Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF GROWTH, FORM AND BRANCHING TRAITS IN AN F2 POPULATION OF THE Pinus elliottii

--The developmental biology of the haploid conifer megagametophyte has been exploited using dominant RAPD markers to construct a reference genetic map for an individual Pinus elliottii x Pinus caribaea Fl hybrid tree forming part of a three generation pedigree. This map incorporates 186 markers across 17 linkage groups. At 1595cM, the map is estimated to cover 80% of the genome at an average marker density of 8.6cM. Using this reference genetic map with phase known, genotype information obtained using dominant RAPD markers in the F2 population was successfully used to determine full genotype classification. This genotype information was used to map QTL for five growth and form traits and seven branching traits in six year old trees. A total of 57 putative QTL were identified for all 12 traits examined. Considerable overlap existed between QTL identified for under and overbark diameter and bark thickness, and between QTL for branch angle, regularity of branch spacing, ramicorn number, and occurrence of double leaders, suggesting common genetic control of physiologically related traits. Detailed analysis of QTL for bark thickness indicated cryptic genetic variation not evident from the phenotype of either parent, as well as additive, dominant, overdominant and underdominant modes of gene action. Loci whose effects are stable across environments and specific to particular environments were also indicated. An approach was developed to accumulate confidence in QTL results obtained from small populations: potentially a common limitation to QTL mapping in forest trees. The implications of these findings for both conventional tree breeding and marker assisted breeding are discussed.
Character-Level Translation with Self-attention

We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolutions. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese). Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety. While this has recently motivated the development of methods to detect such out-of-distribution (OoD) inputs, a robust solution is still lacking. We propose a new probabilistic, unsupervised approach to this problem based on a Bayesian variational autoencoder model, which estimates a full posterior distribution over the decoder parameters using stochastic gradient Markov chain Monte Carlo, instead of fitting a point estimate. We describe how information-theoretic measures based on this posterior can then be used to detect OoD inputs both in input space and in the model's latent space. We empirically demonstrate the effectiveness of our proposed approach.
Invited Review Gap junction channels: new roles in disease

The importance of intercellular communication to complex cellular processes such as development, differentiation , growth , propagation of electrical impulses and diffusional feeding has long been appreciated. The realization that intercellular communication is mediated by gap junction channels , which are in turn comprised of a diverse family of proteins called the connexins, has provided new tools and avenues for studying the role of intercellular communication in these important cellular processes. The identification of different connexin isoforms has not only enabled the development of specific reagents to study connexin expression patterns, but has also allowed the functional properties of the different connexin isoforms and how they interact with each other, to be explored. Increasingly , the knowledge gained from studying connexin diversity is being used to investigate the role played by gap junction channels in a number of diseases . In this article we highlight selected cases where gap junction channels have been shown or are believed to be directly involved in the disease process.
Single episode transfer for differing environmental dynamics in reinforcement learning

Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation. To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy. This modular approach enables integration of state-of-the-art algorithms for variational inference or RL. Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot. In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.
Wide Voltage Range Dual Active Bridge PV Retrofit Storage System

This paper presents a storage system integration on the photovoltaic module side of a traditional grid-connected PV plant. The proposed retrofit application is based on a wide voltage range Dual Active Bridge topology, used as bidirectional DC/DC converter. Wide voltage operation capability at PV side is required to allow the integration of the retrofit storage system on a broader range of existing PV plants, characterized by different PV string operating voltage and commercial grid-connected inverters. Dual Active Bridge topology was chosen as a galvanically-insulated bidirectional single stage conversion stage to interface storage batteries to the PV side of the existing photovoltaic plant. The storage battery is charged during the overproduction of the PV plant and the storage system sustains the end-user domestic loads when solar irradiation is insufficient. The proper operation of the commercial inverter, the design and control strategy of the whole system showed the effectiveness of the proposed solution. A full-size 3.2kW converter prototype was manufactured and experimental validation was carried out.
Recognition of the Hungarian Fingerspelling Alphabet using Convolutional Neural Network based on Depth Data

The aim of this paper is to introduce that Convolutional Neural Networks based on depth data can be applied to recognize the signs of the Hungarian fingerspelling alphabet. After a comparison with end-to-end style RGB images, depth data is chosen as input. With the method recommended in this paper, a 94% classification accuracy was measured for the test subjects.
Analysis of DC-link Current of Parallel Three-phase Inverters under Unbalanced Output Currents

