Miracle-dz/UniFAR-FTUs huggingface dataset:
Meat and Muscle BiologyTM Introduction

The objective was to investigate the effects of blast chilling on pork quality in cuts from the Longissimus thoracic et lumborum (LM), Psoas major (PM), Semimembranosus (SM; both superficial [SMS] and deep [SMD] portions) and the Triceps brachii (TB). Forty carcasses (10 carcasses per replication) were split and sides were assigned to either blast chill (BC, –32°C for 90 min, followed by spray chill at 2°C for 22.5 h) or conventional chill (CC, spray chilled at 2°C for 24 h) regimens. The LM from BC sides had lower (P < 0.05) temperature at 2 h postmortem (CC 21.8°C, BC 9.7°C), 4 h (CC 13.3°C, 3.8°C BC), 22 h (CC 4.2°C, BC 1.4°C), and 30 h (CC 0.4°C, BC –0.2°C). The LM pH in BC sides was higher at 4 h (CC 6.09, BC 6.34), 22 h (CC 5.81, BC 5.89), and 30 h (CC 5.68, BC 5.74) postmortem. The BC resulted in higher (P < 0.05) 30 h postmortem pH in the SM compared to the CC regime (CC 5.68, BC 5.74). The BC sides had increased (P < 0.05) purge in the PM (CC 0.48%, BC 0.74%) and increased (P < 0.05) cook loss in chops from the LM (CC 22.37%, BC 24.24%). The PM from BC sides were more juicy (CC 7.50, BC 8.30), less chewy (CC 2.80, BC 2.10), and more tender (CC 7.90, BC 8.60). Chops from the LM of BC sides had greater Warner-Bratzler shear force (CC 2.00, BC 2.30). Color was affected in the SM with BC sides showing darker color score (CC 3.00, BC 3.20) and redder Hunter a value (CC 16.35, BC 16.02). Chilling treatment did not affect sarcomere length in the LM. Treatment did not affect postmortem proteolysis in any cut. The response to chilling regimen is different across different muscles which may be caused by location, rate of chilling, and fiber type.
Situation Coverage Testing for a Simulated Autonomous Car - an Initial Case Study

It is hard to test autonomous robot (AR) software because of the range and diversity of external situations (terrain, obstacles, humans, peer robots) that AR must deal with. Common measures of testing adequacy may not address this diversity. Explicit situation coverage has been proposed as a solution, but there has been little empirical study of its effectiveness. In this paper, we describe an implementation of situation coverage for testing a simple simulated autonomous road vehicle, and evaluate its ability to find seeded faults compared to a random test generation approach. In our experiments, the performance of the two methods is similar, with situation coverage having a very slight advantage. We conclude that situation coverage probably does not have a significant benefit over random generation for the type of simple, research-grade AR software used here. It will likely be valuable when applied to more complex and mature software.
Entanglement-Assisted Quantum Codes from Cyclic Codes

Entanglement-assisted quantum-error-correcting (EAQEC) codes are quantum codes which use entanglement as a resource. These codes can provide better error correction than the (entanglement unassisted) codes derived from the traditional stabilizer formalism. In this paper, we provide a general method to construct EAQEC codes from cyclic codes. Afterwards, the method is applied to Reed–Solomon codes, BCH codes, and general cyclic codes. We use the Euclidean and Hermitian construction of EAQEC codes. Three families have been created: two families of EAQEC codes are maximal distance separable (MDS), and one is almost MDS or almost near MDS. The comparison of the codes in this paper is mostly based on the quantum Singleton bound.
Photometric and Spectroscopic Studies of Superoutbursts of Three Dwarf Novae Independently Identified by the SVOM/GWAC System in 2018

