Nonredundant Representation Research Articles

An Efficient Procedure to Compensate for the Errors Due to the Probe Mispositioning in a Cylindrical Near-Field Facility.

This paper deals with the compensation of the probe mispositioning errors occurring in a cylindrical near-field (NF) facility due to the imprecise control of the linear and azimuthal positioners allowing the cylindrical scanning and/or to their limited resolution and to defects in the rails guiding the linear motion. As a result, 3-D errors in the positioning of the probe at any sampling point, as prescribed by the adopted non-redundant representation, affect the accuracy of the NF measurements. An efficient procedure is here proposed to properly compensate for these errors. It involves two steps. The former allows one to correct the mispositioning errors due to the deviation of each actual sampling point from the nominal measurement cylinder. The latter makes use of an iterative technique to restore the NF samples at any sampling point fixed by the used non-redundant representation from the ones obtained at the previous step and affected by 2-D mispositioning errors. Once these steps have been fruitfully applied, the so-compensated NF samples are effectively interpolated through a 2-D optimal sampling interpolation (OSI) formula to accurately reconstruct the input data required to perform the traditional cylindrical near-to-far-field transformation. The OSI representation is here developed by considering an elongated antenna under test as enclosed either in a prolate spheroid or in a cylinder terminated by two half spheres (rounded cylinder) in order to make the representation effectively non-redundant. Numerical test results, which thoroughly prove the efficacy of the devised procedure in correcting even severe 3-D mispositioning errors, are reported.

Calibrating an unfocused plenoptic camera based on parameters grouping and the light field structure point.

Accurately calibrating an unfocused plenoptic camera is essential to its applications. Rapid progress has been made in this area in the past decades. In this paper, detailed analysis is first performed toward the state-of-the-art projection model. Based on the analysis, parameters in the projection model are divided into two groups. Then, based on the parameter analysis, a new, to the best of our knowledge, form of the projection model, together with a new image feature light field structure point (LF-structure-point), is proposed. The LF-structure-point provides a completely non-redundant representation of the signal structure of the recorded light field raw data and induces a virtual space, "light field structure space," which is related to the real physical space by a 3D-to-3D projective transformation. The extracting algorithm of the LF-structure-point is also presented. Finally, based on the 3D-to-3D projective transformation and parameter grouping, a simple two-step calibration method using the LF-structure-point as the input data is then proposed and achieves satisfactory experimental results.

82. Enabling large scale precision oncology research with a new standard for genomic variants: OMOP Genomic

Precision Oncology requires the application of genomic variants to epidemiological methods used in clinical research. Variants are detected through traditional immunohistochemistry, microarray or PCR panels, or large-scale next generation sequencing, either for purposes of clinical care or understanding of biological processes of carcinogenesis and cancer treatment. However, we see few variants used in standard epidemiological research in the context of rich phenotypes: longitudinal observational patient data of treatment, progression and survival. The reason for this lies in the incompatibility of data representations and methods. In a typical observational study, cohorts, exposures, and outcomes are defined through clinical events encoded by predefined concepts. Epidemiological methods use this closed system model to measure the statistical change in rates, which are calculated with the denominator of all possible clinical states. The same is very challenging for genomic variants, which work like open systems: The representation of possibly infinite variants, whether previously observed or not. The Oncology Working Group of OHDSI has been working on the development of a canonical, comprehensive, and non-redundant representation of genomic variants that are clinically relevant for cancer. The resulting reference list, provided as part of the OMOP Standardized Vocabularies, is called 'OMOP Genomic'. It was constructed by consolidating genomic variants from public somatic cancer variant knowledgebases and contains +95,000 variations from 575 cancer genes. Assessment of the current gap, integration of other genomic knowledgebases are essential to improve the coverage of important and clinically relevant mutations implicated in cancer. Precision Oncology requires the application of genomic variants to epidemiological methods used in clinical research. Variants are detected through traditional immunohistochemistry, microarray or PCR panels, or large-scale next generation sequencing, either for purposes of clinical care or understanding of biological processes of carcinogenesis and cancer treatment. However, we see few variants used in standard epidemiological research in the context of rich phenotypes: longitudinal observational patient data of treatment, progression and survival. The reason for this lies in the incompatibility of data representations and methods. In a typical observational study, cohorts, exposures, and outcomes are defined through clinical events encoded by predefined concepts. Epidemiological methods use this closed system model to measure the statistical change in rates, which are calculated with the denominator of all possible clinical states. The same is very challenging for genomic variants, which work like open systems: The representation of possibly infinite variants, whether previously observed or not. The Oncology Working Group of OHDSI has been working on the development of a canonical, comprehensive, and non-redundant representation of genomic variants that are clinically relevant for cancer. The resulting reference list, provided as part of the OMOP Standardized Vocabularies, is called 'OMOP Genomic'. It was constructed by consolidating genomic variants from public somatic cancer variant knowledgebases and contains +95,000 variations from 575 cancer genes. Assessment of the current gap, integration of other genomic knowledgebases are essential to improve the coverage of important and clinically relevant mutations implicated in cancer.

