Miracle-dz/UniFAR-FTUs huggingface dataset:
Ground Testing Strategies for Verifying the Slew Rate Tolerance of Star Trackers

The performance of a star tracker is largely based on the availability of its attitude solution. Several methods exist to assess star tracker availability under both static and dynamic imaging conditions. However, these methods typically make various idealizations that can limit the accuracy of these results. This study aims to increase the fidelity of star tracker availability modeling by accounting for the effects of detection logic and pixel saturation on star detection. We achieve this by developing an analytical model for the focal plane intensity distribution of a star in the presence of sensor slew. Using the developed model, we examine the effects of slew rate on star detection using simulations and lab tests. The developed approach allows us to determine the maximum slew rate for which a star of a given stellar magnitude can still be detected. This information can then be used to describe the availability of a star tracker attitude solution as a function of slew rate, both spatially, across the entire celestial sphere, or locally, along a specified orientation track.
Fabrication and Characterization of Integrable GaAs-Based High-Contrast Grating Reflector and Fabry–Pérot Filter Array With GaInP Sacrificial Layer

Integrable GaAs-based high-contrast gratings (HCGs) are fabricated and characterized, targeting applications in high-speed vertical-cavity surface-emitting lasers (VCSELs). A Ga0.51In0. 49P sacrificial layer beneath the GaAs layer is employed to create a low index surrounding HCG strips by selective etching. Experimental results show that the finite-size HCG has a reflectivity of 93% from 1270 to 1330 nm for the transverse magnetic polarization, which is consistent with the calculated results. An HCG-based Fabry-Perot filter array formed by the different HCGs, air gap, and GaAs substrate is demonstrated. The measured resonance wavelengths of the filter arrays are consistent with the theoretical results, which implies that the resonance wavelength of such filters can be tuned by parameters of the HCG itself.
High-resolution transcriptome and genome-wide dynamics of RNA polymerase and NusA in Mycobacterium tuberculosis

To construct a regulatory map of the genome of the human pathogen, Mycobacterium tuberculosis, we applied two complementary high-resolution approaches: strand-specific RNA-seq, to survey the global transcriptome, and ChIP-seq, to monitor the genome-wide dynamics of RNA polymerase (RNAP) and the anti-terminator NusA. Although NusA does not bind directly to DNA, but rather to RNAP and/or to the nascent transcript, we demonstrate that NusA interacts with RNAP ubiquitously throughout the chromosome, and that its profile mirrors RNAP distribution in both the exponential and stationary phases of growth. Generally, promoter-proximal peaks for RNAP and NusA were observed, followed by a decrease in signal strength reflecting transcriptional polarity. Differential binding of RNAP and NusA in the two growth conditions correlated with transcriptional activity as reflected by RNA abundance. Indeed, a significant association between expression levels and the presence of NusA throughout the gene body was detected, confirming the peculiar transcription-promoting role of NusA. Integration of the data sets pinpointed transcriptional units, mapped promoters and uncovered new anti-sense and non-coding transcripts. Highly expressed transcriptional units were situated mainly on the leading strand, despite the relatively unbiased distribution of genes throughout the genome, thus helping the replicative and transcriptional complexes to align.
Efficient Probabilistic Supergraph Search Over Large Uncertain Graphs

In recent years, with the emergence of a number of new real applications, such as protein-protein interaction (PPI) networks, visual pattern recognition, and intelligent traffic systems, managing huge volumes of uncertain graphs has attracted much attention in the database community. Currently, most existing fundamental queries over graphs only support deterministic (or certain) graphs, although real graph data are often noisy, inaccurate, and incomplete. In this paper, we study a new type of uncertain graph query, probabilistic supergraph containment query over large uncertain graphs. Specifically, given an uncertain graph database UGD which contains a set of uncertain graphs, a deterministic query graph q, and a probabilistic threshold δ, a probabilistic supergraph containment query is to find the set of uncertain graphs from UGD, denoted as UGDq, such that UGDq={ugi∈ UGD|Pr(ugi⊆ q)≥δ} where Pr(ugi⊆q) means the likelihood that ugi is a subgraph of q. We prove that the computation of Pr(ugi⊆q) is #P-hard and design an efficient filtering-and-verification framework to avoid the expensive computation. In particular, we propose an effective filtering strategy and a novel probabilistic inverted index, called PS-Index, to enhance pruning power in the filtering phase. Furthermore, the candidate graphs which pass the filtering phase are tested in the verification phase via an efficient unequal probability sampling-based approximation algorithm. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.
A hybrid channel assignment approach using an efficient evolutionary strategy in wireless mobile networks

