Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of spironolactone and benazepril on furosemide‐induced diuresis and renin‐angiotensin‐aldosterone system activation in normal dogs

Abstract Background Diuretic braking during furosemide continuous rate infusion (FCRI) curtails urine production. Hypothesis Renin‐angiotensin‐aldosterone system (RAAS) activation mediates braking, and RAAS inhibition will increase urine production. Animals Ten healthy purpose‐bred male dogs. Methods Dogs received placebo, benazepril, or benazepril and spironolactone PO for 3 days before a 5‐hour FCRI (0.66 mg/kg/h) in a 3‐way, randomized, blinded, cross‐over design. Body weight (BW), serum creatinine concentration (sCr), serum electrolyte concentrations, PCV, and total protein concentration were measured before PO medications, at hours 0 and 5 of FCRI, and at hour 24. During the FCRI, water intake, urine output, urine creatinine concentration, and urine electrolyte concentrations were measured hourly. Selected RAAS components were measured before and after FCRI. Variables were compared among time points and treatments. Results Diuretic braking and urine production were not different among treatments. Loss of BW, hemoconcentration, and decreased serum chloride concentration occurred during FCRI with incomplete recovery at hour 24 for all treatments. Although unchanged during FCRI, sCr increased and serum sodium concentration decreased at hour 24 for all treatments. Plasma aldosterone and angiotensin‐II concentrations increased significantly at hour 5 for all treatments, despite suppressed angiotensin‐converting enzyme activity during benazepril background treatment. Conclusions The neurohormonal profile during FCRI supports RAAS mediation of diuretic braking in this model. Background treatment with benazepril with or without spironolactone did not mitigate braking, but was well tolerated. Delayed changes in sCr and serum sodium concentration and incomplete recovery of hydration indicators caused by furosemide hold implications for clinical patients.
Floating Tunable Coupler for Scalable Quantum Computing Architectures

We propose a floating tunable coupler that does not rely on direct qubit-qubit coupling capacitances to achieve the zero-coupling condition. We show that the polarity of the qubit-coupler couplings can be engineered to offset the otherwise constant qubit-qubit coupling and attain the zero-coupling condition when the coupler frequency is above or below the qubit frequencies. We experimentally demonstrate these two operating regimes of the tunable coupler by implementing symmetric and asymmetric configurations of the superconducting pads of the coupler with respect to the qubits. Such a floating tunable coupler provides flexibility in designing large scale quantum processors while reducing the always-on residual couplings.
Cooperative Learning of Zero-Shot Machine Reading Comprehension

Pretrained language models have significantly improved the performance of down-stream language understanding tasks, including extractive question answering, by providing high-quality contextualized word embeddings. However, learning question answering models still need large-scaled data annotation in specific domains. In this work, we propose a cooperative, self-play learning framework, REGEX, for question generation and answering. REGEX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor. Given a passage with a masked entity, the generator generates a question around the entity, and the extractor is trained to extract the masked entity with the generated question and raw texts. The framework allows the training of question generation and answering models on any text corpora without annotation. We further leverage a reinforcement learning technique to reward generating high-quality questions and to improve the answer extraction model's performance. Experiment results show that REGEX outperforms the state-of-the-art (SOTA) pretrained language models and zero-shot approaches on standard question-answering benchmarks, and yields the new SOTA performance under the zero-shot setting.
Dynamics of small heavy particles in homogeneous turbulence: a Lagrangian experimental study

Abstract We investigate the behaviour of microscopic heavy particles settling in homogeneous air turbulence. The regimes are relevant to the airborne transport of dust and droplets: the Taylor-microscale Reynolds number is $Re_\lambda = 289\text {--}462$, the Kolmogorov-scale Stokes number is $St = 1.2\text {--}13$ and the Kolmogorov acceleration is comparable to the gravitational acceleration (i.e. the Froude number $Fr = O(1)$). We use high-speed laser imaging to track the particles and simultaneously characterize the air velocity field, resolving all relevant spatio-temporal scales. The role of the flow sampled by the particles is spotlighted. In the present range of parameters, the particle settling velocity is enhanced proportionally to the velocity scale of the turbulence. Both gravity and inertia reduce the velocity fluctuations of the particles compared to the fluid; while they have competing effect on the particle acceleration, through the crossing trajectories and inertial filtering mechanisms, respectively. The preferential sampling of high-strain/low-vorticity regions is measurable, but its impact on the global statistics is moderate. The inertial particles have large relative velocity at small separations, which increases their pair dispersion; however, gravity offsets this effect by causing them to experience fluid velocities that decorrelate faster in time compared to tracers. Based on the observations, we derive an analytical model to predict the particle velocity and acceleration variances for arbitrary $St$, $Fr$ and $Re_\lambda$. This agrees well with the present observations and previous simulations and captures the respective effects of inertia and gravity, both of which play crucial roles in the transport.
Improving Translation Robustness with Visual Cues and Error Correction

