Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of the conduit size on middle-term outcomes in patients with extracardiac total cavopulmonary connection

Abstract OBJECTIVES The 18- and 16-mm conduits in extracardiac total cavopulmonary connection (eTCPC) were reported to be optimal based on energy loss and flow stagnation at the relatively early phase. However, because the artificial conduit lacks growth potential, we have recently encountered some cases in which the conduit needs to be changed several years after eTCPC. These cases prompted us to reconsider the surgical strategy for eTCPC. METHODS We reviewed our 20-year single-centre experience with eTCPC patients (n = 256) to compare the 18-mm conduit (n = 195) and 16-mm conduit (n = 61) in terms of mortality and morbidity. RESULTS The 16-mm conduit was used significantly more frequently in patients whose main chamber was right ventricle (P < 0.001). There was also a significant difference in preoperative inferior vena cava pressure (P = 0.008). There was a significant difference in the actuarial rate of freedom from late-occurring complications, including mortality, between the 2 groups (P = 0.003). There was a significant difference in the actuarial rate of reoperation-free survival (P = 0.042); however, there was no significant difference in resurgical intervention for the conduit (P = 0.333). In multivariate analysis, preoperative inferior vena cava pressure was an independent predictor for late-occurring complications (hazard ratio 1.19; P = 0.026). Conduit size (18 or 16 mm) itself was not an independent predictive factor for late-occurring complications (P = 0.690). CONCLUSIONS The mid-term clinical outcomes in patients who underwent eTCPC were excellent with low mortality. Preoperative inferior vena cava pressure was the only predictive risk factor for postoperative morbidity, and the 16 mm conduit was not predictive thereof.
Effect of the polysaccharide capsule and its heptose on the resistance of Campylobacter jejuni to innate immune defenses

Abstract Campylobacter jejuni is a commensal in many animals but causes diarrhea in humans. Its polysaccharide capsule contributes to host colonization and virulence in a strain‐ and model‐specific manner. We investigated if the capsule and its heptose are important for interactions of strain NCTC 11168 with various hosts and their innate immune defenses. We determined that they support bacterial survival in Drosophila melanogaster and enhance virulence in Galleria mellonella. We showed that the capsule had limited antiphagocytic activity in human and chicken macrophages, decreased adherence to chicken macrophages, and decreased intracellular survival in both macrophages. In contrast, the heptose increased uptake by chicken macrophages and supported adherence to human macrophages and survival within them. While the capsule triggered nitric oxide production in chicken macrophages, the heptose mitigated this and protected against nitrosative assault. Finally, the C. jejuni strain NCTC 11168 elicited strong cytokine production in both macrophages but quenched ROS production independently from capsule and heptose, and while the capsule and heptose did not protect against oxidative assault, they favored growth in biofilms under oxidative stress. This study shows that the wild‐type capsule with its heptose is optimized to resist innate defenses in strain NCTC 11168 often via antagonistic effects of the capsule and its heptose.
A Novel CNN-BiLSTM Ensemble Model With Attention Mechanism for Sit-to-Stand Phase Identification Using Wearable Inertial Sensors

Sit-to-stand transition phase identification is vital in the control of a wearable exoskeleton robot for assisting patients to stand stably. In this study, we aim to propose a method for segmenting and identifying the sit-to-stand phase using two inertial sensors. First, we defined the sit-to-stand transition into five phases, namely, the initial sitting phase, the flexion momentum phase, the momentum transfer phase, the extension phase, and the stable standing phase based on the preprocessed acceleration and angular velocity data. We then employed a threshold method to recognize the initial sitting and the stable standing phases. Finally, we designed a novel CNN-BiLSTM-Attention algorithm to identify the three transition phases, namely, the flexion momentum phase, the momentum transfer phase, and the extension phase. Fifteen subjects were recruited to perform sit-to-stand transition experiments under a specific paradigm. A combination of the acceleration and angular velocity data features for the sit-to-stand transition phase identification were validated for the model performance improvements. The integration of the CNN, Bi-LSTM, and Attention modules demonstrated the reasonableness of the proposed algorithms. The experimental results showed that the proposed CNN-BiLSTM-Attention algorithm achieved the highest average classification accuracy of 99.5% for all five phases when compared to both traditional machine learning algorithms and deep learning algorithms on our customized dataset (STS-PD). The proposed sit-to-stand phase recognition algorithm could serve as a foundation for the control of wearable exoskeletons and is important for the further development of intelligent wearable exoskeleton rehabilitation robots.
Towards audio language modeling - an overview

Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency. Researchers recently discovered the potential of codecs as suitable tokenizers for converting continuous audio into discrete codes, which can be employed to develop audio language models (LMs). Numerous high-performance neural audio codecs and codec-based LMs have been developed. The paper aims to provide a thorough and systematic overview of the neural audio codec models and codec-based LMs.
A unifying primary framework for quantum graph neural networks from quantum graph states

Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.
Soft-bottom intertidal ecosystems shaped by ecosystem engineers

Inter-getijde kustgebieden met zandige bodem worden gevormd door biobouwers: Gevolgen voor de trofische structuur Biobouwers zijn soorten die hun omgeving sterk veranderen. Door dit te doen, kunnen ze ze het aantal en de verspreiding van andere soorten beinvloeden. Met monitoren en experimenten in het veld, hebben we de effecten van biobouwers op de gemeenschap van soorten in inter-getijde kustgebieden met zandige bodem onderzocht. We vonden dat de aanwezigheid van rifvormende schelpdieren (d.w.z. mosselen en oesters), zeegras en krabben, het aantal soorten en de samenstelling van de voedselketen sterk kunnen veranderen, zowel in de ruimte als over de tijd. Door het verstrekken van structuur en het stabiliseren van de bodem, kunnen rifvormende schelpdieren namelijk meerdere voedselketen-niveaus beinvloeden en deze effecten zijn zichtbaar tot op honderden meters buiten de grenzen van het rif zelf. Daarnaast spelen de effecten van deze rifvormende schelpdieren in combinatie met andere interacties zoals predatie en competitie, ook een sterke rol in het structureren van populaties van andere soorten schelpdieren, zoals kokkels. Interessant is ook dat koloniserend zeegras dat fijn sediment invangt, in combinatie met gravende krabben die grote poelen maken in deze sedimentlaag, ervoor zorgen dat de structuur van de voedselketen en de temporele ontwikkeling hiervan drastisch veranderen. De bevindingen van dit onderzoek onderstrepen het grote belang van biobouwers in inter-getijde kustgebieden met een zandige bodem.
High-harmonic generation in semi-Dirac and Weyl semimetals with broken time-reversal symmetry: Exploring merging of Weyl nodes

We explore anomalous high-harmonic generation in a model that realizes a transition from a broken time-reversal symmetry Weyl-semimetal to a semi-Dirac regime, i.e. a gapless semimetal with dispersion that is parabolic in one direction and conical in the other two. We point out the intensity of the induced anomalous high harmonics is high in the semi-Dirac regime. For Weyl semimetals, we reveal anomalous high harmonics are due to excitations at momenta where the dispersion is not strictly linear and that in the linearized low-energy theory the anomalous response is harmonic only. Our findings aid experimental characterization of Weyl, Dirac, and semi-Dirac semimetals.
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification

Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user data privacy concerns. We address this limitation by storing embeddings of the underlying training data independently of the model weights, enabling dynamic data modifications without retraining. Specifically, our approach integrates the $k$-Nearest Neighbor ($k$-NN) classifier with a vision-based foundation model, pre-trained self-supervised on natural images, enhancing interpretability and adaptability. We share open-source implementations of a previously unpublished baseline method as well as our performance-improving contributions. Quantitative experiments confirm improved classification across established benchmark datasets and the method's applicability to distinct medical image classification tasks. Additionally, we assess the method's robustness in continual learning and data removal scenarios. The approach exhibits great promise for bridging the gap between foundation models' performance and challenges tied to data privacy. The source code is available at https://github.com/TobArc/privacy-aware-image-classification-with-kNN.
Towards a new model-independent calibration of Gamma-Ray Bursts

Current data on baryon acoustic oscillations and Supernovae of Type Ia cover up to $z\sim2.5$. These low-$z$ observations play a very important role in the determination of cosmological parameters and have been widely used to constrain the $\Lambda$CDM and models beyond the standard. To extend this investigation to higher redshifts, Gamma-Ray Bursts (GRBs) stand out as one of the most promising observables since can probe the universe up to $z\sim9.4$. The use of GRB correlations is still a challenge due to the spread in their intrinsic properties. In this work, we propose an innovative and cosmology-independent method of calibration of the so-called 3D Dainotti correlation. We employ state-of-the-art data on Cosmic Chronometers (CCH) at $z\lesssim2$ and use the Gaussian Processes Bayesian reconstruction tool. To match the CCH redshift range, we select 20 long GRBs in $0.553 \leq z \leq 1.96$ from the Platinum sample, which consists of well-defined GRB plateau properties that obey the fundamental plane relation. To ensure the generality of our method, we verify that the choice of priors on the parameters of the Dainotti relation and the modelling of CCH uncertainties and covariance have negligible impact on our results. Moreover, we consider the case in which the redshift evolution of the physical features of the plane is accounted for. We find that the use of CCH allows us to identify a sub-sample of GRBs that adhere even more closely to the fundamental plane relation, with an intrinsic scatter of $\sigma_{int}=0.20^{+0.03}_{-0.05}$ obtained in this analysis when evolutionary effects are considered. In an epoch in which we strive to reduce uncertainties on the variables of the GRB correlations to tighten constraints on cosmological parameters, we have found a novel model-independent approach to pinpoint a sub-sample that can thus represent a valuable set of standardizable candles.
Truncated Polynomial Expansion-Based Detection in Massive MIMO: A Model-Driven Deep Learning Approach

