Miracle-dz/UniFAR-FTUs huggingface dataset:
A knowledge graph completion model integrating entity description and network structure

PurposeIn recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.Design/methodology/approachThe authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.FindingsThe experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.Originality/valueThe research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.
Maternal first trimester metabolic profile in TGA pregnancies - a case-control study

Objective: To evaluate the association between maternal first trimester metabolic profile and transposition of the great arteries (TGA) in the offspring. Design: A matched case-control study using national register data and maternal first trimester blood samples. Settings: Finland Patients: A total of 100 mothers with TGA in their offspring and 200 controls matched by the year of birth and sex of the child, and the age and the body mass index (BMI) of the mother. Interventions: None. Main outcome measures: To identify the impact of 73 metabolic measure concentrations on the likelihood that a child would be born with TGA. Results: A higher concentration of four subtypes in extremely large very-low-density lipoprotein (VLDL) particles and one in large VLDL particles were observed in mothers with TGA in their offspring. This finding did not reach statistical significance after multiple testing correction. In the analysis of the subgroups of maternal BMI over 25 and 30 only, the odds ratios (OR) of the metabolic variables were higher compared to the original population. In the group with a BMI over 25 the mean OR was 1.3 (N= 111, total) and in the group with a BMI over 30 was 2.1 (N= 42, total). Conclusions: We found no statistically significant difference between the metabolic profiles of mothers with TGA in their offspring, and their controls. However, the trend in our BMI-matched study potentially indicates that the likelihood of TGA development associates with maternal early-pregnancy metabolic profile, and the association is more pronounced in the subgroups of BMI over 25 and 30. Further studies are needed especially in the higher BMI groups.
Generative time series models using Neural ODE in Variational Autoencoders

In this paper, we implement Neural Ordinary Differential Equations in a Variational Autoencoder setting for generative time series modeling. An object-oriented approach to the code was taken to allow for easier development and research and all code used in the paper can be found here: https://github.com/simonmoesorensen/neural-ode-project The results were initially recreated and the reconstructions compared to a baseline Long-Short Term Memory AutoEncoder. The model was then extended with a LSTM encoder and challenged by more complex data consisting of time series in the form of spring oscillations. The model showed promise, and was able to reconstruct true trajectories for all complexities of data with a smaller RMSE than the baseline model. However, it was able to capture the dynamic behavior of the time series for known data in the decoder but was not able to produce extrapolations following the true trajectory very well for any of the complexities of spring data. A final experiment was carried out where the model was also presented with 68 days of solar power production data, and was able to reconstruct just as well as the baseline, even when very little data is available. Finally, the models training time was compared to the baseline. It was found that for small amounts of data the NODE method was significantly slower at training than the baseline, while for larger amounts of data the NODE method would be equal or faster at training. The paper is ended with a future work section which describes the many natural extensions to the work presented in this paper, with examples being investigating further the importance of input data, including extrapolation in the baseline model or testing more specific model setups.
SparseP: Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Systems

Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield significant performance and energy improvements in parallel applications by alleviating data access costs. Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse Matrix Vector Multiplication (SpMV) kernel. This paper provides the first comprehensive analysis of SpMV on a real-world PIM architecture, and presents SparseP, the first SpMV library for real PIM architectures. We make three key contributions. First, we implement a wide variety of software strategies on SpMV for a multithreaded PIM core and characterize the computational limits of a single multithreaded PIM core. Second, we design various load balancing schemes across multiple PIM cores, and two types of data partitioning techniques to execute SpMV on thousands of PIM cores: (1) 1D-partitioned kernels to perform the complete SpMV computation only using PIM cores, and (2) 2D-partitioned kernels to strive a balance between computation and data transfer costs to PIM-enabled memory. Third, we compare SpMV execution on a real-world PIM system with 2528 PIM cores to state-of-the-art CPU and GPU systems to study the performance and energy efficiency of various devices. SparseP software package provides 25 SpMV kernels for real PIM systems supporting the four most widely used compressed matrix formats, and a wide range of data types. Our extensive evaluation provides new insights and recommendations for software designers and hardware architects to efficiently accelerate SpMV on real PIM systems.
Observables from spherically symmetric modified dispersion relations

