Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptome dynamics uncovers long non-coding RNAs response to salinity stress in Chenopodium quinoa

Chenopodium quinoa is a crop with outstanding tolerance to saline soil, but long non-coding RNAs (LncRNAs) expression profile driven by salt stress in quinoa has rarely been observed yet. Based on the high-quality quinoa reference genome and high-throughput RNA sequencing (RNA-seq), genome-wide identification of LncRNAs was performed, and their dynamic response under salt stress was then investigated. In total, 153,751 high-confidence LncRNAs were discovered and dispersed intensively in chromosomes. Expression profile analysis demonstrated significant differences between LncRNAs and coding RNAs. Under salt stress conditions, 4,460 differentially expressed LncRNAs were discovered, of which only 54 were differentially expressed at all the stress time points. Besides, strongly significantly correlation was observed between salt-responsive LncRNAs and their closest neighboring genes (r = 0.346, p-value < 2.2e-16). Furthermore, a weighted co-expression network was then constructed to infer the potential biological functions of LncRNAs. Seven modules were significantly correlated with salt treatments, resulting in 210 hub genes, including 22 transcription factors and 70 LncRNAs. These results indicated that LncRNAs might interact with transcription factors to respond to salinity stress. Gene ontology enrichment of the coding genes of these modules showed that they were highly related to regulating metabolic processes, biological regulation and response to stress. This study is the genome-wide analysis of the LncRNAs responding to salt stress in quinoa. The findings will provide a solid framework for further functional research of salt responsive LncRNAs, contributing to quinoa genetic improvement.
Ensuring the take-over readiness of the driver based on the gaze behavior in conditionally automated driving scenarios

Conditional automation is the next step towards the fully automated vehicle. Under prespecified conditions an automated driving function can take-over the driving task and the responsibility for the vehicle, thus enabling the driver to perform secondary tasks. However, performing secondary tasks and the resulting reduced attention towards the road may lead to critical situations in take-over situations. In such situations, the automated driving function reaches its limits, forcing the driver to take-over responsibility and the control of the vehicle again. Thus, the driver represents the fallback level for the conditionally automated system. At this point the question arises as to how it can be ensured that the driver can take-over adequately and timely without restricting the automated driving system or the new freedom of the driver. To answer this question, this work proposes a novel prototype for an advanced driver assistance system which is able to automatically classify the driver’s take-over readiness for keeping the driver ”in-the-loop”. The results show the feasibility of such a classification of the take-over readiness even in the highly dynamic vehicle environment using a machine learning approach. It was verified that far more than half of the drivers performing a low-quality take-over would have been warned shortly before the actual take-over, whereas nearly 90% of the drivers performing a high-quality take-over would not have been interrupted by the driver assistance system during a driving simulator study. The classification of the take-over readiness of the driver is performed by means of machine learning algorithms. The underlying features for this classification are mainly based on the head and eye movement behavior of the driver. It is shown how the secondary tasks currently being performed as well as the glances on the road can be derived from these measured signals. Therefore, novel, online-capable approaches for driver-activity recognition and Eyes-on-Road detection are introduced, evaluated, and compared to each other based on both data of a simulator and real-driving study. These novel approaches are able to deal with multiple challenges of current state-of-the-art methods such as: i) only a coarse separation of driver activities possible, ii) necessity for costly and time-consuming calibrations, and iii) no adaption to conditionally automated driving scenarios.
Compliant control of Uni/ Multi- robotic arms with dynamical systems

A ccomplishment of many interactive tasks hinges on the compliance of humans. Humans demonstrate an impressive capability of complying their behavior and more particularly their motions with the environment in everyday life. In humans, compliance emerges from different facets. For example, many daily activities involve reaching for grabbing tasks, where compliance appears in a form of coordination. Humans comply their hands’ motions with each other and with that of the object not only to establish a stable contact and to control the impact force but also to overcome sensorimotor imprecisions. Even though compliance has been studied from different aspects in humans, it is primarily related to impedance control in robotics. In this thesis, we leverage the properties of autonomous dynamical systems (DS) for immediate re-planning and introduce active complaint motion generators for controlling robots in three different scenarios, where compliance does not necessarily mean impedance and hence it is not directly related to control in the force/velocity domain. In the first part of the thesis, we propose an active compliant strategy for catching objects in flight, which is less sensitive to the timely control of the interception. The soft catching strategy consists in having the robot following the object for a short period of time. This leaves more time for the fingers to close on the object at the interception and offers more robustness than a “hard” catching method in which the hand waits for the object at the chosen interception point. We show theoretically that the resulting DS will intercept the object at the intercept point, at the right time with the desired velocity direction. Stability and convergence of the approach are assessed through Lyapunov stability theory. In the second part, we propose a unified compliant control architecture for coordinately reaching for grabbing a moving object by a multi-arm robotic system. Due to the complexity of the task and of the system, each arm complies not only with the object’s motion but also with the motion of other arms, in both task and joint spaces. At the task-space level, we propose a unified dynamical system that endows the multi-arm system with both synchronous and asynchronous behaviors and with the capability of smoothly transitioning between the two modes. At the joint space level, the compliance between the arms is achieved by introducing a centralized inverse kinematics (IK) solver under selfcollision avoidance constraints; formulated as a quadratic programming problem
Nonlinear estimation of torque in switched reluctance motors using grid locking and preferential training techniques on self-organizing neural networks

