Biased Graph Research Articles

The Rhodes semilattice of a biased graph

The Rhodes semilattice of a biased graph

Impression management strategy — The relationship between accounting narrative thematic bias and financial graph distortion

Prior literature has examined 10-K narrative thematic bias and financial graph distortion as two independent outcomes that might arise from managements' efforts at impression management. Largely unexplored is an analysis of narrative thematic bias and financial graph distortion as joint and interrelated outcomes that would arise if management coordinates both in the same 10-K report as part of an impression management strategy. We fill this void in the literature by using a simultaneous equation system to examine the joint relationship between narrative thematic bias and graph distortion in the 10-K filings of S&P 500 firms from 2014 to 2018. We draw upon Paivio's (1986) dual coding theory to predict a positive association between narrative thematic bias and financial graph distortion based on the insight that graph distortion helps reinforce the effects of thematic bias, and vice versa. Consistently, we find a simultaneous and positive relationship between thematic bias and graph distortion. Further, we find that this complementary relationship is more pronounced in firms with weak corporate governance and weak external monitoring. Our findings suggest that management may exploit the reinforcing effects of thematic bias and graph distortion to leave financial statement users with a more favorable impression of firm performance.

A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches learning sparse point-based representations through graph CNNs (GCN) become more popular. Yet, these graph techniques hold lower performance than their frame-based counterpart due to two issues: (i) Biased graph structures that don't properly incorporate varied attributes (such as semantics, and spatial and temporal signals) for each vertex, resulting in inaccurate graph representations. (ii) A shortage of robust pretrained models. Here we solve the first problem by proposing a new event-based GCN (EDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To address the second problem, we introduce a novel learning framework called cross-representation distillation (CRD), which leverages the dense representation of events as a cross-representation auxiliary to provide additional supervision and prior knowledge for the event graph. This frame-to-graph distillation allows us to benefit from the large-scale priors provided by CNNs while still retaining the advantages of graph-based models. Extensive experiments show our model and learning framework are effective and generalize well across multiple vision tasks.

Postoperative nausea and vomiting in orthognathic surgery: systematic review and meta-analysis.

Postoperative nausea and vomiting (PONV) is a frequently reported adverse event following orthognathic surgery. The aim of this work is to conduct a systematic review of the literature on the subject, and to discuss the role of maxillofacial surgeons and the steps that can be taken to prevent or control PONV in orthognathic surgery. A systematic review was conducted according to the PRISMA guidelines, using the search strategy: (orthognathic AND (nausea OR vomiting)). The authors searched PubMed, Embase, Dimensions, Web of Science and Google Scholar databases, without any language restrictions. RevMan 5.4 was used to create a risk of bias graph and a forest plot. The included articles were classified as having a low risk of bias, despite the limited literature on the subject. Various measures have been reported to be beneficial in preventing or managing PONV, such as the use of dexamethasone, antiemetic drugs, gastric aspiration, and anesthetic blocks. Effective bleeding control and faster surgeries can also be helpful. Throat packs have not been found to be effective in preventing PONV. Although no definitive protocol has been established in the literature, the Enhanced Recovery After Surgery (ERAS) protocol could be a useful approach. Overall, a multimodal approach may be taken to prevent PONV, and further research is needed to establish definitive protocols.

Cobiased Graphs: Single-Element Extensions and Elementary Quotients of Graphic Matroids

Zaslavsky (1991) introduced a graphical structure called a biased graph and used it to characterize all single-element coextensions and elementary lifts of graphic matroids. We introduce a new graphical structure that we call a cobiased graph and use it to characterize single-element extensions and elementary quotients of graphic matroids.

Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants.

There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.

