Miracle-dz/UniFAR-FTUs huggingface dataset:
Shoulder Implant X-Ray Manufacturer Classification: Exploring with Vision Transformer

Shoulder replacement surgery, also called total shoulder replacement, is a common and complex surgery in Orthopedics discipline. It involves replacing a dead shoulder joint with an artificial implant. In the market, there are many artificial implant manufacturers and each of them may produce different implants with different structures compares to other providers. The problem arises in the following situation: a patient has some problems with the shoulder implant accessory and the manufacturer of that implant maybe unknown to either the patient or the doctor, therefore, correctly identification of the manufacturer is the key prior to the treatment. In this paper, we will demonstrate different methods for classifying the manufacturer of a shoulder implant.
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis tasks such as node classification and node clustering. However, most methods ignore the heterogeneity in real-world graphs. Methods designed for heterogeneous graphs, on the other hand, fail to learn complex semantic representations because they only use meta-paths instead of meta-graphs. Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors. We propose a novel Higher-order Attribute-Enhancing (HAE) framework that enhances node embedding in a layer-by-layer manner. Under the HAE framework, we propose a Higher-order Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network representation learning. HAEGNN simultaneously incorporates meta-paths and meta-graphs for rich, heterogeneous semantics, and leverages the self-attention mechanism to explore content-based nodes’ interactions. The unique higher-order architecture of HAEGNN allows examining the first-order as well as higher-order neighborhoods. Moreover, HAEGNN shows good explainability as it learns the importances of different meta-paths and meta-graphs. HAEGNN is also memory-efficient, for it avoids per meta-path based matrix calculation. Experimental results not only show HAEGNN's superior performance against the state-of-the-art methods in node classification, node clustering, and visualization, but also demonstrate its superiorities in terms of memory efficiency and explainability.
SDSS-IV MaNGA: 3D spin alignment of spiral and S0 galaxies

We investigate the 3D spin alignment of galaxies with respect to the large-scale filaments using the MaNGA survey. The cosmic web is reconstructed from the Sloan Digital Sky Survey using disperse and the 3D spins of MaNGA galaxies are estimated using the thin disc approximation with integral field spectroscopy kinematics. Late-type spiral galaxies are found to have their spins parallel to the closest filament’s axis. The alignment signal is found to be dominated by low-mass spirals. Spins of S0-type galaxies tend to be oriented preferentially in perpendicular direction with respect to the filament’s axis. This orthogonal orientation is found to be dominated by S0s that show a notable misalignment between their kinematic components of stellar and ionized gas velocity fields and/or by low-mass S0s with lower rotation support compared to their high-mass counterparts. Qualitatively similar results are obtained when splitting galaxies based on the degree of ordered stellar rotation, such that galaxies with high spin magnitude have their spin aligned, and those with low spin magnitude in perpendicular direction to the filaments. In the context of conditional tidal torque theory, these findings suggest that galaxies’ spins retain memory of their larger scale environment. In agreement with measurements from hydrodynamical cosmological simulations, the measured signal at low redshift is weak, yet statistically significant. The dependence of the spin-filament orientation of galaxies on their stellar mass, morphology, and kinematics highlights the importance of sample selection to detect the signal.
Optimizing Multiple Multi-Way Stream Joins

We address the joint optimization of multiple stream joins in a scale-out architecture by tailoring prior work on multi-way stream joins to predicate-driven data partitioning schemes. We present an integer linear programming (ILP) formulation for selecting the partitioning and tuple routing with minimal probe load. The presented algorithms and optimization schemes are implemented in CLASH, a data stream processor developed in our group that translates queries to deployable Apache Storm topologies after optimization. The experiments conducted on TPC-H data exhibit the potential of multi-query optimization of multi-way stream joins and the effectiveness and feasibility of the ILP optimization problem.
J2229+2725: an extremely low metallicity dwarf compact star-forming galaxy with an exceptionally high [O iii]λ5007/[O ii]λ3727 flux ratio of 53

