Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Systematic Relationships of Arabidopsis: A Molecular and Morphological Perspective

Our rapidly expanding knowledge of the molecular and developmental biology of Arabidopsis thaliana is of fundamental importance to a better understanding of biological systems in crops and other plants. This knowledge will be particularly valuable in future examinations of evolutionary processes at the generic, specific, and population levels. To effectively utilize the knowledge gained from A. thaliana in an evolutionary context, information concerning its phylogenetic relationships is essential. However, despite the great utility of A. thaliana for comparative studies, remarkably little basic information is available concerning the systematics and evolution of the genus Arabidopsis and its relationships within the mustard family. The boundaries of the genus Arabidopsis are very poorly known, and the majority of species now assigned to the genus are not yet accessible for scientific study because of their restricted geographic distributions in remote mountainous areas of Asia. In the following sections, we summarize available information concerning the systematics and phylogenetic position of A. thaliana both from the standpoint of traditional morphologically based classifications and from new data from molecular comparisons. We review the geographic distribution and chromosome numbers of the other species currently placed in the genus Arabidopsis and the problematic nature of its delimitation and tribal placement in the Brassicaceae. We also present new information on the phylogenetic relationships of A. thaliana within the family obtained from sequence comparisons of the chloroplast rbc L gene and from preliminary analyses of restriction site data from the chloroplast genome. SYSTEMATIC RELATIONSHIPS OF ARABIDOPSIS Familial and Tribal Classification of Arabidopsis
Textural and deep learning methods in recognition of renal cancer types based on CT images

Recent advancements in deep learning have opened new prospects in many areas of research. Especially interesting field is biomedical image analysis, where plenty of problems wait for efficient solution. The aim of this work is to develop new approaches to recognition of different types of renal cancer on the basis of Computed Tomography (CT) imaging. Two different directions will be investigated. One uses the texture descriptors of the images to define the diagnostic features. They are next combined with support vector machine responsible for final recognition and classification. The second applies deep learning approach using different configurations of Convolutional Neural Networks. The experimental research for both textural and deep learning approaches was conducted on real world dataset of CT scans consisting of eight types of renal cell carcinomas. The proposed structures of predictive system were able to achieve the level of accuracy around 90% for complex and unbalanced datasets.
Stockgram: Deep Learning Model for Digitizing Financial Communications via Natural Language Generation

This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
Prime Inner Product Encoding for Effective Wildcard-Based Multi-Keyword Fuzzy Search

With the prevalence of cloud computing, a growing number of users are delegating clouds to host their sensitive data. To preserve user privacy, it is suggested that data is encrypted before outsourcing. However, data encryption makes keyword-based searches over ciphertexts extremely difficult. This is even challenging for <italic>fuzzy search</italic> that allows uncertainties or misspellings of keywords in a query. In this article, we propose a prime inner product encoding (PIPE) scheme, which makes use of the <italic>indecomposable</italic> property of prime numbers to provide efficient, highly accurate, and flexible multi-keyword fuzzy search. Our main idea is to encode either a query keyword or an index keyword into a vector filled with primes or reciprocals of primes, such that the result of vectors’ inner product is an integer only when two keywords are similar. Specifically, we first construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>PIPE</mml:mtext><mml:mn>0</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq1-3020688.gif"/></alternatives></inline-formula> that is secure in the known ciphertext model. Unlike existing works that have difficulty supporting AND and OR semantics simultaneously, <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>PIPE</mml:mtext><mml:mn>0</mml:mn></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq2-3020688.gif"/></alternatives></inline-formula> gives users the flexibility to specify different search semantics in their queries. Then, we construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{\text{S}}$</tex-math><alternatives><mml:math><mml:msub><mml:mtext>PIPE</mml:mtext><mml:mtext>S</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="liu-ieq3-3020688.gif"/></alternatives></inline-formula> that subtly adds random noises to a query vector to resist linear analyses. Both theoretical analyses and experiment results demonstrate the effectiveness of our scheme.
Model Predictive Control Guided Reinforcement Learning Control Scheme

Deep Reinforcement Learning (DRL) is an artificial intelligence technology that can complete decision-making tasks by interaction. It has been successfully applied to various games. However, there are still many challenges when this technique is applied to the industrial process control due to the low sample efficiency and the inability to deal with large time delay. In this paper, a novel Model Predictive Control (MPC) guided Reinforcement Learning Control (MP-RLC) scheme is proposed for the process control. In this scheme, Model predictive control is directly combined with Reinforcement Learning (RL) to guide the training process, thus greatly improving the sample efficiency of reinforcement learning and effectively solving the problem of time delay. The simulation results on both a third-order linear system and a nonlinear continuous stirred tank reactor (CSTR) system with large time delay demonstrate that this scheme can not only accelerate the training process but also improve the control performance, which is superior to both standalone RL and MPC schemes. The proposed approach may help to pave the way for DRL applied to industrial processes.
Inspecting Gradual and Abrupt Changes in Emotion Dynamics With the Time-Varying Change Point Autoregressive Model

