Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive analysis of mitogen-activated protein kinase cascades in chrysanthemum

Background Mitogen-activated protein kinase (MAPK) cascades, an important type of pathway in eukaryotic signaling networks, play a key role in plant defense responses, growth and development. Methods Phylogenetic analysis and conserved motif analysis of the MKK and MPK families in Arabidopsis thaliana, Helianthus annuus and Chrysanthemum morifolium classified MKK genes and MPK genes. qRT-PCR was used for the expression patterns of CmMPK and CmMKK genes, and yeast two-hybrid assay was applied to clear the interaction between CmMPKs and CmMKKs. Results We characterized six MKK genes and 11 MPK genes in chrysanthemum based on transcriptomic sequences and classified these genes into four groups. qRT-PCR analysis demonstrated that CmMKKs and CmMPKs exhibited various expression patterns in different organs of chrysanthemum and in response to abiotic stresses and phytohormone treatments. Furthermore, a yeast two-hybrid assay was applied to analyze the interaction between CmMKKs and CmMPKs and reveal the MAPK cascades in chrysanthemum. Discussion Our data led us to propose that CmMKK4-CmMPK13 and CmMKK2-CmMPK4 may be involved in regulating salt resistance and in the relationship between CmMKK9 and CmMPK6 and temperature stress.
A tree topology power allocation algorithm for non-orthogonal multiple access system in 5G system

Due to its superior spectral efficiency and higher system capacity, non-orthogonal multiple access (NOMA) has recently become a promising candidate scheme in 5G systems. In this paper, we discussed the basic theory of NOMA system first of all. Then, we studied the systems capacity optimization problem of NOMA system under the constraints of total transmit power and the minimum rate of the weaker user. Then a tree topology power allocation algorithm is discussed, which achieves the same performance as the FSPA method. but its computation complexity decreases from exponential to constant. The results of numerical simulation showed proposed algorithm has lower computational complexity and can realize higher system capacity. Meanwhile, higher system capacity can be achieved through adjusting the power allocation factor α in FPA algorithm.
Behavioral Authentication Method Utilizing Wi-Fi History Information Captured by IoT Device

Lifestyle authentication, a new user authentication method that uses human lifestyle patterns, has recently been proposed. This method exists in part because of progress in IoT technology, which has facilitated the gathering of lifestyle behavioral information as lifelogs. However, large-scale data generally cannot be used for research. In order to promote research on lifestyle authentication, we carried out a demonstration experiment and succeeded in gathering large-scale lifelogs. Moreover, we applied the lifelog data to the proposed authentication method using Wi-Fi information which is one of lifestyle authentication factors. As a result of the experiment, we confirmed the validity of the proposed authentication method.
On Stubborn Sets and Planning with Resources

Stubborn sets are a well-established technique to admissibly prune permutable parts of forward search in classical planning. But what about planning with resources? The identification of effective stubborn sets relies on non-interfering actions. Yet, a priori, all actions affecting the same resource interfere. We show how to exploit the fact that, nevertheless, with commutativity of addition and subtraction, many resource-affecting action sequences are permutable. We design suitable notions of stubborn sets for planning with resources, with and without resource production. We show empirically, on classical IPC benchmarks with discrete resource variables, that our new pruning methods are often, and sometimes dramatically, superior to previous ones. Together with a novel way of automatically identifying the resource variables, this result holds under IPC conditions.
CNN-based Action Recognition and Supervised Domain Adaptation on 3D Body Skeletons via Kernel Feature Maps

Deep learning is ubiquitous across many areas areas of computer vision. It often requires large scale datasets for training before being fine-tuned on small-to-medium scale problems. Activity, or, in other words, action recognition, is one of many application areas of deep learning. While there exist many Convolutional Neural Network architectures that work with the RGB and optical flow frames, training on the time sequences of 3D body skeleton joints is often performed via recurrent networks such as LSTM. In this paper, we propose a new representation which encodes sequences of 3D body skeleton joints in texture-like representations derived from mathematically rigorous kernel methods. Such a representation becomes the first layer in a standard CNN network e.g., ResNet-50, which is then used in the supervised domain adaptation pipeline to transfer information from the source to target dataset. This lets us leverage the available Kinect-based data beyond training on a single dataset and outperform simple fine-tuning on any two datasets combined in a naive manner. More specifically, in this paper we utilize the overlapping classes between datasets. We associate datapoints of the same class via so-called commonality, known from the supervised domain adaptation. We demonstrate state-of-the-art results on three publicly available benchmarks.
Strategies for imputing missing covariates in accelerated failure time models

