Miracle-dz/UniFAR-FTUs huggingface dataset:
A kiloparsec view of a typical star-forming galaxy when the Universe was ~1 Gyr old. Part I. Outflow, halo, and interstellar medium properties

We present new Atacama Large Millimeter/Submillimeter Array (ALMA) observations of the [C ii] 158 μm transition and the dust continuum in HZ4, a typical star-forming galaxy when the Universe was only ∼ 1 Gyr old (z ≈ 5.5). Our high ≈ 0.3 spatial resolution allow us to study the relationships between [C ii] line emission, star formation rate (SFR), and far-infrared (FIR) emission on spatial scales of ∼ 2 kpc. In the central ∼4 kpc of HZ4 the [C ii]/FIR ratio is ∼ 3 × 10−3 on global scales as well as on spatially-resolved scales of ∼2 kpc, comparable to the ratio observed in local moderate starburst galaxies such as M82 or M83. For the first time in an individual normal galaxy at this redshift, we find evidence for outflowing gas from the central star-forming region in the direction of the minor-axis of the galaxy. The projected velocity of the outflow is ∼ 400 km s−1, and the neutral gas mass outflow rate is ∼ 3 − 6 times higher than the SFR in the central region. Finally, we detect a diffuse component of [C ii] emission, or [C ii]-halo, that extends beyond the star-forming disk and has a size of ∼ 12 kpc in diameter. Most likely the outflow, which has a velocity approximately half the escape velocity of the system, is in part responsible for fueling the [C ii] extended emission. Together with the kinematic analysis of HZ4 (presented in a forthcoming paper), the analysis supports that HZ4 is a typical star-forming disk at z ∼ 5 with interstellar medium (ISM) conditions similar to present-day galaxies forming stars at a similar level, driving a galactic outflow that may already play a role in its evolution.
Leader Confirmation Replication for Millisecond Consensus in Geo-distributed Private Chains

—Geo-distributed private chain and database have created higher performance requirements for consistency models. However, with millisecond network latency between nodes, the widely used leader-based SMR models cause frequent retransmission of logs since they cannot know the logs replication status in time, which resulting in the leader costing high network and computing resource. To address the problem, we proposed a Leader Conﬁrmation based Replication (LCR) model. First, we demonstrate the efﬁcacy of the approach by designing the Future-Log Replication model, which the followers are responsible for non-transactional log replication. It reduces the leader’s network load using the signal log. Secondly, we designed a Generation Re-replication strategy, which can ensure the security and consistency of future-logs when the number of nodes changes. Finally, we implemented LCR-Raft and designed experiments. The results show that in the single-ms network latency environments, LCR-Raft can provide higher TPS (1.5X 1.9X), and reduce the network trafﬁc of the leader by 20%-30% with acceptable network trafﬁc and CPU cost on followers. Besides, LCR can provide high portability since it does not change the number of leader and election process.
Text Augmentation in a Multi-Task View

Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective—a multi-task view (MTV) of data augmentation—in which the primary task trains on original examples and the auxiliary task trains on augmented examples. In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger augmentation functions. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that using the MTV leads to higher and more robust performance than traditional augmentation.
Electroabsorption by confined excitons with Gaussian interaction potential

We consider the effects of electron-hole interaction, 2D confinement and applied electric field on direct allowed transitions in III-V semiconductors, with InGaAs as a study case. Instead of Coulomb interaction, we use Gaussian potential. It is finite at the origin and has a finite effective range, which allows for a more efficient numerical solution of Schrödinger equation. Yet, we can expect electroabsorption phenomena to remain qualitatively similar to the ones observed for Coulomb excitons. Moreover, we use variation of parameters to fit both position and magnitude of the first absorption peak to the Coulomb case.
A search for runaway stars in 12 Galactic supernova remnants

Runaway stars can result from core-collapse supernovae in multiple stellar systems. If the supernova disrupts the system, the companion gets ejected with its former orbital velocity. A clear identification of a runaway star can yield the time and place of the explosion as well as orbital parameters of the pre-supernova binary system. Previous searches have mostly considered O- and B-type stars as runaway stars because they are always young in absolute terms (not much older than the lifetime of the progenitor) and can be detected up to larger distances. We present here a search for runaway stars of all spectral types. For late-type stars, a young age can be inferred from the lithium test. We used Gaia data to identify and characterise runaway star candidates in nearby supernova remnants, obtained spectra of 39 stars with UVES at the VLT and HDS at the Subaru telescope and found a significant amount of lithium in the spectra of six dwarf stars. We present the spectral analysis, including measurements of radial velocities, atmospheric parameters and lithium abundances. Then we estimate the ages of our targets from the Hertzsprung-Russell diagram and with the lithium test, present a selection of promising runaway star candidates and draw constraints on the number of ejected runaway stars compared to model expectations.
Group Testing with a Graph Infection Spread Model