Abstract. Recent studies have shown that emotion dynamics such as inertia (i.e., autocorrelation) can change over time. Importantly, current methods can only detect either gradual or abrupt changes in inertia. This means that researchers have to choose a priori whether they expect the change in inertia to be gradual or abrupt. This will leave researchers in the dark regarding when and how the change in inertia occurred. Therefore in this article, we use a new model: the time-varying change point autoregressive (TVCP-AR) model. The TVCP-AR model can detect both gradual and abrupt changes in emotion dynamics. More specifically, we show that the inertia of positive affect and negative affect measured in one individual differs qualitatively in how it changes over time. Whereas the inertia of positive affect increased only gradually over time, negative affect changed both in a gradual and abrupt fashion over time. This illustrates the necessity of being able to model both gradual and abrupt changes in order to detect meaningful quantitative and qualitative differences in temporal emotion dynamics.
Impacts of high intensity crop rotation and N management on oilseed rape productivity in Germany

Abstract. A rotational field experiment was established in the year 2002 at the experimental farm Etzdorf in the Hercynian dry region of central Germany. Since 2005 field measured datasets were used to determine the effect of different preceding crop combinations and different nitrogen (N) fertilisation treatments on the seed yield, oil content, oil yield and N-use efficiency of oilseed rape (Brassica napus L.). The preceding crop combinations compared were winter wheat (Triticum aestivum L.)-winter wheat (WW), WW-oilseed rape (OSR), OSR-OSR and an OSR monoculture. In addition to the preceding crop combination, N fertiliser treatments with either 120 kg N ha–1 or 180 kg N ha–1 were established in the year 2013. Overall the results demonstrated that seed yield, oil yield and N-use efficiency all declined with an increased cropping intensity for the period 2005–2012. Higher N rates in the 2013–2014 seasons increased seed yield and oil yield when OSR followed WW-WW pre-crops. OSR monoculture had lowest yield independent of applied N. Seed yield declined from 4.61 t ha–1 (OSR following WW-WW) to 4.28 t ha–1 in the OSR monoculture with 120 kg N ha–1, and from 4.81 t ha–1 (following WW-WW) to 4.42 t ha–1 in the OSR monoculture with 180 kg N ha–1. Higher N rates generally reduced N-use efficiency, with highest N-efficiency for WW-WW-OSR (38.4 kg kg–1), and lowest for continuous OSR receiving 180 kg N ha–1 (24.5 kg kg–1). These results emphasise the importance of crop rotation to maintain seed yield and oil yield of oilseed rape, and to maximise the response to applied N. A reduced N rate increased N-use efficiency and reduced the risk of high N surpluses without a significant/equivalent decrease of the seed yield when the rotation was optimised.
Identification of Topoisomerase I Inhibitor from Gardenia Jasminoides Ellis and its Anti - Cancer Activity in Oral Cancer Cell

Topoisomerases are essential enzymes involved in all processes of DNA metabolism, and their inhibitors have been identified as potential anti-cancer agents. The present study examined the effect of linoelaidic acid (C18 polyunsaturated fatty) compounds derived from Gardenia jasminoids Ellis extract on the activity of eukaryotic topoisomerases inhibition. The present study identified linoelaidic acid compounds using open column fraction, HPLC, NMR and LC/MS which have effects on cell death in oral cancer cell line, FaDu, but not in immortalized normal cell line, HaCaT. Subsequent studies revealed linoelaidic acid-induced autophagy through LC3 activation. Finally, its inhibition of topoisomerase I and selectively induction of oral cancer cell death possibly implies that linoelaidic acid can be a role as potenial agents in the prevention and therapy of oral cancer.
A strategy to label RNA for visualization

