Miracle-dz/UniFAR-FTUs huggingface dataset:
A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs

The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.
Distribution Regression for Continuous-Time Processes via the Expected Signature

We introduce a learning framework to infer macroscopic properties of an evolving system from longitudinal trajectories of its components. By considering probability measures on continuous paths we view this problem as a distribution regression task for continuous-time processes and propose two distinct solutions leveraging the recently established properties of the expected signature. Firstly, we embed the measures in a Hilbert space, enabling the application of an existing kernel-based technique. Secondly, we recast the complex task of learning a non-linear regression function on probability measures to a simpler functional linear regression on the signature of a single vector-valued path. We provide theoretical results on the universality of both approaches, and demonstrate empirically their robustness to densely and irregularly sampled multivariate time-series, outperforming existing methods adapted to this task on both synthetic and real-world examples from thermodynamics, mathematical finance and agricultural science.
Adaptive Gradient Coding

This paper focuses on mitigating the impact of stragglers in distributed learning system. Unlike the existing results designated for a fixed number of stragglers, we develop a new scheme called Adaptive Gradient Coding (AGC) with flexible communication cost for varying number of stragglers. Our scheme gives an optimal tradeoff between computation load, straggler tolerance and communication cost by allowing workers to send multiple signals sequentially to the master. In particular, it can minimize the communication cost according to the unknown real-time number of stragglers in practical environments. In addition, we present a Group AGC (G-AGC) by combining the group idea with AGC to resist more stragglers in some situations. The numerical and simulation results demonstrate that our adaptive schemes can achieve the smallest average running time.
Coronal-jet-producing Minifilament Eruptions as a Possible Source of Parker Solar Probe Switchbacks

The Parker Solar Probe (PSP) has observed copious rapid magnetic field direction changes in the near-Sun solar wind. These features have been called “switchbacks,” and their origin is a mystery. But their widespread nature suggests that they may be generated by a frequently occurring process in the Sun’s atmosphere. We examine the possibility that the switchbacks originate from coronal jets. Recent work suggests that many coronal jets result when photospheric magnetic flux cancels, and forms a small-scale “minifilament” flux rope that erupts and reconnects with coronal field. We argue that the reconnected erupting-minifilament flux rope can manifest as an outward propagating Alfvénic fluctuation that steepens into an increasingly compact disturbance as it moves through the solar wind. Using previous observed properties of coronal jets that connect to coronagraph-observed white-light jets (a.k.a. “narrow CMEs”), along with typical solar wind speed values, we expect the coronal-jet-produced disturbances to traverse near-perihelion PSP in ≲25 minutes, with a velocity of ∼400 km s−1. To consider further the plausibility of this idea, we show that a previously studied series of equatorial latitude coronal jets, originating from the periphery of an active region, generate white-light jets in the outer corona (seen in STEREO/COR2 coronagraph images; 2.5–15 R⊙), and into the inner heliosphere (seen in Solar-Terrestrial Relations Observatory (STEREO)/Hi1 heliospheric imager images; 15–84 R⊙). Thus it is tenable that disturbances put onto open coronal magnetic field lines by coronal-jet-producing erupting-minifilament flux ropes can propagate out to PSP space and appear as switchbacks.
Avaliação eletrocardiográfica de equinos após exercício de polo (baixo handicap)

Electrocardiography can be used to quantify the training and athletic performance as well as evaluating the cardiac function under the effect of exercise identifying the influence of cardiac anomalies, and deleterious effects of stress ahead of heart function. Considering the complexity of the physical efforts inherent in polo ponies in competitions, together with the lack of reports in the literature, on the demand resulting for heart. The aim of this study was to identify and evaluate the cardiac effects of electrocardiographic exercise in polo horses in order to support data for understanding the physiological cardiac demand of this sport. We evaluated 27 equine practitioner polo at rest and between five and ten minutes after exercise. The results showed that the observed changes in the duration and P wave amplitude and duration of PR and QT interval were considered in physiological response to increased heart rate. The diphasic P wave found at rest and when atrial hypertrophy represented bifida represented different points of activation of the sinoatrial node. As expected, the QRS complex has not undergone major changes. The ST-segment elevation and T wave changes observed after exercise could pose adverse effects to the myocardium, but studies examining multiple factors are needed to confirm this association and define your actual question. The increase in QTc suggested fatigue mild myocardial represented high heart demand for this type of exercise. The heart score showed that these animals were within the pattern of cardiac adaptation to a horse athlete. The rapid recovery of HR showed good conditioning of these animals. The pacemaker migration was observed in most animals proven to be a normal finding horse athlete. We observed a low incidence of changes in cardiac rhythm.
Understanding Points of Correspondence between Sentences for Abstractive Summarization

