Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Species of Aphyllon (Orobanchaceae) Parasitic On Galium In the Western USA

Abstract Aphyllon epigalium Colwell & A.C. Schneid. is described as a new species from Oregon and California. This taxon is distinguishable from the other members of Aphyllon Mitch. sect. Aphyllon in Western North America (i.e., A. fasciculatum [Nutt.] Torr. & A. Gray, A. purpureum [A. Heller] Holub, and A. uniflorum [L.] Torr. & A. Gray) in its host preference for Galium L. , by having 2–4 yellow flowers per stem, and pedicels longer than the stem. Two subspecies are described: Aphyllon epigalium subsp. epigalium, which occurs in the Cascade and Klamath ranges in Oregon and California, and the Coast Ranges and Sierra Nevada in California; and A. epigalium subsp. notocalifornicum Colwell & A.C. Schneid. , currently known from few sites in montane southern California. The new subspecies differ from one another in flower size, corolla lobe shape, host preference, geographic range, and nuclear and plastid genetic markers. An updated key to California and Oregon Aphyllon sect. Aphyllon is presented.
Reconstruction and Scalable Detection and Tracking of 3D Objects

The task of detecting objects in images is essential for autonomous systems to categorize, comprehend and eventually navigate or manipulate its environment. Since many applications demand not only detection of objects but also the estimation of their exact poses, 3D CAD models can prove helpful since they provide means for feature extraction and hypothesis refinement. This work, therefore, explores two paths: firstly, we will look into methods to create richlytextured and geometrically accurate models of real-life objects. Using these reconstructions as a basis, we will investigate on how to improve in the domain of 3D object detection and pose estimation, focusing especially on scalability, i.e. the problem of dealing with multiple objects simultaneously. A fundamental aspect of the thesis is the usage of RGB-D sensors to tackle the above-mentioned tasks. In contrast to standard color cameras, these sensors provide an additional depth channel that supplies to each pixel the metric distance to the camera. This allows for correct depth perception and alleviates many typical problems such as scale estimation and occlusion reasoning. The part on reconstruction will start with a method that allows for recovering the full colored geometry of arbitrarily shaped objects. By tracking the camera movement via the object’s support surface, we can eventually fuse keyframes into a colored signed distance field after global pose optimization. By repositioning the object to expose unseen parts, we create multiple partial scans and propose a novel variational fusion scheme. The reconstruction quality exhibited supersedes related methods and allows for metrically accurate models. In a follow-up work, we focus on a more efficient method to achieve the fusion by means of Octrees. These spatial look-up structures allow for memory-efficient storage but are not straightforward to use in optimization tasks. The part on detection will first focus on hashing of templated object views to achieve scalability. While discriminative, most template approaches suffer from a linear time complexity since each template has to be matched against the scene. With our learned hashing scheme, we decrease the computational complexity with only a small penalty on the detection accuracy. From there, we present our second work in that domain that employs Deep Learning of local RGB-D patches to allow for robust voting of object instances. This method is scalable and performs well in cluttered scenes at reasonable speeds. Following up, we introduce a novel deeply-learned detection scheme that predicts 2D bounding boxes together with scored 6D poses in a single shot. Our approach scales well to many objects and can run at 10Hz. Lastly, we present a model tracker in RGB-D data by means of a direct energy minimization over contour and object-interior cues. Our elegant method is robust to occlusion and scale changes and runs on a single CPU core for multiple objects in real-time.
Data-driven tool construction in exploratory programming environments