Exposing the Self-Supervised Space-Time Correspondence Learning via Graph Kernels

Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or optical flow constraints. Moreover, video correspondence predictive models require mining more inherent properties in videos, such as structural information. In this work, we propose the VideoHiGraph, a space-time correspondence framework based on a learnable graph kernel. Concerning the video as the spatial-temporal graph, the learning objectives of VideoHiGraph are emanated in a self-supervised manner for predicting unobserved hidden graphs via graph kernel manner. We learn a representation of the temporal coherence across frames in which pairwise similarity defines the structured hidden graph, such that a biased random walk graph kernel along the sub-graph can predict long-range correspondence. Then, we learn a refined representation across frames on the node-level via a dense graph kernel. The self-supervision of the model training is formed by the structural and temporal consistency of the graph. VideoHiGraph achieves superior performance and demonstrates its robustness across the benchmark of label propagation tasks involving objects, semantic parts, keypoints, and instances. Our algorithm implementations have been made publicly available at https://github.com/zyqin19/VideoHiGraph.

Distinct degrees and homogeneous sets

In this paper we investigate the extremal relationship between two well-studied graph parameters: the order of the largest homogeneous set in a graph G and the maximal number of distinct degrees appearing in an induced subgraph of G, denoted respectively by hom⁡(G) and f(G).Our main theorem improves estimates due to several earlier researchers and shows that if G is an n-vertex graph with hom⁡(G)≥n1/2 then f(G)≥(n/hom⁡(G))1−o(1). The bound here is sharp up to the o(1)-term, and asymptotically solves a conjecture of Narayanan and Tomon. In particular, this implies that max⁡{hom⁡(G),f(G)}≥n1/2−o(1) for any n-vertex graph G, which is also sharp.The above relationship between hom⁡(G) and f(G) breaks down in the regime where hom⁡(G)<n1/2. Our second result provides a sharp bound for distinct degrees in biased random graphs, i.e. on f(G(n,p)). We believe that the behaviour here determines the extremal relationship between hom⁡(G) and f(G) in this second regime.Our approach to lower bounding f(G) proceeds via a translation into an (almost) equivalent probabilistic problem, and it can be shown to be effective for arbitrary graphs. It may be of independent interest.

Matrix representations of frame and lifted-graphic matroids correspond to gain functions

Let M be a 3-connected matroid and let F be a field. Let A be a matrix over F representing M and let (G,B) be a biased graph representing M. We characterize the relationship between A and (G,B), settling four conjectures of Zaslavsky. We show that for each matrix representation A and each biased graph representation (G,B) of M, A is projectively equivalent to a canonical matrix representation arising from G as a gain graph over F+ or F× realizing B. Further, we show that the projective equivalence classes of matrix representations of M are in one-to-one correspondence with the switching equivalence classes of gain graphs arising from (G,B), except in one degenerate case.

The role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography in oral squamous cell carcinoma: A systematic review

Objective: The aim of this study was to systematically review the literature to assess the role of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in oral squamous cell carcinoma (OSCC). Materials and Methods: The literature was searched using MEDLINE and ScienceDirect databases. Studies comparing fusion imaging PET/CT with other imaging modalities in the staging, identifying secondary metastasis, treatment response assessment, and follow-up of OSCC, were deemed eligible. Results: Six studies met the inclusion criteria. The average mean was found to be 85.47%, 78.80%, 78.50%, and 89.22% for sensitivity, specificity, positive predictive value (PPV), and negative predictive value, respectively. The risk of bias graph shows that there was 50% low risk of bias in selection of patients, more than 25% low risk of bias in conduct/interpretation of the index test, and > 25% risk of bias in reference standard. 18F-FDG PET/CT is a good prognostic factor for overall and progression-free survival. Conclusion: FDG PET/CT has a good diagnostic accuracy for identifying metastatic nodes, especially subcentimeter metastatic nodes that appear morphologically normal on CT images. PET/CT is promising in the diagnosis and evaluation of distant metastases and for identifying unknown and second primary malignancies. Advances in Knowledge: Fusion imaging is gaining importance with its ability to identify tumor as small as 0.5 mm and provide early diagnosis, thereby increasing quality of life of patients. PET/CT provides valuable information for therapeutic planning, therapeutic assessment, follow-up, and surveillance in the care of patients with OSCC.

The Protective Effect of Metformin on Abdominal Aortic Aneurysm: A Systematic Review and Meta-Analysis.

