Miracle-dz/UniFAR-FTUs huggingface dataset:
Unfolded protein response in rice (Oryza sativa L.) varieties with different level of salt stress tolerance

Plants activate the unfolded protein response as part of cellular adaptation, thereby maintaining the endoplasmic reticulum homeostasis during external stresses exposure. In this study, we examined the relationship between the degree of salt tolerance and unfolded protein response-related gene expression in India salt-tolerant Pokkali and INPARI 35 varieties compared to the Indica salt-sensitive counterpart IR64 and INPARI 4 varieties. Our result showed that the salt tolerance of Pokkali and INPARI 35 had been confirmed by their higher survival rate, higher chlorophyll content, lower electrolyte leakage, and lower H2O2 and malondialdehyde content under salt stress conditions. Furthermore, the expression of unfolded protein response genes was highest in INPARI 35, whereas IR64 and INPARI 4 exhibited low gene induction during endoplasmic reticulum stress conditions. Among the four examined varieties the salt tolerant Pokkali surprisingly showed the lowest induction of all examined unfolded protein response-related genes. These results indicated the possibility that unfolded protein response supports the rice plant for adapting to the saline environment.
Genetic diversity and population structure of Eurycoma apiculata in Eastern Sumatra, Indonesia

Abstract. Zulfahmi, Parjanto, Purwanto E, Yunus A. 2021. Genetic diversity and population structure of Eurycoma apiculata in Eastern Sumatra, Indonesia. Biodiversitas 22: 4431-4439. Information on genetic variation within and among populations of Eurycoma apiculata plants is important to develop strategies for their conservation, sustainable use, and genetic improvement. To date, no information on genetic variation within and among populations of the E. apiculata has been reported. This study aims to assess genetic diversity within and among populations of E. apiculata based on RAPD markers, and to determine populations to collect E. apiculata genetic material for conservation and breeding programs. Young leaves of E. apiculata were collected from six natural populations. Fifteen RAPD primers were used to assess the genetic diversity of each population. The data obtained were analyzed with POPGEN and Arlequin software. The amplification results of 15 selected primers produced 3-16 loci with all primers 100% polymorphic. At the species level, the mean allele per locus (Na), number of effective alleles (Ne), percentage of polymorphic loci (PPL), Nei’s gene diversity index (He) and Shannon information index (I) were 2.000, 1.244, 100%, 0.167, and 0.286, respectively. At the population level, the mean values for Na, Ne, PPL, He and I were 1.393, 1.312, 39.27%, 0.119, and 0.186, respectively. The highest value of gene diversity within population (He) was found in the Lingga-1 population and the lowest value was found in the Rumbio population. The value of genetic differentiation among populations (GST) of E. apiculata is 0.284, consistent with the results of the AMOVA analysis which found that genetic variation among populations was 23.14%, indicates that the genetic variation of E. apiculata was more stored within populations than among populations. The gene flow (Nm) value of E. apiculata was 1.259 migrants per generation among populations. The Nm value of this species was high category, and could inhibit genetic differentiation among populations. The clustering of E. apiculata population based on the UPGMA dendrogram and PCA was inconsistent with its geographic distribution, reflecting the possibility that genes migration occurred between islands in the past. The main finding of this study was the genetic variation of the E. apiculata mostly stored within the population. Therefore, the population with the highest genetic diversity is a priority for in-situ conservation, and collection of E. apiculata genetic material for ex-situ conservation and breeding programs should be carried out minimum from Lingga-1 and Pokomo populations.
Tracking the movements of North Atlantic seabirds: steps towards a better understanding of population dynamics and marine ecosystem conservation

Many seabird species undergo extensive seasonal migrations, often across large marine ecosystems or between marine areas under different national jurisdictions. With the advances of electronic tracking, especially of the application of Global Location Sensors (GLS or geolocators), it is now possible to study the seasonal movements of seabirds and link breeding populations to non-breeding habitats. To take full advantage of this development for better management and conservation, and to broaden the scope of scientific questions that can be assessed, there is a need for large-scale and multi-species programmes. The SEATRACK project with partners from 10 countries is ongoing and aims to identify the year-round distribution and movements of seabirds breeding in colonies across the northern part of the North Atlantic. By 2020, 14534 loggers were deployed on 11 species, and data from 5440 retrieved loggers have been analyzed and compiled. This Theme Section assembles original research articles based on data collected as part of the SEATRACK project from 2014 to 2019. A series of 11 papers advances the knowledge within 4 research themes: (1) variation in migration strategies among individuals, populations and species; (2) linking migration strategies and winter distribution to seabird demography and population dynamics; (3) linking migration and winter distribution to contaminants in seabirds and (4) the use of GLS data in marine spatial planning. We review existing literature within SEATRACK’s 4 themes with a focus on the temperate and arctic zones of the North Atlantic to provide a framework within which to discuss the 11 contributions and provide recommendations for future research.
ROBUST FINITE-TIME SUPOPTIMAL CONTROL OF LARGE-SCALE SYSTEMS WITH INTERACTED STATE AND CONTROL DELAYS

