Miracle-dz/UniFAR-FTUs huggingface dataset:
T-DNA Insertion Mutagenesis in Arabidopsis: Prospects and Perspectives

Approximately 13 000 independent transformants of Arabidopsis have been generated utilising the Agrobacterium-mediated seed infection/transformation procedure. The average number of functional independent T-DNA insertion events per transformed line is 1.5, thus constituting a population with 19 500 independent inserts. These transformed lines have been screened under two environmental regimes for visible alterations in phenotype. In one screen the lines were grown under greenhouse conditions and examined at weekly intervals. In the second, seeds from each line were plated under axenic conditions in vertically oriented square petri plates and examined 4 and 9 days after germination. The mutants observed were placed into 7 distinct phenotypic classes: seedling-lethal, size variant, reduced-fertility, pigment defect, embryo-defective, dramatic morphological, and physiological. The mutational spectrum of the first 8000 transformed lines has been reported earlier. The mutational spectrum for a further 5000 transformed lines is reported here and, with several notable exceptions, is similar to that observed previously.
The implications for climate sensitivity of Bjorn Stevens' new aerosol forcing paper

In a paper published last year (Lewis & Curry 2014), discussed here, Judith Curry and I derived best estimates for equilibrium/effective climate sensitivity (ECS) and transient climate response (TCR). At 1.64°C, our estimate for ECS was below all those exhibited by CMIP5 global climate models, and at 1.33°C for TCR, nearly all. However, our upper (95%) uncertainty bounds, at 4.05°C for ECS and 2.5°C for TCR, ruled out only a few CMIP5 climate models. The main reason was that they reflected the AR5 aerosol forcing best estimate and uncertainty range of −0.9 W/m 2 (for 2011 vs 1750), with a 5–95% range of −1.9 to −0.1 W/m 2 . The strongly negative 5% bound of that aerosol forcing range accounts for the fairly high upper bounds on ECS and TCR estimates derived from AR5 forcing estimates.
An Efficient Hardware design of MED based Spectrum Sensor using VLSI architecture for Cognitive Radio

: Cognitive Radio Network is an intelligent piece of machine which can be programmed and configured dynamically. It is an adaptive technology that can automatically detect the available channels in a wireless spectrum and change the parameters in the transmission, enabling us to communicate more effectively. In this paper, a Maximum Eigenvalue based Detection (MED) algorithm is used as a spectrum sensor for cognitive radio network. Out contributions focus on providing a hardware friendly MED based spectrum sensing algorithm. In addition, we propose a resource-shared VLSI architecture for this algorithm to enhance the efficiency of the hardware with low sensing time. A performance comparison is carried out between MED and cyclostationary based spectrum sensing algorithm. In that, MED based spectrum sensing algorithm delivered better performance than cyclostationary based algorithm at the detection probability of 0.5 based on the simulation result.
Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems

Ensuring quality of service (QoS) guarantees in service systems is a challenging task, particularly when the system is composed of more fine-grained services, such as service function chains. An important QoS metric in service systems is the end-to-end delay, which becomes even more important in delay-sensitive applications, where the jobs must be completed within a time deadline. Admission control is one way of providing end-to-end delay guarantee, where the controller accepts a job only if it has a high probability of meeting the deadline. In this paper, we propose a reinforcement learning-based admission controller that guarantees a probabilistic upper-bound on the end-to-end delay of the service system, while minimizes the probability of unnecessary rejections. Our controller only uses the queue length information of the network and requires no knowledge about the network topology or system parameters. Since long-term performance metrics are of great importance in service systems, we take an average-reward reinforcement learning approach, which is well suited to infinite horizon problems. Our evaluations verify that the proposed RL-based admission controller is capable of providing probabilistic bounds on the end-to-end delay of the network, without using system model information.
Geographic Variation in the Seasonal Activity Cycle of Spotted Turtles, Clemmys guttata

to northern Florida, and west through Pennsylvania and the lower Great Lakes region to northeastern Illinois (Ernst, 1972). Previous investigators have noted that northern populations of C. guttata are active primarily during the spring (Conant, 1951; Nemuras, 1966; Ernst, 1976), and appear to shun high environmental temperatures (Ward et al., 1976; Ernst, 1982). However, there are no published accounts regarding the seasonal activity cycle of this essentially northern species in the southern part of its range. If aversion to high temperature is consistent in the species, then turtles at lower latitudes should initiate their activity cycle earlier than populations at higher latitudes. The objective of this study was to compare the seasonal activity cycles of C. guttata from various geographic regions. Previously published seasonal activity cycles for C. guttata (Ohio-Conant, 1951; Maryland-Nemuras, 1966; Pennsylvania-Ernst, 1976) were compared, along with unpublished records for South Carolina. Records for South Carolina were based on live ani-
Accurate initial conditions for cosmological N-body simulations: minimizing truncation and discreteness errors

