Miracle-dz/UniFAR-FTUs huggingface dataset:
SEED PRIMING WITH SALICYLIC ACID IMPROVE SEED GERMINATION AND PHYSIOLOGICAL RESPONSES OF CARROT SEEDS

Carrot cultivation is a profitable practice for the farmers in Punjab province of Pakistan. Mostly, farmers grow it on small scale i.e., around 1-5 acres, indicating that majority of them grow it for the local market (Ahmad et al., 2012). Although it is a winter crop, usually sown during October, but to earn higher profit, farmers sow it during August-September as early crop. High temperature during August-September causes germination problem. To get optimum plant population, farmers use high seed rate (1.5 to 2 times more) as compared to normal seed rate. Abiotic stresses delays or inhibits the germination process through reducing water availability, affecting the structural organization of proteins, and changing or creating imbalance for mobilization of stored reserves inside the cells (Ibrahim, 2016). Carrot crop is usually established by direct seeding and therefore, high or low temperature may lead to poor stand. In tropics, carrot production is vulnerable to loss due to high temperature during germination of seed and stand establishment (Vieira et al., 2005). High temperature ≥35°C during or after seeding may completely inhibit or delay carrot seed germination as well as reduce uniformity ultimately resulting in poor stand establishment (Nascimento and Pereira, 2007). At advanced stages of plant life, high temperature could influence photosynthesis, respiration, water relations or stability of membrane and can alter endogenous levels of plant hormones and primary/secondary metabolites (Wahid et al., 2007). Most of the commercial carrot cultivars exhibit very low seed germination at high temperature (Pereira et al., 2007). An alternating day and night temperature (30 and 20°C) for the period of 8 h and 16 h is recommended for proper germination (AOSA, 1993). Seedling emergence rate and vigor of seedling directly affects yield and grades of carrot crop, therefore rapidness and uniformity in emergence of seedling is prerequisite (Kaur et al., 2005) and such characters are influenced by the quality of seed and adverse conditions of environment e.g., drought, high temperature and high concentration of salts in the soil (Sarlikioti et al., 2010). A considerable decrease was Pak. J. Agri. Sci., Vol. 57(2), 351-359; 2020 ISSN (Print) 0552-9034, ISSN (Online) 2076-0906 DOI:10.21162/PAKJAS/20.8975 http://www.pakjas. com.pk
Design and Modelling of Generator for Wave Energy Conversion System in Malaysia

This paper presents the design and modelling of generator for wave energy conversion system in Malaysia. This paper aims to identify the most promising topology to be used and to propose initial designs of the generator. Literature review on the previous technologies, power take-offs and linear generator have been completed and from the review, the criteria for the proposed generator designs are acquired. Two designs have been proposed in which one of the designs utilizes alternate slot winding concept. Both of the designs are simulated using Finite Element software, Ansys Maxwell. The preliminary results of flux distribution, air gap flux density, flux linkage and induced back EMF are obtained. From the simulation results, air gap flux density of both designs is within the expected range. It is also found that by implementing alternate slot winding concept, even though the total number of turn is the same, the induced back EMF produces is slightly lower than the full slot winding.
Constraints and creativity at the workplace: The role of constructive non-compliance

Constraints have been shown to have an inconsistent effect on creativity, sometimes hindering it and other times acting as triggers to creativity. To clarify this relationship, we explored how constraints force employees to solve problems, thereby leading them to be more creative at work in the face of obstacles. Thus, we identified and validated a new behaviour called constructive non-compliance (CNC) through which employees follow their own course of action and ignore rules or social norms in order to optimally pursue work-related goals. A questionnaire diary study was conducted with employees (N = 170) over three weeks. Results showed that both organizational constraints and CNC behaviour had a positive effect on creativity. Although CNC did not strengthen the relationship between organizational or job constraints and creativity as hypothesized, our results nonetheless show that triggers previously thought to be inhibitors of creativity, such as constraints and rule-deviating behavior, can lead people to be more creative. These findings have practical implications for managers, who can expend their effort by supporting employees to work within the existing constraints in the organization and make do with the resources which are available.
EFFECT OF NICKEL ON SOME BIOCHEMICAL PARAMETERS OF TISSUES LIKE LIVER, KIDNEY, BRAIN AND OVARY OF THE FRESH WATER FISH Oreochromis mossambicus

