Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Remote Sensing Image Registration With Accuracy Estimation at Local and Global Scales

This paper focuses on the potential accuracy of remote sensing (RS) image registration. We investigate how this accuracy can be estimated without ground truth available and used to improve registration quality of mono- and multimodal pair of images. At the local scale of image fragments, the Cramér-Rao lower bound (CRLB) on registration error is estimated for each local correspondence between coarsely registered pair of images. This CRLB is defined by local image texture and noise properties. Opposite to the standard approach, where registration accuracy is only evaluated at the output of the registration process, such valuable information is used by us as an additional input knowledge. It greatly helps in detecting and discarding outliers and refining the estimation of geometrical transformation model parameters. Based on these ideas, a new area-based registration method called registration with accuracy estimation (RAE) is proposed. In addition to its ability to automatically register very complex multimodal image pairs with high accuracy, the RAE method is able to provide registration accuracy at the global scale as a covariance matrix of estimation error of geometrical transformation model parameters or as pointwise registration standard deviation. This accuracy does not depend on any ground truth availability and characterizes each pair of registered images individually. Thus, the RAE method can identify image areas for which a predefined registration accuracy is guaranteed. This is essential for RS applications imposing strict constraints on registration accuracy such as change detection, image fusion, and disaster management. The RAE method is proved successful with reaching subpixel accuracy while registering eight complex mono-/multimodal and multitemporal image pairs including optical-to-optical, optical-to-radar, optical-to-digital elevation model (DEM) images, and DEM-to-radar cases. Other methods employed in comparisons fail to provide in a stable manner accurate results on the same test cases.
Signaling in the phytomicrobiome: breadth and potential

Higher plants have evolved intimate, complex, subtle, and relatively constant relationships with a suite of microbes, the phytomicrobiome. Over the last few decades we have learned that plants and microbes can use molecular signals to communicate. This is well-established for the legume-rhizobia nitrogen-fixing symbiosis, and reasonably elucidated for mycorrhizal associations. Bacteria within the phytomircobiome communicate among themselves through quorum sensing and other mechanisms. Plants also detect materials produced by potential pathogens and activate pathogen-response systems. This intercommunication dictates aspects of plant development, architecture, and productivity. Understanding this signaling via biochemical, genomics, proteomics, and metabolomic studies has added valuable knowledge regarding development of effective, low-cost, eco-friendly crop inputs that reduce fossil fuel intense inputs. This knowledge underpins phytomicrobiome engineering: manipulating the beneficial consortia that manufacture signals/products that improve the ability of the plant-phytomicrobiome community to deal with various soil and climatic conditions, leading to enhanced overall crop plant productivity.
Detection of Protective Coating Disbonds in Pipe Using Ci rcumferential Guided Waves

Disbonded protective coatings greatly increase the corr osion rates in pipeline. A new guidedwave technology is developed for the detection of disbon ds. Theoretical dispersion curves are generated for shear horizontal waves propagating around the circumference of a pipe. Three parameters are identified for the detection of disbonde d coatings. Disbond detection is demonstrated experimentally using a 20” diameter pipe with a hot-applied coal-tar coating. Results show that there is potential for disbond sizin g in addition to detection.
Low-Density Parity-Check Code with Fast Decoding Speed

Low-Density Parity-Check (LDPC) codes received much attention recently due to their capacity-approaching performance. The iterative message-passing algorithm is a widely adopted decoding algorithm for LDPC codes \cite{Kschischang01}. An important design issue for LDPC codes is designing codes with fast decoding speed while maintaining capacity-approaching performance. In another words, it is desirable that the code can be successfully decoded in few number of decoding iterations, at the same time, achieves a significant portion of the channel capacity. Despite of its importance, this design issue received little attention so far. In this paper, we address this design issue for the case of binary erasure channel. We prove that density-efficient capacity-approaching LDPC codes satisfy a so called "flatness condition". We show an asymptotic approximation to the number of decoding iterations. Based on these facts, we propose an approximated optimization approach to finding the codes with good decoding speed. We further show that the optimal codes in the sense of decoding speed are "right-concentrated". That is, the degrees of check nodes concentrate around the average right degree.
Monitoring temporal development and healing of diabetic foot ulceration using hyperspectral imaging

