Miracle-dz/UniFAR-FTUs huggingface dataset:
Nebulized Antibiotics for Ventilator-associated Pneumonia: Next Steps After the Meta-analyses

Ventilator-associated pneumonia (VAP) represents a major clinical challenge for all physicians caring for critically ill patients. Important concerns regarding the optimum treatment regimen for the management of pulmonary infections—such as the choice of antibiotic, the dosing, the duration of therapy, and drug delivery to the infected tissue—play an even more prominent role in the management of VAP. Patients with VAP are usually severely ill and have already been exposed to prolonged periods of intensive care; therefore, the safety margin for error is narrowed. Nebulized antibiotics represent a promising way to deliver high doses to the lung tissue with fewer concerns regarding systemic toxicity. In the context of multidrug-resistant VAP, where therapeutic options may be reduced to more toxic drugs, nebulized antibiotics may be a reasonable and logical choice. While most studies focused on the adjunctive role (ie, complementary to intravenous infusion) of nebulized antibiotics, some reports have suggested that nebulization may be effective even as a single therapy. Nevertheless, important differences in nebulization techniques contribute to the difficulty in obtaining robust data regarding the efficacy of nebulized therapy. Two recent meta-analyses have evaluated the role of nebulized antibiotics for VAP, one of them focusing specifically on nebulized polymyxin and the other assessing the role of nebulization therapy regardless of the drug used. Although conceptually different, both studies concluded that nebulized therapy could indeed play a significant role in the management of VAP. In this review, we reassess the evidence for nebulized antibiotics and provide guidance for future studies in this field to fill the knowledge gaps.
Video Compression Coding via Colorization: A Generative Adversarial Network (GAN)-Based Approach

Under the limited storage, computing and network bandwidth resources, the video compression coding technology plays an important role for visual communication. To efficiently compress raw video data, a colorization-based video compression coding method is proposed in this paper. In the proposed encoder, only the video luminance components are encoded and transmitted. To restore the video chrominance information, a generative adversarial network (GAN) model is adopted in the proposed decoder. In order to make the GAN work efficiently for video colorization, the generator of the proposed GAN model adopts an optimized MultiResUNet, an attention module, and a mixed loss function. Experimental results show that when compared with the H.265/HEVC video compression coding standard using all-intra coding structure, the proposed video compression coding method achieves an average of 72.05% BDBR reduction, and an average of 4.758 dB BDPSNR increase. Moreover, to our knowledge, this is the first work which compresses videos by using GAN-based colorization, and it provides a new way for addressing the video compression coding problems.
Measuring the Hubble function with standard candle clustering

Supernova Ia magnitude surveys measure the dimensionless luminosity distance $H_{0}D_{L}$. However, from the distances alone one cannot obtain quantities like $H(z)$ or the dark energy equation of state, unless further cosmological assumptions are imposed. Here we show that by measuring the power spectrum of density contrast and of peculiar velocities of supernovae one can estimate also $H(z)/H_{0}$ regardless of background or linearly perturbed cosmology and of galaxy-matter bias. This method, dubbed Clustering of Standard Candles (CSC) also yields the redshift distortion parameter $\beta(k,z)$ and the biased matter power spectrum in a model-independent way. We forecast that LSST may be able to constrain $H(z)/H_{0}$ to 5-13% in redshift bins of $\Delta z=0.1$ up to $z=0.8$.
Sharing nonlocality and nontrivial preparation contextuality using the same family of Bell expressions

In [Phys. Rev. Lett. 114, 250401 (2015)] the sharing of non-locality by multiple observers was demonstrated through the quantum violation of Clauser-Horne-Shimony-Halt inequality. In this paper we provide a scheme for sharing of non-locality and non-trivial preparation contextuality sequentially through the quantum violation of a family of Bell's inequalities where Alice and Bob perform $2^{n-1}$ and $n$ numbers of measurements of dichotomic observables respectively. For this, we consider that Alice always performs projective measurement and multiple Bobs sequentially perform unsharp measurement. We show that when Bob's choices of measurement settings are unbiased, maximum two Bobs can sequentially share the non-locality through the violation of our inequalities. Further, we show that the local bound of the aforementioned family of inequalities gets reduced if non-trivial preparation non-contextuality assumptions are further imposed. Then there is a chance to share the non-trivial preparation contextuality for more number of Bobs than that of non-locality. We demonstrate that the non-trivial preparation contextuality can be sequentially shared by arbitrary numbers of Bob for unbiased choices of his measurement settings.
Layerwise Noise Maximisation to Train Low-Energy Deep Neural Networks