The group testing idea is an efficient infection identification approach based on pooling the test samples of a group of individuals, which results in identification with less number of tests than individually testing the population. In our work, we propose a novel infection spread model based on a random connection graph which represents connections between n individuals. Infection spreads via connections between individuals, and this results in a probabilistic cluster formation structure as well as non-i.i. d. (correlated) infection statuses for individuals. We propose a class of two-step sampled group testing algorithms where we exploit the known probabilistic infection spread model. We investigate the metrics associated with two-step sampled group testing algorithms. To demonstrate our results, for analytically tractable exponentially split cluster formation trees, we calculate the required number of tests and the expected number of false classifications in terms of the system parameters, and identify the trade-off between them. For such exponentially split cluster formation trees, for zero-error construction, we prove that the required number of tests is O(log2n). Thus, for such cluster formation trees, our algorithm outperforms any zero-error non-adaptive group test, binary splitting algorithm, and Hwang’s generalized binary splitting algorithm. Our results imply that, by exploiting probabilistic information on the connections of individuals, group testing can be used to reduce the number of required tests significantly even when the infection rate is high, contrasting the prevalent belief that group testing is useful only when the infection rate is low.
Rescaling CNN Through Learnable Repetition of Network Parameters

Deeper and wider CNNs are known to provide improved performance for deep learning tasks. However, most such networks have poor performance gain per parameter increase. In this paper, we investigate whether the gain observed in deeper models is purely due to the addition of more optimization parameters or whether the physical size of the network as well plays a role. Further, we present a novel rescaling strategy for CNNs based on learnable repetition of its parameters. Based on this strategy, we rescale CNNs without changing their parameter count, and show that learnable sharing of weights itself can provide significant boost in the performance of any given model without changing its parameter count. We show that small base networks when rescaled, can provide performance comparable to deeper networks with as low as 6% of optimization parameters of the deeper one. The relevance of weight sharing is further highlighted through the example of group-equivariant CNNs. We show that the significant improvements obtained with group-equivariant CNNs over the regular CNNs on classification problems are only partly due to the added equivariance property, and part of it comes from the learnable repetition of network weights. For rot-MNIST dataset, we show that up to 40% of the relative gain reported by state-of-the-art methods for rotation equivariance could actually be due to just the learnt repetition of weights.11Code is publicly available at https://github.com/transmuteAI/RepeatNet
Newton-Okounkov bodies and Picard numbers on surfaces

We study the shapes of all Newton-Okounkov bodies $\Delta_{v}(D)$ of a given big divisor $D$ on a surface $S$ with respect to all rank 2 valuations $v$ of $K(S)$. We obtain upper bounds for, and in many cases we determine exactly, the possible numbers of vertices of the bodies $\Delta_{v}(D)$. The upper bounds are expressed in terms of Picard numbers and they are birationally invariant, as they do not depend on the model $\tilde{S}$ where the valuation $v$ becomes a flag valuation. We also conjecture that the set of all Newton-Okounkov bodies of a single ample divisor $D$ determines the Picard number of $S$, and prove that this is the case for Picard number 1, by an explicit characterization of surfaces of Picard number 1 in terms of Newton-Okounkov bodies.
Kernel-based ANOVA decomposition and Shapley effects -- Application to global sensitivity analysis

Global sensitivity analysis is the main quantitative technique for identifying the most influential input variables in a numerical simulation model. In particular when the inputs are independent, Sobol' sensitivity indices attribute a portion of the output of interest variance to each input and all possible interactions in the model, thanks to a functional ANOVA decomposition. On the other hand, moment-independent sensitivity indices focus on the impact of input variables on the whole output distribution instead of the variance only, thus providing complementary insight on the inputs / output relationship. Unfortunately they do not enjoy the nice decomposition property of Sobol' indices and are consequently harder to analyze. In this paper, we introduce two moment-independent indices based on kernel-embeddings of probability distributions and show that the RKHS framework used for their definition makes it possible to exhibit a kernel-based ANOVA decomposition. This is the first time such a desirable property is proved for sensitivity indices apart from Sobol' ones. When the inputs are dependent, we also use these new sensitivity indices as building blocks to design kernel-embedding Shapley effects which generalize the traditional variance-based ones used in sensitivity analysis. Several estimation procedures are discussed and illustrated on test cases with various output types such as categorical variables and probability distributions. All these examples show their potential for enhancing traditional sensitivity analysis with a kernel point of view.
Hyper Suprime-Cam Legacy Archive

