Variational adiabatic transport of tensor networks

We discuss a tensor network method for constructing the adiabatic gauge potential -- the generator of adiabatic transformations -- as a matrix product operator, which allows us to adiabatically transport matrix product states. Adiabatic evolution of tensor networks offers a wide range of applications, of which two are explored in this paper: improving tensor network optimization and scanning phase diagrams. By efficiently transporting eigenstates to quantum criticality and performing intermediary density matrix renormalization group (DMRG) optimizations along the way, we demonstrate that we can compute ground and low-lying excited states faster and more reliably than a standard DMRG method at or near quantum criticality. We demonstrate a simple automated step size adjustment and detection of the critical point based on the norm of the adiabatic gauge potential. Remarkably, we are able to reliably transport states through the critical point of the models we study.Comment: 11 pages, 7 figures; added note in discussion section and new reference

On Dirac equations with Hartree type nonlinearity in modulation spaces

We obtain well-posedness for Dirac equations with a Hartree-type nonlinearity derived by decoupling the Dirac-Klein-Gordon system. We extend the function space of initial data, enabling us to handle initial data that were not addressed in previous studies.Comment: 8 page

Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network

Along with generative AI, interest in scene graph generation (SGG), which comprehensively captures the relationships and interactions between objects in an image and creates a structured graph-based representation, has significantly increased in recent years. However, relying on object-centric and dichotomous relationships, existing SGG methods have a limited ability to accurately predict detailed relationships. To solve these problems, a new approach to the modeling multiobject relationships, called edge dual scene graph generation (EdgeSGG), is proposed herein. EdgeSGG is based on a edge dual scene graph and Dual Message Passing Neural Network (DualMPNN), which can capture rich contextual interactions between unconstrained objects. To facilitate the learning of edge dual scene graphs with a symmetric graph structure, the proposed DualMPNN learns both object- and relation-centric features for more accurately predicting relation-aware contexts and allows fine-grained relational updates between objects. A comparative experiment with state-of-the-art (SoTA) methods was conducted using two public datasets for SGG operations and six metrics for three subtasks. Compared with SoTA approaches, the proposed model exhibited substantial performance improvements across all SGG subtasks. Furthermore, experiment on long-tail distributions revealed that incorporating the relationships between objects effectively mitigates existing long-tail problems

Positional Effects of Fluorination in Conjugated Side Chains on Photovoltaic Properties of Donor-Acceptor Copolymers

The position at which conjugated side chains were fluorinated, the meta- or ortho-position in phenyl side chains, was varied to investigate the positional effects of fluorination on the energy levels, crystalline ordering, and photovoltaic properties of the polymers. The fluorine in the ortho-position achieved a lower HOMO energy level than that in the meta-position, but reduced the chain rigidity.1116Ysciescopu

Dual-Organ Transcriptomic Analysis of Rainbow Trout Infected With Ichthyophthirius multifiliis Through Co-Expression and Machine Learning

Ichthyophthirius multifiliis is a major pathogen that causes a high mortality rate in trout farms. However, systemic responses to the pathogen and its interactions with multiple organs during the course of infection have not been well described. In this study, dual-organ transcriptomic responses in the liver and head kidney and hemato-serological indexes were profiled under I. multifiliis infection and recovery to investigate systemic immuno-physiological characteristics. Several strategies for massive transcriptomic interpretation, such as differentially expressed genes (DEGs), Poisson linear discriminant (PLDA), and weighted gene co-expression network analysis (WGCNA) models were used to investigate the featured genes/pathways while minimizing the disadvantages of individual methods. During the course of infection, 6,097 and 2,931 DEGs were identified in the head kidney and liver, respectively. Markers of protein processing in the endoplasmic reticulum, oxidative phosphorylation, and the proteasome were highly expressed. Likewise, simultaneous ferroptosis and cellular reconstruction was observed, which is strongly linked to multiple organ dysfunction. In contrast, pathways relevant to cellular replication were up-regulated in only the head kidney, while endocytosis- and phagosome-related pathways were notably expressed in the liver. Moreover, interestingly, most immune-relevant pathways (e.g., leukocyte trans-endothelial migration, Fc gamma R-mediated phagocytosis) were highly activated in the liver, but the same pathways in the head kidney were down-regulated. These conflicting results from different organs suggest that interpretation of co-expression among organs is crucial for profiling of systemic responses during infection. The dual-organ transcriptomics approaches presented in this study will greatly contribute to our understanding of multi-organ interactions under I. multifiliis infection from a broader perspective.publishedVersio

