Dual Connections Research Articles

Scimitar Syndrome in a Pediatric Cohort.

Scimitar syndrome is a rare form of congenital heart disease (CHD) characterized by anomalous pulmonary venous drainage of the right lung to the inferior vena cava. We describe the presentation, diagnosis, therapeutic management and long-term follow-up of 10 pediatric patients with Scimitar Syndrome. We performed a retrospective observational study of all pediatric patients from our institution with scimitar syndrome (March 1996-July 2023). Patients underwent systematic evaluation including medical and family history, chest x-ray, 12-lead electrocardiogram, echocardiogram, angiography and/or computed tomography; or magnetic resonance angiography. Ten patients with scimitar syndrome were included. The median age at diagnosis was 10.4 [0.1-150.2] months and the median follow-up time was 7.7 [1.3-15.3] years. Eight patients presented with aortopulmonary collateral arteries which were embolized. Two patients had dual connections to the inferior vena cava and left atrium; embolization of the inferior vena cava connection was only feasible in one of them. No patients underwent surgery of the scimitar vein. Three patients had surgical correction of CHDs. There were no deaths related to scimitar syndrome during follow-up. All patients with scimitar syndrome need prompt cardiovascular evaluation and follow-up. Our study demonstrates that a conservative approach with aortopulmonary collateral artery embolization, scimitar vein embolization when dual drainage to the left atrium is identified, along with correction of concomitant CHDs might have good results in patients with scimitar syndrome in order to postpone surgical correction of the anomalous pulmonary venous return to an older age when clinically or hemodynamically indicated. Further studies with longer-term follow-up and a larger sample size are needed to more effectively determine treatment strategy.

Dually flat structure of binary choice models

AbstractIn this study, we consider parametric binary choice models from the perspective of information geometry. The set of models is a dually flat manifold with dual connections, naturally derived from the Fisher information metric. Under the dual connections, the canonical divergence and the Kullback–Leibler divergence of the binary choice model coincide if and only if the model is a logit model.

Unusual Doppler in Pulmonary Veins Leads to the Diagnosis of Unusual Partial Anomalous Pulmonary Venous Return with Dual Connections

Unusual Doppler in Pulmonary Veins Leads to the Diagnosis of Unusual Partial Anomalous Pulmonary Venous Return with Dual Connections

ESDMR-Net: A lightweight network with expand-squeeze and dual multiscale residual connections for medical image segmentation

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recognised benchmark datasets. However, most of the existing approaches are computationally demanding, which limits their practical applicability. This paper presents an expand-squeeze dual multiscale residual network (ESDMR-Net), which is a full y convolutional network that is particularly well-suited for resource-constrained computing hardware such as mobile devices. ESDMR-Net focusses on extracting multiscale features, enabling the learning of contextual dependencies among semantically distinct features. The ESDMR-Net architecture allows dual-stream information flow within encoder–decoder pairs. The expansion operation (depthwise separable convolution) makes all of the rich features with multiscale information available to the squeeze operation (bottleneck layer), which then extracts the necessary information for the segmentation task. The Expand-Squeeze (ES) block helps the network pay more attention to under-represented classes, which contributes to improved segmentation accuracy. To enhance the flow of information across multiple resolutions or scales, we integrated dual multiscale residual (DMR) blocks into the skip connection. This integration enables the decoder to access features from various levels of abstraction, ultimately resulting in more comprehensive feature representations. We present experiments on seven datasets from five distinct examples of applications: segmentation of retinal vessels (2×), skin lesions (2×), digestive tract polyps, lung regions, and cells. Our model demonstrates strong performance, with an F1 score of 0.8287%, 0.8211%, 0.9034%, 0.9451%, 0.9543%, 0.9840%, and 0.8424% on the DRIVE, CHASE, ISIC2017, ISIC2016, CVC-ClinicDB, MC and MoNuSeg datasets, respectively. Remarkably, our model achieves these results despite having significantly fewer trainable parameters, with a reduction of two or even three orders of magnitude.