We report our photometric and spectroscopic follow-up observations of the superoutbursts of three dwarf novae (DNe; GWAC 180415A, GWAC 181017A, and GWAC 181211A) identified independently by the Ground Wide-angle Cameras system, one of the ground-based instruments of the China–France SVOM mission. Based on a combination of our photometry and that taken from the American Association of Variable Star Observers, our period analysis of the superhumps enables us to determine the mass ratios to be 0.0967–0.1163, 0.1879–0.1883, and 0.0981–0.1173 for GWAC 180415A, GWAC 181017A, and GWAC 181211A, respectively. GWAC 180415A can be firmly identified as a WZ sge-type DNe due to its long duration (∼2 weeks) multiple rebrightenings with amplitudes of 3–4 mag, the early superhump associated with a double-wave modulation and the low-mass ratio. The inferred low-mass ratio and location in the diagram suggest that GWAC 181211A is a WZ sge-type DNe candidate. The measured Balmer decrements suggest the Balmer line emission is produced from an optical thick region in GWAC 180415A and GWAC 181017A, and from an optically thin region in GWAC 181211A.
Automated derivation of parametric data movement lower bounds for affine programs

Researchers and practitioners have for long worked on improving the computational complexity of algorithms, focusing on reducing the number of operations needed to perform a computation. However the hardware trend nowadays clearly shows a higher performance and energy cost for data movements than computations: quality algorithms have to minimize data movements as much as possible. The theoretical operational complexity of an algorithm is a function of the total number of operations that must be executed, regardless of the order in which they will actually be executed. But theoretical data movement (or, I/O) complexity is fundamentally different: one must consider all possible legal schedules of the operations to determine the minimal number of data movements achievable, a major theoretical challenge. I/O complexity has been studied via complex manual proofs, e.g., refined from Ω(n3/√S) for matrix-multiply on a cache size S by Hong & Kung to 2n3/√S by Smith et al. While asymptotic complexity may be sufficient to compare I/O potential between broadly different algorithms, the accuracy of the reasoning depends on the tightness of these I/O lower bounds. Precisely, exposing constants is essential to enable precise comparison between different algorithms: for example the 2n3/√S lower bound allows to demonstrate the optimality of panel-panel tiling for matrix-multiplication. We present the first static analysis to automatically derive non-asymptotic parametric expressions of data movement lower bounds with scaling constants, for arbitrary affine computations. Our approach is fully automatic, assisting algorithm designers to reason about I/O complexity and make educated decisions about algorithmic alternatives.
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization

Spatiotemporal action localization requires the incorporation of two sources of information into the designed architecture: (1) temporal information from the previous frames and (2) spatial information from the key frame. Current state-of-the-art approaches usually extract these information with separate networks and use an extra mechanism for fusion to get detections. In this work, we present YOWO, a unified CNN architecture for real-time spatiotemporal action localization in video streams. YOWO is a single-stage architecture with two branches to extract temporal and spatial information concurrently and predict bounding boxes and action probabilities directly from video clips in one evaluation. Since the whole architecture is unified, it can be optimized end-to-end. The YOWO architecture is fast providing 34 frames-per-second on 16-frames input clips and 62 frames-per-second on 8-frames input clips, which is currently the fastest state-of-the-art architecture on spatiotemporal action localization task. Remarkably, YOWO outperforms the previous state-of-the art results on J-HMDB-21 and UCF101-24 with an impressive improvement of ~3% and ~12%, respectively. We make our code and pretrained models publicly available.
Thermoelectricity Modeling with Cold Dipole Atoms in Aubry Phase of Optical Lattice

We study analytically and numerically the thermoelectric properties of a chain of cold atoms with dipole-dipole interactions placed in an optical periodic potential. At small potential amplitudes the chain slides freely that corresponds to the Kolmogorov-Arnold-Moser phase of integrable curves of a symplectic map. Above a certain critical amplitude the chain is pinned by the lattice being in the cantori Aubry phase. We show that the Aubry phase is characterized by exceptional thermoelectric properties with the figure of merit Z T = 25 being 10 times larger than the maximal value reached in material science experiments. We show that this system is well accessible for magneto-dipole cold atom experiments that opens new prospects for investigations of thermoelectricity.
WST 49.2 SC26 corr