Neural Machine Translation models are brittle to input noise. Current robustness techniques mostly adapt models to existing noisy texts, but these models generally fail when faced with unseen noise and their performance degrades on clean texts. In this paper, we introduce the idea of visual context to improve translation robustness against noisy texts. In addition, we propose a novel error correction training regime by treating error correction as an auxiliary task to further improve robustness. Experiments on English-French and English-German translation show that both multimodality and error correction training are beneﬁcial for model robustness to known and new types of errors, while keeping the quality on clean texts.
Contribution of XP-Endo files to the root canal filling removal: A systematic review and meta-analysis of in vitro studies.

This review was designed to analyse whether the supplementary use of XP-Endo Finisher and XP-Endo Finisher R contributes to the removal of root canal filling materials. Databases were searched up to November 2020. Studies were critically assessed according to inclusion and exclusion criteria. Risk of bias evaluation was performed. Pooled standardised mean differences (SMD) and 95% confidence intervals (95% CI) were calculated. Ten studies involving 192 teeth were eligible for systematic review and meta-analysis could be performed with eight of these studies. Strong evidence showed that XP-Endo Finisher R file contributed significantly to filling material removal from root canals (SMD (95% CI): 0.41 (0.06-0.76)). Although the evidence was conflicting, XP-Endo Finisher R file was superior than other irrigant activation techniques (SMD (95% CI): 0.47 (0.02-0.93)). The supplementary use of XP-Endo Finisher and XP-Endo Finisher R is beneficial in further removal of root canal filling materials from root canal system.
Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal

Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops. Inside the DAM, there are two attentive maps - each of which attends to the heavy and light rainy regions, respectively, to guide the deraining process differently for applicable regions. In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a “heavy-to-light” scheme to remove rain via addressing the unsatisfying deraining regions. Extensive experiments on one public raindrop dataset, one public rain streak and our synthesized joint rain streak and raindrop (JRSRD) dataset have demonstrated that the proposed method not only is capable of removing rain streaks and raindrops simultaneously, but also achieves the state-of-the-art performance on both tasks.
A Lyapunov-Based ISS Small-Gain Theorem for Infinite Networks of Nonlinear Systems

In this article, we show that an infinite network of input-to-state stable (ISS) subsystems, admitting ISS Lyapunov functions, itself admits an ISS Lyapunov function, provided that the couplings between the subsystems are sufficiently weak. The strength of the couplings is described in terms of the properties of an infinite-dimensional nonlinear positive operator, built from the interconnection gains. If this operator induces a uniformly globally asymptotically stable (UGAS) system, a Lyapunov function for the infinite network can be constructed. We analyze necessary and sufficient conditions for UGAS and relate them to small-gain conditions used in the stability analysis of finite networks.
N\'eron models of Jacobians over bases of arbitrary dimension

We work with a smooth relative curve $X_U/U$ with nodal reduction over an excellent and locally factorial scheme $S$. We show that blowing up a nodal model of $X_U$ in the ideal sheaf of a section yields a new nodal model, and describe how these models relate to each other. We construct a N\'eron model for the Jacobian of $X_U$, and describe it locally on $S$ as a quotient of the Picard space of a well-chosen nodal model. We provide a combinatorial criterion for the N\'eron model to be separated.
Continuously Managing NFRs: Opportunities and Challenges in Practice

Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. However, research has shown NFRs are, in practice, poorly defined and difficult to verify. Continuous software engineering practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to handling NFRs. In this multi-case study we empirically investigated how three organizations, for which NFRs are paramount to their business survival, manage NFRs in their continuous practices. We describe four practices these companies use to manage NFRs, such as offloading NFRs to cloud providers or the use of metrics and continuous monitoring, both of which enable almost real-time feedback on managing the NFRs. However, managing NFRs comes at a cost—as we also identified a number of challenges these organizations face while managing NFRs in their continuous software engineering practices. For example, the organizations in our study were able to realize an NFR by strategically and heavily investing in configuration management and infrastructure as code, in order to offload the responsibility of NFRs; however, this offloading implied potential loss of control. Our discussion and key research implications show the opportunities, trade-offs, and importance of the unique give-and-take relationship between continuous software engineering and NFRs. Research artifacts may be found at https://doi.org/10.5281/zenodo.3376342.
Asymptotic Theory of $\ell_1$-Regularized PDE Identification from a Single Noisy Trajectory