This paper is concerned with a problem of supoptimal nite-time control for a class of linear large-scale delay systems. The system under consideration is subjected to the state and control delays interacted between subsystems. Based on improved LMI-based approach combining with new estimation techniques, we derive sucient conditions for solving nite-time H1 control and guaranteed cost control of the system. Numerical examples are given to illustrate the validity and eectiveness of the theoretical results.
Nutrients of Carob and Seed Powders and Its Application in Some Food Products

The current study aimed to utilize carob pulp powder (due to its nutritional and healthy values) through substitution in various amounts (25, 50, and 75 %) in cocoa milk, brownies and chocolate cake as a cocoa replacer. Studying the impact of carob seed powder substitutions as a coffee replacer was also carried out. Sensory properties of all application were performed. In the present study the proximate chemical composition of Carob pods and seeds were analyzed for moisture, ash, fat, protein, carbohydrate and determination of tannins in pulp and seeds of carob. The most acceptable roasted carob powder was obtained by roasting kibbled carob at 150°C for 60 min. Carob pod powder contained high levels of carbohydrate (68.54± 0.31) appreciable amount of protein (6.83± 0.12), and low level of fat (0.24± 0.04) . They also contains appreciable amounts of minerals (mg/100g) such as phosphorus: (2255.21), potassium (8637.64) calcium (2123.00), selenium (1757. 80), iron (381. 80), zinc (24, 61). Carob pods and seeds contain 19.5 mg and 40 (mg of Gallic acid/ mg dry weight) of total polyphenols, respectively. Carob seeds contain higher percentages of protein (10.40± 0.20), fiber (76±0.05) and fat (1.67±0.05) as compared to pods, whereas pods contained higher percentages of carbohydrate than seeds. Sugars and amino acid composition were also determined. The carob pods powder was used because it possesses the advantage being free from caffeine and theobromine.
Mathematical modeling of the trajectory of a ship as a control object in local planning

In the article proposes the decomposition of the global planning task in local planning. It is proposed to combine the segment method and the sector method of division of the circle to construct the ship’s trajectory in local planning. Corrective coefficients were selected for the correct geometry of the turning trajectory, combining the segment method and the sector method of constructing the ship’s trajectory. The article uses formulas of spherical trigonometry; the trajectory of the turn depends on the rudder angle, which in turn depends on the turning ability of the vessel under the given conditions. In determining the value of the angle of the rudder, the control device must take into account the ship’s turning ability in the conditions set, the value of the angle of the turn and the constrained water area. The combination of all factors allows to differentiate the ship’s turning ability such as: high HG, middle MD or low LW with regard to passage area. The ME shifting operating modes matrix proposed in the article works as a filter of modes of operation and is completely dependent on the readings of pressure, temperature and vibration sensors. The ship’s trajectory is constructed using calculations in MS Excel and graphic simulations in the MATLAB environment.
The Social Relations Model for Count Data An Exploration of Intergenerational Co-Activity Within Families

The social relations model (SRM) is typically used to identify sources of variance in interpersonal dispositions in families. Traditionally, it uses dyadic measurements that are obtained from a round-robin design, where each family member rates each other family member. Those dyadic measurements are mostly considered to be continuous, but we, however, will discuss how the SRM can be adapted to count dyadic measurements. Such SRM for count data can be formulated in the SEM-framework by viewing it as a confirmatory factor analysis (CFA), but it can also be defined in the multilevel framework. These two frameworks result in equivalent models of which the parameters can be estimated using maximum likelihood estimation or a Bayesian approach. We perform a simulation study to compare the performance of those two estimators. As an illustration, we consider intergenerational co-activity data from a block design and contrast family dynamics between non-divorced families and stepfamilies.
Confirmation of Leptobrachellaventripunctata (Fei, Ye, and Li, 1990), based on molecular and morphological evidence in Thailand