Missing covariates often occur in biomedical studies with survival outcomes. Multiple imputation via chained equations (MICE) is a semi‐parametric and flexible approach that imputes multivariate data by a series of conditional models, one for each incomplete variable. When applying MICE, practitioners tend to specify the conditional models in a simple fashion largely dictated by the software, which could lead to suboptimal results. Practical guidelines for specifying appropriate conditional models in MICE are lacking. Motivated by a study of time to hip fractures in the Women's Health Initiative Observational Study using accelerated failure time models, we propose and experiment with some rationales leading to appropriate MICE specifications. This strategy starts with specifying a joint model for the variables involved. We first derive the conditional distribution of each variable under the joint model, then approximate these conditional distributions to the extent which can be characterized by commonly used regression models. We propose to fit separate models to impute incomplete variables by the failure status, which is key to generating appropriate MICE specifications for survival outcomes. The proposed strategy can be conveniently implemented with all available imputation software that uses fully conditional specifications. Our simulation results show that some commonly used simple MICE specifications can produce suboptimal results, while those based on the proposed strategy appear to perform well and be robust toward model misspecifications. Hence, we warn against a mechanical use of MICE and suggest careful modeling of the conditional distributions of variables to ensure proper performance.
How phyllosilicate mineral structure affects fault strength in Mg‐rich fault systems

The clay mineralogy of fault gouges has important implications for the frictional properties of faults, often identified as a major factor contributing to profound fault weakness. This work compares the frictional strength of a group of Mg‐rich minerals common in the Mg‐Al‐Si‐O compositional space (talc, saponite, sepiolite, and palygorskite) by conducting triaxial frictional tests with water or argon as pore fluid. The studied minerals are chemically similar but differ in their crystallographic structure. Results show that fibrous Mg‐rich phyllosilicates are stronger than their planar equivalents. Frictional strength in this group of minerals is highly influenced by strength of the atomic bonds, continuity of water layers within the crystals, and interactions of mineral surfaces with water molecules, all of which are dictated by crystal structure. The formation and stability of the minerals studied are mainly controlled by small changes in pore fluid chemistry, which can lead to significant differences in fault strength.
Non-linear FEM simulation of the elasto-plastic behaviour in MEMS specimens

Main goal of the present paper is the experimental characterization of the elastic-plastic behavior of beam structure MEMS test specimen. For this purpose, a non-linear FE-based model is studied and results were successively benchmarked with experimental data obtained in laboratory. In this paper non-linear FEM simulation is uses to design and characterize a specimen embedded in the test MEMS structure.
Relative indexed compressed sparse filter encoding format for hardware-oriented acceleration of deep convolutional neural networks

To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a computation flow, stacked filters stationary flow (SFS), and a corresponding data encoding format, relative indexed compressed sparse filter format (CSF), to make the best of data sparsity, and simplify data handling at execution time. And we also propose a three dimensional Single Instruction Multiple Data (3D-SIMD) processor architecture to illustrate how to accelerate deep CNNs by taking advantage of the SFS flow and CSF format. Comparing with the state-of-the-art result [1], our methods achieve 1.11 x improvement in reducing the storage required by AlexNet, and 1.09 x improvement in reducing the storage required by SqueezeNet, without loss of accuracy on the ImageNet dataset. Moreover, using these approaches, chip area for logics handling irregular sparse data access can be saved (about 19.1% chip area in [2]). Comparing with the 2D-SIMD processor structures in DVAS, ENVISION, etc., our methods achieve about 3.65x processing element (PE) array utilization rate improvement (from 26.4% to 96.5%) on the data from Deep Compression on AlexNet 1.
A recommendation model of smart parking

Parking search is a challenging task in most urban areas. The traffic resulting from drivers cruising for parking has a great effect on the environment, increasing energy consumption and emissions of CO2. Existing parking search applications only provide the information such as the address of parking spot, turn-by-turn navigation to the parking destination and available parking space. We have proposed a real-time parking android-based APP for Pingtung city, which connects with different parking management servers to locate the best parking spaces based on the drivers needs, such as driving distance to parking spot, walking distance from parking spot to destination, available parking space and parking price. The results have shown that the APP helps to reduce the environmental burden as well as the time spent looking for parking.
The Pfaffian Technique: A (2 + 1)-Dimensional Korteweg de Vries Equation

The (2 + 1)-dimensional Korteweg de Vries (KdV) equation, which was first derived by Boiti et al., has been studied by various distinct methods. It is known that this (2 + 1)-dimensional KdV equation has rich solutions, such as multi-soliton solutions and dromion solutions. In the present article, a unified representation of its N-soliton solution is given by means of pfaffian. We’ll show that this (2 + 1)-dimensional KdV equation is nothing but the Plucker identity when its τ-function is given by pfaffian.
Improving Subject-independent Human Emotion Recognition Using Electrodermal Activity Sensors for Active and Assisted Living

