deficits in developing countries are a combined consequence of a paucity of specific vitamins in major food staple crops, losses during crop processing, and/or overreliance on a single species as a primary food source. We discuss the role that plant science can play in addressing this problem and review successful engineering of vitamin pathways. We conclude that while considerable advances have been made in understanding vitamin metabolic pathways in plants, more cross-disciplinary approaches must be adopted to provide adequate levels of all vitamins in the major staple crops to eradicate vitamin deficiencies from the global population.
Phone calls and political ping-pong: nodding syndrome and healthcare provision in Uganda

ABSTRACT This article pays attention to the poorly understood nodding syndrome (NS) and its political connotations, while exploring a case of mortality in northern Uganda. The appearance of a novel set of symptoms combined with heated debates in national media and parliament, resulted in the creation of a parallel healthcare system for children afflicted by NS. This article increases our understanding of the role of the state in healthcare provision and the range of factors influencing the outcomes of, and perceptions of, healthcare programmes. The political constellations that arise from the emergence of NS should be seen within a historical framework of regional politics. The government is perceived as an important actor in the delivery of healthcare, but is also subjected to a lot of distrust that has built up over recent decades in northern Uganda. The response to NS raises questions on the status of Acholi as citizens of Uganda and reveals feelings of detachment from the national healthcare system. By publicly questioning the role of government, the affected families have become participants in the political arena.
Using Model-based Overlapping Seed Expansion to detect highly overlapping community structure

As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to degrade when the ground-truth contains a more highly overlapping community structure, with nodes assigned to more than two communities. Such highly overlapping structure is likely to exist in many social networks, such as Facebook friendship networks. In this paper we present a scalable algorithm, MOSES, based on a statistical model of community structure, which is capable of detecting highly overlapping community structure, especially when there is variance in the number of communities each node is in. In evaluation on synthetic data MOSES is found to be superior to existing algorithms, especially at high levels of overlap. We demonstrate MOSES on real social network data by analyzing the networks of friendship links between students of five US universities.
DINARY BOUNDARY CONDITIONS ON NATURAL LAW*

It is refreshing to see a scholar who is largely sympathetic to the so-called information processing or representational/computational approach to cognitive systems recognizing its fundamental inadequacies. To be blunt, that approach fails to come to terms with either information or intentionality. Sayre's response to these inadequacies, however, keeps close to the received view. He assumes that a biologically and psychologically relevant sense of information can be provided by the mathematical theory of communication; he assumes that intentionality amounts to representation. These assumptions are bolstered by the closely cognate beliefs that intentionality is to be ascribed to some roughly midway-state in the classical afferent-efferent link and that there is a metamorphosis from meaningless states to meaningful states. To his credit, Sayre aspires to make the representations genuine. He wants them to stand for real things. He wants the transition from meaningless sensory states to meaningful perceptual states to be (mathematically) principled.
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning

We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-dependent versions of the Local Rademacher Complexity (LRC). We also give a new bound on the LRC for norm regularized as well as strongly convex hypothesis classes, which applies not only to MTL but also to the standard i.i. d. setting. Combining both results, one can now easily derive fast-rate bounds on the excess risk for many prominent MTL methods, including---as we demonstrate---Schatten-norm, group-norm, and graph-regularized MTL. The derived bounds reflect a relationship akeen to a conservation law of asymptotic convergence rates. This very relationship allows for trading off slower rates w.r. t. the number of tasks for faster rates with respect to the number of available samples per task, when compared to the rates obtained via a traditional, global Rademacher analysis.
Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System

In this contribution we present the results of a pilot study in which an Extended Range Telepresence System is used to calibrate parameters of a pedestrian model for simulation. The parameters control a model element that is intended to make simulated agents walk in the direction of the estimated smallest remaining travel time. We use this to, first, show that that an Extended Range Telepresence System can serve for such a task in general and second to actually find simulation parameters that yield realistic results.
Hierarchical testing of variable importance