In this work we continue the systematic study of observable eﬀects emerging from mod- iﬁed dispersion relations. We study the motion of test particles subject to a general ﬁrst order modiﬁcation of the general relativistic dispersion relation as well as subject to the κ -Poincar´e dispersion relation in spherical symmetry. We derive the corrections to the photon sphere, the black hole shadow, the Shapiro delay and the light deﬂection and identify the additional dependence of these observables on the photons’ four momentum, which leads to measurable eﬀects that can be compared to experimental data. The results presented here can be interpreted in two ways, depending on the origin of the modiﬁed dispersion relation: on the one hand as prediction for traces of quantum grav- ity, when the modiﬁed dispersion relation is induced by phenomenological approaches to quantum gravity, on the other hand as predictions of observables due to the presence of a medium, like a plasma, which modiﬁes the dispersion relation of light on curved spacetimes.
CompanyName2Vec: Company Entity Matching based on Job Ads

Entity Matching is an essential part of all real-world systems that take in structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data cleaning and integration processes require completion before any data analytics, or further processing can be performed. Although record linkage is frequently regarded as a somewhat tedious but necessary step, it reveals valuable insights, supports data visualization, and guides further analytic approaches to the data. Here, we focus on organization entity matching. We introduce CompanyName2Vec, a novel algorithm to solve company entity matching (CEM) using a neural network model to learn company name semantics from a job ad corpus, without relying on any information on the matched company besides its name. Based on a real-world data, we show that CompanyName2Vec outperforms other evaluated methods and solves the CEM challenge with an average success rate of 89.3%.
Derivative interactions during inflation: a systematic approach

We present a systematic prescription for calculating cosmological correlation functions for models with derivative interactions through the wavefunction of the universe and compare this result with the “in-in” formalism — canonical approach. The key step in this procedure is to perform the path integral over conjugate momenta after which a straightforward generalisation of Feynman's Rules can be applied. We show that this integral recovers the classical action plus some additional divergent contributions which are necessary to cancel other divergences that arise due to loop diagrams involving time derivatives. As a side project, for the first time, we introduce the “off-shell” version of the in-in formalism that is sometimes more straightforward, especially for the models with derivative coupling. To examine our prescription, as a specific example, we work out the trispectra of the scalar fluctuation in the model with the λϕ' 3 derivative coupling.
Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm

The present paper focuses on the problem of sampling from a given target distribution $\pi$ defined on some general state space. To this end, we introduce a novel class of non-reversible Markov chains, each chain being defined on an extended state space and having an invariant probability measure admitting $\pi$ as a marginal distribution. The proposed methodology is inspired by a new formulation of Kac's theorem and allows global and local dynamics to be smoothly combined. Under mild conditions, the corresponding Markov transition kernel can be shown to be irreducible and Harris recurrent. In addition, we establish that geometric ergodicity holds under appropriate conditions on the global and local dynamics. Finally, we illustrate numerically the use of the proposed method and its potential benefits in comparison to existing Markov chain Monte Carlo (MCMC) algorithms.
Flavor anomalies in supersymmetric scenarios with nonminimal flavor violation

Motivated by tensions between experimental measurements and SM predictions in b → s(cid:96) + (cid:96) − transitions, we present the ﬁrst study of non-minimal ﬂavour-violating Minimal Supersymmetric Standard Model (MSSM) scenarios contributing to the relevant Wilson coeﬃcients to address the observed anomalies using SuperIso and MARTY . We calculate the full one-loop analytical contributions of the general MSSM to Wilson coeﬃcients relevant for ﬂavour anomalies, together with the anomalous muon magnetic dipole moment ( g − 2) µ . We show that, after imposing theoretical constraints on the ﬂavour-violating parameters we can ﬁnd scenarios in agreement with the experimental measurements that can address at the same time the tensions in ﬂavour observables and in ( g − 2) µ .
Largest Eigenvalues of the Conjugate Kernel of Single-Layered Neural Networks

