Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic Lateral Inhibition Facilitates Winner-Take-All in Domain Wall Racetrack Arrays for Neuromorphic Computing

Neuromorphic computing is a promising candidate for beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. Lateral inhibition and winner-take-all (WTA) features play a crucial role in neuronal competition of the nervous system as well as neuromorphic hardwares. The domain wall - magnetic tunnel junction (DWMTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic lateral inhibition. In this paper we show that lateral inhibition parameters modulate the neuron firing statistics in a DW-MTJ neuron array, thus emulating soft-winner-take-all (WTA) and firing group selection.
Performance Loss Rate Estimation of Fielded Photovoltaic Systems Based on Statistical Change-Point Techniques

The precise estimation of performance loss rate (PLR) of photovoltaic (PV) systems is vital for reducing investment risks and increasing the bankability of the technology. Until recently, the PLR of fielded PV systems was mainly estimated through the extraction of a linear trend from a time series of performance indicators. However, operating PV systems exhibit failures and performance losses that cause variability in the performance and may bias the PLR results obtained from linear trend techniques. Change-point (CP) methods were thus introduced to identify nonlinear trend changes and behaviour. The aim of this work is to perform a comparative analysis among different CP techniques for estimating the annual PLR of eleven grid-connected PV systems installed in Cyprus. Outdoor field measurements over an 8-year period (June 2006-June 2014) were used for the analysis. The obtained results when applying different CP algorithms to the performance ratio time series (aggregated into monthly blocks) demonstrated that the extracted trend may not always be linear but sometimes can exhibit nonlinearities. The application of different CP methods resulted to PLR values that differ by up to 0.85% per year (for the same number of CPs/segments).
Tracing back the evolution of the candidate LBV HD 168625

Context. The luminous blue variable phase is a crucial transitory phase that is not clearly understood in the massive star evolution. Aims. We have obtained far-infrared Herschel /PACS imaging and spectroscopic observations of the nebula surrounding the candidate LBV HD 168625. By combining these data with optical spectra of the central star, we want to constrain the abundances in the nebula and in the star and compare them to trace back the evolution of this object. Methods. We use the CMFGEN atmosphere code to determine the fundamental parameters and the CNO abundances of the central star whilst the abundances of the nebula are derived from the emission lines present in the Herschel /PACS spectrum. Results. The far-infrared images show a nebula composed of an elliptical ring/torus of ejecta with a ESE-WNW axis and of a second perpendicular bipolar structure composed of empty caps/rings. We detect equatorial shells composed of dust and ionized material with different sizes when observed at different wavelengths, and bipolar caps more of less separated from the central star in H α and mid-IR images. This complex global structure seems to show two different inclinations: ~40° for the equatorial torus and ~ 60° for the bipolar ejections. From the Herschel /PACS spectrum, we determine nebular abundances of N/H = 4.1 ± 0.8 × 10 -4 and C/H ~ 1.6 -0.35 +1.16 × 10 -4 , as well as a mass of ionized gas of 0.17 ± 0.04 M ⊙ and a neutral hydrogen mass of about 1.0 ± 0.3 M ⊙ which dominates. Analysis of the central star reveals T eff = 14 000 ± 2000 K, log g = 1.74 ± 0.05 and log ( L / L ⊙ ) = 5.58 ± 0.11. We derive stellar CNO abundances of about N/H = 5.0 ± 1.5 × 10 -4 , C/H = 1.4 ± 0.5 × 10 -4 and O/H = 3.5 ± 1.0 × 10 -4 , not significantly different from nebular abundances. All these measurements taken together are compatible with the evolutionary tracks of a star with an initial mass between 28 and 33 M ⊙ and with a critical rotational rate between 0.3 and 0.4 that has lost its material during or just after the blue supergiant phase.
Enhancing Sustainability in Machine Learning-based Android Malware Detection using API calls

