bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-Tail Entity Alignment
Authors: Zhao Xiang , Zeng Weixin , Tang Jiuyang 
Published: October 2023

AbstractMost entity alignment solutions currently rely on structural information, specifically KG embedding, to align entities. However, in real-life KGs, the majority of entities have a sparse neighborhood structure, while only a few entities are densely connected to others. These less-connected entities are referred to as long-tail entities, and this phenomenon limits the effectiveness of using structural information for entity alignment.To address this issue, we propose an approach that incorporates entity name information, which is often overlooked but readily available. We amplify the weak structural information of long-tail entities with concatenated power mean word embeddings of their names during pre-alignment. To align entities, we introduce a novel complementary framework that combines both structural and name signals. It uses the entity’s degree as a guide to fuse the two sources of information effectively and proposes a degree-aware co-attention network that dynamically adjusts the significance of features in a degree-aware manner. Finally, we propose using confident entity alignment results as anchors to complement original KGs with facts from their counterparts via iterative training during post-alignment. Experimental evaluations show the effectiveness of the proposed techniques.
Unsupervised Entity Alignment
Authors: Zhao Xiang , Zeng Weixin , Tang Jiuyang 
Published: October 2023

AbstractState-of-the-art entity alignment solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance by enriching the pseudo-labeled data with alignment results from the previous round. Our solution does not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate its superiority.
Multimodal Entity Alignment
Authors: Zhao Xiang , Zeng Weixin , Tang Jiuyang 
Published: October 2023

AbstractIn various tasks related to artificial intelligence, data is often present in multiple forms or modalities. Recently, it has become a popular approach to combine these different forms of information into a knowledge graph, creating a multi-modal knowledge graph (MMKG). However, multi-modal knowledge graphs (MMKGs) often face issues of insufficient data coverage and incompleteness. In order to address this issue, a possible strategy is to incorporate supplemental information from other multi-modal knowledge graphs (MMKGs). To achieve this goal, current methods for aligning entities could be utilized; however, these approaches work within the Euclidean space, and the resulting entity representations can distort the hierarchical structure of the knowledge graph. Additionally, the potential benefits of visual information have not been fully utilized.To address these concerns, we present a new approach for aligning entities across multiple modalities, which we call hyperbolic multi-modal entity alignment (). This method expands upon the conventional Euclidean representation by incorporating a hyperboloid manifold. Initially, we utilize hyperbolic graph convolutional networks() to acquire structural representations of entities. In terms of visual data, we create image embeddings using the model and subsequently map them into the hyperbolic space utilizing . Lastly, we merge the structural and visual representations within the hyperbolic space and utilize the combined embeddings to forecast potential entity alignment outcomes. Through a series of thorough experiments and ablation studies, we validate the efficacy of our proposed model and its individual components.
Giant gravitons in twisted holography
Authors: Budzik Kasia , Gaiotto Davide 
Published: October 2023

Abstract
We study correlation functions of determinant-like operators in the “chiral algebra subsector” of four-dimensional $$ \mathcal{N} $$
N
= 4 gauge theory with U(N) gauge group. We map the large N saddles of the correlation functions to specific semiclassical D-branes in the holographic dual BCOV theory. We present a detailed match of several gauge-theory and BCOV calculations.
Kinematic twist-three contributions to pseudo- and quasi-GPDs and translation invariance
Authors: Braun V. M. 
Published: October 2023

Abstract
We present explicit expressions for the tree-level “kinematic” twist-three contributions to the nucleon matrix elements of gauge-invariant nonlocal quark-antiquark operators which can be used in lattice calculations of generalized parton distributions (GPDs). These contributions in particular restore the translation invariance of the results up to higher twist four. The calculated twist-three corrections are logarithmically enhanced as compared to the leading twist, and are discontinuous at the kinematic points x = ±ξ.
A half de Sitter holography
Authors: Kawamoto Taishi , Ruan Shan-Ming , Suzuki Yu-ki , Takayanagi Tadashi 
Published: October 2023