Dockground scoring benchmarks for protein docking.

Protein docking protocols typically involve global docking scan, followed by re-ranking of the scan predictions by more accurate scoring functions that are either computationally too expensive or algorithmically impossible to include in the global scan. Development and validation of scoring methodologies are often performed on scoring benchmark sets (docking decoys) which offer concise and nonredundant representation of the global docking scan output for a large and diverse set of protein-protein complexes. Two such protein-protein scoring benchmarks were built for the Dockground resource, which contains various datasets for the development and testing of protein docking methodologies. One set was generated based on the Dockground unbound docking benchmark 4, and the other based on protein models from the Dockground model-model benchmark 2. The docking decoys were designed to reflect the reality of the real-case docking applications (e.g., correct docking predictions defined as near-native rather than native structures), and to minimize applicability of approaches not directly related to the development of scoring functions (reducing clustering of predictions in the binding funnel and disparity in structural quality of the near-native and nonnative matches). The sets were further characterized by the source organism and the function of the protein-protein complexes. The sets, freely available to the research community on the Dockground webpage, present a unique, user-friendly resource for the developing and testing of protein-protein scoring approaches.

Visual Semantic-Based Representation Learning Using Deep CNNs for Scene Recognition

In this work, we address the task of scene recognition from image data. A scene is a spatially correlated arrangement of various visual semantic contents also known as concepts, e.g., “chair,” “car,” “sky,” etc. Representation learning using visual semantic content can be regarded as one of the most trivial ideas as it mimics the human behavior of perceiving visual information. Semantic multinomial (SMN) representation is one such representation that captures semantic information using posterior probabilities of concepts. The core part of obtaining SMN representation is the building of concept models. Therefore, it is necessary to have ground-truth (true) concept labels for every concept present in an image. Moreover, manual labeling of concepts is practically not feasible due to the large number of images in the dataset. To address this issue, we propose an approach for generating pseudo-concepts in the absence of true concept labels. We utilize the pre-trained deep CNN-based architectures where activation maps (filter responses) from convolutional layers are considered as initial cues to the pseudo-concepts. The non-significant activation maps are removed using the proposed filter-specific threshold-based approach that leads to the removal of non-prominent concepts from data. Further, we propose a grouping mechanism to group the same pseudo-concepts using subspace modeling of filter responses to achieve a non-redundant representation. Experimental studies show that generated SMN representation using pseudo-concepts achieves comparable results for scene recognition tasks on standard datasets like MIT-67 and SUN-397 even in the absence of true concept labels.

SIKE in 32-bit ARM Processors Based on Redundant Number System for NIST Level-II

We present an optimized implementation of the post-quantum Supersingular Isogeny Key Encapsulation (SIKE) for 32-bit ARMv7-A processors supporting NEON engine (i.e., SIMD instruction). Unlike previous SIKE implementations, finite field arithmetic is efficiently implemented in a redundant representation, which avoids carry propagation and pipeline stall. Furthermore, we adopted several state-of-the-art engineering techniques as well as hand-crafted assembly implementation for high performance. Optimized implementations are ported to Microsoft SIKE library written in “a non-redundant representation” and evaluated in high-end 32-bit ARMv7-A processors, such as ARM Cortex-A5, A7, and A15. A full key-exchange execution of SIKEp503 is performed in about 109 million cycles on ARM Cortex-A15 processors (i.e., 54.5 ms @2.0 GHz), which is about 1.58× faster than previous state-of-the-art work presented in CHES’18.

Reconstruction of the Far-Field Pattern of Volumetric AUTs from a Reduced Set of Near-Field Samples Collected along a Planar Spiral with a Uniform Step.