In wireless mobile communication systems, radio spectrum is a limited resource. However, efficient use of available channels has been shown to improve the system capacity. The role of a channel assignment scheme is to allocate channels to cells or mobiles in such a way as to minimize call blocking or call dropping probabilities, and also to maximize the quality of service. Channel assignment is known to be an NP-hard optimization problem. In this paper, we have developed an evolutionary strategy (ES) which optimizes the channel assignment. The proposed ES approach uses an efficient problem representation as well as an appropriate fitness function. Our paper deals with a novel hybrid channel assignment based scheme called D-ring. Our D-ring method yields a faster running time and simpler objective function. We also propose a novel way of generating the initial population which reduces the number of channels reassignments and therefore yields a faster running time and may generate a possibly better initial parent. We have obtained better results (as well as faster running time) than a similar approach in literature.
A Valence-Dependent Group-Specific Recall Bias of Retrospective Self-Reports: A Study of Borderline Personality Disorder in Everyday Life

Recall is an active reconstruction process likely to distort past experiences. This distortion, known as recall bias, seems to manifest itself differently in sick and healthy people. A recall bias has been documented in several disorders, but never investigated in borderline personality disorder (BPD). To determine recall bias in BPD, we assessed momentary and retrospective ratings of specific emotions in 50 patients with BPD and 50 healthy controls (HCs), using the methodology of 24-hour ambulatory monitoring. Our data reveal a group-specific valence-dependent recall bias of retrospective self-report, indicated by a different overall recall pattern in HCs and BPD. BPD patients show an overall negative recall pattern, whereas HCs show a positive recall pattern. A traditional questionnaire approach does not distinguish between symptoms of the disorder and recall bias, although the pathological mechanisms underlying them as well as the appropriate treatment strategies may be different.
Strathprints Institutional Repository (2010) High-accuracy Sub- Pixel Motion Estimation from Noisy Images in Fourier Domain. Ieee

Strathprints is designed to allow users to access the research output of the University of Strathclyde. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, or not-for-profit purposes without prior permission or charge. 1  Abstract—In this paper, we propose a new method for estimating sub-pixel motion via exploiting the principle of phase correlation in the Fourier domain. The method is based on linear weighting of the height of the main peak on the one hand and the difference between its two neighboring side-peaks on the other. Using both synthetic and real data we show that the proposed method outperforms many established approaches and achieves improved accuracy even in the presence of noisy samples.
Methods for Evaluating Software Architecture: A Survey

Software architectural evaluation becomes a familiar practice in software engineering community for developing quality software. Architectural evaluation reduces software development efiort and costs, and enhances the quality of the software by verifying the addressability of quality requirements and identifying potential risks. There have been several methods and techniques to evaluate software architectures with respect to the desired quality attributes such as maintainability, usability and performance. This paper presents a discussion on difierent software architectural evaluation methods and techniques using a taxonomy. The taxonomy is used to distinguish architectural evaluation methods based on the artifacts on which the methods are applied and two phases (early and late) of software life cycle. The artifacts include speciflcation of a whole software architecture and its building blocks: software architectural styles or design patterns. The role of this paper discussion is to review difierent existing well known architectural evaluation methods in order to view the state of the art in software architectural evaluation. This paper also concentrates on summarizing the importance of the difierent
Combining Real-Time Polymerase Chain Reaction using SYBR Green I Detection and Sequencing to Identify Vertebrate Bloodmeals in Fleas

ABSTRACT Programs that aim to control vector-borne zoonotic diseases require information on zoonotic hosts and on the feeding behavior of bridging vectors that are capable of transmitting pathogens from those hosts to humans. Here we describe an assay developed to identify bloodmeals in field-collected cat fleas (Ctenocephalides felis Bouché) to assess this species' potential role as a Yersinia pestis bridging vector in a plague-endemic region of Uganda. Our assay uses a single primer set and SYBR Green I-based real-time polymerase chain reaction to amplify a segment of the 12S mitochondrial ribosomal RNA gene for identification by sequencing. The assay capitalizes on the sensitivity of real-time polymerase chain reaction and the specificity of sequencing and can be used to differentiate vertebrate bloodmeals to the genus or species level without a priori knowledge of the host community. Because real-time assays that detect vertebrate DNA are highly sensitive to human DNA contamination, we analyzed detection in artificially fed and unfed fleas to establish a Ct cutoff that optimized specificity without completely sacrificing sensitivity. Using the established cutoff, our assay detected human, rat, and goat DNA in artificially fed C. felis up to 72 h postfeeding.
Resource Optimization for Content Distribution Networks in Shared Infrastructure Environment

