Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon and Nitrogen Metabolism Are Jointly Regulated During Shading in Roots and Leaves of Camellia Sinensis

Numerous studies have shown that plant shading can promote the quality of green tea. However, the association of shading with metabolic regulation in tea leaves and roots remains unelucidated. Here, the metabolic profiling of two tea cultivars (“Xiangfeicui” and “Jinxuan”) in response to shading and relighting periods during the summer season was performed using non-targeted metabolomics methods. The metabolic pathway analyses revealed that long-term shading remarkably inhibit the sugar metabolism such as glycolysis, galactose metabolism, and pentose phosphate pathway in the leaves and roots of “Xiangfeicui,” and “Jinxuan” were more sensitive to light recovery changes. The lipid metabolism in the leaves and roots of “Xiangfeicui” was promoted by short-term shading, while it was inhibited by long-term shading. In addition, the intensity of the flavonoid metabolites in the leaves and roots of “Jinxuan” were upregulated with a trend of rising first and then decreasing under shading, and five flavonoid synthesis genes showed the same trend (F3H, F3′5′H, DFR, ANS, and ANR). Simultaneously, the amino acids of the nitrogen metabolism in the leaves and roots of the two cultivars were significantly promoted by long-term shading, while the purine and caffeine metabolism was inhibited in the leaves of “Xiangfeicui. ” Interestingly, CsGS1.1 and CsTSI, amino acid synthase genes was upregulated in the leaves and roots of two cultivars. These results indicated that shading could participate in carbon and nitrogen metabolic regulation of both leaf and root, and root metabolism could have a positive association with leaf metabolism to promote the shaded tea quality.
Cross-Lingual Training for Automatic Question Generation

Automatic question generation (QG) is a challenging problem in natural language understanding. QG systems are typically built assuming access to a large number of training instances where each instance is a question and its corresponding answer. For a new language, such training instances are hard to obtain making the QG problem even more challenging. Using this as our motivation, we study the reuse of an available large QG dataset in a secondary language (e.g. English) to learn a QG model for a primary language (e.g. Hindi) of interest. For the primary language, we assume access to a large amount of monolingual text but only a small QG dataset. We propose a cross-lingual QG model which uses the following training regime: (i) Unsupervised pretraining of language models in both primary and secondary languages and (ii) joint supervised training for QG in both languages. We demonstrate the efficacy of our proposed approach using two different primary languages, Hindi and Chinese. Our proposed framework clearly outperforms a number of baseline models, including a fully-supervised transformer-based model trained on the QG datasets in the primary language. We also create and release a new question answering dataset for Hindi consisting of 6555 sentences.
COMPARISON OF MUSCLE ACTIVATION USING VARIOUS HAND POSITIONS DURING THE PUSH‐UP EXERCISE

Popular fitness literature suggests that varied hand placements during push-ups may isolate different muscles. Scientific literature, however, offers scant evidence that varied hand placements elicit different muscle responses. This study examined whether different levels of electromyographic (EMG) activity in the pectoralis major and triceps brachii muscles are required to perform push-ups from each of 3 different hand positions: shoulder width base, wide base, and narrow base hand placements. Forty subjects, 11 men and 29 women, performed 1 repetition of each push-up. The EMG activity for subjects' dominant arm pectoralis major and triceps brachii was recorded using surface electrodes. The EMG activity was greater in both muscle groups during push-ups performed from the narrow base hand position compared with the wide base position (p < 0.05). This study suggests that, if a goal is to induce greater muscle activation during exercise, then push-ups should be performed with hands in a narrow base position compared with a wide base position.
A Dual Process Model of Perfectionism Based on Reinforcement Theory

This article begins with a brief review of the current literature on the structure and measurement of perfectionism. It is concluded from this review that two major types can be distinguished, a normal/healthy form and a pathological form. These two forms are then defined as positive and negative perfectionism and related directly to Skinnerian concepts of positive and negative reinforcement. The positive/negative distinction is then further elaborated on in terms of approach/avoidance behavior, goal differences, self-concept involvement, emotional conrelates, and the promoting environment. Finally, some of the more obvious theoretical and practical implications are briefly explored.
Verification of programs under the release-acquire semantics

We address the verification of concurrent programs running under the release-acquire (RA) semantics. We show that the reachability problem is undecidable even in the case where the input program is finite-state. Given this undecidability, we follow the spirit of the work on context-bounded analysis for detecting bugs in programs under the classical SC model, and propose an under-approximate reachability analysis for the case of RA. To this end, we propose a novel notion, called view-switching, and provide a code-to-code translation from an input program under RA to a program under SC. This leads to a reduction, in polynomial time, of the bounded view-switching reachability problem under RA to the bounded context-switching problem under SC. We have implemented a prototype tool VBMC and tested it on a set of benchmarks, demonstrating that many bugs in programs can be found using a small number of view switches.
Throughput and Optimal Threshold for FFR Schemes in OFDMA Cellular Networks

