Miracle-dz/UniFAR-FTUs huggingface dataset:
Unresolved Atelectasis in Refractory Mycoplasma Pneumoniae Pneumonia: Predictive Factors and the Influence of Corticosteroids and Bronchoscopy Lavage Therapy

Background Unresolved atelectasis occurs in some children with refractory mycoplasma pneumoniae pneumonia (RMPP). The aim was to analyze factors predicting unresolved atelectasis in RMPP and the impact of corticosteroids and bronchoscopy lavage therapy (BLT) on developing atelectasis.Methods We retrospectively analyzed data for 230 pediatric RMPP from January 2013 to June 2017 in Beijing Children’s Hospital. In this study, we diagnosed RMPP when patients' clinical and radiological findings deteriorated after 7 days of macrolide therapy, peripheral blood C-reactive protein (CRP) was higher than 40mg/L, and chest imaging showed consolidation with high density > 1/2 pulmonary lobe. We divided patients into two groups according to the presence/absence of atelectasis on chest imaging after a 6-month follow-up. We calculated the predictive value of fever duration, levels of CRP and lactate dehydrogenase (LDH), and the size of lobe consolidation, regarding atelectasis. Additionally, we compared the starting time and dosage of corticosteroids and the starting time of BLT between the two groups.Results Ninety-five patients developed atelectasis (atelectasis group/group A), and 135 patients did not (non-atelectasis group/group NA). Chest imaging showed > 2/3 pulmonary lobe consolidation in 93.7% of patients in group A and 54.1% of patients in group NA. Multiple logistic regression analysis showed that fever duration, CRP and LDH levels, and lobe consolidation were related to developing atelectasis. Areas under the curve revealed that CRP ≥ 137 mg/L had 82.11% sensitivity and 80.07% specificity, and LDH ≥ 471 IU/L had 62.65% sensitivity and 60.31% specificity to predict atelectasis. Fewer patients receiving corticosteroids and BLT within 10 days after illness onset developed atelectasis.Conclusions Fever duration>10 days, CRP and LDH levels, and lobe consolidation are risk factors for developing atelectasis in RMPP. CRP ≥ 137 mg/L, LDH ≥ 471 IU/L, and >2/3 pulmonary lobe consolidation were significant predictors of atelectasis, which can aid in early recognition. Corticosteroid administration and subsequent BLT within 10 days of the disease onset, and increased corticosteroid dosage may help reduce the incidence of atelectasis in these RMPP patients.
EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System

State-of-the-art extractive question answering models achieve superhuman performances on the SQuAD benchmark. Yet, they are unreasonably heavy and need expensive GPU computing to answer questions in a reasonable time. Thus, they cannot be used for real-world queries on hundreds of thousands of documents in the open-domain question answering paradigm. In this paper, we explore the possibility to transfer the natural language understanding of language models into dense vectors representing questions and answer candidates, in order to make the task of question-answering compatible with a simple nearest neighbor search task. This new model, that we call EfficientQA, takes advantage from the pair of sequences kind of input of BERT-based models to build meaningful dense representations of candidate answers. These latter are extracted from the context in a question-agnostic fashion. Our model achieves state-of-the-art results in Phrase-Indexed Question Answering (PIQA) beating the previous state-of-art by 1.3 points in exact-match and 1.4 points in f1-score. These results show that dense vectors are able to embed very rich semantic representations of sequences, although these ones were built from language models not originally trained for the use-case. Thus, in order to build more resource efficient NLP systems in the future, training language models that are better adapted to build dense representations of phrases is one of the possibilities.
ON A FAMILY OF SELF-AFFINE IFS WHOSE ATTRACTORS HAVE A NON-FRACTAL TOP

Let [Formula: see text] and [Formula: see text]. In this paper, we prove that for the vast majority of such parameters the top of the planar attractor [Formula: see text] of the IFS [Formula: see text] is the graph of a continuous, strictly increasing function. Despite this, for most parameters, [Formula: see text] has a lower box dimension strictly greater than 1, showing that the upper boundary is not representative of the complexity of the fractal. Finally, we prove that if [Formula: see text], then [Formula: see text] has a non-empty interior.
Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the vehicle cabin plays a key role in ensuring a safe and pleasant journey for driver and passenger alike. At the same time, recent advances in the field of artificial intelligence (AI) have enabled a whole range of new applications and assistance systems to solve automated problems in the vehicle cabin. This article presents a thorough survey on existing work that utilizes AI methods for use cases inside the driving cabin, focusing, in particular, on application scenarios related to driving safety and driving comfort. Results from the surveyed works indicate that AI technology has a promising future in tackling in-cabin tasks within the autonomous driving aspect.
Switchgrass Influences on Soil Biogeochemical Processes in the Dryland Region of the Pacific Northwest