Current Content Distribution Networks (CDN) deployment requires heavy infrastructure investment since a large number of servers have to be deployed over a wide area. This paper proposes a new paradigm where future CDNs are to be deployed over a leased server and network infrastructure. While this paradigm shift introduces a new dimension of flexibility, it requires the resource provisioning and object placement problems to be jointly considered. This paper formulates this new optimization problem, and presents solution to it based on Lagrangian relaxation and greedy heuristics.
Identification of differentially expressed circular RNAs in human colorectal cancer

Circular RNA, a class of non-coding RNA, is a new group of RNAs and is related to tumorigenesis. Circular RNAs are suggested to be ideal candidate biomarkers with potential diagnostic and therapeutic implications. However, little is known about their expression in human colorectal cancer. In our study, differentially expressed circular RNAs were detected using circular RNA array in paired tumor and adjacent non-tumorous tissues from six colorectal cancer patients. Expression levels of selected circular RNAs (hsa_circRNA_103809 and hsa_circRNA_104700) were measured by real-time polymerase chain reaction in 170 paired colorectal cancer samples for validation. Statistical analyses were conducted to investigate the association between hsa_circRNA_103809 and hsa_circRNA_104700 expression levels and respective patient clinicopathological features. Receiver operating characteristic curve was constructed to evaluate the diagnostic values. Our results indicated that there were 125 downregulated and 76 upregulated circular RNAs in colorectal cancer tissues compared with normal tissues. We also first demonstrated that the expression levels of hsa_circRNA_103809 (p < 0.0001) and hsa_circRNA_104700 (p = 0.0003) were significantly lower in colorectal cancer than in normal tissues. The expression level of hsa_circRNA_103809 was significantly correlated with lymph node metastasis (p = 0.021) and tumor-node-metastasis stage (p = 0.011), and the expression level of hsa_circRNA_104700 was significantly correlated with distal metastasis (p = 0.036). The area under receiver operating characteristic curves of hsa_circRNA_103809 and hsa_circRNA_104700 were 0.699 (p < 0.0001) and 0.616 (p < 0.0001), respectively. In conclusion, these results suggest that hsa_circRNA_103809 and hsa_circRNA_104700 may be potentially involved in the development of colorectal cancer and serve as potential biomarkers for the diagnosis of colorectal cancer.
Synthesis and Mechanical Characterisation of Aluminium-Copper-Alumina Nano Composites Powder Embedded in Glass/Epoxy Laminates

This paper presents the synthesis and mechanical properties study of Aluminum-Copper nanocomposite powders with variation in volume percentages of alumina. The powders were synthesized using mechanical alloying (high energy ball milling technique). Samples of size 2010 mm were produced from nanocomposite powders by spark plasma sintering technique and conventional sintering method. The microstructural verifications were carried out using X-ray diffraction. Transition electron microscopy were used to determine the phases formed and size of the particles. Thermal analysis and hardness of these samples were measured by conducting DSC and Vickers’s Hardness Test. Also, the powders of ACANC were embedded into Glass/Epoxy laminates for further identification of NC powders effects on mechanical properties like tensile and compressive strength. The samples prepared using conventional sintering technique had gone through two different types of annealing before sintering and shown enhanced hardness, yield strength and increment in density. The nanocomposite embedded laminates have shown improved tensile, compression and hardness values in compare to virgin specimens.
Formative Evaluation of Ontology Learning Methods for Entity Discovery by Using Existing Ontologies as Reference Standards

Summary Objective: Developing a two-step method for formative evaluation of statistical Ontology Learning (OL) algorithms that leverages existing biomedical ontologies as reference standards. Methods: In the first step optimum parameters are established. A ‘gap list’ of entities is generated by finding the set of entities present in a later version of the ontology that are not present in an earlier version of the ontology. A named entity recognition system is used to identify entities in a corpus of biomedical documents that are present in the ‘gap list’, generating a reference standard. The output of the algorithm (new entity candidates), produced by statistical methods, is subsequently compared against this reference standard. An OL method that performs perfectly will be able to learn all of the terms in this reference stand ard. Using evaluation metrics and precision-recall curves for different thresholds and parameters, we compute the optimum parameters for each method. In the second step, human judges with exper tise in ontology development evaluate each candidate suggested by the algorithm con figured with the optimum parameters previously established. These judgments are used to compute two performance metrics developed from our previous work: Entity Suggestion Rate (ESR) and Entity Acceptance Rate (EAR). Results: Using this method, we evaluated two statistical OL methods for OL in two medical domains. For the pathology domain, we obtained 49% ESR, 28% EAR with the Lin method and 52% ESR, 39% EAR with the Church method. For the radiology domain, we obtain 87% ESA, 9% EAR using Lin method and 96% ESR, 16% EAR using Church method. Conclusion: This method is sufficiently general and flexible enough to permit comparison of any OL method for a specific corpus and ontology of interest.
Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study.
Negative refraction in visible region using nano-structured metallo-dielectric photonic crystal

