Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Feed Variation on Jowo Super Chicken (Gallus domesticus) Growth in the Integrated System of Crops – Livestock – Fish

The need of people’s animal protein increases every year. One of the alternative sources of animal protein that can be easily obtained is Jowo Super chicken (Gallus domesticus), a hybrid resulting from Kampong chicken and laying hens. However, the high cost of the chicken food causes hardships for breeders in the process of achieving production efficiency whereas it contributes 60%-70% of the whole production cost. One of the ways that can be used and be the solution in the effort to suppress the food and production cost is by applying the Integrated System of Crops – Livestock – Fish. This research has the purpose to find the effect on the feed variation on Jowo Super chicken (Gallus domesticus) based on the treatments towards each food variation. This research was done in Biological Physics Laboratory of Faculty of Agricultural Technology Universitas Gadjah Mada. The livestock used in this research are 32 Jowo Super chicken (Gallus domesticus) aged 3 weeks at the start of the research and 8 weeks at the end of the research. The chickens were placed inside 4 cages with each containing 8 chickens as replications. This research used Completely Randomized Design (CRD) with 4 treatments and 8 replications. The variations of chicken foods are i) 100% commercial food, ii) 50% commercial foods, 30% BSF maggot (Hermetia illucens), 20% mustard waste (Brassica rapa L), iii) 50% BSF maggot (Hermetia illucens), 30% commercial food, 20% mustard waste (Brassica rapa L), iv) 100% BSF maggot (Hermetia illucens). The result of this research is that the P2 treatment has the most optimum level of growth efficiency in chickens.
Zb tetraquark channel from lattice QCD and Born-Oppenheimer approximation

Article history: Received 30 January 2020 Received in revised form 10 April 2020 Accepted 29 April 2020 Available online 7 May 2020 Editor: B. Grinstein Two Zb hadrons with exotic quark structure b̄bd̄u were discovered by Belle experiment. We present a lattice QCD study of the b̄bd̄u system in the approximation of static b quarks, where the total spin of heavy quarks is fixed to one. The energies of eigenstates are determined as a function of the separation r between b and b̄. The lower eigenstates are related to a bottomonium and a pion. The eigenstate dominated by B B̄∗ has energy significantly below mB + mB∗ , which points to a sizable attraction for small r. The attractive potential V (r) between B and B̄∗ is extracted assuming that this eigenstate is related exclusively to B B̄∗. The Schrödinger equation for B B̄∗ within the extracted potential leads to a virtual bound state, whose mass depends on the parametrization of the lattice potential. For certain parametrizations, we find a virtual bound state slightly below B B̄∗ threshold and a narrow peak in the B B̄∗ rate above threshold these features could be related to Zb(10610) in the experiment. We surprisingly find also a deep bound state within the undertaken approximations. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3.
Taking 200mg Vitamin C Three Times per Day Improved Extraction Socket Wound Healing Parameters: A Randomized Clinical Trial

Vitamin C is essential for wound healing. However, there are no reports concerning the effect of a different dose of vitamin C on extraction wound size clinically. )erefore, the aim of this study was to investigate the effect of different oral vitamin C doses on extraction wound healing. A split-mouth, double-blind randomized clinical trial was performed in 42 patients who underwent symmetric bilateral noninfected premolar extraction. )e patients were randomly divided into 3 groups, namely, P/600, P/1,500, and 600/1,500 (14 patients for each group); P/600: placebo vs. 600mg vitamin C/d, P/1,500: placebo vs. 1,500mg vitamin C/d, and 600/1,500: 600mg vitamin C/d vs. 1,500mg vitamin C/d. Patients were prescribed placebo or/and vitamin C three times a day for 10 days after each tooth extraction. Extraction wound size and pain score were evaluated. )e wound assessment was performed on day 0, 7, and 21; and then the tooth on the other side was extracted using the same protocol. Pain score was recorded on the first three days after extraction. )e reduced size of mesiodistal extraction wound in percentage reduction between day 0 and 7 of teeth receiving vitamin C 600mg/d was more than that in placebo (P< 0.05). Pain scores on day 1–3 of teeth receiving vitamin C 600mg/d were significantly lower than the placebo side (P< 0.05). Taking oral vitamin C 600mg/d over three doses for 10 days after tooth extraction enhances extraction wound healing by reducing mesiodistal extraction wound and reduces postoperative pain.
Temperature forecasting by deep learning methods