The number of malware such as banking Trojans, spyware, and ransomware in Android devices has been rising. In addition, the recent evolution of Android malware makes existing malware detection techniques less effective. This paper shows that existing Android malware detection techniques based on Random Forest classifiers using Application Programming Interface (API) calls as a feature set are not sustainable on a relatively long-time scale. Then, we introduce two new machine learning techniques that exhibit high sustainability. By applying the proposed techniques to 126,000 Android apps, we obtained the highest accuracy of 97,8% and an F1-score of 98.8%.
Standard Cell and Supergates Designs: An Electrical Comparison on 4-Input Logic Functions

This paper presents an electrical study on logic functions with up to 4 inputs designed with a standard cell mapping and two automatically generated supergates methodologies. The results indicate that supergate-based designs reduce the average power in 84.4% of the studied cases while reducing area by 12.9%. Despite the supergate design increasing in average the circuit critical delay by 5.8%, it achieves better power-delay-product in 2823 (70.9%) of the 3982 studied logic functions. The reduction of logic levels is the main factor for gains obtained with supergates due to the glitch power reduction.
A Highly Efficient Fully Integrated Active Rectifier for Ultrasonic Wireless Power Transfer

Ultrasonic wireless power transfer (WPT) has been proved to be a promising approach to power biomedical implants. To extract the energy generated from the transducer, a rectifier is typically required. Previous inductor-based rectifiers (SSHI and SECE) require a large off-chip inductor to achieve good performance, which is not desired for miniaturization and safety reasons. Synchronized switch harvesting on capacitors (SSHC) rectifiers have been proved to achieve high performance without inductors; however, they are mainly designed for low-frequency kinetic energy harvesting. In this paper, an improved SSHC rectifier is designed to achieve a fully integrated design with all flying capacitors implemented on-chip. The proposed SSHC rectifier can properly operate at ultrasonic excitation frequency (100 KHz) with precise switching time control and ultrafast voltage flipping techniques. In addition, an on-chip ultralow-power LDO allows the system to be self-sustained. The system is designed in a TSMC 180nm BCD technology and post-layout simulation results are presented.
ACCEPTED FROM OPEN CALL

applications, and a corresponding detector should be implemented in MEC. Due to the rise of low-cost Software-Defined Radio (SDR), malicious attacks using rogue base stations (BSs) have become more popular. It implies that security protection at MEC in the communications layer is necessary. This article, therefore, proposes a novel platform, M3Inspector, where inspectors located in mobiles and AIoT machines collect information from surrounding BSs and provide them to MEC. MEC determines the rogue BS and makes a notification to users subscribing to the local service. A realistic 5G experimental platform with rogue BSs is developed. The results demonstrate that attack detection and mitigation can be implemented in the MEC paradigm to significantly improve the security protection of AIoT from the perspectives of rogue BS attacks and file-less attacks.
Learning-based Algorithm for Real Imaging System Enhancement: Acoustic Resolution to Optical Resolution Photoacoustic Microscopy

Optical resolution photoacoustic microscopy (OR-PAM) imaging method can achieve high lateral resolution $(\lt 5 \mu \mathrm{m})$, while the penetration depth for OR is shallow (up to $1 \sim 2$ mm). In contrast, acoustic resolution photoacoustic microscopy (AR-PAM) imaging only has limited lateral resolution $(\gt 50 \mu \mathrm{m})$ but with deeper penetration depth up to several millimeters (3-10 mm). Enlighted by the recent progress in the field of machine learning, we proposed to enhance AR-PAM to OR-PAM while maintaining its high penetration depth merit with deep neural network, where a novel network structure named MultiResU-Net is employed. By training the network with OR images obtained with real setup and AR images simulated with physical model, the network is able to enhance the image quality of simulated AR image a huge extent that is similar to OR image. More importantly, the trained model is applied to real AR imaging system for both phantom and in vivo image enhancement. When compared with corresponding ground truth OR images, it can be fully substantiated that our proposed method realized the AR to OR target in real photoacoustic microscopy imaging system.
Ali266: an optimized VVC Software Encoder Implementation Towards Real-time Applications

