Miracle-dz/UniFAR-FTUs huggingface dataset:
Ferric carboxymaltose has a higher distribution into myocardium than gadobutrol - a quantitative T1 mapping study in healthy volunteers

Background: The dynamic tissue distribution of the clinically available intravenous iron substitution agent ferric carboxymaltose (FCM) is largely unknown. Notably, T1 mapping cardiovascular magnetic resonance (CMR) is highly sensitive for detecting myocardial iron. Purpose: To evaluate and quantify the dynamic tissue distribution of FCM using CMR. Materials and methods: T1 mapping was prospectively performed to determine T1 and the partition coefficient (lambda) in myocardium, liver, spleen and skeletal muscle up to 60 minutes after onset of a 15-minute-long infusion of 20 ml (50 mg iron/ml) FCM in healthy male volunteers. For comparison, myocardial lambda for gadobutrol (0.2 mmol/kg) was measured in a separate group of age-matched healthy male volunteers. The t-test was used for group comparisons. Results: A total of 25 healthy male participants (mean{+/-}SD age 27{+/-}3 years) were evaluated. Subjects underwent CMR with intravenous FCM (n=8) or gadobutrol (n=17, age matched). T1 values of myocardium, blood, liver, spleen and skeletal muscle were all shortened after intravenous injection of FCM (p<0.001 for all). Lambda for FCM in myocardium and spleen remained constant over time after injection of FCM (mean{+/-}SEM 64{+/-}8% and 81{+/-}20%, respectively, 30 minutes after injection start), while lambda for FCM in liver and skeletal muscle increased over time. Myocardial lambda for FCM was higher than myocardial lambda for gadobutrol (64{+/-}8 vs 45{+/-}1%, p=0.003). Conclusions: T1 mapping can detect and quantify the dynamic tissue distribution of iron from ferric carboxymaltose in the myocardium, liver, spleen and skeletal muscle. Lambda in healthy myocardium for ferric carboxymaltose was markedly higher than lambda for gadobutrol, indicating that ferric carboxymaltose distributes to a greater extent into the myocardium than extracellular contrast agents, most likely due to additional distribution into the intracellular space.
Isolated Tracheabronchomalacia Misdiagnosed for Years as Bronchial Asthma

Tracheomalacia (TM) is an abnormal collapse of the tracheal lumen, which often occurs when the cartilaginous part of the trachea has not developed. It is a rare condition but is seen often in infancy and childhood period. The incidence of primary airway malacia in children was estimated to be at least one in 2,100. It has a wide range of etiologies, and it is often localized but rarely generalized as in our case. It could be severe enough to indicate frequent admission and might expose the patient to multiple unnecessary medications. We are reporting a case with unusual primary tracheobronchomalacia (TBM) that was missed for several years with a huge burden on both families and healthcare providers. A five-year-old Saudi girl had multiple admissions to the intensive care unit with similar presentation each time, and she was misdiagnosed as having asthma exacerbation with an occasional chest infection. Bronchoscopy revealed the underlying condition, and the patient was kept on the minimal intervention of nasal continuous positive airway pressure (CPAP) and aggressive airway hydration therapy, all with the goal of improving the patient's outcome and reducing hospital admissions. We emphasize the importance of alerting physicians about malacia as an important cause of recurrent wheezy chest, which is one of the common asthma mimickers; in such cases, flexible bronchoscopy remains the gold standard diagnostic test, while the treatment remained supportive.
Hybrid high‐ and low‐molecular weight chains of hyaluronan is an effective modality treatment for increasing female sexual satisfaction: An interventional, randomized‐control parallel study

Psychological, behavioral, hormonal, surgical, and psychopharmacologic approaches are the only acknowledged treatments for Female Sexual Dysfunction (FSD). The study is conducted to assess the efficacy of hybrid cooperative complexes of high and low molecular weight hyaluronan (hybrid H‐HA/L‐HA) in treating females with varying of sexual dysfunction and to assess the female genital self‐image scale (FGSIS), female sexual function index (FSFI), and dermatology life quality index (DLQI) before and after treatment.
Investigating the relationship between right ventricular size and function with pre‐eclampsia: A two‐group cross‐sectional study

Pre‐eclampsia is a multisystem disorder characterized by symptoms of high blood pressure and proteinuria during pregnancy. It is associated with many complications and maternal and fetal mortality. This disorder may be associated with many cardiovascular complications and affect the function of the heart. Therefore, in this study, the structure and function of the right ventricle (RV) in patients with pre‐eclampsia have been investigated using echocardiography.
Determining optical-correlator filter capacity using fractal dimension as a complexity measure