A frequently encountered challenge in high-dimensional regression is the detection of relevant variables. Variable selection suffers from instability and the power to detect relevant variables is typically low if predictor variables are highly correlated. When taking the multiplicity of the testing problem into account, the power diminishes even further. To gain power and insight, it can be advantageous to look for influence not at the level of individual variables but rather at the level of clusters of highly correlated variables. We propose a hierarchical approach. Variable importance is first tested at the coarsest level, corresponding to the global null hypothesis. The method then tries to attribute any effect to smaller subclusters or even individual variables. The smallest possible clusters, which still exhibit a significant influence on the response variable, are retained. It is shown that the proposed testing procedure controls the familywise error rate at a prespecified level, simultaneously over all resolution levels. The method has power comparable to the Bonferroni--Holm procedure on the level of individual variables and dramatically larger power for coarser resolution levels. The best resolution level is selected adaptively. Copyright 2008, Oxford University Press.
Robust non-parametric statistics method for joint angle-Doppler estimation in non-Gaussian clutter

In this paper, a new method is proposed for the problem of joint angle and Doppler estimation in non-Gaussian clutter which is modeled as the complex symmetric alpha stable (SalphaS) process. The proposed method normalizes each space-time snapshot vector by its infinity-norm, so that the second-order statistics-based superresolution estimators can become applicable to the non-Gaussian heavy-tailed clutter environments. Unlike the well-known fractional lower-order moment (FLOM)-based methods, the proposed method does not require any priori knowledge of the non-Gaussian clutterpsilas statistics, and hence, it is ldquoblindrdquo. Numerical results show that the proposed method outperforms the FLOM-based algorithms in the presence of non-Gaussian clutter.
A Preliminary Report on Disordered Speech with Deep Brain Stimulation in Individuals with Parkinson's Disease

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has proven effective in treating the major motor symptoms of advanced Parkinson's disease (PD). The aim of this study was to learn which laryngeal and articulatory acoustic features changed in patients who were reported to have worse speech with stimulation. Six volunteers with PD who had bilateral STN electrodes were recorded with DBS turned on or off. Perceptual ratings reflected poorer speech performance with DBS on. Acoustic measures of articulation (corner vowel formants, diphthong slopes, and a spirantization index) and phonation (perturbation, long-term average spectrum) as well as verbal fluency scores showed mixed results with DBS. Some speakers improved while others became worse on individual measures. The magnitude of DBS effects was not predictable based on the patients' demographic characteristics. Future research involving adjustments to stimulator settings or electrode placement may be beneficial in limiting the negative effects of DBS on speech.
Cervical Sagittal Alignment Parameters of Patients Admitted to Neurosurgery and Emergency Clinics in a State Hospital at Eastern Part of Turkey

Objective Cervical spine encompasses a bridge role between the head and the lower parts of the spine and therefore has unique properties. Our aim in this study was to evaluate the cervical sagittal alignment parameters in pediatric and adult non-surgical patients and to find any differences in respect of age, sex and admission type. Methods All patients who were admitted to emergency and neurosurgery clinics of Diyarbakir Bismil State Hospital due to cervical spine problems (trauma, radiculopathy, paraspinal pain) in 2014 were enrolled retrospectively into the study. Cervical anterior-posterior and lateral X-rays were obtained. Our exclusion criteria were cervical coronal deformity, multitrauma, Glasgow Coma Scale <15, traumatic disruption of the cervical spine, history of malignancy, spinal infection, metabolic or rheumatologic diseases. Results There were 44 female and 55 male patients (n=99) in the study. Thirty-five (35.35%) of the patients were younger than 18 years of age. Mean cervical spinal alignment parameters were as follows: -42.81±11.23° (OC2), -17.15±11.48° (C2-C7), -29.82±7.60° (T1 slope), -3.62±3.05° (C3), -3.14±3.05 (C4), -3.80±2.74° (C5), -3.12±2.36° (C6), -3.43±2.53° (C7). Positive correlations were observed between age-C2C7 angle, C2C7 angle-T1 slope, C3 angle-C4 angle, C4 angle-OC2 angle, C4 angle-T1 slope, C4 angle-C5 angle. The one only negative correlation was between OC2 angle-C2C7 angle. Conclusion In this regional study, it has been observed that global cervical lordosis increases as age increases. C4 vertebra is in the middle of this evaluation as it has many correlations with other cervical segments, which should be kept in mind when making surgical plans for this delicate spine region.
Reliability of power devices: Bias-induced threshold voltage instability and dielectric breakdown in GaN MIS-HEMTs