As the latest international video coding standard finalized in July 2020, the Versatile Video Coding (VVC) standard achieves around 50% bitrate savings at the same video quality over its predecessor, the High Efficiency Video Coding (HEVC) standard. The improvement of coding efficiency brings great value with the rapid growth of video applications. However, increased coding efficiency also comes at the cost of increased encoding complexity, which makes it rather difficult to apply VVC to real-time video services using general-purpose processors. In this paper, an optimized VVC software encoder implementation towards real-time applications, named Ali266, is introduced, including coding efficiency optimization and computational complexity reduction algorithms in Ali266. Our experiments demonstrate that Ali266 with the “faster” preset enables real-time encoding for 1080p video-on-demand (VOD) content, while providing more than 40% bitrate savings over x265 at the medium preset with the same objective video quality.
Dynamic Network Slicing using Deep Reinforcement Learning

5G network slicing provides service flexibility and enables the operator to optimize network resources and provide differentiated services at scale. Slicing in real-time is a challenge due to a large number of connected devices with wide-ranging quality of service requirements. A dynamic slicing method is needed to provide the operator the flexibility to alter the number of slices and change the resource allocation in RAN for each slice. We developed a fast and efficient deep reinforcement learning model to do dynamic network slicing that optimizes the service quality in real-time. Our solution is able to cater to a large number of users with different service requirements due to the use of neural networks to carry out the state-action mapping. Our model uses prioritized experience replay to reduce the training time which will allow the operator to update the model at frequent intervals. To prove the feasibility of the proposed model, we trained the learning agent using our model to carry out the network slicing task in VRAN. Our simulations show that the model learns the characteristics of the network slices and our novel reward mechanism enables the base station to make intelligent decisions to maximize network utility.
An Accurate Modelling of CMOS Folded Cascode Op-Amp with Negative Transconductance

This paper presents a comprehensive analysis of the impact of negative transconductance in folded cascode Operational Amplifier (Op-Amp). An analytical study with small signal model is presented and summarized with respect to the performance of folded cascode op-amp implemented in 110nm CMOS process. The small signal model satisfactorily illustrates the behavior of the amplifier’s output resistance taking negative transconductance into account. Furthermore, design guidelines and mathematical equations are provided for maximizing output resistance and small signal gain corroborated with simulation results.
Spin-incoherent Luttinger liquid of one-dimensional spin-1 Tonks-Girardeau Bose gases: Spin-dependent properties

Spin-incoherent Luttinger liquid (SILL) is a different universal class from the Luttinger liquid. \ This difference results from the spin incoherence of the system when the thermal energy of the system is higher than the spin excitation energy. \ We consider one-dimensional spin-$1$ Bose gas in the SILL regime and investigate its spin-dependent many-body properties. \ In Tonks-Girardeau limit, we are able to write down the general wave functions in a harmonic trap. \ We numerically calculate the spin-dependent (spin-plus, minus, and $0$) momentum distributions in the sector of zero magnetization which allows to demonstrate the most significant spin-incoherent feature compared to the spinless or spin-polarized case. \ In contrast to the spinless Bose gas, the momentum distributions are broadened and in the large momentum limit follow the same asymptotic $1/p^4$ dependence but with reduced coefficients. \ While the density matrices and momentum distributions differ between different spin components for small $N$, at large $N$ they approach each other. \ We show these by analytic arguments and numerical calculations up to $N$ $=$ $16$.
A Morphological Fingerprint Minutiae Annotation Algorithm for Deep Learning Datasets

Minutiae extraction is vital for automatic finger-print identification system. Fingerprint datasets (databases) for minutiae extraction are beneficial to improve the performance of subsequent indexing and matching tasks, particularly combined with advanced deep learning technique. However, the learning-based methods are restricted by the limitation of public labeled datasets due to legitimate privacy concerns. To thoroughly address these issues (composed of lacking of the available databases and general annotation technique), a novel fingerprint image labeling approach is first focused on in this paper. Specifically, we devise a knowledge-oriented minutiae annotation method, which consists of image segmentation, normalization, orientation and frequency estimations, enhancement, binarization, thining, minutiae extraction, deleting redundancy and then revising the wrong annotations manually. Next, a minutiae database is created via the approach, and then experiments verify its effectiveness for training deep learning model. Comprehensive experimental results on two datasets manifest the ability of the constructed database to improve the prediction accuracy of CNN (achieving precision and recall up to 89.97% and 88.38%, respectively) compared with public software and other similar deep learning extraction model which in turn reveals the effectiveness of our method.
Berry-phase theory of polar discontinuities at oxide-oxide interfaces