BackgroundType 2 diabetes mellitus (T2DM) patients have a lower risk of abdominal aortic aneurysm (AAA) and its comorbidities, which might be associated with the usage of metformin. The objective of the study was to evaluate the role of metformin in the process of AAA development.MethodPubMed, Embase and Cochrane Library were searched up to May 15th, 2021. We implemented several methods including the risk of bias graph, GRADE system and funnel plot to assess the quality and possible bias of this study. Subgroup analysis and sensitivity analysis were applied to address quality differences and validate the robustness of the final results.ResultTen articles were enrolled after screening 151 articles searched from databases. The pooled results showed that, compared with T2DM patients without metformin, metformin prescription was associated with a slower annual growth rate of the aneurysm (mean difference (MD) -0.67 cm [95% confidence interval (CI) -1.20 ~ -0.15 cm]). Besides, metformin exposure was associated with a lower frequency of AAA events (odds ratio (OR) 0.61 [95% CI 0.41-0.92]).ConclusionMetformin alleviated both annual expansion rate and aneurysm rupture frequency in AAA patients with T2DM.Systematic Review RegistrationPROSPERO, identifier https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=217859 (CRD42020217859).

Graphical indices of some chemical structure graphs

In present situations for theory and applications point of view graph theory is useful. Graphs are used as a representative tool for many problems of applied consequence. For illustration, a system of towns, which are considered by vertices, and connections among them make a biased graph. In present work graphical structure of graphene and ferroelectric hysteresis curve is explain.

The efficacy of novel metabolic targeted agents and natural plant drugs for nonalcoholic fatty liver disease treatment: A PRISMA-compliant network meta-analysis of randomized controlled trials.

Background:Nonalcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver disease characterized by excess accumulation of fat in hepatocytes. Because no drug has been approved for NAFLD treatment, this work analyzed the effects of agents resulting from 2 research hotspots, metabolic target agents, and natural plant drugs, on NAFLD with network meta-analysis.Methods:Public databases were searched through August 14, 2020. Randomized controlled trials that compared obeticholic acid, elafibranor, cenicriviroc, selonsertib, curcumin, silymarin, and resveratrol to placebo were included. Liver pathology improvement, hepatic biochemical indicators, and lipid metabolism indicators were analyzed.Results:Thirty-five studies were included in the meta-analysis. Obeticholic acid was found to significantly increase the frequency of liver biopsy improvement compared to placebo (OR: 2.10; 95% CI: 1.60, 2.77). The ranking results among the hepatic biochemical indicators showed that obeticholic acid (94.9%) and elafibranor (86.3%) have a relative advantage in reducing alanine aminotransferase (ALT) levels, and obeticholic acid also had an advantage (95.4%) in reducing aspartate aminotransferase (AST) levels. Considering lipid metabolic indicators, elafibranor (expSMD: 0.01; 95% CI: 0.00, 0.05; SUCRA: 100%), and obeticholic acid (expSMD: 0.48; 95% CI: 0.28,0.84; SUCRA: 75.6%) significantly reduced triglyceride (TG) levels compared with placebo; moreover, obeticholic acid, but not elafibranor, caused a serious increase in total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels and a decrease in high-density lipoprotein cholesterol (HDL-C) levels.Conclusions:Novel metabolic targeted agents generally have better effects than natural plant drugs, especially obeticholic acid, and elafibranor. However, obeticholic acid showed serious adverse effects such as increasing LDL-C levels and decreasing HDL-C levels. Curcumin showed potential advantages for NAFLD but lacked statistical significance.

Polynomial approximation based spectral dual graph convolution for scene parsing and segmentation

Semantic segmentation requires both a large receptive field and accurate spatial information. Although existing methods based on the FCN have greatly improved the accuracy, it still does not show satisfactory results on complex scene parsing and tiny object identification. The convolution operation in FCN suffers from a restricted receptive field, while global modeling is fundamental to dense prediction tasks. In this work, we apply graph convolution into the semantic segmentation task and propose a spectral dual graph convolution module to solve the above problems. Moreover, the semantic segmentation task can be divided into two directions, one of which is to get a large receptive field and consider the global context information; the other is to focus on extracting spatial and contour clues, such as sharply changing curves and tiny objects. From the spectral-domain, it is supposed that low-frequency information is critical to the former task, while high-frequency information is vital to the latter task. Accordingly, high-frequency and low-frequency biased graph convolutions are proposed to process the above information separately. Experiments on Cityscapes, COCO Stuff, PASCAL Context, and PASCAL VOC demonstrate the effectiveness of our methods on semantic segmentation. The proposal achieves comparable performance with advantages in computational and memory overhead.