Analysis and calculation of DC-link current of three-phase inverter is one of significant part of the selection of de capacitors, system life expectation and the stability of converter control. However, almost all DC-link current analysis have focused on balanced three-phase ac output currents so far. For applications of four-wire power quality improvement, like the active power filter (APF) and the static VAR generator (SVG), unbalanced output current are required. In this paper, considering unbalanced output currents, the analysis and calculation of the de-link current for three-phase inverter are firstly presented and the sideband harmonic orders are derived subsequently. Then, for parallel three-phase inverters with a shared dc bus, the effect of interleaved carrier phase shift PWM on DC-link current is studied and the optimal interleaving angle is derived. Finally, the analysis and calculation of the dc-link current is verified by simulation, which shows the good accuracy of the analysis results under unbalanced output currents.
Development and Control of an MR Brake-Based Passive Force Feedback Data Glove

This paper presents the development and feedback force control of a safe, lightweight, yet powerful, and stable passive force feedback data glove (FFDG), utilizing a disc-type magnetorheological (MR) brake as the force feedback device. Firstly, a detailed illustration of the mechanical design of the FFDG, including transmission structure, MR brake, and angle measuring mechanism, is presented. Then, to achieve a precise feedback force control, the design and modelling of a current controller based on the buck circuit are carried out. This is followed by the tracking control of the feedback force, using conventional PID and adaptive fuzzy-PID control schemes. The control effect of the two methods is evaluated and compared through simulations. Finally, an experimental set-up is established, and a series of experiments are carried out on both the manufactured MR brake and the FFDG prototype. The results verify the feasibility and stability of the developed FFDG, and demonstrate that the feedback force tracking accuracy of the adaptive fuzzy-PID controller is satisfactory.
Reliability Study of Input Side Capacitors in Impedance-Source PV Microconverters

This paper discusses reliability issues of three different types of capacitors (ceramic, electrolytic and film) applied in the input side of an impedance-source photovoltaic (PV) microconverter having an ultra-wide dc gain range. The study considers a wear-out failure analysis based on a daily PV mission profile. The general operation and control principle are first described. Then the selection criteria of the capacitors are analyzed. Experimental measurements of capacitors temperature are translated into accumulated damage using their corresponding lifetime models. Finally, conclusions are drawn regarding the applicability of different capacitor technologies in impedance-source PV microconverters, where the film capacitor has the lowest damage.
Research on display that reproduces the thermal sensation perceived by human

In this paper, in order to propose an interface that reproduces a thermal sensation, the skin temperature change when a human touches an object was measured, and temperature reproduction was performed using a Peltier device. The response and followability of the Peltier device were evaluated in experiments using objects with different thermal properties. The results showed that the heat transfer was generally terminated when the object is touched for about 10 seconds, and after that the temperature change was small. Furthermore, using the manufactured thermal sensation presentation device, we attempted to generate the wet sensation of a plurality of dry fabrics. The subjects evaluated the wet sensation of the initial temperature controlled the fabrics (dry or having a moisture content of 20-30%). As a result we concluded that the wet sensation can be generated when the initial temperature of the dry fabrics is lower than the skin temperature.
Basal Strain Estimation in Transesophageal Echocardiography (TEE) using Deep Learning based Unsupervised Deformable Image Registration

As of today, perioperative monitoring in the operating room is based on vital signs and clinical observations by the anesthesiologist. This, however, does not offer a complete monitoring of left ventricular function throughout the intervention. We hypothesize that functional monitoring of the heart can be performed automatically based on transesophageal echocardiographic (TEE) images. The main goal of this work is therefore to compute the non-linear deformation between subsequent images in a TEE sequence of the left ventricle and estimate basal longitudinal strain to assess regional myocardial function using a deep learning approach. An unsupervised approach based on a convolutional neural network (CNN) was implemented. The output of the CNN network is a dense vector field that describes the non-linear deformation required to maximize the similarity between two images.Recordings from 42 consecutive complete TEE exams from the Echocardiography Unit were anonymized and used for training. Recordings from 5 consecutive TEE exams performed during heart surgery, also anonymized, were used as test set. For the test set patients, the basal strain was manually annotated in EchoPac by an expert echocardiographer. For this work, 19 heart cycles were randomly selected from the test set and checked to ensure visibility of the points to be tracked. Overall when estimating strain there was a mean difference of 7.25%(±4.56%) in the strain values when compared to expert annotations.We have proven that point tracking is working as expected in most images, where the myocardium is well depicted. However, dropouts, noise generated by implants or air bubbles after surgery, confuse the tracker and drifting occurs.
ClusVNFI: A Hierarchical Clustering-Based Approach for Solving VNFI Dilemma in NFV Orchestration