This study combines non‐invasive hyperspectral imaging with an experimentally validated skin optical model and inverse algorithm to monitor diabetic feet of two representative patients. It aims to observe temporal changes in local epidermal thickness and oxyhemoglobin concentration and to gain insight into the progression of foot ulcer formation and healing. Foot ulceration is a debilitating comorbidity of diabetes that may result in loss of mobility and amputation. Inflammation and necrosis preempt ulceration and can result in changes in the skin prior to ulceration and during ulcer healing that affect oxygen delivery and consumption. Previous studies estimated oxyhemoglobin and deoxyhemoglobin concentrations around pre‐ulcerative and ulcer sites on the diabetic foot using commercially available hyperspectral imaging systems. These measurements were successfully used to detect tissue at risk of ulceration and predict the healing potential of ulcers. The present study shows epidermal thickening and decrease in oxyhemoglobin concentration can also be detected prior to ulceration at pre‐ulcerative sites. The algorithm was also able to observe reduction in the epidermal thickness combined with an increase in oxyhemoglobin concentration around the ulcer as it healed and closed. This methodology can be used for early prediction of diabetic foot ulceration in a clinical setting. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Universal Lax pairs for spin Calogero-Moser models and spin exchange models

For any root system Δ and a set of vectors which form a single orbit of the reflection (Weyl) group GΔ generated by Δ, a spin Calogero–Moser model can be defined for each of the potentials: rational, hyperbolic, trigonometric and elliptic. For each member μ of , to be called a 'site', we associate a vector space Vμ whose element is called a 'spin'. Its dynamical variables are the canonical coordinates of a particle in Rr (r = rank of Δ) and spin exchange operators (ρ Δ) which exchange the spins at the sites μ and sρ(μ). Here sρ is the reflection generated by ρ. For each Δ and a spin exchange model can be defined. The Hamiltonian of a spin exchange model is a linear combination of the spin exchange operators only. It is obtained by 'freezing' the canonical variables at the equilibrium point of the corresponding classical Calogero–Moser model. For Δ = Ar and = set of vector weights it reduces to the well-known Haldane–Shastry model. Universal Lax pair operators for both spin Calogero–Moser models and spin exchange models are presented which enable us to construct as many conserved quantities as the number of sites for degenerate potentials.
On the Need for Identity in Ontology-Based Conceptual Modelling

Conceptual modelling is often argued to be a core technique in information systems development. An important aspect of conceptual modelling is the ontological and philosophical questions of how to conceive of object existence and identity. Despite significant interest in the topic, formal agreement on how object identity should be represented in modelling languages remains an open question. In the literature, the predominant view is that an object, or entity, is a modelling construct used to represent things. Contrary to this view, we draw on theories of language use and social ontology to understand object identity based on the notion that identity cannot be limited to the identity of physical things. The emphasis is on how language is used to create conceptual entities in a way that maintains fidelity to physical reality and ensures reliable identification of entities across domains. The theoretical implications of this work are primarily the new perspective of conceptual modelling that social ontology affords and the formal introduction and ontological grounding of institutional entities, which have so far been treated rather incidentally. Practical implications include a better foundation for designing and selecting identifiers and classes.
The Prevalence and Effects of Premenstrual Syndrome among Female Health Science Students in Eritrea

Premenstrual syndrome (PMS) is a combination of physical and behavioral symptoms that occur in women which can adversely affect their social interaction, educational performance and emotional well-being. The objective of the study was to assess the prevalence of Premenstrual syndrome and its effects on the academic and social aspects of students in Asmara College of Health sciences. A cross sectional study was conducted among 240 female students of Asmara College of Health Sciences from January to March 2016 in Asmara, Eritrea. Respondents were selected using stratified systematic random sampling method. Data was analyzed using the Statistical Package for Social Sciences Version 20. The criterion proposed by the International Classification of Diseases 10 (ICD 10) was used for diagnosis and performance indicators were employed to measure the prevalence and effects of PMS. The results of the study show that the prevalence of PMS was found to be 17.5%. 96.5% had experienced at least one premenstrual symptom. Out of the students diagnosed with PMS, 78.6% reported their concentration in class was affected, 90.5% had stopped studying and class missing was reported by 45% of the students. Poor social interaction was also reported with their family (66.7%) and friends (40.5%). PMS affected the ability of performing home chores in 83% of respondents and 57.1% socially isolated themselves due to its symptoms. There was no significant association of any demographic characteristics with PMS. The effect of PMS with social indicators such as impaired interaction with family members and friends, inability to perform chores and social isolation were statistically significant (CI 95%, p<0.05). All academic effect indicators such as loss of concentration in class, stopping studying and missing classes were significantly associated with PMS (CI 95%, p<0.05). However there was no significant association of PMS with scoring lower grade and applying for academic withdrawal. In conclusion, although the prevalence of PMS was relatively low, it was seen to affect students negatively in their academic activities and social lives.
Control of an autonomous personal transporter towards moving targets

