Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Fumigants on Soil Microbial Communities

ABSTRACT Agricultural soils are typically fumigated to provide effective control of nematodes, soilborne pathogens, and weeds in preparation for planting of high-value cash crops. The ability of soil microbial communities to recover after treatment with fumigants was examined using culture-dependent (Biolog) and culture-independent (phospholipid fatty acid [PLFA] analysis and denaturing gradient gel electrophoresis [DGGE] of 16S ribosomal DNA [rDNA] fragments amplified directly from soil DNA) approaches. Changes in soil microbial community structure were examined in a microcosm experiment following the application of methyl bromide (MeBr), methyl isothiocyanate, 1,3-dichloropropene (1,3-D), and chloropicrin. Variations among Biolog fingerprints showed that the effect of MeBr on heterotrophic microbial activities was most severe in the first week and that thereafter the effects of MeBr and the other fumigants were expressed at much lower levels. The results of PLFA analysis demonstrated a community shift in all treatments to a community dominated by gram-positive bacterial biomass. Different 16S rDNA profiles from fumigated soils were quantified by analyzing the DGGE band patterns. The Shannon-Weaver index of diversity,H, was calculated for each fumigated soil sample. High diversity indices were maintained between the control soil and the fumigant-treated soils, except for MeBr (H decreased from 1.14 to 0.13). After 12 weeks of incubation, Hincreased to 0.73 in the MeBr-treated samples. Sequence analysis of clones generated from unique bands showed the presence of taxonomically unique clones that had emerged from the MeBr-treated samples and were dominated by clones closely related to Bacillus spp. andHeliothrix oregonensis. Variations in the data were much higher in the Biolog assay than in the PLFA and DGGE assays, suggesting a high sensitivity of PLFA analysis and DGGE in monitoring the effects of fumigants on soil community composition and structure. Our results indicate that MeBr has the greatest impact on soil microbial communities and that 1,3-D has the least impact.
Antagonistic action of harpin proteins: HrpWea from Erwinia amylovora suppresses HrpNea-induced cell death in Arabidopsis thaliana

Harpins are proteins secreted by the type-three secretion system of phytopathogenic bacteria. They are known to induce a hypersensitive response (HR) in non-host plant leaf tissue. Erwinia amylovora, the fire blight pathogen of pear and apple trees, secretes two different harpins, HrpNea and HrpWea. In the present study, we showed that an Erwinia amylovora hrpWea mutant induces stronger electrolyte leakages in Arabidopsis thaliana foliar disks than the wild-type strain, thus suggesting that HrpWea could function as a HR negative modulator. We confirmed this result by using purified HrpWea and HrpNea. HrpWea has dual effects depending on its concentration. At 200 nM, HrpWea, like HrpNea, provoked the classical defense response – active oxygen species (AOS) production and cell death. However, at 0.2 nM, HrpWea inhibited cell death and AOS production provoked by HrpNea. HrpWea probably inhibits HrpNea-induced cell death by preventing anion channel inhibition, confirming that anion channel regulation is a determinant feature of the plant response to harpins. Collectively our data show that the HrpWea harpin can act antagonistically to the classical HrpNea harpin by suppressing plant defense mechanisms.
High-Efficiency, Broadband Doherty Power Amplifier for 5G Applications

This paper presents a broadband Doherty Power Amplifier (DPA) for 5G applications. The proposed DPA uses high electron mobility transistor (HEMT) Gallium nitride (GaN) based packaged device as an active nonlinear device. DPA uses active load modulation to get high efficiency at back-off power operation. With the simulation results it is observed that the proposed DPA can achieve the highest efficiency of 75.3% at saturation and around 64.3% at 6 dB output back off (OBO) at the center frequency of 3.5 GHz. The achieved bandwidth (BW) is 3.1-3.8 GHz with a gain of more than 8 dB and output power (Pout) of 41.4-44.6 dBm. The proposed DPA can accomplish a wide band characteristic with high efficiency and output power.
Fitness-Assisted Counting Based on Lightweight Pose Recognition Network and k-NN Classification

In this paper, propose a fitness assistance algorithm based on the combination of a lightweight network human posture detection model, BlazePose, and a k-NN classification algorithm for real-time counting on mobile devices. Firstly, the BlazePose model is used to predict the target pose on the given sample image, and the obtained Landmark is normalized to build the training set of the k-NN classifier. Then Pose Classification Colab classifies each sample by targeting the entire training set while excluding outliers and underrepresented categories. Finally, the implementation of the classifier itself can realize the real-time counting of fitness exercises. In this study, squats and push-ups, two of the most representative sports, are selected as the verification objects. The experimental results show that, with real-time and accuracy guaranteed This algorithm is capable of classifying and counting push-ups and squats at a rate of 30 frames on an ordinary mobile device.
Challenges and Opportunities for Trust Management in Crowdsourcing