We prove the support recovery for a general class of linear and nonlinear evolutionary partial differential equation (PDE) identification from a single noisy trajectory using $\ell_1$ regularized Pseudo-Least Squares model~($\ell_1$-PsLS). In any associative $\mathbb{R}$-algebra generated by finitely many differentiation operators that contain the unknown PDE operator, applying $\ell_1$-PsLS to a given data set yields a family of candidate models with coefficients $\mathbf{c}(\lambda)$ parameterized by the regularization weight $\lambda\geq 0$. The trace of $\{\mathbf{c}(\lambda)\}_{\lambda\geq 0}$ suffers from high variance due to data noises and finite difference approximation errors. We provide a set of sufficient conditions which guarantee that, from a single trajectory data denoised by a Local-Polynomial filter, the support of $\mathbf{c}(\lambda)$ asymptotically converges to the true signed-support associated with the underlying PDE for sufficiently many data and a certain range of $\lambda$. We also show various numerical experiments to validate our theory.
An Empirical Study on the Usage of BERT Models for Code Completion

Code completion is one of the main features of modern Integrated Development Environments (IDEs). Its objective is to speed up code writing by predicting the next code token(s) the developer is likely to write. Research in this area has substantially bolstered the predictive performance of these techniques. However, the support to developers is still limited to the prediction of the next few tokens to type. In this work, we take a step further in this direction by presenting a large-scale empirical study aimed at exploring the capabilities of state-of-the-art deep learning (DL) models in supporting code completion at different granularity levels, including single tokens, one or multiple entire statements, up to entire code blocks (e.g., the iterated block of a for loop). To this aim, we train and test several adapted variants of the recently proposed RoBERTa model, and evaluate its predictions from several perspectives, including: (i) metrics usually adopted when assessing DL generative models (i.e., BLEU score and Levenshtein distance); (ii) the percentage of perfect predictions (i.e., the predicted code snippets that match those written by developers); and (iii) the "semantic" equivalence of the generated code as compared to the one written by developers. The achieved results show that BERT models represent a viable solution for code completion, with perfect predictions ranging from ∼7%, obtained when asking the model to guess entire blocks, up to ∼58%, reached in the simpler scenario of few tokens masked from the same code statement.
Price and Fulfillment Strategies in Omnichannel Retailing

Omnichannel retailing, a new form of distribution system, seamlessly integrates the Internet and physical stores. This study considers the pricing and fulfillment strategies of a retailer that has two sales channels: online and one physical store. The retailer offers consumers three purchasing options: delivery from the fulfillment center, buy online and pick up in-store (BOPS), and purchasing at the store. Consumers choose one of these options to maximize their utility, dividing them into several segments. Given the retailer can induce consumers to the profitable segment by adjusting the online and store prices, our analysis shows that it has three optimal strategies: (1) The retailer excludes consumers far from the physical store from the market and lets the others choose BOPS or purchasing at the store. (2) It lets consumers far from the physical store choose delivery from the fulfillment center and the others choose BOPS or purchasing at the store. (3) It lets all consumers choose delivery from the fulfillment center. Finally, we present simple dynamic simulations that considers how the retailer’s optimal strategy changes as consumers’ subjective probability of believing the product is in stock decreases. The results show that the retailer should offer BOPS in later periods of the selling season to maximize its profit as the subjective probability decreases.
Turbulent particle transport driven by ion and electron modes

The topic of the present work is the turbulent transport of main ions and impurities driven by ion modes (ITG) and electron modes (TE and electron scale ETG). Particle transport in regions relevant to the pedestal region of H-mode plasmas, i.e. with steep density gradients, are of particular interest. Using the code GENE, quasi- and nonlinear gyrokinetic simulations are performed, and the results are compared with a computationally efficient fluid model. The transport properties are quantified by locally finding density gradients (R/Ln ) yielding zero particle flux, which are directly related to the balance of convective and diffusive transport. This measure of the impurity peaking is calculated for ITG and TE mode turbulence in source-free regions of the plasma, and scalings are obtained for the driving gradients and the impurity charge number (Z). Further, the quality of the helium ash removal is studied for ITG and TE mode drive turbulence. For ETG modes, the focus is on the main ion transport, and the density gradient leading to zero main ion particle flux, related to the formation and sustainment of the edge pedestal, is estimated.
Exploratory study on the combustion and PAH emissions of selected municipal waste plastics

Polynuclear aromatic hydrocarbon (PAH) emissions from the combustion of selected synthetic polymers (plastics), commonly found in municipal solid waste streams, were monitorsd using bench-scale furnaces. Experiments were conducted burning aerosol of poly(styrene), poly(ethylsns), poly(propylene), poly(methyl methacrylate), and poly(vinyl chloride) particles, in size cuts in the range of 150-300 μm. Most experiments involved dilute dispersions of polymer particles free-falling in a drop-tube laminarflow furnace, at gas temperatures of 750-1150 o C and residence times of 0.5-2 s in air. Upon melting and pyrolysis, the polymer particles burned with luminous envelope flames, whose temperature and duration were measured pyrometrically in the near infrared
TACC3 is a prognostic biomarker for kidney renal clear cell carcinoma and correlates with immune cell infiltration and T cell exhaustion