Deep neural networks (DNNs) depend on the storage of a large number of parameters, which consumes an important portion of the energy used during inference. This paper considers the case where the energy usage of memory elements can be reduced at the cost of reduced reliability. A training algorithm is proposed to optimize the reliability of the storage separately for each layer of the network, while incurring a negligible complexity overhead compared to a conventional stochastic gradient descent training. For an exponential energy-reliability model, the proposed training approach can decrease the memory energy consumption of a DNN with binary parameters by 3.3×at isoaccuracy, compared to a reliable implementation.
Improved central limit theorem and bootstrap approximations in high dimensions

This paper deals with the Gaussian and bootstrap approximations to the distribution of the max statistic in high dimensions. This statistic takes the form of the maximum over components of the sum of independent random vectors and its distribution plays a key role in many high-dimensional econometric problems. Using a novel iterative randomized Lindeberg method, the paper derives new bounds for the distributional approximation errors. These new bounds substantially improve upon existing ones and simultaneously allow for a larger class of bootstrap methods.
A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D Images

ABSTRACT Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high-level and hierarchical image features; several deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorized the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analyzed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localization and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved.
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach

Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by health organizations worldwide and has been estimated to decrease breast cancer mortality by 20-40%. Nevertheless, significant false positive and false negative rates, as well as high interpretation costs, leave opportunities for improving quality and access. To address these limitations, there has been much recent interest in applying deep learning to mammography; however, obtaining large amounts of annotated data poses a challenge for training deep learning models for this purpose, as does ensuring generalization beyond the populations represented in the training dataset. Here, we present an annotation-efficient deep learning approach that 1) achieves state-of-the-art performance in mammogram classification, 2) successfully extends to digital breast tomosynthesis (DBT; "3D mammography"), 3) detects cancers in clinically-negative prior mammograms of cancer patients, 4) generalizes well to a population with low screening rates, and 5) outperforms five-out-of-five full-time breast imaging specialists by improving absolute sensitivity by an average of 14%. Our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.
Molecular gas in distant brightest cluster galaxies

The mechanisms governing the stellar mass assembly and star formation history of brightest cluster galaxies (BCGs) are still being debated. By means of new and archival molecular gas observations we investigate the role of dense megaparsec-scale environments in regulating the fueling of star formation in distant BCGs, through cosmic time. We observed in CO with the IRAM 30 m telescope two star-forming BCGs belonging to SpARCS clusters, namely, 3C 244.1 (z = 0.4) and SDSS J161112.65+550823.5 (z = 0.9), and compared their molecular gas and star formation properties with those of a compilation of ∼100 distant cluster galaxies from the literature, including nine additional distant BCGs at z ∼ 0.4 − 3.5. We set robust upper limits of MH2 < 1.0 × 1010 M⊙ and < 2.8 × 1010 M⊙ to their molecular gas content, respectively, and to the ratio of molecular gas to stellar mass M(H2)/M⋆ ≲ 0.2 and depletion time τdep ≲ 40 Myr of the two targeted BCGs. They are thus among the distant cluster galaxies with the lowest gas fractions and shortest depletion times. The majority (64%±15% and 73%±18%) of the 11 BCGs with observations in CO have lower M(H2)/M⋆ values and τdep, respectively, than those estimated for main sequence galaxies. Statistical analysis also tentatively suggests that the values of M(H2)/M⋆ and τdep for the 11 BCGs deviates, with a significance of ∼2σ, from those of the comparison sample of cluster galaxies. A morphological analysis for a subsample of seven BCGs with archival HST observations reveals that 71%±17% of the BCGs are compact or show star-forming components or substructures. Our results suggest a scenario where distant star-forming BCGs assemble a significant fraction ∼16% of their stellar mass on the relatively short timescale ∼τdep, while environmental mechanisms might prevent the replenishment of gas feeding the star formation. We speculate that compact components also favor the rapid exhaustion of molecular gas and ultimately help to quench the BCGs. Distant star-forming BCGs are excellent targets for ALMA and for next-generation telescopes such as the James Webb Space Telescope.
Power Control for Massive MIMO Systems with Nonorthogonal Pilots

This letter shows that optimizing the transmit powers along with judiciously designed nonorthogonal pilots can significantly reduce pilot contamination and improve the overall throughput of the uplink multi-cell massive multiple-input multiple-output (MIMO) system as compared to the conventional schemes that use orthogonal pilots. Given the optimized nonorthogonal pilots, power control as a function of the large-scale path-loss can be thought of as a stochastic optimization problem due to the presence of fast fading. This letter advocates a deterministic approach to solving this problem, then further proposes a stochastic optimization method that utilizes successive convex approximation as a benchmark to quantify the performance of the proposed approach. Simulation results reveal significant advantage of using optimized nonorthogonal pilots together with power control to combat pilot contamination.
The Black-Scholes-Merton Dual Equation