Strategies to label RNA for visualization A thesis presented to the Department of Biology Graduate School of Arts and Sciences Brandeis University Waltham, Massachusetts By Chinmoyee Karmakar Many experimental studies require the isolation of multi-subunit RNA-protein complexes for detailed analysis and characterization. It would be highly advantageous if an investigator could site-specifically label the RNA molecule(s) in such a native complex with a probe, such as a flourophore, to aid the characterization and biochemical as well as structural study of the complex. However, the commonly used RNA labeling techniques require chemical modifications of RNA that either cannot be done on a natively purified RNA or may be deleterious to the structure of an assembly. I propose an RNA labelling strategy that is gentle and simple and accomplished following purification of a native RNA-protein complex. The strategy uses: a small truncated RNA binding protein (U1A), fused with ten or six consecutive Histidine residues or a His-tag at the N-terminus; a short RNA stem-loop recognized by U1A; and a chemical reagent that recognizes the His-tag fused to U1A. The combination of the His-tagged U1A bound to a short RNA stem-loop and the His-tag bound to a functional chemical probe can iv serve as a ‘binding module’. Such a binding module can be made to recognize a specific RNA sequence in a target RNA molecule by including in the RNA stem-loop of the module a single-stranded sequence that is complementary to a single-stranded sequence in the RNA target. Importantly, the His-tag on the U1A protein serves two uses: (1) for purification of the U1A protein; and (2) as the binding target for a reagent composed of a tris-NTA moiety conjugated with a functional probe, such as a gold nanoparticle or flourophore. In this thesis, I describe the preparation of the U1A protein, the RNA short stem-loop RNA molecules to which the U1A protein binds, and the design and preparation of the target RNA molecules to establish the utility of the ‘binding module’ approach for RNA labeling.
Single Negative Metamaterials take on Negative Indices owing to Multiple Scattering: Demonstration with an Acoustic Super-lens

Abstract— We evidence that single negative metamaterials can turn into double negative ones, hence leading to a negative band solely by breaking the symmetry. We explain this phenomenon from multiple scattering effects and give an analogy with the phonon optical branch. We experimentally demonstrate a negative index acoustic super-lens using a soda can hexagonal array with a focal spot 15 times smaller than the wavelength.
Penalized variance components for association of multiple genes with traits

Variance component models have gained popularity for genetic analyses, driven by their flexibility to simultaneously analyze multiple genetic variants in a gene by kernel statistics, and their ability to account for population stratification via genomic relationship matrices. For exploratory analyses with modest sample sizes and a potentially large number of variance components, it can be challenging to use standard maximum‐likelihood or restricted maximum‐likelihood methods to estimate variance components, because these iterative methods often fail to converge when likelihood surfaces are fairly flat, and standard‐likelihood ratio statistical tests are not adequate. To overcome these limitations, we developed a penalized‐likelihood model, whereby the penalty function follows the popular elastic‐net approach, applying both L1 and L2 penalties to the variance components. By simulations, we demonstrate the potential gain in power by using both L1 and L2 penalties, and results from our simulations suggest that assigning 80% of the penalty parameter to the L1 penalty and 20% to the L2 penalty provides a reasonable balance between false‐positive and false‐negative results. Larger sample size improves the properties of our methods, at the cost of longer computation time. Application of our methods to a study of the influence of DNA methylation on levels of cortisol in reaction to stress testing shows how our method can be used to prioritize findings for further functional studies.
The Effects of Locational Point Representation of Apartment Complexes on Hedonic Valuation of Air Quality

The marginal implicit price of air quality can be measured by taking a partial derivative of hedonic price function (HPF) with respect to the level of air quality. It has been pointed out that the size of the marginal implicit price varies with the use of different function forms, different estimation methods, and the different ways of measuring air quality level in estimating HPF. In addition to these factors, this study shows theoretically and empirically the way housing properties are represented on a digital map could differentiate the size of marginal implicit price of air quality when GIS is used to measure location attributes of the housing properties in the Korean apartment market. Furthermore, this study shows that the degree of difference in the marginal implicit price due to the manner in which housing properties are represented on a digital map can be larger than the degree of difference in the marginal implicit price due to using different function forms and estimation methods. The major implication from the results of this study is that one should carefully try diverse ways of representing housing properties in the Korean apartment market on a digital map in the process of estimating HPF, as he or she usually tries diverse function forms and estimation methods, to see if the value of the marginal implicit price of air quality varies substantially.
Kinetics Models of Granular and Active Matter: Hydrodynamics and Fluctuations

Out of equilibrium statistical mechanics is a wide research field that deals with all those phenomenons that require a statistical description, focusing on their behaviour while they are going to thermodynamical equilibrium or when an equilibrium state cannot be reached at all. Such systems are a common part of every day life, e.g. in weather changes, chemical reactions, biological activity. My thesis will concentrate mostly on two kind of systems: granular matter and active matter. Both are intrinsically out of equilibrium, presenting symmetric properties: granular matter ceaselessly dissipates energy by mean of inelastic collisions, while active matter needs to produce energy to keep its state of continuous motion. Generally, such systems are made by an amount of particles so large that statistical mechanics is necessary to represent their dynamics, but not so large that fluctuations can be easily neglected as in the usual thermodynamic limit. The lack of an established general method has led to the formulation of new mathematical methods such as fluctuating hydrodynamics and macroscopic fluctuation theory [1], which rely on the existence of a scale separation in time and space for some observables. Furthermore, granular and active matter models are driving intense research and applications in biophysics, material science, nanotechnologies [2].
A ug 2 01 3 Deterministic photonic spatial-polarization hyper-controlled-not gate assisted by quantum dot inside one-side optical microcavity