Inaccuracies in the initial conditions for cosmological N-body simulations could easily be the largest source of systematic error in predicting the non-linear large-scale structure. From the theory side, initial conditions are usually provided by using low-order truncations of the displacement field from Lagrangian perturbation theory, with the first and second-order approximations being the most common ones. Here we investigate the improvement brought by using initial conditions based on third-order Lagrangian perturbation theory (3LPT). We show that with 3LPT, truncation errors are vastly suppressed, thereby opening the portal to initializing simulations accurately as late as z=12 (for the resolution we consider). We analyse the competing effects of perturbative truncation and particle discreteness on various summary statistics. Discreteness errors are essentially decaying modes and thus get strongly amplified for earlier initialization times. We show that late starting times with 3LPT provide the most accurate configuration, which we find to coincide with the continuum fluid limit within 1 per cent for the power- and bispectrum at z=0 up to the particle Nyquist wave number of our simulations (k ~ 3h/Mpc). In conclusion, to suppress non-fluid artefacts, we recommend initializing simulations as late as possible with 3LPT. We make our 3LPT initial condition generator publicly available.
Radar Imaging Based on IEEE 802.11ad Waveform

The extension to millimeter-wave (mmWave) spectrum of communication frequency band makes it easy to implement a joint radar and communication system using single hardware. In this paper, we propose radar imaging based on the IEEE 802.11ad waveform for a vehicular setting. The necessary parameters to be estimated for inverse synthetic aperture radar (ISAR) imaging are sampled version of round-trip delay, Doppler shift, and vehicular velocity. The delay is estimated using the correlation property of Golay complementary sequences embedded on the IEEE 802.11ad preamble. The Doppler shift is first obtained from least square estimation using radar return signals and refined by correcting the phase uncertainty of Doppler shift by phase rotation. The vehicular velocity is determined from the estimated Doppler shifts and an equation of motion. Finally, an ISAR image is formed with the acquired parameters. Simulation results show that it is possible to obtain recognizable ISAR image from a point scatterer model of a realistic vehicular setting.
CDE-GAN: Cooperative Dual Evolution-Based Generative Adversarial Network

Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are still open problems caused by their adversarial optimization difficulties. In this article, motivated by the cooperative co-evolutionary algorithm, we propose a cooperative dual evolution-based GAN (CDE-GAN) to circumvent these drawbacks. In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multiobjective optimization. Thus, it exploits the complementary properties and injects dual mutation diversity into the training, to steadily diversify the estimated density in capturing multimodes and improve generative performance. Specifically, CDE-GAN decomposes the complex adversarial optimization problem into two subproblems (generation and discrimination), and each subproblem is solved with a separated subpopulation (E-Generators and E-Discriminators), evolved by its own evolutionary algorithm. Additionally, we further propose a Soft Mechanism to balance the tradeoff between E-Generators and E-Discriminators to conduct steady training for CDE-GAN. Extensive experiments on one synthetic dataset and three real-world benchmark image datasets demonstrate that the proposed CDE-GAN achieves a competitive and superior performance in generating good quality and diverse samples over baselines. The code and more generated results are available at our project homepage https://shiming-chen.github. io/CDE-GAN-website/CDE-GAN.html.
A Systematic Assessment of Deep Learning Models for Molecule Generation

In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.
Higher order initial conditions for mixed baryon–CDM simulations

We present a novel approach to generate higher order initial conditions (ICs) for cosmological simulations that take into account the distinct evolution of baryons and dark matter. We focus on the numerical implementation and the validation of its performance, based on both collisionless N-body simulations and full hydrodynamic Eulerian and Lagrangian simulations. We improve in various ways over previous approaches that were limited to first-order Lagrangian perturbation theory (LPT). Specifically, we (1) generalize nth-order LPT to multifluid systems, allowing 2LPT or 3LPT ICs for two-fluid simulations, (2) employ a novel propagator perturbation theory to set up ICs for Eulerian codes that are fully consistent with 1LPT or 2LPT, (3) demonstrate that our ICs resolve previous problems of two-fluid simulations by using variations in particle masses that eliminate spurious deviations from expected perturbative results, (4) show that the improvements achieved by going to higher order PT are comparable to those seen for single-fluid ICs, and (5) demonstrate the excellent (i.e. few per cent level) agreement between Eulerian and Lagrangian simulations, once high-quality initial conditions are used. The rigorous development of the underlying perturbation theory is presented in a companion paper. All presented algorithms are implemented in the monofonic music-2 package that we make publicly available.
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition

Gesture recognition has attracted considerable attention owing to its great potential in applications. Although the great progress has been made recently in multi-modal learning methods, existing methods still lack effective integration to fully explore synergies among spatio-temporal modalities effectively for gesture recognition. The problems are partially due to the fact that the existing manually designed network architectures have low efficiency in the joint learning of multi-modalities. In this paper, we propose the first neural architecture search (NAS)-based method for RGB-D gesture recognition. The proposed method includes two key components: 1) enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and 2) optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities. The resultant multi-modal multi-rate network provides a new perspective to understand the relationship between RGB and depth modalities and their temporal dynamics. Comprehensive experiments are performed on three benchmark datasets (IsoGD, NvGesture, and EgoGesture), demonstrating the state-of-the-art performance in both single- and multi-modality settings. The code is available at https://github.com/ZitongYu/3DCDC-NAS.
Counterfactual-based minority oversampling for imbalanced classification

A key challenge of oversampling in imbalanced classification is that the generation of new minority samples often neglects the usage of majority classes, resulting in most new minority sampling spreading the whole minority space. In view of this, we present a new oversampling framework based on the counterfactual theory. Our framework introduces a counterfactual objective by leveraging the rich inherent information of majority classes and explicitly perturbing majority samples to generate new samples in the territory of minority space. It can be analytically shown that the new minority samples satisfy the minimum inversion, and therefore most of them locate near the decision boundary. Empirical evaluations on benchmark datasets suggest that our approach significantly outperforms the state-of-the-art methods.
Phylogenetic analysis and comparative study of Sahiwal cattle CD14 gene.

Cluster of Differentiation-14 (CD14) gene is one of the strong candidate genes for mastitis resistance trait. Bovine CD14 gene spans over 2630 bp having 2 exons. Present investigation is based on phylogenetic analysis and comparative study of CD14 gene in Sahiwal cattle. A total of 100 lactating Sahiwal cattle maintained at NDRI, Karnal (Haryana) were included in the present study. Sequence analysis revealed that Sahiwal cattle CD14 gene sequence is 73 to 99% identical with several species. Phylogenetic analysis indicated that Sahiwal cattle (Bos indicus) and Bos taurus are genetically the most similar (99%) followed by Bubalus bubalis. The predicted peptide sequence revealed 373 amino acids precursor corresponding to coding sequence of CD14 gene of Sahiwal cattle. Amino acid length of CD14 protein was found different in reported species. BLAST analysis also revealed that Sahiwal cattle CD14 protein is 73 to 100% similar with other reported species. The sites for leucine-rich repeats, leucine rich nuclear export signal, putative N-linked glycosylation, O-linked glycosylation and disulphide bridges were predicted and comparative study was done.
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics

Annotating histopathological images is a time-consuming andlabor-intensive process, which requires broad-certificated pathologistscarefully examining large-scale whole-slide images from cells to tissues.Recent frontiers of transfer learning techniques have been widely investi-gated for image understanding tasks with limited annotations. However,when applied for the analytics of histology images, few of them can effec-tively avoid the performance degradation caused by the domain discrep-ancy between the source training dataset and the target dataset, suchas different tissues, staining appearances, and imaging devices. To thisend, we present a novel method for the unsupervised domain adaptationin histopathological image analysis, based on a backbone for embeddinginput images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels. The graph model isset up by connecting every image with its close neighbors in the embed-ded feature space. Then graph neural network is employed to synthesizenew feature representation from every image. During the training stage,target samples with confident inferences are dynamically allocated withpseudo labels. The cross-entropy loss function is used to constrain thepredictions of source samples with manually marked labels and targetsamples with pseudo labels. Furthermore, the maximum mean diversityis adopted to facilitate the extraction of domain-invariant feature repre-sentations, and contrastive learning is exploited to enhance the categorydiscrimination of learned features. In experiments of the unsupervised do-main adaptation for histopathological image classification, our methodachieves state-of-the-art performance on four public datasets
To Paraphrase or Not To Paraphrase: User-Controllable Selective Paraphrase Generation

