Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Object Detection and Grasping: A Survey

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed.
Deep-Rice: Deep Multi-sensor Image Recognition for Grading Rice*

Rice grading is an important topic in the research of food security, which targets at assessing the quality of rice. However, there is seldom attention from researchers. In this paper, we propose a novel rice grading system, named as Deep-Rice, which is built upon a deep learning framework. Specifically, Deep-Rice employs a multi-view CNN architecture to extract the discriminative features from different views of rice images, and tries to optimize the CNN parameters by using a modified softmax loss function. In accompany with this deep model, we also build a large-scaled rice dataset, which is denoted as FIST-Rice, to provide a baseline database for the research of food security. FIST-Rice is the first publicly available multi-sensor rice dataset, which contains sound and unsound kernel samples with the total number of 30,000. Each sample is captured under three different illumination conditions. We evaluate the proposed Deep-Rice model on the FIST-Rice dataset by comparing with the AdaBoost and SVM methods. The experimental results indicate the Deep-Rice model achieves improved performance in different conditions of light intensity. The FIST-Rice dataset and baseline codes will be released for further public research.
Point cloud based iterative segmentation technique for 3D plant phenotyping

The segmentation of 3D point clouds is an important prerequisite step for many plant phenotype and data analysis. One of the main challenges is high resolution 3D plant model segmentation. In this paper, we present an iteration based approach for 3D segmentation directly from the dense point clouds that are reconstructed from multi-view images. We extend the existing euclidean distance and spectral clustering (SC) algorithms and using iteration approach to segment the 3D point clouds into elementary shape units that could represent the plant organs (stems, branches, leaves, etc.). Such approach only requires the 3D coordinate data information. Experimental results show our approach can effectively segmented the obtained point cloud for various plant species.
Driver’s Head Behavior Detection Using Binocular Infrared Camera*

Driver’s head behavior detection is very important for safe driving and smart driving. It can be judged based on changes in driver’s head pose. In this paper, we propose a driver's head pose estimation method based on binocular infrared camera. The head coordinate system can be established by combining two-dimensional image feature point information and three-dimensional point cloud information. With the camera coordinate system as a reference, the pose of the head coordinate system is obtained in real time, which indicate the driver's head pose. The experiments of head rotation are conducted in a real vehicle environment, and the results show that the proposed method can effectively detect driver's head behavior.
Combined Planning of Medium and Low Voltage Grids

Typically, low-voltage (LV) and medium-voltage (MV) grids are planned separately. Grid planners define limits that divide the available voltage band into fixed ranges for different voltage levels. MV grids are then planned so that these limits are not violated. LV grids are planned under the worst-case assumption that the voltage at all MV connection points is equal to the voltage limit in the MV grid. This approach is likely to overestimate the demand for reinforcement, possibly leading to unnecessary investments. To analyze this effect, the capital expenditure on grid reinforcement caused by additional renewable generation are calculated for a rural MV grid in Germany and all connected LV grids. Reinforcement can be reduced if it is known which MV feeder an LV grid is connected to. On-Load Tap Changer (OLTC) transformers can lead to additional capex reductions if applied selectively. The combined MV and LV grid planning leads to the most efficient solution in terms of capital expenditure.
‘Expert’ and ‘Casual’ Interfaces for Museums in Digital Times : Board and Field Games : MUDs and MMORPGs : Things and Networks

This paper explores game-like designs for interventions of digital devices, media and administrative tools in networks of museum functions. Specifically, it focuses on the ubiquity of networked mobile devices, and argues that game play presents a powerful model for understanding the impact of non-linear, multi-vocal, poly-agentic systems on the role of museums as sites of culture construction.The paper argues for digital initiatives to take a dynamic systems approach to the development of museums. It draws on the work of Latour and others on Actant-Network Theory and of Bryant on Onto-Cartography. These works offer a framework for understanding transactions and interactions among such heterogeneous entities as software codes, game mechanics and gallery structures.The site of any such transaction may be described as an ‘interface’, and the paper sets out proposals for the design of two sites, the so-called ‘expert-interface’ and its complementary partner, the ‘casual-interface’. The paper opens with descriptions of the two interfaces, sets them in the context of the museum problematic and the systems approach, then turns to detailed discussions of the workings of each and ends with brief notes on developing experimental prototypes.
Active Interposer Technology for Chiplet-Based Advanced 3D System Architectures

We report the first successful technology integration of chiplets on an active silicon interposer, fully processed, packaged and tested. Benefits of chiplet-based architectures are discussed. Built up technology is presented and focused on 3D interconnects process and characterization. 3D packaging is presented up to the successful structural test and characterization of the demonstrator.
Jicama (Pachyrhizus erosus) fiber prevents excessive blood glucose and body weight increase without affecting food intake in mice fed with high-sugar diet