An efficient near-to-far-field transformation (NTFFT) technique, wherein the near-field (NF) measurements are acquired along a planar spiral with a uniform step to make the control of the involved positioners easier, is developed in this article. Such a technique is tailored for quasi-spherical, i.e., volumetric, antennas under test and makes use of a reduced number of NF data. An effective two-dimensional sampling interpolation algorithm, allowing the accurate reconstruction of the input NF data for the standard NTFFT with plane-rectangular scan, is obtained by setting the spiral step equal to the sample spacing required for interpolating along a radial line according to the spatial bandlimitation properties of electromagnetic fields, and by properly developing a non-redundant representation along such a spiral. Tests results are reported to demonstrate that the proposed NTFFT technique retains the same accuracy as the standard plane-rectangular one.

Pattern Evaluation From NF Spherical Spiral Data in the Noncentered Quasi-Planar Antennas Case

The far-field reconstruction of noncentered quasi-planar antennas is here addressed by using a spherical spiral scan in the near-field region. A reduction of the near-field data is achieved by suitably exploiting the unified theory of spiral scannings for nonspherical antennas. Such a technique, relying on the nonredundant representation of electromagnetic fields, adopts an oblate spheroid as source modeling and uses a two-dimensional optimal sampling interpolation algorithm to recover the data needed to execute the classical near-field–far-field transformation. In this case, the reduction process is required since an inadequate mounting system increases the number of needed data when compared to facilities, which allow one to center the antenna under test with respect to the spherical scanning surface. The above-mentioned statement is based on the following rule: the classical near-field–far-field transformation requires a number of data depending on the smallest spherical surface, which encloses the antenna and shares the same center as the scanning surface. The reported results thoroughly prove the efficacy of the developed technique.

CATEGORY-LEARNING WITH CONTEXT-AUGMENTED AUTOENCODER

Finding an interpretable non-redundant representation of real-world data is one of the key problems in Machine Learning. Biological neural networks are known to solve this problem quite well in unsupervised manner, yet unsupervised artificial neural networks either struggle to do it or require fine tuning for each task individually. We associate this with the fact that a biological brain learns in the context of the relationships between observations, while an artificial network does not. We also notice that, though a naive data augmentation technique can be very useful for supervised learning problems, autoencoders typically fail to generalize transformations from data augmentations. Thus, we believe that providing additional knowledge about relationships between data samples will improve model's capability of finding useful inner data representation. More formally, we consider a dataset not as a manifold, but as a category, where the examples are objects. Two these objects are connected by a morphism, if they actually represent different transformations of the same entity. Following this formalism, we propose a novel method of using data augmentations when training autoencoders. We train a Variational Autoencoder in such a way, that it makes transformation outcome predictable by auxiliary network in terms of the hidden representation. We believe that the classification accuracy of a linear classifier on the learned representation is a good metric to measure its interpretability. In our experiments, present approach outperforms $\beta$-VAE and is comparable with Gaussian-mixture VAE.

A Spherical Near-to-far-Field Transformation Using a Non-Redundant Voltage Representation Optimized for Non-Centered Mounted Quasi-Planar Antennas

This research falls in the antenna measurements related topic, and deals with the problem occurring in the classical spherical near-to-far-field (NTFF) transformation, when it becomes unpractical to mount the antenna under test (AUT) with its center at the center of the scanning sphere. This issue reflects in a growth of the number of near-field (NF) samples to be acquired, since this number depends on the radius of the minimum sphere, which contains the antenna, and is centered at the scanning sphere center. The non-redundant sampling representations of the electromagnetic field are conveniently exploited, to develop an effective spherical NTFF transformation for non-centered AUTs with quasi-planar geometry, requiring a minimum amount of NF samples, and nearly the same as that for a centered mounting of the AUT. Then, the NF data needed to perform the classical NTFF transformation are determined in efficient way from the acquired non-redundant NF samples by employing an accurate 2-D sampling interpolation scheme. Thus, it is possible to significantly save measurement time. Some simulation and laboratory results are reported to show the effectiveness of the developed technique, which takes into account a non-centered AUT mounting.

Neuronal coding of multiscale temporal features in communication sequences within the bat auditory cortex

Experimental evidence supports that cortical oscillations represent multiscale temporal modulations existent in natural stimuli, yet little is known about the processing of these multiple timescales at a neuronal level. Here, using extracellular recordings from the auditory cortex (AC) of awake bats (Carollia perspicillata), we show the existence of three neuronal types which represent different levels of the temporal structure of conspecific vocalizations, and therefore constitute direct evidence of multiscale temporal processing of naturalistic stimuli by neurons in the AC. These neuronal subpopulations synchronize differently to local-field potentials, particularly in theta- and high frequency bands, and are informative to a different degree in terms of their spike rate. Interestingly, we also observed that both low and high frequency cortical oscillations can be highly informative about the listened calls. Our results suggest that multiscale neuronal processing allows for the precise and non-redundant representation of natural vocalizations in the AC.