In the framework of the modern theory of polarization, we rigorously establish the microscopic nature of the electric displacement field D. In particular, we show that the longitudinal component of D is preserved at a coherent and insulating interface. To motivate and elucidate our derivation, we use the example of LAO/STO interfaces and superlattices, where the validity of the above conservation law is not immediately obvious. Our results generalize the "locality principle" of constrained-D density-functional theory to the first-principles modeling of charge-mismatched systems.
HDC8192: A General Purpose Mixed-Signal CMOS Architecture for Massively Parallel Hyperdimensional Computing

This paper addresses a mixed-mode CMOS circuit for Hyperdimensional Computing (HDC). HDC is based on the use of binary vectors with thousands dimensions to represent data in a holistic way. During the last years HDC has shown to be a powerful approach to solve classification problems. The proposed circuit architecture in this paper is made up of an array of 128 × 64 (8192) processing units (PUs) with a 1-bit ALU, local memory and connectivity to their 4 nearest neighbors to run the basic operations of HDC, i.e, binding, bundling and permutation. The architecture also includes a module to calculate Hamming distance to address classification. Post-layout simulations of the complete system working on various basic operations in 0.18 μ m CMOS technology are shown.
Editable asynchronous control logic for SAR ADCs

This paper presents a novel design method for asynchronous control logic targeting successive approximation register (SAR) analog-to-digital converters (ADCs). This work is based on modeling the control logic for SAR ADCs using signal transition graphs (STGs). Different from conventional synchronous controllers, the proposed method results in asynchronous controllers driven by the causality of signals rather than relying on clocks to control the conversion process. Moreover, the proposed asynchronous control logic can be modularized through the handshake protocol, making it possible to build ADCs of arbitrary precision based on single-bit control units. This work results in a formal, model-based asynchronous design flow for SAR ADC control, which is shown to produce resulting circuits of similar speeds but great power efficiency improvements.
A 0.45 pJ/bit 20 Gb/s/Wire Parallel Die-to-Die Interface with Rotary Traveling Wave Oscillators

In this work, a die-to-die communication architecture with the integration of resonant clocking is presented. The novelty of the architecture are the rotary traveling wave oscillators, designed across the interposer of a 2. 5D multi-die system to provide a synchronous high frequency clock to all chiplets simultaneously. The transmitter and receiver interface circuits of the architecture benefit from the use of the low power, low skew, multiple phase clock signals across the chiplets. In experimentation, a channel length of 4 mm between transceivers is investigated over a 5 mm $\times 5$ mm silicon interposer. SPICE based simulations with post-layout, parasitic extracted models are performed. The proposed architecture demonstrates 20 Gb/s operation at 0.45 pJ/bit over a 4mm channel at a nominal 1 V supply voltage. The overall clock power of the proposed architecture is 56% lower than prior works at 20 Gb/s.
Quantified Satisfiability-based Simultaneous Selection of Multiple Local Approximate Changes under Maximum Error Bound

Approximate computing is an emerging low-power design technique for error-tolerant applications. One key enabling technique for approximate circuit design is approximate logic synthesis (ALS). Many ALS methods are based on a scheme that iteratively selects one single local approximate change (LAC) in each round until the error bound is reached. However, this scheme fails to consider the joint effect of multiple LACs whose induced errors may counteract with each other when applied simultaneously. In this work, we propose a method to select multiple LAC candidates in a single round under a given bound on maximum error distance (MaxED). It first builds a miter by adding a multiplexer into the network for each LAC candidate in the network. Then, a quantified satisfiability problem is formulated on the miter and solved to obtain a maximal set of LACs that can be applied simultaneously. The experimental results show that under the normalized MaxED bound of 1%, our method reduces the circuit area by up to 39%, which is 20% higher than the area reduction achieved by a baseline method that iteratively selects one single LAC per round.
Physics-based modeling of a bi-layer Al₂O₃/Nb₂O₅ analog memristive device