Fusing sentences containing disparate content is a remarkable human ability that helps create informative and succinct summaries. Such a simple task for humans has remained challenging for modern abstractive summarizers, substantially restricting their applicability in real-world scenarios. In this paper, we present an investigation into fusing sentences drawn from a document by introducing the notion of points of correspondence, which are cohesive devices that tie any two sentences together into a coherent text. The types of points of correspondence are delineated by text cohesion theory, covering pronominal and nominal referencing, repetition and beyond. We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences. Our dataset bridges the gap between coreference resolution and summarization. It is publicly shared to serve as a basis for future work to measure the success of sentence fusion systems.
Automatic Code Summarization via Multi-dimensional Semantic Fusing in GNN

Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. Recent works attempt to encode programs into graphs for learning program semantics and yield promising results. However, these methods only use simple code representations(e.g., AST), which limits the capability of learning the rich semantics for complex programs. Furthermore, these models primarily rely on graph-based message passing, which only captures local neighborhood relations. To this end, in this paper, we combine diverse representations of the source code (i.e., AST, CFG and PDG)into a joint code property graph. To better learn semantics from the joint graph, we propose a retrieval-augmented mechanism to augment source code semantics with external knowledge. Furthermore, we propose a novel attention-based dynamic graph to capture global interactions among nodes in the static graph and followed a hybrid message passing GNN to incorporate both static and dynamic graph. To evaluate our proposed approach, we release a new challenging benchmark, crawledfrom200+diversified large-scale open-source C/C++projects. Our method achieves the state-of-the-art performance, improving existing methods by1.66,2.38and2. 22in terms of BLEU-4, ROUGE-L and METEOR metrics.
Neural Network Activation Quantization with Bitwise Information Bottlenecks

Recent researches on information bottleneck shed new light on the continuous attempts to open the black box of neural signal encoding. Inspired by the problem of lossy signal compression for wireless communication, this paper presents a Bitwise Information Bottleneck approach for quantizing and encoding neural network activations. Based on the rate-distortion theory, the Bitwise Information Bottleneck attempts to determine the most significant bits in activation representation by assigning and approximating the sparse coefficient associated with each bit. Given the constraint of a limited average code rate, the information bottleneck minimizes the rate-distortion for optimal activation quantization in a flexible layer-by-layer manner. Experiments over ImageNet and other datasets show that, by minimizing the quantization rate-distortion of each layer, the neural network with information bottlenecks achieves the state-of-the-art accuracy with low-precision activation. Meanwhile, by reducing the code rate, the proposed method can improve the memory and computational efficiency by over six times compared with the deep neural network with standard single-precision representation. Codes will be available on GitHub when the paper is accepted \url{this https URL}.
Thermal tolerance of sympatric hymenopteran parasitoid species: does it match seasonal activity?

Climatic changes result in an increased in mean temperature and in a higher incidence of extreme weather events such as heat and cold waves. For ectotherms, such as insect parasitoids, the ability to remain active under extreme climatic conditions is a significant key to fitness. The body size of individuals, and in particular their surface to volume ratio, may play a role in their resistance to thermal conditions. The thermal tolerances are investigated of two closely‐related sympatric parasitoid species [Aphidius avenae Haliday and Aphidius rhopalosiphi De Stefani‐Perez (Hymenoptera: Aphidiinae)] that have a similar ecology but differ in body size and phenologies. The critical thermal limits of individuals are assessed in both sexes of each parasitoid species and the influence of surface–volume ratios on their thermal tolerances. Aphidius avenae is less resistant to low temperatures and more resistant to high temperatures than A. rhopalosiphi. The lower surface to volume ratio of A. avenae individuals may help them to remain active in summer when experiencing heat waves. However, body size is not the sole factor that plays a role in differences of thermal tolerance between species and body size may not be an adaptation to extreme temperatures but rather a by‐product of developmental regulation. Closely‐related sympatric species from the same ecological guild can have different thermal tolerances that may allow them to occur within the same habitat. The present study also highlights the importance of clearly defining how to measure critical thermal limits to determine the thermal tolerance of a species.
Differentiable Meta-Learning in Contextual Bandits

