Id Pairing Research Articles

Beyond the Bound: A New Performance Perspective for Identification via Channels

Identification via channels (ID) is a goal-oriented (Post-Shannon) communications paradigm that verifies the matching of message (identity) pairs at source and sink. To date, ID research has focused on the upper bound λ for the probability of a false-positive (FP) identity match, mainly through ID tagging codes that represent the identities through ID codeword sets consisting of position-tag tuples. We broaden the ID research scope by introducing novel ID performance metrics: the expected FP-error probability <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fp</sub> which considers distance properties of ID codeword sets in conjunction with the probability for selecting ID pairs, the threshold probabilities <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</sub> that characterize quantiles of FP-probabilities, and the distance tail uplift ratio DiTUR giving the fraction of ID pairs whose distance is increased above the minimum distance (which corresponds to λ). We define a No-Code (NC) approach that directly conducts the ID operations with the messages (identities) without any additional coding as a baseline for ID. We investigate a concatenated Reed-Solomon ID code and a Reed-Muller ID code, and find that they do not always yield advantages over using no ID code. We analytically characterize the reduction of error-prone ID pairs through sending multiple tags. Overall, our insights point to investigating the distance distribution of ID codes and incorporating the ID pair distributions of real ID systems in future ID research.

Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Due to the lack of CAN frame encryption and authentication, CAN bus is vulnerable to various attacks, which can in general be divided into message injection, suspension, and falsification. Existing CAN bus anomaly detection mechanisms either can only detect one or two of these attacks, or require numerous CAN messages during predictions, which can hardly realize real-time performance. In this paper, we propose a CAN bus anomaly detection system that can detect all these attacks simultaneously in as short as 3 milliseconds (ms) based on Graph Neural Network (GNN). This work generates directed attributed graphs based on CAN message streams in given message intervals. Node attributes denote data contents in CAN messages while each edge attribute represents the frequency of a typical CAN ID pair in the given interval. Afterwards, a GNN is trained based on generated CAN message graphs. Considering highly imbalanced training data, a two-stage classifier cascade is developed in this paper, which is composed of a one-class classifier for anomaly detection and a multi-class classifier for attack classification. An openmax layer is further introduced to the multi-class classifier to tackle new anomalies from unknown classes. To take advantage of crowdsourcing while protecting user data privacy, we adopt federated learning to train a universal model that covers different driving scenarios and vehicle states. Extensive experiment results show the effectiveness and efficiency of our methodology.

EmiRIT: a text-mining-based resource for microRNA information.

microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA –PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains ‘miRNA –gene regulation’ (69 ,152 relations), ‘miRNA disease (cancer)’ (12 ,300 relations), ‘miRNA –biological process and pathways’ (23, 390 relations) and circulatory ‘miRNAs in extracellular locations’ (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information. Database URL: https://research.bioinformatics.udel.edu/emirit/

Impurity effect as a probe for the pairing symmetry of graphene-based superconductors

We study theoretically the single impurity effect on graphene-based superconductors. Four different pairing symmetries are discussed. Sharp in-gap resonant peaks are found near the impurity site for the d + id pairing symmetry and the p + ip pairing symmetry when the chemical potential is large. As the chemical potential decreases, the in-gap states are robust for the d + id pairing symmetry while they disappear for the p + ip pairing symmetry. Such in-gap peaks are absent for the fully gapped extended s-wave pairing symmetry and the nodal f-wave pairing symmetry. The existence of the ingap resonant peaks can be explained well based on the sign-reversal of the superconducting gap along different Fermi pockets and by analyzing the denominator of the T-matrix. All of the features may be checked by the experiments, providing a useful probe for the pairing symmetry of graphene-based superconductors.