Using the Large Binocular Telescope (LBT)/Multi-Object Dual Spectrograph (MODS), we have obtained optical spectroscopy of one of the most metal-poor dwarf star-forming galaxies (SFGs) in the local Universe, J2229+2725. This galaxy with a redshift z = 0.0762 was selected from the Data Release 16 of the Sloan Digital Sky Survey (SDSS). Its properties derived from the LBT observations are most extreme among SFGs in several ways. Its oxygen abundance 12 + logO/H = 7.085 ± 0.031 is among the lowest ever observed for an SFG. With its very low metallicity, an absolute magnitude Mg = −16.39 mag, a low stellar mass M⋆ = 9.1 × 106 M⊙, and a very low mass-to-light ratio M⋆/Lg ∼ 0.0166 (in solar units), J2229+2725 deviates strongly from the luminosity–metallicity relation defined by the bulk of the SFGs in the SDSS. J2229+2725 has a very high specific star formation rate sSFR ∼ 75 Gyr−1, indicating very active ongoing star formation. Three other features of J2229+2725 are most striking, being the most extreme among lowest metallicity SFGs: (1) a ratio O32 = I([O iii]λ5007)/I([O ii]λ3727) ∼ 53, (2) an equivalent width of the H β emission line EW(H β) of 577 Å, and (3) an electron number density of ∼1000 cm−3. These properties imply that the starburst in J2229+2725 is very young. Using the extremely high O32 in J2229+2725, we have improved the strong-line calibration for the determination of oxygen abundances in the most metal-deficient galaxies, in the range 12 + logO/H $\lesssim$ 7.3.
Finding Motifs in Knowledge Graphs using Compression

We introduce a method to find network motifs in knowledge graphs. Network motifs are useful patterns or meaningful subunits of the graph that recur frequently. We extend the common definition of a network motif to coincide with a basic graph pattern. We introduce an approach, inspired by recent work for simple graphs, to induce these from a given knowledge graph, and show that the motifs found reflect the basic structure of the graph. Specifically, we show that in random graphs, no motifs are found, and that when we insert a motif artificially, it can be detected. Finally, we show the results of motif induction on three real-world knowledge graphs.
Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors

We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier. The latent code guides the pair representations and influences sentence reconstruction. Experimental results on two datasets created via distant supervision indicate that multi-task learning results in performance benefits. Additional exploration of employing Knowledge Base priors into theVAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results.
Electronic and magnetic properties of VOCl/FeOCl antiferromagnetic heterobilayers

We study the electronic properties of the heterobilayer of vanadium and iron oxychlorides, VOCl and FeOCl, two layered air stable van der Waals insulating oxides with different types of antiferromagnetic order in bulk: VOCl monolayers are ferromagnetic (FM) whereas the FeOCl monolayers are antiferromagnetic (AF). We use density functional theory calculations, with Hubbard correction that is found to be needed to describe correctly the insulating nature of these compounds. We compute the magnetic anisotropy and propose a spin model Hamiltonian. Our calculations show that interlayer coupling is weak and hence the magnetic order of each monolayers is preserved in the heterobilayer. Thus, the heterobilayer combines antiferromagnetic and ferromagnetic orders. Interlayer exchange should lead both to exchange bias and to the emergence of hybrid collective modes that combine FM and AF magnons. The energy band of the heterobilayer show a type II band alignment, and feature spin-splitting of the states of the AF layer due to the breaking of the inversion symmetry.
Learning Feature Interactions With and Without Specifications

Features in product lines and highly configurable systems can interact in ways that are contrary to developers' intent. Current methods to identify such unanticipated feature interactions are costly and inadequate. To address this problem we propose a new approach to learn feature interactions, both in those product lines where constraints on feature combinations are specified and in feature-rich configurable systems where such specifications often are not available. The contribution of the paper is to use program analysis to extract feature-relevant learning models from the source code in order to detect unwanted feature interactions. Where specifications of feature constraints are unavailable, our approach infers the constraints using feature-related data-flow dependency information. Evaluation in experiments on three software product line benchmarks and a highly configurable system shows that this approach is fast and effective. The contribution is to support developers by automatically detecting feature combinations in a new product or version that can interact in unwanted or unrecognized ways. This enables better understanding of latent interactions and identifies software components that should be tested together because their features interact in some configurations.
Development and validation of liquid chromatographic method for estimation of escitalopram oxalate in tablet dosage forms

A simple, specific, accurate and precise RP-HPLC method was developed and validated for the determination of escitalopram oxalate in tablet dosage forms. A hypersil BDS C8, 5μ column having 250x4.6mm internal diameter in isocratic mode with mobile phase containing methanol: disodium hydrogen phosphate: acetonitrile (28:44:28v/v, pH 7.0±0.05) was used. The flow rate was 1.5ml/min and effluents were monitored at 226nm. The retention time of escitalopram oxalate was 8.45 min. The linearity range is 250-1500μg/ml with coefficient of correlation 0.9999. The method was validated in terms of accuracy, precision, repeatability. The percentage recovery for escitalopram oxalate was found to be 99.0%. The proposed method was successfully applied for quantitative determination of escitalopram oxalate in single dosage form for routine analysis.
The origin of bulges and discs in the CALIFA survey – I. Morphological evolution