Future city mobility concepts will contain personalized navigation aspects. One important role will be played by autonomous and semi-autonomous transportation platforms. This article proposes a framework for dynamic navigation towards moving targets. A personal transporter, which is also capable to navigate autonomously, is introduced. This platform can approach a localized target and at the same time avoid collisions with obstacles.
Long-Term Impact of Investments in Early Schooling: Empirical Evidence from Rural Ethiopia

This paper identifies the cumulative impact of early schooling investments on later schooling outcomes in a developing country context using enrollment status and relative grade attainment as short-run and long-run measures of schooling. Using a child-level longitudinal data set from rural Ethiopia, we estimate a dynamic conditional schooling demand function where the coefficient estimate on the lagged dependent variable captures the impact of all previous periods schooling inputs and resources. We find that this lagged dependent variable indicates a strong positive association between current and lagged schooling. Past history matters more for girls than boys and for children from higher income households compared to the poor.
Boundary control synthesis for hyperbolic systems: A singular perturbation approach

In this paper, we consider the problem of boundary control of a class of linear hyperbolic systems of conservation laws based on the singular perturbation method. The full hyperbolic system is written as two subsystems, namely the reduced system representing the slow dynamics and the boundary-layer system standing for the fast dynamics. By choosing the boundary conditions for the reduced system as zero, the slow dynamics is stabilized in finite time. The main result is illustrated with a design of boundary control for a linearized Saint-Venant-Exner system. The stabilization of the full system is achieved with different boundary conditions for the fast dynamics.
Transcription Regulatory Regions Database (TRRD): its status in 2002

Transcription Regulatory Regions Database (TRRD) is an informational resource containing an integrated description of the gene transcription regulation. An entry of the database corresponds to a gene and contains the data on localization and functions of the transcription regulatory regions as well as gene expression patterns. TRRD contains only experimental data that are inputted into the database through annotating scientific publication. TRRD release 6.0 comprises the information on 1167 genes, 5537 transcription factor binding sites, 1714 regulatory regions, 14 locus control regions and 5335 expression patterns obtained through annotating 3898 scientific papers. This information is arranged in seven databases: TRRDGENES (general gene description), TRRDLCR (locus control regions); TRRDUNITS (regulatory regions: promoters, enhancers, silencers, etc.), TRRDSITES (transcription factor binding sites), TRRDFACTORS (transcription factors), TRRDEXP (expression patterns) and TRRDBIB (experimental publications). Sequence Retrieval System (SRS) is used as a basic tool for navigating and searching TRRD and integrating it with external informational and software resources. The visualization tool, TRRD Viewer, provides the information representation in a form of maps of gene regulatory regions. The option allowing nucleotide sequences to be searched for according to their homology using BLAST is also included. TRRD is available at http://www.bionet. nsc.ru/trrd/.
TimerMon: A Time-Tracing Hardware for Instrumenting Real-Time Software

In the design and analysis of real-time systems, accurate timing measurement of various events in the system is required for validation purposes. In this paper, we present the design and implementation of a time-tracing hardware that gathers timing information on the execution of programs including the operating system. The hardware traces the timing behavior of programs including the total execution times, the time consumed in the system calls of the operating system, the time spent by the scheduler, etc. The results show that the hardware measures the execution times of programs with a resolution of 50 ns, while incurring only a small amount of overhead in the execution time. The timing information that this hardware provides can be used for validating various aspects of real-time systems such as worst case execution time analysis and schedulability analysis.
Relating topological determinants of complex networks to their spectral properties: structural and dynamical effects

The largest eigenvalue of a network’s adjacency matrix and its associated principal eigenvector are key elements for determining the topological structure and the properties of dynamical processes mediated by it. We present a physically grounded expression relating the value of the largest eigenvalue of a given network to the largest eigenvalue of two network subgraphs, considered as isolated: the hub with its immediate neighbors and the densely connected set of nodes with maximum K -core index. We validate this formula by showing that it predicts, with good accuracy, the largest eigenvalue of a large set of synthetic and real-world topologies. We also present evidence of the consequences of these findings for broad classes of dynamics taking place on the networks. As a by-product, we reveal that the spectral properties of heterogeneous networks built according to the linear preferential attachment model are qualitatively different from those of their static counterparts.
DEGREE SEQUENCE OF RANDOM PERMUTATION GRAPHS BY BHASWAR B. BHATTACHARYA

