Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning of TaPRP from Wheat and Its Cold Tolerance Analysis in Transgenic Tobacco

TaPRP , a proline rich protein (PRP) cDNA, was cloned by RT-PCR from winter wheat. Nucleotide sequence analysis showed TaPRP is composed of 1137 bp (378 amino acid residues with a Mr of 42.19 kD). TaPRP shows 92.6%, 89.3%, 73.0%, and 73.3% sequence homologies with PRP genes from wheat, sorghum, rice, and maize, respectively. The deduced protein includes 170 prolines, presenting a normal PRP primary structure. Expression vector pBI -TaPRP was constructed, in which TaPRP was driven by CaMV35S promoter and stopped by NospolyA. Tobaccos were transformed by Agrobacterium containing the constructed vectors. Three transgenic lines were confirmed by PCR detection and Southern blot. Under the same low temperature stress conditions, transgenic plants had lower conductivity rate compared with the non-transgenic plants, suggesting that cold tolerance in transgenic tobacco plants was improved. However, the different transgenic plants showed significant differences in cold resistant, and there also existed significant interactions between plant and treatment temperature. TaPRP might have an important role in wheat in cold adaptation process.
Changes in Arterial Pressure during Mechanical Ventilation

Mechanical ventilation induces cyclic changes in vena cava blood flow, pulmonary artery blood flow, and aortic blood flow. At the bedside, respiratory changes in aortic blood flow are reflected by “swings” in blood pressure whose magnitude is highly dependent on volume status. During the past few years, many studies have demonstrated that arterial pressure variation is neither an indicator of blood volume nor a marker of cardiac preload but a predictor of fluid responsiveness. That is, these studies have demonstrated the value of this physical sign in answering one of the most common clinical questions, Can we use fluid to improve hemodynamics? , while static indicators of cardiac preload (cardiac filling pressures but also cardiac dimensions) are frequently unable to correctly answer this crucial question. The reliable analysis of respiratory changes in arterial pressure is possible in most patients undergoing surgery and in critically ill patients who are sedated and mechanically ventilated with conventional tidal volumes.
Who is moving where? Molecular evidence reveals patterns of range shift in the acorn barnacle Hexechamaesipho pilsbryi in Asia

Global warming is predicted to cause a shift in the geographic ranges of marine organisms. Such shifts have already been observed with the polar retreat of temperate species and northern expansion of more tropical species. Hexechamaesipho pilsbryi is an intertidal acorn barnacle first identified by Hiro in 1936. Hiro reported the southern limit of H. pilsbryi as southern Kyushu Island, Japan. However, H. pilsbryi has recently been recorded in Taiwan as well as Maritime Southeast Asia — suggesting a recent southward range extension into tropical waters that would be contrary to general predictions. To test this hypothesis, we compared the mitochondrial COI gene sequences of 200 individuals sampled from two sites in the previously reported range, and nine sites in the newly reported localities. Two highly diverged lineages were found: a northern lineage, predominantly in Japan and Okinawa, and a southern lineage, primarily in Taiwan and Southeast Asia. The unimodal mismatch distribution and the star-like shaped haplotype network suggest recent demographic expansion in the southern lineage. The molecular data, therefore, rejected the southward extension hypothesis, and instead supported an alternative scenario of a northward range shift of the southern lineage from Southeast Asia to Taiwan. Our results reveal that range shifts in cryptic or poorly studied marine taxa may confound the identification of species’ distributions and consequent interpretation of possible changes in ranges and sources of invasion. The application of molecular data to identify and monitor such changes can address these problems and permits a rapid and sensitive confirmation of such events.
A Natural Language Processing Based Model for Quality Assurance of Software Requirements Document

— Software projects failure has been a reoccurring problem in software development domain. The advent of applying engineering principles to software development initiated the need to produce software products systematically to solve the problems of poor software quality, late deliveries, and cost overruns. Requirement engineering is the first phase in software development process and the major output of this phase is the Software requirements document. This document usually written in natural language contains both functional and non-functional system requirements. However, due to the informal nature of natural language, it is prone to a lot of errors, which if not detected early enough has an adverse effect on the success of the software project. Hence, this work aimed at the development of an NLP based model for quality assurance of Software requirements. Using two major techniques which are Natural Language Processing and information extraction techniques, the developed NLP model was able to assure the quality of software requirements based on testability metrics. This was done by performing syntactic and semantic analysis on requirements. A Software Requirement quality assurance system was developed using JAPE grammar rules to extract thematic roles and detect non-testability features of software requirements. We report on the performance of our model using datasets of Promise data repository.
Text Mining for Studying Management's Confidence in IPO Prospectuses and IPO Valuations

