Miracle-dz/UniFAR-FTUs huggingface dataset:
Sickle cell cerebrovascular reactivity to a CO2 stimulus: Too little, too slow

Background: Despite increased cerebral blood flow (CBF), cerebral infarcts occur in patients with sickle cell disease (SCD). This suggests increased CBF does not meet metabolic demand possibly due to compromised cerebral vasodilatory response. Hypothesis: In adult SCD patients, cerebrovascular reactivity (CVR) and speed of vasodilatory response (tau) to a standardized vasodilatory stimulus, are reduced compared to normal subjects. Methods: Functional brain imaging performed as part of routine care in adult SCD patients without known large vessel cerebral vasculopathy was reviewed retrospectively. CVR was calculated as the change in CBF measured as the blood-oxygenation-level-dependent (BOLD)-magnetic resonance imaging signal, in response to a standard vasoactive stimulus of carbon dioxide (CO2). The tau corresponding to the best fit between the convolved end-tidal partial pressures of CO2 and BOLD signal was defined as the speed of vascular response. CVR and tau were normalized using a previously generated atlas of 42 healthy controls. Results: Fifteen patients were included. CVR was reduced in grey and white matter (mean Z-score for CVR −0.5 [−1.8 to 0.3] and −0.6 [−2.3 to 0.7], respectively). Tau Z-scores were lengthened in grey and white matter (+0.9 [−0.5 to 3.3] and +0.8 [−0.7 to 2.7], respectively). Hematocrit was the only significant independent predictor of CVR on multivariable regression. Conclusion: Both measures of cerebrovascular health (CVR and tau) in SCD patients were attenuated compared to normal controls. These findings show that CVR represents a promising tool to assess disease state, stroke risk, and therapeutic efficacy of treatments in SCD and merits further investigation.
Endocrine disrupting chemicals interfere with decidualization of human primary endometrial stromal cells in vitro

Multiple studies have shown associations between exposure to endocrine disrupting chemicals (EDCs) and reduced fertility in women. However, little is known about the target organs of chemical disruption of female fertility. Here, we focus on the hormone-sensitive uterine lining, the endometrium, as a potential target. Decidualization is the morphological and functional change that endometrial stromal cells undergo to support endometrial receptivity, which is crucial for successful implantation, placentation, and pregnancy. We investigated the effect of nine selected EDCs on primary human endometrial stromal cell decidualization in vitro. The cells were exposed to a decidualization-inducing mixture in the presence or absence of 1 μM of nine different EDCs for nine days. Extent of decidualization was assessed by measuring the activity of cAMP dependent protein kinase, Rho-associated coiled-coil containing protein kinase, and protein kinase B in lysates using photoluminescent probes, and secretion of prolactin into the media by using ELISA. Decidualization-inducing mixture upregulated activity of protein kinases and prolactin secretion in cells derived from all women. Of the tested chemicals, dichlorodiphenyldichloroethylene (p,p’-DDE), hexachlorobenzene (HCB) and perfluorooctanesulfonic acid (PFOS) significantly reduced decidualization as judged by the kinase markers and prolactin secretion. In addition, bisphenol A (BPA) reduced prolactin secretion but did not significantly affect activity of the kinases. None of the EDCs was cytotoxic, based on the assessment of total protein content or activity of the viability marker casein kinase 2 in lysates. These results indicate that EDCs commonly present in the blood circulation of reproductive-aged women can reduce decidualization of human endometrial stromal cells in vitro. Future studies should focus on detailed hazard assessment to define possible risks of EDC exposure to endometrial dysfunction and implantation failure in women.
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings

Learning fine-grained embeddings is essential for extending the generalizability of models pre-trained on “coarse” labels (e.g., animals). It is crucial to fields for which fine-grained labeling (e.g., breeds of animals) is expensive, but fine-grained prediction is desirable, such as medicine. The dilemma necessitates adaptation of a “coarsely” pre-trained model to new tasks with a few “finer-grained” training labels. However, coarsely supervised pre-training tends to suppress intra-class variation, which is vital for cross-granularity adaptation. In this paper, we develop a training framework underlain by a novel superclass-conditional Gaussian mixture model (SCGM). SCGM imitates the generative process of samples from hierarchies of classes through latent variable modeling of the fine-grained subclasses. The framework is agnostic to the encoders and only adds a few distribution related parameters, thus is efficient, and flexible to different domains. The model parameters are learned end-to-end by maximum-likelihood estimation via a principled Expectation-Maximization algorithm. Extensive experiments on benchmark datasets and a real-life medical dataset indicate the effectiveness of our method.
Imaginary-Time Approach to the Kondo Effect out of Equilibrium

An artificial quantum dot which is attached to two leads exhibits a non-equilibrium Kondo effect due to the presence of a finite source-drain voltage. The construction of a theory which provides numerically computable expectation values of physical observables for this non-perturbative quantum effect within an appropriate non-equilibrium quantum statistical ensemble is an unsolved problem. The present thesis investigates an imaginary-time approach by Han and Heary [1] by means of its formal structure and computational feasibility. The theory maps the non-equilibrium system to an infinite set of auxiliary effective-equilibrium systems which can be examined with conventional techniques. An analytic continuation is necessary to reobtain non-equilibrium expectation values from effective-equilibrium ones. It gives rise to an inverse problem. We show that continuous-time quantum Monte-Carlo (CT-QMC) methods provide a numerically controlled solution of the set of auxiliary systems, in particular for parameter values which are deep inside the non-equilibrium Kondo regime. For static local observables on the quantum dot we provide reasonable numerical estimates with clear signatures of non-equilibrium Kondo physics, using a standard MaxEnt approach to the inverse problem of analytic continuation. In order to compute local dynamical observables and transport characteristics, a theoretical framework based on the function theory of several complex variables is developed. It defines the inverse problem for the MaxEnt procedure. Resulting predictions for spectral functions and I-V characteristics in the weak- and intermediate-coupling regime are in good agreement with other methods. [1] J. E. Han, R. J. Heary, Phys. Rev. Lett. 99, 236808 (2007)
Robust Higher-Order Filtering of Points

Processing noisy point data is a tedious task. It often requires smoothing and meshing, followed by mesh processing algorithms. In this work we present a robust filter which operates directly on points using higher order surface approximations, computed iteratively. We extend the bilateral filter by using and filtering surface curvature, and introducing a quality parameter based on our local surface approximation. Using this process input point data is denoised and smoothed, while preserving features. We also extend our filter to allow feature enhancement, by controlling shock formation resulting in sharp edges in regions of high curvature. We show results of our algorithms on several scanned point sets.
Image-Text Re-Matching Using Swin Transformer and DistilBERT

In recent years, the news media has become multimodal. The relationship between text and images in news is complex and needs to be understood. In this paper, we work on Image-Text Re-Matching to understand the relationship between images and text, and apply and improve the image retrieval method, ADAPT. Improvements are made by reconsidering the feature extraction methods in image retrieval. We employ Swin Transformer for image feature extraction and DistilBERT for text feature extraction. According to the report from organizers, our runs resulted in MRR@100 score of 0.0789 and Recall@100 score of 0.5781 for test set.
Stiffness-aware neural network for learning Hamiltonian systems

We propose stiffness-aware neural network (SANN), a new method for learning Hamiltonian dynamical systems from data. SANN identiﬁes and splits the training data into stiff and nonstiff portions based on a stiffness-aware index, a simple, yet effective metric we introduce to quantify the stiffness of the dynamical system. This classiﬁcation along with a resampling technique allows us to apply different time integration strategies such as step size adaptation to better capture the dynamical characteristics of the Hamiltonian vector ﬁelds. We evaluate SANN on complex physical systems including a three-body problem and billiard model. We show that SANN is more stable and can better preserve energy when compared with the state-of-the-art methods, leading to signiﬁcant improvement in accuracy.
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds

Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability density for the variables of interest is known, as it involves estimating a potentially high-dimensional log partition function. In this work, we present a unifying view of existing MI bounds from the perspective of importance sampling, and propose three novel bounds based on this approach. Since accurate estimation of MI without density information requires a sample size exponential in the true MI, we assume either a single marginal or the full joint density information is known. In settings where the full joint density is available, we propose Multi-Sample Annealed Importance Sampling (AIS) bounds on MI, which we demonstrate can tightly estimate large values of MI in our experiments. In settings where only a single marginal distribution is known, we propose Generalized IWAE (GIWAE) and MINE-AIS bounds. Our GIWAE bound unifies variational and contrastive bounds in a single framework that generalizes InfoNCE, IWAE, and Barber-Agakov bounds. Our MINE-AIS method improves upon existing energy-based methods such as MINE-DV and MINE-F by directly optimizing a tighter lower bound on MI. MINE-AIS uses MCMC sampling to estimate gradients for training and Multi-Sample AIS for evaluating the bound. Our methods are particularly suitable for evaluating MI in deep generative models, since explicit forms of the marginal or joint densities are often available. We evaluate our bounds on estimating the MI of VAEs and GANs trained on the MNIST and CIFAR datasets, and showcase significant gains over existing bounds in these challenging settings with high ground truth MI.
OrientDB: A NoSQL, Open Source MMDMS

OrientDB is a full-function, NoSQL MMDMS (multi-model database management system), addressing the big data variety problem with one single, multi-model store and a SQL-based, multi-model query language. It combines graph and semistructured data management with object-oriented, text and spatial capabilities, and features a variety of deployment and distribution / replication options, transactional / ACID storage and indexing, making it a commercially successful MMDMS. With that, OrientDB is well-suited for novel adaptations of applications like smart logistics, asset management, social data storage and analysis, and other use cases that require multiple perspectives on the data. While OrientDB’s initial open source release was in 2010, many improvements like the multi-model API / query language and full ACID support came with a recent re-design. This paper gives the first comprehensive description of the revamped system, focusing on its data model, query language, distribution models and software architecture. We assess the current state of OrientDB’s performance compared to other multi-model, but also “best-in-class” NoSQL single-model document systems.
Causal Contextual Bandits with Targeted Interventions

We study a contextual bandit setting where the learning agent has the ability to perform interventions on targeted subsets of the population, apart from possessing qualitative causal side-information. This novel formalism captures intricacies in real-world scenarios such as software product experimentation where targeted experiments can be conducted. However, this fundamentally changes the set of options that the agent has, compared to standard contextual bandit settings, necessitating new techniques. This is also the first work that integrates causal side-information in a contextual bandit setting, where the agent aims to learn a policy that maps contexts to arms (as opposed to just identifying one best arm). We pro-pose a new algorithm, which we show empirically performs better than baselines on experiments that use purely synthetic data and on real world-inspired experiments. We also prove a bound on regret that theoretically guards performance.
Bacterial communities of premise plumbing systems in 1 four European cities , and their association with 2 culturable Legionella 3 Running title : Bacterial communities and culturable Legionella in four European 4 cities 5

Maria Scaturro1,9, Federica Del Chierico2, Yair Motro3, Angeliki Chaldoupi4, Anastasia 6 Flountzi4,9, Jacob Moran-Gilad3,9, Antonietta Girolamo1, Thomai Koutsiomani4, 7 Bozena Krogulska5, Diane Lindsay 6,9, Renata Matuszewska5, Georgios 8 Papageorgiou4, Katarzyna Pancer5, Nikolaos Panoussis4, Maria Cristina Rota1, Søren 9 Anker Uldum7,9, Emmanuel Velonakis8, Dominique Louise Chaput6†, Maria Luisa 10 Ricci1,9†, 11
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness

Among a wide range of success of deep learning, convolutional neural networks have been extensively utilized in several tasks such as speech recognition, image processing, and natural language processing, which require inputs with large dimensions. Several studies have investigated function estimation capability of deep learning, but most of them have assumed that the dimensionality of the input is much smaller than the sample size. However, for typical data in applications such as those handled by the convolutional neural networks described above, the dimensionality of inputs is relatively high or even infinite. In this paper, we investigate the approximation and estimation errors of the (dilated) convolutional neural networks when the input is infinite dimensional. Although the approximation and estimation errors of neural networks are affected by the curse of dimensionality in the existing analyses for typical function spaces such as the Hölder and Besov spaces, we show that, by considering anisotropic smoothness, they can alleviate exponential dependency on the dimensionality but they only depend on the smoothness of the target functions. Our theoretical analysis supports the great practical success of convolutional networks. Furthermore, we show that the dilated convolution is advantageous when the smoothness of the target function has a sparse structure.
Exploring Multimodality, Perplexity and Explainability for Memorability Prediction

This paper describes several approaches proposed by the MeMAD Team for the MediaEval 2021 “Predicting Media Memorability” task. Our best approach is based on early fusion of multimodal (visual and textual) features. We also designed one of our run to be explainable in order to give new insights into the topic of audio visual content memorability. Finally, one of our runs is an experiment in analysing the potential role played by text perplexity in video content memorability.
Antiderivatives Exist without Integration

We present a proof that any continuous function with domain including a closed interval yields an antiderivative of that function on that interval. This is done without the need of any integration comparable to that of Riemann, Cauchy, or Darboux. The proof is based on one given by Lebesgue in 1905.
Path Auxiliary Proposal for MCMC in Discrete Space

Energy-based Models (EBMs) offer a powerful approach for modeling discrete structure, but both inference and learning of EBM are hard as it involves sampling from discrete distributions. Recent work shows Markov Chain Monte Carlo (MCMC) with the informed proposal is a powerful tool for such sampling. How-ever, an informed proposal only allows local updates as it requires evaluating all energy changes in the neighborhood. In this work, we present a path auxiliary algorithm that uses a composition of local moves to efﬁciently explore large neigh-borhoods. We also give a fast version of our algorithm that only queries the evaluation of energy function twice for each proposal via linearization of the energy function. Empirically, we show that our path auxiliary algorithms considerably outperform other generic samplers on various discrete models for sampling, inference, and learning. Our method can also be used to train deep EBMs for high dimensional discrete data.
Variational oracle guiding for reinforcement learning

How to make intelligent decisions is a central problem in machine learning and artificial intelligence. Despite recent successes of deep reinforcement learning (RL) in various decision making problems, an important but under-explored aspect is how to leverage oracle observation (the information that is invisible during online decision making, but is available during offline training) to facilitate learning. For example, human experts will look at the replay after a Poker game, in which they can check the opponents’ hands to improve their estimation of the opponents’ hands from the visible information during playing. In this work, we study such problems based on Bayesian theory and derive an objective to leverage oracle observation in RL using variational methods. Our key contribution is to propose a general learning framework referred to as variational latent oracle guiding (VLOG) for DRL. VLOG is featured with preferable properties such as its robust and promising performance and its versatility to incorporate with any value-based DRL algorithm. We empirically demonstrate the effectiveness of VLOG in online and offline RL domains with tasks ranging from video games to a challenging tilebased game Mahjong. Furthermore, we publish the Mahjong environment and an offline RL dataset as a benchmark to facilitate future research on oracle guiding1.
NewsSeek-NOVA at MediaEval 2021: Context-enriched Multimodal Transformers For News Images Re-matching

