Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and real-time estimation of signal-dependent noise in quantum-limited imaging

Many established and emerging image processing applications rely on quantum-limited imaging, i.e., imaging in extremely poor illumination. At this, images are corrupted by severe signal-dependent Poisson noise. For optimal noise reduction the noise characteristics must be estimated and integrated into the method. Common noise estimators, however, assume Gaussian noise which is not signal-dependent. In this paper, we describe the modeling process exemplarily for low-dose medical X-ray imaging. In this context, we formulate functional models for detector images and images which have undergone nonlinear white compression prior to further processing. Furthermore, we present a robust estimator for signal-dependent noise suited for real-time applications.
Design of a compact 5-DOF surgical robot of a spherical mechanism: CURES

In recent years, several surgical or assistant robots have made a successful debut to surgical theatre. Due to their large size, and high price, there is a growing need for the miniaturization of these surgical robots. In this paper, a design methodology for a compact surgical robot is presented. Considering workspace and force requirements, a simulation and experiments are conducted to determine the design parameters. The compact surgical robot CURES of 5-DOF spherical mechanism is developed from the analysis and its properties are presented.
A Kernel-Based Method for Semi-Supervised Learning

In recent years there has been growing interest in applying techniques that incorporate knowledge from unlabeled data into systems performing supervised learning. The main motivation for this is the belief that classification performance can be improved by utilizing the contextual information provided by unlabeled data. This paper approaches the problem from a generative classifier perspective, and proposes a new kernel-based method based on combining likelihoods from the labeled examples with those of unlabeled examples. Preliminary results on synthetic low-dimensional data show that the performance of the technique is comparable to that of existing semi-supervised generative approaches based on mixture models trained using Expectation-Maximization. However, a distinct advantage of the proposed approach is that it relies on optimizing only a single parameter. The paper describes how this can be done using cross- validation.
Palm-dorsa vein recognition based on independent principle component analysis

The traditional Principal Component Analysis (PCA) can only remove first- and second-order correlation between various components of data. To solve this problem and gain the features sensitive to the high-order information, two different architectures of independent principle component analysis (ICA) for palm-dorsa vein recognition are discussed, ICA architecture I based on statistically independent basis images and ICA architecture II based on statistically independent coefficients. A new approach based on invariant features is utilized to find the region of interest (ROI) in the palm-dorsa vein images so as to increase the recognition accuracy and reliability. We compare ICA with PCA on an existing palm-dorsa vein set. Experimental results show that two ICA architectures perform better than PCA and ICA, architecture I performs well, but not as well as ICA architecture II. Furthermore, the squared prediction error (SPE) of ICA is much smaller than that of PCA. It is illustrated that ICA can describe image features more essentially.
Detection of Cancer DNA in Plasma of Patients with Early-Stage Breast Cancer

Purpose: Detecting circulating plasma tumor DNA (ptDNA) in patients with early-stage cancer has the potential to change how oncologists recommend systemic therapies for solid tumors after surgery. Droplet digital polymerase chain reaction (ddPCR) is a novel sensitive and specific platform for mutation detection. Experimental Design: In this prospective study, primary breast tumors and matched pre- and postsurgery blood samples were collected from patients with early-stage breast cancer (n = 29). Tumors (n = 30) were analyzed by Sanger sequencing for common PIK3CA mutations, and DNA from these tumors and matched plasma were then analyzed for PIK3CA mutations using ddPCR. Results: Sequencing of tumors identified seven PIK3CA exon 20 mutations (H1047R) and three exon 9 mutations (E545K). Analysis of tumors by ddPCR confirmed these mutations and identified five additional mutations. Presurgery plasma samples (n = 29) were then analyzed for PIK3CA mutations using ddPCR. Of the 15 PIK3CA mutations detected in tumors by ddPCR, 14 of the corresponding mutations were detected in presurgical ptDNA, whereas no mutations were found in plasma from patients with PIK3CA wild-type tumors (sensitivity 93.3%, specificity 100%). Ten patients with mutation-positive ptDNA presurgery had ddPCR analysis of postsurgery plasma, with five patients having detectable ptDNA postsurgery. Conclusions: This prospective study demonstrates accurate mutation detection in tumor tissues using ddPCR, and that ptDNA can be detected in blood before and after surgery in patients with early-stage breast cancer. Future studies can now address whether ptDNA detected after surgery identifies patients at risk for recurrence, which could guide chemotherapy decisions for individual patients. Clin Cancer Res; 20(10); 2643–50. ©2014 AACR.
Nitrate aerosols today and in 2030: a global simulation including aerosols and tropospheric ozone