The torque of a switched reluctance motor (SRM) can be estimated using a topology-preserving self-organizing neural network map. Since self-organizing maps tend to contract at region boundaries, a procedure for locking neuron weights at specific locations in a region is presented. A strategy for preferentially training neuron weights on the region boundaries is introduced. As an example of these training techniques, a one-dimensional neural network will approximate a nonlinear function. In general an n-dimension mapping can be used to approximate an m-dimensional system for n<or=m. As a practical implementation of this technique, the modeling of the theoretical torque of a SRM as a function of position and current is presented. A two-dimensional neural network estimates a three-dimensional highly nonlinear surface. <<ETX>>
Variation of winter road surface temperature due to topography and application of Thermal Mapping

It is known that topography is an important factor controlling the variation of road surface temperature (RST). In order to explore possible relationships between RST and topography, the authors used Thermal Mapping data obtained by a vehicle‐mounted thermometer in a mountainous area in Nevada, USA, under different weather conditions in December 1994. The data were validated against the measurements of road surface sensors. After that, a step‐wise regression technique is employed to find out possible statistic relationships between RST and altitude at different surveying routes. It was found that the relationships exhibited different characteristics in different climate domains and under different weather types. In most cases, the relationships were non‐linear. In order to obtain more information about the cause of the residuals and the variation, the error of regression fitting is related to near‐ground meteorological parameters (air temperature, dew point and wind, etc.). This study proved that Thermal Mapping is a reliable and effective method to display spatial variation of road surface temperature. Copyright © 1997 Royal Meteorological Society
Speech synthesis using neural network

In this paper, we develop a speech learning machine by using Neural-Network. The work is based on a previous work of Neural Network, named Net Talk and compare Net Talk model with Hidden Markov Model (HMM). The work presents simulated result for the simulated neural network to mimic the pronunciation of English vocabulary especially the vowel sounds and the silent pronounced letters.
An Introduction to Machine Learning Methods for Survey Researchers

Machine learning techniques comprise an array of computer-intensive methods that aim at discovering patterns in data using flexible, often nonparametric, methods for modeling and variable selection. These methods offer an expansion to the more traditional methods, such as OLS or logistic regression, which have been used by survey researchers and social scientists. Many of the machine learning methods do not require the distributional assumptions of the more traditional methods, and many do not require explicit model specification prior to estimation. Machine learning methods are beginning to be used for various aspects of survey research including responsive/adaptive designs, data processing and nonresponse adjustments and weighting. This special issue aims to familiarize survey researchers and social scientists with the basic concepts in machine learning and highlights five common methods. Specifically, articles in this issue will offer an accessible introduction to: LASSO models, support vector machines, neural networks, and classification and regression trees and random forests. In addition to a detailed description, each article will highlight how the respective method is being used in survey research along with an application of the method to a common example. The introductory article will provide an accessible introduction to some commonly used concepts and terms associated with machine learning modeling and evaluation. The introduction also provides a description of the data set that was used as the common application example for each of the five machine learning methods.
Auditory Neuron Responses to Predicted Synaptic Input Derived from Cochlear Prosthetic Devices

