Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Latent Factor Models for Undirected and Sparse Networks with Large Scales

Nonnegative latent factor (NLF) models are highly efficient in extracting useful information from undirected, high-dimension, nonnegative and sparse (SHiDS) networks, which are commonly encountered in various industrial applications. The conventional double factorization (DF)-based matrix factorization (MF) technique contributes to obtaining LFs from SHiDs networks, whereas might lead to a fairly low prediction accuracy due to the limitation of the number of variables in NLF matrix. In order to tackle this problem, a novel NLF model via the trip factorization (TF)-based matrix MF technique is proposed, which is definitely equipped with 1) fulfilling the constrains on the symmetry; 2) a higher prediction accuracy; and 3) the nonnegativity of the NLF matrix. Experimental results on two data sets from real industrial applications demonstrate that the proposed TF-based NLF model has a better capability of guaranteeing the desirable performance at a bit expense of computational efficiency.
Rumor transmission on homogeneous networks with both oblivion-recall and loss-interest mechanisms

In this paper, the reality of people’s forgetting and losing interest in the process of rumor spreading are considered. We introduce both the oblivion-recall mechanism and the loss-interest mechanism to propose a novel susceptible-infected-removed (SIR) model. In the present SIR model, we regard the forgetting as an independent state type of the population, and use the forgetting factor and the recall factor to characterize the oblivion-recall mechanism. The relationship between these two factors and the propagation critical threshold is investigated by means of stable state analysis. It shows that there is a linear proportional relationship between the forgetting factor and the propagation critical threshold. So it can be concluded that the existence of oblivion-recall mechanism reduces the outbreak probability of rumors.
A Vision-Assisted Semi-Automatic Uterus Manipulation Approach Based on a Pose Estimating Trocar

In this paper, we propose a new vision-assisted semi-automatic uterus manipulation approach based on a pose estimating trocar. In this approach, a trocar integrated with an inertial measurement unit and a range sensor is used to estimate the position of the tip of the laparoscopic tool with respected to the trocar’s base frame and a robot which grasps a uterine manipulator is used to perform uterus manipulation. Position of the tip of the uterine manipulator with respected to the robot’s base frame can be obtained by forward kinematics. The relationship between the trocar’s and the robot’s base frames are estimated with a vision based approach by considering the laparoscopic camera’s extrinsic parameters. By this, the relative position between the tips of the uterine manipulator and the laparoscopic tool can thus be estimated. Automatic uterus manipulation which aims to maximize the distance between the fundus of the uterus and the laparoscopic tool tip can then be achieved. Experiments are conducted to verify the feasibility of the proposed approach.
A Novel Design of Cable-Driven Neck Rehabilitation Robot (CarNeck)

Dropped Head Syndrome (DHS) is usually found in patients with neurological or neuromuscular disorders, such as amyotrophic lateral sclerosis. In this condition, patients experience weakness and pain in the neck muscles and do not have enough power to lift or mobilize their head. This situation causes severe challenges for the patients in breathing, swallowing, and talking. Static neck braces are commonly prescribed to the patients to support their head, but they restrict the human head in a single configuration, and progressively cause the neck muscles to atrophy. In order to deal with this problem, this paper introduces a novel design of dynamic neck rehabilitation robot, which assists DHS’s patients to achieve the complete natural range of head rotation, while ensuring their safety. The proposed robot is driven by cables, and it is the first time a cable transmission is used for neck rehabilitation robot. The proposed cable driven neck rehabilitation robot (CarNeck) offers three degrees of freedom (DOF), each of which is controlled by separate 2N type cable driven joint module. Kinematics and statics analysis of CarNeck are also presented in this paper.
Analysis of a Two-Degree-of-Freedom Piezoelectric Energy Harvester from Vortex-Induced Vibrations

This paper presents a 2-DOF piezoelectric energy harvester for vortex-induced vibrations. This 2-DOF structure is composed of an outer beam, an inner beam, a cylinder and tip masses attached to the beams. The inner beam is attached to the free end of the outer beam to make the first two resonant frequencies of the structure close. This 2-DOF harvester is meant to improve the harvesting range and scavenge wind energy even when the wind speed is beyond the lock-in range. A prototype is fabricated and experimentally tested in a wind tunnel. A 1-DOF piezoelectric energy harvester is also built for comparison. The experimental results show that the 2-DOF design achieves higher voltage than the 1-DOF counterpart at both low speed and high speed. A parametric study of the tip masses is also conducted to understand their impact on the harvesting voltage.
Regularized Nonnegative Matrix Factorization with Real Data for Hyperspectral Unmixing