Abstract
A long-standing and intriguing question is: does the holographic principle apply to cosmologies like de Sitter spacetime? In this work, we consider a half dS spacetime wherein a timelike boundary encloses the bulk spacetime, presenting a version of de Sitter holography. By analyzing the holographic entanglement entropy in this space and comparing it with that in AdS/CFT, we argue that gravity on a half dSd+1 is dual to a highly non-local field theory residing on dSd boundary. This non-locality induces a breach in the subadditivity of holographic entanglement entropy. Remarkably, this observation can be linked to another argument that time slices in global de Sitter space overestimate the degrees of freedom by redundantly counting the same Hilbert space multiple times.
New shape of parity-violating graviton non-Gaussianity
Authors: Gong Jinn-Ouk , Mylova Maria , Sasaki Misao 
Published: October 2023

Abstract
We show that the general vacuum states that respect the de Sitter symmetry, known as the α-vacua, can introduce non-vanishing parity-violating tensor non-Gaussianities. This is due to the mixing by the Bogoliubov transformation of the positive and negative frequency modes of the Bunch-Davies vacuum. We calculate explicitly the bispectra of tensor perturbations and show that the amplitude can be exponentially enhanced for certain choices of the squeezing parameter α and the phase ϕ of the α-vacua. We find a new shape for the parity-violating tensor bispectrum which peaks in the flattened configuration.
Higgs inflation at the pole
Authors: Cléry Simon , Lee Hyun Min , Menkara Adriana G. 
Published: October 2023

Abstract
We propose a novel possibility for Higgs inflation where the perturbative unitarity below the Planck scale is ensured by construction and the successful predictions for inflation are accommodated. The conformal gravity coupling for the Higgs field leads to the proximity of the effective Planck mass to zero in the Jordan frame during inflation, corresponding to a pole in the Higgs kinetic term in the Einstein frame. Requiring the Higgs potential to vanish at the conformal pole in the effective theory in the Jordan frame, we make a robust prediction of the successful Higgs inflation. For a successful Higgs inflation at the pole, we take the running quartic coupling for the Higgs field to be small enough at the inflation scale, being consistent with the low-energy data, but we need a nontrivial extension of the SM with extra scalar or gauge fields in order to keep the running Higgs quartic coupling small during inflation. Performing the perturbative analysis of reheating with the known couplings of the SM particles to the Higgs boson, we show that a concrete realization of the Higgs pole inflation can be pinned down by the reheating processes with a general equation of state for the Higgs inflaton. We illustrate some extensions of the simple Higgs pole inflation to the general pole expansions, the running Higgs quartic coupling in the Standard Model and its extension with a singlet scalar field, a supergravity embedding of the Higgs pole inflation.
Photon sphere and quasinormal modes in AdS/CFT
Authors: Hashimoto Koji , Sugiura Kakeru , Sugiyama Katsuyuki , Yoda Takuya 
Published: October 2023

Abstract
Photon spheres are the characteristic of general black holes, thus are a suitable touchstone for the emergence of gravitational spacetime in the AdS/CFT correspondence. We provide a spectral analysis of an AdS Schwarzschild black hole near its photon sphere. We find that quasinormal modes near the photon sphere reflect the AdS boundary, resulting in a peculiar spectral pattern. Our large angular momentum analysis owes to an analogue to solvable Schrödinger equations such as an inverted harmonic oscillator and the Pöschl-Teller model, with a Dirichlet boundary condition. Through the AdS/CFT dictionary, it predicts the existence of a peculiar subsector in the large angular momentum spectrum of thermal holographic CFTs on a sphere.
The five-point bootstrap
Authors: Poland David , Prilepina Valentina , Tadić Petar 
Published: October 2023

