Miracle-dz/UniFAR-FTUs huggingface dataset:
Inconstant Update of Reference Point Value for Parallel and Distributed MOEA/D

This paper proposes an update method to determine the reference point and nadir point when speeding up multi-objective evolutionary optimization algorithm based on decomposition (MOEA/D) with parallel and distributed processing in a many-core environment. We have already proposed a migration method that uses a virtual overlapping zone between partitions. Compared with simply partitioning the objective space or applying the standard island migration model, this method is shown to be effective in improving the hypervolume (HV) value, increasing diversity, and reducing regions where non-dominated solutions do not exist between partitions (sparse areas). However, this proposed method could not completely deal with the sparse areas’ problem in the early stage of solution searching. To resolve this problem, in addition to the virtual overlap method, we propose here a method that switches the update interval for determining the reference point and nadir point by taking into account the progress of solution searching. To evaluate our newly proposed method, we compare both convergence and diversity on single-core MOEA/D and parallel MOEA/D using a two-objective constrained knapsack problem, and show that our method is effective not only in reducing sparse areas in the early stage but also in improving diversity and increasing the HV value.
Forksheet FETs for Advanced CMOS Scaling: Forksheet-Nanosheet Co-Integration and Dual Work Function Metal Gates at 17nm N-P Space

We report on forksheet N- and PFETs co-integrated with gate-all-around nanosheet FETs. The forksheet short-channel control is on par with nanosheets down to 22nm gate length (SSSAT=66-68mV/dec). Forksheet ION and IOFF characteristics are improved by post-channel-release wet clean optimization, attributed to gate stack interface trap density reduction. Dual work function metal gates are integrated at 17nm N-P space, highlighting a key benefit of forksheets for CMOS area scaling.
Efficient Reduction of the Transmission Delay of the Authentication Based Elliptic Curve Cryptography in 6LoWPAN Wireless Sensor Networks in the Internet of Things

Wireless Sensor Network (WSN) is considered as the backbone of Internet of Things (IoT) networks. Authentication is the most important phase that guarantees secure access to such networks but it is more critical than that in traditional Internet because the communications are established between constrained devices that could not compute heavy cryptographic primitives. In this paper, we are studying with real experimentation the efficiency of HIP Diet EXchange header (HIP DEX) protocol over IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) in IoT. The adopted application layer protocol is Constrained Application Protocol (CoAP) and as a routing protocol, the Routing Protocol for Low power and lossy networks (RPL). The evaluation concerns the total End-to-End transmission delays during the authentication process between the communicating peers regarding the processing, propagation, and queuing times' overheads results. Most importantly, we propose an efficient handshake packets' compression header, and we detailed a comparison of the above evaluation's criteria before and after the proposed compression. Obtained results are very encouraging and reinforce the efficiency of HIP DEX in IoT networks during the handshake process of constrained nodes.
An Immune-Inspired Approach to Macro-Level Neural Ensemble Search

Recent years have seen a renewed interest in evolutionary computation applied to the automatic design of deep neural network architectures, i.e. Neural Architecture Search (NAS). The advantages of evolutionary approaches in NAS include their conceptual simplicity and their flexibility with regards to search space definition and/or optimization objective.However, Artificial Immune Systems (AIS) that follow the evolutionary computation paradigm are less explored in NAS. In this research, we aim to leverage their intrinsic and excellent ability to balance performance and population diversity to develop a novel Neural Ensemble Search method, based on the Clonal Selection Algorithm [1]. For more generality, we focus on designing macro-architectures rather than architectural components.Experiments on popular computer vision benchmarks demonstrate that our method reaches competitive accuracy and efficiency despite minimal augmentation and post-processing. We show that the AIS brings tangible benefits, including maintaining the diversity of solutions, a semantically straightforward implementation, and high efficiency. Moreover, this AIS can exhibit a "secondary response": when presented with a related but more difficult task, the ensemble will perform competently with zero modification to the architectures or the training protocol.
A CMOS Low-Power Variable-Gain LNA Based on Triple Cascoded Common-Source Amplifiers and Forward-Body-Bias Technology