Fractional frequency reuse (FFR) is an efficient way to mitigate inter-cell interference (ICI) in multi-cell orthogonal frequency division multiple access (OFDMA) networks. In this paper, we investigate the throughput and the optimal threshold for the FFR scheme. The average cell throughputs are derived for both round robin (RR) and maximum SINR (MSINR) scheduling strategies when users are uniformly distributed in the cell region. It is shown from the analysis and simulation results that the throughput increases and the optimal distance threshold decreases with the number of users for both scheduling strategies. The optimal distance threshold approaches the minimum distance that users can be away from the base station when the number of users goes to infinity. The optimal distance threshold increases with the frequency reuse factor of the cell-edge region when the MSINR scheduling is used. The impact of the RR scheduling strategy on the optimal threshold of the FFR scheme is negligible. Simulation also demonstrates that the FFR scheme with the optimal threshold significantly outperforms that with the existing fixed threshold.
Sparse computation data dependence simplification for efficient compiler-generated inspectors

This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse matrix codes and evaluates its performance in the context of wavefront parallellism. Sparse computations incorporate indirect memory accesses such as x[col[j]] whose memory locations cannot be determined until runtime. The key contributions of this paper are two compile-time techniques for significantly reducing the overhead of runtime dependence testing: (1) identifying new equality constraints that result in more efficient runtime inspectors, and (2) identifying subset relations between dependence constraints such that one dependence test subsumes another one that is therefore eliminated. New equality constraints discovery is enabled by taking advantage of domain-specific knowledge about index arrays, such as col[j]. These simplifications lead to automatically-generated inspectors that make it practical to parallelize such computations. We analyze our simplification methods for a collection of seven sparse computations. The evaluation shows our methods reduce the complexity of the runtime inspectors significantly. Experimental results for a collection of five large matrices show parallel speedups ranging from 2x to more than 8x running on a 8-core CPU.
Second Harmonic Generation from Phononic Epsilon-Near-Zero Berreman Modes in Ultrathin Polar Crystal Films

Enormous optical field enhancement was predicted to occur for the Berreman mode in ultrathin films in the vicinity of epsilon near zero (ENZ). Here, we report the first experimental proof of this prediction in the mid-infrared by probing the resonantly enhanced second harmonic generation (SHG) at the longitudinal optic phonon frequency from a deeply subwavelength-thin aluminum nitride (AlN) film. Employing a transfer matrix formalism, we show that the field enhancement is completely localized inside the AlN layer, revealing that the observed SHG signal of the Berreman mode is solely generated in the AlN film. Our results demonstrate that ENZ Berreman modes in intrinsically low-loss polar dielectric crystals constitute a promising platform for nonlinear nanophotonic applications.
Regulation of NADPH oxidase subunit p22phox by NF-kB in human aortic smooth muscle cells

Abstract Accumulating evidence demonstrates the involvement of oxidative stress in the pathophysiology of cardiovascular diseases. The molecular mechanisms accountable for the increased production of reactive oxygen species remain uncertain. Among others, NADPH oxidase is one of the most important sources of superoxide in vascular cells. Here we investigate the role of NF-kB in the regulation of p22phox subunit and NADPH oxidase activity, in human aortic smooth muscle cells. Overexpression of p65/RelA or IKKβ up-regulated p22phox gene promoter activity. Transcription factor pull-down assays demonstrated the physical interaction of p65/RelA protein with predicted NF-kB binding sites. Real time PCR and Western blotting analysis showed that p22phox mRNA and protein expression are significantly down-regulated by NF-kB decoy oligodeoxynucleotides and N-alpha-tosyl-l-phenylalanine chloromethyl ketone (TPCK). Lucigenin-enhanced chemiluminescence assay revealed that NF-kB inhibitors reduce the NADPH-dependent superoxide production. Regulation of NADPH oxidase by NF-kB may represent a possible mechanism whereby pro-inflammatory factors induce oxidative stress in atherosclerosis, hypertension, diabetes, stroke or heart failure.
Securing the Legacy of TESS through the Care and Maintenance of TESS Planet Ephemerides