LIGHTLIKE SUBMANIFOLDS OF AN INDEFINITE LORENTZIAN PARA-SASAKIAN STATISTICAL MANIFOLD

In this paper, we introduce an indefinite LP-Sasakian statistical manifold and study lightlike submanifold of an indefinite LP-Sasakian statistical manifold. We also introduce some relations among induced geometrical objects with respect to dual connections in a lightlike submanifold of an indefinite LP-Sasakian statistical manifold. One example related to this concept is also presented. Finally, we show that an invariant lightlike submanifold of an indefinite LP-Sasakian statistical manifold is an indefinite LP-Sasakian statistical manifold.

On a torsion/curvature analogue of dual connections and statistical manifolds

In analogy to the concept of a non-metric dual connection, which is essential in defining statistical manifolds, we develop that of a torsion dual connection. Consequently, we illustrate the geometrical meaning of such a torsion dual connection and show how the use of both connections preserves the cracking of parallelograms in spaces equipped with a connection and its torsion dual. The coefficients of such a torsion dual connection are essentially computed by demanding a vanishing mutual torsion among the two connections. For this manifold we then prove two basic Theorems. In particular, if both connections are metric-compatible we show that there exists a specific 3-form measuring how the connection and its torsion dual deviate away from the Levi-Civita one. Furthermore, we prove that for these torsion dual manifolds flatness of one connection does not necessary impose flatness on the other but rather that the curvature tensor of the latter is given by a specific divergence. Finally, we give a self-consistent definition of the mutual curvature tensor of two connections and subsequently define the notion of a curvature dual connection.

Doubly autoparallel structure and curvature integrals

On a statistical manifold, we can define autoparallel submanifolds and path integrals of the second fundamental forms (curvature integrals) for its primal and dual affine connections, respectively. A submanifold is called doubly autoparallel if it is simultaneously autoparallel with respect to the both connections. In this paper we first discuss common properties of such submanifolds. In particular we next give an algebraic characterization of them in Jordan algebras and show their applications. Further, we exhibit that both curvature integrals induced from dually flat structure are interestingly related to an unexpected quantity, i.e., iteration-complexity of the interior-point algorithms for convex optimization defined on a submanifold that is not doubly autoparallel.

Geometry of Lightlike Hypersurfaces of a Statistical Manifold

In this paper, we introduced the lightlike hypersurfaces of a statistical manifold. It is shown that a lightlike hypersurface of a statistical manifold is not a statistical manifold with respect to the induced connections, but the screen distribution has a canonical statistical structure. Some relations between induced geometric objects with respect to dual connections in a lightlike hypersurface of a statistical manifold are obtained. An example is presented. Induced Ricci tensors for lightlike hypersurface of a statistical manifold are computed.

Network dynamics and action space

PurposeIn today’s business settings, most firms strive to closely integrate their resources and activities with those of their business partners. However, these linkages tend to create lock-in effects when changes are needed. In such situations, firms need to generate new space for action. The purpose of this paper is twofold: analysis of potential action spaces for restructuring; and examination of how action spaces can be exploited and the consequences accompanying this implementation.Design/methodology/approachNetwork dynamics originate from changes in the network interdependencies. This paper is focused on the role of the three dual connections – actors–activities, actors–resources and activities–resources, identified as network vectors. In the framing of the study, these network vectors are combined with managerial action expressed in terms of networking and network outcome. This framework is then used for the analysis of major restructuring of the car industries in the USA and Europe at the end of the 1900s.FindingsThis study shows that the restructuring of the car industry can be explained by modifications in the three network vectors. Managerial action through changes of the vector features generated new action space contributing to the transition of the automotive network. The key to successful exploitation of action space was interaction – with individual business partners, in triadic constellations, as well as on the network level.Originality/valueThis paper presents a new view of network dynamics by relying on the three network vectors. These concepts were developed in the early 1990s. This far, however, they have been used only to a limited extent.

