Miracle-dz/UniFAR-FTUs huggingface dataset:
Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments

Deep neural networks (DNNs) have shown prominent performance in the field of object detection. However, DNNs usually run on powerful devices with high computational ability and sufficient memory, which have greatly limited their deployment for constrained environments such as embedded devices. YOLO is one of the state-of-the-art DNN-based object detection approaches with good performance both on speed and accuracy and Tiny-YOLO-V3 is its latest variant with a small model that can run on embedded devices. In this paper, Tinier-YOLO, which is originated from Tiny-YOLO-V3, is proposed to further shrink the model size while achieving improved detection accuracy and real-time performance. In Tinier-YOLO, the fire module in SqueezeNet is appointed by investigating the number of fire modules as well as their positions in the model in order to reduce the number of model parameters and then reduce the model size. For further improving the proposed Tinier-YOLO in terms of detection accuracy and real-time performance, the connectivity style between fire modules in Tinier-YOLO differs from SqueezeNet in that dense connection is introduced and fine designed to strengthen the feature propagation and ensure the maximum information flow in the network. The object detection performance is enhanced in Tinier-YOLO by using the passthrough layer that merges feature maps from the front layers to get fine-grained features, which can counter the negative effect of reducing the model size. The resulting Tinier-YOLO yields a model size of 8.9MB (almost $4\times $ smaller than Tiny-YOLO-V3) while achieving 25 FPS real-time performance on Jetson TX1 and an mAP of 65.7% on PASCAL VOC and 34.0% on COCO. Tinier-YOLO alse posses comparable results in mAP and faster runtime speed with smaller model size and BFLOP/s value compared with other lightweight models like SqueezeNet SSD and MobileNet SSD.
Low-Complexity Non-Data-Aided SNR Estimation for Multilevel Constellations

Empirical-distribution-function (EDF)-based non-data-aided signal-to-noise ratio (SNR) estimation methods are effective for multilevel constellations but require many matching operations between reference cumulative distribution functions and EDF. To reduce resource consumption and improve real-time performance, we propose a low-complexity method by simplifying the matching test method. The proposed method modifies the matching operation from two-dimensions (2-D) to 1-D, reducing the number of matching operations and memory resource. Compared to the moment-based methods and the existing EDF-based reduced-complexity methods, the proposed method has the lowest complexity and provides better estimation performance in the medium-to-high SNR range.
Radiation Characteristics of Chebyshev Function-Based Power Weighted Linear Array Antennas Influenced by Elements Configuration

The radiation characteristics of Chebyshev function-based power weighted linear array antennas influenced by elements configuration are investigated in terms of width of main lobe (WML) and sidelobe level (SLL). The investigation is also performed using a theoretical approach focused on synthesizing process. The use of Chebyshev function as weighting coefficient for linear array antennas with number of elements less than 10 shows that the variation of array-elements-number (N) with a constant inter-elements-spacing (d) can improve WML and provide notable impacts to SLL. While the variation of inter-elements-spacing (d) with a constant array-elements-number (N) yields to the WML improvement without significant effect on SLL. The theoretical approach is then validated by the experimentation with the error rate in SLL and WML of 1.68% and 3 %, respectively.
Toward Real-Time 3D Shape Tracking of Deformable Objects for Robotic Manipulation and Shape Control

Dexterous robotic manipulation of complex nonrigid objects is a requirement for automating many delicate tasks. This work explores various sensing and modeling strategies for tracking the 3D shape of objects during manipulation, allowing for more accurate and responsive planning and control of robotic manipulators. These approaches are evaluated in terms of their ability to correctly capture the behaviour of non-rigid objects as well as their computational complexity, which is strongly constrained by the need for real-time control on robotic platforms with limited computing power.
Design of Cantilever Diaphragm Array Piezoelectric MEMS Microphone for Signal-To-Noise Ratio Enhancement

This study realizes an improved signal-to-noise ratio (SNR) MEMS microphone based on piezoelectric sensing principle. In Fig. 1a, the proposed square-shaped and the reference 45-degree right triangle cantilever diaphragm (PZT/Silicon unimorph) arrays are designed and implemented for comparison. The proposed microphone has two merits, (1) enhance SNR by increasing sensitivity: with the same diaphragm area, the proposed square cantilever diaphragm design can provide larger stress and wider stress distribution than the reference type, as in Fig. 1b, which increase the sensitivity. Measurement shows the proposed microphone has a higher SNR of 70.1dB (at 1 kHz). As compared with the reference type, the proposed design has a 2.3dB SNR improvement (at 1 kHz). (2) prevent sensitivity loss at low frequency by reducing the gap between cantilever diaphragms: the proposed arrangement of cantilever diaphragms reduces the gap between the bent cantilever diaphragms (caused by residual stress, as in Fig. 1a) to prevent the low-frequency acoustic loss. Acoustic frequency response measurement shows successful prevention of sensitivity loss at low frequency.
Fast Coding Unit Splitting Decisions for the Emergent AVS3 Standard