Water pollution is recognized as a potential threat to aquatic organisms. Water resources are polluted due to discharge of effluents from various industries, agricultural run-off having insecticides, pesticides, heavy metals, fertilizers, chemicals, sewage and other domestic wastes. The present study was designed to analyse the toxic effect of nickel on the fish Oreochromis mossambicus. The protein, glucose and cholesterol content of tissues from l iver, kidney, brain and ovary were analysed. The protein content of all the tissues are significantly decreased upto 121.44%, Glucose content increased significantly upto 26.40%, Cholesterol increased significantly upto 23.37% and Lipid decreased upto 34.48% significantly.
Estimating Treatment Effects with Observed Confounders and Mediators

Given a causal graph, the do-calculus can express treatment effects as functionals of the observational joint distribution that can be estimated empirically. Sometimes the do-calculus identifies multiple valid formulae, prompting us to compare the statistical properties of the corresponding estimators. For example, the backdoor formula applies when all confounders are observed and the frontdoor formula applies when an observed mediator transmits the causal effect. In this paper, we investigate the over-identified scenario where both confounders and mediators are observed, rendering both estimators valid. Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters. Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators. We also present a procedure for combining two datasets, with confounders observed in one and mediators in the other. Finally, we evaluate our methods on both simulated data and the IHDP and JTPA datasets.
A Topological Characterization of Modulo-p Arguments and Implications for Necklace Splitting

The classes PPA-$p$ have attracted attention lately, because they are the main candidates for capturing the complexity of Necklace Splitting with $p$ thieves, for prime $p$. However, these classes are not known to have complete problems of a topological nature, which impedes any progress towards settling the complexity of the problem. On the contrary, such problems have been pivotal in obtaining completeness results for PPAD and PPA, for several important problems, such as finding a Nash equilibrium [Daskalakis et al., 2009, Chen et al., 2009b] and Necklace Splitting with 2 thieves [Filos-Ratsikas and Goldberg, 2019]. In this paper, we provide the first topological characterization of the classes PPA-$p$. First, we show that the computational problem associated with a simple generalization of Tucker's Lemma, termed $p$-polygon-Tucker, as well as the associated Borsuk-Ulam-type theorem, $p$-polygon-Borsuk-Ulam, are PPA-$p$-complete. Then, we show that the computational version of the well-known BSS Theorem [Barany et al., 1981], as well as the associated BSS-Tucker problem are PPA-$p$-complete. Finally, using a different generalization of Tucker's Lemma (termed $\mathbb{Z}_p$-star-Tucker), which we prove to be PPA-$p$-complete, we prove that $p$-thieves Necklace Splitting is in PPA-$p$. All of our containment results are obtained through a new combinatorial proof for $\mathbb{Z}_p$-versions of Tucker's lemma that is a natural generalization of the standard combinatorial proof of Tucker's lemma by Freund and Todd [1981]. We believe that this new proof technique is of independent interest.
The modelling of COVID19 pathways sheds light on mechanisms, opportunities and on controversial interpretations of medical treatments. v2