Efficient Single-image Super-resolution Using Dual path Connections with Multiple scale Learning

Deep convolutional neural networks have been demonstrated to be effective for single-image super-resolution in recent years. On the one hand, residual connections and dense connections have been used widely to ease forward information and backward gradient flows to boost performance. However, current methods use residual connections and dense connections separately in most network layers in a sub-optimal way. On the other hand, although various networks and methods have been designed to improve computation efficiency, save parameters, or utilize training data of multiple scale factors for each other to boost performance, they either do super-resolution in high-resolution space to have a high computation cost or cannot share parameters between models of different scale factors to save parameters and inference time. To tackle these challenges, we propose an efficient single-image super-resolution network using dual path connections with multiple scale learning (EMSRDPN). By introducing dual path connections inspired by Dual path Networks into EMSRDPN, it uses residual connections and dense connections in an integrated way in most network layers. Dual path connections have the benefits of both reusing common features of residual connections and exploring new features of dense connections to learn a good representation for single-image super-resolution. To utilize the feature correlation of multiple scale factors, EMSRDPN shares all network units in low-resolution space between different scale factors to learn shared features and only uses a separate reconstruction unit for each scale factor, which can utilize training data of multiple scale factors to help each other to boost performance, meanwhile, which can save parameters and support shared inference for multiple scale factors to improve efficiency. Experiments show EMSRDPN achieves better performance and comparable or even better parameter and inference efficiency over state-of-the-art methods. Code will be available at https://github.com/yangbincheng/EMSRDPN .

Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of -12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.

Lifting statistical structures

In this paper, we consider some natural (functorial) lifts of geometric objects associated with statistical manifolds (metric tensor, dual connections, skewness tensor, etc.) to higher tangent bundles. It turns out that the lifted objects form again a statistical manifold structure, this time on the higher tangent bundles, with the only difference that the metric tensor is pseudo-Riemannian. What is more, natural lifts of potentials (called also divergence or contrast functions) turn out to be again potentials, this time for the lifted statistical structures. We propose an analogous procedure for lifting statistical structures on Lie algebroids and lifting contrast functions which are defined on Lie groupoids. In particular, we study in detail Lie groupoid structures of higher tangent bundles of Lie groupoids. Our geometric constructions of lifts are illustrated by explicit examples, including some important statistical models and potential functions on Lie groupoids.

Abstract 15712: Imaging Conundrum: The Diagnosis of Unusual Partial Anomalous Pulmonary Venous Return With Dual Connections

Case: A 68 year old male with a past medical history of hypertension, hyperlipidemia and chronic kidney disease presented for progressive dyspnea on exertion and bilateral lower extremity edema over three weeks. He was found to be in decompensated heart failure and new onset Atrial Flutter with rapid ventricular rate. Transthoracic echocardiography revealed severely impaired left ventricular systolic function with ejection fraction <20%, enlarged right ventricle, moderate right ventricular systolic dysfunction, biatrial enlargement, mild pulmonary hypertension. He was started on guideline directed medical therapy and referred for transesophageal echocardiogram (TEE) with cardioversion for new onset Atrial Flutter and possible tachycardia induced cardiomyopathy. Imaging findings: During TEE, routine doppler of right upper pulmonary vein showed markedly dilated superior vena cava (SVC) (3.6 cm) and unusual predominant outflow with brief mid late systolic inflow (Fig A and B), suggestive of partial anomalous pulmonary venous return (PAPVR). All three other pulmonary veins appeared normal. Also noted to have positive bubble study likely related to PAPVR. Subsequent right heart catheterization left to right shunt with Qp/Qs 1.6, step up noted between SVC and right atrium. CTA revealed partial anomalous right upper lobe pulmonary vein (RUPV) had dual connection to both SVC via window like communication and left atrium (Fig C), no sinus venosus defect seen. Conclusion: This case illustrates an unusual example of PAPVR with bidirectional flow, as well as the importance of thorough evaluation even on routine transesophageal echocardiograms especially for patients with right heart enlargement.

