Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyclic di-GMP interact with putrescine via a PilZ domain receptor YcgR

The cell motility is one of the key pathogenic factors that contribute to the virulence of Dickeya oryzea, which is a prevalent bacterial pathogen capable of infecting a range of crops and plants. We showed recently that the bacterial second messenger c-di-GMP, and the putrescine-mediated quorum sensing (QS) system, are both involved in the regulation of the bacterial motility in D. oryzea EC1. In this study, we set to determine whether and how there two signaling mechanisms work together to modulate the bacterial motility. The results showed that the second messenger signaling system interacts with the putrescine QS system via the c-di-GMP receptor YcgR, which could promote the activity of SpeA, the rate-limiting enzyme in the putrescine biosynthesis pathway, thereby increasing the intracellular putrescine levels. However, it was shown that this facilitative effect could be inhibited by c-di-GMP molecules. In addition, we demonstrated the dominance of c-di-GMP over putrescine in the regulation of bacterial motility. The findings from this study provide the first insight into the interaction between c-di-GMP and putrescine in bacteria and provide a valuable reference for the study of intracellular second messenger system and polyamine-mediated quorum sensing system in other bacteria. Importance Dickea oryzea is a major bacterial pathogen capable of infesting many plants and crops, causing significant economic damage to rice and banana production especially. Bacterial motility is a key pathogenic factor of D. oryzea to compete for food resources and infect their host species, which is negatively regulated by c-di-GMP and positively regulated by putrescine, respectively. However, the connection between c-di-GMP and putrscine in regulating the motility of D.oryzea is not understood. Here we revealed the link and the mechanism of interaction between them, showing that c-di-GMP interact with putrescine via a receptor of c-di-GMP. The significance of our research is in providing the first insight into the interaction between c-di-GMP and putrescine and the methods and experimental designs in our study will provide a valuable reference for subsequent studies on the link between c-di-GMP and putrescine in other bacteria and even the regulatory mechanisms of complex bacterial motility networks, respectively.
Local Minimum Energy of LBP Algorithm Concrete CT Image Segmentation

Concrete CT image segmentation is a hot civil engineering research field in recent years. An algorithm of minimizing the energy of local region is proposed to solve the problem of interaction fails to capture statistical property of natural image and to segment concrete CT image. At first, the algorithm utilizes local region information of image to construct the energy model of local region, establishes a segmentation model of local interaction region based on MRF. Then using loopy belief propagation (LBP) algorithm and other algorithms optimizes global energy. In process of optimization, local region energy is converged, and the label of local region is estimated based on MAP algorithm. Then the information of local region is transferred to the region of neighborhood. The result of experiment shows that comparing with standard LBP algorithm, the new algorithm has a better segmentation result, and efficiently restrained effect on image noise and texture for segmentation. Result of concrete CT image segmentation will simplify the following CT statistical analysis and provide an important method for reaching real meso-structure of concrete’s finite element network.
Working memory-based and -free reward prediction in a dual dopamine system in the basal ganglia

Animals optimize their actions by predicting and verifying their outcomes (e.g., rewards). Reward prediction often requires working memory (WM)-based information. To elucidate the neural basis of WM-based reward prediction, we compared the activity of dopamine (DA) neurons in the ventral tegmental area (VTA) in an alternate reward condition (WM-based) with that in a random (WM-free) reward condition in rats and mice. Positron emission tomography revealed greater VTA activation in the WM-based than the WM-free condition. Lateral and medial VTA neurons displayed differential electrophysiological spike activities reflecting WM-based and WM-free reward prediction error, respectively. Furthermore, phasic DA release in the dorsal and ventral striatum changed as WM-based and WM-free classical conditioning progressed. Consistent with our WM-based model, reward acquisition caused a DA dip only in the dorsal striatum. Thus a dual DA system processes WM-based and WM-free reward prediction in parallel.
Oxide thickness mapping of ultrathin Al2O3 at nanometer scale with conducting atomic force microscopy

In this work, we introduce conducting atomic force microscopy (C-AFM) for the quantitative electrical characterization of ultrathin Al2O3 films on a nanometer scale length. By applying a voltage between the AFM tip and the conductive Co substrate direct tunneling currents in the sub pA range are measured simultaneously to the oxide surface topography. From the microscopic I–V characteristics the local oxide thickness can be obtained with an accuracy of 0.03 nm. A conversion scheme was developed, which allows the calculation of three-dimensional maps of the local electrical oxide thickness with sub-angstrom thickness resolution and nanometer lateral resolution from the tunneling current images. Local tunneling current variations of up to three decades are correlated with the topography and local variations of the electrical oxide thickness of only a few angstroms.In this work, we introduce conducting atomic force microscopy (C-AFM) for the quantitative electrical characterization of ultrathin Al2O3 films on a nanometer scale length. By applying a voltage between the AFM tip and the conductive Co substrate direct tunneling currents in the sub pA range are measured simultaneously to the oxide surface topography. From the microscopic I–V characteristics the local oxide thickness can be obtained with an accuracy of 0.03 nm. A conversion scheme was developed, which allows the calculation of three-dimensional maps of the local electrical oxide thickness with sub-angstrom thickness resolution and nanometer lateral resolution from the tunneling current images. Local tunneling current variations of up to three decades are correlated with the topography and local variations of the electrical oxide thickness of only a few angstroms.
MULTIGRIDWAVEFORM RELAXATIONON SPATIAL FINITEELEMENT MESHES: THE DISCRETE-TIME CASE*