Tutte’s dichromate for signed graphs

We introduce the trivariate Tutte polynomial of a signed graph as an invariant of signed graphs up to vertex switching that contains among its evaluations the number of proper colorings and the number of nowhere-zero flows. In this, it parallels the Tutte polynomial of a graph, which contains the chromatic polynomial and flow polynomial as specializations. The number of nowhere-zero tensions (for signed graphs they are not simply related to proper colorings as they are for graphs) is given in terms of evaluations of the trivariate Tutte polynomial at two distinct points. Interestingly, the bivariate dichromatic polynomial of a biased graph, shown by Zaslavsky to share many similar properties with the Tutte polynomial of a graph, does not in general yield the number of nowhere-zero flows of a signed graph. Therefore the “dichromate” for signed graphs (our trivariate Tutte polynomial) differs from the dichromatic polynomial (the rank–size generating function).The trivariate Tutte polynomial of a signed graph can be extended to an invariant of ordered pairs of matroids on a common ground set — for a signed graph, the cycle matroid of its underlying graph and its frame matroid form the relevant pair of matroids. This invariant is the canonically defined Tutte polynomial of matroid pairs on a common ground set in the sense of a recent paper of Krajewski, Moffatt and Tanasa, and was first studied by Welsh and Kayibi as a four-variable linking polynomial of a matroid pair on a common ground set.

Disjoint non-balanced A-paths in biased graphs

Let A be a set of vertices in a graph G. An A-path is a non-trivial path in G that has both of its ends in A. In 1961, Gallai showed that, for any integer k, either G has k disjoint A-paths or there is a set of at most 2(k−1) vertices that hits all of the A-paths. There have been a number of extensions of this result; in each of these extensions we want to find a maximum collection of disjoint “allowable” A-paths, where the collection of allowed A-paths varies according to the application. We prove a new extension of this type, in the context of biased graphs, unifying many of the others.

Effects of respiratory muscle therapy on obstructive sleep apnea: a systematic review and meta-analysis

The purpose of this study is to conduct a systematic review and meta-analysis evaluating the effects of respiratory muscle therapy (ie, oropharyngeal exercises, speech therapy, breathing exercises, wind musical instruments) compared with control therapy or no treatment in improving apnea-hypopnea index ([AHI] primary outcome), sleepiness, and other polysomnographic outcomes for patients diagnosed with obstructive sleep apnea (OSA). Only randomized controlled trials with a placebo therapy or no treatment searched using PubMed, EMBASE, Cochrane, and Web of Science up to November 2018 were included, and assessment of risk of bias was completed using the Cochrane Handbook. Nine studies with 394 adults and children diagnosed with mild to severe OSA were included, all assessed at high risk of bias. Eight of the 9 studies measured AHI and showed a weighted average overall AHI improvement of 39.5% versus baselines after respiratory muscle therapy. Based on our meta-analyses in adult studies, respiratory muscle therapy yielded an improvement in AHI of -7.6 events/h (95% confidence interval [CI] = -11.7 to -3.5; P ≤ .001), apnea index of -4.2 events/h (95% CI = -7.7 to -0.8; P ≤ .016), Epworth Sleepiness Scale of -2.5 of 24 (95% CI= -5.1 to -0.1; P ≤ .066), Pittsburgh Sleep Quality Index of -1.3 of 21 (95% CI= -2.4 to -0.2; P ≤ .026), snoring frequency (P = .044) in intervention groups compared with controls. This systematic review highlights respiratory muscle therapy as an adjunct management for OSA but further studies are needed due to limitations including the nature and small number of studies, heterogeneity of the interventions, and high risk of bias with low quality of evidence.