Location of semi-rigid connections effect on the seismic performance of steel frame structures

In design steel frames, combining semi-rigid and rigid connections can result in better structural performance, particularly in seismic locations. In this study, the effects of semi-rigid beam-to-column connections located on the seismic performance of steel frame structures are investigated. The analysis uses six and twelve-story moment resisting steel frames (MRSF) with rigid, semi-rigid, and dual beam-column connections. These frames are designed according to the Egyptian design codes. Drain-2Dx computer program and seven earthquake ground motions are used in the non-linear dynamic analysis. The rotational stiffness of beam-to-column connections is indicated through the end fixity factors with a value equal to 0.6. The performances of these frames are evaluated through the roof drift ratio (RDR), the maximum story drift ratios (SDR), and the maximum column axial compression force (MACF). The results indicated that the quantities of fundamental periods, roof drift ratio, the story drift ratio, and the column axial compression force are related to stiffness, rigidity, and the number of semi-rigid connections in steel frames.

The information geometry of two-field functional integrals

Two-field functional integrals (2FFI) are an important class of solution methods for generating functions of dissipative processes, including discrete-state stochastic processes, dissipative dynamical systems, and decohering quantum densities. The stationary trajectories of these integrals describe a conserved current by Liouville’s theorem, despite the absence of a conserved kinematic phase space current in the underlying stochastic process. We develop the information geometry of generating functions for discrete-state classical stochastic processes in the Doi-Peliti 2FFI form, and exhibit two quantities conserved along stationary trajectories. One is a Wigner function, familiar as a semiclassical density from quantum-mechanical time-dependent density-matrix methods. The second is an overlap function, between directions of variation in an underlying distribution and those in the directions of relative large-deviation probability that can be used to interrogate the distribution, and expressed as an inner product of vector fields in the Fisher information metric. To give an interpretation to the time invertibility implied by current conservation, we use generating functions to represent importance sampling protocols, and show that the conserved Fisher information is the differential of a sample volume under deformations of the nominal distribution and the likelihood ratio. We derive a pair of dual affine connections particular to Doi-Peliti theory for the way they separate the roles of the nominal distribution and likelihood ratio, distinguishing them from the standard dually-flat connection of Nagaoka and Amari defined on the importance distribution, and show that dual flatness in the affine coordinates of the coherent-state basis captures the special role played by coherent states in Doi-Peliti theory.

From Dual Connections to Almost Contact Structures

A dualistic structure on a smooth Riemaniann manifold M is a triple (M,g,∇) with g a Riemaniann metric and ∇ an affine connection generally assumed to be torsionless. From g and ∇, dual connection ∇* can be defined. In this work, we give conditions on the basis of this notion for a manifold to admit an almost contact structure and some related structures: almost contact metric, contact, contact metric, cosymplectic, and co-Kähler in the three-dimensional case.

Nanoarchitectonics of graphene oxide with functionalized cellulose nanocrystals achieving simultaneous dual connections and defect repair through catalytic graphitization for high thermal conductivity

Graphene, with an extremely high intrinsic thermal conductivity of 5300 W/mK, is of special interest to meet thermal requirements in future wearable and high-power electronic devices. Generally, microscopic graphene sheets can be built into a macroscopic material by using graphene oxide (GO) as the precursor. However, both the defects on the basal plane of GO sheets and the lack of connections at their boundaries induce massive scattering of phonons and a low thermal conductivity. Herein, we solve the problem through the nanoarchitectonics of GO with nano Fe3O4 functionalized cellulose nanocrystals (Fe–CNC). During the assembly process, Fe–CNC were compounded with GO homogeneously and formed a composite GO/Fe–CNC film with an aligned lamellar structure. A subsequent thermal reduction at 1500 °C resulted in the generation of Fe/Fe3C nanoparticles in the matrix of the reduced GO/Fe–CNC. During this process, the reduced GO (R-GO) sheets were dually connected by the Fe/Fe3C nanoparticles as well as the carbonized cellulose nanocrystals. Furthermore, the defects on the R-GO are repaired by Fe through a catalytic graphitization process. Thus, the scatterings of phonons at the adjacent sheet boundaries and on the basal plane of the R-GO sheets were reduced. A flexible and light weight composite film of reduced GO/Fe–CNC with a thermal conductivity of 1958.14 W/mK was obtained. The thermal conductivity is much higher than that of a pure R-GO film (388.72 W/mK) prepared under the same conditions. This assembly process achieved dual connections and defect repairs simultaneously and it provides an avenue for the design of graphene-based films with a high thermal conductivity.