Nonnegative matrix factorization has been applied in hyperspectral unmixing, while the accuracy of unmixing is closely related with the local minimizers. In this paper, we present a new regularized cost function of nonnegative matrix factorization by fully considering the real data information of the endmember signature. The endmember signatures can be easily found in the United States Geological Survey spectral library. The multiplicative update rules are employed to obtain the factor matrices, because it is easy to implement and often yields good results. We demonstrate the success of regularized nonnegative matrix factorization by applying it on hyperspectral unmixing.
Identifying Worldwide Interests in Organic Foods by Google Search Engine Data

Global interests in organic foods are of importance to researchers and the food industry. Traditional questionnaire-based methods do not provide a broad picture. To meet this need, worldwide interests in organic foods were studied by integrating query data from the Google search engine and deep learning methods. The results show that organic oil, organic milk, organic chicken, and organic apples are the most interested organic foods; people from Singapore, US, New Zealand, Australia, United Kingdom and Canada care about organic foods the most; consumers’ interest in organic foods has no correlation with GDP and life expectancy but has significant correlations with other dimensions of culture such as individualism, uncertainty avoidance, and long-term orientation. A recurrent neural network (RNN) model structure is useful in predicting people’s interests in major organic foods over time.
Piezoelectric Mems Microspeaker with Suspension Springs and Dual Electrode to Enhance Sound Pressure Level

This study based on piezoelectric actuating principle to design and realize a high sound pressure level (SPL) MEMS microspeaker for in-ear applications. The proposed suspension-spring design enables larger out-of-plane displacement to improve SPL and the piston movement of diaphragm to generate sound waves with better quality. Moreover, applying dual electrode pattern on the suspension springs further enhances diaphragm out-of-plane displacement and SPL. Measurements demonstrate the highest SPL above 100dB at 3.13kHz in a 3cm long canal. SPL higher than 70dB is obtained from 400Hz to 8.5kHz with only 2Vpp driving voltage and 1.67mm2 diaphragm. Furthermore, total harmonic distortion (THD) is less than 3% in most of the audio range.
Efficient Coupled-mode space based Non-Equilibrium Green’s Function Approach for Modeling Quantum Transport and Variability in Vertically Stacked SiNW FETs

In this paper we present state of the art coupled-mode space based Non-Equilibrium Green Function approach for modeling quantum transport accurately in the vertically stacked Silicon nanowire (SiNW) FETs. Random discrete dopants (RDD) and metal grain granularity (MGG) induced variability in stacked SiNW FETs are also investigated. Furthermore, charge spectrum, current spectrum w.r. t. sub bands and the space-resolved Local Density of States (LDOS) corresponding to the location of band edge are analyzed in detail. The newly developed flexible and computationally efficient models implemented in quantum transport simulation tool NESS provides valuable insights on the effect of RDD and MGG variability on Sub-Threshold Swing (SS), Threshold Voltage $( \mathrm {V}_{TH})$ shift, On/Off Current $( \mathrm {I}_{ON}/ \mathrm {I}_{OFF})$ ratio and quantum confined charge transport mechanism.
Linear Stages for Next Generation Precision Motion Systems

This paper presents the design, fabrication, and testing of two linear stages for next generation precision mechatronic motion systems such as in semiconductor photolithography machines. We have designed a new linear iron-core permanent-magnet motor that can simultaneously achieve high force and low noise, providing a promising candidate for actuating systems for high-throughput and high-precision servo applications. The vibro-acoustic noise mechanism of linear iron-core synchronous motors and a new magnetic design approach to mitigate such noise issue are presented in this paper, along with the experimental validation of significant higher-force and lower-noise performance as compared to a conventional linear iron-core motor. In this paper, we also present another type of novel linear stage for in-vacuum transportation tasks in precision manufacturing systems. This new linear stage system is driven by a hysteresis motor and magnetically-levitated with two-degrees-of-freedom (DOF) active bearing controls and three-DOF passive magnetic bearings. This paper discusses the design, operating principles, and experimental results of this magnetically levitated linear motor system.
Application of Logistic Regression with Filter in Data Classification

Logistic regression is the main method to deal with data classification in the field of large data and machine learning. The traditional logistic regression uses gradient descent method to solve the optimal parameters of loss function with convexity to a certain extent. In the case of non-convex loss function, the filter method can replace the traditional penalty function method to ensure the global convergence of the optimization algorithm. The filter method does not need to consider the selection of parameters, thus avoiding the shortcomings of the penalty function method. The main idea of the filter method is to accept the trial point when the objective function or constraint violation degree is improved. In this paper, filter theory in the field of operations research and cybernetics is used to solve the optimal parameters of loss function, which overcomes the limitation of gradient descent method on convexity requirement of loss function. In this paper, we use logistic regression with filters to deal with the problem of actual data classification, and verify the effectiveness of the algorithm.
Estimating Biological Gender from Panoramic Dental X-Ray Images