Similarity query support in big data management systems

Similarity query processing is becoming increasingly important in many applications such as data cleaning, record linkage, Web search, and document analytics. In this paper we study how to provide end-to-end similarity query support natively in a parallel database system. We discuss how to express a similarity predicate in its query language, how to build indexes, how to answer similarity queries (selections and joins) efficiently in the runtime engine, possibly using indexes, and how to optimize similarity queries. One particular challenge is how to incorporate existing similarity join algorithms, which often require a series of steps to achieve a high efficiency, including collecting token frequencies, finding matching record id pairs, and reassembling result records based on id pairs. We present a novel approach that uses existing runtime operators to implement such complex join algorithms without reinventing the wheel; doing so positions the system to automatically benefit from future improvements to those operators. The approach includes a technique to transform a similarity join plan into an efficient operator-based physical plan during query optimization by using a template expressed largely in the system’s user-level query language; this technique greatly simplifies the specification of such a transformation rule. We use Apache AsterixDB, a parallel Big Data management system, to illustrate and validate our techniques. We conduct an experimental study using several large, real datasets on a parallel computing cluster to assess the similarity query support. We also include experiments involving three other parallel systems and report the efficacy and performance results.

Antiferromagnetically ordered Mott insulator and [formula omitted] superconductivity in twisted bilayer graphene: a quantum Monte Carlo study

Using exact quantum Monte Carlo method, we examine the recent novel electronic states seen in magic-angle graphene superlattices. From the Hubbard model on a double-layer honeycomb lattice with a rotation angle θ=1.08°, we reveal that an antiferromagnetically ordered Mott insulator emerges beyond a critical Uc at half filling, and with a small doping, the pairing with d+id symmetry dominates over other pairings at low temperature. The effective d+id pairing interaction strongly increases as the on-site Coulomb interaction increases, indicating that the superconductivity is driven by electron-electron correlation. Our non-biased numerical results demonstrate that the twisted bilayer graphene shares the similar superconducting mechanism of high temperature superconductors, which is a new and ideal platform for further investigating the strongly correlated phenomena.

Phase diagrams of singlet superconducting states with mixed symmetry

The competition between the singlet superconducting states with s- and d-wave symmetry of the order parameter is studied within a single-band model with nearest-neighbor attractive interaction. The ground state and finite-temperature phase diagrams are constructed for different ratios between the nearest- and next-nearest-neighbor electron transfer integrals. The mixed s+id pairing state is shown to be formed in the intermediate region between s- and d-waves. The temperature phase transitions between the pure and the mixed pairing state are found.

Contribution of factor H-Binding protein sequence to the cross-reactivity of meningococcal native outer membrane vesicle vaccines with over-expressed fHbp variant group 1.

Factor H-binding protein (fHbp) is an important meningococcal vaccine antigen. Native outer membrane vesicles with over-expressed fHbp (NOMV OE fHbp) have been shown to induce antibodies with broader functional activity than recombinant fHbp (rfHbp). Improved understanding of this broad coverage would facilitate rational vaccine design. We performed a pair-wise analysis of 48 surface-exposed amino acids involved in interacting with factor H, among 383 fHbp variant group 1 sequences. We generated isogenic NOMV-producing meningococcal strains from an African serogroup W isolate, each over-expressing one of four fHbp variant group 1 sequences (ID 1, 5, 9, or 74), including those most common among invasive African meningococcal isolates. Mice were immunised with each NOMV, and sera tested for IgG levels against each of the rfHbp ID and for ability to kill a panel of heterologous meningococcal isolates. At the fH-binding site, ID pairs differed by a maximum of 13 (27%) amino acids. ID 9 shared an amino acid sequence common to 83 ID types. The selected ID types differed by up to 6 amino acids, in the fH-binding site. All NOMV and rfHbp induced high IgG levels against each rfHbp. Serum killing from mice immunised with rfHbp was generally less efficient and more restricted compared to NOMV, which induced antibodies that killed most meningococci tested, with decreased stringency for ID type differences. Breadth of killing was mostly due to anti-fHbp antibodies, with some restriction according to ID type sequence differences. Nevertheless, under our experimental conditions, no relationship between antibody cross-reactivity and variation fH-binding site sequence was identified. NOMV over-expressing different fHbp IDs belonging to variant group 1 induce antibodies with fine specificities against fHbp, and ability to kill broadly meningococci expressing heterologous fHbp IDs. The work reinforces that meningococcal NOMV with OE fHbp is a promising vaccine strategy, and provides a basis for rational selection of antigen sequence types for over-expression on NOMV.