Transfer Learning Featured Short-Term Combining Forecasting Model for Residential Loads With Small Sample Sets

In recent years, a large variety of short-term load forecasting methodologies have been proposed, but a common drawback of them is that require a large amount of historical data to train the model. However, in reality, the cases in limited historical data would be more frequently occurred, for example, a new house or substation is built. To tackle this issue, a combining forecasting method of deep residual neural network (ResNet)-based transfer learning for residential loads is proposed in this article. To improve the accuracy and reliability of transfer learning, a novel deep ResNet with dual skip connections (ResNet-DSC) is proposed as the base model. Then with sparse training data, a Bayesian probability weighted averaging (approach is proposed, to address the model combination parameters estimation problem. In addition, probability density function forecasting is also delivered by the method. For demonstration, a series of experiments, with actual residential load data, was performed. With the comparison with other transfer learning and nontransfer learning approaches, the effectiveness and improvement of the proposed method have been validated.

Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images.

Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography. Automated pulmonary nodule detection is an essential part of computer-aided diagnosis, which is still facing great challenges and difficulties to quickly and accurately locate the exact nodules' positions. This paper proposes a dual skip connection upsampling strategy based on Dual Path network in a U-Net structure generating multiscale feature maps, which aims to minimize the ratio of false positives and maximize the sensitivity for lesion detection of nodules. The results show that our new upsampling strategy improves the performance by having 85.3% sensitivity at 4 FROC per image compared to 84.2% for the regular upsampling strategy or 81.2% for VGG16-based Faster-R-CNN.

Energy dissipation capacity of precast concrete beam-column joint connected by double notch subjected to cyclic lateral loading

This paper presents the experimental investigation of full-scale precast concrete beam-column connections subjected to cyclic lateral loading. The specimen consists of three models. Two of which are precast beam-column connections and one is monolith. The precast and monolith specimen were designed for the same strength. The cross-sectional dimensions of the beam 250 mm x 300 mm and column 300 mm x 300 mm. Connections are placed in plastic hinge area, with a distance of h (beam height) from the face of the column which is expected to occur first destruction. Precast construction joints are distinguished, with 3 different models, namely monolith, double notch type 1 (STR-1), and double notch type 2 (STR-2). Maximum load capacity, hysterical behavior and energy dissipation are measured, and capacity is compared. The results showed that beam-column joint STR-2 are better able to absorb energy than beam column joint monolith and beam-column joint STR-1. Kumulative energy dissipation of monolith about 9333.07 kN-mm, STR-1 is 8336.76 kN-mm, and STR-2 is 10162.52 kN-mm. The use of dual notch connections (STR-1 and STR-2) provides satisfactory performance, which is marked by meeting the minimum relative energy dissipation ratio at a 3.5% drift according to the ACI Committee 374.1-05. The results show that the STR-2 beam-column connection is more capable of absorbing energy than the monolith beam-column connection and STR-1 beam connection. the value of monolithic cumulative energy dissipation is around 9333.07 kN-mm, STR-1 is 8336.76 kN-mm, and STR-2 is 10162.52 kN-mm. in principle, all three test specimens, monoliths, STR-1 and STR-2 provide satisfactory stability performance under lateral cyclic loads, because they still meet the minimum relative energy dissipation ratio at a deviation of 3.5% according to the ACI Committee 374.1 -05