Miracle-dz/UniFAR-FTUs huggingface dataset:
PRODUCTIVE AND REPRODUCTIVE PERFORMANCE OF ZEBU × HOLSTEIN-FRIESIAN CROSS-BRED DAIRY COWS IN JIMMA TOWN, OROMIA, ETHIOPIA

2 Abstract: The aim of the study was to assess productive and reproductive performance of crossbred (Zebu x Holstein-Friesian) dairy cows and factors affecting their performance in Jimma town, Oromia State, Ethiopia. A total of 54 small-scale dairy farm owners were randomly selected and interviewed with pre-tested structured questionnaire to obtain information on the productive and reproductive performance of cows. A follow-up study was also conducted to obtain milk production based on lactation stages. The results of the study showed that from the follow-up study, the mean daily milk yield for the first, second and third stage of lactations was 7.01± 2.73, 5.55±2.83 and 3.50±1.64 liters per cow, respectively. The milk yield was decreased significantly in the third stage of lactation than that of first and second (P<0.01). The milk yield was observed significantly (P<0.01) higher in the morning than in the evening for the three stage of lactations. From the survey results, the mean milk production per day/cow and per lactation was 8.45±1.23 and 2314.45 litres, respectively. The overall average lactation length was 9.13±1.99 months The mean age at first service(Months), age at first calving (year), days open till conception, calving interval (Months) and number of services per conception from survey data were 24.30±8.01, 3.05±0.65, 21.36±3.84 months and 1.56±0.57, respectively. The present study showed that productive and reproductive performance of crossbred cows owned by small-scale dairy producers in Jimma town was low. Thus, there is a need to improve feed supply, health care and services, access to artificial insemination, proper breeding management and supply of improved genotypes in order to improve the performance of dairy cows in the study area.
CMEs and SEPs During November‐December 2020: A Challenge for Real‐Time Space Weather Forecasting

Predictions of coronal mass ejections (CMEs) and solar energetic particles (SEPs) are a central issue in space weather forecasting. In recent years, interest in space weather predictions has expanded to include impacts at other planets beyond Earth as well as spacecraft scattered throughout the heliosphere. In this sense, the scope of space weather science now encompasses the whole heliospheric system, and multipoint measurements of solar transients can provide useful insights and validations for prediction models. In this work, we aim to analyze the whole inner heliospheric context between two eruptive flares that took place in late 2020, that is, the M4.4 flare of 29 November and the C7.4 flare of 7 December. This period is especially interesting because the STEREO‐A spacecraft was located ∼60° east of the Sun–Earth line, giving us the opportunity to test the capabilities of “predictions at 360°” using remote‐sensing observations from the Lagrange L1 and L5 points as input. We simulate the CMEs that were ejected during our period of interest and the SEPs accelerated by their shocks using the WSA‐Enlil‐SEPMOD modeling chain and four sets of input parameters, forming a “mini‐ensemble. ” We validate our results using in situ observations at six locations, including Earth and Mars. We find that, despite some limitations arising from the models' architecture and assumptions, CMEs and shock‐accelerated SEPs can be reasonably studied and forecast in real time at least out to several tens of degrees away from the eruption site using the prediction tools employed here.
PP-YOLOE: An evolved version of YOLO

In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment. We optimize on the basis of the previous PP-YOLOv2, using anchor-free paradigm, more powerful backbone and neck equipped with CSPRepResStage, ET-head and dynamic label assignment algorithm TAL. We provide s/m/l/x models for different practice scenarios. As a result, PP-YOLOE-l achieves 51.4 mAP on COCO test-dev and 78.1 FPS on Tesla V100, yielding a remarkable improvement of (+1.9 AP, +13.35% speed up) and (+1.3 AP, +24.96% speed up), compared to the previous state-of-the-art industrial models PP-YOLOv2 and YOLOX respectively. Further, PP-YOLOE inference speed achieves 149.2 FPS with TensorRT and FP16-precision. We also conduct extensive experiments to verify the effectiveness of our designs. Source code and pre-trained models are available at https://github.com/PaddlePaddle/PaddleDetection.
Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging

—In this study, the problem of computing a sparse representation for datasets of inter-dependent signals, given a ﬁxed dictionary, is considered. A dataset of inter-dependent signals is deﬁned as a matrix whose columns demonstrate strong dependencies. A computational efﬁcient sparse coding optimization problem is derived by employing regularization terms that are adapted to the properties of the signals of interest. Exploiting the merits of the learnable regularization techniques, a neural network is employed to act as structure prior and reveal the underlying signal inter-dependencies. To solve the optimization problem Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures, that process the input dataset in a block-by-block fashion. Extensive simulation results, in the context of hyperspectral image denoising, are provided, that demonstrate that the proposed algorithms outperform sig-niﬁcantly other sparse coding approaches and exhibit superior performance against recent state-of-the-art deep-learning-based denoising models. In a wider perspective, our work provides a unique bridge between a classic approach, that is the sparse representation theory, and modern representation tools that are based on deep learning modeling.
4-bit Conformer with Native Quantization Aware Training for Speech Recognition

Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios. Along this direction, model quantization has become an increasingly popular approach to compress neural networks and reduce computation cost. Most of the existing practical ASR systems apply post-training 8-bit quantization. To achieve a higher compression rate without introducing additional performance regression, in this study, we propose to develop 4-bit ASR models with native quantization aware training, which leverages native integer operations to effectively optimize both training and inference. We conducted two experiments on state-of-the-art Conformer-based ASR models to evaluate our proposed quantization technique. First, we explored the impact of different precisions for both weight and activation quantization on the LibriSpeech dataset, and obtained a lossless 4-bit Conformer model with 5.8x size reduction compared to the float32 model. Following this, we for the first time investigated and revealed the viability of 4-bit quantization on a practical ASR system that is trained with large-scale datasets, and produced a lossless Conformer ASR model with mixed 4-bit and 8-bit weights that has 5x size reduction compared to the float32 model.
Large surface acoustic wave nonreciprocity in synthetic antiferromagnets

We have studied the transmission of surface acoustic waves (SAWs) in ferromagnetic/non-magnetic/ferromagnetic tryilayers. The SAW scattering matrix is studied for devices with various non-magnetic spacer thickness, which defines the strength of the interlayer exchange coupling. We find the SAW transmission amplitude depends on its propagation direction when the two ferromagnetic layers are coupled antiferromagnetically. The degree of such SAW nonreciprocity increases with increasing exchange coupling strength and reaches 37 dB mm−1 for a device with the thinnest spacer layer. These results show the potential of interlayer exchange coupled synthetic antiferromagnets for viable acoustic nonreciprocal transmission devices, such as circulators and isolators.
Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network

—In this work, we present a method for synthetic CT (sCT) generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. We propose a loss function that favors a spatially sparse region in the image. We harness the ability of a multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via classiﬁcation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task. We demonstrate how the multi-task network with RoI focused loss offers an advantage over other conﬁgurations of the network to achieve higher accuracy of performance. This is relevant to sCT where failure to accurately estimate high Hounsﬁeld Unit values of bone could lead to impaired accuracy in clinical applications. We compare the dose calculation maps from the proposed sCT and the real CT in a radiation therapy treatment planning setup.
On the Double Doppler Effect Generated by Scatterer Motion

In a time-varying transmission channel, the received signals are subject to frequency shifts due to the Doppler effect. The Doppler frequency is dependent on the carrier frequency and channel variation rate. In a fixed wireless channel, the channel variations are caused by scatterer motion. In this paper, we investigate analytically the Doppler effects generated by scatterer motion under different scatterer velocity distributions using the ring-of-scatterers geometric model. The proposed model considers Doppler frequency components caused by scatterer mobility to both received and reflected signals at each scatterer, and therefore is called the double Doppler model. The analytical curves are compared and statistically tested with several measurement results published in the literature. At low scatterer speeds, e.g., generated by moving foliage, the exponential velocity distribution is an appropriate model to describe the time-varying nature of the fixed wireless channels. The curve fitting results also show that our analytical model better approaches the empirical curves than the single Doppler model does. However, further investigation is still needed to find a suitable scatterer velocity distribution that closely describes the double Doppler effect in fast-variation fixed wireless channels, e.g., caused by passing vehicles.
Crowdsourcing Creative Work