This paper presents a 15.8-30 GHz CMOS variable-gain LNA based on triple cascoded common-source (CS) amplifiers and forward-body-bias technology. The forward body bias (FBB) is utilized to effectively reduce the threshold voltage of the MOS transistor, and the minimum supply voltage for a MOSFET operating in the saturation region can be relaxed accordingly. Based on above strategy, a triple cascaded CS topology with low power is proposed with a 1.2 V standard power supply in 65-nm CMOS. In addition, the gain of the proposed topology can be adjusted via the body biasing, resulting in a variable-gain LNA successfully. For demonstration, a prototype of 15.8-30GHz variable-gain LNA is designed and fabricated in 65-nm CMOS technology with 0.3 mm2 chip size. The prototype achieves a minimum noise figure of 4.7 dB, a maximum gain of 11.4 dB with 16.4 dB gain tuning range, while consuming only 2.16 mW from a 1.2 V supply.
Mechanical Property Evolution in SAC+Bi Lead Free Solders Subjected to Various Thermal Exposures

Lead free solder materials are widely used as interconnects in electronic assemblies. They are often subjected to different thermal exposures such as isothermal aging and/or thermal cycling during their service life in various applications such as well drilling, automotive power electronics, aerospace engines, and geothermal energy. Therefore, the evolutions of mechanical properties of lead-free solder materials subjected to different thermal exposure profiles are of great importance and need to be investigated.In our previous work, mechanical behavior evolutions of SAC305 and SAC+3%Bi lead free solder materials were reported under different thermal exposure profiles for up to 5 days of exposure. In the present work, we have extended this work to investigate the evolution of additional SAC+Bi lead free solder alloys for up to 20 days of thermal exposure. In particular, SAC+Bi alloys with 1%, and 2% of Bi have been studied. Several thermal exposure profiles between T = -40°C and T = 125°C were investigated: (1) 150 minutes cycles with 45 minute ramps and 30 minute dwells (slow thermal cycling), (2) air-to-air thermal shock exposures with 30 minute dwells and near instantaneous ramps (thermal shock), (3) 90 minute cycles with 45 minute ramps and 0 minute dwells (thermal ramping), and (4) isothermal aging at the high temperature extreme (aging). Stress-strain tests were performed on the uniaxial test specimens to investigate the change in mechanical properties which includes effective elastic modulus (E) and ultimate tensile strength (UTS). The test results were compared with each other in order to understand the effect of different thermal exposure profiles on the mechanical properties of SAC+Bi solder materials. It was observed that the more the exposure time per cycle, the degradations in mechanical properties are higher. The degradation was greatest for the thermal cycling with the longest ramp period (slow thermal cycling). After 20 days of isothermal aging, the UTS and elastic modulus were reduced by 10%, and 30%, respectively for SAC+2%Bi; whereas in case of slow thermal cycling, these properties were reduced by 12%, and 35%, respectively. Also, the degradation of mechanical properties during thermal shock, and slow thermal ramping was higher than pure aging after 20 days.
MTL-LSTM: Multi-Task Learning-based LSTM for Urban Traffic Flow Forecasting

Predicting traffic flow in large cities is beneficial for a wide range of applications, including vehicle navigation services, vehicle routing, and traffic congestion management. In this scenario, deep learning approaches such as Recurrent Neural Networks (RNN) and its variant Long Short Term Memory (LSTM) are excellent alternatives due to their ability to learn long-term dependencies. However, these neural networks only learn the temporal traffic information for each trajectory (moving object), failing to take advantage of spatial information shared by neighboring trajectories. This paper introduces MTL-LSTM (Multi-Task Learning-based LSTM) traffic flow estimator, which attempts to explore both temporal and spatial dependencies among adjacent trajectories. Specifically, we employ LSTM predictors with the MTL approach to explore traffic flow patterns across urban trajectories. To examine the proposed model, we predict traffic flow in Porto's city using a data set from buses and taxies. Our experiments show improvements of 10% to 15% over the state-of-the-art.
Towards Real-Time Detection of Auditory Steady-State Responses: A Comparative Study