Abstract
We study five-point correlation functions of scalar operators in d-dimensional conformal field theories. We develop a new approach to computing the five-point conformal blocks for exchanged primary operators of arbitrary spin by introducing a generalization of radial coordinates, using an appropriate ansatz, and perturbatively solving two quadratic Casimir differential equations. We then study five-point correlators 〈σσϵσσ〉 in the critical 3d Ising model. We truncate the operator product expansions (OPEs) in the correlator by including a finite number of primary operators with conformal dimension below a cutoff ∆ ⩽ ∆cutoff. We then compute several OPE coefficients involving ϵ and two spinning operators by demanding that the truncated correlator approximately satisfies the crossing relation.
Predictor bias in genomic and phenomic selection
Authors: Dallinger Hermann Gregor , Löschenberger Franziska , Bistrich Herbert , Ametz Christian , Hetzendorfer Herbert , Morales Laura , Michel Sebastian , Buerstmayr Hermann 
Published: October 2023

Abstract
Key message
NIRS of wheat grains as phenomic predictors for grain yield show inflated prediction ability and are biased toward grain protein content.

Abstract
Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, phenomic technologies, including remote sensing and aerial hyperspectral imaging of plant canopies, have made it feasible to predict the breeding value of individuals in the absence of genetic marker data. This is commonly referred to as phenomic prediction. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 1980 s to predict compositional parameters of harvest products. Moreover, in recent studies NIRS from grains was used to predict grain yield. The same studies showed that phenomic prediction can outperform genomic prediction for grain yield. The genome is static and not environment dependent, thereby limiting genomic prediction ability. Gene expression is tissue specific and differs under environmental influences, leading to a tissue- and environment-specific phenome, potentially explaining the higher predictive ability of phenomic prediction. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions outperform genomic prediction for some traits. However, phenomic predictions are biased toward the information present in the predictor. Future studies on this topic should investigate whether population parameters are retained in phenomic prediction as they are in genomic prediction. Furthermore, we find that unbiased phenomic prediction abilities are considerably lower than previously reported and recommend a method to circumvent this issue.
An inverse method for automatic determination of material models for metal cutting based on multi-objective optimization
Authors: Liu Hui , Kibireva Anna , Meurer Markus , Bergs Thomas 
Published: October 2023

AbstractCutting simulation is a crucial tool that enables engineers and operators to optimize machining processes virtually, before producing physical parts. The accuracy of these simulations relies heavily on validated models, encompassing both friction and material parameters. The prevalent technique for calibrating material models in cutting simulations is the inverse method. This state-of-the-art approach indirectly determines model parameters by comparing simulated outcomes with experimental data. However, the manual calibration process can be complex and time-consuming due to the intricacies of numerical simulation setups and the abundance of material model parameters. To address these challenges, this paper presents a novel fully-automated calibration approach utilizing multi-objective optimization algorithms. This approach integrates a modular design, simplifying the calibration process and enabling automatic calibration of any model parameters within cutting simulations. The approach has been successfully applied to calibrate the model parameters of AISI 1045 and X30CrMoN15-1 materials. Moreover, through a comparison of various optimization algorithms, this paper underscores the efficiency of the swarm optimizer in calibrating model parameters, particularly in scenarios with restricted computational resources.
Vertical contracting between a vertically integrated firm and a downstream rival
Authors: Kourandi Frago , Pinopoulos Ioannis N. 
Published: October 2023

AbstractCompared to linear tariffs, two-part tariffs are generally perceived as being more efficient since double marginalization is avoided. We investigate the efficiency of two-part tariffs vs. linear tariffs when a vertically integrated firm sells its input also to an independent downstream firm selling a differentiated substitute product. We find that a linear tariff can generate higher consumer surplus and overall welfare than a two-part tariff when the independent downstream firm is rather powerful in negotiating the contract terms, and downstream competition is in prices (Bertrand competition). In that case, the integrated firm makes more profits under a linear tariff than under a two-part tariff. In contrast, under downstream Cournot competition two-part tariffs are always welfare-superior. Under linear demand, we find that, irrespective of the mode of downstream competition and the distribution of bargaining power, the preferred contract type of the integrated firm is always welfare-superior.
Prevalence of anatomical variations at the suboccipital (V3) segment of the vertebral artery: a systematic review
Authors: Omotoso Bukola R. , Harrichandparsad Rohen , Lazarus Lelika 
Published: October 2023