Abstract. Numerical weather prediction (NWP) models solve a system of partial differential equations based on physical laws to forecast the future state of the atmosphere. These models are deployed operationally, but they are computationally very expensive. Recently, the potential of deep neural networks to generate bespoke weather forecasts has been explored in a couple of scientific studies inspired by the success of video frame prediction models in computer vision. In this study, a simple recurrent neural network with convolutional filters, called ConvLSTM, and an advanced generative network, the Stochastic Adversarial Video Prediction (SAVP) model, are applied to create hourly forecasts of the 2 m temperature for the next 12 h over Europe. We make use of 13 years of data from the ERA5 reanalysis, of which 11 years are utilized for training and 1 year each is used for validating and testing. We choose the 2 m temperature, total cloud cover, and the 850 hPa temperature as predictors and show that both models attain predictive skill by outperforming persistence forecasts. SAVP is superior to ConvLSTM in terms of several evaluation metrics, confirming previous results from computer vision that larger, more complex networks are better suited to learn complex features and to generate better predictions. The 12 h forecasts of SAVP attain a mean squared error (MSE) of about 2.3 K2, an anomaly correlation coefficient (ACC) larger than 0.85, a structural similarity index (SSIM) of around 0.72, and a gradient ratio (rG) of about 0.82. The ConvLSTM yields a higher MSE (3.6 K2), a smaller ACC (0.80) and SSIM (0.65), and a slightly larger rG (0.84). The superior performance of SAVP in terms of MSE, ACC, and SSIM can be largely attributed to the generator. A sensitivity study shows that a larger weight of the generative adversarial network (GAN) component in the SAVP loss leads to even better preservation of spatial variability at the cost of a somewhat increased MSE (2.5 K2). Including the 850 hPa temperature as an additional predictor enhances the forecast quality, and the model also benefits from a larger spatial domain. By contrast, adding the total cloud cover as predictor or reducing the amount of training data to 8 years has only small effects. Although the temperature forecasts obtained in this way are still less powerful than contemporary NWP models, this study demonstrates that sophisticated deep neural networks may achieve considerable forecast quality beyond the nowcasting range in a purely data-driven way.
The cultivation period effects on heavy metals content of some soils of Sohag governorate

The main objectives of this study are to assess the levels of some heavy metals (i.e. Mn, Cd, Zn, Co, and Cu) in some soils of Sohag governorate under different lands in the respect of cultivation period; and to quantify the metal contamination of these soils. Total number of 114 soil samples was collected from 57 different locations under various land-uses (i.e. old cultivated lands, new cultivated lands, and un-cultivated lands). Un-cultivated soils are considered as a control soil, while old cultivated soils are the oldest soils compared to new reclaimed ones in between. Soil samples were collected from two layers 030 cm and 30-60 cm depth to reflect the various fertilizers application used in studied soils. The geo-coordinates of the studied sites were identified using the global Positioning System (GPS). Soil samples were prepared and analyzed for their physicochemical properties as well as for their content of DTPA-extractable heavy metals in the soil testing laboratories using the standard methods of analysis. Maps of DTPAextractable heavy metals were generated. Correlations between the concentration of the studied elements and some soil properties (i.e. primary sand, silt, clay, pH, OM, CaCO3) was developed whereas, a positive correlation was observed between clay content and heavy metal. The obtained results indicated that the highest concentrations of heavy metals were found in the old cultivated soils compared to un-cultivated soils and newly reclaimed soils. Therefore, it should be mentioned that, the cultivation period affects the levels and quantities of these heavy metals in the studied soils. This may be due to agricultural activities such as the use of chemical fertilization.
The Improving Effect of Intelligent Speech Recognition System on English Learning