Objective: Jicama (Pachyrhizus erosus) fiber has been documented to exert an immunomodulatory effect both in vitro and in vivo. However, its beneficial effect against metabolic syndrome remains unknown. This study aimed to reveal whether the jicama fiber (JF) could prevent the development of diabetes and obesity caused by a high-sugar diet (HSD). Materials and Methods: The JF was isolated from its tuberous part and subsequently used as a supplemental diet for adult male Bagg and Albino (BALB)/c mice fed with a HSD. Four different diet paradigms including normal diet, HSD (30% sucrose), and HSD in combination with 10% and 25% of JF, respectively, were deployed continuously for 8 weeks. Furthermore, the blood glucose level, glucose tolerance, body weight, food and water consumption as well as epididymal white adipose tissue (WAT) and interscapular brown adipose tissue (BAT) mass were determined. Results: Our results revealed that supplementation of 25% JF could significantly prevent the blood glucose increase, excessive body weight gain, and glucose intolerance in mice fed with HSD. Moreover, 10% and 25% JF blunted the HSD-induced WAT mass gain but failed to counteract the depletion of BAT mass. Furthermore, the fiber supplementation elicited a minimum effect on rhythm and total food and water intake. Conclusion: The JF could effectively sustain blood glucose homeostasis as well as improve body weight and WAT mass profile against the development of diabetes and obesity caused by HSD.
A Compact 76-81 GHz 3TX/4RX Transceiver for FMCW Radar Applications in 65-nm CMOS Technology

This paper presents a compact 76-81 GHz 3TX/4RX transceiver for FMCW radar applications in 65-nm CMOS technology. Three individual transmitters (TXs) and four receivers (RXs) are integrated for MIMO operation to achieve higher resolution and sensitivity. A wideband frequency sextupler cascaded with a Wilkinson power divider is employed as the local oscillation (LO) chain to lower down the frequency of FMCW signal generator and simplify the design of LO distribution network. Each transmitter channel achieves above 12.2 dBm output power from 76-81 GHz and a peak 14.2 dBm output power is obtained at 77 GHz. The receiver conversion gain from low noise amplifier input to baseband output is programmable from 46.3 dB to 102.45 dB for adapting to different detection distances. The measured noise figure of the receiver chain is 11 dB at 5 MHz intermediate frequency (IF). The transceiver including all dc bonding pads occupies a silicon area of 1.8 mm× 4 mm, and the power dissipation of the whole chip is 721.8 mW.
Material and Structure Design Optimization for Panel-Level Fan-Out Packaging

In recent years, the evolution of semiconductor packages is driven by the consumer demands of Internet of Things (IoT), mobile phones, wearable devices, and tablets. Manufacturing has evolved from wafer-level chip-scale packaging (WLCSP) to fan-out wafer-level processing (FOWLP) and 2.5/3D packaging. The panel-based fan-out processing was highly noticed. It is the best solution to have both high density and cost efficiency. In this study, the warpage behavior at mold and carrier release stages of the Panel-Level Fan-Out (PLFO) device was optimized by the theoretical and simulation tools. The panel-level (300×300 mm2) warpage of the PLFO test vehicle was measured by advanced metrology analyzer (aMA), which can not only do the global measure of warpage of the full panel, but also do the local measure of the unit package. The Finite Element Method (FEM) analysis was then conducted for the corresponding test vehicle with considering the temperature dependent material properties, including Young's modulus and Coefficient Thermal Expansion (CTE), and the simulated warpage was highly correlated to the measured result for modeling validation. Four-layer and three-layer curvature theoretical formulas were developed for mold and carrier release stages, respectively, and used to predict the trends versus different factors. These trends were verified by FEM simulations. The optimal curvature was then obtained by the optimized factors.
Driver Fatigue Detection Based on Machine Vision*

The eye is the key to detect the driver fatigue, this paper proposes an algorithm to improve the accuracy in the complex lighting environment in the family car. First, facial landmarks are detected based on the ERT algorithm. Eye opening and closure states are detected using HOG feature and support vector machine (SVM). Gaussian mixture model (GMM) is established by calculating values of PERCLOS and ECD to achieve the purpose of fatigue detection. It shows that the algorithm has a certain degree of accuracy and robustness.
Suboptimality of Decentralized Methods for OPF