Understanding pricing strategies in the context of the Initial Public Offering (IPO) process has been receiving much attention. Most prior studies have however focused on information sources from post issuance periods, and understanding such strategies from the management’s perspective during the IPO process is still an open research issue. Form 424 variants, as finalized IPO prospectus approved by Security Exchange Committee (SEC), contain rich and genuine information about the issuing firms. In this study, we analyze the inter-relationships between the management’s confidence (through the proxy of sentiments expressed in textual contents in the Management’s Discussion & Analysis (MD&A) sections in the prospectus) and the pre-/post-IPO valuations. We develop an analytical framework namely FOCAS-IE (Feature-Oriented, Context-Aware, Systematic Information Extraction) to derive sentiments from the MD&A sections. Further, we construct predictive models using information extracted using FOCAS-IE to predict IPO pricings. The results have shown to outperform results from prior related studies.
An Integrated Coupled Inductor and Switched-Capacitor Based High Gain DC/DC Converter for Closed Loop Control of DC Motor

The voltage gain of Conventional boost converter is limited due to the high current ripple, high voltage stress across active switch and diode, and low efficiency associated with large duty ratio operation. High voltage gain is required in applications, such as the renewable energy power systems with low input voltage. A high step-up voltage gain active-network converter with switched capacitor technique is proposed in this project. The proposed converter can achieve high voltage gain without extremely high duty ratio. In addition, the voltage stress of the active switches and output diodes is low. Therefore, low voltage components can be adopted to reduce the conduction loss and cost. The operating principle and steady-state analysis are discussed in detail. Based on the concept of switchedinductor and switchedcapacitor, this project proposes a novel switched-capacitor-based active-network converter (SC-ANC) for high stepup conversion, which has the following advantages: high voltageconversion ratio, low voltage stress across switches and diodes, and self-voltage balancing across the output capacitors. The operating principle and steady-state analysis are discussed in detail. The simulation results are given to verify the analysis and advantages of the proposed converter
Extensive and Intensive Margin E¤ects and Their Hybrids in Sample-Selection Treatment E¤ects

In sample selection models, a treatment can inuence the observed outcome in two di¤erent ways: a¤ecting the binary selection/participation decision, and a¤ecting the latent outcome. The former is called the extensive margin e¤ect, and the latter the intensive margin effect. Despite the popularity of these e¤ects in economics, the intensive margin e¤ect is not a proper causal parameter, because it is conditioned on the selecting/participating decision which is a post-treatment variable possibly a¤ected by the treatment. This paper presents a proper causal framework, and introduces additional hybrid e¤ects relevant for sample selection models. It is hard to separate those e¤ects in general, but in some popular models (parametric sample-selection models, semiparametric independence models, semiparametric zero-censored models, and polynomial approximation models), they are separately identi ed and estimable. An empirical analysis and a simulation study are provided to illustrate these causal e¤ects as well as conventionalnon-causal e¤ects in sample selection models.
Green-winged Teal (Anas crecca)

Male Green-winged Teal. (Jeff Lemons) The Green-winged Teal is the last of our eight species of dabbling ducks and at 14 1⁄2 inches, it is the smallest dabbler. It is only half an inch larger than the Bufflehead, which is the smallest duck found in our region. Males are easily identified by their gray body, rufous-chestnut head, and metallic-green ear patch. They have a distinct vertical white stripe on the side of their breast. Both sexes have a green speculum that is most obvious in flight. The Green-winged Teal is an uncommon duck here in the Central Carolinas, and is generally found in small numbers between October and March. Our earliest recorded arrival is 13 September in 2011, when Lori Owenby found a flock of three at Mountain Creek Park. A late date of departure is 1 May in 1965 with six counted on the Stanly Spring Bird Count. A high count of 1,773 was estimated on the Pee Dee Christmas Bird Count on January 4, 2003. Most sightings are of 50 or fewer birds. It is interesting to note that the “Eurasian” form of Green-winged Teal (Anas crecca crecca, formerly known as the Common Teal) has been found at least once amongst teal flocks at Pee Dee National Wildlife Refuge. Barbara Gearhart picked one out of a flock that was foraging there on December 11, 1999. This form lacks the vertical white stripe found on the drake of our usual race of Green-winged Teal (Anas crecca carolinensis). Green-winged Teal Anas crecca
The Discretizable Molecular Distance Geometry Problem : from Ideal to Real Instances

The Molecular Distance Geometry Problem (MDGP) is the problem of ﬁnding the conformation of a molecule (i.e. the coordinates in the three-dimensional space of all its atoms) by using known relative distances between pairs of its atoms. The information regarding the relative distances can be obtained by experimental techniques such as Nuclear Magnetic Resonance (NMR) spectroscopy. The following steps are usually performed in order to solve an MDGP : (i) perform NMR experiments ;
Unidirectional quantum transport in optically driven 
V
-type quantum dot chains