Much of the science from the exoplanets detected by the Transiting Exoplanet Survey Satellite (TESS) mission relies on precisely predicted transit times that are needed for many follow-up characterization studies. We investigate ephemeris deterioration for simulated TESS planets and find that the ephemerides of 81% of those will have expired (i.e., 1σ mid-transit time uncertainties greater than 30 minutes) 1 yr after their TESS observations. We verify these results using a sample of TESS planet candidates as well. In particular, of the simulated planets that would be recommended as James Webb Space Telescope (JWST) targets by Kempton et al., ∼80% will have mid-transit time uncertainties >30 minutes by the earliest time JWST would observe them. This rapid deterioration is driven primarily by the relatively short time baseline of TESS observations. We describe strategies for maintaining TESS ephemerides fresh through follow-up transit observations. We find that the longer the baseline between the TESS and the follow-up observations, the longer the ephemerides stay fresh, and that 51% of simulated primary mission TESS planets will require space-based observations. The recently approved extension to the TESS mission will rescue the ephemerides of most (though not all) primary mission planets, but the benefits of these new observations can only be reaped 2 yr after the primary mission observations. Moreover, the ephemerides of most primary mission TESS planets (as well as those newly discovered during the extended mission) will again have expired by the time future facilities such as the ELTs, Ariel, and the possible LUVOIR/Origins Space Telescope missions come online, unless maintenance follow-up observations are obtained.
Deep Square Similarity Learning for Person Re-Identification in the Edge Computing System

The increasing number of mobile phones and we-bcams has led to exponential growth in video data. Because of data transmission delay and privacy, cloud computing has no advantage in processing video data. Therefore, the edge computing is the first choice for video analysis and processing. The main computing task is placed on the edge nodes, and the central node mainly performs task scheduling. One of the key technologies of video surveillance systems is Person Re-IDentification (Re-ID). The Re-ID is used to identify whether the target pedestrian is the same person that was collected by cameras that are not spatially overlapped. In this paper, we propose a deep square similarity learning (DSSL), which is used to measure the similarity of image pairs. The algorithm takes into account the difference correlation, first-order correlation and two-order correlation of image pairs. Finally, the training data automatically adjusts the network parameters and the weights of the three correlations to minimize the loss of the training set. Consequently, the proposed DSSL can improve the performance of Re-ID. We conducted experiments on the challenging Re-ID databases CuHK03 and Male1501. The results show that the proposed DSSL method is superior to many of the most advanced Re-ID methods, and the primary recognition rate is increased by 5% and 5.47% respectively.
Boosting operational DNN testing efficiency through conditioning

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions. A challenge is that the testing often needs to produce precise results with a very limited budget for labeling data collected in field. Viewing software testing as a practice of reliability estimation through statistical sampling, we re-interpret the idea behind conventional structural coverages as conditioning for variance reduction. With this insight we propose an efficient DNN testing method based on the conditioning on the representation learned by the DNN model under testing. The representation is defined by the probability distribution of the output of neurons in the last hidden layer of the model. To sample from this high dimensional distribution in which the operational data are sparsely distributed, we design an algorithm leveraging cross entropy minimization. Experiments with various DNN models and datasets were conducted to evaluate the general efficiency of the approach. The results show that, compared with simple random sampling, this approach requires only about a half of labeled inputs to achieve the same level of precision.
Deep Q-Learning for Directed Acyclic Graph Generation

We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning. Generating graphs with specified structures is an important and challenging task in various application fields, however most current graph generation methods produce graphs with undirected edges. We demonstrate that this method is capable of generating DAGs with topology and node types satisfying specified criteria in highly sparse reward environments.
Sperm penetration in vitro of pig follicular oocytes matured in culture.

Boar ejaculated and epididymal spermatozoa were preincubated in modified KRB or the isolated oviduct and uterine horn of an oestrous sow for 4.5-5 h at 37 degrees C before introduction into medium containing ovarian oocytes previously cultured for 24 h. At examination 17-20 h after insemination 60.6% of the total oocytes had reached at least the 2nd metaphase. The proportions of oocytes penetrated (i.e. enlarged sperm head or male pronucleus and corresponding sperm tail) were 0, 10.0 and 16.7% with ejaculated spermatozoa, and 3.3, 19.6 and 26.4% with epididymal spermatozoa preincubated in modified KRB, oviduct and uterus, respectively. Although the proportion of oocytes with morphologically normal male and female pronuclei was low (10/36 = 27.8%), the results suggest that boar spermatozoa can be capacitated in the isolated genital tract of an oestrous sow and that capacitation of epididymal is better than that of ejaculated spermatozoa.
Spontaneous recovery of locomotion induced by remaining fibers after spinal cord transection in adult rats.