Crowd sourcing (CS) systems offer a new way for businesses and individuals to leverage on the power of mass collaboration to accomplish complex tasks in a divide-and-conquer manner. In existing CS systems, no facility has been provided for analyzing the trustworthiness of workers and providing decision support for allocating tasks to workers, which leads to high dependency of the quality of work on the behavior of workers in CS systems as shown in this paper. To address this problem, trust management mechanisms are urgently needed. Traditional trust management techniques are focused on identifying the most trustworthy service providers (SPs) as accurately as possible. Little thoughts were given to the question of how to utilize these SPs due to two common assumptions: 1) an SP can serve an unlimited number of requests in one time unit, and 2) a service consumer (SC) only needs to select one SP for interaction to complete a task. However, in CS systems, these two assumptions are no longer valid. Thus, existing models cannot be directly used for trust management in CS systems. This paper takes the first step towards a systematic investigation of trust management in CS systems by extending existing trust management models for CS trust management and conducting extensive experiments to study and analyze the performance of various trust management models in crowd sourcing. In this paper, the following key contributions are made. We 1) propose extensions to existing trust management approaches to enable them to operate in CS systems, 2) design a simulation test-bed based on the system characteristics of Amazon's Mechanical Turk (AMT) to make evaluation close to practical CS systems, 3) discuss the effect of incorporating trust management into CS system on the overall social welfare, and 4) identify the challenges and opportunities for future trust management research in CS systems.
A Gestural Language For A Humanoid Robot by Aaron Ladd Edsinger

This thesis describes work done at the MIT Artificial Intelligence Laboratory on the humanoid robot platform, Cog. Humanoid research has long been concerned with the quality of the robot’s movement. However, obtaining the elusive tempo and grace of the human motor system has proven to be a very difficult problem. The complexity of controlling high degree of freedom (DOF) humanoid robots, combined with insights provide by neurophysiological findings, has lead researchers to look at motor primitives (Williamson 1996) as an organizing methodology. We propose a data-driven approach to motor primitives in building a motor language for Cog. The proposed model is implemented on Cog and applied to the task of human motor mimicry. Thesis Supervisor: Rodney Brooks Title: Fujitsu Professor of Computer Science and Engineering
One table stores all: Enabling painless free-and-easy data publishing and sharing

In this paper, we present a free-and-easy data publishing and sharing system based on folksonomy. The system accepts data objects described with user-created metadata, called data units. The system supports flexible structure on the data units, and places no restrictions on the vocabulary used. We devise a generic table model for storing and representing the data units of various structures. We propose a framework for managing the data units and providing browsing, searching and querying services over them. We present our current approaches and discuss relevant research issues.
Homecare Robotic Systems: Wearable Remote Sensing with Interactive AI Features

Driven by the advent of new technologies in robotics, healthcare technologies have also been revolutionized (Healthcare 4.0) and there have been significant improvements in healthcare. Newer technologies such as homecare robotic systems (HRS) based on cyber-physical systems (CPS) are also emerging rapidly. In this paper, a new-cost cost-effective, easy-to-use system is proposed for the CPS-based HRS combined with artificial intelligence (AI) and intelligent wearable sensors and remote sensing. The sensing system created measures the important parameters required to monitor a user’s namely SPO2, Heart Rate, and Temperature using the photoplethysmography method. It also provides additional features namely, Fall detection and GPS Location of the user. These features are essential for monitoring the well-being and safety of elderly users. The HRS system is integrated with Deep Neural Networks (DNN) to provide intelligent accurate predictions based on the real-time sensing data uploaded to the cloud alerting the user of any advent health dangers that might occur to the user.
Assessing Robustness and Vulnerability in Interdependent Network Infrastructure: A Multilayer Network Approach

Modern societies profoundly depend upon interdependent critical infrastructure systems, such as the power grid, oil pipeline, gas pipeline, and transportation system, for proper functioning. These systems are extensively interconnected, characterized by their interconnected layers of infrastructures, which presents unique complexities that can complicate their analysis. Furthermore, if one system fails, it can have a ripple effect throughout the entire network. Hence, it is crucial to evaluate the robustness and vulnerability of interdependent critical infrastructure systems with complex inter-layer and intra-layer dependencies to ensure their continued operation and after-math resilience. In response to these challenges, this study presents a method for evaluating the robustness, vulnerability, and efficiency of multi-layer infrastructures using various probabilistic evaluation measures. We considered three key parameters: the number of nodes (N), the denseness of network connections (DL), and node failure probability (p). We modeled these complex systems using Erdos-Renyi graphs and investigated the impact of three parameters: N, DL, and p. We considered two network sizes, N = 50 and N = 100, each divided into two layers with equal nodes. The DL and $p$ parameters represent the distinct characteristics of different infrastructures, which are considered independently for each layer. We simulated four scenarios varying DL and $p$ across layers and examined the topological transformations induced by these variations. The results indicated that a larger network size generally enhances network robustness and efficiency while reducing vulnerability, highlighting the importance of considering network size in multi-layer network design. Also, the interplay between DL and $p$ significantly influences network behavior, with a high DL mitigating the impact of high node failure probabilities on network robustness. These insights offer valuable guidelines for improving network resilience against potential node failures and are crucial for infrastructure designers and network analysts.
Empirical Mode Decomposition with Envelope Extraction and LSTM for univariate time series forecasting