This series of papers aims at understanding the formation and evolution of non-barred disc galaxies. We use the new spectro-photometric decomposition code, c2d, to separate the spectral information of bulges and discs of a statistically representative sample of galaxies from the CALIFA survey. Then, we study their stellar population properties analising the structureindependent datacubes with the Pipe3D algorithm. We find a correlation between the bulgeto-total ( /)) luminosity (and mass) ratio and galaxy stellar mass. The /) mass ratio has only a mild evolution with redshift, but the bulge-to-disc ( / ) mass ratio shows a clear increase of the disc component since redshift I < 1 for massive galaxies. The masssize relation for both bulges and discs describes an upturn at high galaxy stellar masses (log ("★/"⊙) > 10.5). The relation holds for bulges but not for discs when using their individual stellar masses. We find a negligible evolution of the mass-size relation for both the most massive (log ("★,b,d/"⊙) > 10) bulges and discs. For lower masses, discs show a larger variation than bulges. We also find a correlation between the Sérsic index of bulges and both galaxy and bulge stellar mass, which does not hold for the disc mass. Our results support an inside-out formation of nearby non-barred galaxies, and they suggest that i) bulges formed early-on and ii) they have not evolved much through cosmic time. However, we find that the early properties of bulges drive the future evolution of the galaxy as a whole, and particularly the properties of the discs that eventually form around them.
Dynamical Sweet Spot Engineering via Two-Tone Flux Modulation of Superconducting Qubits

Current superconducting quantum processors require strategies for coping with material defects and imperfect parameter targeting in order to scale up while maintaining high performance. To that end, in-situ control of qubit frequencies with magnetic flux can be used to avoid spurious resonances. However, increased dephasing due to 1/f flux noise limits performance at all of these operating points except for noise-protected sweet spots, which are sparse under DC flux bias and monochromatic flux modulation. Here we experimentally demonstrate that two-tone flux modulation can be used to create a continuum of dynamical sweet spots, greatly expanding the range of qubit frequencies achievable while first-order insensitive to slow flux noise. To illustrate some advantages of this flexibility, we use bichromatic flux control to reduce the error rates and gate times of parametric entangling operations between transmons. Independent of gate scheme, the ability to use flux control to freely select qubit frequencies while maintaining qubit coherence represents an important step forward in the robustness and scalability of near-term superconducting qubit devices.
On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective

With the increasing scale, complexity, and heterogeneity of the next generation networked systems, seamless control, management, and security of such systems becomes increasingly challenging. Many diverse applications have driven interest in networked systems, including large-scale distributed learning, multi-agent optimization, 5G service provisioning, and network slicing, etc. In this paper, we propose trust as a measure to evaluate the status of network agents and improve the decision-making process. We interpret trust as a relation among entities that participate in various protocols. Trust relations are based on evidence created by the interactions of entities within a protocol and may be a composite of multiple metrics such as availability, reliability, resilience, etc. depending on application context. We first elaborate on the importance of trust as a metric and then present a mathematical framework for trust computation and aggregation within a network. Then we show in practice, how trust can be integrated into network decision-making processes by presenting two examples. In the first example, we show how utilizing the trust evidence can improve the performance and the security of Federated Learning. Second, we show how a 5G network resource provisioning framework can be improved when augmented with a trust-aware decision-making scheme. We verify the validity of our trust-based approach through simulations. Finally, we explain the challenges associated with aggregating the trust evidence and briefly explain our ideas to tackle them.
Spectroscopie optique de nanotubes de carbone : complexes excitoniques et cavités plasmoniques