We study a contextual bandit setting where the learning agent has access to sampled bandit instances from an unknown prior distribution $\mathcal{P}$. The goal of the agent is to achieve high reward on average over the instances drawn from $\mathcal{P}$. This setting is of a particular importance because it formalizes the offline optimization of bandit policies, to perform well on average over anticipated bandit instances. The main idea in our work is to optimize differentiable bandit policies by policy gradients. We derive reward gradients that reflect the structure of our problem, and propose contextual policies that are parameterized in a differentiable way and have low regret. Our algorithmic and theoretical contributions are supported by extensive experiments that show the importance of baseline subtraction, learned biases, and the practicality of our approach on a range of classification tasks.
Making Non-Stochastic Control (Almost) as Easy as Stochastic

Recent literature has made much progress in understanding \emph{online LQR}: a modern learning-theoretic take on the classical control problem in which a learner attempts to optimally control an unknown linear dynamical system with fully observed state, perturbed by i.i. d. Gaussian noise. It is now understood that the optimal regret on time horizon $T$ against the optimal control law scales as $\widetilde{\Theta}(\sqrt{T})$. In this paper, we show that the same regret rate (against a suitable benchmark) is attainable even in the considerably more general non-stochastic control model, where the system is driven by \emph{arbitrary adversarial} noise (Agarwal et al. 2019). In other words, \emph{stochasticity confers little benefit in online LQR}. We attain the optimal $\widetilde{\mathcal{O}}(\sqrt{T})$ regret when the dynamics are unknown to the learner, and $\mathrm{poly}(\log T)$ regret when known, provided that the cost functions are strongly convex (as in LQR). Our algorithm is based on a novel variant of online Newton step (Hazan et al. 2007), which adapts to the geometry induced by possibly adversarial disturbances, and our analysis hinges on generic "policy regret" bounds for certain structured losses in the OCO-with-memory framework (Anava et al. 2015). Moreover, our results accomodate the full generality of the non-stochastic control setting: adversarially chosen (possibly non-quadratic) costs, partial state observation, and fully adversarial process and observation noise.
On the linear evolution of disturbances in plane Poiseuille flow

The linear evolution of disturbances due to a ribbon vibrating at frequency $\omega_0$ in plane Poiseuille flow is computed by solving the associated initial boundary value problem in the Fourier-Laplace plane, followed by inversion. A novel algorithm for identifying the temporal modes of the Orr-Sommerfeld equation (OSE) in the complex wavenumber plane, which are required in the inversion, is presented. Unlike in many prior studies, the performance of the Laplace integral first, not only avoids complicated causality arguments and confusion, in locating upstream and downstream modes, that is prevalent in literature but also yields a spatio-temporally uniform solution. It also reveals that the solution consists of a time-periodic part at $\omega_0$ , associated with the relevant spatial mode (the Tollmein-Schlichting wave) and a transient wavepacket, associated mainly with the saddle points of the OSE and is computed by the method of steepest descents, which can also include contributions from the spatial pole. Which of these parts dominates depends on the Reynolds number and {\omega}0. A secondary stability analysis of this dominant part is seen to explain the disturbance growth observed in the seminal experiments of Nishioka, Iida & Ichikawa (J. Fluid Mech., vol.72 , 1975, p.731) and Nishioka, Iida & Kanbayashi (NASA TM-75885, 1981). Threshold amplitudes for instability at a subcritical Reynolds number Re = 5000 are obtained from the time-averaged three dimensional disturbances, by combining the secondary base states and the growing Floquet modes. The observed minima of the threshold amplitude curves in the experiments are explained in terms of the instabilities of these two base states. Computations, for another subcritical (4000) and a supercritical (6000) Reynolds number, are also validated with the experimental data.
Towards an Intrinsic Definition of Robustness for a Classifier