To objectively evaluate weak neural responses such as auditory steady-state responses (ASSRs) often involves repetitive measurements, after which the accumulated trials are averaged or analyzed by statistical methods to detect a significant response. Such detection methods are often performed off-line on all measured trials. However, the number of required trials, and therefore the measurement time, is often heuristically chosen, and might be suboptimal. In this work, we compared three real-time signal-detection algorithms that could yield improved detection performance, at reduced measurement time. The classical Neyman-Pearson (NP) detector was evaluated by quantifying the signal-to-noise ratio (SNR), and detection probability of ASSRs, as a function of accumulated trial number. We also analyzed the performance of the Bayes factor (BF) to detect ASSRs at different thresholds. Finally, we modified the sequential probability ratio test (SPRT), with a cropped maximum likelihood (ML) estimator, such that it can detect ASSRs (with unknown SNRs) sequentially. We compared the three real-time detectors by using Monte Carlo simulation, and evaluated their performance on detecting ASSRs, generated from the superposition of a pair of amplitude-modulated (AM) tones near 40 Hz in the EEG of nine subjects with normal hearing. The low-order ASSRs (i.e., envelope frequency-following responses) have been sufficiently evaluated in the literature. However, the higher-order ASSRs might reflect further nonlinear mechanisms in the upper ascending auditory pathway. Results show that the real-time detectors can detect not only all low-order ASSRs but also higher-order ASSRs at frequencies with lower SNR. The NP detector yielded the best simulation and actual detection performance. For all subjects, the second-order ASSRs could already be detected with the NP and BF detectors within five trials, but more trials were needed for the modified SPRT detector. In general, higher-order ASSRs require more trials to detect, with the exception of ASSRs near 40 Hz and 80 Hz. In conclusion, compared with traditional off-line detectors, both (real-time) NP and BF detectors showed improved detection performance, and their application in EEG experiments can save valuable measurement time.
Gain-Boosted Super Class AB OTAs Based on Nested Local Feedback

A new approach to design super class AB operational transconductance amplifiers (OTAs) with enhanced large-signal and small-signal performance is presented. It is based on employing two nested positive and negative feedback loops at the active load of an adaptively biased differential pair in weak inversion region. As a result, DC gain, gain-bandwidth product, settling time and noise are improved compared to conventional super class AB OTAs without extra circuit nodes or power consumption. Measurement results of a 180 nm CMOS test chip prototype show a current boosting factor higher than 5000 and a nearly ideal current efficiency. Due to the ultra-low quiescent currents and high driving capability, the circuit exhibits an excellent large-signal figure-of-merit (FOML) of 236 $\text{V}^{-1}$ . To illustrate the applicability of the proposed approach, a differential sample-and-hold (S/H) circuit was designed and fabricated on the same test chip. Measurement results of the S/H validate the advantages of the proposal.
A Compact 26.5–29.5-GHz LNA-Phase-Shifter Combo With 360° Continuous Phase Tuning Based on All-Pass Networks for Millimeter-Wave 5G

This article presents a compact integrated receiver front-end for phased-arrays at 5G n257/n261 band (26.5–29.5 GHz) employing both a low-noise amplifier (LNA) and a novel passive phase-shifter based on variable-phase all-pass networks. The proposed phase-shifter topology is analysed and designed using $0.25~\mu \text{m}$ BiCMOS achieving continuous tuning capabilities with 360° phase range over a 25–33 GHz frequency range under 0.1 mm2 of area. The proposed phase-shifter is measured standalone, achieving 8.3 ± 0.55 dB losses at 29 GHz with an RMS gain error below 0.6 dB across the entire frequency range. When quantizing the continuous phase-shift into 6 bits, an RMS phase error < 3° is obtained. To build the LNA-Phase-shifter combo, the phase-shifter is then integrated with an LNA optimized for compactness, which was measured standalone, showing a noise figure below 3.2 dB across the n257/n261 band and 20 dB gain under 0.0625 mm2 of area. The small size of both the phase-shifter and LNA enables an ultra-compact phased-array receiver front-end under 0.2 mm2 of area. The front-end is measured, achieving 11.5 ± 0.53 dB of gain, RMS gain error below 0.42 dB and RMS phase error below 3.2° over the entire n257 band, measured input-referred P1dB of −25 dBm and a 15 mW DC power consumption.
Mars Image Super-Resolution Based on Generative Adversarial Network