We present the launch of the Hyper Suprime-Cam Legacy Archive (HSCLA), a public archive of processed, science-ready data from Hyper Suprime-Cam (HSC). HSC is an optical wide-field imager installed at the prime focus of the Subaru Telescope and has been in operation since 2014. While ~1/3 of the total observing time of HSC has been used for the Subaru Strategic Program (SSP), the remainder of the time is used for PI programs. We have processed the data from these PI programs and make the processed, high quality data available to the community through HSCLA. The current version of HSCLA includes data taken in the first year of science operation, 2014. We provide both individual and coadd images as well as photometric catalogs. The photometric catalog from the coadd is loaded to the database, which offers a fast access to the large catalog. There are other online tools such as image browser and image cutout tool and they will be useful for science analyses. The coadd images reach 24-27th magnitudes at $5\sigma$ for point sources and cover approximately 580 square degrees in at least one filter with 150 million objects in total. We perform extensive quality assurance tests and verify the photometric and astrometric quality of the data to be good enough for most scientific explorations. However, the data are not without problems and users are referred to the list of known issues before exploiting the data for science. All the data and documentations can be found at the data release site, this https URL.
FabricNet: A Fiber Recognition Architecture Using Ensemble ConvNets

Fabric is a planar material composed of textile fibers. Textile fibers are generated from many natural sources; including plants, animals, minerals, and even, it can be synthetic. A particular fabric may contain different types of fibers that pass through a complex production process. Fiber identification is usually carried out through chemical tests and microscopic tests. However, these testing processes are complicated as well as time-consuming. We propose FabricNet, a pioneering approach for the image-based textile fiber recognition system, which may have a revolutionary impact from individual to the industrial fiber recognition process. The FabricNet can recognize a large scale of fibers by only utilizing a surface image of fabric. The recognition system is constructed using a distinct category of class-based ensemble convolutional neural network (CNN) architecture. The experiment is conducted on recognizing 50 different types of textile fibers. This experiment includes a significantly large number of unique textile fibers than previous research endeavors to the best of our knowledge. We experiment with popular CNN architectures that include Inception, ResNet, VGG, MobileNet, DenseNet, and Xception. Finally, the experimental results demonstrate that FabricNet outperforms the state-of-the-art popular CNN architectures by reaching an accuracy of 84% and F1-score of 90%.
Practical Face Reconstruction via Differentiable Ray Tracing

We present a differentiable ray‐tracing based novel face reconstruction approach where scene attributes – 3D geometry, reflectance (diffuse, specular and roughness), pose, camera parameters, and scene illumination – are estimated from unconstrained monocular images. The proposed method models scene illumination via a novel, parameterized virtual light stage, which in‐conjunction with differentiable ray‐tracing, introduces a coarse‐to‐fine optimization formulation for face reconstruction. Our method can not only handle unconstrained illumination and self‐shadows conditions, but also estimates diffuse and specular albedos. To estimate the face attributes consistently and with practical semantics, a two‐stage optimization strategy systematically uses a subset of parametric attributes, where subsequent attribute estimations factor those previously estimated. For example, self‐shadows estimated during the first stage, later prevent its baking into the personalized diffuse and specular albedos in the second stage. We show the efficacy of our approach in several real‐world scenarios, where face attributes can be estimated even under extreme illumination conditions. Ablation studies, analyses and comparisons against several recent state‐of‐the‐art methods show improved accuracy and versatility of our approach. With consistent face attributes reconstruction, our method leads to several style – illumination, albedo, self‐shadow – edit and transfer applications, as discussed in the paper.
Morpho-kinematics of the circumstellar envelope of the AGB star R Dor: a global view

We analyse new ALMA observations of the 29SiO (ν = 0, J = 8–7) and SO2 (ν = 0, 343,31−342,32) line emissions of the circumstellar envelope (CSE) of the oxygen-rich asymptotic giant branch (AGB) star, R Dor. With a spatial resolution of ∼2.3 au, the observations cover distances below ∼30 au from the star providing a link between earlier observations and clarifying some open issues. The main conclusions are the following. (i) Rotation is confined below ∼15 au from the star, with velocity reaching a maximum below 10 au and morphology showing no significant disc-like flattening. (ii) In the south-eastern quadrant, a large Doppler velocity gas stream is studied in more detail than previously possible and its possible association with an evaporating planetary companion is questioned. (iii) A crude evaluation of the respective contributions of rotation, expansion and turbulence to the morpho-kinematics is presented. Significant line broadening occurs below ∼12 au from the star and causes the presence of high Doppler velocity components near the line of sight pointing to the centre of the star. (iv) Strong absorption of the continuum emission of the stellar disc and its immediate dusty environment is observed to extend beyond the disc in the form of self-absorption. The presence of a cold SiO layer extending up to some 60 au from the star is shown to be the cause. (v) Line emissions from SO, 28SiO, CO and HCN molecules are used to probe the CSE up to some 100 au from the star, revealing the presence of two broad back-to-back outflows, the morphology of which is studied in finer detail than in earlier work.
Partial removal of water from red pepper by immersion in an osmotic solution before drying