Creative work is launched on paid crowdsourcing platforms, yet we lack an in-depth understanding of how the two key stakeholders of crowdsourcing platforms (crowd workers and requesters) perceive and experience creative work. Creativity is a human characteristic that is difficult to automate by machines, and supplying requesters with crowdsourced human insights and complex creative work is, therefore, a timely topic for research. According to value-sensitive design, the integration of human insight into complex socio-technical systems will need to consider the perspectives of the two key stakeholders. This article-based doctoral thesis explores the stakeholder perspectives and experiences of crowdsourced creative work on two of the leading crowdsourcing platforms. The thesis has two parts. In the first part, we explore creative work from the perspective of the crowd worker. In the second part, we explore and study the requester's perspective in different contexts and several case studies. The research is exploratory and we contribute empirical insights using survey-based and artefact-based approaches common in the field of Human-Computer Interaction (HCI). In the former approach, we explore the key issues that may limit creative work on paid crowdsourcing platforms. In the latter approach, we create computational artefacts to elicit authentic experiences from both crowd workers and requesters of crowdsourced creative work. The thesis contributes a classification of crowd workers into five archetypal profiles, based on the crowd workers’ demographics, disposition, and preferences for creative work. We propose a three-part classification of creative work on crowdsourcing platforms: creative tasks, creativity tests, and creativity judgements (also referred to as creative feedback). The thesis further investigates the emerging research topic of how requesters can be supported in interpreting and evaluating complex creative work. Last, we discuss the design implications for research and practice and contribute a vision of creative work on future crowdsourcing platforms with the aim of empowering crowd workers and fostering an ecosystem around tailored platforms for creative microwork.
An Analysis of the Operational Time and Productivity in Whole-tree and Cut-to-Length Logging Operation System

This study was conducted to analyze on the operational time and productivities of logging operations in whole-tree logging operation system by tower-yarder and swing-yarder, and in cut-to-length logging operation system by excavator with grapple in order to establish the efficient logging operation system and to spread logging operation technique. In the analysis of operational time, in case of whole-tree logging operation system, the felling time was 46.6 sec/cycle by chain saw, the yarding time was 480.6 sec/cycle by tower-yarder, the yarding time was 287.4 sec/cycle by swing-yarder and the bucking time was 155.14 sec/cycle by chain saw. In case of the cut-to-length logging operation system, the felling and bucking time was 225.65 sec/cycle by chain saw, the cut-to-length extraction time was 4,972 sec/cycle by excavator with grapple, the branches and leaves extraction time was 3,143 sec/cycle by excavator with grapple. The forwarding time was 4,688 sec/cycle by wheel type mini-forwarder, the forwarding time was 2,118 sec/cycle by excavator with grapple and small forwarding vehicle. In the analysis of operational productivities, in case of whole-tree logging operation system, the average felling performance was by chain saw, the average yarding performance was by tower-yarder, by swing-yarder respectively, the average bucking performance was by chain saw. In case of the cut-to-length logging operation system, the average felling and bucking performance was by chain saw, the average cut-to-length extraction performance was by excavator with grapple, the average branches and leaves extraction performance was by excavator with grapple, the average length of operation road construction was 73.8 m/day by excavator with grapple. The average forwarding performance by wheel type mini-forwarder and the average forwarding performance by excavator with grapple and small forwarding vehicle was and , respectively.
Continual Normalization: Rethinking Batch Normalization for Online Continual Learning

Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i. d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose a simple yet effective method that we call Continual Normalization (CN) to facilitate training similar to BN while mitigating its negative effect. Extensive experiments on different continual learning algorithms and online scenarios show that CN is a direct replacement for BN and can provide substantial performance improvements. Our implementation is available at \url{https://github.com/phquang/Continual-Normalization}.
Gauged sigma models and exceptional dressing cosets

The Poisson–Lie (PL) T-duality is a generalized T-duality based on the Lie algebra of the Drinfel’d double. In particular, when we consider the PL T-duality of a coset space, the dual space is found to be a generalized coset space, which is called the dressing coset. In this paper, we investigate an extension of the dressing cosets to the U-duality setup. We propose the gauged actions for various branes in M-theory and type IIB theory, where the generalized metric is constructed by using the Exceptional Drinfel’d Algebra (EDA) and the gauge algebra is a certain isotropic subalgebra of the EDA. By eliminating the gauge fields, the gauged action reduces to the standard brane action on a certain reduced background, which we call the exceptional dressing coset. We also propose an alternative definition of the exceptional dressing cosets based on Sfetsos’s approach and reproduce a known example of non-Abelian T-duality in the U-duality-covariant formulation.
Sex differences in the association of math achievement with visual‐spatial and verbal working memory: Does the type of math test matter?

