Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative transcriptome profiling reveals the cold stress responsiveness in two contrasting Chinese jujube cultivars

Background Low temperature is a major factor influences the growth and development of Chinese jujube ( Ziziphus jujuba Mill.) in cold winter and spring. Little is known about the molecular mechanisms for coping with different freezing stress conditions in jujube. To gain insight into the freezing-related molecular changes, we conducted comparative transcriptome analyses from the cold-sensitive cultivar ‘Dongzao’ and cold-tolerant cultivar ‘Jinsixiaozao’ using RNA-Seq. Results In our study, more than 20,000 genes were detected at chilling (4°C) and freezing (-10°C, -20°C, -30°C and -40°C) stress between two cultivars. The numbers of differentially expressed genes (DEGs) between the two cultivars was 1831, 2030, 1993, 1845 and 2137 under the five treatments, respectively. Functional enrichment analysis suggested that metabolic pathway, response to stimulus and catalytic activity were significantly enriched at stronger freezing stress. Among them, nine DEGs participated in the pathway of Ca 2+ signal, thirty-two DEGs were identified to take part in sucrose metabolism, and other DEGs were identified to participate in the regulation of ROS, plant hormones and antifreeze protein. In addition, important transcription factors ( WRKY , AP2 / ERF , NAC and bZIP ) participating in the freezing stress were activated under different degrees of freezing stress. Conclusions Our research provides a more comprehensive understanding of DEGs involved in freezing stress at the transcriptome levels in Z. jujuba , especially two cultivars with different cold tolerance. These results expanding our understanding on the complex molecular mechanism of jujube, which also provide new insights and candidate genes for genetic improvement of jujube tolerance to freezing stress.
Comparison of TRMM-Derived Rainfall Products for General and Extreme Rains over the Maritime Continent

AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain ra.
A Weak Galerkin Finite Element Method for the Linear Elasticity Problem in Mixed Form

In this paper, we use the weak Galerkin (WG) finite element method to solve the mixed form linear elasticity problem. In the mixed form, we get the discrete of proximation of the stress tensor and the displacement field. For the WG methods, we define the weak function and the weak differential operator in an optimal polynomial approximation spaces. The optimal error estimates are given and numerical results are presented to demonstrate the efficiency and the accuracy of the weak Galerkin finite element method. Mathematics subject classification: 65N30, 65N15, 65N12, 35J50, 74B05.
HyperDense-Net : A densely connected CNN for multi-modal image segmentation

Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions. Most existing approaches for this problem are based on multi-atlas label fusion strategies, which are timeconsuming and sensitive to registration errors. As alternative to these methods, we propose a hyper-densely connected 3D convolutional neural network that employs MR-T1 and T2 images as input, which are processed independently in two separated paths. An important difference with previous densely connected networks is the use of direct connections between layers from the same and different paths. Adopting such dense connectivity helps the learning process by including deep supervision and improving gradient flow. We evaluated our approach on data from the MICCAI Grand Challenge on 6-month infant Brain MRI Segmentation (iSEG), obtaining very competitive results. Among 21 teams, our approach ranked first or second in most metrics, translating into a state-of-the-art performance.
Method for improving the bank-to-turn missile manoeuvrability

This study addresses the autopilot design problem for a bank-to-turn missile to improve the manoeuvrability. A modified dynamic model is introduced by adding new reaction jet inputs to provide extra manouvrability. The H_ index, which is defined as the minimum singular value of the transfer function, is utilised to assess the manoeuvrability and the autopilot design problem is then transformed to a problem of H_ index controller design. An iterative linear matrix inequality algorithm is presented to obtain the control law. Simulation results illustrate the effectiveness of the proposed approach.
Methods for the inclusion of real world evidence in network meta-analysis