To improve the effect of English learning in the context of smart education, this study combines speech coding to improve the intelligent speech recognition algorithm, builds an intelligent English learning system, combines the characteristics of human ears, and studies a coding strategy of a psychoacoustic masking model based on the characteristics of human ears. Moreover, this study analyzes in detail the basic principles and implementation process of the psychoacoustic model coding strategy based on the characteristics of the human ear and completes the channel selection by calculating the masking threshold. In addition, this study verifies the effectiveness of the algorithm in this study through simulation experiments. Finally, this study builds a smart speech recognition system based on this model and uses simulation experiments to verify the effect of smart speech recognition on English learning. To improve the voice recognition effect of smart speech, band-pass filtering and envelope detection adopt the gammatone filter bank and Meddis inner hair cell model in the mathematical model of the cochlear system; at the same time, the masking effect model of psychoacoustics is introduced in the channel selection stage to prevent noise. Sex has been improved, and the recognition effect of smart voice has been improved. The analysis shows that the intelligent speech recognition system proposed in this study can effectively improve the effect of English learning. In particular, it has a great effect on improving the effect of oral learning.
Semi-supervised novelty detection using ensembles with regularized disagreement

Deep neural networks often predict samples with high confidence even when they come from unseen classes and should instead be flagged for expert evaluation. Current novelty detection al-gorithms cannot reliably identify such near OOD points unless they have access to labeled data that is similar to these novel samples. In this paper, we develop a new ensemble-based procedure for semi-supervised novelty detection (SSND) that successfully leverages a mixture of unlabeled ID and novel-class samples to achieve good detection performance. In particular, we show how to achieve disagreement only on OOD data using early stopping regularization. While we prove this fact for a simple data distribution, our extensive experiments suggest that it holds true for more complex scenarios: our approach significantly outperforms state-of-the-art SSND methods on standard image data sets (SVHN/CIFAR-10/CIFAR-100) and medical image data sets with only a negligible increase in computation cost.
Electronic and optical properties of InAs/InAs0.625Sb0.375 superlattices and their application for far-infrared detectors

We calculate the electronic and optical properties of InAs/InAs0.625Sb0. 375 superlattices (SLs) within relativistic density functional theory. To have a good description of the electronic and optical properties, the modified Becke–Johnson exchange-correlation functional is employed to describe the band gaps correctly. First, we analyze the electronic and optical characteristics of bulk InAs and InSb, and then we investigate the InAs/InAs0.625Sb0. 375 SL. The optical gaps deduced from the imaginary part of the dielectric function are associated with the characteristic interband transitions. We investigate the electronic and optical properties of the InAs/InAs0.625Sb0. 375 SL with three lattice constants of the bulk InAs, GaSb and AlSb, respectively. It is observed that the electronic and optical properties strongly depend on the lattice constant. Our results support the presence of two heavy-hole bands with increasing in-plane effective mass as we go far from the Fermi level. We notice a considerable decrease in the energy gaps and the effective masses of the heavy-holes in the k x –k y plane compared to the bulk phases of the parent compounds. We demonstrate that the electrons are s-orbitals delocalized in the entire SL, while the holes have mainly p-Sb character localized in the In(As,Sb) side of the SL. In the SL, the low-frequency absorption spectra greatly increase when the electric field is polarized orthogonal to the growth axis allowing the applicability of III–V compounds for the long-wavelength infrared detectors.
Electrical conductivity of iron in Earth's core from microscopic Ohm's law

Understanding the electronic transport properties of iron under high temperatures and pressures is essential for constraining geophysical processes. The difficulty of reliably measuring these properties under Earth-core conditions calls for sophisticated theoretical methods that can support diagnostics. We present results of the electrical conductivity within the pressure and temperature ranges found in Earth's core from simulating microscopic Ohm's law using time-dependent density functional theory. Our predictions provide a new perspective on resolving discrepancies in recent experiments.
Dual reparametrized Variational Generative Model for Time-Series Forecasting