Identifying the gender of a person is one of the fundamental tasks in forensic medicine. One possible application is right after a catastrophic event such as a mass disaster with a high victim count. In such cases it is necessary to identify the people involved which can require a high number of forensic experts, depending on the scale of the event. With panoramic dental x-ray images the biological gender of a person can be estimated by analyzing skeletal structures that express sexual dimorphism. Current methods require the manual measurement of a wide array of mandibular parameters which are then manually compared to references based on these measurements and assumed ethnicity of the people involved. We propose an automated solution based on deep learning techniques using convolutional neural networks. Our data consists of 4000 panoramic dental x-ray images of patients with European origin, with the images being taken by a wide range of orthopantomographs. Our automated method can estimate 64 images per second on contemporary hardware, it doesn't require human intervention for estimation and it achieves state-of-the-art results with an accuracy of 96.87% ± 0.96%.
Convex Relaxation with Log-Determinant Divergence-L1 Regularization for 3D Shape Reconstruction

Investigated is the problem of estimating the 3D shape of an object defined by a set of 3D landmarks with their 2D correspondences in a single image. To solve this problem, we use a dictionary of the basic shape with LDD-LI regularization, which is the construction of the shape space model. Based on the proposed convex optimization method, 3D human pose reconstruction by shape space model and 3D variable shape model was carried out on the mocap database. To improve accuracy and reduce the number of iterations, we use PSO algorithm to optimize initial value of the key parameter. The experimental results show that the improved algorithm exhibits less iterations but higher accuracy, which can be much helpful in practical applications.
A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

Automated visual inspection in the semiconductor industry aims to detect and classify manufacturing defects utilizing modern image processing techniques. While an earliest possible detection of defect patterns allows quality control and automation of manufacturing chains, manufacturers benefit from an increased yield and reduced manufacturing costs. Since classical image processing systems are limited in their ability to detect novel defect patterns, and machine learning approaches often involve a tremendous amount of computational effort, this contribution introduces a novel deep neural network-based hybrid approach. Unlike classical deep neural networks, a multi-stage system allows the detection and classification of the finest structures in pixel size within high-resolution imagery. Consisting of stacked hybrid convolutional neural networks (SH-CNN) and inspired by current approaches of visual attention, the realized system draws the focus over the level of detail from its structures to more task-relevant areas of interest. The results of our test environment show that the SH-CNN outperforms current approaches of learning-based automated visual inspection, whereas a distinction depending on the level of detail enables the elimination of defect patterns in earlier stages of the manufacturing process.
Pumpless Solution Exchange for Repeatable Nanopore Biosensor Driven by Superabsorbent Polymer and Hydrostatic Pressure

This paper reports a compact pumpless analyte exchange of a droplet based lipid bilayer. This system allows the repeatable detection. The flow in the system is caused by superabsorbent polymer and hydrostatic pressure, which realize a simple exchange system without power source. The retention time to replace the analyte could be controlled by flow rate. We succeeded in detecting nanopore signal formed by $\alpha$ -hemolysin and observing the decreasing of blockage signal from cyclodextrin. This data indicates that the concentration of cyclodextrin in a droplet based lipid bilayer decreased. This exchange system working without external power supply is significantly simple and compact comparing with the conventional method that required two pumps.
Factors predicting inefficacy of lithium in patients with Bipolar I Disorder

Introduction: The aim of this study was to investigate whether factors, that are related to a more severe course of bipolar disorder, will predict the efficacy of lithium in BPI patients. For this purpose it was examined whether earlier onset, comorbid anxiety, comorbid alcohol dependence, comorbid substance dependence and early extreme adversity predict lithium response in a large sample of BPI patients. Method: Based on a questionnaire about lithium response, 446 lithium users were selected and groups were created: A group that used lithium at the time of inclusion and was (very) satisfied with the performance, versus a group that once used lithium but discontinued the treatment because of inefficacy. Furthermore an additional group was created which contained all patients who discontinued lithium treatment due to reasons other than inefficacy, including motivation and negative side effects. Results: As expected, the current study found an association between lifetime alcohol dependence and bad lithium response, alcohol dependence was associated with the discontinuation of lithium treatment due to motivational reasons and due to inefficiency. However, none of the other factors of the severity profile i.e. earlier onset, comorbid anxiety, comorbid substance dependence and early extreme adversity were related to bad lithium response. Discussion: Apart from alcohol dependence, the severity profile appeared unusable for drafting a predictive model for discontinuation of lithium treatment. This makes further research based on other profiles necessary. However, the results may reflect an influence of motivation on lithium treatment in BPI patients with comorbid alcohol dependence. Therefore, strictly monitoring motivation for treatment and enhancing compliance within this group should be an essential component in treatment with lithium.
Calculating the Complex Permittivity of Powdered Crystalline Materials