This work presents a new design for programming environments that promote the exploration of domain-specific software artifacts and the construction of graphical tools for such program comprehension tasks. In complex software projects, tool building is essential because domain- or task-specific tools can support decision making by representing concerns concisely with low cognitive effort. In contrast, generic tools can only support anticipated scenarios, which usually align with programming language concepts or well-known project domains. However, the creation and modification of interactive tools is expensive because the glue that connects data to graphics is hard to find, change, and test. Even if valuable data is available in a common format and even if promising visualizations could be populated, programmers have to invest many resources to make changes in the programming environment. Consequently, only ideas of predictably high value will be implemented. In the non-graphical, command-line world, the situation looks different and inspiring: programmers can easily build their own tools as shell scripts by configuring and combining filter programs to process data. We propose a new perspective on graphical tools and provide a concept to build and modify such tools with a focus on high quality, low effort, and continuous adaptability. That is, (1) we propose an object-oriented, data-driven, declarative scripting language that reduces the amount of and governs the effects of glue code for view-model specifications, and (2) we propose a scalable UI-design language that promotes short feedback loops in an interactive, graphical environment such as Morphic known from Self or Squeak/Smalltalk systems. We implemented our concept as a tool building environment, which we call VIVIDE, on top of Squeak/Smalltalk and Morphic. We replaced existing code browsing and debugging tools to iterate within our solution more quickly. In several case studies with undergraduate and graduate students, we observed that VIVIDE can be applied to many domains such as live language development, source-code versioning, modular code browsing, and multi-language debugging. Then, we designed a controlled experiment to measure the effect on the time to build tools. Several pilot runs showed that training is crucial and, presumably, takes days or weeks, which implies a need for further research. As a result, programmers as users can directly work with tangible representations of their software artifacts in the VIVIDE environment. Tool builders can write domain-specific scripts to populate views to approach comprehension tasks from different angles. Our novel perspective on graphical tools can inspire the creation of new trade-offs in modularity for both data providers and view designers.
Bacterial Growth and Mortality after Deposition of Saharan Dust and Mixed Aerosols in the Eastern Mediterranean Sea: A Mesocosm Experiment

The impact of viral lysis and grazing by flagellates on bacterioplankton production was assessed during a mesocosm experiment in the Eastern Mediterranean Sea, in response to Saharan dust (SD) vs. mixed aerosols (A) addition. The results highlight a positive effect on bacterial abundance, production and growth rate (~1.2, ~2.4 and ~1.9 –fold higher than the controls) in both SD and A, which was also confirmed by the increased portion of high DNA content bacteria (up to 48% of the bacterial community). Lytic viral production and the portion of bacterial production lost due to viral lysis were lower in SD and A after dust addition than in the controls (0.33 ± 0.17 x106 virus-like particles mL-1 h-1 and 6 ± 4%, respectively). Potential ingestion rate of bacteria by flagellates increased upon dust enrichment, but did not differ between mesocosms. Larger predators possibly down regulated flagellate abundance, and the calculated portion of bacterial production lost due to flagellate grazing was probably an artifact. Higher frequency of lysogenic cells in A compared to SD and the controls four days after dust addition may reflect faster phosphorus limitation in A, due to receiving less dissolved inorganic phosphorus and more dissolved inorganic nitrogen than SD.
Diet of the Crested Guan (Penelope purpurascens) in Panama: leaf-eating by a tropical frugivore

ABSTRACT We studied the diet of Crested Guans (Penelope purpurascens; Galliformes: Cracidae) in lowland tropical forest on Barro Colorado Island, Panama. From June-August 2015 we collected observations of foraging adults using five-minute focal follows of randomly chosen individuals. A total of 141 food items were recorded over 565 minutes of observations. Although Crested Guans are believed to be primarily frugivorous, we found that they consumed a high proportion of leaves (61.7% of items) and insects (24.8%), with fruits comprising a minority of items (13.5%). Guans preferred to eat young leaves, which are more tender and contain higher levels of nitrogen than mature leaves; however, young leaves also contain higher levels of tannins and other secondary defensive compounds, raising new questions about the mechanisms by which arboreal birds digest leaves. Recent studies have found similarly high levels of leaf consumption in other species of guans and chachalacas, suggesting that folivory may be more widespread in the family Cracidae than previously realized.
A Comparative Study on Demand Forecast of Car Sharing Users Based on ARIMA and LSTM

With the gradual growth of car-sharing market, it is particularly important to predict and analyze the demand of users. In this study, the K-means clustering method is used to cluster users into three types based on three indexes of the first use interval days, last use interval days, and monthly usage frequency. Each type of user demand has different time series trends. According to the ARIMA and LSTM models, respectively, the short-term demand of the three types of users is predicted. It is found that the LSTM model has a higher demand prediction accuracy for each type of user and the ARIMA model has a higher fitting accuracy. User demand forecasting can provide a reference for car-sharing managers to make scientific decisions.
Mobile Robot Localization Using Soft Sensor

This paper presents a new approach to fuse information in special form. To determine the position and heading of mobile robot, the information from fixation vision geometric constraint must be exploited. However, geometric constraint is not measurement data from sensor, cannot be fused directly. Therefore the concept of “soft sensor” is adopted. Its application to mobile robot localization is shown with simulation in this paper.
Bit Error Probability and Capacity Bound of OFDM Systems in Deterministic Doubly-Selective Channels