This paper propose DualVDT, a generative model for Time-series forecasting. Introduced dual reparametrized variational mechanisms on variational autoencoder (VAE) to tighter the evidence lower bound (ELBO) of the model, prove the advance performance analytically. This mechanism leverage the latent score based generative model (SGM), explicitly denoising the perturbation accumulated on latent vector through reverse time stochastic differential equation and variational ancestral sampling. The posterior of denoised latent distribution fused with dual reparametrized variational density. The KL divergence in ELBO will reduce to reach the better results of the model. This paper also proposed a latent attention mechanisms to extract multivariate dependency explicitly. Build the local temporal dependency simultaneously in factor wised through constructed local topology and temporal wised. The proven and experiment on multiple datasets illustrate, DualVDT, with a novel dual reparametrized structure, which denoise the latent perturbation through the reverse dynamics combining local-temporal inference, has the advanced performance both analytically and experimentally.
Enhancing Adversarial Training with Second-Order Statistics of Weights

Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over model weights and adversarial perturbations, where the weights can be optimized through gradient descent methods like SGD. In this paper, we show that treating model weights as random variables allows for enhancing adversarial training through Second-Order Statistics Optimization (S2O) with respect to the weights. By relaxing a common (but unrealistic) assumption of previous PAC-Bayesian frameworks that all weights are statistically independent, we derive an improved PAC-Bayesian adversarial generalization bound, which suggests that optimizing second-order statistics of weights can effectively tighten the bound. In addition to this theoretical insight, we conduct an extensive set of experiments, which show that S2O not only improves the robustness and generalization of the trained neural networks when used in isolation, but also integrates easily in state-of-the-art adversarial training techniques like TRADES, AWP, MART, and AVMixup, leading to a measurable improvement of these techniques. The code is available at https://github.com/Alexkael/S2O.
Explicit isogenies of prime degree over number fields

We provide an explicit and algorithmic version of a theorem of Momose classifying isogenies of prime degree of elliptic curves over number ﬁelds, which we implement in Sage and PARI/GP. Combining this algorithm with recent work of Box-Gajovi´c-Goodman we determine the ﬁrst instances of isogenies of prime degree for cubic number ﬁelds, as well as for several quadratic ﬁelds not previously known. While the correctness of the general algorithm relies on the Generalised Riemann Hypothesis, the algorithm is unconditional for the restricted class of semistable elliptic curves.
Ages of Main-sequence Turnoff Stars from the GALAH Survey

Main-sequence turnoff (MSTO) stars are good tracers of Galactic populations since their ages can be reliably estimated from atmospheric parameters. Based on the GALAH survey, we use the Yale rotation evolution code to determine the ages of 2926 MSTO stars with a mean age uncertainty of ∼10% considering the variation of C and O abundances. The ages of CO-poor stars are systematically affected by ∼10% due to the C and O abundances, globally shifting to ∼0.5 Gyr older compared to the results using solar metal mixture. Of the stars with [Fe/H] ∼ 0.3–0.5 or [O/Fe] ≤ −0.25, many have fractional age differences of ≥20%, and even reach up to 36%. The age–metallicity relation appears to possibly indicate the existence of two distinct sequences: a young sequence of stars with ages mostly <7 Gyr, and a relatively older sequence of stars with ages mostly >7 Gyr, overlapping at 5 Gyr ≤ age ≤ 7 Gyr. Moreover, the trends of abundances-to-age ratios show two corresponding sequences, especially in the [O/Fe]–age plane. We also find that [Y/Mg] is a good chemical clock in disk populations. The young sequence and the old sequence cannot be separated based on chemistry or kinematics; therefore, stellar age is an important parameter to distinguish these two sequences in our sample.
Adaptive POD-DEIM correction for Turing pattern approximation in reaction–diffusion PDE systems