High-resolution (HR) Mars images have great significance for studying the land-form features of Mars and analyzing the climate on Mars. Nowadays, the mainstream image super-resolution methods are based on deep learning or CNNs, which are better than traditional methods. However, these deep learning based methods obtain low-resolution(LR) images usually by using an ideal down-sampling method (e.g. bicubic interpolation). There are two limitations in the existing SR methods: 1) The paired LR-HR data by using such methods can achieve a satisfactory results when tested on an ideal datasets. But, these methods always fail in real Mars image super-resolution, since real Mars images rarely obey an ideal down-sampling rule. 2) The LR images obtained by ideal down-sampling methods have no noise while real Mars images usually have noise, which leads to the super-resolved images are not realistic in texture details. To solve the above-mentioned problems, in this article, we propose a novel two-step framework for Mars image super-resolution. Specifically, to address limitation 1), we focus on designing a new degradation framework by estimating blur-kernels. To address limitation 2), a Generative Adversarial Network (GAN) is trained to generate noise distribution. Extensive experiments on the Mars32k dataset demonstrate the effectiveness of the proposed method, and we achieve better qualitative and quantitative results compared to other SOTA methods.
Multi-Feature Tensor Neighborhood Preserving Embedding for 3D Facial Expression Recognition

To investigate an effective representation model for 3D facial expression recognition, this paper proposes a multi-feature tensor neighborhood preserving embedding (MFTNPE) method, which seeks various attribute features from raw textured shape scan models of 3D facial expression to construct a multi-feature tensor model. With MFTNPE method, we can project the model into a tensor subspace, explore the interactions and correlations among multimodal data and then extract discriminative low-dimensional tensor features for 3D facial expression recognition. In the study, we first introduce a tensor model based on multi-feature integration for 3D facial expression recognition. Via exploring the textural and geometric features of 3D facial expression data, an original multi-feature tensor model is constructed by stacking various attribute maps orderly. Second, we utilize the tensor completion approach as the scheme of data preprocessing to correct the original multi-feature tensor model. Due to the presence of imperfect data in the projected attribute maps, the tensor completion can predict the missing data in tensor by the trace norm of multi-feature tensor model along each mode. Third, we redefine a weighted matrix for use in the orthogonal-based tensor neighborhood preserving embedding algorithm. In the weighted matrix, the class information is used to guarantee the correct neighborhood relationships for multi-feature tensor samples. This effectively avoids the disturbances and confusions between similar samples while preserving spatial structure and inter-class information. Finally, we conduct extensive experiments on two 3D facial expression recognition datasets. The experimental results show that the proposed method has much better efficiency and performance compared with the state-of-the-art methods.
A Survey about Gender Diversity in a More Gender-Balanced IT Firm

We present a case study of Truewind, a mid-size IT consulting company whose distribution of employees is significantly more gender-balanced than the norm. Whereas worldwide employment of women in the ICT sector hardly reaches 20%, women make up almost one third of the developers of the main office of this company. We conducted a case study to gain an understanding of which factors are perceived to contribute to such a gender distribution, and how its employees see it as an advantage. A survey revealed that, independently of gender, employees value gender diversity, pointing out advantages on teamwork, organizational climate, and critical thinking. Further, we collected factors that contribute to the retention of women, which in great part refer to organizational climate and work conditions. Up to a quarter of the IT consultants of Truewind have majored in non-ICT fields, changing their career path mostly from other STEM disciplines.
Neuromorphic Implementation of a Continuous Attractor Neural Network With Various Synaptic Dynamics