High-sensitivity C-reactive Protein Can Predict Major Adverse Cardiovascular Events in Korean Patients with Type 2 Diabetes

Inflammation is thought to play a role in the pathogenesis of major adverse cardiovascular events (MACE). It has been suggested that the measurement of markers of inflammation may aid in predicting the risk of such events. Here, the relationship between high-sensitivity C-reactive protein (hs-CRP) levels and MACE in Korean patients with type 2 diabetes is assessed. A retrospective cohort study was conducted as a follow-up among 1,558 patients with type 2 diabetes and without cardiovascular diseases over a mean period of 55.5 months. A Cox proportional-hazards model was used to determine whether increased hs-CRP levels are useful as a predictor for future MACE. The hazard ratio of MACE was 1.77 (95% CI; 1.16-2.71) in subjects who had the highest hs-CRP levels (> 0.21 mg/dL) compared to subjects who had the lowest hs-CRP levels (< 0.08 mg/dL), after adjusting for age, regular physical activity, current smoking, and duration of diabetes. The present results indicate that high hs-CRP levels can act as a predictor for the MACE occurrence in Korean patients with type 2 diabetes

A Terahertz CMOS V-Shaped Patch Antenna with Defected Ground Structure

In this paper, a V-shaped patch antenna with defected ground structure is proposed at terahertz to overcome the limited performance of a standard complementary metal-oxide semiconductor (CMOS) patch antenna consisting of several metal layers and very thin interdielectric layers. The proposed V-shaped patch with slots allows the increased radiation resistance and broadband performance. In addition, the patch resonating at different frequency from the V-shaped patch is stacked on the top to broaden the impedance-matching bandwidth. More importantly, the slots are formed in the ground plane, which is called the defected ground structure, to further increase the radiation resistance and thus improve the bandwidth and efficiency. It is verified from electromagnetic simulations that the leakage waves from the defected ground can enhance the antenna directivity and gain by coherently interfering with the topside radiation. The proposed on-chip antenna is fabricated using a standard 65 nm CMOS process. The on-wafer measurement shows very wide bandwidth in input reflection coefficient (&lt;&minus;10 dB), greater than 28.7% from 240 to &gt;320 GHz. The measured peak gain was as high as 5.48 dBi at 295 GHz. To the best of the authors&rsquo; knowledge, these results belong to the best performance among the terahertz CMOS on-chip antennas without using additional components or processes such as dielectric resonators, lens, or substrate thinning

Genotypic and Phenotypic Characterization of Highly Alkaline-Resistant Carnobacterium maltaromaticum V-Type ATPase from the Dairy Product Based on Comparative Genomics

Although Carnobacterium maltaromaticum derived from dairy products has been used as a lactic acid bacterium industrially, several studies have reported potential pathogenicity and disease outbreaks. Because strains derived from diseased fish and dairy products are considered potentially virulent and beneficial, respectively, their genotypic and phenotypic characteristics have attracted considerable attention. A genome-wide comparison of 30 genome sequences (13, 3, and 14 strains from diseased aquatic animals, dairy products, and processed food, respectively) was carried out. Additionally, one dairy and two nondairy strains were incubated in nutrient-rich (diluted liquid media) and nutrient-deficient environments (PBS) at pH 10 to compare their alkaline resistance in accordance with different nutritional environments by measuring their optical density and viable bacterial cell counts. Interestingly, only dairy strains carried 11 shared accessory genes, and 8 genes were strongly involved in the V-type ATPase gene cluster. Given that V-type ATPase contributes to resistance to alkaline pH and salts using proton motive force generated via sodium translocation across the membrane, C. maltaromaticum with a V-type ATPase might use nutrients in food under high pH. Indeed, the dairy strain carrying the V-type ATPase exhibited the highest alkaline resistance only in the nutrient-rich environment with significant upregulation of V-type ATPase expression. These results suggest that the gene cluster of V-type ATPase and increased alkaline resistance of dairy strains facilitate adaptation in the long-term ripening of alkaline dairy products

Strichartz estimates for the Dirac flow in Wiener amalgam spaces

In this paper we obtain some new Strichartz estimates for the Dirac flow in the context of Wiener amalgam spaces which control the local regularity of a function and its decay at infinity separately unlike $L^p$ spaces. While it is well understood recently for some flows such as the Schr\"odinger and wave flows that work in the non-relativistic regime, nothing is known about the Dirac flow which governs a physical system in the case of relativistic fields.Comment: 16 pages. arXiv admin note: text overlap with arXiv:2012.0727