Switchgrass and other perennial grasses have been promoted as biomass crops for production of renewable fuels. The objective of this study was to evaluate the effect of biomass removal on soil biogeochemical processes. A 3-year field study consisting of three levels of net primary productivity (NPP; low, medium, and high growing season precipitation) and two biomass crops (winter wheat and switchgrass) was conducted near Pendleton, Oregon. Switchgrass increased soil carbon (C)–nitrogen (N) ratio, but the effect varied with net primary productivity (NPP) and soil depth. In situ soil respiration (carbon dioxide; CO2) rate from switchgrass increased with NPP level but switchgrass had greater cumulative flux than wheat in medium and low NPP. Nitrogen mineralization and microbial biomass carbon were significantly greater under switchgrass than under wheat at high and medium NPP. Introduction of switchgrass initiates major changes in soil nutrient dynamics through organic-matter input.
Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps

​. Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression. Keywords​. Regression; Deep Learning; Mobile Application
Online Multivalid Learning: Means, Moments, and Prediction Intervals

We present a general, efficient technique for providing contextual predictions that are"multivalid"in various senses, against an online sequence of adversarially chosen examples $(x,y)$. This means that the resulting estimates correctly predict various statistics of the labels $y$ not just marginally -- as averaged over the sequence of examples -- but also conditionally on $x \in G$ for any $G$ belonging to an arbitrary intersecting collection of groups $\mathcal{G}$. We provide three instantiations of this framework. The first is mean prediction, which corresponds to an online algorithm satisfying the notion of multicalibration from Hebert-Johnson et al. The second is variance and higher moment prediction, which corresponds to an online algorithm satisfying the notion of mean-conditioned moment multicalibration from Jung et al. Finally, we define a new notion of prediction interval multivalidity, and give an algorithm for finding prediction intervals which satisfy it. Because our algorithms handle adversarially chosen examples, they can equally well be used to predict statistics of the residuals of arbitrary point prediction methods, giving rise to very general techniques for quantifying the uncertainty of predictions of black box algorithms, even in an online adversarial setting. When instantiated for prediction intervals, this solves a similar problem as conformal prediction, but in an adversarial environment and with multivalidity guarantees stronger than simple marginal coverage guarantees.
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation

In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories of VAE, GAN, and pretrained language models. Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, and learning process into highly reusable modules, which allows users to easily incorporate new models into our framework. The above features make TextBox especially suitable for researchers and practitioners to quickly reproduce baseline models and develop new models. TextBox is implemented based on PyTorch, and released under Apache License 2.0 at the link https://github.com/RUCAIBox/TextBox.
A Unified Learning Approach for Hand Gesture Recognition and Fingertip Detection

In human-computer interaction or sign language interpretation, recognizing hand gestures and detecting ﬁngertips become ubiquitous in computer vision research. In this paper, a uniﬁed approach of convolutional neural network for both hand gesture recognition and ﬁngertip detection is introduced. The proposed algorithm uses a single network to predict the probabilities of ﬁnger class and positions of ﬁngertips in one forward propagation of the network. Instead of directly regressing the positions of ﬁngertips from the fully connected layer, the ensemble of the position of ﬁngertips is regressed from the fully convolutional network. Subsequently, the ensemble average is taken to regress the ﬁnal position of ﬁngertips. Since the whole pipeline uses a single network, it is signiﬁcantly fast in computation. The proposed method results in remarkably less pixel error as compared to that in the direct regression approach and it outperforms the existing ﬁngertip detection approaches including the Heatmap-based framework.
Particle Dynamics in 3D Self-gravitating Disks. I. Spirals