Scaling is a major problem and bottleneck in operation of all types of equipment used for handling and/or processing saline streams. The challenge is to be able to determine the criteria to define and predict the onset and rate of scale formation. Even though precipitating salts co-exist in industrial water systems, due to the complexity of the fouling process, research has primarily been concentrated on a single salt precipitation. And solubility concentrations obtained for single salt crystallization have been used as a measure of scaling potential in actual practical situations and usually not with much success. This paper critically assesses the current practices and shows based on the recent work by the author’s research group that the thermodynamics and kinetics of single salt crystallization are not applicable to practical conditions of feed water when a mixture of salts is present. Presence of other species, salinity effects and even presence of trace components could have a detrimental effect on scaling potential of given water and cannot be ignored. Application of Debye Huckle theory or a modified version of that though essential, is not sufficient.
Manipulating the Difference between the Day and Night Temperatures Can Enhance the Quality of Astragalus membranaceus and Codonopsis lanceolata Plug Seedlings

Astragalus membranaceus Bunge and Codonopsis lanceolata Benth. et Hook. f. are two famous medical species in Korea, China, and Japan, mainly used for treating diseases including cancer, obesity, and inflammation. Manipulation of the difference between the day and night temperatures (DIF) is an efficient horticultural practice to regulate the growth and development of vegetables in a glasshouse. However, little research has focused on how the DIF influences the plug seedling quality of medicinal plants. In this study, uniform plug seedlings were cultivated in three environmentally controlled chambers under an average daily temperature of 20 °C with negative (−10 °C), zero, or positive (+10 °C) DIFs, and the same relative humidity (75%), photoperiod (12 h), and light intensity (150 μmol·m−2·s−1 photosynthetic photon flux density with white LEDs). The results showed that the DIF had a noticeable effect on the growth, development, and morphology of A. membranaceus and C. lanceolata plug seedlings. The positive DIF (+10 °C) significantly increased the biomass (shoot, root, and leaf), stem diameter, and Dickson’s quality index, indicating an enhanced plug seedling quality. Moreover, the contents of primary and secondary metabolites, including soluble sugar, starch, total phenols and flavonoids, were higher with higher DIFs, where the maximum values were found at 0 °C or +10 °C DIF. Furthermore, the increases in the chlorophyll content and stomatal conductance were obtained in a positive DIF, indicating that a positive DIF was favorable to photosynthesis. An analysis of the gene expression showed that a positive DIF (+10 °C) up-regulated the expression of photosynthetic genes, including GBSS, RBCL, and FDX. In conclusion, the results of this study recommend a positive DIF (+10 °C) for enhancing the quality of A. membranaceus and C. lanceolata plug seedlings.
Skeletal age estimation and the epiphyseal scar : challenging the status quo

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www.econstor.eu How to Estimate Beta?∗

Researchers and practitioners face many choices when estimating an asset’s sensitivities toward risk factors, i.e., betas. We study the effect of different data sampling frequencies, forecast adjustments, and model combinations for beta estimation. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as a shrinkage toward the industry average yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors. JEL classification: G12, G11, G17
On the Time-Based Conclusion Stability of Software Defect Prediction Models

Researchers in empirical software engineering often make claims based on observable data such as defect reports. Unfortunately, in many cases, these claims are generalized beyond the data sets that have been evaluated. Will the researcher's conclusions hold a year from now for the same software projects? Perhaps not. Recent studies show that in the area of Software Analytics, conclusions over different data sets are usually inconsistent. In this article, we empirically investigate whether conclusions in the area of defect prediction truly exhibit stability throughout time or not. Our investigation applies a time-aware evaluation approach where models are trained only on the past, and evaluations are executed only on the future. Through this time-aware evaluation, we show that depending on which time period we evaluate defect predictors, their performance, in terms of F-Score, the area under the curve (AUC), and Mathews Correlation Coefficient (MCC), varies and their results are not consistent. The next release of a product, which is significantly different from its prior release, may drastically change defect prediction performance. Therefore, without knowing about the conclusion stability, empirical software engineering researchers should limit their claims of performance within the contexts of evaluation, because broad claims about defect prediction performance might be contradicted by the next upcoming release of a product under analysis.
Optimal adaptive group testing