An artificial engineered structure of nano-inclusion made of metallic nano-rods embedded in a dielectric (ε=12.96) matrix with hexagonal arrangement is proposed. New improved designed structure exhibits Negative Refraction (NR) in visible region by using surface plasmon wave in metallo-dielectric photonic crystal operating in a dispersion regime with anti-parallel refracted wave vector and Poynting vector. Finite Difference Time Domain (FDTD) simulations are carried out to study the reflection and transmission properties and obtained Far-field pattern. Designed structure gives NR with high transmission and act as a filter with a quality factor ≈ 102 with strong application potential in nano-optics and nano-technology.
Modeling and Identification of Ultra-Wideband Analog Multipliers

Analog multipliers are employed in many applications. In conventional RF front ends, for example, they are widely used for frequency conversion tasks. In noncoherent energy detectors or autocorrelation receivers, they multiply the (broadband) input signal by itself to achieve a down-conversion. Unfortunately, there exist no ideal hardware realizations of such devices, hence multipliers inevitably create undesired signal content at their output. To be able to deal with these effects or correct for them, we need to be able to model and identify realistic RF multipliers. This paper proposes and validates a multiple-input single-output Wiener–Hammerstein model for ultra-wideband (UWB) analog multipliers. The structure of the proposed model gives insight in the distortions created. It thus provides the possibility to study the realistic behavior of systems involving those multipliers, e.g., the influence of undesired nonlinear signal content onto noncoherent UWB receivers. A comparison of the model performance is shown with respect to measurements and circuit simulations.
VCluster: a thread‐based Java middleware for SMP and heterogeneous clusters with thread migration support

Clusters, composed of symmetric multiprocessor (SMP) machines and heterogeneous machines, have become increasingly popular for high‐performance computing. Message‐passing libraries, such as message‐passing interface (MPI) and parallel virtual machine (PVM), are de facto parallel programming libraries for clusters that usually consist of homogeneous and uni‐processor machines. For SMP machines, MPI is combined with multithreading libraries like POSIX Thread and OpenMP to take advantage of the architecture. In addition to existing parallel programming libraries that are in C/C++ and FORTRAN programming languages, the Java programming language presents itself as another alternative with its object‐oriented framework, platform neutral byte code, and ever‐increasing performance. This paper presents a new parallel programming model and a library, VCluster, which implements this model. VCluster is based on migrating virtual threads instead of processes to support clusters of SMP machines more efficiently. The implementation uses thread migration, which can be used in dynamic load balancing. VCluster was developed in pure Java, utilizing the portability of Java to support clusters of heterogeneous machines. Several applications are developed to illustrate the use of this library and compare the usability and performance of VCluster with other approaches. Copyright © 2007 John Wiley & Sons, Ltd.
COEVOLUTION AS AN AUTONOMOUS LEARNING STRATEGY FOR NEUROMODULES

The training of artificial neural networks to solve a particular problem usually affects only the weight space of the network. In contrast, the network architecture has to be predefined by the user. For this pretraining choice of network architecture profound skill and experience may be required on the part of the user — an improperly predefined architecture easily renders a learning problem insoluble. Relevant parameters of a network architecture include the choice of feedforward or recurrent structure, of network size and connectivity level, and of the number of layers. The appropriateness of such choices can only be determined posttraining. Consequently, methods have been devised either for determining upper bounds for network sizes (e.g. the application of Kolmogorov’s theorem to 3-layered network structures by Hecht-Nielsen) or for automatically pruning neurons from an initial structure in order to adjust network size (e.g. Bartlett’s dynamic node architecture learning). However, these methods are restricted to a particular subclass of network architectures involving only feedforward connections. Here we suggest a general approach allowing for a joint adaptation of neural networks with respect both to weight dynamics and architecture dynamics. All the relevant parameters of network architecture mentioned above are subject to the learning process and thus are adjusted to the specific problem considered. This applies in particular to the crucial alternative of feedforward or recurrent network structure, in fact, the proper choice between these paradigms is automatically made.