Abstract We investigate a suitable application of Model Order Reduction (MOR) techniques for the numerical approximation of Turing patterns, that are stationary solutions of reaction–diffusion PDE (RD-PDE) systems. We show that solutions of surrogate models built by classical Proper Orthogonal Decomposition (POD) exhibit an unstable error behaviour over the dimension of the reduced space. To overcome this drawback, first of all, we propose a POD-DEIM technique with a correction term that includes missing information in the reduced models. To improve the computational efficiency, we propose an adaptive version of this algorithm in time that accounts for the peculiar dynamics of the RD-PDE in presence of Turing instability. We show the effectiveness of the proposed methods in terms of accuracy and computational cost for a selection of RD systems, i.e., FitzHugh–Nagumo, Schnakenberg and the morphochemical DIB models, with increasing degree of nonlinearity and more structured patterns.
Moser-Tardos Algorithm with small number of random bits

We study a variant of the parallel Moser-Tardos Algorithm. We prove that if we restrict attention to a class of problems whose dependency graphs have subexponential growth, then the expected total number of random bits used by the algorithm is constant; in particular, it is independent from the number of variables. This is achieved by using the same random bits to resample variables which are far enough in the dependency graph. There are two corollaries. First, we obtain a deterministic algorithm for finding a satisfying assignment, which for any class of problems as in the previous paragraph runs in time O(n), where n is the number of variables. Second, we present a Borel version of the Lov\'asz Local Lemma.
Anti-Oversmoothing in Deep Vision Transformers via the Fourier Domain Analysis: From Theory to Practice

Vision Transformer (ViT) has recently demonstrated promise in computer vision problems. However, unlike Convolutional Neural Networks (CNN), it is known that the performance of ViT saturates quickly with depth increasing, due to the observed attention collapse or patch uniformity. Despite a couple of empirical solutions, a rigorous framework studying on this scalability issue remains elusive. In this paper, we first establish a rigorous theory framework to analyze ViT features from the Fourier spectrum domain. We show that the self-attention mechanism inherently amounts to a low-pass filter, which indicates when ViT scales up its depth, excessive low-pass filtering will cause feature maps to only preserve their Direct-Current (DC) component. We then propose two straightforward yet effective techniques to mitigate the undesirable low-pass limitation. The first technique, termed AttnScale, decomposes a self-attention block into low-pass and high-pass components, then rescales and combines these two filters to produce an all-pass self-attention matrix. The second technique, termed FeatScale, re-weights feature maps on separate frequency bands to amplify the high-frequency signals. Both techniques are efficient and hyperparameter-free, while effectively overcoming relevant ViT training artifacts such as attention collapse and patch uniformity. By seamlessly plugging in our techniques to multiple ViT variants, we demonstrate that they consistently help ViTs benefit from deeper architectures, bringing up to 1.1% performance gains"for free"(e.g., with little parameter overhead). We publicly release our codes and pre-trained models at https://github.com/VITA-Group/ViT-Anti-Oversmoothing.
How to Train a (Bad) Algorithmic Caseworker: A Quantitative Deconstruction of Risk Assessments in Child Welfare

Child welfare (CW) agencies use risk assessment tools as a means to achieve evidence-based, consistent, and unbiased decision-making. These risk assessments act as data collection mechanisms and have been further developed into algorithmic systems in recent years. Moreover, several of these algorithms have reinforced biased theoretical constructs and predictors because of the easy availability of structured assessment data. In this study, we critically examine the Washington Assessment of Risk Model (WARM), a prominent risk assessment tool that has been adopted by over 30 states in the United States and has been repurposed into more complex algorithmic systems. We compared WARM against the narrative coding of casenotes written by caseworkers who used WARM. We found significant discrepancies between the casenotes and WARM data where WARM scores did not not mirror caseworkers’ notes about family risk. We provide the SIGCHI community with some initial findings from the quantitative de-construction of a child-welfare risk assessment algorithm.