AVS3 adopts quad-tree (QT) with nested binary tree (BT) and extended quad-tree (EQT) partitioning, which shows promising compression performance when compared to the conventional QT partitioning in AVS2 and HEVC. However, the interleaved and recursive splitting manner significantly increases the computational complexity of the encoder, which may impede the real applications of AVS3. This paper proposes fast coding unit splitting decision methods for QT, BT and EQT partitioning. In particular, the Bayesian decision rule is employed with Skip states, and BT and EQT partitioning can be early terminated. Additionally, the average splitting depth that is produced by BT partitioning, is incorporated as the prior information for terminating the EQT and QT splittings. Moreover, we explore the splitting settings for CUs that are predicted with Skip mode, with the aim of eliminating unnecessary partition attempts. Experimental results show that the proposed fast algorithms are effective and provide a good trade-off between computational complexity and coding performance. In particular, 69% encoding time reduction is achieved with only 0.55% increase in terms of BD-Rate on average, which greatly benefits the practical implementations of the AVS3 in real applications. The proposed methods have been adopted into TAVS3 reference software.
Parallel MOEA Based on Consensus and Membrane Structure for Inferring Phylogenetic Reconstruction

In recent years, inferring phylogenies has attracted lots of attention in both academic community and various application fields. Phylogenetic inference usually consists of a couple of evolutionary relationships, which can be represented as a phylogenetic tree. The phylogenetic reconstruction problem can be defined as an optimization problem, targeting at finding the most eligible tree among all possible topologies according to a selected criterion. Since the combinatorial number of possible topologies exceeds tolerance, various heuristic and metaheuristic methods have been proposed to find approximate solutions according to the selected criterion. However, different criterions are based on different principle and conflict with each other basically. In this line, scholars has proposed multi-objective evolutionary algorithm (MOEA) based on diverse criteria. Nevertheless, MOEA has suffered unbearable time consumption due to its inherent drawbacks of computational complexity and convergence. By studying the independence between the sub-populations in each time-consuming step of MOEA, the steps without global information can be designed to be executed in parallel, which can fundamentally address computational problems. Effective parallel algorithms designed with the characteristics of modern multicore clusters can solve such problems. In this sense, we propose a parallelized multi-objective evolutionary algorithm (MOEA-MC) by deploying on Spark, which added consensus into evolutionary algorithm to improve the quality of convergence and used membrane structure to keep equal solutions under different weights. In order to assess the performance achieved by the proposal, we have performed comparison among different methods on three real-world datasets separately. The results have certified that the solutions derived from MOEA-MC are superior to traditional methods in all studied datasets. And parallelized MOEA-MC can get dominant position and optimal Pareto-frontier simultaneously within minimal runtime.
Cryptocurrency Pump and Dump Schemes: Quantification and Detection

In the past years, cryptocurrencies have received a lot of attention in popular media. Having attracted significant speculation, prices have soared in 2017, fell in 2018 and are generally known to be very volatile. However, some of the price changes have been due to organized manipulation. Traditionally known in the world of penny stocks and made illegal in most countries, pump and dump schemes are frequent in cryptocurrencies, and mostly unregulated. In this paper, we perform quantification and detection of pump and dump schemes that are coordinated through Telegram chats and executed on Binance – one of the most popular cryptocurrency exchanges. We detail how pumps are organized on Telegram, and quantify the properties of 149 confirmed events with respect to market capitalization, trading volume, price impact and profitability. Based on this ground truth, and regular trading intervals obtained from twitter timestamps, we optimize a binary classifier in order to be able to detect additional suspicious trading activity. Our results indicate that pump and dump schemes occur frequently in cryptocurrencies with market capitalizations below $50 million, that scheme operators often organize their actions across multiple channels, that such activity tends to lead to inflated prices over longer time periods and machine learning can help to identify activity that is similar to known pump and dump schemes.
Experimental Investigation of GaN Transistor Current Collapse on Power Converter Efficiency for Electrical Vehicles