This paper reviews the most relevant threshold-voltage instabilities and dielectric breakdown mechanisms in GaN-based transistors with metal-insulator-semiconductor gate. Metal-insulator-semiconductor (MIS) high electron mobility transistors (HEMTs) with partially-recessed gate have been submitted to pulsed and constant voltage stress, with the aim of evaluating the impact of charge trapping processes and dielectric breakdown on device performance and reliability. Three different dielectrics were considered for this investigation: SiN deposited by rapid thermal chemical vapour deposition (RTCVD), SiN deposited by plasma enhanced atomic layer deposition (PE-ALD), and Al2O3 deposited by atomic layer deposition (ALD). The role of bias and temperature is extensively investigated and discussed. The results obtained within this paper are critically compared to previous literature reports, to provide a more complete view of the state-of-the-art.
Correlation of outer nuclear layer thickness with cone density values in patients with retinitis pigmentosa and healthy subjects.

PURPOSE We studied the correlation between outer nuclear layer (ONL) thickness and cone density in normal eyes and eyes with retinitis pigmentosa (RP). METHODS Spectral-domain optical coherence tomography (SD-OCT) scans were acquired using a displaced pupil entry position of the scanning beam to distinguish Henle's fiber layer from the ONL in 20 normal eyes (10 subjects) and 12 eyes with RP (7 patients). Cone photoreceptors were imaged using adaptive optics scanning laser ophthalmoscopy. The ONL thickness and cone density were measured at 0.5° intervals along the horizontal meridian through the fovea nasally and temporally. The ONL thickness and cone density were correlated using Spearman's rank correlation coefficient r. RESULTS Cone densities averaged over the central 6° were lower in eyes with RP than normal, but showed high variability in both groups. The ONL thickness and cone density were significantly correlated when all retinal eccentricities were combined (r = 0.74); the correlation for regions within 0.5° to 1.5° eccentricity was stronger (r = 0.67) than between 1.5° and 3.0° eccentricity (r = 0.23). Although cone densities were lower between 0.5° and 1.5° in eyes with RP, ONL thickness measures at identical retinal locations were similar in the two groups (P = 0.31), and interindividual variation was high for ONL and cone density measures. Although ONL thickness and retinal eccentricity were important predictors of cone density, eccentricity was over 3 times more important. CONCLUSIONS The ONL thickness and cone density were correlated in normal eyes and eyes with RP, but both were strongly correlated with retinal eccentricity, precluding estimation of cone density from ONL thickness. (ClinicalTrials.gov number, NCT00254605.).
RALF: A reinforced active learning formulation for object class recognition

Active learning aims to reduce the amount of labels required for classification. The main difficulty is to find a good trade-off between exploration and exploitation of the labeling process that depends - among other things - on the classification task, the distribution of the data and the employed classification scheme. In this paper, we analyze different sampling criteria including a novel density-based criteria and demonstrate the importance to combine exploration and exploitation sampling criteria. We also show that a time-varying combination of sampling criteria often improves performance. Finally, by formulating the criteria selection as a Markov decision process, we propose a novel feedback-driven framework based on reinforcement learning. Our method does not require prior information on the dataset or the sampling criteria but rather is able to adapt the sampling strategy during the learning process by experience. We evaluate our approach on three challenging object recognition datasets and show superior performance to previous active learning methods.
Decreased CD161+CD8+ T cells in the peripheral blood of patients suffering from rheumatic diseases.