In this paper, we propose a deep learning (DL)-based approach for efficiently computing the inverse of Hermitian matrices using truncated polynomial expansion (TPE). Our model-driven approach involves optimizing the coefficients of the TPE during an offline training procedure for a given number of TPE terms. We apply this method to signal detection in uplink massive multiple-input multiple-output (MIMO) systems, where the matrix inverse operation required by linear detectors, such as zero-forcing (ZF) and minimum mean square error (MMSE), is approximated using TPE. Our simulation results demonstrate that the proposed learned TPE-based method outperforms the conventional TPE method with optimal coefficients in terms of asymptotic convergence speed and reduces the computational complexity of the online detection stage, albeit at the expense of the offline training stage. However, the limited number of trainable parameters leads to a swift offline training process.
On Binary Formation from Three Initially Unbound Bodies

We explore three-body binary formation (3BBF), the formation of a bound system via gravitational scattering of three initially unbound bodies (3UB), using direct numerical integrations. For the first time, we consider systems with unequal masses, as well as finite-size and post-Newtonian effects. Our analytically derived encounter rates and numerical scattering results reproduce the 3BBF rate predicted by Goodman&Hut (1993) for hard binaries in dense star clusters. We find that 3BBF occurs overwhelmingly through nonresonant encounters and that the two most massive bodies are never the most likely to bind. Instead, 3BBF favors pairing the two least massive bodies (for wide binaries) or the most plus least massive bodies (for hard binaries). 3BBF overwhelmingly favors wide binary formation with super-thermal eccentricities, perhaps helping to explain the eccentric wide binaries observed by Gaia. Hard binaries form much more rarely, but with a thermal eccentricity distribution. The semimajor axis distribution scales cumulatively as $a^3$ for hard and slightly wider binaries. Though mergers are rare between black holes when including relativistic effects, direct collisions occur frequently between main-sequence stars -- more often than hard 3BBF. Yet, these collisions do not significantly suppress hard 3BBF at the low velocity dispersions typical of open or globular clusters. Energy dissipation through gravitational radiation leads to a small probability of a bound, hierarchical triple system forming directly from 3UB.
Finding Cross-rule Optimization Bugs in Datalog Engines

Datalog is a popular and widely-used declarative logic programming language. Datalog engines apply many cross-rule optimizations; bugs in them can cause incorrect results. To detect such optimization bugs, we propose an automated testing approach called Incremental Rule Evaluation (IRE), which synergistically tackles the test oracle and test case generation problem. The core idea behind the test oracle is to compare the results of an optimized program and a program without cross-rule optimization; any difference indicates a bug in the Datalog engine. Our core insight is that, for an optimized, incrementally-generated Datalog program, we can evaluate all rules individually by constructing a reference program to disable the optimizations that are performed among multiple rules. Incrementally generating test cases not only allows us to apply the test oracle for every new rule generated-we also can ensure that every newly added rule generates a non-empty result with a given probability and eschew recomputing already-known facts. We implemented IRE as a tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely Souffl\'e, CozoDB, $\mu$Z, and DDlog, and discovered a total of 30 bugs. Of these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the bugs identified by Deopt, queryFuzz might be unable to detect 5. Our incremental test case generation approach is efficient; for example, for test cases containing 60 rules, our incremental approach can produce 1.17$\times$ (for DDlog) to 31.02$\times$ (for Souffl\'e) as many valid test cases with non-empty results as the naive random method. We believe that the simplicity and the generality of the approach will lead to its wide adoption in practice.
Conditional convergence: how long is the long-run?