This paper is concerned with the asymptotic distribution of the largest eigenvalues for some nonlinear random matrix ensemble stemming from the study of neural networks. More precisely we consider $M= \frac{1}{m} YY^\top$ with $Y=f(WX)$ where $W$ and $X$ are random rectangular matrices with i.i. d. centered entries. This models the data covariance matrix or the Conjugate Kernel of a single layered random Feed-Forward Neural Network. The function $f$ is applied entrywise and can be seen as the activation function of the neural network. We show that the largest eigenvalue has the same limit (in probability) as that of some well-known linear random matrix ensembles. In particular, we relate the asymptotic limit of the largest eigenvalue for the nonlinear model to that of an information-plus-noise random matrix, establishing a possible phase transition depending on the function $f$ and the distribution of $W$ and $X$. This may be of interest for applications to machine learning.
Flexible Style Image Super-Resolution using Conditional Objective

Recent studies have significantly enhanced the performance of single-image super-resolution (SR) using convolutional neural networks (CNNs). While there can be many high-resolution (HR) solutions for a given input, most existing CNN-based methods do not explore alternative solutions during the inference. A typical approach to obtaining alternative SR results is to train multiple SR models with different loss weightings and exploit the combination of these models. Instead of using multiple models, we present a more efficient method to train a single adjustable SR model on various combinations of losses by taking advantage of multi-task learning. Specifically, we optimize an SR model with a conditional objective during training, where the objective is a weighted sum of multiple perceptual losses at different feature levels. The weights vary according to given conditions, and the set of weights is defined as a style controller. Also, we present an architecture appropriate for this training scheme, which is the Residual-in-Residual Dense Block equipped with spatial feature transformation layers. At the inference phase, our trained model can generate locally different outputs conditioned on the style control map. Extensive experiments show that the proposed SR model produces various desirable reconstructions without artifacts and yields comparable quantitative performance to state-of-the-art SR methods.
When geometry meets optimization theory: partially orthogonal tensors

Due to the multi-linearity of tensors, most algorithms for tensor optimization problems are designed based on the block coordinate descent method. Such algorithms are widely employed by practitioners for their implementability and effectiveness. However, these algorithms usually suffer from the lack of theoretical guarantee of global convergence and analysis of convergence rate. In this paper, we propose a block coordinate descent type algorithm for the low rank partially orthogonal tensor approximation problem and analyse its convergence behaviour. To achieve this, we carefully investigate the variety of low rank partially orthogonal tensors and its geometric properties related to the parameter space, which enable us to locate KKT points of the concerned optimization problem. With the aid of these geometric properties, we prove without any assumption that: (1) Our algorithm converges globally to a KKT point; (2) For any given tensor, the algorithm exhibits an overall sublinear convergence with an explicit rate which is sharper than the usual $O(1/k)$ for first order methods in nonconvex optimization; {(3)} For a generic tensor, our algorithm converges $R$-linearly.
Influence of the thickness of Si and Ge films deposited on Si$_3$N$_4$/SiO$_2$/Si substrates on their structure and diffusion of hydrogen atoms from Si$_3$N$_4$ layers

The results of RHEED, FTIR and Raman spectroscopy study of silicon and germanium films with the thickness up to 200 nm grown from molecular beams on dielectric Si3N4/SiO2/Si(001) substrates are presented. Noticeable changes of the intensity of the N–H and Si–N absorption bands have been observed in the IR absorbance spectra as a result of the deposition of the silicon and germanium films. The thicker was the deposited film, the more considerable were the decrease of N– H absorption band intensity and the increase in that of the Si–N band. This tendency has been observed during the growth of both amorphous and polycrystalline Si or Ge films. The reduction of IR absorption at the band assigned to the N–H bond vibration is explained by breaking of these bonds followed by the diffusion of the hydrogen atoms from the Si3N4 layer into the growing film of
Monolithic integration of 940 nm AlGaAs distributed Bragg reflectors on bulk Ge substrates

High quality 940 nm AlxGa1-xAs n-type distributed Bragg reflectors (DBRs) were successfully monolithically grown on off-cut Ge (100) substrates. The Ge-DBRs have reflectivity spectra comparable to those grown on conventional bulk GaAs substrates and have smooth morphology, reasonable periodicity and uniformity. These results strongly support VCSEL growth and
Stem–loop formation drives RNA folding in mechanical unzipping experiments