We derive the Black-Scholes-Merton dual equation, which has exactly the same form as the Black-Scholes-Merton equation. The new equation is general and works for European, American, Bermudan, Asian, barrier, lookback, etc. options and leads to new insights into pricing and hedging. Trivially, a put-call equality emerges - all the above-mentioned put (call) options can be priced as their corresponding calls (puts) by simply swapping stock price (dividend yield) for strike price (risk-free rate) simultaneously. More importantly, deltas (gammas) of such puts and calls are linked via analytic formulas. As one application in hedging, the dual equation is utilized to improve the accuracy of the recently proposed approach of hedging options statically with short-maturity contracts.
Learning for Safety-Critical Control with Control Barrier Functions

Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model uncertainty remains a significant challenge in synthesizing safe controllers, leading to degradation in the properties provided by the controllers. This paper develops a machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system. This approach iteratively collects data and updates a controller, ultimately achieving safe behavior. We validate this method in simulation and experimentally on a Segway platform.
ANTI-INFLAMMATORY AND ANTIOXIDANT ACTIVITIES OF CASSIA FISTULA LINN BARK EXTRACTS

Anti-inflammatory and Antioxidant activities of the aqueous (CFA) and methanolic extracts (CFM) of the Cassia fistula Linn. bark were assayed in wistar albino rats. The extracts were found to posses significant anti-inflammatory effect in both acute and chronic models. Cassia fistula bark extracts showed significant radical scavenging by inhibiting lipid peroxidation initiated by CCl4 and FeSO4 in rat liver and kidney homogenates. Both extracts exhibited significant antioxidant activity in DPPH, Nitric oxide and Hydroxyl radical induced invitro assay methods. Both extracts showed Dose-dependent protective effect against lipid peroxidation and free radical generation in liver and kidney homogenates. Further, the acute toxicity study with the extracts showed no sign of toxicity up to a dose level of 2000 mg/ po. Thus it could be concluded that Cassia fistula bark extracts (CFA & CFM) possess significant anti-inflammatory and anti oxidant properties.
Deep Learning via Dynamical Systems: An Approximation Perspective

We build on the dynamical systems approach to deep learning, where deep residual networks are idealized as continuous-time dynamical systems, from the approximation perspective. In particular, we establish general sufficient conditions for universal approximation using continuous-time deep residual networks, which can also be understood as approximation theories in $L^p$ using flow maps of dynamical systems. In specific cases, rates of approximation in terms of the time horizon are also established. Overall, these results reveal that composition function approximation through flow maps present a new paradigm in approximation theory and contributes to building a useful mathematical framework to investigate deep learning.
Teaching robots to perceive time - A reinforcement learning approach (Extended version)

Time perception is the phenomenological experience of time by an individual. In this paper, we study how to replicate neural mechanisms involved in time perception, allowing robots to take a step towards temporal cognition. Our framework follows a twofold biologically inspired approach. The first step consists of estimating the passage of time from sensor measurements, since environmental stimuli influence the perception of time. Sensor data is modeled as Gaussian processes that represent the second-order statistics of the natural environment. The estimated elapsed time between two events is computed from the maximum likelihood estimate of the joint distribution of the data collected between them. Moreover, exactly how time is encoded in the brain remains unknown, but there is strong evidence of the involvement of dopaminergic neurons in timing mechanisms. Since their phasic activity has a similar behavior to the reward prediction error of temporal-difference learning models, the latter are used to replicate this behavior. The second step of this approach consists therefore of applying the agent's estimate of the elapsed time in a reinforcement learning problem, where a feature representation called Microstimuli is used. We validate our framework by applying it to an experiment that was originally conducted with mice, and conclude that a robot using this framework is able to reproduce the timing mechanisms of the animal's brain.
On the implosion of a three dimensional compressible fluid

We consider the compressible three dimensional Navier Stokes and Euler equations. In a suitable regime of barotropic laws, we construct a set of finite energy smooth initial data for which the corresponding solutions to both equations implode (with infinite density) at a later time at a point, and completely describe the associated formation of singularity. Two essential steps of the analysis are the existence of $\mathcal C^\infty$ smooth self-similar solutions to the compressible Euler equations for quantized values of the speed and the derivation of spectral gap estimates for the associated linearized flow which are addressed in the companion papers \cite{MRRSprofile, MRRSdefoc}. All blow up dynamics obtained for the Navier-Stokes problem are of type II (non self-similar).
PR ] 2 2 D ec 2 01 9 Random Čech complexes on R d : decrackling the noise with local scalings