The new coronavirus (2019-nCoV or SARS-CoV2), inducing the current pandemic disease (COVID-19) and causing pneumoniae in humans, is dramatically increasing in epidemic scale since its first appearance in Wuhan, China, in December 2019. The first infection from epidemic coronaviruses in 2003 fostered the spread of an overwhelming amount of related scientific efforts. The manifold aspects that have been raised, as well as their redundancy offer precious information that has been underexploited and needs to be critically re-evaluated, appropriately used and offered to the whole community, from scientists, to medical doctors, stakeholders and common people. These efforts will favour a holistic view on the comprehension, prevention and development of strategies (pharmacological, clinical etc) as well as common intervention against the new coronavirus spreading. Here we describe a model that emerged from our analysis that was focused on the Renin Angiotensin System (RAS) and the possible routes linking it to the viral infection. because the infection is mediated by the viral receptor on human cell membranes Angiotensin Converting Enzyme (ACE2), which is a key component in RAS signalling. The model depicts the main pathways determining the disease and the molecular framework for its establishment, and can help to shed light on mechanisms involved in the infection. It promptly gives an answer to some of the controversial, and still open, issues concerning predisposing conditions and medical treatments that protect from or favour the severity of the disease (such as the use of ACE inhibitors or ARBs/sartans), or to the sex related biases in the affected population. The model highlights novel opportunities for further investigations, diagnosis and appropriate intervention to understand and fight COVID19.
Robust Least Squares for Quantized Data

In this paper we formulate and solve a robust least squares problem for a system of linear equations subject to quantization error. Ordinary least squares fails to consider uncertainty in the data matrices, modeling all noise in the observed signal. Total least squares accounts for uncertainty in the data matrix, but necessarily increases the condition number of the system compared to ordinary least squares. Tikhonov regularization or ridge regression, is frequently employed to combat ill-conditioning, but requires heuristic parameter tuning which presents a host of challenges and places strong assumptions on parameter prior distributions. The proposed method also requires selection of a parameter, but it can be chosen in a natural way, e.g., a matrix rounded to the 4th digit uses an uncertainty bounding parameter of 0.5e-4. We show here that our robust method is theoretically appropriate, tractable, and performs favorably against ordinary and total least squares for both residual and absolute error reduction.
RelatIF: Identifying Explanatory Training Examples via Relative Influence

In this work, we focus on the use of influence functions to identify relevant training examples that one might hope "explain" the predictions of a machine learning model. One shortcoming of influence functions is that the training examples deemed most "influential" are often outliers or mislabelled, making them poor choices for explanation. In order to address this shortcoming, we separate the role of global versus local influence. We introduce RelatIF, a new class of criteria for choosing relevant training examples by way of an optimization objective that places a constraint on global influence. RelatIF considers the local influence that an explanatory example has on a prediction relative to its global effects on the model. In empirical evaluations, we find that the examples returned by RelatIF are more intuitive when compared to those found using influence functions.
Clusters have edges: the projected phase-space structure of SDSS redMaPPer clusters

We study the distribution of line-of-sight velocities of galaxies in the vicinity of Sloan Digital Sky Survey (SDSS) red-sequence Matched-filter Probabilistic Percolation (redMaPPer) galaxy clusters. Based on their velocities, galaxies can be split into two categories: galaxies that are dynamically associated with the cluster, and random line-of-sight projections. Both the fraction of galaxies associated with the galaxy clusters, and the velocity dispersion of the same, exhibit a sharp feature as a function of radius. The feature occurs at a radial scale Redge ≈ 2. 2Rλ, where Rλ is the cluster radius assigned by redMaPPer. We refer to Redge as the ‘edge radius’. These results are naturally explained by a model that further splits the galaxies dynamically associated with a galaxy cluster into a component of galaxies orbiting the halo and an infalling galaxy component. The edge radius Redge constitutes a true ‘cluster edge’, in the sense that no orbiting structures exist past this radius. A companion paper tests whether the ‘halo edge’ hypothesis holds when investigating the full three-dimensional phase-space distribution of dark matter substructures in numerical simulations, and demonstrates that this radius coincides with a suitably defined splashback radius.
Giant linear non-reciprocal charge transport in one-dimensional channels with a magnetic proximity effect