Network functions virtualization (NFV) is becoming a prevailing design for future Internet by migrating network functions from dedicated hardware appliances to software instances running in virtual computing platforms. NFV resource allocation approaches can dynamically instantiate network functions by using virtualized network functions (VNFs) to satisfy various quality-of-service (QoS) requirements with minimum network costs. The operator can launch a new VNF instance (VNFI) for each VNF required for flows, or assign it to the established VNFI. This makes NFV orchestration (NFVO) even more complicated. In addition, the challenges of developing NFVO scheme include how to manage the dependency between VNFs placement (network-level) and routing of flows (flow-level) through ordered VNFs, and how to efficiently utilize the available network resources. In this paper, ClusVNFI, a hierarchical resource allocation approach based on clustering, is proposed to address these challenges. To be specific, VNFs are proposed to be clustered based on their correlation. Then VNFs belonging to the same cluster are inclined to be deployed on one node to reduce the occupied link bandwidth. Moreover, network nodes are clustered based on the similarity on end-to-end flow latency information. Accordingly, flows in the same cluster intend to share the instantiated VNFIs, aiming at improving VNFI utilization while avoiding path stretch. By capturing the dependency between network-level and flow-level through clustering, ClusVNFI can achieve the tradeoff among multiple objectives including maximizing the number of admitted flows, minimizing path stretch, and improving VNFI utilization. Extensive simulation results show that the proposed ClusVNFI can balance multiple objectives comparing with other typical heuristic algorithms. Moreover, ClusVNFI can reduce network resource occupation effectively, while guaranteeing the average delay and network hops.
A Neural Network-Based ECG Classification Processor With Exploitation of Heartbeat Similarity

This paper presents a neural network based processor with improved computation efficiency, which aims at multiclass heartbeat recognition in wearable devices. A lightweight classification algorithm that integrates both bi-directional long short-term memory (BLSTM) and convolutional neural networks (CNN) is proposed to deliver high accuracy with minimal network scale. To reduce energy consumption of the classification algorithm, the similarity between consecutive heartbeats is exploited to achieve a high degree of computation reuse in hardware architecture. In addition, neural network compression techniques are adopted in the procedure of inference to save hardware resources. Synthesized in the SMIC 40LL CMOS process, the prototype design has a total area of 1.40 mm2 with 186.2 kB of static random-access memory (SRAM) capacity. Based on the simulation, this processor achieves an average energy efficiency of 3.52 GOPS/mW under 1.1 V supply at 100 MHz frequency. Compared with the design without computation reuse, the proposed processor provides a speedup by 2.58x and an energy dissipation reduction by 61.27% per classification. This work is a valuable exploration of neural network based design for long-term arrhythmia monitoring in daily life.
Similar User Assisted Mobility Prediction

In 5G systems, user mobility research can provide basic support for mobile communications in terms of handover management, resource reservation, etc., improve the robustness of mission-critical services, and reduce total energy consumption and communication delay. In this paper, the association analysis algorithm is used to mine the key movement patterns of users, and the definition of user mobility similarity is proposed. Based on the similar user found by similarity calculation, this paper proposes a similar user assisted mobility prediction model for predicting the mobility of individual users in the dataset, and discusses the impact of two important parameters of the model. Compared with the single-user model, this model achieves a 4% performance improvement, which verifies the validity of the model and the importance of similar user for prediction.
Reliable Motion Estimation in Super-Resolution US by Reducing the Interference of Microbubble Movement

In ultrasound localization microscopy, microbubbles are localized and tracked in contrast enhanced ultrasound sequences to reconstruct the microvasculature. The accurate localization of MBs is essential to achieve super-resolved images, but it is strongly disturbed by tissue motion. Therefore, the MB positions have to be corrected for this tissue motion. Usually, a foreground-background separation is applied, to localize the MBs in the foreground, and to estimate the tissue motion in the background for the following correction of the MB positions. However, we recently showed that strong interferences can be introduced into motion estimation, depending on the choice of separation algorithm. Therefore, we propose to apply an optical flow approach to the non-separated B-mode images. During nonrigid motion estimation, the regions of the MBs are excluded. This leads to incomplete vector fields whose gaps are estimated afterwards.With simulations, we show an improvement of the localization accuracy over other approaches proposed, e.g. a reduction of the displacement error of 40 µm to around 6 µm. On preclinical data, we achieved an increased correlation between the motion corrected frames. Furthermore, the reconstructed microvasculature appears clearer.
Doppler Spread Suppression for Massive MIMO High-Mobility CoMP Uplink Transmission