Finding good measures of robustness – i.e. the ability to correctly classify corrupted input signals – of a trained classifier is an important question for sensitive practical applications. In this paper, we point out that averaging the radius of robustness of samples in a validation set is a statistically weak measure. We propose instead to weight the importance of samples depending on their difficulty. We motivate the proposed score by a theoretical case study using logistic regression. We also empirically demonstrate the ability of the proposed score to measure robustness of classifiers with little dependence on the choice of samples in more complex settings, including deep convolutional neural networks and real datasets.
Training with Multi-Layer Embeddings for Model Reduction

Modern recommendation systems rely on real-valued embeddings of categorical features. Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost to model size. We introduce a multi-layer embedding training (MLET) architecture that trains embeddings via a sequence of linear layers to derive superior embedding accuracy vs. model size trade-off. Our approach is fundamentally based on the ability of factorized linear layers to produce superior embeddings to that of a single linear layer. We focus on the analysis and implementation of a two-layer scheme. Harnessing the recent results in dynamics of backpropagation in linear neural networks, we explain the ability to get superior multi-layer embeddings via their tendency to have lower effective rank. We show that substantial advantages are obtained in the regime where the width of the hidden layer is much larger than that of the final embedding (d). Crucially, at conclusion of training, we convert the two-layer solution into a single-layer one: as a result, the inference-time model size scales as d. We prototype the MLET scheme within Facebook's PyTorch-based open-source Deep Learning Recommendation Model. We show that it allows reducing d by 4-8X, with a corresponding improvement in memory footprint, at given model accuracy. The experiments are run on two publicly available click-through-rate prediction benchmarks (Criteo-Kaggle and Avazu). The runtime cost of MLET is 25%, on average.
Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image

In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP problems are formalized by combining reasoning on a symbolic, discrete level (e.g. first-order logic) with continuous motion planning such as nonlinear trajectory optimization. Due to the great combinatorial complexity of possible discrete action sequences, a large number of optimization/motion planning problems have to be solved to find a solution, which limits the scalability of these approaches. To circumvent this combinatorial complexity, we develop a neural network which, based on an initial image of the scene, directly predicts promising discrete action sequences such that ideally only one motion planning problem has to be solved to find a solution to the overall TAMP problem. A key aspect is that our method generalizes to scenes with many and varying number of objects, although being trained on only two objects at a time. This is possible by encoding the objects of the scene in images as input to the neural network, instead of a fixed feature vector. Results show runtime improvements of several magnitudes. Video: this https URL
Atacama Compact Array observations of the pulsar-wind nebula of SNR 0540-69.3

We present observations of the pulsar-wind nebula (PWN) region of SNR 0540-69.3. The observations were made with the Atacama Compact Array (ACA) in Bands 4 and 6. We also add radio observations from the Australia Compact Array at 3 cm. For 1.449–233.50 GHz, we obtain a synchrotron spectrum Fν∝ν−αν⁠, with the spectral index αν = 0.17 ± 0.02. To conclude how this joins the synchrotron spectrum at higher frequencies, we include hitherto unpublished AKARI mid-infrared data, and evaluate published data in the ultraviolet (UV), optical, and infrared (IR). In particular, some broad-band filter data in the optical must be discarded from our analysis due to contamination by spectral line emission. For the UV/IR part of the synchrotron spectrum, we arrive at αν=0.87+0.08−0.10⁠. There is room for 2.5 × 10−3 M⊙ of dust with a temperature of ∼55 K if there are dual breaks in the synchrotron spectrum, one around ∼9 × 1010 Hz and another at ∼2 × 1013 Hz. The spectral index then changes at ∼9 × 1010 Hz from αν = 0.14 ± 0.07 in the radio to αν=0.35−0.07+0.05 in the millimetre-to-far-IR range. The ACA Band 6 data marginally resolve the PWN. In particular, the strong emission ∼1. ′′5 south-west of the pulsar, seen at other wavelengths, and resolved in the 3 cm data with its 0. ″8 spatial resolution, is also strong in the millimetre range. The ACA data clearly reveal the supernova remnant shell ∼20–35 arcsec west of the pulsar, and for the shell we derive αν = 0.64 ± 0.05 for the range 8.6–145 GHz.