Effects of Meloidogyne Incognita, Soil Physical Parameters, and Thielaviopsis Basicola on Cotton Root Architecture and Plant Growth

The root-knot nematode, Meloidogyne incognita, and the seedling pathogen, Thielaviopsis basicola, commonly co-exist in Arkansas cotton fields and may interact resulting in increased losses. The primary objective of this research was to evaluate the effects of soil physical parameters on these soilborne pathogens and cotton growth in controlled environmental, field, and microplot studies. Controlled environmental experiments used two soil bulk densities and four pathogen treatments: non-infested soil, soil infested with M. incognita or T. basicola and soil infested with both pathogens. The results indicated bulk density generally did not affect seedling growth or disease since soils had low penetration resistance under well-watered conditions. The combination of M. incognita with T. basicola reduced seedling stands and root volume more than either pathogen alone. Both M. incognita and T. basicola reduced root topological characters, but only M. incognita changed the root topological index. The effects of subsoiling and application of the nematicide 1,3-dichloropropene (Telone II®) on root system development and plant growth were investigated from 2009 to 2011 in a cotton field in northeastern Arkansas. Subsoiling did not consistently affect early season growth. Nematicide treatment consistently improved seedling growth for one or more parameters in 2010 and 2011. Root galling and the population of M. incognita were suppressed by Telone II®. Neither subsoiling nor nematicide application affected cotton development or root topology. The effects of a soil hard pan (HP) and M. incognita on cotton root architecture and plant growth were evaluated in a microplot study in 2010 and 2011 at Hope, Arkansas. An artificial HP was created 20 cm below the soil surface in half of the microplots. Pathogen treatments included soil infested with T. basicola plus four different M. incognita levels (0, 4, 8, 12 eggs/cm3 soil). Generally, soil HP improved seedling growth due to higher soil water contents above the HP layer. M. incognita reduced taproot length, delayed cotton maturity and reduced seed cotton yield. Root topology provides a new approach to quantify the changes caused by soilborne pathogens and soil physical factors and will help in crop management in the future

Manifold Regularized Correlation Object Tracking

In this paper, we propose a manifold regularized correlation tracking method with augmented samples. To make better use of the unlabeled data and the manifold structure of the sample space, a manifold regularization-based correlation filter is introduced, which aims to assign similar labels to neighbor samples. Meanwhile, the regression model is learned by exploiting the block-circulant structure of matrices resulting from the augmented translated samples over multiple base samples cropped from both target and nontarget regions. Thus, the final classifier in our method is trained with positive, negative, and unlabeled base samples, which is a semisupervised learning framework. A block optimization strategy is further introduced to learn a manifold regularization-based correlation filter for efficient online tracking. Experiments on two public tracking data sets demonstrate the superior performance of our tracker compared with the state-of-the-art tracking approaches

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio

Visual Tracking by Sampling in Part Space

In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods

ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic Corpora

Generating visually grounded image captions with specific linguistic styles using unpaired stylistic corpora is a challenging task, especially since we expect stylized captions with a wide variety of stylistic patterns. In this paper, we propose a novel framework to generate Accurate and Diverse Stylized Captions (ADS-Cap). Our ADS-Cap first uses a contrastive learning module to align the image and text features, which unifies paired factual and unpaired stylistic corpora during the training process. A conditional variational auto-encoder is then used to automatically memorize diverse stylistic patterns in latent space and enhance diversity through sampling. We also design a simple but effective recheck module to boost style accuracy by filtering style-specific captions. Experimental results on two widely used stylized image captioning datasets show that regarding consistency with the image, style accuracy and diversity, ADS-Cap achieves outstanding performances compared to various baselines. We finally conduct extensive analyses to understand the effectiveness of our method. Our code is available at https://github.com/njucckevin/ADS-Cap.Comment: Accepted at Natural Language Processing and Chinese Computing (NLPCC) 202