Abstract Background Thailand is considered a global biodiversity hotspot that is known to harbour a striking diversity of endemic species. However, several research studies have determined that the level of amphibian diversity in the country has been significantly underestimated. The megophryid genus Leptobrachella Smith, 1925 is currently known to include 89 species that are primarily distributed throughout southern China and Southeast Asia; however, only seven species have been found in Thailand. New information Based on an integrative approach encompassing genetic and morphological analyses, we have concluded that the population identified from Chiang Rai Province of Thailand is conspecific with Leptobrachellaventripunctata (Fei, Ye, and Li, 1990). Importantly, this is the first confirmation record of this species, based on molecular and morphological evidence in Thailand. The discovery of this species reaffirms that the diversity within the genus has been underestimated with many species yet to be discovered. In addition, the findings of our study further highlight the lack of existing knowledge on amphibian taxonomy and an underestimation of the biodiversity that exists along these national border areas.
Global Correlations Between Chronic Inflammation and Violent Incidents: Potential Behavioral Consequences of Inflammatory Illnesses Across Socio-Demographic Levels

Introduction This review explores the potential correlation between conditions associated with chronic inflammation and measures of violence across five socioeconomic subgroups. The hypothesis being that since chronic inflammation is associated with increased aggression, an extreme version of which is violence, there should be a correlation between incidents of violence and diseases with one or more inflammatory factors, without an equivalent correlation with the contrast group. An extension of this reasoning would predict a higher correlation among lower socio-demographic index (SDI) populations as a result of fewer resources to prevent either inflammatory disease or violent crime. Methods In order to examine this potential correlation, an analysis was made comparing rates of change in incidence between violence, inflammatory conditions, and a contrast group disease of noninflammatory nature, as determined by Pearson’s correlation coefficient. Results In the low socio-demographic index, inflammatory conditions demonstrated 80% correlation with interpersonal violence, middle-low socio-demographic index inflammatory conditions demonstrated 60% correlation with interpersonal violence, middle socio-demographic index inflammatory conditions demonstrated 0% correlation with interpersonal violence, middle-high socio-demographic index inflammatory conditions demonstrated 60% correlation with interpersonal violence, and high socio-demographic index inflammatory conditions demonstrated 40% correlation with interpersonal violence. Discussion The majority of socio-demographic groups showed a significant correlation between rates of change in incidence of violence and inflammatory conditions. This correlation was not found with a similar frequency or strength in diseases not causally linked to inflammation. As predicted in the hypothesis, the highest correlations of inflammatory diseases with violence existed in the lower socio-demographic populations, supporting a link between inflammatory levels and incidences of violence.
How useful are credit gap-based early warning indicators of financial crises?

Are early warning indicators based on credit-gaps good predictors of financial crises? We systematically investigate the impact of modeling choices on the the ability of credit gap-based early warning indicators (EWIs) to predict financial crises in both advanced and emerging economies. The experiment is intentionally rigged to maximize the forecasting ability of EWIs in real time. Even after rigging the experiment, the performance of EWIs is mixed for advanced economies and poor for emerging economies. We offer several important practical implications.
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval

The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches. However, neural models’ dense representations are more suitable for re-ranking, due to their inefficiency. Sparse representations, either in symbolic or latent form, are more efficient with an inverted index. Taking the merits of the sparse and dense representations, we propose an ultra-high dimensional (UHD) representation scheme equipped with directly controllable sparsity. UHD’s large capacity and minimal noise and interference among the dimensions allow for binarized representations, which are highly efficient for storage and search. Also proposed is a bucketing method, where the embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects. We test our models with MS MARCO and TREC CAR, showing that our models outperforms other sparse models.
Fast Private Parameter Learning and Inference for Sum-Product Networks

A sum-product network (SPN) is a graphical model that allows several types of inferences to be drawn efficiently. There are two types of learning for SPNs: Learning the architecture of the model, and learning the parameters. In this paper, we tackle the second problem: We show how to learn the weights for the sum nodes, assuming the architecture is fixed, and the data is horizontally partitioned between multiple parties. The computations will preserve the privacy of each participant. Furthermore, we will use secret sharing instead of (homomorphic) encryption, which allows fast computations and requires little computational resources. To this end, we use a novel integer division to compute approximate real divisions. We also show how simple and private inferences can be performed using the learned SPN.
Large Scale Substitution-based Word Sense Induction

We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words. Evaluation on English Wikipedia that was sense-tagged using our method shows that both the induced senses, and the per-instance sense assignment, are of high quality even compared to WSD methods, such as Babelfy. Furthermore, by training a static word embeddings algorithm on the sense-tagged corpus, we obtain high-quality static senseful embeddings. These outperform existing senseful embeddings methods on the WiC dataset and on a new outlier detection dataset we developed. The data driven nature of the algorithm allows to induce corpora-specific senses, which may not appear in standard sense inventories, as we demonstrate using a case study on the scientific domain.
SAFFRON and LORD Ensure Online Control of the False Discovery Rate Under Positive Dependence