In Active and Assisted Living environments (AAL), one of the major tasks is to make sure that old people or disabled persons do feel well in their environment. Unfortunately, it is still a difficult task to design a learning system or build a machine learning model which can be trained on a group of subjects using physiological sensors and performs well when testing it on other subjects. This paper proposes a dynamic calibration algorithm which presents promising results for subject-independent human emotion recognition. The goal of the calibration module is to calibrate itself with respect to the features of a new subject by finding the most similar subject in the training data. In order to check the overall performance, this approach is tested using the well-known MAHNOB dataset. The results show a promising improvement based on different evaluation metrics, e.g., sensitivity and specificity.
Low-capture-power X-filling method based on architecture using selection expansion

In the system-on-chip(SOC) design, there are a huge volume of test data. The arrival of test data compression is because of lacking in channel capacity and the restricted memory of Automatic Test Equipment (ATE). Therefore, the technique of test data compression is very important to save time and memory. Because of MISR's characteristic, we can use one ATE data to run lots of times. In this paper we use selection with Flip-Flops to spread MISR data. Each Flip-Flop of MISR is connecting with 2 MUXs of selection. And selection connected with MISR. To use Flip-Flops so we can restore bits in it, just changing Flip-Flops bits when data changing. It is not frequently changing of the bits of Flip-Flops, so we can decrease power consumption. In our architecture we already saved some shift power. In this paper we will approach a Low-Capture-Power X-Filling Method to decrease shift times of Flip-Flops. Compare to Random X-filling Method, the FC will be the same but less average and maximum shift times.
All That Glitters Is Gold - An Attack Scheme on Gold Questions in Crowdsourcing

One of the most popular quality assurance mechanisms in paid micro-task crowdsourcing is based on gold questions: the use of a small set of tasks of which the requester knows the correct answer and, thus, is able to directly assess crowd work quality. In this paper, we show that such mechanism is prone to an attack carried out by a group of colluding crowd workers that is easy to implement and deploy: the inherent size limit of the gold set can be exploited by building an inferential system to detect which parts of the job are more likely to be gold questions. The described attack is robust to various forms of randomisation and programmatic generation of gold questions. We present the architecture of the proposed system, composed of a browser plug-in and an external server used to share information, and briefly introduce its potential evolution to a decentralised implementation. We implement and experimentally validate the gold detection system, using real-world data from a popular crowdsourcing platform. Finally, we discuss the economic and sociological implications of this kind of attack.
Applying convolutional networks to underwater tracking without training

Underwater target tracking is one of the most important tasks in recent upsurge of smart aquaculture, especially in the application of AI-driven cage culture. In [1] CNT was shown quite effective in the task of land-based object tracking, it is fast as no huge amount of data is required for training. However, an initial bounding box must be selected manually in CNT for tracking a single object, not to mention the fact that applications of CNT and other convolutional networks in underwater operations are rarely reported. In this paper we present an improved version of CNT (called Fast-CNT2) which is capable of performing underwater multi-target tracking. Firstly, GMM is applied to the input video for extracting regions each containing a moving target (e.g. fish); then a respective region containing a fish is identified and bounded with a box; finally, multi-target tracking is implemented with Fast-CNT2. Experimental results show that our method can successfully track multiple fish.
Impaired Taste Associative Memory and Memory Enhancement by Feeding Omija in Parkinson’s Disease Fly Model

Neurodegeneration can result in memory loss in the central nervous system (CNS) and impairment of taste and smell in the peripheral nervous system (PNS). The neurodegeneration seen in Parkinson’s disease (PD) is characterized by functional loss of dopaminergic neurons. Recent studies have also found a role for dopaminergic neurons in regulating taste memory rewards in insects. To investigate how taste memories and sugar sensitivity can be affected in PD, we utilized the DJ-1β mutant fruit fly, DJ-1βex54, as a PD model. We performed binary choice feeding assays, electrophysiology and taste-mediated memory tests to explore the function of the DJ-1β gene in terms of sugar sensitivity as well as associative taste memory. We found that PD flies exhibited an impaired ability to discriminate sucrose across a range of sugar concentrations, with normal responses at only very high concentrations of sugar. They also showed an impairment in associative taste memory. We highlight that the taste impairment and memory defect in DJ-1βex54 can be recovered by the expression of wild-type DJ-1β gene in the dopaminergic neurons. We also emphasized the role of dopaminergic neurons in restoring taste memory function. This impaired memory property of DJ-1βex54 flies also allows them to be used as a model system for finding supplementary dietary foods that can improve memory function. Here we provide evidence that the associative taste memory of both control and DJ-1βex54 flies can be enhanced with dietary supplementation of the medicinal plant, omija.
The Impact of Recipe Features, Social Cues and Demographics on Estimating the Healthiness of Online Recipes