This paper proposes the derivation of a physics-based model of an analog memristive device realized as a bi-layer Al2 O3/NB2 O5 stack. Memristive crossbar arrays implementing matrix-vector multiplications are a central building block of novel computing-in-memory architectures for artificial neural network and neuromorphic computing applications. The presented memristor shows analog, multi-level switching at high resistances without electroforming and is suitable for crossbar operations with low energy consumption. By including a graphical analysis method of the I-V curves obtained in a quasi-static approach, the dynamic behavior is analyzed with regard to ohmic and Poole-Frenkel behavior. Finally, a compact model, represented by an algebraic differential equation, is proposed and verified by fitting calculated solutions to experimental data.
Domain Specific ReRAM Computation-in-Memory Design Considering Bit Precision and Memory Errors for Simulated Annealing

In this paper, domain specific ReRAM-based Computation-in-Memory (CiM) design for simulated annealing (SA) is proposed. This paper reveals that the influence of bit precision and memory cell errors of ReRAM CiM on the accuracy for SA depends on the domains of combinatorial optimization problems, such as Max-Cut and Knapsack problems. It is found that Max-Cut problem has smaller circuit structure and is 3-bit higher tolerant of bit precision, but 4% lower bit-error rate (BER) tolerant, compared with Knapsack problem. In this paper, considering the requirements of bit precision and BER from each domain, examples of case studies and design strategy are presented such that ReRAM CiMs are best optimized in terms of reliability and array area of memory cells. Furthermore, the proposed best-optimized ReRAM CiM for Max-Cut problem improves the quality of SA by introducing approximate answers and avoiding local minimum.
Thermoelectric enhancement in PbTe with K or Na codoping from tuning the interaction of the light- and heavy-hole valence bands

The effect of K and K-Na substitution for Pb atoms in the rocksalt lattice of PbTe was investigated to test a hypothesis for development of resonant states in the valence band that may enhance the thermoelectric power. We combined high-temperature Hall-effect, electrical conductivity, and thermal conductivity measurements to show that K-Na codoping do not form resonance states but can control the energy difference of the maxima of the two primary valence subbands in PbTe. This leads to an enhanced interband interaction with rising temperature and a significant rise in the thermoelectric figure of merit of p-type PbTe. The experimental data can be explained by a combination of a single- and two-band models for the valence band of PbTe depending on hole density that varies in the range of 1-15 x 10{sup 19} cm{sup -3}.
A Hybrid Video-to-Text Summarization Framework and Algorithm on Cascading Advanced Extractive- and Abstractive-based Approaches for Supporting Viewers' Video Navigation and Understanding

In this work, we propose the development of a hybrid video-to-text summarization (VTS) framework on cascading the advanced and code-accessible extractive and abstractive (EA) approaches for supporting viewers' video navigation and understanding. More precisely, the contributions of this paper are three-fold. First, we devise an automated and unified hybrid VTS framework that takes an arbitrary video as an input, generates the text transcripts from its human dialogues, and then summarizes the text transcripts into one short video synopsis. Second, we advance the binary merge-sort approach and expand its use to develop an intuitive and heuristic abstractive-based algorithm, with the time complexity $O(T_{L}logT_{L})$ and the space complexity $O(T_{L})$, where TL is the total number of word tokens on a text, to dynamically and successively split and merge a long piece of text transcripts, which exceeds the input text size limitation of an abstractive model, to generate one final semantic video synopsis. At the end, we test the feasibility of applying this proposed framework and algorithm in conducting the preliminarily experimental evaluations on three different videos, as a pilot study, in genres, contents, and lengths. We show that our approach outperforms and/or levels most of the individual EA methods stated above by 75% in terms of the ROUGE F1-Score measurement.