This paper proposes a forecasting method for uni-variate time series based on a combination of Long-Short-Term-Memory (LSTM) Neural Network and Empirical Mode Decomposition (EMD) with envelope extraction. The basic steps of the method are 1) to split each Intrinsic Mode Function (IMF) into its envelope and detail, 2) to forecast separately these two components for each IMF, using LSTM and 3) to recombine the different forecasts. As already proven, the use of EMD in a forecast setting improves the accuracy. In this paper, we show that the splitting step on each IMF provides further significant improvement on the forecast. This is developed to be applied to treasury transactions forecast. The method relies on well known and easily implementable techniques and is usable with most forecasting algorithms. We experimented it on 3 non-stationary time series, showing its portability to a wide variety of univariate time series.
Low Overhead PIM-to-PIM Communication on PCIe-based Multi-PIM Platforms for Executing Large-Scale AI Models

The emerging transformers suffer from the memory bottleneck because of their low data locality and large data sizes, and we are actively studying Processing in Memory (PIM), which computes inside memory, to overcome the bottleneck. However, as their model parameters get bigger, a single PIM device would be insufficient because of its limited memory capacity and computing resources. In this paper, we develop a low-overhead data communication method between PIMs on a PCIe-based multi-PIM platform. We adopt an XDMA-based PIM-to-PIM (P2P) direct data communication mechanism to eliminate redundant data movement between CPU and PIMs. Our P2P achieves 1.69x, 1.70x, 1.61x, and 1.63x speedups compared to the DMA using the system memory as a buffer when transferring 16MB, 32MB, 64MB, and 128MB data, respectively.
A Quick Search Method for Audio Signals Based on a Piecewise Linear Representation of Feature Trajectories

This paper presents a new method for a quick similarity-based search through long unlabeled audio streams to detect and locate audio clips provided by users. The method involves feature-dimension reduction based on a piecewise linear representation of a sequential feature trajectory extracted from a long audio stream. Two techniques enable us to obtain a piecewise linear representation: the dynamic segmentation of feature trajectories and the segment-based Karhunen-Loeve (KL) transform. The proposed search method guarantees the same search results as the search method without the proposed feature-dimension reduction method in principle. Experimental results indicate significant improvements in search speed. For example, the proposed method reduced the total search time to approximately 1/12 that of previous methods and detected queries in approximately 0.3 s from a 200-h audio database.
Code-Mixing and Code-Switching on Social Media Text: A Brief Survey

In the diverse linguistic landscape of India, individuals employ a variety of languages (codes) in their online communications, much like non-English speakers worldwide. Code-mixing is predominating in today's communication-driven culture and no natural language has escaped these trends. This paper explores the expanding subfield of Natural Language Processing (NLP) known as code mixing (CM) and code switching (CS). It examines how individuals exhibit their linguistic versatility by seamlessly blending English terms with regional languages (e.g., English-Hindi, English-Punjabi, English-Telugu) through techniques such as transliteration, Romanization, and language fusion on popular social media platforms such as Facebook, Google Reviews, and Twitter. The pervasive use of social media applications has resulted in a substantial influx of unstructured text, much of which is characterized by code-mixing and multi-script writing, where words are expressed in scripts distinct from their native tongue.
SCASAN: Spatial Central Attention-Spectral Attention Network Based Feature Extractor for Hyperspectral Image Classification

Due to the influence of space, time, environmental changes, and types of ground objects on the lighting conditions of high-light falling images, Hyperspectral image (HSI) classification is challenging. Recently, attention-based HSI classification has received increasing attention. It uses both spatial attention and spectral attention to capture the required spatial-spectral features, thereby achieving better classification accuracy. However, it is challenging to connect the two attention mechanisms without compromising the spatial-spectral features. In addition, how to obtain the attention weight matrix which determines the importance of each channel or spatial location is also an important problem in the use of attention mechanisms. In response to the above issues, the Spatial Central Attention-Spectral Attention network (SCASAN) is designed to efficiently model the spatial-spectral information for HSI classification. In this network, we propose the novel spectral weight spatial attention (SWSA) module to solve the connection problem between spatial attention and spectral attention. Furthermore, we utilize a spatial central attention network (spatial CAN) to obtain a spatial attention weight matrix to avoid the impact of non-homogeneous adjacent pixels on the classified pixel. Our proposed approach demonstrates superior performance in hyperspectral image classification compared to state-of-the-art methods, as evidenced by comprehensive experiments conducted on three publicly available datasets.
A Level-Increment Circuit for Multilevel Inverter Based on Cross-Connected Sources