Mainstream theories of economic growth predict that countries across the world will converge in per capita income. This has not happened. The theory has been modified, hypothesises that conditional convergence takes place not across all countries, but within groups of countries which have similar political and cultural backgrounds. Much of the literature analyses data sets which cover short periods of just a few decades in the late 20 th century. This paper uses a much longer period, in particular 1820-1998. A wide range of countries is examined. The paper examines two concepts of convergence: the dispersion of incomes across countries at a point in time; the mobility of countries across time Even within the culturally homogenous group of countries which make up Western Europe, the time scale of convergence is of the order of 150 years from the spread of industrial capitalism some 200 years ago. Data sets which focus purely on the second half of the 20 th century alone will give a misleading picture of convergence.
Target Speech Extraction with Pre-trained Self-supervised Learning Models

Pre-trained self-supervised learning (SSL) models have achieved remarkable success in various speech tasks. However, their potential in target speech extraction (TSE) has not been fully exploited. TSE aims to extract the speech of a target speaker in a mixture guided by enrollment utterances. We exploit pre-trained SSL models for two purposes within a TSE framework, i.e., to process the input mixture and to derive speaker embeddings from the enrollment. In this paper, we focus on how to effectively use SSL models for TSE. We first introduce a novel TSE downstream task following the SUPERB principles. This simple experiment shows the potential of SSL models for TSE, but extraction performance remains far behind the state-of-the-art. We then extend a powerful TSE architecture by incorporating two SSL-based modules: an Adaptive Input Enhancer (AIE) and a speaker encoder. Specifically, the proposed AIE utilizes intermediate representations from the CNN encoder by adjusting the time resolution of CNN encoder and transformer blocks through progressive upsampling, capturing both fine-grained and hierarchical features. Our method outperforms current TSE systems achieving a SI-SDR improvement of 14.0 dB on LibriMix. Moreover, we can further improve performance by 0.7 dB by fine-tuning the whole model including the SSL model parameters.
Comparing MCMC algorithms in Stochastic Volatility Models using Simulation Based Calibration

Simulation Based Calibration (SBC) is applied to analyse two commonly used, competing Markov chain Monte Carlo algorithms for estimating the posterior distribution of a stochastic volatility model. In particular, the bespoke 'off-set mixture approximation' algorithm proposed by Kim, Shephard, and Chib (1998) is explored together with a Hamiltonian Monte Carlo algorithm implemented through Stan. The SBC analysis involves a simulation study to assess whether each sampling algorithm has the capacity to produce valid inference for the correctly specified model, while also characterising statistical efficiency through the effective sample size. Results show that Stan's No-U-Turn sampler, an implementation of Hamiltonian Monte Carlo, produces a well-calibrated posterior estimate while the celebrated off-set mixture approach is less efficient and poorly calibrated, though model parameterisation also plays a role. Limitations and restrictions of generality are discussed.
SoftQE: Learned Representations of Queries Expanded by LLMs

We investigate the integration of Large Language Models (LLMs) into query encoders to improve dense retrieval without increasing latency and cost, by circumventing the dependency on LLMs at inference time. SoftQE incorporates knowledge from LLMs by mapping embeddings of input queries to those of the LLM-expanded queries. While improvements over various strong baselines on in-domain MS-MARCO metrics are marginal, SoftQE improves performance by 2.83 absolute percentage points on average on five out-of-domain BEIR tasks.
Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression

Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may arise due to privacy concerns, e.g., to avoid the release of sensitive genetic information. We extend GhostKnockoffs He et al. [2022] and introduce variable selection methods based on penalized regression achieving false discovery rate (FDR) control. We report empirical results in extensive simulation studies, demonstrating enhanced performance over previous work. We also apply our methods to genome-wide association studies of Alzheimer’s disease, and evidence a significant improvement in power.
Revisiting the Second Vassiliev (In)variant for Polymer Knots

Knots in open strands such as ropes, fibers, and polymers, cannot typically be described in the language of knot theory, which characterizes only closed curves in space. Simulations of open knotted polymer chains, often parameterized to DNA, typically perform a closure operation and calculate the Alexander polynomial to assign a knot topology. This is limited in scenarios where the topology is less well-defined, for example when the chain is in the process of untying or is strongly confined. Here, we use a discretized version of the Second Vassiliev Invariant for open chains to analyze Langevin Dynamics simulations of untying and strongly confined polymer chains. We demonstrate that the Vassiliev parameter can accurately and efficiently characterize the knotted state of polymers, providing additional information not captured by a single-closure Alexander calculation. We discuss its relative strengths and weaknesses compared to standard techniques, and argue that it is a useful and powerful tool for analyzing polymer knot simulations.