Doubly-selective channels, such as those that occur when the transmitter and the receiver move relative to each other at high speeds, are a key scenario for fifth generation (5G) cellular systems, which are mostly based in the use of the orthogonal frequency-division multiplexing (OFDM) modulation. In this paper, we consider an OFDM system using quadrature amplitude modulation (QAM) symbols and we show that, when transmitting over deterministic doubly-selective channels, the inter-carrier-interference (ICI) affecting a symbol can be well approximated by a complex-valued normal distribution. Based on this, we derive a lower bound for the link capacity using the Shannon-Hartley theorem. Finally, we provide an approximation of the bit error probability (BEP) using the well-known BEP expressions for Gray-coded QAM constellations over additive white Gaussian noise (AWGN) channels, and show numerical results that confirm that the proposed BEP expression approximates accurately the bit error ratio (BER) of the OFDM system for standardized channel models. The proposed closed-form analytical expressions for the capacity and the BEP do not only allow for discarding the need of computationally-costly Monte-Carlo system simulations, but also provide a theoretical framework to optimize the system parameters directly impacting on the achievable performance.
Generic Intent-based Networking Platform for E2E Network Slice Orchestration & Lifecycle Management

5G claims the service provisioning for a variety of new applications increasing orchestration complexity. Additionally, many different orchestrators and platforms have been developed over recent years to handle and control the network infrastructures. However, each of the orchestration platforms requires different expertise for handling them. The diversity and dynamicity in terms of requirements made it very complex for the operators to handle runtime operations. As with the increasing number of devices, the amount of traffic streamed over network deviates drastically making it impossible for network operators to handle the network operations manually. Hence automated platforms are a dire need, this work proposed a generic intent-based system that can automatically orchestrate and manage network lifecycle over multiple domains, sites and orchestrators. This system will include the orchestration over multiple domains access, transport and core and it also includes the use of Machine Learning for prediction of resource status for proactive decision making to manage the slice lifecycle automatically.
Deep Chaos Synchronization

In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).
Scaling and Variation Predictions for Silicon Fin-Based High Electron Mobility Transistor

We present scaling and variation predictions for a strained-silicon (s-Si) fin-based high electron mobility transistor (FinHEMT) with well-tempered, short-channel characteristics. Using device simulation calibrated with experimental data, we predict that the FinHEMT can achieve high electron mobility (~1100 cm<sup>2</sup>/Vs) and enhance effective mobility (up to 2x) by suppressing the surface roughness scattering effect in the Si quantum well (QW) channel. Moreover, excellent scalability of the FinHEMT ON-current (<inline-formula> <tex-math notation="LaTeX">${I}_{{\textit {ON}}} >1.1$ </tex-math></inline-formula> mA/<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${L}_{G} < 10$ </tex-math></inline-formula> nm) is predicted as the high channel mobility can reduce the underlap series resistance in the scaled device. Owing to this low underlap resistivity, geometrical variations of fin width and underlap length have little effect on the ON-current in FinHEMT.
Electromicrofluidic Device on Multilayered Laser-Induced Polyamide Substrate for Diverse Electrochemical Applications

Microfluidic devices with integrated electrodes, called electromicrofluidic (EMF) device, have been reported for multiple applications. In this work, a unique approach to realizing a multilayered EMF device, with microchannel and integrated electrodes on the same polyamide (PI) substrate, has been presented. A computer-controlled CO<sub>2</sub> laser ablation method, with varying speeds and power values, was employed to form laser-induced graphene (LIG) on a PI substrate as per the desired design. Initially, to create a microchannel layer, the formed LIG was peeled off which left behind an etched pattern. Subsequently, to realize an electrode layer, the PI substrate with microchannel was further ablated to create patterned LIG. An optimal flow rate of <inline-formula> <tex-math notation="LaTeX">$100~\mu \text{L}$ </tex-math></inline-formula>/min via a controlled syringe pump was established in EMF device. As a prototype, the developed platform, with microchannel and electrodes, was explored for variable electrochemical applications. First, the electrochemical sensing of uric acid displayed a limit of detection (LOD) as <inline-formula> <tex-math notation="LaTeX">$0.61~\mu \text{M}$ </tex-math></inline-formula> in a linear range from <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{M}$ </tex-math></inline-formula> to 3 mM with significant recovery values. Furthermore, the polarization performance for fuel cell application was evaluated on the developed EMF platform using the chronoamperometry (CA) method with a stable open-circuit potential (OCP), harnessing the maximum power density obtained was 3.027 nW/cm<sup>2</sup>. Finally, an array of interdigitated microelectrodes (IDEs) was realized to examine impedance-based nitrite detection. Overall, the presented multilayered EMF devices with microchannel and electrodes on the single sheet authenticate the applicability of the designed platform for a variety of sensing and energy harvesting applications.
A Multiclassification Model of Sentiment for E-Commerce Reviews