In this article, we propose a paraphrase generation technique to keep the key phrases in source sentences during paraphrasing. We also develop a model called TAGPA with such technique, which has multiple pre-configured or trainable key phrase detector and a paraphrase generator. The paraphrase generator aims to keep the key phrases and increase the diversity of the paraphrased sentences. The key phrases can be entities provided by our user, like company names, people's names, domain-specific terminologies, etc., or can be learned from a given dataset.
A Monte Carlo Tree Search Framework for Quantum Circuit Transformation

In Noisy Intermediate-Scale Quantum (NISQ) era, quantum processing units (QPUs) suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit executable, a circuit transformation process is necessary to transform it into a functionally equivalent one so that the connectivity constraints imposed by the QPU are satisfied. Due to the high branching factor and vast search space, almost all existing algorithms search very shallowly and thus, very often, can reach (at most) locally optimal solutions. We propose a Monte Carlo Tree Search framework to tackle this problem, which enables the search process to go deeper. In particular, we design, by taking both short- and long-term rewards into consideration, a scoring mechanism, and propose to use a fast random strategy for simulation. The thus designed search algorithm is polynomial in all relevant parameters and empirical results on extensive realistic circuits show that it can reduce, in average, the size of the output circuits by at least 30% when compared with the state-of-the-art algorithms on IBM Q20.
Towards Designing a Self-Managed Machine Learning Inference Serving System inPublic Cloud

We are witnessing an increasing trend towardsusing Machine Learning (ML) based prediction systems, span-ning across different application domains, including productrecommendation systems, personal assistant devices, facialrecognition, etc. These applications typically have diverserequirements in terms of accuracy and response latency, thathave a direct impact on the cost of deploying them in a publiccloud. Furthermore, the deployment cost also depends on thetype of resources being procured, which by themselves areheterogeneous in terms of provisioning latencies and billingcomplexity. Thus, it is strenuous for an inference servingsystem to choose from this confounding array of resourcetypes and model types to provide low-latency and cost-effectiveinferences. In this work we quantitatively characterize the cost,accuracy and latency implications of hosting ML inferenceson different public cloud resource offerings. In addition, wecomprehensively evaluate prior work which tries to achievecost-effective prediction-serving. Our evaluation shows that,prior work does not solve the problem from both dimensionsof model and resource heterogeneity. Hence, we argue that toaddress this problem, we need to holistically solve the issuesthat arise when trying to combine both model and resourceheterogeneity towards optimizing for application constraints.Towards this, we envision developing a self-managed inferenceserving system, which can optimize the application require-ments based on public cloud resource characteristics. In orderto solve this complex optimization problem, we explore the highlevel design of a reinforcement-learning based system that canefficiently adapt to the changing needs of the system at scale.
ConiVAT: Cluster Tendency Assessment and Clustering with Partial Background Knowledge

The VAT method is a visual technique for determining the potential cluster structure and the possible number of clusters in numerical data. Its improved version, iVAT, uses a path-based distance transform to improve the effectiveness of VAT for "tough" cases. Both VAT and iVAT have also been used in conjunction with a single-linkage(SL) hierarchical clustering algorithm. However, they are sensitive to noise and bridge points between clusters in the dataset, and consequently, the corresponding VAT/iVAT images are often in-conclusive for such cases. In this paper, we propose a constraint-based version of iVAT, which we call ConiVAT, that makes use of background knowledge in the form of constraints, to improve VAT/iVAT for challenging and complex datasets. ConiVAT uses the input constraints to learn the underlying similarity metric and builds a minimum transitive dissimilarity matrix, before applying VAT to it. We demonstrate ConiVAT approach to visual assessment and single linkage clustering on nine datasets to show that, it improves the quality of iVAT images for complex datasets, and it also overcomes the limitation of SL clustering with VAT/iVAT due to "noisy" bridges between clusters. Extensive experiment results on nine datasets suggest that ConiVAT outperforms the other three semi-supervised clustering algorithms in terms of improved clustering accuracy.
Primal-Dual Sequential Subspace Optimization for Saddle-point Problems

We introduce a new sequential subspace optimization method for large-scale saddle-point problems. It solves iteratively a sequence of auxiliary saddle-point problems in low-dimensional subspaces, spanned by directions derived from first-order information over the primal \emph{and} dual variables. Proximal regularization is further deployed to stabilize the optimization process. Experimental results demonstrate significantly better convergence relative to popular first-order methods. We analyze the influence of the subspace on the convergence of the algorithm, and assess its performance in various deterministic optimization scenarios, such as bi-linear games, ADMM-based constrained optimization and generative adversarial networks.