Multilevel inverters (MLIs) are growing as a potential solution for DC to AC power conversion. This work describes the design and implementation of a single-phase newly developed multilevel inverter configuration that combines a cross connected sources (CCS) based MLI and a half-bridge inverter with considerable decrease in component count for a large number of output voltage levels. The level-increment circuit (LIC) is a type of half-bridge inverter that boosts the output voltage levels in the CCS-MLI topology by nearly twice. The aforesaid approach minimises the amount of power switching devices utilised compared to conventional topologies, resulting in higher output voltage levels by minimising the THD and dv/dt stress on the load. The proposed configuration can be designed to operate in both symmetrical and asymmetrical source patterns. Symmetrical arrangement allows for the formation of four more levels in each sequence with addition of each pair of switches to the basic module. The concept has been studied using MATLAB/Simulink and a laboratory prototype.
Inkjet Printed Wideband Frequency Reconfigurable Antenna for Wireless Applications

In this study, a reconfigurable wideband antenna with inkjet printing is created using a low-cost resin coated commercial photo paper substrate. A PIN diode is primarily utilized to disperse the surface current, which gives the proposed antenna reconfigurable features in the lower frequency region. The antenna’s dimensions are 28. 94mmx36.36mmx0. 44mm, and it has a partial ground that covers the mobile and wireless bands from 3. 32GHz to 7. 73GHz. For the antenna’s design parameter to be optimized, parametric analysis is done. The primary radiator is intended to operate at a frequency range of 4-7. 7GHz, while the L-shaped radiators are intended to work between 3. 3-3.9GHz. Peak gain and radiation efficiency for the suggested antenna were 3. 45dBi and 65%, respectively. Reflection coefficient, gain, and efficiency simulation and measurement findings indicate that the antenna design is in good agreement. This method eliminates the requirement for several electronic parts to provide multiband frequency.
Improving Contextual Quality Models for MT Evaluation Based on Evaluators’ Feedback

The Framework for the Evaluation for Machine Translation (FEMTI) contains guidelines for building a quality model that is used to evaluate MT systems in relation to the purpose and intended context of use of the systems. Contextual quality models can thus be constructed, but entering into FEMTI the knowledge required for this operation is a complex task. An experiment has been set up in order to transfer knowledge from MT evaluation experts into the FEMTI guidelines, by polling experts about the evaluation methods they would use in a particular context, then inferring from the results generic relations between characteristics of the context of use and quality characteristics. The results of this hands-on exercise, carried out as part of a conference tutorial, have served to refine FEMTIs generic contextual quality model and to obtain feedback on the FEMTI guidelines in general.
What Do Thinking-Aloud Participants Say? A Comparison of Moderated and Unmoderated Usability Sessions

The value of thinking aloud in usability tests depends on the content of the users’ verbalizations. We investigated moderated and unmoderated users’ verbalizations during relaxed thinking aloud (i.e., verbalization at Levels 1–3). Verbalizations of user experience were frequent and mostly relevant to the identification of usability issues. Explanations and redesign proposals were also mostly relevant, but infrequent. The relevance of verbalizations of user experience, explanations, and redesign proposals showed the value of relaxed thinking aloud but did not clarify the trade-off between rich verbalizations and test reactivity. Action descriptions and system observations—two verbalization categories consistent with both relaxed and classic thinking aloud—were frequent but mainly of low relevance. Across all verbalizations, the positive or negative verbalizations were more often relevant than those without valence. Finally, moderated and unmoderated users made largely similar verbalizations, the main difference being a higher percentage of high-relevance verbalizations by unmoderated users.
Eurographics/ Acm Siggraph Symposium on Computer Animation (2006) Learning a Correlated Model of Identity and Pose-dependent Body Shape Variation for Real-time Synthesis

We present a method for learning a model of human body shape variation from a corpus of 3D range scans. Our model is the first to capture both identity-dependent and pose-dependent shape variation in a correlated fashion, enabling creation of a variety of virtual human characters with realistic and non-linear body deformations that are customized to the individual. Our learning method is robust to irregular sampling in pose-space and identity-space, and also to missing surface data in the examples. Our synthesized character models are based on standard skinning techniques and can be rendered in real time.