Spiral arms are distinctive features of many circumstellar disks, observed in scattered light, which traces the disk surface; millimeter dust emission, which probes the disk midplane; as well as molecular emission. The two leading explanations for spirals are wakes generated by a massive planet and the density waves excited by disk self-gravity. We use stratified 3D hydrodynamic shearing-box simulations including dust particles and disk self-gravity to investigate how gas and dust spirals in a self-gravitating disk depend on the simulation size, the cooling efficiency, and the aerodynamic properties of particles. We find that the opening angles of spirals are universal (∼10°) and not significantly affected by the size of the computational domain, the cooling time, or the particle size. In simulations with the biggest domain, the spirals in the gaseous disk become slightly more open with a higher cooling efficiency. Small dust follows the gaseous spirals very well, while intermediate-sized dust with dimensionless stopping time (St) close to 1 is more concentrated in the spirals and shows stronger spirals. However, large dust with St > 1 also shows spirals, which is different from some previous simulations. We identify that this is due to the gravity from the gas to the dust component. We show that when St ≳ Q, the gravitational force from the gaseous spirals on the dust particles becomes stronger than the particles’ aerodynamic drag force, so that the gas significantly affects these large particles through gravitational interaction. This has important implications for both spiral observations and planetesimal formation/dynamics.
Phase transitions for the geodesic flow of a rank one surface with nonpositive curvature

We study the one parameter family of potential functions associated with the unstable Jacobian potential (or geometric potential) for the geodesic flow of a compact rank 1 surface of nonpositive curvature. For q<1, it is known that there is a unique equilibrium state associated with , and it has full support. For q>1 it is known that an invariant measure is an equilibrium state if and only if it is supported on the singular set. We study the critical value q = 1 and show that the ergodic equilibrium states are either the restriction to the regular set of the Liouville measure or measures supported on the singular set. In particular, when q = 1, there is a unique ergodic equilibrium state that gives positive measure to the regular set.
Fabrication and electrical properties of all-printed carbon nanotube thin film transistors on flexible substrates

In this manuscript, we developed a high-performance, printable and water-based semiconducting SCNT ink, and fabricated all-printed chemically functionalized CoMoCat 76 SCNT thin film transistors (TFTs) on flexible substrates via a suite of printing technologies. The metallic species in the pristine CoMoCat 76 SCNTs were effectively eliminated by organic radicals, and the functionalized SCNTs were characterized by UV-Vis-NIR spectroscopy and Raman spectra. The high quality, printable and water-based functionalized SCNT inks were obtained by tuning the ink ingredients, such as the concentrations of surfactants and additives. The printing methods we investigated include inkjet printing, aerosol jet printing and a hybrid with nanoimprinting. Large area source and drain electrode patterns were first fabricated on flexible substrates by a hybrid printing method, and then the optimal SCNT ink was printed on the channel of the TFT devices by ink-jet printing. Subsequently, the silver side-gate electrode and ion gel dielectric layer were deposited by aerosol jet printing. The all-printed flexible TFTs exhibited an effective mobility up to 1.5 cm2 V−1 s−1 and an on/off ratio up to 4 × 103. This work opens up a way to fabricate scalable and all-printed flexible electronics, and it is of benefit to generalize the practical applications of flexible electronics in the future.
Relic density of dark matter in the inert doublet model beyond leading order for the low mass region. II. Coannihilation

We examine the relic density of the light mass dark matter region in the inert doublet model (IDM) when the dominant process is due to co-annihilation between the lightest neutral scalars of the model. The full one-loop electroweak corrections are computed in an on-shell scheme and are found to be well approximated as an effective cross-section expressed in terms of Z-observables. The electroweak corrections to the subdominant process which consists of an annihilation into an on-shell W and an off-shell W , that is calculated as a annihilation into a 3-body final state, is also performed. The latter reveals an important dependence on a parameter that describes the self-interaction of the new scalars (solely within the dark sector), a parameter which is not accessible in tree-level calculations of standard model (SM)-IDM interactions. ar X iv :2 10 1. 02 16 6v 1 [ he pph ] 6 J an 2 02 1
Integrated Use of Organic Manure and Chemical Fertilizer on Growth, Yield, and Quality of Sugarcane in High Ganges River Floodplain Soils of Bangladesh

Abstract A study was undertaken to evaluate the effects of integrated use of organic wastes such as press mud, farmyard manure (FYM), and green manure (Crotalaria juncea) along with chemical fertilizer on the yield and quality of sugarcane in High Ganges River Floodplain soils of Bangladesh. Chlorophyll a and Chlorophyll b contents of cane leave tissues were slightly increased by organic manure and green manure. Leaf area index (LAI) and total dry matter (TDM) production were higher during rapid grand growth phase. Application of fertilizer (based on soil test value) accompanied with FYM at 15 t ha−1 produced the maximum plant population density of 163.4×103 ha−1 and resulted in the highest cane and sugar yields of 108.4 and 9.47 t ha−1, respectively. The phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), copper (Cu), iron (Fe), zinc (Zn), and manganese (Mn) contents in leaves were higher in organic manured plots over chemical fertilizer. The organic matter, total nitrogen (N), available P, K, and sulphur (S) contents of the soil increased slightly due to use of organic manure. It appeared that 25% reduction of fertilizer application is possible if FYM or press mud at 15 t ha−1 is used in successful sugarcane production.
Commissioning of a clinical pencil beam scanning proton therapy unit for ultra-high dose rates (FLASH).