Identifying the chiral d-wave superconductivity by Josephson \u03c60-states

We propose the Josephson junctions linked by a normal metal between a d + id superconductor and another d + id superconductor, a d-wave superconductor, or a s-wave superconductor for identifying the chiral d + id superconductivity. The time-reversal breaking in the chiral d-wave superconducting state is shown to result in a Josephson φ0-junction state where the current-phase relation is shifted by a phase φ0 from the sinusoidal relation, other than 0 and π. The ground-state phase difference φ0 and the critical current can be used to definitely confirm and read the information about the d + id superconductivity. A smooth evolution from conventional 0-π transitions to tunable φ0-states can be observed by changing the relative magnitude of two types of d-wave components in the d + id pairing. On the other hand, the Josephson junction involving the d + id superconductor is also the simplest model to realize a φ0- junction, which is useful in superconducting electronics and superconducting quantum computation.

트리 자료구조를 이용한 비 휘발성 메모리의 가비지 수집 기법

Most traditional garbage collectors commonly use the language level metadata, which is designed for pointer type searching. However, because it is difficult to use this metadata in non-volatile memory allocation platforms, a new garbage collection technique is essential for non-volatile memory utilization. In this paper, we design new metadata for managing information regarding non-volatile memory allocation called Allocation Tree. This metadata is comprised of tree data structure for fast information lookup and a node that holds an allocation address and an object ID pair in key-value form. The Garbage Collector starts collecting when there are insufficient non-volatile memory spaces, and it compares user data and the allocation tree for garbage detection. We develop this algorithm in a persistent heap based non-volatile memory allocation platform called HEAPO for demonstration.

Predicting Unconventional High-Temperature Superconductors in Trigonal Bipyramidal Coordinations

Cuprates and iron-based superconductors are two classes of unconventional high Tc superconductors based on 3d transition elements. Recently, two principles, correspondence principle and magnetic selective pairing rule, have been emerged to unify their high Tc superconducting mechanisms. These principles strongly regulate electronic structures that can host high Tc superconductivity. Guided by these principles, here we propose high Tc superconducting candidates that are formed by cation-anion trigonal bipyramidal complexes with a d^7 filling configuration on the cation ions. Their superconducting states are expected to be dominated by the d+id pairing symmetry.

Doping effect on the evolution of the pairing symmetry in n-type superconductor near antiferromagnetic phase boundary

We present the investigation results of the in-plane ρ(T) resistivity tensor at the temperature range 0.4–40 K in magnetic fields up to 90 kOe (H || c, J || ab) for electron-doped Nd2−xCexCuO4+δ with different degree of disorder near antiferromagnetic-superconducting phase boundary. We have experimentally found that for optimally doped compound both the upper critical field slope and the critical temperature decrease with increasing of the disorder parameter (d-wave pairing) while in the case of the underdoped system the critical temperature remains constant and dHc2/dT increases with increasing of the disorder (s-wave pairing). These features suggest a possible implementation of the complex mixture state as the s + id pairing.

Quantum Monte Carlo study of magnetic and superconducting properties of graphene

We apply quantum Monte Carlo technique to address the issue of high controllability of ferromagnetism in graphene and the issue of electron correlation driven superconductivity in graphene, by simulating the t − U − V Hubbard model on a honeycomb lattice. In the region of low electron band filling below the Van Hove singularity, the system shows a short‐range ferromagnetic correlation, which is strengthened slightly by the on‐site Coulomb interaction and markedly by the next nearest‐neighbor hoping integral. The strong dependence of ferromagnetism on the electron band filling can be manipulated by applying electric gate voltage. For V=0 and close to the half filling, we find that pairing with d + id symmetry dominates pairing with extended‐s symmetry. However, as the system size increases the long‐range part of the d + id pairing correlation decreases and tends to vanish in the thermodynamic limit. An inclusion of nearest‐neighbor interaction V, either repulsive or attractive, has a small effect on the extended‐s pairing correlation, but strongly suppresses the d + id pairing correlation. Copyright © 2013 John Wiley &amp; Sons, Ltd.