Nitrate aerosols are expected to become more important in the future atmosphere due to the expected increase in nitrate precursor emissions and the decline of ammonium-sulphate aerosols in wide regions of this planet. The GISS climate model is used in this study, including atmospheric gas- and aerosol phase chemistry to investigate current and future (2030, following the SRES A1B emission scenario) atmospheric compositions. A set of sensitivity experiments was carried out to quantify the individual impact of emission- and physical climate change on nitrate aerosol formation. We found that future nitrate aerosol loads depend most strongly on changes that may occur in the ammonia sources. Furthermore, microphysical processes that lead to aerosol mixing play a very important role in sulphate and nitrate aerosol formation. The role of nitrate aerosols as climate change driver is analyzed and set in perspective to other aerosol and ozone forcings under pre-industrial, present day and future conditions. In the near future, year 2030, ammonium nitrate radiative forcing is about ? 0.14 W/m² and contributes roughly 10% of the net aerosol and ozone forcing. The present day nitrate and pre-industrial nitrate forcings are ? 0.11 and ? 0.05 W/m², respectively. The steady increase of nitrate aerosols since industrialization increases its role as a non greenhouse gas forcing agent. However, this impact is still small compared to greenhouse gas forcings, therefore the main role nitrate will play in the future atmosphere is as an air pollutant, with annual mean near surface air concentrations, in the fine particle mode, rising above 3 ? g/m³ in China and therefore reaching pollution levels, like sulphate aerosols.
Multihop mobile communications system using MC-CDMA in forward links

This paper proposes a multihop mobile communications system using an MC-CDMA transmission technique in the forward links. In the system, it is assumed henceforth that each mobile station has a relaying function where a frequency band is used both in the 1st and 2nd hops. Then, we examined a radio resource allocation method in the forward links. In this method, an orthogonal codeword is assigned for each group that consists of one or more mobile stations and the resources of time and frequency are distributed to each link within a group. To clarify the characteristics of this method, we compared the proposed system and a conventional one without relaying, in terms of the base station connection rate, throughput, and so on. Simulation results indicate that the proposed system improves the connection rate without further orthogonal codeword consumption, and user throughput in an appropriate mobile station density.
Ecological Vehicle Control on Roads With Up-Down Slopes

This paper presents a novel development of an ecological (eco) driving system for running a vehicle on roads with up-down slopes. Fuel consumed in a vehicle is greatly influenced by road gradients, aside from its velocity and acceleration characteristics. Therefore, optimum control inputs can only be computed through anticipated rigorous reasoning using information concerning road terrain, model of the vehicle dynamics, and fuel consumption characteristics. In this development, a nonlinear model predictive control method with a fast optimization algorithm is implemented to derive the vehicle control inputs based on road gradient conditions obtained from digital road maps. The fuel consumption model of a typical vehicle is formulated using engine efficiency characteristics and used in the objective function to ensure fuel economy driving. The proposed eco-driving system is simulated on a typical road with various shapes of up-down slopes. Simulation results reveal the ability of the eco-driving system in significantly reducing fuel consumption of a vehicle. The fuel saving behavior is graphically illustrated, compared, and analyzed to focus on the significance of this development.
Brain Region-Dependent Rejection of Neural Precursor Cell Transplants

The concept of CNS as an immune-privileged site has been challenged by the occurrence of immune surveillance and allogeneic graft rejection in the brain. Here we examined whether the immune response to allogeneic neural grafts is determined by the site of implantation in the CNS. Dramatic regional differences were observed between immune responses to allogeneic neural precursor/stem cell (NPC) grafts in the striatum vs. the hippocampus. Striatal grafts were heavily infiltrated with IBA-1+ microglia/macrophages and CD3+ T cells and completely rejected. In contrast, hippocampal grafts exhibited milder IBA-1+ cell infiltration, were not penetrated efficiently by CD3+ cells, and survived efficiently for at least 2 months. To evaluate whether the hippocampal protective effect is universal, astrocytes were then transplanted. Allogeneic astrocyte grafts elicited a vigorous rejection process from the hippocampus. CD200, a major immune-inhibitory signal, plays an important role in protecting grafts from rejection. Indeed, CD200 knock out NPC grafts were rejected more efficiently than wild type NPCs from the striatum. However, lack of CD200 expression did not elicit NPC graft rejection from the hippocampus. In conclusion, the hippocampus has partial immune-privilege properties that are restricted to NPCs and are CD200-independent. The unique hippocampal milieu may be protective for allogeneic NPC grafts, through host-graft interactions enabling sustained immune-regulatory properties of transplanted NPCs. These findings have implications for providing adequate immunosuppression in clinical translation of cell therapy.
Towards an Elastic and Autonomic Multitenant Database