Cette these porte d'une part sur l'etude de la stabilite du biexciton dans le nanotube de carbone et, d'autre part, sur le controle de l'emission du nanotube par le couplage des nanotubes de carbone a des antennes plasmoniques. La technique de spectroscopie optique non-lineaire de saturation d'absorption, appliquee aux nanotubes de carbone, nous a permis d'effectuer la premiere observation du biexciton dans cette nanostructure. Plus precisement, deux raies d'absorptions induites sont observees et attribuees au trion et a l'exciton par des etudes en temperatures et en puissance du laser de pompe. La mise en oeuvre d'une configuration a trois faisceaux basee sur un schema en double-pompe permet de confirmer la photo-generation du biexciton en tant qu'excitation elementaire du nanotube de carbone. Le biexciton est observe avec une energie de liaison de 107 ± 1 meV pour la chiralite (9,7) et presente un profil asymetrique de Fano. Une premiere estimation de la dynamique de recombinaisondu biexciton est donnee par description quantitative du processus de Fano. Le modele est base sur le formalisme de la susceptibilite non-lineaire du troisieme ordre χ(3) incluant le couplage coulombien entre le biexciton et le continuum des paires electron-trou de la premiere singularite de Van Hove. Le facteur de Fano est evaluee a q = 5 et conduit a l'estimation du taux de recombinaison Auger du biexciton B ∈ [0.1; 1] μm·ps−1 . Le rendement radiatif du biexciton est ainsi estime a 10−6 . Dans le but d'etudier les nanotubes en cavite plasmonique, une experience de micro-photoluminescence et une experience de spectroscopie en champ sombre, sont developpes dans le domaine spectrale des telecommunications (1.3 μm et 1.55 μm). La caracterisation de divers types echantillons de nanotube et des antennes plasmoniques sont presentes. Des resultats preliminaires sur un echantillon de nanotubes associes dans une configuration patch a des antennes plasmoniques montrent une correlation entre la position des antennes et les zones luminescentes, ainsi qu'un changement de l'allure des spectres de photoluminescence. Ces premiers resultats constituent une transition dans la demarche de notre projet. L'etape de calibration des experiences est en phase de finition et l'etude des proprietes physique des nanotubes en cavite plasmonique represente desormais une activite operationnelle au sein de notre equipe.
Probing artificial neural networks: insights from neuroscience

A major challenge in both neuroscience and machine learning is the development of useful tools for understanding complex information processing systems. One such tool is probes, i.e., supervised models that relate features of interest to activation patterns arising in biological or artificial neural networks. Neuroscience has paved the way in using such models through numerous studies conducted in recent decades. In this work, we draw insights from neuroscience to help guide probing research in machine learning. We highlight two important design choices for probes $-$ direction and expressivity $-$ and relate these choices to research goals. We argue that specific research goals play a paramount role when designing a probe and encourage future probing studies to be explicit in stating these goals.
A Comparative Study on Collecting High-Quality Implicit Reasonings at a Large-scale

Explicating implicit reasoning (i.e. warrants) in arguments is a long-standing challenge for natural language understanding systems. While recent approaches have focused on explicating warrants via crowdsourcing or expert annotations, the quality of warrants has been questionable due to the extreme complexity and subjectivity of the task. In this paper, we tackle the complex task of warrant explication and devise various methodologies for collecting warrants. We conduct an extensive study with trained experts to evaluate the resulting warrants of each methodology and find that our methodologies allow for high-quality warrants to be collected. We construct a preliminary dataset of 6,000 warrants annotated over 600 arguments for 3 debatable topics. To facilitate research in related downstream tasks, we release our guidelines and preliminary dataset.
Multivalent Entailment Graphs for Question Answering

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding. There has been much progress in unsupervised learning of entailment graphs for this purpose. We make three contributions: (1) we reinterpret the Distributional Inclusion Hypothesis to model entailment between predicates of different valencies, like DEFEAT(Biden, Trump) entails WIN(Biden); (2) we actualize this theory by learning unsupervised Multivalent Entailment Graphs of open-domain predicates; and (3) we demonstrate the capabilities of these graphs on a novel question answering task. We show that directional entailment is more helpful for inference than non-directional similarity on questions of fine-grained semantics. We also show that drawing on evidence across valencies answers more questions than by using only the same valency evidence.
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction

Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.
A Study of Graph-Based Approaches for Semi-Supervised Time Series Classification

Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series learning tasks of great importance, we here focus on semi-supervised learning which beneﬁts of a graph representation of the data. Two main aspects are involved in this task: A suitable distance measure to evaluate the similarities between time series, and a learning method to make predictions based on these distances. However, the relationship between the two aspects has never been studied systematically. We describe four different distance measures, including (Soft) DTW and Matrix Proﬁle, as well as four successful semi-supervised learning methods, including the graph Allen– Cahn method and a Graph Convolutional Neural Network. We then compare the performance of the algorithms on standard data sets. Our ﬁndings show that all measures and methods vary strongly in accuracy between data sets and that there is no clear best combination to employ in all cases.