A Ramsey Theorem for Biased Graphs

A biased graph is a pair $(G,\mathcal{B})$, where $G$ is a graph and $\mathcal{B}$ is a collection of “balanced” cycles of $G$ such that no $\Theta$-subgraph of $G$ contains precisely two balanced cycles. We prove a Ramsey-type theorem, showing that if $(G,\mathcal{B})$ is a biased graph for which $G$ is a very large complete graph, then $G$ contains a large complete subgraph $H$ such that the set of balanced cycles within $H$ has one of three specific, highly symmetric structures, all of which can be described naturally via group-labelings.

Fast Graph Sampling Set Selection Using Gershgorin Disc Alignment

Graph sampling set selection, where a subset of nodes are chosen to collect samples to reconstruct a smooth graph signal, is a fundamental problem in graph signal processing (GSP). Previous works employ an unbiased least-squares (LS) signal reconstruction scheme and select samples via expensive extreme eigenvector computation. Instead, we assume a biased graph Laplacian regularization (GLR) based scheme that solves a system of linear equations for reconstruction. We then choose samples to minimize the condition number of the coefficient matrix---specifically, maximize the smallest eigenvalue $\lambda_{\min}$. Circumventing explicit eigenvalue computation, we maximize instead the lower bound of $\lambda_{\min}$, designated by the smallest left-end of all Gershgorin discs of the matrix. To achieve this efficiently, we first convert the optimization to a dual problem, where we minimize the number of samples needed to align all Gershgorin disc left-ends at a chosen lower-bound target $T$. Algebraically, the dual problem amounts to optimizing two disc operations: i) shifting of disc centers due to sampling, and ii) scaling of disc radii due to a similarity transformation of the matrix. We further reinterpret the dual as an intuitive disc coverage problem bearing strong resemblance to the famous NP-hard set cover (SC) problem. The reinterpretation enables us to derive a fast approximation scheme from a known SC error-bounded approximation algorithm. We find an appropriate target $T$ efficiently via binary search. Extensive simulation experiments show that our disc-based sampling algorithm runs substantially faster than existing sampling schemes and outperforms other eigen-decomposition-free sampling schemes in reconstruction error.

Describing quasi-graphic matroids

The class of quasi-graphic matroids recently introduced by Geelen, Gerards, and Whittle generalises each of the classes of frame matroids and lifted-graphic matroids introduced earlier by Zaslavsky. For each biased graph (G,B) Zaslavsky defined a unique lift matroid L(G,B) and a unique frame matroid F(G,B), each on ground set E(G). We show that in general there may be many quasi-graphic matroids on E(G) and describe them all: for each graph G and partition (B,L,F) of its cycles such that B satisfies the theta property and each cycle in L meets each cycle in F, there is a quasi-graphic matroid M(G,B,L,F) on E(G). Moreover, every quasi-graphic matroid arises in this way. We provide cryptomorphic descriptions in terms of subgraphs corresponding to circuits, cocircuits, independent sets, and bases. Equipped with these descriptions, we prove some results about quasi-graphic matroids. In particular, we provide alternate proofs that do not require 3-connectivity of two results of Geelen, Gerards, and Whittle for 3-connected matroids from their introductory paper: namely, that every quasi-graphic matroid linearly representable over a field is either lifted-graphic or frame, and that if a matroid M has a framework with a loop that is not a loop of M then M is either lifted-graphic or frame. We also provide sufficient conditions for a quasi-graphic matroid to have a unique framework.Zaslavsky has asked for those matroids whose independent sets are contained in the collection of independent sets of F(G,B) while containing those of L(G,B), for some biased graph (G,B). Adding a natural (and necessary) non-degeneracy condition defines a class of matroids, which we call biased-graphic. We show that the class of biased-graphic matroids almost coincides with the class of quasi-graphic matroids: every quasi-graphic matroid is biased-graphic, and if M is a biased-graphic matroid that is not quasi-graphic then M is a 2-sum of a frame matroid with one or more lifted-graphic matroids.