Red pepper is dried for use as a spice. When using solar energy to dry it, there are no energy costs but using other energy sources such as fuel or electricity, the production cost of red pepper spice increase. Due to the fact that energy is becoming expensive globally, it can be envisaged that osmotic dehydration gains more popularity as a pre-processing dehydration step. In this study, red pepper samples were cut into square and immersed in the osmotic solutions prepared at three different concentrations, a defined ratio (sample/solution; g/ml) and several temperatures during osmotic dehydration process time. Water activity, color, solid content, water loss and solid gain of samples were determined during osmotic dehydration process taking place in solutions at different concentration, temperature and time. Statistically, all dependent variables have effect on osmotic dehydration. 60 min of the process was found as the critical point for water loss-solid gain relation. The effect of concentration was significant on water activity of sample. Water activities of samples whose initials were greater than 0.90 reduced below the level of 0.8 at the end of dehydration time, with the exception of a few samples; visible color values were influenced by the process. Key words: Red pepper, sea salt, water activity color, osmotic dehydration.
The Monocyte-to-Lymphocyte Ratio at Hospital Admission Is a Novel Predictor for Acute Traumatic Intraparenchymal Hemorrhage Expansion after Cerebral Contusion

Purpose To explore the potential of monocyte-to-lymphocyte ratio (MLR) at hospital admission for predicting acute traumatic intraparenchymal hematoma (tICH) expansion in patients with cerebral contusion. Patients and Methods. This multicenter, observational study included patients with available at-hospital admission (baseline) and follow-up computed tomography for volumetric analysis (retrospective development cohort: 1146 patients; prospective validation cohort: 207 patients). Semiautomated software assessed tICH expansion (defined as ≥33% or 5 mL absolute growth). MLR was acquired from routine blood tests upon admission. We constructed two predictive models: basic combined model of clinical and imaging variables and MLR combined model of both MLR and other variables in the basic model. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to estimate the performance of MLR for predicting acute tICH expansion. Results MLR was significantly larger in patients with acute tICH expansion compared to those without acute tICH expansion (mean [SD], 1.08 [1.05] vs. 0.59 [0.37], P < 0.001). A nonlinear positive relationship between MLR and the incidence of acute tICH expansion was observed. Multivariate logistic regression indicated MLR as an independent risk factor for acute tICH expansion (odds ratio (OR), 5.88; 95% confidence interval (CI), 4.02-8.61). The power of the multivariate model for predicting acute tICH expansion was substantially improved with the inclusion of MLR (AUC 0.86 vs. AUC 0.74, P < 0.001), as was also observed in an external validation cohort (AUC 0.83 vs. AUC 0.71, P < 0.001). The net benefit of MLR model was higher between threshold probabilities of 20-100% in DCA. For clinical application, a nomogram derived from the multivariate model with MLR was introduced. In addition, MLR was positively associated with 6-month unfavorable outcome. Conclusion MLR is a novel predictor for traumatic parenchymatous hematoma expansion. A nomogram derived from the MLR model may provide an easy-to-use tool for predicting acute tICH expansion and promoting the individualized treatment of patients with hemorrhagic cerebral contusion. MLR is associated with long-term outcome after cerebral contusion.
Comprehensive Knowledge towards Cervical Cancer and Associated Factors among Women in Durame Town, Southern Ethiopia

Low awareness about cervical cancer and poor screening practice are some of the contributing factors for the high burden of cervical cancer in sub-Saharan Africa. The aim of this study was to assess comprehensive knowledge towards cervical cancer and associated factors among reproductive age women visiting Durame General Hospital. Institution-based cross-sectional study was conducted in April 2019. Systematic random sampling technique was employed to select study participants. Pretested interviewer administered questionnaire was used for data collection. Binary and multiple logistic regression analysis was done. Adjusted odds ratio with a 95% CI was used to determine the presence and strength of associations between independent and outcome variable. Variables with p value less than 0.05 were considered as statistically significant. Among the 237 women enrolled, more than half (55.7%) have ever heard about cervical cancer. Health professionals were major source of information. Half of respondents (51.5%) had good knowledge towards cervical cancer. Urban residence (AOR = 2.28, 95% CI (1.19-4.35)), having formal employment (AOR = 2.92, 95% CI (1.53-5.59)), and knowing someone with cervical cancer (AOR = 5.21, 95% CI (2.32-11.71)) were found to have significant association with good knowledge towards cervical cancer. The comprehensive knowledge of women towards cervical cancer was found to be insufficient. Provision of community-based health education with health professionals needs to be emphasized.