PURPOSE A major issue in analysis of experimental results after spinal cord injury is spontaneous functional recovery induced by remaining nerve fibers. The authors investigated the relationship between the degree of locomotor recovery and the percentage and location of the fibers that spared spinal cord transection. METHODS The spinal cords of 12 adult rats were transected at T9 with a razor blade, which often resulted in sparing of nerve fibers in the ventral spinal cord. The incompletely-transected animals were used to study the degree of spontaneous recovery of hindlimb locomotion, evaluated with the BBB rating scale, in correlation to the extent and location of the remaining fibers. RESULTS Incomplete transection was found in the ventral spinal cord in 42% of the animals. The degree of locomotor recovery was highly correlated with the percentage of the remaining fibers in the ventral and ventrolateral funiculi. In one of the rats, 4.82% of remaining fibers in unilateral ventrolateral funiculus were able to sustain a certain recovery of locomotion. CONCLUSIONS Less than 5% of remaining ventrolateral white matter is sufficient for an unequivocal motor recovery after incomplete spinal cord injury. Therefore, for studies with spinal cord transection, the completeness of sectioning should be carefully checked before any conclusion can be reached. The fact that the degree of locomotor recovery is correlated with the percentage of remaining fibers in the ventrolateral spinal cord, exclusive of most of the descending motor tracts, may imply an essential role of propriospinal connections in the initiation of spontaneous locomotor recovery.
Non-bouncing solutions in loop quantum cosmology

According to the Belinskii-Khalatnikov-Lifshitz scenario, a collapsing universe approaching a spacelike singularity can be approximated by homogeneous cosmological dynamics, but only if asymptotically small spatial regions are considered. It is shown here that the relevant small-volume behavior in solvable models of loop quantum cosmology is crucially different from the large-volume behavior exclusively studied so far. While bouncing solutions exist and may even be generic within a given quantum representation, they are not generic if quantization ambiguities such as choices of representations are taken into account. The analysis reveals an interesting interplay between sl(2,R)-representation theory and canonical effective theory.
Robust real-time monitoring of high-dimensional data streams

Robust real-time monitoring of high-dimensional data streams has many important real-world applications such as industrial quality control, signal detection, biosurveillance, but unfortunately it is highly non-trivial to develop efficient schemes due to two challenges: (1) the unknown sparse number or subset of affected data streams and (2) the uncertainty of model specification for high-dimensional data. In this article, motivated by the detection of smaller persistent changes in the presence of larger transient outliers, we develop a family of efficient real-time robust detection schemes for high-dimensional data streams through monitoring feature spaces such as PCA or wavelet coefficients when the feature coefficients are from Tukey-Huber's gross error models with outliers. We propose to construct a new local detection statistic for each feature called $L_{\alpha}$-CUSUM statistic that can reduce the effect of outliers by using the Box-Cox transformation of the likelihood function, and then raise a global alarm based upon the sum of the soft-thresholding transformation of these local $L_{\alpha}$-CUSUM statistics so that to filter out unaffected features. In addition, we propose a new concept called false alarm breakdown point to measure the robustness of online monitoring schemes, and also characterize the breakdown point of our proposed schemes. Asymptotic analysis, extensive numerical simulations and case study of nonlinear profile monitoring are conducted to illustrate the robustness and usefulness of our proposed schemes.
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video

In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video. Specifically, given a natural sentence and a video, we localize a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training. First, a set of spatio-temporal tubes, referred to as instances, are extracted from the video. We then encode these instances and the sentence using our newly proposed attentive interactor which can exploit their fine-grained relationships to characterize their matching behaviors. Besides a ranking loss, a novel diversity loss is introduced to train our attentive interactor to strengthen the matching behaviors of reliable instance-sentence pairs and penalize the unreliable ones. We also contribute a dataset, called VID-sentence, based on the ImageNet video object detection dataset, to serve as a benchmark for our task. Results from extensive experiments demonstrate the superiority of our model over the baseline approaches.
What you need is a more professional teacher

We propose a simple and efficient method to combine semi-supervised learning with weaklysupervised learning for deep neural networks. Designing deep neural networks for weaklysupervised learning is always accompanied by a tradeoff between fine-information and coarse-level classification accuracy. While using unlabeled data for semi-supervised learning, in contrast to seeking for this tradeoff, we design two extremely different models for different targets, one of which just pursues finer information for the final target. Another one is more professional to achieve higher coarse-level classification accuracy so that it is regarded as a more professional teacher to teach the former model using unlabeled data. We present an end-to-end semi-supervised learning process termed guided learning for these two different models so that improve the training efficiency. Our approach improves the 1 place result on Task4 of the DCASE2018 challenge from 32.4% to 38.3%, achieving start-of-art performance.