The efficiency ofnumerically solving time-dependent partial differentialequationson parallelcomput- ers canbe greatly improvedby computing the solution onmany time levels simultaneously. The theoretical properties of one such method, namely the discrete-time multigrid waveform relaxation method, are investigated for systems of ordinary differential equations obtained by spatial finite-element discretisation of linear parabolic initial-boundary value problems. The results are compared to the corresponding continuous-time results. The theory is illustrated for a one-dimensional and a two-dimensional model problem and checked against results obtained by numerical experiments. with B the symmetric positive definite mass matrix, A the stiffness matrix, and u(t) (Ul (t), u2(t) Ud(t)) the unknown solution.vector. In 10) we considered solving (1.1) with the continuous-time multigrid waveform relax- ation method. This method is based on waveform relaxation, a highly parallel technique for solving very large systems of ODEs (12), (15). It is accelerated by using multigrid, a very efficient method for solving elliptic partial differential equations, see e.g. 1 ), (4), (24). The continuous-timewaveformrelaxationmethod differs from standardODE solvers in that itcom- putes a solution along a continuous time interval. It requires the analytical solution of certain ODEs and the exact continuous representation of certain functions. The method is therefore mainly of theoretical interest. In an actual implementation of the method, the algorithm is replaced by a discrete-time algorithm. That is, functions are represented discretely as vectors defined on successive time levels, and the ODEs are solved by using standard time-stepping techniques. The resulting discrete-time multigrid waveform relaxation method belongs to the class of parabolic multigrid methods. These are multigrid methods for time-dependent problems designed to operate on grids extending in space and time. Other examples of such meth- ods are the time:parallel multigrid method (3), (7) and the space-time multigrid method (8). These methods are highly efficient oa parallel computers, possibly outperforming parallel implementations of standard time-stepping methods by orders of magnitude (9), (23). Their convergence characteristics as iterative solvers are often similar to the convergence charac- teristics of multigrid methods for stationary problems, although different parabolic multigrid methods may have very different robustness characteristics. The waveform method, in par-
How to Count Bots in Longitudinal Datasets of IP Addresses

—Estimating the size of a botnet is one of the most basic and important queries one can make when trying to understand the impact of a botnet. Surprisingly and unfortunately, this seemingly simple task has confounded many measurement efforts. While it may seem tempting to simply count the number of IP addresses observed to be infected, it is well-known that doing so can lead to drastic overestimates, as ISPs commonly assign new IP addresses to hosts. As a result, estimating the number of infected hosts given longitudinal datasets of IP addresses has remained an open problem. In this paper, we present a new data analysis technique, CARDCount , that provides more accurate size estimations by accounting for IP address reassignments. CARDCount can be applied on longer windows of observations than prior approaches (weeks compared to hours), and is the ﬁrst technique of its kind to provide conﬁdence intervals for its size estimations. We evaluate CARDCount on three real world datasets and show that it performs equally well to existing solutions on synthetic ideal situations, but drastically outperforms all previous work in realistic botnet situations. For the Hajime and Mirai botnets, we estimate that CARDCount, is 51.6% and 69.1% more accurate than the state of the art techniques when estimating the botnet size over a 28-day window.
Three-Dimensional Instability of Finite-Amplitude Water Waves

Computations based on the full water-wave equations reveal that there are two distinct types of instabilities for gravity waves of finite amplitude on deep water. One is predominantly two dimensional and is related to all the known results for special cases. The other is predominantly three dimensional and becomes dominant when the wave steepness is sufficiently large.
The 'best-correlated pixels' method for solid mass flow measurements using electrical capacitance tomography

A novel method of calculating a solid velocity field based on a twin-plane electrical capacitance system is presented. This method introduces the concept of the best-correlated pair of pixels instead of the classical assumption that a flow pattern between two sensor planes is frozen. Preliminary results and future applications of the proposed method are discussed.
Shape representation for optimisation