Growing evidence has demonstrated that transforming acidic coiled-coil protein 3 (TACC3), a member of the TACC family, may be involved in regulating cell mitosis, transcription, and tumorigenesis. However, the role of TACC3 in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, multiple databases were used to determine the pattern of TACC3 in KIRC. We found that high TACC3 expression was associated with poor overall survival (OS) in stage I, II, IV and grade 3 KIRC patients. Univariate and multivariate Cox regression analyses showed that TACC3 was an independent risk factor for OS among KIRC patients. Moreover, TACC3 expression correlated with immune cell infiltration levels of B cells, T cells (CD8+, CD4+, follicular helper, regulatory and gamma delta), total and resting natural killer cells, total and activated dendritic cells, and resting mast cells. Furthermore, T cell exhaustion markers, such as PD1, CTLA4, LAG3 and TIM-3 were highly expressed in TACC3 overexpressing tissues. In addition, GSEA analysis revealed that the role of TACC3 in KIRC may be closely linked to immune-associated pathways. Therefore, our study reveals that TACC3 is a prognostic biomarker for OS among KIRC patients and may be associated with immune cell infiltration and T cell exhaustion.
Characterization of the gut‐liver‐muscle axis in cirrhotic patients with sarcopenia

Sarcopenia is frequent in cirrhosis and is associated with unfavourable outcomes. The role of the gut‐liver‐muscle axis in this setting has been poorly investigated. The aim of this study was to identify gut microbiota, metabolic and inflammatory signatures associated with sarcopenia in cirrhotic patients.
Hybridization, ecogeographical displacement and the emergence of new lineages – A genotyping‐by‐sequencing and ecological niche and species distribution modelling study of Sempervivum tectorum L. (Houseleek)

Ecogeographical displacement of homoploid hybrid lineages from their parents is well documented and considered an important mechanism to achieve reproductive isolation. In this study, we investigated the origin of the flowering plant species Sempervivum tectorum in the Massif Central (France) through homoploid hybridization between lineages of the species from the Rhine Gorge area (Germany) and the Pyrenees (France). We used genotyping‐by‐sequencing genetic data as evidence for the hybrid origin of the Massif Central lineage, and WorldClim climatic data and soil pH and soil temperature data collected by us for ecological niche and species distribution modelling. We could show that the Massif Central lineage shows hybrid admixture and that the niche of this lineage is significantly different from those of the parental lineages. In comparison with the parental niches, different variables of the niche of the hybrid lineage are intermediate, parental‐combined or extreme. The different niche of the Massif Central populations thus can plausibly be interpreted as hybridization‐derived. Our species distribution modelling for the Last Glacial Maximum and Mid‐Holocene showed that the potential distribution of the hybrid lineage at the likely time of its origin in the Quaternary possibly was parapatric in relation to the largely sympatric distributions of the parental lineages. We hypothesize that reproductive isolation of the hybrid lineage from the parental lineages resulted from the segregation of distribution ranges by a differential response of the three lineages to a warming climate.
Minimax optimal conditional density estimation under total variation smoothness

This paper studies the minimax rate of nonparametric conditional density estimation under a weighted absolute value loss function in a multivariate setting. We first demonstrate that conditional density estimation is impossible if one only requires that $p_{X|Z}$ is smooth in $x$ for all values of $z$. This motivates us to consider a sub-class of absolutely continuous distributions, restricting the conditional density $p_{X|Z}(x|z)$ to not only be H\"older smooth in $x$, but also be total variation smooth in $z$. We propose a corresponding kernel-based estimator and prove that it achieves the minimax rate. We give some simple examples of densities satisfying our assumptions which imply that our results are not vacuous. Finally, we propose an estimator which achieves the minimax optimal rate adaptively, i.e., without the need to know the smoothness parameter values in advance. Crucially, both of our estimators (the adaptive and non-adaptive ones) impose no assumptions on the marginal density $p_Z$, and are not obtained as a ratio between two kernel smoothing estimators which may sound like a go to approach in this problem.
Electricity intraday price modeling with marked Hawkes processes

We consider a 2-dimensional marked Hawkes process with increasing baseline intensity in order to model prices on electricity intraday markets. This model allows to represent different empirical facts such as increasing market activity, random jump sizes but above all microstructure noise through the signature plot. This last feature is of particular importance for practitioners and has not yet been modeled on those particular markets. We provide analytic formulas for first and second moments and for the signature plot, extending the classic results of Bacry et al. (2013) in the context of Hawkes processes with random jump sizes and time dependent baseline intensity. The tractable model we propose is estimated on German data and seems to fit the data well. We also provide a result about the convergence of the price process to a Brownian motion with increasing volatility at macroscopic scales, highlighting the Samuelson effect.