Miracle-dz/UniFAR-FTUs huggingface dataset:
Security Analysis of QKD Protocols: Simulation & Comparison

The inception of quantum key distribution (QKD) empowered the communication networks with unconditional security because of the unique characteristics of quantum mechanics. Since QKD genesis, various protocols have been proposed to augment the feasibility of its real-world implementation and to overcome the inherent vulnerabilities of communication channel. In the past several years' studies emphasize the significance of QKD in the field of secure communication, because it can counter critical attacks quite effectively. In this paper, we present in-depth security analysis of the notable QKD protocols which encompasses from their theoretical assertions to their practical implementation factors and simulation outcomes.
Cooperative Autonomous Driving Oriented MEC-Aided 5G-V2X: Prototype System Design, Field Tests and AI-Based Optimization Tools

Vehicle-to-Everything (V2X) requirements from cooperative autonomous driving can be characterized as ultra-reliable, low latency, high traffic, and high mobility. These requirements introduce great challenges in the air interface and protocol stack design, resource allocation, network deployment, and all the way up to mobile (or multi-access) edge computing (MEC), cloud and application layer. In this paper, we present a cooperative autonomous driving oriented MEC-aided 5G-V2X prototype system design and the rationale behind the design choices. The prototype system is developed based on a next-generation radio access network (NG-RAN) experimental platform, a cooperative driving vehicle platoon, and an MEC server providing high definition (HD) 3D dynamic map service. Field tests are conducted and the results demonstrate that the combination of 5G-V2X, MEC and cooperative autonomous driving can be pretty powerful. Considering the remaining challenges in the commercial deployment of 5G-V2X networks and future researches, we propose two artificial intelligence (AI) based optimization tools. The first is a deep-learning-based tool called deep spatio-temporal residual networks with a permutation operator (PST-ResNet). By providing city-wide user and network traffic prediction, PST-ResNet can help to reduce the capital expense (CAPEX) and operating expense (OPEX) costs of commercial 5G-V2X networks. The second is a swarm intelligence based optimization tool called subpopulation collaboration based dynamic self-adaption cuckoo Search (SC-DSCS), which can be widely used to solve complex optimization problems in future researches. The effectiveness of proposed optimization tools is verified by real-world data and benchmark functions.
Investigation on Thermal Stability of Quasi-Isotropic Superconducting Strand Stacked by 2 mm Wide REBCO Tapes and Cu Tapes

In order to improve the thermal stability of high-temperature superconducting (HTS) strand, a quasi-isotropic superconducting strand (Q-IS) stacked by 2 mm wide REBCO tapes and Cu tapes with geometrical symmetric configuration were developed, in this paper. A three dimensional (3D) numerical model considering the anisotropic thermal conductivity was built to study the thermal stability of the strand, including minimum quench energy (MQE) and quench propagation velocity (QPV). The temperature distribution in the cross section at different positions along the longitudinal direction of the strand during the quench was obtained as well. Then, a strand was prepared to investigate the thermal stability experimentally. Results show that the Q-IS stacked by REBCO tapes and copper tapes has higher MQE than Q-IS without copper tapes. The results can provide a fundamental analysis for further developing device of high-current electric power transmission with high thermal stability.
Rapid Optimization of Compact Microwave Passives Using Kriging Surrogates and Iterative Correction

Design of contemporary microwave components is—in a large part—based on full-wave electromagnetic (EM) simulation tools. The primary reasons for this include reliability and versatility of EM analysis. In fact, for many microwave structures, notably compact components, EM-driven parameter tuning is virtually imperative because traditional models (analytical or network equivalents) are unable to account for the cross-coupling effects, strongly present in miniaturized layouts. At the same time, the cost of simulation-based design procedures may be significant due to a typically large number of evaluations of the circuit at hand involved. In this paper, a novel approach to expedited design closure of compact microwave passives is presented. The proposed procedure incorporates available designs (e.g., existing from the previous design work on the same structure) in the form of the kriging interpolation models, utilized to yield a reasonable initial design and to accelerate its further refinement. An important component of the framework is an iterative correction procedure that feeds the accumulated discrepancies between the target and the actual design objective values back to the kriging surrogate to produce improved predictions. The efficacy of our methodology is demonstrated using two miniaturized impedance matching transformers with the optimized designs obtained at the cost of a few EM simulations of the respective circuits. The relevance of the iterative correction is corroborated through the comparative studies showing its superiority over rudimentary gradient-based refinement.
Variability-Aware Predictive Modeling of Line-to-Line Dielectric Reliability