FUTURE Binaural hearing enables multiple functions of the hearing system, including sound localization. In the mammalian auditory circuitry, the first neurons responsible for comparing sound from the two ears are found in the Medial Superior Olive (MSO). While there have been many studies on MSO neuron physiology, details concerning how these neurons generate meaningful patterns of activity in response to synaptic input are poorly understood. In this study, we used dynamic clamp to study neurons from acute brain slices from Mongolian gerbils in vitro. Whole cell dynamic clamp provides naturalistic synaptic input that neurons are predicted to receive in vivo. We simulated synaptic input to MSO neurons evoked by acoustic stimuli at both 100 Hz (low frequency) and 1000 Hz (high frequency). We found that with lower frequency stimuli, the cells respond significantly more at 180 degrees out of phase in comparison to 90 degrees out of phase. At the higher frequency, the opposite is true (which is the situation in vivo.) We attempted to modulate this effect by changing the strength of the synapse. Neither increasing nor decreasing the strength of each synapse eliminated the out of phase spiking. We also changed the number of inputs per side, and while the sample size is small, the data show a trend toward no effect on out of phase spiking. These results suggest that inhibition may be required to process low frequencies compared to higher frequencies. We speculate that this could be a result of the interaction between stimulus frequency and the refractory period of the neuron. Future studies will include adding monaural (EEI) and binaural (EEII) inhibition to the dynamic clamp protocols in order to observe the changes to the out of phase stimuli.
Detection of License Plate using Sliding Window, Histogram of Oriented Gradient, and Support Vector Machines Method

The license plate recognition usually used as part of system such as parking system. License plate detection considered as the most important step in the license plate recognition system. We propose methods that can be used to detect the vehicle plate on mobile phone. In this paper, we used Sliding Window, Histogram of Oriented Gradient (HOG), and Support Vector Machines (SVM) method to license plate detection so it will increase the detection level even though the image is not in a good quality. The image proceed by Sliding Window method in order to find plate position. Feature extraction in every window movement had been done by HOG and SVM method. Good result had shown in this research, which is 96% of accuracy.
Analysis of Misheard Phrases in Nursing Dialogue

We are now collecting, transcribing and analyzing conversation data in medical and nursing facilities. This is part of nursing assistance research being conducted based on ubiquitous computing technologies. In this paper, we focus on communication error analysis. We analyzed 30 hours and 45 minutes of transcribed voice data collected in a hospital. Incorrectly transcribed phrases are analyzed to find features of incorrect transcription.
On Integration of Qualitative and Quantitative Analysis of Manufacturing Systems Using Petri Nets

Among those methods, which aim at the improvement of the dependability of any system, different kinds of Petri net based validation techniques to avoid faults during the development phase have attracted a lot of attention in the last two decades. Within this general framework, various Petri net based methodologies of dependability engineering have been proposed. At the beginning, only qualitative (i. e. time-less) properties have been discussed. But because of the crucial impact of performance on parallel or distributed (shortly called concurrent) systems, special emphasis has been laid more and more on incorporation of quantitative (i. e. timedependent) criteria as part of the system development cycle [Heiner 94].
Parallel randomized controlled feasibility trials of the “Active Brains” digital intervention to protect cognitive health in adults aged 60–85

Introduction Multidomain interventions to address modifiable risk factors for dementia are promising, but require more cost-effective, scalable delivery. This study investigated the feasibility of the “Active Brains” digital behavior change intervention and its trial procedures. Materials and methods Active Brains aims to reduce cognitive decline by promoting physical activity, healthy eating, and online cognitive training. We conducted 12-month parallel-design randomized controlled feasibility trials of “Active Brains” amongst “lower cognitive scoring” (n = 180) and “higher cognitive scoring” (n = 180) adults aged 60–85. Results We collected 67.2 and 76.1% of our 12-month primary outcome (Baddeley verbal reasoning task) data for the “lower cognitive score” and “higher cognitive score” groups, respectively. Usage of “Active Brains” indicated overall feasibility and satisfactory engagement with the physical activity intervention content (which did not require sustained online engagement), but engagement with online cognitive training was limited. Uptake of the additional brief telephone support appeared to be higher in the “lower cognitive score” trial. Preliminary descriptive trends in the primary outcome data might indicate a protective effect of Active Brains against cognitive decline, but further investigation in fully-powered trials is required to answer this definitively. Discussion Whilst initial uptake and engagement with the online intervention was modest, it was in line with typical usage of other digital behavior change interventions, and early indications from the descriptive analysis of the primary outcome and behavioral data suggest that further exploration of the potential protective benefits of Active Brains are warranted. The study also identified minor modifications to procedures, particularly to improve online primary-outcome completion. Further investigation of Active Brains will now seek to determine its efficacy in protecting cognitive performance amongst adults aged 60–85 with varied levels of existing cognitive performance.
Towards Large Scale Summarization