OBJECTIVES Although it has been reported that the numbers of both CD4(-)CD8(-) and CD4(+) natural killer T (NKT) cells are selectively decreased in the peripheral blood of patients with rheumatic diseases, there have been no reports concerning a novel subpopulation of CD8(+) NKT cells. To examine whether CD161(+)CD8(+) T cells, which are closely related to CD8(+) NKT cells, are also decreased in patients with rheumatic diseases, we have investigated the expression of CD161, together with that of CD28, CD25 and CD62L, on T cells in the peripheral blood of these patients. METHODS The rheumatic diseases evaluated in this study were systemic lupus erythematosus (SLE) (n= 54), mixed connective-tissue disease (MCTD) (n= 15), systemic sclerosis (SSc) (n= 14), polymyositis/dermatomyositis (PM/DM) (n= 13) and rheumatoid arthritis (RA) (n= 24). Healthy donors were examined as controls (n= 18). The expression of CD161, CD28, CD25 and CD62L on T cells was analysed by flow cytometry. RESULTS Both the frequency of CD161 expression on CD8(+) cells and the absolute number of CD161(+)CD8(+) cells were significantly decreased in patients with SLE, MCTD, SSc and PM/DM. Only the absolute number of CD161(+)CD8(+) T cells was significantly decreased in RA. CD161 expression on CD28(-)CD8(+) T cells was significantly decreased in SLE, MCTD and SSc. The absolute number of CD161(+)CD8(+)CD62L(-) T cells was significantly decreased in SLE, MCTD and SSc. CONCLUSIONS Both the frequency and the absolute number of CD161(+)CD8(+) T cells were decreased in the peripheral blood of patients suffering from SLE, MCTD, SSc and PM/DM. This result suggests that there is also an abnormality of NKT cells in the CD8(+) population.
Adaptive ingress admission control for differentiated services

Abstrncr . Admission eontml is a critical element for supporting Quality-of-Service in networks. We pmpase and evaluate the performance of an adaptive inadmission contml scheme that is suitable for use in a Differentiated Services Internet backbone. The pmposed scheme impmves upon the admission cuotml scheme by measur. ing the maximal arrival rate envelope at an ingress node and adding an adaptive adjustment scheme that dynamically adjusts the perceived network capacity io correlation with the rate at which a speeilied end4o-end delay bound is violated. Simulalion results are presented to show that the pmposed adaptive ingress admission mntml scheme is more effective than the egress admission mntml scheme.
CommonCode: a code reuse platform for co-simulation and experimentation

Experimentation of a wireless network protocol over the air is of significant interest. However it is rarely performed compared to the simulation because of the difficulties in coding and debugging as well as scalability and repeatability. CommonCode is a unique protocol validation platform reusing the simulation codes for the experiments. It helps fast, convenient and accurate validation of protocols.
Tour recommendation system based on web information and GIS

Information recommendation and filtering techniques have been studied intensively. Traditional tour recommendation systems, which can be considered one of information recommendation systems, usually calculate the shortest path in terms of time or distance. Recently, tour recommendation systems for more general purposes have become an important research topic. In this paper we propose an efficient tourist route search system which not only recommends the path simply connecting several tourist spots, but also recommends the path with beautiful scenic sights. We focus on the visibility of scenic sights between one tourist spot and another, which is an important factor for choosing a driving route, but has not been considered in traditional tour recommendation systems. To automatically retrieve tourist spots, we propose a personalized tourist spot recommendation technique using the Web information. Although, for some regions, databases of the famous spots exist and are published, such regions are limited and usually outdated. Our method automatically extracts spots from the Web, thus our system is versatile and up-to-date for large regions. To find a route with attractive scenery, we calculate scores for paths based on the visibility of scenic sights. After generating route candidates using GIS, a 3D virtual space is constructed and the Z-Buffer method is used to decide the visibility of scenic sights for each route candidate. We implemented a prototype and tested the effectiveness of the system.