Consumer reviews are important information that reflects the quality of E-commerce goods and services and their existing problems after shopping. Due to the possible differences in consumers’ experiences with goods and service quality, consumer reviews can involve multiple-aspect expressions of emotions or opinions. This may result in attitudes expressed by a consumer in the same review sometimes having a variety of emotions. We introduce a sentiment multiclassification method based on a directed weighted model. The model represents the sentiment entity vocabulary as the sentiment nodes and represents the relation between nodes as the directed weighted link. The sentiment entity vocabulary is the entity with attributes, which can express sentiment meaning in related reviews. Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and determined by the direct correlation calculation between them. The paths are all connected directed links from one node to another, which are composed of several nodes and links with close sentiment similarity. Then, we can establish a directed weighted model concerning the sentiments. Directed weighted links having similar sentiment relations with each other may constitute a directed weighted path. There are several directed weighted paths from a start node to the end nodes of the sentiment entity vocabulary in the directed weighted model. Each different path is a different sentiment expression, which represents a different sentiment type. The different sentiment classifications can be obtained through the restriction of path length. Experiments and analysis of the results show that the sentiment multiclassification model based on the directed weighted model proposed in this paper can classify the review sentiments according to different limited threshold rules. Comprehensive analysis indicates the classification results have good accuracy and high efficiency.
An Optimized Point Cloud Classification and Object Extraction Method Using Graph Cuts

The requirement for 3D scene classification and understanding has dramatically increased with the widespread use of airborne Light Detection And Ranging (LiDAR). This paper focuses on precise classification and object extraction based on point cloud data in complex scenes. There are usually gross errors in the initial classification based on locally independent classifiers, due to the over- and under-clustering in the feature extraction. We introduce a graph-cut based method to improve the classification precision and eliminate errors by using contextual information. The intuition behind our algorithm is based on the fact that nearby points have a high probability of belonging to the same object, while the distances of points belonging to different objects will be large. Based on this assumption, objects of interest can be precisely extracted, and we can thus optimize the classification results. The experiments undertaken in this study proved that the classification method we propose can be effectively used for point cloud classification in complex scenes.
Robust Model Predictive Control for a Three-Phase PMSM Motor With Improved Control Precision

This article proposes a novel model predictive control for a three-phase permanent-magnet synchronous motor (PMSM) with enhanced robustness against parameter variation and higher current control precision. First, the extended set of voltage vectors are adopted to increase the current control precision. Then, the parameter sensitivity problem is avoided in the first stage using an indirect reference vector strategy for the sake of computation time reduction. Subsequently, the inductance disturbance observer is adopted in the current prediction process to enhance the robustness against machine parameter mismatch. The intrinsic connection between the discrete control sets precision and disturbance observer is analyzed. The proposed method improves the robustness against parameter mismatch and current control precision simultaneously. Experimentations are conducted to verify the effectiveness of the proposed method.
A Universal Wideband Device-Level Parallel Simulation Method and Conducted EMI Analysis for More Electric Aircraft Microgrid

The validation of electromagnetic compatibility for the microgrid of a more electric aircraft (MEA) is an essential test item before delivery for a trial flight, and it has always been urgently expected to be involved during the design stage. This article presents a universal method for wideband modeling and simulation of the MEA microgrid system in the time domain, regardless of the fact that motors are driven by which kind of converter, e.g., modular multilevel converter (MMC), 3-L neutral-point clamped (NPC), or 2-L pulsewidth modulation (PWM) converters. The insulated gate bipolar transistor and diodes are modeled with the physics-based dynamic model to emulate not only precise system-level performance of the system, but also to get an insight into the high-frequency oscillation between junction capacitance of the semiconductor modules and the parasitic parameters and high-frequency branch of other components, such as the permanent magnet synchronous motor (PMSM), transformer, and generator. To alleviate the attendant computational challenge, which could be extremely time-consuming (if no nonconvergence problem is encountered) when solved on traditional simulation platform, circuit partition based on transmission line decoupling, Norton equivalent parameter extraction, and TLM-link decoupling of submodules from the MMC bridge arms are utilized. The simulation program is executed on GPU to achieve massively parallel and accelerated solution. The accuracy and efficiency of the GPU-based parallel algorithm are validated by the comparison with the experimentally verified model in ANSYS Simplorer.