In non-centrosymmetric conducting channels, the electrical resistance $R$ differs when the current flows in forward ($R^+$) and backward ($R^-$) directions under an external magnetic field $B$, which results from breaking of both the spatial inversion symmetry (SIS) and time reversal symmetry (TRS). Thus far, this nonreciprocal charge transport has been observed only in the non-linear transport regime by using second harmonic measurements, where the resistance difference $R^+ - R^-$ is proportional to both the current intensity $I$ and magnetic field $B$, and these $R^+ - R^-$ values were minuscule, ~0.01$\%$ of the total resistance. Here, we report a new non-reciprocal charge transport phenomenon observed in the linear response regime, which is independent of $I$, in chiral one-dimensional (1D) edge channels of an InAs thin film interfaced with a ferromagnetic semiconductor (FMS) (Ga,Fe)Sb layer. By switching the $B$ direction, the resistance change between $\pm$ 10 T of the 1D channels is as large as 27$\%$ and is clearly detected by DC transport measurements. Our theoretical analysis using Boltzmann's equation suggests that the relativistic spin-orbit interaction (SOI) in the 1D channels and the spin-dependent scattering at the InAs/(Ga,Fe)Sb interface are the origins of this phenomenon. These findings suggest that utilizing the magnetic proximity effect (MPE) is a revolutionary way to study non-reciprocal electrical transport, which is directly related to fundamental issues in condensed matter physics such as symmetries.
Synthesis and Biological Evaluation of Trehalose-Schiff base Fragments of Cell Wall in Mycobacterium tuberculosis on Some Microorganisms

A series of α,α`-Dtrehalose-shiff base had been prepared through the trityl protection of the two primary hydroxyls of α,α`-D-trehaloses, benzoylation, removal of the trityl protective group, mesylation, azidation, catalytic hydrogenation in the presence of hydrochloride, coupling reaction with a variety of aromatic aldehydes, and subsequent debenzoylation. All new compounds were characterized by 1 H NMR, FTIR spectroscopy. Synthesised compounds were screened for antimicrobial bacterial activity against several pathogenic Microorganisms, namely Escherichia coli, Staphylococcus aureus, Mycobacterium phlei, Bacillus subtilis, Candida albicans and Aspergillus niger, and they showed enhanced antimicrobial activity.
DETERMINANTS OF INTERNATIONAL MIGRATION: AN APPLIED STUDY ON SELECTED ARAB COUNTRIES (1995-2017)

The objective of this study is to analyze the pull and push factors as determination of international migration from selected Arab countries (Algeria, Egypt, Iraq, Jordan, Lebanon, Libya, Mauritania, Morocco, Sudan, Syria, Tunisia, Yemen) to western countries (Canada, France, Germany, Britain, USA), using Unbalanced Panel Data For the period of (1995-2017). The study aimed at developing an extended gravity model to investigate economic and non-economic determinants of international immigration using negative binomial regression; this is considered as the most appropriate to estimate the relationship between the number of immigrants as a dependent variable and other explanatory variables in this study. The dependent variable is an example of a count data, which takes positive integers numbers. After examining the hypotheses of the study, the results showed that the economic factor represented by per capita income in the receiving country is the strongest attraction for migrants from Arab countries. In aWddition to the presence of former immigrants from the same immigrant country in the receiving State, the study also found an increase in the number of immigrants from Arab countries since 2011. In contrast, distance between countries and poverty in the Arab countries are the main obstacles to international migration.
Search for long-lived, massive particles in events with a displaced vertex and a muon with large impact parameter in $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