The chosen shape representation defines the subset of the shape space that the Genetic Algorithm can search. If the fitness function can not correctly evaluate all parts of the shape space then we must ensure that the shape representation only defines that subset which can be correctly evaluated. This paper details a parametric description which was used after earlier less restrictive shape representations produced totally impractical designs.
The role of quantum intramolecular dynamics in unimolecular reactions

The dynamics of unimolecular reactions can be modelled by classical mechanics for the motion of nuclei on Born-Oppenheimer or other effective potential surfaces, by the corresponding quantum mechanical equations of motion and, perhaps, by quantum statistical treatments. In this paper I provide a synopsis of fundamental, qualitatively important effects arising from the quantum nature of intramolecular dynamics, as opposed to classical mechanics, and illustrate these with theoretical predictions and experimental examples from the work of my group in Zurich. These include quantum nonlinearity in infrared (IR) multiphoton excitation and reaction, non-classical wavepacket spreading in the Fermi resonance coupled modes in CHX3 molecules, effects of zero point energy and angular momentum in unimolecular reactions, nuclear spin symmetry conservation and inter con version and the hypothetical effects arising from the violation of parity and time reversal symmetry in unimolecular reactions. Specific applications to experiments include IR laser chemistry of CF3I and CF3Br, IR spectroscopy and dynamics of CHF3 and predissociation spectra and dynamics of II
Parallel Resultant Computation

A resultant is a purely algebraic criterion for determining when a finite collection of polynomials have a common zero. It has been shown to be a useful tool in the design of efficient parallel and sequential algorithms in symbolic algebra, computational geometry, computational number theory, and robotics. We begin with a brief history of resultants and a discussion of some of their important applications. Next we review some of the mathematical background in commutative algebra that will be used in subsequent sections. The Nullstellensatz of Hilbert is presented in both its strong and weak forms. We also discuss briefly the necessary background on graded algebras, and define affine and projective spaces over arbitrary fields. We next present a detailed account of the resultant of a pair of univariate polynomials, and present efficient parallel algorithms for its computation. The theory of subresultants is developed in detail, and the computation of polynomial remainder sequences is derived. A resultant system for several univariate polynomials and algorithms for the gcd of several polynomials are given. Finally, we develop the theory of multivariate resultants as a natural extension of the univariate case. Here we treat both classical results on the projective (homogeneous) case, as well as more recent results on the affine (inhomogeneous) case. The u-resultant of a set of multivariate polynomials is defined and a parallel algorithm is presented. We discuss the computation of generalized characteristic polynomials and relate them to the decision problem for the theories of real closed and algebraically closed fields.
On recurrence relations for the probability function of multivariate generalized Poisson distribution

Multivariate Poisson distribution is a well known distribution in multivariate discrete distributions. The author: Kawamura had discussed around the distribution in [1], [2] and shown recurrence relations for the distribution in [3], [4]. These are the recurrence relations in bivariate case, see [3], [4]. In this paper it will be discussed that a similar relation also holds in the case of generalized multivariate distribution. A relation between algebraic structure of the range space and its probability function concerning the distribution will be investigated in detail, especially,by the recurrencerelations: .
Research Review of Dispensing Based on Machine Vision

Aiming at the low efficiency, the fixed product size and the limited dispensing precision of the traditional dispensing of manual dispensing, a research review of automatic dispensing machine based on machine vision that the efficiency and automation is much higher than manual degree is presented. Automatic operation of automatic dispensing machine is simple and controllable, not only promotes the production efficiency, but also can replace the labor force and reduce largely production costs. With its application industry in further development, its production technology is also in continuous innovation. This paper summarizes the research and application of fully automatic dispensing machine, the development process of dispensing machine and the domestic and foreign situation and the way of dispensing category, the visual identification dispensing research results and development trend, finally for the future research content made the prospect and summary.
Deep Learning for Alzheimer's Disease Detection using Multimodal MRI-PET Fusion

Alzheimer's disease (AD) is an irremediable brain disorder that is progressive and causes irreparable damage to brain cells, neurotransmitters, and nerves. This in turn severely affects brain functionalities and ultimately leads to dementia. Although there is currently no cure for AD, there are treatments that can slow down the disease's development. Hence, early diagnosis of AD is the need of the hour and researchers across the world have shifted their focus on developing robust and intelligent systems that can aid in early and accurate diagnosis of AD this has been the main motivation behind this study. The main objective of this paper is to present a comparative study of 2D and 3D Convolutional Neural Network (CNN) architectures for AD classification and to choose the most robust model for AD classification. The models are trained on MRI and PET individually and with the fusion of MRI and PET. 2D feature fusion is performed using pre-trained neural networks and 3D fusion involves a series of operations such as skull-stripping, image segmentation, and co-registration. 2D CNN provided the highest accuracy of 91.29% on MRI images followed by 3D CNN with an accuracy of 91.07%. Comparing the performance on multimodal fusion, 3D MRI -PET fusion exhibited a significantly good accuracy of 86.90%. This paper briefly describes the GUI developed for easy visualization of AD classification and the possibilities of integrating the trained machine learning models with various mobile and web applications and with instruments that facilitate real-time diagnosis and classification of AD.
Binary Variational (BiV) Channel State Information Coding