Background: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. We investigate methods for the inclusion of RWE in NMA and its impact on the uncertainty around the effectiveness estimates. Methods: A range of methods for inclusion of RWE in evidence synthesis, including Bayesian hierarchical and power prior models, were investigated by applying them to an example in relapsing remitting multiple sclerosis. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. Power prior NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs; however, this also increased the level of uncertainty. Conclusion: The power prior approach for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.
Optimization and performance evaluation of an automated ﬁ ltration method for the recovery of SARS-CoV-2 and other viruses in wastewater

(cid:129) Testing CP Select automated ﬁ ltration method for virus detection in wastewater (cid:129) The addition of protease solution increased SARS-CoV-2 recovery from wastewater. (cid:129) Higher performance was achieved using 0.05 μ m PS hollow ﬁ ber ﬁ lter tips. (cid:129) SARS-CoV-2 RNA was detected in 75 % samples using the optimized CP Select protocol. (cid:129) Comparable sensitivity of enteric virus de- tection was observed with other methods.
Discovery of natural language concepts in individual units

Although deep convolutional networks have achieved improved performance in 1 many natural language tasks, they have been treated as black boxes because they are 2 difficult to interpret. Especially, little is known about how they represent language 3 in their intermediate layers. In an attempt to understand the representations of 4 deep convolutional networks trained on language tasks, we show that individual 5 units are selectively responsive to specific morphemes, words, and phrases, rather 6 than responding to arbitrary and uninterpretable patterns. In order to quantitatively 7 analyze such intriguing phenomenon, we propose a concept alignment method 8 based on how units respond to replicated text. We conduct analyses with different 9 architectures on multiple datasets for classification and translation tasks and provide 10 new insights into how deep models understand natural language. 11
Complex Network Community Detection by Local Similarity

Complex networks are a typical form of representation of complex systems. Community structure is one of the most important structural characteristics of complex networks. In this paper, we propose a new measurement of similarity based on local structures for the purpose of detecting communities in complex networks. Compared to the similarity measures based on the entire network, the proposed similarity measure requires less computation and produces good descriptions of the structural characteristics of the networks. Meanwhile, it reverses the tendency of under-estimating produced by some existing similarity measures based on local structures. To utilize our measurement of similarity to the detection of community structures, we also generalize the Ward hierarchical clustering algorithm so that it is applicable to any object that has similarity measurement. And as an application we particularly employ this algorithm to detect community structures in complex networks. The proposed method is tested on both computer-generated and real-world networks, and is compared with the typical algorithms in community detection. Experimental results verify and conflrm the feasibility and validity of the proposed method.
On Sampling Distribution of Improved Estimators for Coefficients in Seemingly Unrelated Regression SUR Models

The main objective of this study is to estimate the parameters of the SUR models. A new family of biased estimators, called k-class that has the same asymptotic normal distribution as the Aitken generalized least squares (GLS) with the assumption that the covariance matrix is known. The exact bias have been studied and derived.
Rate-Accuracy Trade-Off in Video Classification with Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet -of- Things applications, surveillance systems and semantic crawlers of large video repositories, the compressed video content and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification that ingests AVC/H.264 encoded videos. Instead of entire compressed video bitstreams, we only retain motion vector and selected texture information at significantly reduced bitrates. Based on two CNN architectures and two action recognition datasets, we achieve 38%-59% saving in bitrate with marginal impact in classification accuracy. A simple rate-based selection between the two CNNs shows that even further bitrate savings are possible with graceful degradation in accuracy. This may allow for rate/accuracy-optimized CNN-based video classification over networks.
Sustainability of Current Account Deficit in Turkey and an Ampirical Analysys for Unit Root Test with Two Structural Breaks