We examine the fact that decentralized methods may converge to suboptimal solution. Observed in numerous studies of decentralized optimal power flow problem it has a simple explanation: the curse of non-convexity. We numerically assess the performance of decentralized interior point method (DIPM) and alternating direction method of multipliers (ADMM). The algorithms were tested in two situations: grid decomposition for a few areas associated with independent Transmission System Operators (TSOs) as in super grids and total decomposition till node level that models prosumers behavior. In particular, we demonstrate that the obtained optimal state and convergence rate depends on the starting point. The algorithms were tested on IEEE 9, 14, 118 bus systems. Besides, we discuss the advantages and drawbacks of decentralized optimization approaches.
A Joint Calibration Method for the 3D Sensing System Composed with ToF and Stereo Camera

Time of Flight (ToF) and stereo vision are the two major technologies for rapid depth information acquisition. The ToF sensor is able to obtain depth map directly at a high rate of speed, but the resolution and accuracy is relatively low. The binocular stereo vision means can obtain high-accuracy disparity map based on triangulation principle, but its performance depends on the textures of the target surface. Therefore it is an interesting research topic to combine above two techniques. In order to carry out the fusion of the two 3D techniques, an efficient calibration method is investigated in this work. Based on a simple calibration rig, point correspondences between the depth image and color image are extracted and then used to calculate both intrinsic and extrinsic parameters of two devices. The experimental result shows that the proposed method could achieve better accuracy in comparison with classical calibration methods.
Growth Responses of Arben (Rubus fraxinifolius Poir.) Seedling to Various Planting Media Respon

Study of the effects of various media on growth of Arben (Rubus fraxinifoilus Poir.) seedlings through seed multiplication was carried out. This study was aimed to determine the effect of planting media on the growth of arben seedlings. The experiment used various media i.e. compost, sand + husk (1:1), compost + husk (1:1), compost + sand + husk (1:1:1). The study was arranged by completely randomized design with three replicates. The results showed that statistically almost of parameters were significant by Tukey test at 1% level. The best media of all of parameters was compost and the low result was showed by sand and husk media.
Real-time Bleeding Simulation with Improved SPH Method

Bleeding simulation using traditional Smoothed Particle Hydrodynamics (SPH) methods cannot satisfy well the visual realism requirement in virtual surgery. An improved real-time SPH method is developed for bleeding simulation in this paper. In order to improve the degree of visual realism, this model incorporates and refines more physical properties of blood such as weakly compressive pressure, stabilized forces on the boundary and improved quality of bleeding surface. The novelties of this work lie in two aspects. First, the stability of the model is enhanced by considering the impact of neighboring density on compressive pressure and using modified supporting area for boundary particles. Second, the surface tension is able to change according to the forms of blood. The breakage of the tumor body of aneurysm is simulated. Compared with the traditional SPH method, the edge of the blood-drops and the bleeding boundary on the tissue in the proposed method are clearer and smoother. The simulation results show that the developed method improves the dynamic effect on bleeding simulation.
Type VI Choledochal Cysts—Case Report and Review of Literature

Abstract Choledochal cysts (CDC), are rare congenital dilations involving the extra hepatic biliary apparatus with or without dilation of the intrahepatic bile ducts. They are conventionally classified into five types. A new type, type VI, causing dilation of the cystic duct between the neck of the gall bladder and the common hepatic duct (CHD) has been described in medical literature which is the rarest of all these subtypes. They are commonly observed in middle aged females and are mostly symptomatic. Most of these cysts need magnetic resonance cholangiopancreatography (MRCP) for accurate diagnosis. Treatment options for these lesions are not well defined but range from simple cholecystectomy to complete excision of the entire bile duct and biliary reconstruction, as there is a concern of malignant transformation in these cysts. Hence, these rare cysts, though rare, must be borne in mind when dealing with suspicious cystic lesions in the biliary tract. Here, we present an interesting case of such a rare cyst and its management in a middle aged woman.
Apertium-fin-eng–Rule-based Shallow Machine Translation for WMT 2019 Shared Task

In this paper we describe a rule-based, bi-directional machine translation system for the Finnish—English language pair. The baseline system was based on the existing data of FinnWordNet, omorfi and apertium-eng. We have built the disambiguation, lexical selection and translation rules by hand. The dictionaries and rules have been developed based on the shared task data. We describe in this article the use of the shared task data as a kind of a test-driven development workflow in RBMT development and show that it suits perfectly to a modern software engineering continuous integration workflow of RBMT and yields big increases to BLEU scores with minimal effort.
Facial Expression Recognition Using Partial Gabor and Neural Network*