Apart from taste, people increasingly consider nutritional facts when choosing a recipe or a meal. Researchers and companies alike aim to support health-conscious choices by providing estimates of a meal's calories and levels of sugar, fat, protein and salt. Features that are typically considered for these automatic estimates include a recipe's title, ingredients and cooking directions, as well as photographic material. Little is known based on which features users estimate the healthiness of online recipes themselves. Making use of data derived from a large online food community, and data collected via crowdsourcing, we compare the performance of algorithmic nutritional estimation with the performance of human-provided estimates, and analyze the most influential features used by humans and machines. Our results indicate that simple models already outperform human raters. Basic features such as title, ingredients and cooking directions were more informative than pictures of a recipe or user comments. For human estimates, we observed effects due to age and gender, but not due to dietary preferences or cooking habits. Our quantitative and qualitative results provide guidance for the development and evaluation of methods for nutrition estimation, and give insight in which features are most useful for nudging people into making healthier diet choices.
AutoGrowCut — Automatic image segmentation by salient labeling

Interactive segmentation algorithm has widespread use in image segmentation literature due to its good segmentation results. However, enormous amount of manual labor, namely interactive object and background labeling, is indispensable for a large image database. In this paper, we present an automatic image segmentation method — AutoGrowCut, which is an improved version of GrowCut and uses our visual attention model to obtain the salient labels. Concretely, we propose a new saliency detection model to do saliency map calculation, and adaptive thresholding is conducted on the saliency map to obtain the mask map, which is exploited as initial seed pixels. Then the seed pixels and saliency map are used as the inputs of the algorithm. Finally, the image segmentation process is executed automatically. Experiments with 500 images from MSRA Salient Object Database show that the improved version AutoGrowCut is not only labor-saving, but also has good performance compared to state-of-the-art algorithms.
Plan Relaxation via Action Debinding and Deordering

While seminal work has studied the problem of relaxing the ordering of a plan’s actions, less attention has been given to the problem of relaxing and modifying a plan’s variable bindings. This paper studies the problem of relaxing a plan into a partial plan which specifies which operators must be executed, but need not completely specify their order or variable bindings. While partial plans can provide an agent with additional flexibility and robustness at execution time, many operations over partial plans are intractable. This paper tackles this problem by proposing and empirically evaluating a fixed-parameter tractable algorithm which searches for tractable, flexible partial plans.
Complex role of IL-23R polymorphisms on ankylosing spondylitis: a meta-analysis

ABSTRACT Background: The aim of the meta-analysis was to evaluate the association between 10 widely studied polymorphisms of interleukin-23 receptor gene (IL-23R) and ankylosing spondylitis (AS). Methods: A comprehensive literature search, screening of eligible articles and data extraction was performed independently by two investigators. Further meta-analysis was conducted with STATA 12.0 software (Stata Corp.; College Station, TX, USA). The association between IL-23R polymorphisms and AS was evaluated by odds ratio (OR) and 95% confidence intervals (95% CI). Results: Twenty-five case–control studies with 8431 cases and 8972 controls were included in this meta-analysis. Quantitative meta-analysis revealed that minor allele frequency (MAF) of rs1004819, rs1495965, and rs2201841 was significantly higher in the AS group (p value < .001, < .001, = .010, respectively). MAF of rs10489629, rs11209026, rs11465804, and rs1343151was significantly lower in the AS group (p value = .002, < .001, = .032, < .001, respectively). However, there is no significant difference between these two groups in rs10889677, rs11209032, and rs7517847 frequency (p value = .128, .237, .131, respectively). Conclusions: The present study indicates that minor allele carriers of rs1004819, rs1495965, and rs2201841 are susceptible to AS. Conversely, minor alleles of rs10489629, rs11209026, rs11465804, and rs1343151 have protective effect on AS.
The functional organization of descending sensory-motor pathways in Drosophila

In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck and terminate in post-cranial regions of the nervous system. This critical neural population is thought to activate, maintain and modulate locomotion and other behaviors. Although individual members of this cell class have been well-studied across species ranging from insects to primates, little is known about the overall connectivity pattern of DNs as a population. We undertook a systematic anatomical investigation of descending neurons in the fruit fly, Drosophila melanogaster, and created a collection of over 100 transgenic lines targeting individual cell types. Our methods allowed us to describe the morphology of roughly half of an estimated 400 DNs and create a comprehensive map of connectivity between the sensory neuropils in the brain and the motor neuropils in the ventral nerve cord. Like the vertebrate spinal cord, our results show that the fly nerve cord is a highly organized, layered system of neuropils, an organization that reflects the fact that insects are capable of two largely independent means of locomotion – walking and fight – using distinct sets of appendages. Our results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of sensory-motor circuits.