Up to now, all the works about constructing quantum logic gates, an essential part in quantum computing, are focused on operating on one degree of freedom (DOF) of quantum systems. Here, we investigate the possibility to achieve a scalable photonic quantum computing based on two DOFs of quantum systems and construct a deterministic hyper-controlled-not (hyper-CNOT) gate operating on both spatial-mode and polarization DOFs of a photon pair simultaneously, by using the giant optical Faraday rotation induced by a single-electron spin in a quantum dot inside a one-side optical microcavity as a result of cavity quantum electrodynamics. With this hyper-CNOT gate and linear optical elements, two-photon four-qubit cluster entangled states can be prepared and analyzed, which gives an application to manipulate more information with less resources. We analyze the experimental feasibility of this hyper-CNOT gate and show that it can be implemented with current technology.
Earthquakes clustering based on maximum likelihood estimation of point process conditional intensity function

In this paper we propose a clustering technique to separate and find out the two main component of seismicity: the background seismicity and the triggered one. The method here proposed assigns each earthquakes to the cluster of earthquakes or to the set of isolated event according to the intensity function. This function is estimated by maximum likelihood methods following the theory of point process and iteratively changing the assignment of the events. This technique develops nonparametric estimation methods and computational procedure for the maximization of the point process likelihood function.
Virgo: Zero Knowledge Proofs System without Trusted Setup

—We present a new succinct zero knowledge argument scheme for layered arithmetic circuits without trusted setup. The prover time is O ( C + n log n ) and the proof size is O ( D log C + log 2 n ) for a D -depth circuit with n inputs and C gates. The veriﬁcation time is also succinct, O ( D log C +log 2 n ) , if the circuit is structured. Our scheme only uses lightweight cryptographic primitives such as collision-resistant hash functions and is plausibly post-quantum secure. We implement a zero knowledge argument system, Virgo, based on our new scheme and compare its performance to existing schemes. Experiments show that it only takes 53 seconds to generate a proof for a circuit computing a Merkle tree with 256 leaves, at least an order of magnitude faster than all other succinct zero knowledge argument schemes. The veriﬁcation time is 50ms, and the proof size is 253KB, both competitive to existing systems. Underlying Virgo is a new transparent zero knowledge veri-ﬁable polynomial delegation scheme with logarithmic proof size and veriﬁcation time. The scheme is in the interactive oracle proof model and may be of independent interest.
Effect of light from cold cathode fluorescent lamps on tree peony (Paeonia suffruticosa) calli

Summary Light quality has significant effects on the growth of Paeonia suffruticosa calli in vitro. In this study, callus growth was examined under six different combinations of light of different wavelengths using cold cathode fluorescent lamps (CCFLs): 100% white (W; 410 - 730 nm); 100% red (R; 622 - 625 nm); 100% blue (B; 467 - 470 nm); 80% R plus 20% B; 70% R plus 30% B; or 60% R plus 40% B. Calli grown under regular fluorescent lamps served as the controls. Calli cultured under red light at 622 - 625 nm had the highest average fresh weight (FW; 1.2206 g per callus) and average increase in callus FW (1.1519-fold per callus), compared to calli grown under the other six light conditions. Chlorophyll concentrations per callus were highest in calli cultured under 60% R plus 40% B light. Callus physiology and biochemistry, including total soluble sugar concentrations and peroxidase activity, were higher under 100% R light than under the other six light conditions. Total soluble protein and phenolic compound concentrations were higher under 100% B light than under the other six light conditions.This study indicates that CCFLs are a more effective light source than regular fluorescent lamps for callus growth in P. suffruticosa.
TECHNOLOGICAL AND GEAR CHANGES AFFECTING THE CAPTURE OF BILLFISH 1973-2019

ICCAT stock assessments on blue and white marlin have assumed constant catchability for marlin species throughout the duration of the National Marine Fisheries Service Recreational Billfish Survey (RBS, 1973-2019). However, scientists and anglers have both expressed concerns that there has been an increase in catchability throughout the 46-year dataset. Tournament fishers are highly motivated to adopt measures that increase the probability of successful trips. Literature review and in-person interviews revealed numerous changes in gear and technology utilized by the recreational fishery which have likely contributed to an increase in billfish catchability since 1973.