Significance Understanding how complementary RNA single strands anneal to form the native structure requires accurate knowledge of the energetics of hybridization and the kinetics driving the hairpin fold. The annealing dynamics are highly complex for RNA, where multiple long-lived intermediate states occur along the folding pathway. In this framework, single-molecule experiments allow us to derive the thermodynamics and kinetics of RNA folding and the characterization of the intermediates along the folding pathway. We mechanically unzip a 2-kbp RNA hairpin with optical tweezers in sodium and magnesium, deriving the 10 RNA nearest-neighbor free energies and modeling the hybridization kinetics in a barrier energy landscape of stem–loop secondary structures. Our study contributes to a better understanding of the RNA folding problem. Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force–distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping–rezipping process, we introduce a barrier energy landscape of the stem–loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem–loops that kinetically trap the long-lived intermediates observed in the FDC. Stem–loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.
Small-scale magnetic fields of the spectroscopic binary T Tauri stars V1878 Ori and V4046 Sgr

Aims. The goal of this study is to investigate the small-scale magnetic ﬁelds of the two spectroscopic binary T Tauri stars V1878 Ori and V4046 Sgr. This is done to complete the observational characterisation of the surface magnetic ﬁelds of these stars because only their large-scale magnetic ﬁelds have been studied with Zeeman Doppler imaging (ZDI) so far. Methods. To investigate the small-scale magnetic ﬁelds, the di ﬀ erential Zeeman intensiﬁcation of near-infrared Ti i lines was investigated using high-resolution archival spectra obtained with the ESPaDOnS spectrograph at the CFHT. In order to study the binary components separately, the spectra were disentangled by considering observations taken at di ﬀ erent orbital phases. The Zeeman-intensiﬁcation analysis was performed based on detailed polarised radiative transfer calculations aided by the Markov chain Monte Carlo inference, treating magnetic ﬁeld ﬁlling factors and other stellar parameters that could a ﬀ ect the spectra as free parameters. Results. The obtained average magnetic ﬁeld strengths of the components of V1878 Ori are 1 . 33 ± 0 . 08 and 1 . 57 ± 0 . 09 kG, re-spectively. Previous ZDI studies of V1878 Ori recovered about 14 and 20% of this total magnetic ﬁeld strength. For V4046 Sgr, the magnetic ﬁeld strengths are 1 . 96 ± 0 . 18 and 1 . 83 ± 0 . 18 kG, respectively. In this case, about 12 and 9% of the total magnetic ﬁeld strength was detected by ZDI. intensiﬁcation for the two components of each binary. This is in contrast to the large-scale magnetic ﬁelds obtained from ZDI investigations, performed using the same observations, which found that magnetic ﬁeld strengths and topologies vary signiﬁcantly in the components of the two binaries. While the large-scale ﬁeld might look signiﬁcantly di ﬀ erent, the overall magnetic energy, primarily carried by the small-scale magnetic ﬁelds, appears to be quite similar. This indicates that the e ﬃ ciency of the magnetic dynamo is comparable for the components of the two binaries.
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examples. In previously proposed few-shot visual classifiers, it is assumed that the feature manifold, where classifier decisions are made, has uncorrelated feature dimensions and uniform feature variance. In this work, we focus on addressing the limitations arising from this assumption by proposing a variance-sensitive class of models that operates in a low-label regime. The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks. We further extend this approach to a transductive learning setting, proposing Transductive CNAPS. This transductive method combines a soft k-means parameter refinement procedure with a two-step task encoder to achieve improved test-time classification accuracy using unlabelled data. Transductive CNAPS achieves state of the art performance on Meta-Dataset. Finally, we explore the use of our methods (Simple and Transductive) for"out of the box"continual and active learning. Extensive experiments on large scale benchmarks illustrate robustness and versatility of this, relatively speaking, simple class of models. All trained model checkpoints and corresponding source codes have been made publicly available.