In this paper, we study the asymptotics of the degree sequence of permutation graphs associated with a sequence of random permutations. The limiting finite-dimensional distributions of the degree proportions are established using results from graph and permutation limit theories. In particular, we show that for a uniform random permutation, the joint distribution of the degree proportions of the vertices labeled nr1 , nr2 , . , nrs in the associated permutation graph converges to independent random variables D(r1),D(r2), . ,D(rs), where D(ri) ∼ Unif(ri ,1 − ri ), for ri ∈ [0,1] and i ∈ {1,2, . , s}. Moreover, the degree proportion of the mid-vertex (the vertex labeled n/2) has a central limit theorem, and the minimum degree converges to a Rayleigh distribution after an appropriate scaling. Finally, the asymptotic finite-dimensional distributions of the permutation graph associated with a Mallows random permutation is determined, and interesting phase transitions are observed. Our results extend to other nonuniform measures on permutations as well.
Multivariate Dekodierung von fMRT-Daten: Auf dem Weg zu einer inhaltsbasierten kognitiven Neurowissenschaft

Zusammenfassung Seit dem Aufkommen der funktionellen Magnetresonanztomografie (fMRT) vor 20 Jahren steht eine neue Methode zur nicht invasiven Messung von Gehirnfunktionen zur Verfügung, welche in den kognitiven Neurowissenschaften inzwischen weit verbreitet ist. Traditionell wurden fMRT-Daten vor allem verwendet, um globale Änderungen der Aktivität in bestimmten Gehirnregionen zu messen, wie sie etwa während einer kognitiven Verarbeitung auftreten. Die Entwicklung neuer Methoden ermöglicht nun einen verfeinerten, inhaltsbasierten Ansatz. Das „multivariate Decoding“ erlaubt es, die kognitive Information zu untersuchen, die in feinkörnigen fMRT-Aktivitätsmustern enthalten ist. Damit lässt sich die Kodierung spezifischer kognitiver Inhalte und Repräsentationen im Gehirn näher bestimmen. Hier wird ein Überblick über verschiedene Entwicklungen des multivariaten Decoding gegeben von der Anwendung in den kognitiven Neurowissenschaften (Wahrnehmung, Aufmerksamkeit, Belohnung, Entscheidungsfindung, emotionale Kommunikation) über neuere methodische Entwicklungen (Informationsfluss, oberflächenbasiertes Searchlight-Decoding) bis hin zur medizinischen Diagnostik gegeben.
Is broadcast plus multiaccess optimal for Gaussian wireless networks?

In this paper we show that "separation"-based approaches in wireless networks do not necessarily give good performance in terms of the capacity of the network. Therefore in optimal design of a wireless network, its total structure should be considered. In other words, achieving capacity on the subnetworks of a wireless network does not guarantee globally achieving capacity. We will illustrate this fact by considering some examples of multistage Gaussian wireless relay networks. We will consider a wireless Gaussian relay network with one stage in both fading and nonfading environment. We show that as the number of relay nodes, n, grows large, the capacity of this network scales like log n. We then show that with the "separation"-based scheme, in which the network is viewed as the concatenation of a broadcast and a multiaccess network, the achievable rate scales as log log n and as a constant for fading and nonfading environment, respectively, which is clearly suboptimal.
On low-capture-power test generation for scan testing

Research on low-power scan testing has been focused on the shift mode, with little or no consideration given to the capture mode power. However, high switching activity when capturing a test response can cause excessive IR drop, resulting in significant yield loss. This paper addresses this problem with a novel low-capture-power X-filling method by assigning 0's and 1's to unspecified (X) bits in a test cube to reduce the switching activity in capture mode. This method can be easily incorporated into any test generation flow, where test cubes are obtained during ATPG or by X-bit identification. Experimental results show the effectiveness of this method in reducing capture power dissipation without any impact on area, timing, and fault coverage.
Cooperative localization using angle of arrival measurements: sequential algorithms and non-line-of-sight suppression

We investigate localization of a source based on angle of arrival (AoA) measurements made at a geographically dispersed network of cooperating receivers. The goal is to efficiently compute accurate estimates despite outliers in the AoA measurements due to multipath reflections in non-line-of-sight (NLOS) environments. Maximal likelihood (ML) location estimation in such a setting requires exhaustive testing of estimates from all possible subsets of "good" measurements, which has exponential complexity in the number of measurements. We provide a randomized algorithm that approaches ML performance with linear complexity in the number of measurements. The building block for this algorithm is a low-complexity sequential algorithm for updating the source location estimates under line-of-sight (LOS) environments. Our Bayesian framework can exploit the ability to resolve multiple paths in wideband systems to provide significant performance gains over narrowband systems in NLOS environments, and easily extends to accommodate additional information such as range measurements and prior information about location.