An SVD-Based Approach to Reconstruct the Planar Wide-Mesh Scanning NF Data From Inaccurately Probe-Positioned Samples

A laboratory validation of an efficient singular value decomposition (SVD) approach to correct known errors in the probe positioning, affecting a nonconventional plane-rectangular near-to-far-field transformation (NTFFT) with the planar wide-mesh scanning, is here given. Such an NTFFT uses a reduced number of near-field data since those required by the standard plane-rectangular one are precisely recovered from them through a nonredundant representation of the voltage measured by the probe, achieved by considering the antenna as contained in an oblate spheroid. The values of the voltage at the sample points dictated by the representation are unknown and are efficiently retrieved from the positioning-errors affected ones through the SVD-based procedure. Some results of experimental tests, carried out at the University of Salerno and validating the efficacy of the proposed approach, are reported.

Efficient Conversion Technique from Redundant Binary to NonRedundant Binary Representation

Redundant Binary (RB) to Two’s Complement (TC) converter offers nonredundant representation. However, the sign bit of TC representation has to be handled using nonstandard hardware blocks. The concept of Inverted encoding of negative weighted bits (IEN) eliminates the need of sign extension and offers design only using predefined hardware blocks. NonRedundant Binary (NRB) representation refers to both conventional and IEN representations. The NRB representation is also useful considering problem related to shifting in Carry Save (CS) representation of a RB number. In this paper, we have proposed two new conversion circuits for RB to NRB representation. The proposed circuits of the RB to NRB converter are coded in Verilog Hardware Description language (HDL) and synthesized using the Encounter(R) RTL Compiler RC13.10 v13.10-s006_1 of Cadence tool considering ASIC platform. Considering 64 bits’ operand, the delay power product performances of proposed one-bit and two-bit computations offer improvement of almost 29.9% and 47%, respectively as compared to Carry-Look-Ahead (CLA). The proposed one-bit converter is also applied in the final stage of the Modified Redundant Binary Adder (MRBA). The 32-bit MRBA offers a delay improvement of 7.87% replacing conventional converter with proposed one-bit converter in same FPGA 4vfx12sf363-12 device.

A sparse equivalent source method for near-field acoustic holography

This study examines a near-field acoustic holography method consisting of a sparse formulation of the equivalent source method, based on the compressive sensing (CS) framework. The method, denoted Compressive-Equivalent Source Method (C-ESM), encourages spatially sparse solutions (based on the superposition of few waves) that are accurate when the acoustic sources are spatially localized. The importance of obtaining a non-redundant representation, i.e., a sensing matrix with low column coherence, and the inherent ill-conditioning of near-field reconstruction problems is addressed. Numerical and experimental results on a classical guitar and on a highly reactive dipole-like source are presented. C-ESM is valid beyond the conventional sampling limits, making wide-band reconstruction possible. Spatially extended sources can also be addressed with C-ESM, although in this case the obtained solution does not recover the spatial extent of the source.

Optimal representation and processing of optical signals in quadratic-phase systems

Optical fields propagating through quadratic-phase systems (QPSs) can be modeled as magnified fractional Fourier transforms (FRTs) of the input field, provided we observe them on suitably defined spherical reference surfaces. Non-redundant representation of the fields with the minimum number of samples becomes possible by appropriate choice of sample points on these surfaces. Longitudinally, these surfaces should not be spaced equally with the distance of propagation, but with respect to the FRT order. The non-uniform sampling grid that emerges mirrors the fundamental structure of propagation through QPSs. By providing a means to effectively handle the sampling of chirp functions, it allows for accurate and efficient computation of optical fields propagating in QPSs.

Incremental processing and the hierarchical lexicon

Hierarchical lexicon structures are not only of great importance for the nonredundant representation of lexical information, they may also contribute to the efficiency of the actual processing of natural language. Two parsing techniques that render the parsing process efficient are presented. Windowing is a technique for incrementally accessing the hierarchical lexicon. Lexical preferencing implements preferences within the parsing process as a natural consequence of the hierarchical structure of the lexicon. Within a proof-theoretic approach to Categorial Grammar it is possible to implement these techniques in a formal and principled way. Special attention is paid to idiomatic expressions.