Abstract
Background and objective
A recent meta-analysis on the incidence of iatrogenic injury to the VA has revealed that patients with variant anatomy are more prone to iatrogenic injury. Therefore, this review is designed to investigate the incidence of variations in the suboccipital component of the vertebral artery in different population groups according to the available literature.

Methods
This systematic review was conducted according to PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The review is based on a comprehensive and extensive search of PubMed, Google Scholar, and ResearchGate. The following search terms were used: “vertebral artery” AND “suboccipital segment” AND “anomalies/anatomical variations of the V3 segment.” Reference lists of all extracted articles were also extensively searched for references to any further relevant publications.

Results
A total of 17 papers met the inclusion criteria. The 17 studies corresponded to a total of 10,820 patients. A persistent first intersegmental artery was registered in 1.8% (197 out of 10,820) of the patients. Extradural PICA origin was observed in 1.6% (175 out of 10,820) of the patients. Fenestration was detected in 0.7% (72 out of 10,820) of the patients.

Conclusion
The authors summarize the incidence of vascular variation at the suboccipital segment of the VA in different population groups across the Asian, European, American, and African continents. Awareness of the extent of possible anatomical variation will help interpret radiographs, which will enhance the identification of vascular pathologies and reduce the risk of iatrogenic injury.
Severe Prolonged Drought Favours Stress-Tolerant Microbes in Australian Drylands
Authors: Maisnam Premchand , Jeffries Thomas C. , Szejgis Jerzy , Bristol Dylan , Singh Brajesh K. , Eldridge David J. , Horn Sebastian , Chieppa Jeff , Nielsen Uffe N. 
Published: October 2023

AbstractDrylands comprise one-third of Earth’s terrestrial surface area and support over two billion people. Most drylands are projected to experience altered rainfall regimes, including changes in total amounts and fewer but larger rainfall events interspersed by longer periods without rain. This transition will have ecosystem-wide impacts but the long-term effects on microbial communities remain poorly quantified. We assessed belowground effects of altered rainfall regimes (+ 65% and -65% relative to ambient) at six sites in arid and semi-arid Australia over a period of three years (2016–2019) coinciding with a significant natural drought event (2017–2019). Microbial communities differed significantly among semi-arid and arid sites and across years associated with variation in abiotic factors, such as pH and carbon content, along with rainfall. Rainfall treatments induced shifts in microbial community composition only at a subset of the sites (Milparinka and Quilpie). However, differential abundance analyses revealed that several taxa, including Acidobacteria, TM7, Gemmatimonadates and Chytridiomycota, were more abundant in the wettest year (2016) and that their relative abundance decreased in drier years. By contrast, the relative abundance of oligotrophic taxa such as Actinobacteria, Alpha-proteobacteria, Planctomycetes, and Ascomycota and Basidiomycota, increased during the prolonged drought. Interestingly, fungi were shown to be more sensitive to the prolonged drought and to rainfall treatment than bacteria with Basidiomycota mostly dominant in the reduced rainfall treatment. Moreover, correlation network analyses showed more positive associations among stress-tolerant dominant taxa following the drought (i.e., 2019 compared with 2016). Our result indicates that such stress-tolerant taxa play an important role in how whole communities respond to changes in aridity. Such knowledge provides a better understanding of microbial responses to predicted increases in rainfall variability and the impact on the functioning of semi-arid and arid ecosystems.
Movement dynamics, sediment turnover and sheltering behaviours of the nocturnal coral reef sea cucumber, Stichopus cf. monotuberculatus
Authors: Gray Benjamin C. T. , Byrne Maria , Clements Matthew , Purcell Steven W. 
Published: October 2023