Online testing procedures assume that hypotheses are observed in sequence, and allow the signiﬁcance thresholds for upcoming tests to depend on the test statistics observed so far. Some of the most popular online methods include alpha investing, LORD++ (hereafter, LORD), and SAFFRON. These three meth-ods have been shown to provide online control of the “modiﬁed” false discovery rate (mFDR). However, to our knowledge, they have only been shown to control the traditional false discovery rate (FDR) under an independence condition on the test statistics. Our work bolsters these results by showing that SAFFRON and LORD additionally ensure online control of the FDR under nonnegative dependence . Because alpha investing can be recovered as a special case of the SAFFRON framework, the same result applies to this method as well. Our result also allows for certain forms of adaptive stopping times, for example, stopping after a certain number of rejections have been observed. For convenience, we also provide simpliﬁed versions of the LORD and SAFFRON algorithms based on geometric alpha allocations.
Z3RO Precoder Canceling Nonlinear Power Amplifier Distortion in Large Array Systems

Large array-based transmission uses the combination of a massive number of antenna elements and clever precoder designs to achieve array gain and spatially multiplex different users. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs). However, this reduces energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders, such as maximum ratio transmission (MRT), induces a coherent combining of distortion at the user location, degrading the signal quality. In this work, a linear precoder is proposed that allows working close to saturation while canceling the coherent combining of the third order nonlinear PA distortion at the user location. In contrast to other solutions, the zero third-order distortion (Z3RO) precoder does not require prior knowledge of the signal statistics and the PA model. The design consists of saturating a single or a few antennas on purpose together with an opposite phase shift to compensate for the distortion of all other antennas. The resulting array gain penalty becomes negligible as the number of base station antennas grows large.
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning tasks put a high demand on the volume of training samples. Different from its single-agent counterpart, distributed value-based multi-agent reinforcement learning faces the unique challenges of demanding data transfer, inter-process communication management, and high requirement of exploration. We propose a containerized learning framework to solve these problems. We pack several environment instances, a local learner and buffer, and a carefully designed multi-queue manager which avoids blocking into a container. Local policies of each container are encouraged to be as diverse as possible, and only trajectories with highest priority are sent to a global learner. In this way, we achieve a scalable, time-efficient, and diverse distributed MARL learning framework with high system throughput. To own knowledge, our method is the first to solve the challenging Google Research Football full game $5\_v\_5$. On the StarCraft II micromanagement benchmark, our method gets $4$-$18\times$ better results compared to state-of-the-art non-distributed MARL algorithms.
An extension of the Ising-Curie-Weiss model of self-organized criticality with a threshold on the interaction range

In arXiv:1301.6911, Cerf and Gorny constructed a model of self-organized criticality, by introducing an automatic control of the temperature parameter in the generalized Ising Curie-Weiss model. The fluctuations of the magnetization of this spin model are of order $n^{3/4}$ with a limiting law of the form $C\exp(-x^4)$, as in the critical regime of the Curie-Weiss model. In this article, we build upon this model by replacing the mean-field interaction with a one-dimensional interaction with a certain range $r_n$ which varies as a function of the number $n$ of particles. In the Gaussian case, we show that the self-critical behaviour observed in the mean-field case extends to interaction ranges $r_n\gg n^{3/4}$ and we show that this threshold is sharp, with different fluctuations when the interaction range is of order of $n^{3/4}$ or smaller than $n^{3/4}$.
Kronecker Decomposition for GPT Compression

GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain. The success of GPT is mostly attributed to its pre-training on huge amount of data and its large number of parameters. Despite the superior performance of GPT, this overparameterized nature of GPT can be very prohibitive for deploying this model on devices with limited computational power or memory. This problem can be mitigated using model compression techniques; however, compressing GPT models has not been investigated much in the literature. In this work, we use Kronecker decomposition to compress the linear mappings of the GPT-2 model. Our Kronecker GPT-2 model (KnGPT2) is initialized based on the Kronecker decomposed version of the GPT-2 model and then is undergone a very light pre- training on only a small portion of the training data with intermediate layer knowledge distillation (ILKD). Finally, our KnGPT2 is fine-tuned on downstream tasks using ILKD as well. We evaluate our model on both language modeling and General Language Understanding Evaluation benchmark tasks and show that with more efficient pre-training and similar number of parameters, our KnGPT2 outperforms the existing DistilGPT2 model significantly.