The success of cloud computing as a platform for deploying webapplications has led to a deluge of applications characterized by small data footprints with unpredictable access patterns. A scalable multitenant database management system (DBMS) is therefore an important component of the software stack for platforms supporting these applications. Elastic load balancing and efficient database migration techniques are key requirements for effective resource utilization and operational cost minimization. Our vision is a DBMS where multitenancy is viewed as virtualization in the database layer, and elasticity is a first class notion with the same stature as scalability, availability etc. We analyze the various models of database multitenancy, formalize the forms of migration, and identify the design space and research goals for an autonomic and elastic multitenant database.
Performance of strong interference cancellation in flexible UL/DL TDD systems using coordinated muting, scheduling and rate allocation

Flexible UL/DL TDD can be used for efficient spectrum utilisation in 5G small cells. In this paper, we consider fully coordinated (centralized) scheduling which exploits interference cancellation in a cluster of small cells using flexible TDD. We study joint uplink-downlink scheduling, with coordinated link adaptation and/or muting in the flexible TDD system with successive interference cancellation receiver. This coordinated link adaptation is called rate allocation. The novelty is that a scheduler may purposely schedule strong interference situations, with muting and rate allocation decisions in the small cell cluster to facilitate effective interference cancellation. Simulation based packet delay performance is shown in an indoor small cell scenario, where high base station-to-base station interference impacts the uplink. Ideal and non-ideal channel estimation cases are considered. Comparison is made to the baseline centralized scheduling where muting is used for interference mitigation without interference cancellation. Results show that with ideal channel estimation, at a system load of 55% interference cancellation aware scheduling, muting with rate allocation outperforms other schemes. It achieves high gain of 67% in terms of packet delay reduction as compared to the baseline. Interference cancellation aware scheduling and muting with uncoordinated rate adaptation also achieves high gain around 62% at the same load using realistic channel estimation.
Toward accurate species‐level metabarcoding of arthropod communities from the tropical forest canopy

Abstract Metabarcoding of arthropod communities can be used for assessing species diversity in tropical forests but the methodology requires validation for accurate and repeatable species occurrences in complex mixtures. This study investigates how the composition of ecological samples affects the accuracy of species recovery. Starting with field‐collected bulk samples from the tropical canopy, the recovery of specimens was tested for subsets of different body sizes and major taxa, by assembling these subsets into increasingly complex composite pools. After metabarcoding, we track whether richness, diversity, and most importantly composition of any size class or taxonomic subset are affected by the presence of other subsets in the mixture. Operational taxonomic units (OTUs) greatly exceeded the number of morphospecies in most taxa, even under very stringent sequencing read filtering. There was no significant effect on the recovered OTU richness of small and medium‐sized arthropods when metabarcoded alongside larger arthropods, despite substantial biomass differences in the mixture. The recovery of taxonomic subsets was not generally influenced by the presence of other taxa, although with some exceptions likely due to primer mismatches. Considerable compositional variation within size and taxon‐based subcommunities was evident resulting in high beta‐diversity among samples from within a single tree canopy, but this beta‐diversity was not affected by experimental manipulation. We conclude that OTU recovery in complex arthropod communities, with sufficient sequencing depth and within reasonable size ranges, is not skewed by variable biomass of the constituent species. This could remove the need for time‐intensive manual sorting prior to metabarcoding. However, there remains a chance of taxonomic bias, which may be primer‐dependent. There will never be a panacea primer; instead, metabarcoding studies should carefully consider whether the aim is broadscale turnover, in which case these biases may not be important, or species lists, in which case separate PCRs and sequencing might be necessary. OTU number inflation remains an issue in metabarcoding and requires bioinformatic development, particularly in read filtering and OTU clustering, and/or greater use of species‐identifying sequences generated outside of bulk sequencing.
RTT-Based Caching Policies to Improve User-Centric Performance in CCN

Content-Centric Networking (CCN) is an attractive future Internet architecture that enables rapid data retrieval due to its native in-network caching mechanism. However, existing caching policies in CCN do not always perform effectively in terms of the user-centric performance that is typically measured by the content download time. This is because they have been designed to improve network-centric performances, such as cache hit ratio and hop count. In this paper, we focus on the Round-Trip Time (RTT) computed at routers, and present two caching policies based on the RTT in order to achieve the high user centric performance. Our policies have the advantage that they can be deployed with no modification of the CCN packet structure in contrast to the existing ones. This paper demonstrates that our policies achieve much shorter download time than the existing ones over various network topologies, and provides deep insights on caching policies to improve the user-centric performance.
PorA Variable Regions of Neisseria meningitidis