GaN power transistors are drawing extensive research interest to increase power electronics systems efficiency and power density for electrical vehicles (EV). However, GaN devices suffer from current collapse, which results in shifted device static characteristics. In this paper, different measurement methods are proposed to investigate the influence of GaN transistor current collapse on device conduction and switching losses. By proposing a measurement circuit to accurately characterise GaN transistor dynamic RDSon, it is shown that device dynamic RDSon increases to 60\% bigger than its static RDSon value when it is operated at 1MHz converter. By proposing a modified double pulse test fixture, device switching transition can be characterised when it is unbiased. Therefore, it is shown that current collapse increases device switching losses 20\% bigger than unbiased condition. It is finally represented the influence of GaN device current collapse on device safe operation area in terms of operation current and frequency in EV-based power converters.
Communicating with SanTO – the first Catholic robot

In the 1560s Philip II of Spain commissioned the realisation of a “mechanical monk”, a small humanoid automaton with the ability to move and walk. Centuries later, we present a Catholic humanoid robot. With the appearance of a statue of a saint and some interactive features, it is designed for Christian Catholic users for a variety of purposes. Its creation offers new insights on the concept of sacredness applied to a robot and the role of automation in religion. In this paper we present its concept, its functioning, and a preliminary test. A dialogue system, integrated within the multimodal communication consisting of vision, touch, voice and lights, drives the interaction with the users. We collected the first responses, particularly focused on the impression of sacredness of the robot, during an experiment that took place in a church in Peru.
Human-Robot Team: Effects of Communication in Analyzing Trust

Trust is related to the performance of human teams, making it a significant characteristic, which needs to be analyzed inside human-robot teams. Trust was researched for a long time in other domains like social sciences, psychology, and economics. Building trust within a team is formed through common tasks and it depends on team performance and communication. By applying an online game based tasks for human-robot teams, the effects of three communication conditions (communication without text and verbal interaction, communication with text and verbal interaction related/not related to the task) on trust are analyzed. Additionally, we found that the participants’ background is linked to the trust in the interaction with the robot. The results show that in a human-robot team the human trust will increase more over time when he/she is working with a robot that uses text and verbal interaction communication related to the task. They further suggest that human trust will decrease to a lower extent when the robot fails in doing the tasks if it uses text and verbal communication with the human.
Effect of growth hormone treatment on the adult height of children with chronic renal failure

Background Growth hormone treatment stimulates growth in short children with chronic renal failure. However, the extent to which this therapy increases final adult height is not known. Methods We followed 38 initially prepubertal children with chronic renal failure treated with growth hormone for a mean of 5.3 years until they reached their final adult height. The mean (±SD) age at the start of treatment was 10.4±2.2 years, the mean bone age was 7.1±2.3 years, and the mean height was 3.1±1.2 SD below normal. Fifty matched children with chronic renal failure who were not treated with growth hormone served as controls. Results The children treated with growth hormone had sustained catch-up growth, whereas the control children had progressive growth failure. The mean final height of the growth hormone–treated children was 165 cm for boys and 156 cm for girls. The mean final adult height of the growth hormone–treated children was 1.6±1.2 SD below normal, which was 1.4 SD above their standardized height at bas.
Pre-Emission Study of Photoelectron Dynamics in a GaAs/AlGaAs Photocathode

Understanding the carrier dynamics in III–V semiconductor photocathodes is of great importance to the development of many novel optoelectronic systems such as low-light imagers and coherent x-ray sources. Past experimental effort to study ultrafast response of GaAs photocathodes has relied exclusively on post-photoemission measurement, i.e. via characterization of the temporal properties of emitted electron bunches. Such an indirect approach is not only difficult to achieve experimentally but also liable to sample surface conditions when assessing key carrier dynamics such as photoelectron diffusion and decay. In this report, we present a pre-photoemission investigation of photoelectron transport and population decay in a GaAs/AlGaAs photocathode based on pump-probe transient reflectometry. By measuring the ultrafast behaviors of reflectivity after the excitation of a femtosecond optical pulse, we are able to evaluate the transient evolution of photoelectron population near the device surface, which provides a direct picture of electron diffusion and decay. A carrier diffusion model shows excellent agreement with experiment. Experiment-theory comparisons also suggests a diffusion coefficient much greater than some of the post-photoemission predictions. In addition, the free-electron population decay near the surface is found to be of bi-exponential nature. Possible physical mechanisms underlying these decay lifetimes are discussed.
Detecting deception in HRI using minimally-invasive and noninvasive techniques