We investigate the homology of an unbounded noisy sample on Rd, under various assumptions on the sampling density. This investigation is based on previous results by Adler, Bobrowski, and Weinberger ([1]), and Owada and Adler ([19]). There, it was found that unbounded noise generally introduces non-vanishing homology, a phenomenon called topological crackle, unless the density has superexponential decay on Rd. We show how some well-chosen non-trivial variable bandwidth constructions can extend the class of densities where crackle doesn’t occur to any light tail density with mild assumptions, what we call decrackling the noise.
Efficacy and Safety of Sodium Tanshinone IIA Sulfonate Injection on Hypertensive Nephropathy: A Systematic Review and Meta-Analysis

Background: Sodium tanshinone IIA sulfonate (STS) injection, the extractive of traditional Chinese medicine Danshen, is supposed to be a supplementary treatment in hypertensive nephropathy. Objectives: To evaluate the efficacy and safety of STS in treatment of hypertensive nephropathy. Methods: We systematically searched China National Knowledge Infrastructure (CNKI), Chinese Scientific Journals Database (VIP), Wan-fang database, Chinese Biomedicine Database (CBM), PubMed, Embase, Web of Science, and Cochrane Library from their inception to December 2018. All studies were screened by two reviewers according to the inclusion and exclusion criteria independently. The Cochrane Collaboration's risk tool was used to assess the methodological quality of the included studies. Reviewer Manager 5.3 was employed for statistical analysis. Results: Sixteen trials involving 1,696 patients were included. The meta-analysis results indicated a combination of STS and angiotensin receptor blockers (ARBs) was more effective than ARB monotherapy in modulating hypertensive nephropathy, as represented by improved estimated glomerular filtration rate (eGFR) [mean difference (MD) = 6.87, 95% CI (4.47, 9.28), P < 0.00001] and reduced 24 h urinary protein [MD = −0.23, 95% CI (−0.27, −0.19), P < 0.00001], serum creatinine (SCr) [MD = −21.74, 95% CI (−24.11, −19.38), P < 0.00001], cystatin-C [MD = −0.16, 95% CI (−0.24, −0.07), P = 0.0003], urinary immunoglobulin G (IgG) [MD = −0.85, 95% CI (−1.11, −0.59), P < 0.00001], and urinary transferrin [MD = −0.61, 95% CI (−1.04, −0.17), P = 0.007]. In addition, the combination therapy had better control in systolic blood pressure (SBP) [MD = −6.53, 95% CI (−8.19, −4.87), P < 0.00001] and diastolic blood pressure (DBP) [MD = −4.14, 95% CI (−5.69, −2.59), P < 0.00001]. Only three trials reported adverse events, and no adverse drug reactions were observed. Conclusions: STS combined with ARBs had a stronger effect on improving renal function in patients with primary hypertensive nephropathy than ARB monotherapy. The combination therapy also provided auxiliary hypotensive effects. Further large-scale, multicenter, and rigorously designed randomized controlled trials (RCTs) should be conducted to confirm our findings.
Risk factors for postsurgical diabetic foot recurrence after minor or major lower-limb's amputation

Background: Many previous studies on diabetic foot focus on ulceration and amputation, but few studies took on recurrent ulcer or wound infection with several years' follow-up after amputations and studying risk factors such as peripheral arterial disease (PAD), renal disease, cardiovascular disease, and environmental factors which may play a role and explain for that disease recurrence. Aim: The aim of this study was to assess the risk of recurrence of diabetic foot disease after different level of amputations. Patients and Methods: A prospective study was conducted for 32 patients presented with diabetic foot disease. This study started from September 2011 up to December 2016. Those patients who admitted for (minor or major) amputation with risk factor diseases were studied and followed up for any recurrence. Results: Among 32 patients, 17 cases did minor amputation (53.1%), whereas 15 cases (46.9%) did major amputations. The number of patients who developed recurrence was 11 cases distributed, as 8 cases (25%) developed wound infection and other 3 cases (9.4%) developed ulceration. Seven cases of those who developed wound infection (87.5%) after amputation had PAD. All patients who complained recurrent ulceration (100%) had PAD risk factor with P = 0.0001*. Two cases (25%) of those with wound infection and also one case with ulceration had renal disease with P = 0.039*. Conclusion: PAD, renal disease, and renal failure have significant risk factors for recurrence of wound infection and ulceration after diabetic foot–limb amputation.