A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration

BackgroundIn bioinformatics, we pre-process raw data into a format ready for answering medical and biological questions. A key step in processing is labeling the measured features with the identities of the molecules purportedly assayed: “molecular identification” (MI). Biological meaning comes from identifying these molecular measurements correctly with actual molecular species. But MI can be incorrect. Identifier filtering (IDF) selects features with more trusted MI, leaving a smaller, but more correct dataset. Identifier mapping (IDM) is needed when an analyst is combining two high-throughput (HT) measurement platforms on the same samples. IDM produces ID pairs, one ID from each platform, where the mapping declares that the two analytes are associated through a causal path, direct or indirect (example: pairing an ID for an mRNA species with an ID for a protein species that is its putative translation). Many competing solutions for IDF and IDM exist. Analysts need a rigorous method for evaluating and comparing all these choices.ResultsWe describe a paradigm for critically evaluating and comparing IDF and IDM methods, guided by data on biological samples. The requirements are: a large set of biological samples, measurements on those samples from at least two high-throughput platforms, a model family connecting features from the platforms, and an association measure. From these ingredients, one fits a mixture model coupled to a decision framework. We demonstrate this evaluation paradigm in three settings: comparing performance of several bioinformatics resources for IDM between transcripts and proteins, comparing several published microarray probeset IDF methods and their combinations, and selecting optimal quality thresholds for tandem mass spectrometry spectral events.ConclusionsThe paradigm outlined here provides a data-grounded approach for evaluating the quality not just of IDM and IDF, but of any pre-processing step or pipeline. The results will help researchers to semantically integrate or filter data optimally, and help bioinformatics database curators to track changes in quality over time and even to troubleshoot causes of MI errors.

A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network

As information technology has advanced, people are turning more frequently to electronic media for communication, and social relationships are increasingly found in online channels. Discovering the latent communities therein is a useful way to better understand the properties of a virtual social network. Traditional community-detection tasks only consider the structural characteristics of a social organization, but more information about nodes and edges such as semantic information cannot be exploited. What is more, the typical size of virtual spaces is now counted in millions, if not billions, of nodes and edges, most existing algorithms are incapable to analyze such large scale dense networks.In this paper, we first introduce an interesting social network model (Interest Network) in which links between two IDs are built if they both participate to the discussions about one or more topics/stories. In this case, we say both of the connected two IDs have the similar interests. Then, the edges of the initial network are updated using the attitude consistency information of the connected ID pairs. For a given ID pair i and j, they may together reply to some topics/IDs. The implicit orientations/attitudes of these two IDs to their together-reply topics/IDs may not be the same. We use a simple statistical method to calculate the attitude consistency, the value of which is between 0 and 1, and the higher value corresponds to a greater degree of consistency of the given ID pair to topics/IDs. The updated network is called Similar-View Network (SVN). In the second part, a fast parallel modularity optimization algorithm (FPMQA) that performs the analogous greedy optimization as CNM and FUC is used to conduct community discovering. By using the parallel manner and sophisticated data structures, its running time is essentially fast, O(kmax(kmax+〈k〉logkmax)). Finally, we propose an evaluation metric, which is based on the reliable ground truths, for online network community detection. In the experimental work, we evaluate our method using real datasets and compare our approach with several previous methods; the results show that our method is more effective and accurate in find potential online communities.

Field Tolerant Dynamic Intrinsic Chip ID Using 32 nm High-K/Metal Gate SOI Embedded DRAM

A random intrinsic chip ID generation method using retention fails is implemented in 32 nm SOI embedded DRAM. A dynamic key algorithm employs a unique pair of 4 Kb binary strings for an ID record for secure authentication. These strings are generated by controlling a wordline low voltage to search for a number of fails matching the corresponding challenge numbers. The algorithm further includes field-tolerant authentication by detecting a number of common bits analytically guaranteed for successful recognition, while preventing ID spoofing during the read operation. This results in 100% successful unique ID generation and recognition in two temperature and three voltage conditions per chip for a total of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\sim$</tex> </formula> 420 k ID pair comparisons in 266 chips. The analytical model predicts a 99.999% successful recognition rate for 10 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$^{6}$</tex></formula> parts. Finally, a method to enable a field-tolerant ID using multiple domains will be discussed.