A continuous attractor neural network (CANN) configured with various synaptic dynamics can implement many brain cognitive functions. Corresponding neuromorphic implementation can be beneficial to carry out these cognitive computations in real-time. Herein, we present a neuromorphic implementation of the CANN of spiking neurons on the field-programmable gate array (FPGA), in which the synaptic strength of local excitation and global inhibition can be configured to achieve winner-take-all (WTA) competition. In contrast to previously reported neuromorphic CANN, in addition to fast-linear synapses, recurrent connections can also be implemented with slow-nonlinear synapses. This endows the proposed CANN on the FPGA with the capability of performing neural computations that require slow and nonlinear temporal dynamics, such as decision making and working memory. Circuit simulations on the hardware reproduced gradually ramping neural activities observed in decision-making experiments and multiple sustained activities observed during the delay in working memory experiments, especially the fade-out and merging of activity bumps in the latter task. Theoretical analysis illuminated the underlying mechanisms of decision making and working memory on the FPGA. The unstable two-bump steady state of the CANN is responsible for the two-alternative decision making, similar to the saddle-node structure in the reduced two-variable decision model. Meanwhile, the stable uniformly distributed N-bump steady states account for the multiple-item working memory, similar to the multistability in previous mean-field models. Furthermore, the asymmetrical N-bump unstable steady states exhibit attractor dynamics underlying the fade-out and merging of activity bumps. The consistency between simulations on the hardware and the theoretical analysis demonstrated that the neuromorphic CANN is endowed with attractor dynamics relating to slow and nonlinear neural computations and has the potential to implement sophisticated cognitive functions by configuring various synaptic dynamics. This implementation shows promise for integrating the cognitive platform due to its characteristics of various dynamics and flexible configurations.
A Review and Experimental Comparison of Multivariate Decision Trees

Decision trees are popular as stand-alone classifiers or as base learners in ensemble classifiers. Mostly, this is due to decision trees having the advantage of being easy to explain. To improve the classification performance of decision trees, some authors have used Multivariate Decision Trees (MDTs), which allow combinations of features when splitting a node. While there is growing interest in the area, recent research in MDTs all have in common that they do not provide adequate comparison of related work: they do not consider relevant rival techniques, or they test algorithm performance in an insufficient number of databases. As a result, claims have no statistical sustain and, hence, there is a lack of general understanding of the actual capabilities of existing MDT induction algorithms, crucial to improving the state-of-the-art. In this paper, we report on an exhaustive review of MDTs. In particular, we give an overview of 37 MDT induction algorithms, out of which we have experimentally compared 19 of them in 57 databases. We provide a statistical comparison in all databases and subsets of databases according to the number of classes, number of features, number of instances, and degree of class imbalance. This allows us to identify groups of top-performing algorithms for different types of databases.
Preliminary study on the boiling heat transfer performance of R-123 refrigerant with nano-porous aluminum oxide membrane

The boiling heat transfer shows the most efficient heat removal capability due to the large latent heat of vaporization. Various research works have been conducted to enhance boiling heat transfer performance. However, the reported maximum critical heat flux enhancement of boiling heat transfer is far below the required heat removal density for the high heat flux management. Recently, the novel concept of the boiling regime called thin-film boiling is proposed with the unprecedently high critical heat flux enhancement. Nevertheless, the underlying physical mechanism of this unprecedented critical heat flux enhancement is still ambiguous. The objective of the current study is the visualization of the liquid-thin film boiling to elucidate the underlying physical mechanism of critical heat flux enhancement principal of the thin film boiling. The fabrication, characterization, and experimental verification of the Pt deposited nano-porous heater surfaces for the phase change heat transfer experiment was made to build a basis for the thin-film boiling visualization experiment.