Optical correlation is an efficient parallel implementation of image classifiers. One formulation of the holographic filter in the optical correlator is the Least Squares Discriminant Function (LSDF), wherein the shape of the whole correlation function is specified when training the filter. An issue of particular interest is the capacity of the filter, or the number of training images that can be used while still retaining discrimination capability. To investigate the capacity of an LSDF filter, and to be more general than just testing on a particular type of image, the fractal dimension of the images was used as an objective complexity measure of the images that the filter is trained with. Non-linear preprocessing, specifically edge detection, was used to increase the filter capacity and the discriminatory capabilities of the correlator. Simulation experiments of these issues are described and the results are discussed.
Antiviral effect and mechanism of Phillyrin and its reformulated FS21 against influenza

Influenza virus causes significant morbidity and mortality with pandemic threat. Oleaceae Fructus Forsythiae is a medicinal herb. This study aimed to investigate antiviral effect of Phillyrin, a purified bioactive compound from this herb, and its reformulated preparation FS21 against influenza and its mechanism.
Random Fixed Boundary Flows

We consider fixed boundary flow with canonical interpretability as principal components extended on non-linear Riemannian manifolds. We aim to find a flow with fixed starting and ending points for noisy multivariate data sets lying on an embedded non-linear Riemannian manifold. In geometric term, the fixed boundary flow is defined as an optimal curve that moves in the data cloud with two fixed end points. At any point on the flow, we maximize the inner product of the vector field, which is calculated locally, and the tangent vector of the flow. The rigorous definition derives from an optimization problem using the intrinsic metric on the manifolds. For random data sets, we name the fixed boundary flow the random fixed boundary flow and analyze its limiting behavior under noisy observed samples. We construct a high level algorithm to compute the random fixed boundary flow and the convergence of the algorithm is provided. We show that the fixed boundary flow yields a concatenate of three segments, of which one coincides with the usual principal flow when the manifold is reduced to the Euclidean space. We further prove that the random fixed boundary flow converges largely to the population fixed boundary flow with high probability. We illustrate how the random fixed boundary flow can be used and interpreted, and showcase its application in real data sets.
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers

Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we develop and motivate a method for using large language models (LLMs) to generate large numbers of synthetic queries cheaply. The method begins by generating a small number of synthetic queries using an expensive LLM. After that, a much less expensive one is used to create large numbers of synthetic queries, which are used to fine-tune a family of reranker models. These rerankers are then distilled into a single efficient retriever for use in the target domain. We show that this technique boosts zero-shot accuracy in long-tail domains and achieves substantially lower latency than standard reranking methods.
Colloidal smectics in button-like confinements: Experiment and theory

Liquid crystals can self-organize into a layered smectic phase. While the smectic layers are typically straight forming a lamellar pattern in bulk, external confinement may drastically distort the layers due to the boundary conditions imposed on the orientational director field. Resolving this distortion leads to complex structures with topological defects. Here, we explore the configurations adopted by two-dimensional colloidal smectics made from nearly hard rod-like particles in complex confinements, characterized by a button-like structure with two internal boundaries (inclusions): a two-holed disk and a double annulus. The topology of the confinement generates new structures which we classify in reference to previous work as generalized laminar and generalized Shubnikov states. To explore these configurations, we combine particle-resolved experiments on colloidal rods with three complementary theoretical approaches: Monte-Carlo simulation, first-principles density functional theory and phenomenological $\mathbf{Q}$-tensor modeling. This yields a consistent and comprehensive description of the structural details. In particular, we characterize a nontrivial tilt angle between the direction of the layers and symmetry axes of the confinement.
Co-learning Planning and Control Policies Using Differentiable Formal Task Constraints

—This paper presents a hierarchical reinforcement learning algorithm constrained by differentiable signal temporal logic. Previous work on logic-constrained reinforcement learning consider encoding these constraints with a reward function, constraining policy updates with a sample-based policy gradient. However, such techniques oftentimes tend to be inefﬁcient because of the signiﬁcant number of samples required to obtain accurate policy gradients. In this paper, instead of implicitly constraining policy search with sample-based policy gradients, we directly constrain policy search by backpropagating through formal constraints, enabling training hierarchical policies with substantially fewer training samples. The use of hierarchical policies is recognized as a crucial component of reinforcement learning with task constraints. We show that we can stably constrain policy updates, thus enabling different levels of the policy to be learned simultaneously, yielding superior performance compared with training them separately. Experiment results on several simulated high-dimensional robot dynamics and a real-world differential drive robot (TurtleBot3) demonstrate the effectiveness of our approach on ﬁve different types of task constraints. Demo videos, code, and models can be found at our project website: https://sites.google. com/view/dscrl.
Resolution of structure in the protostellar source IRAS 16293-2422