We predict a mechanism for achieving complete population inversion in a continuously driven InAs/GaAs semiconductor quantum dot featuring $V$-type transitions. This highly nonequilibrium steady state is enabled by the interplay between $V$-type interband transitions and a non-Markovian decoherence mechanism, introduced by acoustic phonons. The population trapping mechanism is generalized to a chain of coupled emitters. Exploiting the population inversion, we predict unidirectional excitation transport from one end of the chain to the other without external bias, independent of the unitary interdot coupling mechanism.
Towards Reconstructing Multi-Step Cyber Attacks in Modern Cloud Environments with Tripwires

Rapidly-changing cloud environments that consist of heavily interconnected components are difficult to secure. Existing solutions often try to correlate many weak indicators to identify and reconstruct multi-step cyber attacks. The lack of a true, causal link between most of these indicators still leaves administrators with a lot of false-positives to browse through. We argue that cyber deception can improve the precision of attack detection systems, if used in a structured, and automatic way, i.e., in the form of so-called tripwires that ultimately span an attack graph, which assists attack reconstruction algorithms. This paper proposes an idea for a framework that combines cyber deception, automatic tripwire injection and attack graphs, which eventually enables us to reconstruct multi-step cyber attacks in modern cloud environments.
Thermodynamically consistent nonlinear viscoplastic formulation with well-conditioned recovery of the inviscid solution: Theory and implicit integration algorithm with exact solution for the linear case

In this work, a consistent viscoplasticity formulation is derived from thermodynamical principles and employing the concept of continuum elastic corrector rate. The proposed model is developed based on the principle of maximum viscoplastic dissipation for determining the flow direction. The model uses both the equivalent viscoplastic strain and its rate as state variables. Power balance and energy balance give, respectively, separate evolution equations for the equivalent viscoplastic strain rate and the viscoplastic strain, the former written in terms of inviscid rates. Several key points distinguish our formulation from other proposals. First, the viscoplastic strain rate (instead of a yield function) consistently distinguishes conservative from dissipative behaviours during reverse loading; and the discrete implicit integration algorithm is an immediate implementation of the continuum theory based on the mentioned principles. Second, the inviscid solution is recovered in a well-conditioned manner by simply setting the viscosity to zero. Indeed, inviscid plasticity, viscoelasticity and viscoplasticity are particular cases of our formulation and integration algorithm, and are recovered just by setting the corresponding parameters to zero (viscosity or yield stress). Third, the linear viscoplasticity solution is obtained in an exact manner for proportional loading cases, independently of the time step employed. Four, general nonlinear models (Perzyna, Norton, etc) may be immediately incorporated as particular cases both in the theory and the computational implementation.
An SPQR-Tree-Like Embedding Representation for Level Planarity

An SPQR-tree is a data structure that efficiently represents all planar embeddings of a biconnected planar graph. It is a key tool in a number of constrained planarity testing algorithms, which seek a planar embedding of a graph subject to some given set of constraints. We develop an SPQR-tree-like data structure that represents all level-planar embeddings of a biconnected level graph with a single source, called the LP-tree, and give a simple algorithm to compute it in linear time. Moreover, we show that LP-trees can be used to adapt three constrained planarity algorithms to the level-planar case by using them as a drop-in replacement for SPQR-trees.
A Survey on Model Watermarking Neural Networks

Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered intellectual property of the legitimate parties who have trained them, which makes their protection against stealing, illegitimate redistribution, and unauthorized application an urgent need. Digital watermarking presents a strong mechanism for marking model ownership and, thereby, offers protection against those threats. The emergence of numerous watermarking schemes and attacks against them is pushed forward by both academia and industry, which motivates a comprehensive survey on this field. This document at hand provides the first extensive literature review on ML model watermarking schemes and attacks against them. It offers a taxonomy of existing approaches and systemizes general knowledge around them. Furthermore, it assembles the security requirements to watermarking approaches and evaluates schemes published by the scientific community according to them in order to present systematic shortcomings and vulnerabilities. Thus, it can not only serve as valuable guidance in choosing the appropriate scheme for specific scenarios, but also act as an entry point into developing new mechanisms that overcome presented shortcomings, and thereby contribute in advancing the field.
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation

3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks. Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information. In this paper, we propose a generalization of PointPainting to be able to apply fusion at different levels. After the semantic augmentation of the point cloud, we encode raw point data in pillars to get geometric features and semantic point data in voxels to get semantic features and fuse them in an effective way. Experimental results on the KITTI test set show that SemanticVoxels achieves state-of-the-art performance in both 3D and bird’s eye view pedestrian detection benchmarks. In particular, our approach demonstrates its strength in detecting challenging pedestrian cases and outperforms current state-of-the-art approaches.
Learning in a Small/Big World