Towards Large Scale Summarization Janara Christensen Chair of the Supervisory Committee: Professor Mausam Computer Science and Engineering As the Internet grows and information is increasingly available, it is more and more difficult to understand what is most important without becoming overwhelmed by details. We need systems which can organize this information and present it in a coherent fashion. These systems should also be flexible, enabling the user to tailor the results to his or her own needs. Current solutions such as summarization are static and lack coherent organization. Even structured solutions such as timelines are inflexible. These problems become increasingly important as the size of the information grows. I propose a new approach to scaling up summarization called hierarchical summarization, which emphasizes organization and flexibility. In a hierarchical summary, the top level gives the most general overview of the information, and each subsequent level gives more detail. Hierarchical summarization allows the user to understand at a high level the most important information, and then explore what is most interesting to him or her without being overwhelmed by information. In this work, I formalize the characteristics necessary for good hierarchical summaries and provide algorithms to generate them. I perform user studies which demonstrate the value of hierarchical summaries over competing methods on datasets much larger than used for traditional summarization.
A sensitive and rapid method of lead detection using nanoparticle technology based on monoclonal antibody

Lead (Pb) threatens public health due to its toxicity and nonbiodegradable characteristics. It is of significance to develop a sensitive and rapid method for Pb detection. In this study, monoclonal antibodies against Pb were screened with a high affinity constant (Kaff) of 3.56 × 109 L/mol. Au nanosphere particles (AuNS) and Au nanoflower particles (AuNF) were synthesized with a diameter of 15 nm and 60 nm, respectively. The specific anti-Pb antibodies were then immobilized on AuNS and AuNF for probe development. At last, AuNS- and AuNF-based strips were successfully assembled for comparative study, which were able to effectively detect environmental Pb in 10 min. The limits of detection (LODs) were determined to be 3.91 ng/ml and 0.2 ng/ml, respectively. Thus the developed method provides a feasible solution for sensitive and rapid detection of Pb on site, which is beneficial to food safety and pollution control.
Vegetable cutting machine

The utility model relates to a vegetable cutting machine, in particular to a vegetable cutting machine with a hand vegetable cutting function and an electric vegetable cutting function. The vegetable cutting machine comprises a machine frame, a main shaft and a cutter holder, wherein the top part of the machine frame is provided with a material inlet, the main shaft is pivoted on the machine frame, the cutter holder is arranged on the main shaft, the cutter holder is provided with a cutter blade, the machine frame is provided with a motor, an output shaft of the motor is provided with a first gear, one end of the main shaft is provided with a hand handle, the other end of the main shaft is provided with a second gear, and the machine frame is provided with a switching mechanism which can drive the second gear to slide. When the switching mechanism drives the second gear to slide, a mutual engaging state and a disengaging state of the second gear and the first gear are repetitively switched. When the second gear is mutually engaged with the first gear, the vegetable cutting machine is in an electric vegetable cutting mode. When the second gear is disengaged from the first gear, the vegetable cutting machine is in a hand vegetable cutting mode. The vegetable cutting machine has the advantages that the hand vegetable cutting mode or the electric vegetable cutting mode can be selected, the convenience in use is realized, the structure is simple, the manufacturing is easy, and the cost is low.
Dynamic Selection of Recommender Systems: An Application to Tourism

Recommender Systems are software tools and techniques that seek to suggest items that are likely of interest to a particular user. These systems are a key part of most e-commerce applications, as they ease users to find products that meet their needs while improving sales. Several approaches have been created to determine the users’ preferences by working with different sources and types of information. Collaborative filtering uses the history of ratings, content and knowledge based recommenders work with the features of items, context aware systems provide suggestions based on the situation parameters or conditions that surround the user, while demographic filtering utilizes user’s demographic characteristics. Additionally, there are hybrid approaches that fuse two or more techniques in order to overcome the shortcomings of each method. In this work an application of dynamic selection to the recommender systems field is studied. This selection strategy, taken from Multiple Classifier Systems, consists of selecting a specific set of classifiers for each test pattern. To adapt this notion to the context of this research, it was proposed a hybrid system that dynamically seeks to select the best recommendation method in each prediction. After carrying out experiments, the application of dynamic selection did not provide any significant improvement to recommendations. However, the inclusion of demographic and contextual information in a hybrid content-based basis increased the accuracy of the system considerably. The final solution was evaluated using datasets containing reviews of hotels and books. Results showed that the recommender is capable of working in tourism related scenarios and that can also be parameterized to other recommendation problems as long as content, demographic or contextual features are available.