Solid-state density functional theory calculations including periodic boundary conditions have become well established for calculating the THz spectra of crystalline materials. Here we compare a range of DFT programs and calculation parameters including a number of van der Waals’ dispersive corrections in combination with our post-processing tool, PDielec, to calculate the complex permittivity of a range of powdered crystalline materials.
A Cost-Efficient Iterative Truncated Logarithmic Multiplication for Convolutional Neural Networks

This paper proposes a cost-efficient approximate logarithmic multiplication for convolutional neural networks (CNNs), where two truncated logarithmic multipliers are connected for error correction. The proposed iterative logarithmic multiplication achieves low and unbiased average error while the hardware cost is significantly reduced by utilizing the truncated Mitchell multiplier and approximating error terms from the first stage. The proposed design has error characteristics that are suitable for neural network inferences, and the experiments on contemporary CNNs show that the proposed multiplier does not cause significant degradation on accuracy compared to exact multiplication.
Requirement Analysis for Cybersecurity Solutions in Industry 4.0 Platforms

Increasing technology and the use of the Internet have brought the Industry 4.0 revolution. Internet of Things, smart devices, smart objects, information, and data are in many systems in an era of developing the technology. The main components of the industry 4.0 such as cyber-physical system, the Internet of objects, big data and cloud computing has some of the challenges. One of these is cybersecurity. In this study, the structure of the technologies in industry 4.0 and cybersecurity requirement analyzes of these technologies were discussed. With the given recommendations, it is expected to use these technologies in Industry 4.0 safely. Finally, the necessary measures against cyber-attacks and risks in developing technologies and the use of security systems were discussed.
A 1-GS/s 8-Bit 12.01-fJ/conv.-step Two-Step SAR ADC in 28-nm FDSOI Technology

This letter presents a partially interleaved 1-GS/s 8-bit two-step SAR ADC for low-power operations. A fast noise-reduction technique is proposed to increase the power efficiency without significant degradation of the conversion rate. A modified StrongARM latch is adopted to further reduce the comparator noise. A calibration procedure runs in the background to address the nonuniform comparator offsets and the interstage gain error. Fabricated in a 28-nm FDSOI process, the prototype ADC achieves an SNDR of 46.65 dB at Nyquist with a power consumption of 2.1 mW, leading into a Walden FOM of 12.01 fJ/conv. -step.
Design and Characterization of a CMOS MEMS Capacitive Squeeze-Film Pressure Sensor

A capacitive pressure sensor operated based on the elastic squeeze-film damping effect is implemented, for the first time, in a CMOS process to provide sensor integration and sensitivity improvement. Sensor fabrication is simplified as a squeeze-film pressure sensor does not require a sealed membrane as in conventional sensors; in addition, sensitivity is significantly enhanced with the small gap achieved between capacitive plates. The sensor with a resonant plate size of $200\times 200\ \mu \mathrm{m}^{2}$ demonstrates a sensitivity of 793 Hz/kPa.
The Conical-Fishbone Clock Tree: A Clock-Distribution Network for a Heterogeneous Chip Multiprocessor AI Chiplet

In this paper we present a clock tree network that is inspired by the shape of an inverted cone. Conical sections are created in the inverted cone, and each of these resulting rings is considered to be one of the many nets in a Fishbone clock tree. When a ring is excited at uniform intervals from the ring below, the symmetry in the circular characteristics of the wire will make the effect of reflections be exactly the same along any place in the wire. The proposed clock tree named Conical-Fishbone clock tree allows ultra-low clock skew while offering the modularity to support a hierarchical design using standard CAD flows. The Conical-Fishbone tree network is employed in the L1- NOC, the L2-NOC and the DDR DRAM PHY of the core (17.47 mm X 14.13 mm) “chiplet” in the 2.5D nano-Abacus SOC currently being fabricated in the Global Foundries 55nm CMOS technology.
Emotional State Classification using Pulse Rate Variability

Humans are continually exposed to emotional stimuli. Gesture, voice intonation, and facial expressions are among the most popular cues that describe our changing emotions. However, the physiological systems that govern our bodily functions are also impacted by the different emotions that we feel. Psychophysiology is a science concerned with the existing relationship between the psychic state of a person and the physiological signals their body emits. In fact, this connection is due to the autonomic nervous system activity, whose sympathetic nerves get sparked when a person is emotionally excited. One particular physiological phenomenon has turned out to be an excellent indicator of the autonomic function. It is the spontaneous fluctuations in the heart rhythms, which can be described by the pulse rate variability (PRV). In this work, the PRV is obtained using remote photoplethysmography. We prove that from a simple RGB camera, it is possible to assess the emotional state of a person by analysing their pulse rate variations. This optimistic finding is supported by surprising results and an accuracy rate of around 60% on the CAS(ME)2 dataset. This is the first study to propose an emotion classification based on a physiological signal analysis using CAS(ME)2.