Abstract Previous research on sex differences in mathematical achievement shows mixed findings, which have been argued to depend on types of math tests used and the type of solution strategies (i.e., verbal versus visual‐spatial) these tests evoke. The current study evaluated sex differences in (a) performance (development) on two types of math tests in primary schools and (b) the predictive value of verbal and visual‐spatial working memory on math achievement. Children (N = 3175) from grades 2 through five participated. Visual‐spatial and verbal working memory were assessed using online computerized tasks. Math performance was assessed five times during two school years using a speeded arithmetic test (math fluency) and a word problem test (math problem solving). Results from Multilevel Multigroup Latent Growth Modeling, showed that sex differences in level and growth of math performance were mixed and very small. Sex differences in the predictive value of verbal and visual‐spatial working memory for math performance suggested that boys seemed to rely more on verbal strategies than girls. Explanations focus on cognitive and emotional factors and how these may interact to possibly amplify sex differences as children grow older.
The Gastrointestinal Microbiome in Infant Colic

Abstract The significant crying of infantile colic adds stress to the infant and their family, yet it has no recognized etiology. Gastrointestinal health problems and dysfunction have been suspected in the etiology of colic. Disruptions to the microbiome colonization of the gastrointestinal system may lead to excess gas and inflammation that are associated with the crying of colic. Infants with colic have increased colonization with gas-producing bacteria, like Escherichia coli and Klebsiella, and they have lower colonization of anti-inflammatory bacteria, like Bifidobacterium and Lactobacillus. Colic is known to self-resolve around 3 months of age. However, few researchers have investigated how the microbiome may be changing at colic's natural resolution without the intervention of a probiotic. With a better understanding of what leads to colic's self-resolution, future researchers may be able to identify more effective therapies for colic prevention or treatment. This scoping review presents the collective evidence from 21 original, primary research articles on what is known about the gastrointestinal microbiome at colic onset and resolution. Colic is known to self-resolve around three months of age. However, few researchers have investigated how the microbiome may be changing at colic's natural resolution without the intervention of a probiotic. With a better understanding of what leads to colic's self-resolution, future researchers may be able to identify more effective therapies for colic prevention or treatment. This scoping review presents the collective evidence from 21 original, primary research articles on what is known about the gastrointestinal microbiome at colic onset and resolution.
Is the Protein-Mediated Uptake of Drugs by Organic Anion Transporting Polypeptides a Real Phenomenon or an Artifact?

Plasma proteins or human serum albumin (HSA) have been reported to increase the in vitro intrinsic uptake clearance (CLint,uptake) of drugs by hepatocytes or organic anion transporting polypeptide (OATP)-transfected cell lines. This so-called protein-mediated uptake effect (PMUE) is thought to be due to an interaction between the drug-protein complex and the cell membrane causing an increase in the unbound drug concentration at the cell surface, resulting in an increase in the apparent CLint,uptake of the drug. To determine if the PMUE on OATP-mediated drug uptake is an artifact or a real phenomenon, we determined the effect of 1%, 2%, and 5% HSA on OATP1B1-mediated [human embryonic kidney (HEK)293 transfected cells] and passive CLint,uptake (mock HEK293 cells) on a cocktail of five statins. In addition, we determined the non-specific binding (NSB) of the statin-HSA complex to the cells/labware. The increase in uptake of atorvastatin, fluvastatin, and rosuvastatin in the presence of HSA was completely explained by the extent of NSB of the statin-HSA complex, indicating that the PMUE for these statins is an artifact. In contrast, this was not the case for OATP1B1-mediated uptake of pitavastatin and passive uptake of cerivastatin, suggesting that the PMUE is a real phenomenon for these drugs. Additionally, the PMUE on OATP1B1-mediated uptake of pitavastatin was confirmed by a decrease in its unbound IC50 in the presence of 5% HSA versus Hank’s balanced salt solution buffer (HBSS). These data question the utility of routinely including plasma proteins or HSA in uptake experiments and the previous findings on PMUE on OATP-mediated drug uptake. SIGNIFICANCE STATEMENT Here we report, for the first time, that the protein-mediated uptake effect (PMUE) on organic anion transporting polypeptide (OATP)-transported drugs could be an artifact of the non-specific binding (NSB) of the drug-albumin complex to cells/labware. Future experiments on PMUE must take into consideration such NSB. In addition, mechanisms other than PMUE need to be explored to explain the underprediction of in vivo OATP-mediated hepatic drug clearance from in vitro uptake studies.