With the advent of machine learning applications to physical layer communications problems, neural networkbased auto-encoders are considered for channel state information (CSI) feedback of closed-loop MIMO operations. An autoencoder consists of an encoder and a decoder, where the “bottleneck” connecting the encoder and the decoder can be used for CSI feedback. The quantization process is usually applied to the –bottleneck” for the CSI feedback channel. This paper proposes a new approach without an explicit quantization step for CSI coding. The new proposal, known as binary variational (BiV) CSI coding, is based on the variational autoencoder (VAE) framework, with a Bernoulli distribution assumed for the latent space. A binary sampling step provides binary samples of the latent vector for the variational encoder. The encoder and decoder of the BiV CSI coding can be trained together with the stochastic gradient descent (SGD) method. Several trained BiV models are provided to demonstrate the effectiveness of the BiV CSI coding for 3GPP-based MIMO channels.
Satellite-Retrieved Microstructure of AgI Seeding Tracks in Supercooled Layer Clouds

Abstract NOAA Advanced Very High Resolution Radiometer (AVHRR) images revealed conspicuous tracks of glaciated cloud in thick supercooled layer clouds over central China. These tracks were identified as being artificially produced by cloud-seeding operations at the −10°C isotherm, less than 1 km below cloud tops, aimed at precipitation enhancement, by means of AgI acetone generators. The cloud composition was deduced by retrieving the cloud-top effective radius (re) and analyzing its spatial relations with cloud-top temperatures and with the visible reflectance. Cloud-top temperature varied between −13° and −17°C. The glaciation became apparent at cloud tops about 22 min after seeding. The glaciated tops sank and formed a channel in the supercooled layer cloud. The rate of sinking of about 40 cm s−1 is compatible with the fall velocity of ice crystals that are likely to form at these conditions. A thin line of new water clouds formed in the middle of the channel of the seeded track between 38 and 63 min a.
UAV-Borne Landmine Detection via Intelligent Multispectral Fusion

Compared with traditional landmine detection methods, the unmanned aerial vehicle borne measure is more suitable for rapidly detecting large-area minefields of scatterable landmines. We propose a multispectral fusion strategy based on a deep learning model to facilitate landmine detection. With a UAV-borne multispectral cruise platform, we set up a multispectral dataset of scatterable landmines, where varieties of minefields with different ground vegetation environments are considered. The proposed technique includes a structure with adjustable weights to fulfill the fusion and refinement, and maximize the utilization of multispectral information. Experimental comparisons of the predictions before and after fusion demonstrate that our proposed fusion strategy can improve the recall rate and achieve significant gains over single-spectrum detection, especially for the occluded mines, and confirm the feasibility of the multispectral data with reasonable processing for the landmine detection.
Tracking the Adjacency Spectral Embedding for Streaming Graphs

The popular Random Dot Product Graph (RDPG) generative model postulates that each node has an associated (latent) vector, and the probability of existence of an edge between two nodes is their inner-product (with variants to consider directed and weighted graphs). In any case, the latent vectors may be estimated through a spectral decomposition of the adjacency matrix, the so-called Adjacency Spectral Embedding (ASE). Assume we are monitoring a stream of graphs and the objective is to track the latent vectors. Examples include recommender systems or monitoring of a wireless network. It is clear that performing the ASE of each graph separately may result in a prohibitive computation load. Furthermore, the invariance to rotations of the inner product complicates comparing the latent vectors at different time-steps. By considering the minimization problem underlying ASE, we develop an iterative algorithm that updates the latent vectors' estimation as new graphs from the stream arrive. Differently to other proposals, our method does not accumulate errors and thus does not requires periodically re-computing the spectral decomposition. Furthermore, the pragmatic setting where nodes leave or join the graph (e.g. a new product in the recommender system) can be accommodated as well. Our code is available at https://github.com/marfiori/efficient-ASE
Deep Reinforcement Learning-Based Computation Offloading and Resource Allocation in IoT

To address the issue of offloading decision and resource allocation, this paper proposes an approach based on the Q-learning algorithm and DQN algorithm. To start with, a system model is designed in accordance with the single marginal environment system models, including the network model and computing model. Through definition and design of the aforesaid two models, the issue of optimization and constraints facing the optimization are identified and formularized. Next, Q-learning and DQN are used to design the solution plans. Finally, simulation experiment is conducted to compare the SGN algorithm with other solution plans, and point out advantages of the DQN algorithm in mobile edge computing.