In this study, Turkey’s current account deficit problem between the years 1980-2016 is tested whether it is sustainable or not. For this purpose, the ADF and the PP Unit Root test were applied firstly to the annual current account deficit (CAD)/GDP data from the IMF. Then, Lee Strazicich (2003, 2004) Unit Root Tests with two structural breaks was applied. Allowing Lee Strazicich with two structural breaks differs from the Zivot-Andrews (ZA)(1992) and Lumsdaine-Papell (LP) (1997) unit root tests in establishing the basic hypothesis. The ZA and LP unit root tests are based on the basic hypothesis of unit root existence without structural fracture. Based on the Lagrange multiplier proposed by Schmidt and Phillips (1992), LM unit root test with two structural breaks developed by Lee-Strazicich adopted the structural fractured unit root basic hypothesis. According to the ADF and PP test results, the CAD/GDP series is stationary at the level for both models. According to the LM test results, the unit root null hypothesis could be rejected. It is a stationary process in Turkey. This result is consistent with the ADF and PP test results. Empirical findings obtained from LM unit root test with two structural breaks show that current account deficits in Turkey are sustainable. The fact that the current account deficit is sustainable means the sustainability of external debts. The sustainability of deficit is one of the most emphasized issues especially from the perspective of countries which are dependent on foreign capital to finance their development and also crucial for the global system.
Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?

Many recent works advancing deep learning tend to focus on large scale setting with the goal of more effective training and better fitting. This goal might be less applicable to the case of small to medium scale. Studying deep metric learning under such setting, we reason that better generalization could be a big contributing factor to improvement of previous works, as well as the goal for further improvement. We investigate using other layers in a deep metric learning system (beside the embedding layer) for feature extraction and analyze how well they perform on training data and generalize to testing data. From this study, we suggest a new regularization practice and demonstrate state-of-the-art performance on 3 fine-grained image retrieval benchmarks: Cars-196, CUB-200-2011 and Stanford Online Product.
Progress & Compress : A scalable framework for continual learning

The difference between EWC and online-EWC is in their weighting of the past experiences, with EWC putting more weight on the initial tasks and online-EWC favouring the most recent past. For an optimal setting, where the optimisation converges and all penalty terms can be satisfied (Huszár, 2017), online-EWC is often a better choice. However, it is likely that in difficult problems, the network (an agent) doesn’t get enough time/training data to arrive at the optimal solution.
Subtitles Paraphrase Corpus for Six Languages

This paper accompanies the release of Opusparcus, a new paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The corpus consists of paraphrases, that is, pairs of sentences in the same language that mean approximately the same thing. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows. The informal and colloquial genre that occurs in subtitles makes such data a very interesting language resource, for instance, from the perspective of computer assisted language learning. For each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been checked manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators.
Effect of Dark Energy in Geometrothermodynamics and Phase Transitions of Regular AdS Black Hole

We investigate thermodynamics and geometrothermodynamics of regular Bardeen AdS black hole with a quintessence.The thermodynamics of this black hole was studied and temperature-entropy T-S and P-V plots were obtained, which indicates phase transition behavior. This is also confirmed in the divergence of specific heat against entropy, which shows a second order phase transition. Using the concept of Ruppeiner and Weinhold geometry, we calculated the curvature scalar RR and RW in the quintessence dark energy regime. While these curvature scalars enable us to identify the critical behavior, it does not happen so around phase transition points observed in specific heat divergence. To resolve this puzzle we have adopted the method of geometrothermodynamics due to Quevedo. Choosing Legendre invariant (Quevedo) metric RQ the curvature shows singularity at the same point as seen in specific heat study.
Hibikino-Musashi@Home 2018 Team Description Paper

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.
Cognitive engineering principles for enhancing human-computer performance

Many computer systems today are not satisfactory to their users. Often the user interface does not receive the attention that it deserves, even though to the user, the interface is the most important part of the computer system. Further, many interfaces are not designed with reference to how humans process information. This research addressed this problem by designing and evaluating a cognitively engineered interface. Cognitive engineering of a human‐computer interface is the leveraging of empirical findings from the cognitive sciences and application of those findings to the design of the interface. It was hypothesized that a cognitively engineered interface is superior to interfaces that are not cognitively engineered. Ten cognitive‐design principles were extracted from the literature and explicitly applied to the design of an interface. Reaction time, accuracy, workload, and preference for this interface were experimentally determined and compared with that of two other interfaces. The other two interf.
The SuperKEKB Interaction Region Corrector Magnets