AbstractAnimal movement and feeding studies shed light on ecological roles and can inform management strategies for fished species. However, the feeding and movement of nocturnal sea cucumbers have been rarely studied. We determined the movement dynamics, feeding ecology and sheltering behaviour of the nocturnal Stichopus cf. monotuberculatus (dragonfish) at One Tree Reef and Heron Island Reef on the southern Great Barrier Reef. The short-term movement of individual sea cucumbers were tracked, and sediment defecation rates were measured. Displacement rates averaged 3–33 cm h−1 across the four sites, with the sea cucumbers moving more slowly approaching sunrise and within patches of organically rich sediment. The movement paths were moderately tortuous, with larger sea cucumbers moving along straighter paths. Rates of sediment turnover averaged 7.8 g h−1 and were greatest for larger individuals. The faecal casts were organically richer and composed of finer grains than the ambient surface sediments, implying that S. cf. monotuberculatus avoids consuming coarse sediments and feeds on nutritionally rich particles. The sea cucumbers sheltered under hard reef substrata and moved non-directionally on the seascape, with a few individuals homing back to their original refuges. The affinity to reef substrata and short nightly net displacements of S. cf. monotuberculatus have implications for the spatial management of this and other similar species. Larger individuals occupied deeper refuges, implying that projected declines in substratum rugosity on coral reefs due to climate change could impact the size structure of sea cucumber populations, with implications for fisheries.
High serum uric acid levels are protective against cognitive impairment in amyotrophic lateral sclerosis
Authors: Iazzolino Barbara , Grassano Maurizio , Moglia Cristina , Canosa Antonio , Manera Umberto , Vasta Rosario , Cabras Sara , Callegaro Stefano , Matteoni Enrico , Di Pede Francesca , Palumbo Francesca , Mora Gabriele , Calvo Andrea , Chiò Adriano 
Published: October 2023

Abstract
Background
Uric acid (UA) has emerged as a factor that can modify cognitive function both in the general population and in people with neurodegenerative disorders. Since very few data are available concerning amyotrophic lateral sclerosis (ALS), we explored the correlation of UA levels and cognitive impairment in a large cohort of ALS patients.

Methods
We enrolled ALS patients consecutively seen at the Turin ALS expert center in the 2007–2018 period who underwent both cognitive/behavioral and UA evaluation at diagnosis. Patients were classified in 5 categories: normal cognition (ALS-CN), isolated cognitive impairment (ALSci), isolated behavioural impairment (ALSbi), cognitive and behavioural impairment (ALScbi), frontotemporal dementia (ALS-FTD). For this study, ALSci, ALSbi and ALScbi were merged as ALS with intermediate cognitive impairment (ALS-INT).

Results
Out of the 841 ALS patients, 422 had ALS-CN, 271 ALS-INT and 148 ALS-FTD. The mean values of UA were significantly different among the cognitive subgroups of patients, with the lowest values in the ALS-FTD (ALS-CN, 288.5 ± 78.0 (μmol/L; ALS-INT, 289.7 ± 75.5 μmol/L; ALS-FTD, 271.8 ± 74.9 μmol/L; p = 0.046). The frequency of ALS-FTD was significantly higher in the 1st tertile of UA. Lower UA levels were independently associated with FTD (OR 1.32, 95% c.i. 1.01–1.43; p = 0.038) in binary logistic regression.

Conclusions
We found that in ALS lower UA serum levels are correlated with reduced frequency of co-morbid FTD. Patients with intermediate cognitive impairment showed UA levels similar to ALS-CN but higher than ALS-FTD, implying that higher UA levels can prevent or delay cognitive function deterioration.