Our work focuses on detecting deception in Human-Robot Interactions (HRI) by using measurement techniques that are appropriate for such interactions. In our previous research works, we obtained interesting results by using thermal and RGB-D cameras. In this paper, we approached this aspect from a different angle and used a lab-designed armband to accurately measure the participants’ heart rate and skin conductance. We also developed a deception card game scenario that entices human participants to lie either to a robot or a human game partner, allowing us to monitor and understand the correlations between human physiological manifestations and their trustworthiness. Our results show the existence of statistically significant correlations between the participants’ deceptive behaviour and their heart rate, skin conductance, face position, and face orientation. These results allow us to improve robots’ ability to detect deception in HRI.
Facial expression recognition based on global and local feature fusion with CNNs

The facial expression recognition is a hot research subject of computer vision, which causes a broad range of application in the domains of human-computer interaction, security and robotics. However, due to the influence of illumination and posture, real-time facial recognition is still a challenge. In this work, a facial expression recognition method based on the parallel convolutional neural network is proposed. Firstly, the face region is extracted from the pre-processed image by the face detector, in accordance with the detected feature points, the face image is cropped into three parts: eyes, noses and mouths. Then, Two CNNs were trained on the original and cropped data sets individually. Finally, the output of the two CNNs is gathered together to complete the classification of the expression. The modified AlexNet, VGGNet and ResNet were used to verify the method on the FER2013 data set. The recognition accuracy of the three models reached 66.672%, 69.407% and 70.744%, respectively. Experiments show that this method has a positive outcome on the recognition of expressions.
Multi-Channel MHLF: LSTM-FCN using MACD-Histogram with Multi-Channel Input for Time Series Classification

Time series classification is an important task for the identification of a person, weather, and motion, among others. In this study, the deep learning-based model is used for classification. In many research works, the deep learning-based time series classification has been reported to demonstrate a high performance. In particular, the LSTM-FCN model is a deep learning-based model, which shows the highest performance for time series classification. The proposed model is based on LSTM-FCN and its input consists of a multi-channel time series including the time series data and their MACD-histogram. In the experiments, the proposed model is evaluated using the defacto standard benchmark dataset, namely the UCR time series classification archive. The results show that the proposed model has higher performance than the existing models.
Temperature-Dependent Carrier Recombination and Efficiency Droop of AlGaN Deep Ultraviolet Light-Emitting Diodes

We investigate temperature-dependent carrier transfer and efficiency droop on AlGaN-based deep ultraviolet light-emitting diodes. The Shockley-Read-Hall (SRH) recombination and carrier leakage are highly associated with the poor thermal stability. The existence of Auger recombination and carrier leakage is identified by the m-power method. A modified ABC model with an additional term f(n) related to carrier leakage is employed to analyze the evolution of multiple recombination mechanisms. The SRH process strongly suppresses both Auger recombination and carrier leakage at low currents. At high currents, the latter two processes are responsible for the efficiency droop and exhibit an anti-correlation upon temperature.
Irreversibility , Sunk Costs , “ News ” and Evolutionary Economic Methodology

An enlarged conceptual framework for redefining sunk costs as state dependent evaluations is proposed by highlighting how unforeseen contingencies impact systemic adjustment processes through the interplay between existing and prospective irreversibilities. Associated microeconomic mechanisms define market entry and exit, and thereby capture key channels by which history conditions the evolution of economic systems and the interrelation between market and non-market decisions. A crucial distinction is made between ex ante sunk costs, which are contingent on agents’ initial information spaces, and ex post or endogenous sunk cost evaluations, following agents’ internalization of news and strategic interactions. Paradoxically, such redefined sunk costs can have quite divergent implications for the decisions of individual agents and for the evolution of economic systems, as a whole, through their critical role as buildingblocks, which define strategic interactions and adjustment processes. An examination of the relation between the hold-up and lock-in problems highlights the role of information revelation in defining the endogeneity of sunk cost evaluations. A subsequent extension of Owen and Ulph (2002) focuses on the systemic impact of an unanticipated integration shock, hence “pure news”, on endogenous market entry and exit in a framework of international oligopoly. A central identification issue in economic modeling is suggested, which applies to scenarios with market imperfections and strategic interdependence between agents, as shown by a unique correspondence between alternative trade regimes and configurations of different sunk and fixed costs. Asymmetries between the choice sets and optimization problems of agents are defined by existing and newly incurred endogenous sunk costs. A range of generic implications is suggested. These include a defining role for endogenous sunk costs in explaining theories of commitment, micro-foundations of learning processes and expectations formation, new branches in game-theoretic decision trees and the economics of time. JEL classification codes: B4, D8, F1, L1