The group testing problem is concerned with identifying a small number $k \sim n^\theta$ for $\theta \in (0,1)$ of infected individuals in a large population of size $n$. At our disposal is a testing procedure that allows us to test groups of individuals. This paper considers two-stage designs where the test results of the first stage can inform the design of the second stage. We are interested in the minimum number of tests to recover the set of infected individuals w.h. p. Equipped with a novel algorithm for one-stage group testing from [Coja-Oghlan, Gebhard, Hahn-Klimroth \& Loick 2019], we propose a polynomial-time two-stage algorithm that matches the universal information-theoretic lower bound of group testing. This result improves on results from [Mezard \& Toninelli 2011] and resolves open problems prominently posed in [Aldridge, Johnson \& Scarlett 2019, Berger \& Levenshtein 2002, Damaschke \& Muhammad 2012].
Feedback Linearization Based on Gaussian Processes With Event-Triggered Online Learning

Combining control engineering with nonparametric modeling techniques from machine learning allows for the control of systems without analytic description using data-driven models. Most of the existing approaches separate learning, i.e., the system identification based on a fixed dataset, and control, i.e., the execution of the model-based control law. This separation makes the performance highly sensitive to the initial selection of training data and possibly requires very large datasets. This article proposes a learning feedback linearizing control law using online closed-loop identification. The employed Gaussian process model updates its training data only if the model uncertainty becomes too large. This event-triggered online learning ensures high data efficiency and thereby reduces computational complexity, which is a major barrier for using Gaussian processes under real-time constraints. We propose safe forgetting strategies of data points to adhere to budget constraints and to further increase data efficiency. We show asymptotic stability for the tracking error under the proposed event-triggering law and illustrate the effective identification and control in simulation.
Idealised simulations of the deep atmosphere of hot Jupiters

Context. The anomalously large radii of hot Jupiters has long been a mystery. However, by combining both theoretical arguments and 2D models, a recent study has suggested that the vertical advection of potential temperature leads to a hotter adiabatic temperature profile in the deep atmosphere than the profile obtained with standard 1D models. Aims. In order to confirm the viability of that scenario, we extend this investigation to 3D, time-dependent models. Methods. We use a 3D general circulation model DYNAMICO to perform a series of calculations designed to explore the formation and structure of the driving atmospheric circulations, and detail how it responds to changes in both the upper and deep atmospheric forcing. Results. In agreement with the previous, 2D study, we find that a hot adiabat is the natural outcome of the long-term evolution of the deep atmosphere. Integration times of the order of 1500 yr are needed for that adiabat to emerge from an isothermal atmosphere, explaining why it has not been found in previous hot Jupiter studies. Models initialised from a hotter deep atmosphere tend to evolve faster toward the same final state. We also find that the deep adiabat is stable against low-levels of deep heating and cooling, as long as the Newtonian cooling timescale is longer than ~3000 yr at 200 bar. Conclusions. We conclude that steady-state vertical advection of potential temperature by deep atmospheric circulations constitutes a robust mechanism to explain the inflated radii of hot Jupiters. We suggest that future models of hot Jupiters be evolved for a longer time than currently done, and when possible that models initialised with a hot deep adiabat be included. We stress that this mechanism stems from the advection of entropy by irradiation-induced mass flows and does not require a (finely tuned) dissipative process, in contrast with most previously suggested scenarios.
Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems

Generative adversarial networks (GANs) are initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physical systems such as turbulent flows. However, a critical question must be answered before GANs can be considered trusted emulators for physical systems: do GANs-generated samples conform to the various physical constraints? These include both deterministic constraints (e.g., conservation laws) and statistical constraints (e.g., energy spectrum in turbulent flows). The latter has been studied in a companion paper (Wu et al. 2019. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. arXiv:1905.06841). In the present work, we enforce deterministic yet approximate constraints on GANs by incorporating them into the loss function of the generator. We evaluate the performance of physics-constrained GANs on two representative tasks with geometrical constraints (generating points on circles) and differential constraints (generating divergence-free flow velocity fields), respectively. In both cases, the constrained GANs produced samples that precisely conform to the underlying constraints, even though the constraints are only enforced approximately. More importantly, the imposed constraints significantly accelerate the convergence and improve the robustness in the training. These improvements are noteworthy, as the convergence and robustness are two well-known obstacles in the training of GANs.
RESPONSE OF SENSITIVE CORN HYBRIDS TO THE ASSOCIATION OF NICOSULFURON AND TIMING OF NITROGEN APPLICATION

Nicosulfuron is an herbicide used in corn crop for controlling weeds, some hybrids are sensitivity to nicosulfuron even more when it is applied near to the application of nitrogen fertilizers. Thus, this study aimed to evaluate the response of sensitive corn hybrids to association of urea and coated urea applied near to nicosulfuron application on V4 corn stage. P 3646 hybrid was the most sensitive, and urea and coated urea applied near to nicosulfuron had significant effect on corn growth for all hybrids. Most hybrids showed high or significant (p < 0.10) correlation coefficients for grain yield, cob length and row per ear. SYN 7341 hybrid was the one that showed better results in grain yield with urea and nicosulfuron combination. Coated urea should not be applied after V8corn stage when nicosulfuron is applied at V4.
Alpha Amylase Inhibitory Activity of Different Extract of Terminalia arjuna Bark

In India, medicinal plants are used by all sections of people either directly as folk remedies or in different indigenous system of medicine. The present study was based on the use of ethnomedicinal plant bark to evaluate the alpha amylase inhibitory activity. Bark of Terminalia arjuna was collected and air dried and soxhlet extracted by using standard methods for flavonoid, alkaloid, steroid and different solvents. These extracts obtained were then tested for the alpha amylase inhibitory activity using the chromogenic DNSA method and starch iodine min triplicates. Data were expressed as mean ± SEM (standard error of the mean). Data were analyzed by oneway analysis of variance and P values were considered significant at P<0.05. Among the tested extracts, methanol, free flavonoids and bound flavonoids extract exhibited significant alpha amylase inhibitory activity. Results of the present study revealed that methanol extracts of T. arjuna bark showed great antidiabetic potential and may be exploited for future antidiabetic drugs.
Monte Carlo studies of modified scalable designs for quantum computation

As the building blocks of topological quantum computation, Majorana zero modes (MZMs) have attracted tremendous attention in recent years. Scalable mesoscopic island designs with MZMs show great potential in quantum information processing. However, these systems are susceptible to quasi-particle poisoning which would induce various parity-breaking errors. To solve this problem, we modify the mesoscopic islands with gate-tunable valves and non-topological backbones. We study the lifetime of the Majorana qubits on these modified islands which are coupled to local bosonic and fermionic thermal baths. We consider both the parity-breaking and parity-preserving errors, and propose a parity correction scheme. By using Jordan-Wigner transformation, we analyze the probability of logical X and Y errors. The open quantum system is described by the Pauli master equation, and standard Monte Carlo simulations are applied to observe the behavior of the system when the parity correction proposal is implemented. The results demonstrate that (1) our parity correction proposal is effective to most of the parity-breaking errors; (2) the lifetime of the qubit benefits from larger island size before it meets the threshold; (3) small chemical potential $ \mu $ on the non-topological backbones and fine tuned paring potential $ \Delta $ of the topological bulk segment are required for high probability of correctness. Our results provide an effective error correction scheme for the parity-breaking errors.