A search for long-lived particles decaying into hadrons and at least one muon is presented. The analysis selects events that pass a muon or missing-transverse-momentum trigger and contain a displaced muon track and a displaced vertex. The analyzed dataset of proton-proton collisions at $\sqrt{s} = 13$ TeV was collected with the ATLAS detector and corresponds to 136 fb$^{-1}$. The search employs dedicated reconstruction techniques that significantly increase the sensitivity to long-lived particle decays that occur in the ATLAS inner detector. Background estimates for Standard Model processes and instrumental effects are extracted from data. The observed event yields are compatible with those expected from background processes. The results are presented as limits at 95% confidence level on model-independent cross sections for processes beyond the Standard Model, and interpreted as exclusion limits in scenarios with pair-production of long-lived top squarks that decay via a small $R$-parity-violating coupling into a quark and a muon. Top squarks with masses up to 1.7 TeV are excluded for a lifetime of 0.1 ns, and masses below 1.3 TeV are excluded for lifetimes between 0.01 ns and 30 ns.
A Misreport- and Collusion-Proof Crowdsourcing Mechanism Without Quality Verification

Quality control plays a critical role in crowdsourcing. The state-of-the-art work is not suitable for crowdsourcing applications that require extensive validation of the tasks quality, since it is a long haul for the requestor to verify task quality or select professional workers in a one-by-one mode. In this paper, we propose a misreport- and collusion-proof crowdsourcing mechanism, guiding workers to truthfully report the quality of submitted tasks without collusion by designing a mechanism, so that workers have to act the way the requestor would like. In detail, the mechanism proposed by the requester makes no room for the workers to obtain profit through quality misreport and collusion, and thus, the quality can be controlled without any verification. Extensive simulation results verify the effectiveness of the proposed mechanism. Finally, the importance and originality of our work lie in that it reveals some interesting and even counterintuitive findings: 1) a high-quality worker may pretend to be a low-quality one; 2) the rise of task quality from high-quality workers may not result in the increased utility of the requestor; 3) the utility of the requestor may not get improved with the increasing number of workers. These findings can boost forward looking and strategic planning solutions for crowdsourcing.
Using Counterfactual Reasoning and Reinforcement Learning for Decision-Making in Autonomous Driving

In decision-making for autonomous vehicles, we need to predict other vehicle's behaviors or learn their behavior implicitly using machine learning. However, often the predictions and learned models have errors or might be wrong altogether which can lead to dangerous situations. Therefore, decision-making algorithms should consider counterfactual reasoning such as: what would happen if the other agents will behave in a certain way? The approach we present in this paper is two-fold: First, during training, we randomly select behavior models from a behavior model pool and assign them to the other vehicles in the scenario, such as more passive or aggressive behavior models. Second, during the application, we derive several virtual worlds from the actual world that have the same initial state. In each of these worlds, we also assign behavior models from the behavior model pool to others. We then evolve these virtual worlds for a defined time-horizon. This enables us to apply counterfactual reasoning by asking what would happen if the actual world evolves as in the virtual world. In uncertain environments, this makes it possible to generate more probable risk estimates and, thus, to enable safer decision-making. We conduct studies using a lane-change scenario that shows the advantages of counterfactual reasoning using learned policies and virtual worlds to estimate their risk and performance.
Thermodynamics of ultrastrongly coupled light-matter systems

We study the thermodynamic properties of a system of two-level dipoles that are coupled ultrastrongly to a single cavity mode. By using exact numerical and approximate analytical methods, we evaluate the free energy of this system at arbitrary interaction strengths and discuss strong-coupling modifications of derivative quantities such as the specific heat or the electric susceptibility. From this analysis we identify the lowest-order cavity-induced corrections to those quantities in the collective ultrastrong coupling regime and show that for even stronger interactions the presence of a single cavity mode can strongly modify extensive thermodynamic quantities of a large ensemble of dipoles. In this non-perturbative coupling regime we also observe a significant shift of the ferroelectric phase transition temperature and a characteristic broadening and collapse of the black-body spectrum of the cavity mode. Apart from a purely fundamental interest, these general insights will be important for identifying potential applications of ultrastrong-coupling effects, for example, in the field of quantum chemistry or for realizing quantum thermal machines.