This paper looks into how learning behavior changes with the complexity of the inference problem and the individual's cognitive ability, as I compare the optimal learning behavior with bounded memory in small and big worlds. A learning problem is a small world if the state space is much smaller than the size of the bounded memory and is a big world otherwise. I show that first, optimal learning behavior is almost Bayesian in small worlds but is significantly different from Bayesian in big worlds. Second, ignorant learning behaviors, e.g., availability heuristic, correlation neglect, persistent over-confident, are never optimal in small worlds but could be optimal in big worlds. Third, different individuals are bound to agree in small worlds but could disagree and even be bound to disagree in big worlds. These results suggest that the complexity of a learning problem, relative to the cognitive ability of individuals, could explain a wide range of abnormalities in learning behavior.
Spectrum of the Dirac Hamiltonian for Hydrogenic atoms on spacetimes with mild singularities

We model a single-electron ion (hydrogenic atom) as a static, spherically symmetric electrovacuum spacetime in which the nucleus is treated as a timelike line-singularity and the electron is treated as a test particle following Dirac's equation. The spacetime is a solution of Einstein-Maxwell equations with a non-linear vacuum law. An example is Hoffmann's spacetime obtained using the Born-Infeld law. The Dirac Hamiltonian is shown to be essentially self-adjoint, independent of the atomic number. The essential spectrum and absolutely continuous spectrum are the same as in Dirac's Hamiltonian on Minkowski space with a Coulomb potential. Presence of infinitely many eigenvalues is shown and a clustering result is obtained.
A Parallel Sporadic Decomposition of Hankel Structured Matrix using Optimal Patch Size for Impulse Noise Removal

In image processing, image denoising is the most essential process since it removes the noises and reconstructs the original image with high quality. As a result, an optimal patch size based sporadic decomposition of Hankel structured matrix in gradient transform domain namely OPS-e4-ALOHA was proposed in which the image patch was considered as a sparse in the gradient domain and the optimal patch size was selected by using a Flower Pollination Algorithm (FPA). However, it uses only one image at a time whereas the computation speed was less while different test images were used simultaneously. Hence in this article, a parallel sporadic decomposition of Hankel structured matrix named Enhanced e4-ALOHA (Ee4-ALOHA) is proposed with optimal patch size selection for removing the impulse noise from the images. In this technique, a multivariate polynomial decomposition problem in low-rank Hankel matrix is considered. To avoid this problem, Quasi-Hankel matrix is introduced based on the coefficients of the polynomial. Therefore, the original decomposition problem is reformulated as a structured low-rank matrix completion. Further, this technique is combined together with the parallel computing framework which has multiple processing cores for increasing the computation speed of matrix construction and denoising processes. Finally, the experimental results show that the effectiveness of the proposed technique.
Thermal effects in non-Fermi liquid superconductivity

We revisit the interplay between superconductivity and quantum criticality when thermal effects from virtual static bosons are included. These contributions, which arise from an effective theory compactified on the thermal circle, strongly affect field-theoretic predictions even at small temperatures. We argue that they are ubiquitous in a wide variety of models of non-Fermi liquid behavior, and generically produce a parametric suppression of superconducting instabilities. We apply these ideas to non-Fermi liquids in $d=2$ space dimensions, obtained by coupling a Fermi surface to a Landau-damped soft boson. Extending previous methods developed for $d=3\ensuremath{-}\ensuremath{\epsilon}$ dimensions, we determine the dynamics and phase diagram. It features a naked quantum critical point, separated by a continuous infinite order transition from a superconducting phase with strong non-Fermi liquid corrections. We also highlight the relevance of these effects for (numerical) experiments on non-Fermi liquids.
Virtual Forward Dynamics Models for Cartesian Robot Control

In industrial context, admittance control represents an important scheme in programming robots for interaction tasks with their environments. Those robots usually implement high-gain disturbance rejection on joint-level and hide direct access to the actuators behind velocity or position controlled interfaces. Using wrist force-torque sensors to add compliance to these systems, force-resolved control laws must map the control signals from Cartesian space to joint motion. Although forward dynamics algorithms would perfectly fit to that task description, their application to Cartesian robot control is not well researched. This paper proposes a general concept of virtual forward dynamics models for Cartesian robot control and investigates how the forward mapping behaves in comparison to well-established alternatives. Through decreasing the virtual system's link masses in comparison to the end effector, the virtual system becomes linear in the operational space dynamics. Experiments focus on stability and manipulability, particularly in singular configurations. Our results show that through this trick, forward dynamics can combine both benefits of the Jacobian inverse and the Jacobian transpose and, in this regard, outperforms the Damped Least Squares method.