Subtypes, defined by variation in the outer membrane protein PorA, are an integral part of the characterization scheme for Neisseria meningitidis. Identification of these variants remains important as the PorA protein is a major immunogenic component of several meningococcal vaccines under development, and characteristics of PorA are used to provide detailed epidemiologic information. Historically, serosubtypes have been defined by reactivity with a set of monoclonal antibodies. However, nucleotide sequence analyses of porA genes have established that the panel of serosubtyping monoclonal antibodies is not exhaustive and many porA variants cannot be detected. In addition, the nomenclature system used to define subtypes is inadequate. We examined all available nucleotide sequences of the porA VR1 and VR2 regions to identify and define subtype families. A revised nomenclature scheme, compatible with the previous serologic nomenclature scheme, was devised. A Web-accessible database describing this nomenclature and its relationship to previous schemes was established (available from: http://neisseria.org/nm/typing/pora).
Abstract Semantics for Module Composition

Programmers and software engineers agree in unanimity that a useful characteristic of the programming languages they use for implementations (C++, Java, etc.) is their support for both {\em public} and {\em private} features (types, functions). The public features are often called {\em interfaces}. The private part is not visible outside the module (class, package) that declares it, but it can be used internally to define the visible part. Such distinction helps software engineers abstract their work and ignore details which are a main source of confusion and errors. We claim that the distinction between private and public features might also be a desirable characteristic of formal specifications, not only from the practical point of view but also because of at least two important theoretical reasons. One is the possibility to finitely specify theories which do not admit finite standard presentations. For example, Bergstra and Tucker showed that any recursive algebra is a restriction of an initial algebra presented by a finite number of equations over a larger signature. Another reason is that it allows the user to specify behavioral properties of systems, in the sense that every behavioral specification is equivalent to hiding some operators (i.e., making them private) in a usual specification. We introduce the notion of {\em module specification} as a generalization of the standard specification, having both public (or visible) and private features, and then explore their properties at an abstract level, categorical. To formalize the notion of ``logical system'''' we use {\em institutions} enriched with {\em inclusions}, an abstraction of the natural notion of inclusion of signatures from particular logics. The {\em visible theorems} (or the visible consequences) of a module are those theorems which contain only visible symbols, and a model of that module is a model of its visible consequences. Five basic operations on module specifications are explored: renaming, hiding, enriching, aggregation and parameterization. An internal property of modules, {\em conservativeness}, seems to have a decisive role in giving semantics for module composition.
Theoretical and Experimental Study of Radial Velocity Generation for Extending Bandwidth of Magnetohydrodynamic Angular Rate Sensor at Low Frequency

The magnetohydrodynamics angular rate sensor (MHD ARS) has received much attention for its ultra-low noise in ultra-broad bandwidth and its impact resistance in harsh environments; however, its poor performance at low frequency hinders its work in long time duration. The paper presents a modified MHD ARS combining Coriolis with MHD effect to extend the measurement scope throughout the whole bandwidth, in which an appropriate radial flow velocity should be provided to satisfy simplified model of the modified MHD ARS. A method that can generate radial velocity by an MHD pump in MHD ARS is proposed. A device is designed to study the radial flow velocity generated by the MHD pump. The influence of structure and physical parameters are studied by numerical simulation and experiment of the device. The analytic expression of the velocity generated by the energized current drawn from simulation and experiment are consistent, which demonstrates the effectiveness of the method generating radial velocity. The study can be applied to generate and control radial velocity in modified MHD ARS, which is essential for the two effects combination throughout the whole bandwidth.
Simulation modelling for performance measurement in healthcare

Discrete event simulation is widely used to model healthcare systems with a view to their improvement. Most applications focus on discrete aspects of healthcare, such as accident and emergency rooms or outpatient clinics. However, despite this success with simulation at an operational level, there are no reported uses of discrete event simulation for the development and improvement of health policy. We describe the development of such a policy-oriented model, aimed at improving performance assessment in the UK National Health Service
Estimating confidence using word lattices

For many practical applications of speech recognition systems, it is desirable to have an estimate of con dence for each hypothesized word, i.e. to have an estimate which words of the speech recognizer's output are likely to be correct and which are not reliable. Many of today's speech recognition systems use word lattices as a compact representation of a set of alternative hypothesis. We exploit the use of such word lattices as information sources for the measure-of-con dence tagger JANKA [1]. In experiments on spontaneous human-to-human speech data the use of word lattice related information signi cantly improves the tagging accuracy.