PURPOSE The purpose of this work was to provide a flexible platform for FLASH research with protons by adapting a former clinical pencil beam scanning gantry to irradiations with ultra-high dose rates. METHODS PSI Gantry 1 treated patients until December 2018. We optimized the beamline parameters to transport the 250 MeV beam extracted from the PSI COMET accelerator to the treatment room, maximizing the transmission of beam intensity to the sample. We characterized a dose monitor on the gantry to ensure good control of the dose, delivered in spot-scanning mode. We characterized the beam for different dose rates and field sizes for transmission irradiations. We explored scanning possibilities in order to enable conformal irradiations or transmission irradiations of large targets (with transverse scanning). RESULTS We achieved a transmission of 86 % from the cyclotron to the treatment room. We reached a peak dose rate of 9000 Gy/s at 3 mm water equivalent depth, along the central axis of a single pencil beam. Field sizes of up to 5x5 mm2 were achieved for single spot FLASH irradiations. Fast transverse scanning allowed to cover a field of 16x1.2 cm2 . With the use of a nozzle-mounted range shifter, we are able to span depths in water ranging from 19.6 to 37.9 cm. Various dose levels were delivered with precision within less than 1 %. CONCLUSIONS We have realized a proton FLASH irradiation setup able to investigate continuously a wide dose rate spectrum, from 1 to 9000 Gy/s in single spot irradiation as well as in the pencil beam scanning mode. As such, we have developed a versatile test bench for FLASH research.
A Probabilistic Model for Predicting Shot Success in Football

Football forecasting models traditionally rate teams on past match results, that is based on the number of goals scored. Goals, however, involve a high element of chance and thus past results often do not reflect the performances of the teams. In recent years, it has become increasingly clear that accounting for other match events such as shots at goal can provide a better indication of the relative strengths of two teams than the number of goals scored. Forecast models based on this information have been shown to be successful in outperforming those based purely on match results. A notable weakness, however, is that this approach does not take into account differences in the probability of shot success among teams. A team that is more likely to score from a shot will need fewer shots to win a match, on average. In this paper, we propose a simple parametric model to predict the probability of a team scoring, given it has taken a shot at goal. We show that the resulting forecasts are able to outperform a model assuming an equal probability of shot success among all teams. We then show that the model can be combined with predictions of the number of shots achieved by each team, and can increase the skill of forecasts of both the match outcome and of whether the total number of goals in a match will exceed 2.5. We assess the performance of the forecasts alongside two betting strategies and find mixed evidence for improved performance.
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI

It has been shown in the literature that marijuana use is associated with changes in brain network connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such changes using restingstate fMRI. This method combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among fMRI time-series. It is a multivariate approach, since it is capable of identifying the interdependence of time-series in the presence of all other timeseries of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying marijuana users from typical controls using 126 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. We use brain connections estimated by lsXGC as features for classification. After feature extraction, we perform feature selection by Kendall’s-tau rank correlation coefficient followed by classification using a support vector machine. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. Within a cross-validation scheme of 100 different training/test (90%/10%) data splits, we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912] and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC as a potential biomarker for marijuana use.
ISETAuto: Detecting Vehicles With Depth and Radiance Information

Autonomous driving applications use two types of sensor systems to detect vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the performance (average precision) of a ResNet for vehicle detection in complex, daytime, driving scenes when the input is a depth map [D = d(x,y)], a radiance image [L = r(x,y)], or both [D,L]. (1) When the spatial sampling resolution of the depth map and radiance image are both equal to typical camera resolutions, a ResNet detects vehicles at higher average precision from depth than radiance. (2) When the spatial sampling of the depth map matches the range of current LiDAR devices, the average precision is higher for radiance than depth. (3) A hybrid system that combines depth and radiance has higher average precision than systems using depth or radiance alone. We confirm these observations in both simulation and real-world data. We explain the advantage of combining depth and radiance by noting that the two types of information have complementary weaknesses. The radiance data are limited by dynamic range, motion blur and illumination variation; the LiDAR data have low spatial resolution. The ResNet effectively combines the two data sources to improve vehicle detection.