The traditional facial expression recognition method needs two independent steps of extracting features and classifying facial expression, and the deep neural network that attracted wide attention recently combines these two steps into one and performs well in the field of image recognition. We propose a method using partial Gabor features and neural network. First, we use the method of Ensemble of Regression Trees to identify facial significant landmarks and get important feature regions from them. , such as the eyes, nose, and mouth; then extract the Gabor features using 24 Gabor kernel functions with 8 different orientations and 3 scales; finally classify them by the neural network of fully connected layer. As a result, we attained accuracies of 93.7% and 96.3% on JAFFE and CK+ expression databases respectively. The result shows that the combination of Gabor filtering and neural network can greatly improve the recognition accuracy.
GTCOM Neural Machine Translation Systems for WMT19

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT19 shared news translation task. We participate in six directions: English to (Gujarati, Lithuanian and Finnish) and (Gujarati, Lithuanian and Finnish) to English. Further, we get the best BLEU scores in the directions of English to Gujarati and Lithuanian to English (28.2 and 36.3 respectively) among all the participants. The submitted systems mainly focus on back-translation, knowledge distillation and reranking to build a competitive model for this task. Also, we apply language model to filter monolingual data, back-translated data and parallel data. The techniques we apply for data filtering include filtering by rules, language models. Besides, We conduct several experiments to validate different knowledge distillation techniques and right-to-left (R2L) reranking.
System Description: The Submission of FOKUS to the WMT 19 Robustness Task

This paper describes the systems of Fraunhofer FOKUS for the WMT 2019 machine translation robustness task. We have made submissions to the EN-FR, FR-EN, and JA-EN language pairs. The first two were made with a baseline translator, trained on clean data for the WMT 2019 biomedical translation task. These baselines improved over the baselines from the MTNT paper by 2 to 4 BLEU points, but where not trained on the same data. The last one used the same model class and training procedure, with induced typos in the training data to increase the model robustness.
NJU Submissions for the WMT19 Quality Estimation Shared Task

In this paper, we describe the submissions of the team from Nanjing University for the WMT19 sentence-level Quality Estimation (QE) shared task on English-German language pair. We develop two approaches based on a two-stage neural QE model consisting of a feature extractor and a quality estimator. More specifically, one of the proposed approaches employs the translation knowledge between the two languages from two different translation directions; while the other one employs extra monolingual knowledge from both source and target sides, obtained by pre-training deep self-attention networks. To efficiently train these two-stage models, a joint learning training method is applied. Experiments show that the ensemble model of the above two models achieves the best results on the benchmark dataset of the WMT17 sentence-level QE shared task and obtains competitive results in WMT19, ranking 3rd out of 10 submissions.
Synchronous Machine Representations for Stability Studies of Power Systems with Inverters

Since Power Electronics (PE)-based sources present faster dynamics compared to Synchronous Machines (SMs), questions about system modelling for stability studies are raised: Can the transient associated with the grid still be neglected? Are the simplifications in SM modelling still applicable? Focussing on the latter, the main purpose of this paper is to observe the impacts of SM representations on the stability analysis of a power system with PE-based sources. Here, three different representations of SM have been used. A combination of frequency analysis and time domain simulations has shown that one of the simplified models can be considered to be a good approximation of the reference model for stability studies that do not require high precision analysis. On the contrary, the other model displayed a completely different behaviour, presenting even great dependencies between the SM state variables and the critical eigenvalues of the inverter control.
Behaviour Analysis of Electrical Vehicle Flexibility Based on Large-Scale Charging Data

The massive deployment of electrical vehicles (EV) creates new challenges and some opportunities for power systems. This paper focuses on the potential of EV charging stations to provide flexibility for electrical grids. Since there is no concrete definition for flexibility in power systems, this paper first looks at the market opportunity for flexibility and then proposes a method to calculate the flexibility of EV based on the market definition. Then, the flexibility of EV is derived from data collected from charging stations in the Helsinki area between 2015 and 2018.
A High Efficiency 39GHz CMOS Cascode Power Amplifier for 5G Applications

We present a 39GHz CMOS cascode power amplifier (PA) with a two-step (L-C and C-L) second harmonic termination. We analyze the distortion mechanism in a cross-coupled capacitor neutralization technique and suggest the termination scheme to enhance both linearity and efficiency of PAs. This scheme can suppress the second harmonic feedback components generated in the middle of the cascode cell and extend the range of linear output power (Pout). Our two-stage PA shows power gain of >24.5dB, saturation output power (Psat) of >16.2dBm and power-added-efficiency (PAE) of >32% over the full 39GHz band from 37 to 40GHz. Under the 5G new radio modulation, the PA achieves linear Pout of 8.2dBm/10.3dBm and PAEs of 11.9%/16.5% at EVMs of −30.0dB/-25.3dB, respectively.