Emission from the cold, protostellar source IRAS 16293 - 2422 has been mapped in the 2.7 mm continuum and (C-13)O J = 1-0 line with resolutions of 4 arcsec x 6 arcsec and 5 arcsec x 16 arcsec, respectively. The line and continuum emission arise from a disklike structure whose major axis lies roughly perpendicular to a collimated outflow of molecular gas and the local magnetic field direction. The continuum spectrum of the dust is well fitted by a lambda exp -1.5 + or -0.5 emissivity law and a dust temperature of 41 + or - 2 K. The fits imply a total luminosity of 27 + or - 4 solar luminosities and a mass of gas and dust in the range 0.9-6 solar masses. The (C-13)O kinematics suggests that the region may be rotating. 23 references.
Predicting Stock Price Movement as an Image Classification Problem

The paper studies intraday price movement of stocks that is considered as an image classification problem. Using a CNN-based model we make a compelling case for the high-level relationship between the first hour of trading and the close. The algorithm managed to adequately separate between the two opposing classes and investing according to the algorithm's predictions outperformed all alternative constructs but the theoretical maximum. To support the thesis, we ran several additional tests. The findings in the paper highlight the suitability of computer vision techniques for studying financial markets and in particular prediction of stock price movements.
Evaluation Method and Design Guidance for Direction-Finding Antenna Systems

A deterministic evaluation procedure for multiport direction-finding (DF) antennas is proposed. It is based on a DF uncertainty parameter, which describes how well different directions of arrival and polarizations are distinguishable. By investigating a simple antenna array, it is shown that the proposed parameter provides additional insights into the behavior of an antenna system when compared to established methods. Moreover, since the uncertainty parameter is calculated from a set of far fields, it is applicable to port far fields as well as Characteristic Modes (CMs). This finding is utilized to derive design guidance: starting with a set of CM far fields, the angular distribution of the uncertainty is investigated to verify that no ambiguities are present. Different sets of far fields are compared, and the differences regarding their DF behavior are visualized and explained using the uncertainty in conjunction with an estimate of the incident field. To quantify these differences, a key performance indicator (KPI) is introduced that summarizes the DF capabilities over a selected angular region. To demonstrate the design process, a multimode multiport antenna (M3PA) with three uncorrelated ports is developed, manufactured, and measured.
Differentiable Trajectory Generation for Car-like Robots with Interpolating Radial Basis Function Networks

The design of Autonomous Vehicle software has largely followed the Sense-Plan-Act model. Traditional modular AV stacks develop perception, planning, and control software separately with little integration when optimizing for different objectives. On the other hand, end-to-end methods usually lack the principle provided by model-based white-box planning and control strategies. We propose a computationally efficient method for approximating closed-form trajectory generation with interpolating Radial Basis Function Networks to create a middle ground between the two approaches. The approach creates smooth approximations of local Lipschitz continuous maps of feasible solutions to parametric optimization problems. We show that this differentiable approximation is efficient to compute and allows for tighter integration with perception and control algorithms when used as the planning strategy.
Hybrid optoacoustic and ultrasound biomicroscopy monitors’ laser-induced tissue modifications and magnetite nanoparticle impregnation

Tissue modification under laser radiation is emerging as one of the advanced applications of lasers in medicine, with treatments ranging from reshaping and regeneration of cartilage to normalization of the intraocular pressure. Laser-induced structural alterations can be studied using conventional microscopic techniques applied to thin specimen. Yet, development of non-invasive imaging methods for deep tissue monitoring of structural alterations under laser radiation is of great importance, especially for attaining efficient feedback during the procedures. We developed a fast scanning biomicroscopy system that can simultaneously deliver both optoacoustic and pulse-echo ultrasound contrast from intact tissues and show that both modalities allow manifesting the laser-induced changes in cartilage and sclera. Furthermore, images of the sclera samples reveal a crater developing around the center of the laser-irradiated spot as well as a certain degree of thickening within the treated zone, presumably due to pore formation. Finally, we were able to observe selective impregnation of magnetite nanoparticles into the cartilage, thus demonstrating a possible contrast enhancement approach for studying specific treatment effects. Overall, the new imaging approach holds promise for development of noninvasive feedback control systems that could guarantee efficacy and safety of laser-based medical procedures.
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations

Explainable artificial intelligence techniques are developed at breakneck speed, but suitable evaluation approaches lag behind. With explainers becoming increasingly complex and a lack of consensus on how to assess their utility, it is challenging to judge the benefit and effectiveness of different explanations. To address this gap, we take a step back from sophisticated predictive algorithms and instead look into explainability of simple decision-making models. In this setting, we aim to assess how people perceive comprehensibility of their different representations such as mathematical formulation, graphical representation and textual summarisation (of varying complexity and scope). This allows us to capture how diverse stakeholders -- engineers, researchers, consumers, regulators and the like -- judge intelligibility of fundamental concepts that more elaborate artificial intelligence explanations are built from. This position paper charts our approach to establishing appropriate evaluation methodology as well as a conceptual and practical framework to facilitate setting up and executing relevant user studies.
Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control

Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models. Unfortunately, applying such models to settings with embodied agents, such as robots, is challenging due to their lack of experience with the physical world, inability to parse non-language observations, and ignorance of rewards or safety constraints that robots may require. On the other hand, language-conditioned robotic policies that learn from interaction data can provide the necessary grounding that allows the agent to be correctly situated in the real world, but such policies are limited by the lack of high-level semantic understanding due to the limited breadth of the interaction data available for training them. Thus, if we want to make use of the semantic knowledge in a language model while still situating it in an embodied setting, we must construct an action sequence that is both likely according to the language model and also realizable according to grounded models of the environment. We frame this as a problem similar to probabilistic filtering: de-code a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate this guided decoding strategy is able to solve complex, long-horizon embodiment tasks in a robotic setting by leveraging the knowledge of both models. The project’s website can be found at grounded-decoding.github. io.
Why (and When) does Local SGD Generalize Better than SGD?

Local SGD is a communication-efficient variant of SGD for large-scale training, where multiple GPUs perform SGD independently and average the model parameters periodically. It has been recently observed that Local SGD can not only achieve the design goal of reducing the communication overhead but also lead to higher test accuracy than the corresponding SGD baseline (Lin et al., 2020b), though the training regimes for this to happen are still in debate (Ortiz et al., 2021). This paper aims to understand why (and when) Local SGD generalizes better based on Stochastic Differential Equation (SDE) approximation. The main contributions of this paper include (i) the derivation of an SDE that captures the long-term behavior of Local SGD in the small learning rate regime, showing how noise drives the iterate to drift and diffuse after it has reached close to the manifold of local minima, (ii) a comparison between the SDEs of Local SGD and SGD, showing that Local SGD induces a stronger drift term that can result in a stronger effect of regularization, e.g., a faster reduction of sharpness, and (iii) empirical evidence validating that having a small learning rate and long enough training time enables the generalization improvement over SGD but removing either of the two conditions leads to no improvement.
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds

Despite a great deal of research, it is still not well-understood why trained neural networks are highly vulnerable to adversarial examples. In this work we focus on two-layer neural networks trained using data which lie on a low dimensional linear subspace. We show that standard gradient methods lead to non-robust neural networks, namely, networks which have large gradients in directions orthogonal to the data subspace, and are susceptible to small adversarial L 2 -perturbations in these directions. Moreover, we show that decreasing the initialization scale of the training algorithm, or adding L 2 regularization, can make the trained network more robust to adversarial perturbations orthogonal to the data.
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning

Handling the problem of scalability is one of the essential issues for multi-agent reinforcement learning (MARL) algorithms to be applied to real-world problems typically involving massively many agents. For this, parameter sharing across multiple agents has widely been used since it reduces the training time by decreasing the number of parameters and increasing the sample efficiency. However, using the same parameters across agents limits the representational capacity of the joint policy and consequently, the performance can be degraded in multi-agent tasks that require different behaviors for different agents. In this paper, we propose a simple method that adopts structured pruning for a deep neural network to increase the representational capacity of the joint policy without introducing additional parameters. We evaluate the proposed method on several benchmark tasks, and numerical results show that the proposed method significantly outperforms other parameter-sharing methods.
Improved Space Bounds for Learning with Experts

We give improved tradeoffs between space and regret for the online learning with expert advice problem over $T$ days with $n$ experts. Given a space budget of $n^{\delta}$ for $\delta \in (0,1)$, we provide an algorithm achieving regret $\tilde{O}(n^2 T^{1/(1+\delta)})$, improving upon the regret bound $\tilde{O}(n^2 T^{2/(2+\delta)})$ in the recent work of [PZ23]. The improvement is particularly salient in the regime $\delta \rightarrow 1$ where the regret of our algorithm approaches $\tilde{O}_n(\sqrt{T})$, matching the $T$ dependence in the standard online setting without space restrictions.