The Interaction Region (IR) quadrupoles for the SuperKEKB luminosity upgrade at KEK have multiple superconducting correction coils that are needed to fulfill performance goals. These correctors were produced at BNL and undergo system tests and measurement at KEK before final installation of the completed systems in the IR hall. Here we report on measurement results.
a A cross-sectional

Objectives The aim of this study was to examine the validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF) as a measure of physical activity (PA) in young people with cerebral palsy (CP). Design Cross-sectional. Setting Participants were recruited through 8 National Health Service (NHS) trusts, one school, one university and through organisations that provide services for people with disabilities in England. Participants Sixty-four, ambulatory young people aged 10–19 years with CP [Gross Motor Function Classi ﬁ cation System (GMFCS) levels I–III] participated in this study. Mainoutcomemeasure TheIPAQ-SFwasadministeredtoparticipants.ParticipantswerethenaskedtowearawGT3X-BTtriaxialaccelerom-eter (ActiGraph, Pensacola, FL) for 7 days to objectively assess PA. Time spent in sedentary behaviour, in moderate to vigorous PA (MVPA) and in total PA (TPA) was compared between measures. Results Young people with CP self-reported less time in sedentary behaviour and underestimated the time spent in TPA, when compared to accelerometer measurements. Bland–Altman plots demonstrated poor agreement between the measures for MVPA, with upper and lower 95% limits of agreement of − 147 to 148.9minute. After adjusting for gender and GMFCS level, age was a predictor of the difference between measures for MVPA ( P <0.001) and TPA ( P <0.001). Conclusions These ﬁ ndings suggest that the IPAQ-SF is not a valid method of measuring TPA or sedentary behaviour in young people with CP and it is not appropriate for use when assessing an individual’s time in MVPA. Therefore, where feasible, an objective measure of PA should be used.
libmpk: Software Abstraction for Intel Memory Protection Keys

Intel memory protection keys (MPK) is a new hardware feature to support thread-local permission control on groups of pages without requiring modification of page tables. Unfortunately, its current hardware implementation and software supports suffer from security, scalability, and semantic-gap problems: (1) MPK is vulnerable to protection-key-use-after-free and protection-key corruption; (2) MPK does not scale due to hardware limitations; and (3) MPK is not perfectly compatible with mprotect() because it does not support permission synchronization across threads. In this paper, we propose libmpk, a software abstraction for MPK. libmpk virtualizes protection keys to eliminate the protection-key-use-after-free and protection-key corruption problems while supporting a tremendous number of memory page groups. libmpk also prevents unauthorized writes to its metadata and supports inter-thread key synchronization. We apply libmpk to three real-world applications: OpenSSL, JavaScript JIT compiler, and Memcached for memory protection and isolation. An evaluation shows that libmpk introduces negligible performance overhead (<1%) compared with insecure versions, and improves their performance by 8.1x over secure equivalents using mprotect(). The source code of libmpk will be publicly available and maintained as an open source project.
Wavelength dependent in-plane birefringence of transparent flexible films determined by using transmission ellipsometry

We introduce a simple method to determine the in-plane birefringence of transparent flexible films by using transmission spectroscopic ellipsometry. The pseudo-ellipsometric constants which can represent their sample azimuthal angle dependent characteristics are introduced. The effect of in-plane birefringence and sample azimuthal angle on the pseudo ellipsometric constants is calculated using Jones matrix formalism, and the observed sample azimuthal angle dependence of measured pseudo-ellipsometric data is well understood. Wavelength dependence of in-plane birefringence is expressed in terms of the Sellmeier dispersion equation. The best fit pseudo-ellipsometric spectra to the measured ones at the sample azimuthal angles of every 15° from 0 to 90° are searched. The dispersion coefficients of the Sellmeier equation and the azimuthal angle of the optic axis are determined for polycarbonate (PC), poly(ethylene naphthalate) (PEN), poly(ethylene terephthalate) (PET), polyimide (PI), and colorless polyimide (CPI) films.