A semantic framework for intelligent matchmaking for clinical trial eligibility criteria

An integral step in the discovery of new treatments for medical conditions is the matching of potential subjects with appropriate clinical trials. Eligibility criteria for clinical trials are typically specified as inclusion and exclusion criteria for each study in freetext form. While this is sufficient for a human to guide a recruitment interview, it cannot be reliably and computationally construed to identify potential subjects. Standardization of the representation of eligibility criteria can enhance the efficiency and accuracy of this process. This article presents a semantic framework that facilitates intelligent matchmaking by identifying a minimal set of eligibility criteria with maximal coverage of clinical trials. In contrast to existing top-down manual standardization efforts, a bottom-up data driven approach is presented to find a canonical nonredundant representation of an arbitrary collection of clinical trial criteria. The methodology has been validated with a corpus of 709 clinical trials related to Generalized Anxiety Disorder containing 2,760 inclusion and 4,871 exclusion eligibility criteria. This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to a semantically distinct criterion. Internal and external validation measures provide an objective evaluation of the method. An eligibility criteria ontology has been constructed based on the clustering. The resulting model has been incorporated into the development of the MindTrial clinical trial recruiting system. The prototype for clinical trial recruitment illustrates the effectiveness of the methodology in characterizing clinical trials and subjects and accurate matching between them.

FAR-FIELD RECONSTRUCTION FROM NEAR-FIELD DATA ACQUIRED VIA A FAST SPHERICAL SPIRAL SCAN: EXPERIMENTAL EVIDENCES

A probe-compensated near-fleld-far-fleld (NF-FF) trans- formation with spherical spiral scanning, which makes possible to lower the number of needed measurements, as well as the time required for the data acquisition when characterizing quasi-planar antennas, is experimentally verifled in this paper. Such a technique, based on the nonredundant representation of electromagnetic flelds, has been achieved by properly applying the unifled theory of spiral scans for nonspherical antennas and adopting a very ∞exible source modelling, formed by two circular \bowls with the same aperture diameter but difierent bending radii. A two-dimensional optimal sampling interpo- lation formula allows one to reconstruct the NF data at any point on the measurement sphere and, in particular, at those required by the classical NF-FF transformation with spherical scanning. The reported NF and FF reconstructions, obtained from the nonredundant samples acquired on the spiral, assess the accuracy of the proposed technique.

Deformable Bayesian Network: A Robust Framework for Underwater Sensor Fusion

The dynamic tree (DT) graphical model is a popular analytical tool for image segmentation and object classification tasks. A DT is a useful model in this context because its hierarchical property enables the user to examine information in multiple scales and its flexible structure can more easily fit complex region boundaries compared to rigid quadtree structures such as tree-structured Bayesian networks. This paper proposes a novel framework for data fusion called a deformable Bayesian network (DFBN) by using a DT model to fuse measurements from multiple sensing platforms into a nonredundant representation. The structural flexibility of the DFBN will be used to fuse common information across different sensor measurements. The appropriate structure update strategies for the DFBN and its parameters for the data fusion application are discussed. A real-world example application using sonar images collected from a survey mission is presented. The fusion results using the presented DFBN framework are shown to outperform state-of-the-art approaches such as the Gaussian mean shift and spectral clustering algorithms. The DFBN's complexity and scalability are discussed to address its potential for a larger data set.

Two Techniques for Compensating the Probe Positioning Errors in the Spherical NF-FF Transformation for Elongated Antennas

Two effective approaches for compensating the positioning errors in a near-field-far-field (NF-FF) transformation technique with spherical scanning for long antennas using a nonredundant number of data are presented. The transformation technique relies on the nonredundant sampling representations of the electromagnetic fields and on the optimal sampling interpolation (OSI) expansions, and assumes the antenna under test as enclosed in a prolate ellipsoid, a source modelling particularly suitable to deal with elongated antennas. In order to evaluate the NF data at the points fixed by the nonredundant representation from the acquired irregularly spaced ones, the former approach employs the singular value decomposition method, whereas the latter makes use of an iterative technique. The former can be applied when the irregularly samples lie on nonuniform parallels, thus allowing to reduce the starting two-dimensional problem into two independent one-dimensional ones. The latter can be employed also when such a hypothesis does not hold, but requires the existence of a one-to-one correspondence associating at each uniform sampling point the nearest irregular one. In both the cases, the NF data needed by a probe compensated NF-FF transformation with spherical scanning are efficiently evaluated by using an OSI algorithm. Numerical tests assessing the effectiveness of the proposed approaches and their stability with respect to random errors affecting the NF data are shown.