Coordinated Multi-Point transmission/reception (CoMP) is one of the promising techniques for high-mobility wireless communications systems to increase cell edge user throughput in both uplink and downlink transmission. In this paper, we consider the CoMP uplink transmission, where a high-speed terminal (HST) simultaneously transmits signals to multiple base stations (BSs) with angle-domain Doppler shifts compensation. We derive the exact channel power spectrum density (PSD) for uplink CoMP transmissions. The antenna weighting technique is then proposed to suppress the Doppler spread, where an optimization problem is formulated to minimize the maximum Doppler spread among all the BSs. Numerical results are provided to corroborate both the PSD analysis and the superiority of antenna weighting technique.
Rotational Inertia Analysis and Underfrequency Load Shedding Strategy of a Microgrid System

This paper takes the transient response of the energy storage system (ESS) into account for the study of the rate of change of underfrequency and load shedding strategy of a microgrid system. The rotational inertia of the existing power system can be contributed by tradition power generation sets. The strategy of Underfrequency Load Shedding (UFLS) is based on the rotational inertia of synchronous generation sets and transient response of the system frequency. Since a microgrid system consists of more distributed energy resources, due to its lower rotational inertia, the traditional strategy of UFLS may not be applied to a microgrid. Hence, the formula for the curve of rotational inertia of a voltage source based of an ESS has to be analyzed, to plan for the strategy of UFLS for a microgrid system so that when a failure occurs, the renewable energy keeps generating power and the feeding of important loads. An UFLS strategy is proposed in this paper, and it is verified in the microgrid in Institute of Nuclear Energy Research (INER) in Taiwan. It shows the proposed UFLS strategy can recover the microgrid to be stable in voltage and frequency within 800ms after the disconnection from power grid, while PV and some loads are kept connected. Hence, the proposed UFLS strategy can be applied to keep the important loads in stable operation when the demand from loads is larger than power feeding in a microgrid under the islanded mode operation.
Asymmetrical Modulation Strategies for Partially Covered Inductors in Flexible Induction Heating Appliances

Cost-effective multi-output resonant inverter topologies are a key enabling technology for the development of flexible surfaces for induction heating appliances. These topologies present several challenges when applied to a wide range of IH-loads simultaneously. In this paper, two asymmetrical modulations are proposed as an alternative solution to control output power. The proposed approach has been verified using an experimental prototype featuring 2 induction heating loads up to 3.6 kW with output power control in the whole operating range.
Phase-Coded Pulse Sequence for Passive Detection and Mapping of Ultrasound Cavitation

Focused ultrasound (FUS) therapy requires reliable and effective monitoring tools to understand the underlying physical mechanism, controlling the treatment process, and assessing the treatment effects. Passive acoustic mapping (PAM), extended from passive cavitation detection (PCD), can provide noninvasive real-time quantitative imaging associated with treatment effects, and has been widely used in more and more applications of ultrasound therapy. Most PAM applications are based on common one-dimensional arrays that are used for ultrasound diagnosis, assisting with the simplest time exposure acoustics algorithm. However, due to the limited frequency-dependent diffraction mode of these arrays, this algorithm results in a large point spread function (PSF). To address this challenge, in the present study, we proposed a phase-coded pulse sequence for FUS exposure to improve the PAM performance. In the experiments, K sinusoidal signals with equidistant phases were used to driven the FUS transducer, and a PCD transducer and a linear array were utilized to receive the acoustic emissions. The PCD signals of different phases were summed in time domain and then the spectrum analyses were performed, and the passive array signals were processed by a modified passive beamforming algorithm. Ex vivo experimental results illustrated that in PCD spectrum, the K-order and its integer harmonics were enhanced while the other harmonics were suppressed. The PAM results suggested that compared with single phase and pulse inversion, the PSF size was reduced by using 4-phase coding. This work may be beneficial to the accurate control of ultrasound treatment, particularly cavitation-mediated applications.