In this paper, we present our participation in the NewsImages task where we address the complex challenge of connecting images to news text. We leverage transformer-based multimodal models to jointly attend to different contextual news elements when performing predictions, and transfer learning to improve the performance. Our experiments demonstrate that the models benefit from jointly attending to context-enriched samples, supporting our hypothesis. We also extracted rich insights on the principles underlying the connection between images and news text.
Application of network pharmacology and molecular docking approach to explore active compounds and potential pharmacological mechanisms of Aconiti Lateralis Radix Praeparata and Lepidii Semen Descurainiae Semen for treatment of heart failure

Background: Heart failure (HF) is the end stage of the development of heart disease, whose prognosis is poor. The previous research of our team indicated that the formulae containing Aconiti Lateralis Radix Praeparata and Lepidii Semen Descurainiae Semen (ALRP-LSDS) could inhibit myocardial hypertrophy, inhibit cardiomyocyte apoptosis, delay myocardial remodeling (REM), and improve the prognosis of patients with HF effectively. In order to explore the mechanism of ALRP-LSDS for the treatment of HF, a combined approach of network pharmacology and molecular docking was conducted. Methods: Public database TCMSP was used to screen the active compounds of ALRP-LSDS. The targets of screened active compounds were obtained from the TCMSP database and predicted using the online analysis tool PharmMapper. The targets of HF were obtained from 6 databases including GeneCards, OMIM, DrugBank, TTD, PharmGKB, and DisGeNET. Protein–protein interaction and enrichment analysis were performed, respectively, by STRING and Metascape online tools after merging the targets of active compounds and HF. Cytoscape software was used to conduct networks. Finally, molecular docking was performed by Vina to verify the correlation between key targets and active compounds. Results: Final results indicated that the active compounds including β-sitosterol, isorhamnetin, quercetin, kaempferol, and (R)-norcoclaurine, the targets including AKT1, CASP3, and MAPK1 might be the main active compounds and key targets of ALRP-LSDS for the treatment of HF separately. The binding ability of AKT1 to the main active compounds was better compared with the other 2 key targets, which means it might be more critical. The pathways including AGE-RAGE signaling pathway in diabetic complications, Pathways in cancer, and Fluid shear stress and atherosclerosis might play important roles in the treatment of HF with ALRP-LSDS. In general, ALRP-LSDS could inhibit cardiomyocyte apoptosis, delay REM, and improve cardiac function through multicompound, multitarget, and multipathway, which contributes to the treatment of HF. Conclusions: Based on the combined approach of network pharmacology and molecular docking, this study screened out the main active compounds, key targets, and main pathways of ALRP-LSDS for the treatment of HF, and revealed its potential mechanisms, providing a theoretical basis for further research.
Optimisation à base de flot de graphe pour l'acquisition d'informations 3D à partir de séquences d'images

We show how the graph flow technique can be used to solve some complex optimiza- tion problems. This technique is used here to acquire 3D information using several points of view. We show how - with an adapted graph - it is possible to minimize a category of energies which can reach more precise results than the usual disparity maps. This novel approach also takes object discontinuities into account. The results obtained with the method are exposed.
A single‐aperture duplex antenna with dual circular polarizations

This article proposes a novel design of a compact single‐aperture dual circular polarization (CP) antenna with integrated duplexing functions using a phase controllable coupled resonator structure. To the best of the authors’ knowledge, this is the first report of a multifunctional CP antenna with integrated duplexing performance and dual polarizations. The two orthogonal CPs are realized for the two respective filtering channels. Each channel contains a phased filtering power divider forming the dual‐feed to a patch to achieve right‐hand or left‐hand CP (RHCP or LHCP). The required output phases of the dividers are realized by varied configurations of the coupled λ/2‐resonator pairs. A duplex‐CP‐antenna with RHCP/LHCP at the downlink/uplink (2.45/2.7 GHz) is designed, fabricated, and measured. It achieved a 3‐dB AR bandwidth of 3.2/7.7% at the downlink/uplink and an isolation better than 20 dB. Meanwhile, the antenna showed expected second‐order filtering characteristics at both channels.