On the Generality of the Topological Theory of Visual Shape Perception

This study used a series of six closely related experiments to examine whether individuals use topological structures to discriminate figures. Strict control was exerted over the selection of stimuli, which were a specific type of randomly generated lined figures that can be classified using isomorphic sets defined by graph theory. Any two figures within an isomorphic set possessed the same topological structure. The experiments described here used a same/different discrimination task with simultaneously presented pairs of figures: (a) identical pairs (Id pairs), in which each pair of figures had the same topological and superficial properties; (b) nonidentical and isomorphic pairs (Iso pairs), in which each pair had the same topological but different superficial properties; and (c) nonidentical and nonisomorphic pairs (Noniso pairs), in which each pair had different topological properties. Within these experiments I varied the conditions related to the intersecting line segments, presentation of points defining each figure, figure complexity, stimulus aspect ratios, and the parity of the total line-segment lengths between the figures in each pair. These variations showed that the latencies for making accurate discriminations were shorter for Noniso pairs than for Iso pairs, suggesting that individuals are sensitive to topology when distinguishing figures.

Pairing in graphene: A quantum Monte Carlo study

To address the issue of electron correlation driven superconductivity in graphene, we perform a systematic quantum Monte Carlo study of the pairing correlation in the t-U-V Hubbard model on a honeycomb lattice. For V=0 and close to half filling, we find that pairing with d+id symmetry dominates pairing with extended-s symmetry. However, as the system size or the on-site Coulomb interaction increases, the long-range part of the d+id pairing correlation decreases and tends to vanish in the thermodynamic limit. An inclusion of nearest-neighbor interaction V, either repulsive or attractive, has a small effect on the extended-s pairing correlation, but strongly suppresses the d+id pairing correlation.

Non-Abelian topological orders and Majorana fermions in spin-singlet superconductors

The non-Abelian topological order for superconductors is characterized by the existence of zero-energy Majorana fermions in edges of systems and in a vortex of a macroscopic condensate, which obey the non-Abelian statistics. This paper is devoted to an extensive study on the non-Abelian topological phase of spin-singlet superconductors with the Rashba spin-orbit interaction proposed in our previous letter [M. Sato, Y. Takahashi, and S. Fujimoto, Phys. Rev. Lett. 103, 020401 (2009)]. We mainly consider the s-wave pairing state and the d+id pairing state. In the case of d+id-wave pairing, Majorana fermions appear in almost all parameter regions of the mixed state under an applied magnetic field, provided that the Fermi level crosses k-points in the vicinity of the Gamma point or the M point in the Brillouin zone, while in the case of s-wave pairing, a strong magnetic field, the Zeeman energy of which is larger than the superconducting gap is required to realize the topological phase. We clarify that Majorana fermions in Rashba spin-singlet superconductors are much more stable than those realized in spin-triplet p+ip superconductors in certain parameter regions. We also investigate the topological number which ensures the topological stability of the phase in detail. Furthermore, as a byproduct, we found that topological order is also realized in conventional spin (or charge) density wave states with the Rashba spin-orbit interaction, for which massless Dirac fermions appear in the edge of the systems and charge fractionalization occurs.

Unconventional superconductivity in Li-intercalated layered nitride LixZrNCl

It is inferred from the temperature and magnetic field dependence of superfluid density that the gap parameter (2Δ/kBTc) in LixZrNCl exhibits decrease with increasing x, suggesting development of anisotropic order parameter that leads to the reduction of Δ as a mean value. This observation is quantitatively in line with the d+id pairing predicted by recent theory based on the Hubbard model considering disconnected Fermi surfaces on a honeycomb lattice.