We present a predictive model for line-to-line dielectric reliability that takes variability into account. The lifetime distribution is obtained by performing series-parallel reliability computations, starting from the failure distributions of single dielectric unit cells, assumed to be Weibull. The electric field in between the lines is estimated point-by-point, and the power law is applied to account for the related impact on the Weibull scale parameter of every single cell. Finally, the model is calibrated to actual reliability measurements to fix the reference parameters of the Weibull distribution and power law. We employ our model to predict the impact of line-edge roughness (LER), and die-to-die spacing variations on the reliability of scaled interconnects and derive specs from meeting ten years lifetime at operating conditions. We show that specs can be expressed in terms of a single combined-metric, that is, the line-spacing roughness over the wafer (WLSR). Consequently, tradeoffs can be made between specs on LER and die-to-die spacing variations to achieve the same reliability margin. We calibrate our model to Time-Dependent Dielectric Breakdown (TDDB) data from low-<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> planar capacitors (p-caps) and generate WLSR specs for dimensions ranging from 16- to 8-nm spacing. We predict a significant drop in the reliability margin when scaling down the spacing or the dielectric <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-value. As an example, we observe more than 50% reduction in the allowed <inline-formula> <tex-math notation="LaTeX">$\sigma _{{\text {WLSR}}}$ </tex-math></inline-formula> from 16- to 10-nm spacing. We also compare single with multipatterned interconnects. For spacer-assisted multipatterning, spacing edges are cross-correlated, and we predict that for the same die-to-die spacing variations more than two times higher LER is allowed with respect to single patterning to meet the same reliability requirements.
Variability Effects in Nanowire and Macaroni MOSFETs—Part II: Random Telegraph Noise

In this article and in its Part I, we investigate variability effects on the threshold voltage of nanowire (NW) and Macaroni devices, focusing on random dopant fluctuations and random telegraph noise (RTN) and assessing their dependencies on device radius, channel length, and doping. In this article, we address variability induced by RTN, showing that the slope of the exponential distribution follows different dependencies with respect to planar devices. We highlight the strong sensitivity of the Macaroni devices to traps at the filler-oxide interface, discussing the dependence. Finally, accumulation-mode devices are addressed, showing that they allow to achieve a lower RTN in NWs but a worse one in the Macaroni devices.
GaN based LNA MMICs for X-Band Applications

In this paper, we report two low noise broadband amplifiers based on ABMN's AlGaN/GaN on SiC HEMT technology for X-band applications. Two design topologies, a single-stage (LNA-1) and a two-stage (LNA-2), have been investigated. LNA-1 and the first stage of LNA-2 is based on common source (CS) with inductive source degeneration topology. LNA-1 has a flat gain response of ±1.4 dB gain variation with a gain greater than 8 dB for 9 V drain voltage and 100 mA/mm drain current. Input return loss better than 9.8 dB and output return loss better than 12.8 dB have been achieved. The simulated value of noise figure for this design is less than 1.4 dB. In LNA-2 design, a two-stage topology is implemented to enhance amplifier's gain. The simulated values for LNA-2 show a gain greater than 16.8 dB with ±2.9 dB gain variation. Input and output return loss values are better than 8.8 dB and 10 dB, respectively. The value of noise figure for this design is less than 1.7 dB in the desired frequency range. Both designs, having state-of-the art small dimensions, are suitable for their potential applications for space communications, Radar, satellite communications etc.
A Model of Text-Enhanced Knowledge Graph Representation Learning With Mutual Attention

Recently, it has gained lots of interests to jointly learn the embeddings of knowledge graph (KG) and text information. However, previous work fails to incorporate the complex structural signals (from structure representation) and semantic signals (from text representation). This paper proposes a novel text-enhanced knowledge graph representation model, which can utilize textual information to enhance the knowledge representations. Especially, a mutual attention mechanism between KG and text is proposed to learn more accurate textual representations for further improving knowledge graph representation, within a unified parameter sharing semantic space. Different from conventional joint models, no complicated linguistic analysis or strict alignments between KG and text are required to train our model. Besides, the proposed model could fully incorporate the multi-direction signals. Experimental results show that the proposed model achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation models.
SHyLoC 2.0: A Versatile Hardware Solution for On-Board Data and Hyperspectral Image Compression on Future Space Missions

In this paper, we present the design, implementation and results of a set of IP cores that perform on-board hyperspectral image compression according to the CCSDS 123.0-B-1 lossless standard, specifically designed to be suited for on-board systems and for any kind of hyperspectral sensor. As entropy coder, the sample-adaptive entropy coder defined in the 123.0-B-1 standard or the low-complexity block-adaptive encoder defined by the CCSDS 121.0-B-2 lossless standard could be used. Both IPs, 123.0-B-1 and 121.0-B-2, are part of SHyLoC 2.0, and can be used together for compression of hyperspectral images, being also possible the compression of any kind of data using only the 121-IP. SHyLoC 2.0 improves and extends the capabilities of SHyLoC 1.0, currently available at the ESA IP Cores library, increasing its compression efficiency and throughput, without compromising the resources footprint. Moreover, it incorporates new features, such as the unit-delay predictor option defined by the CCSDS 121.0-B-2 standard, and burst capabilities in the external memory interface of the CCSDS 123-IP, among others. Dedicated architectures have been designed for all the possible input image sample arrangements, in order to maximise throughput and reduce the hardware resources utilization. The design is technology-agnostic, enabling the mapping of the VHDL code in different FPGAs or ASICs. Results are presented for a representative group of well-known space-qualified FPGAs, including the new NanoXplore BRAVE family. A maximum throughput of 150 MSamples/s is obtained for Xilinx Virtex XQR5VFX130 when the SHyLoC 2.0 CCSDS-123 IP is configured in Band-Interleaved by Pixel (BIP) order, using only the 4% of LUTs and less than the 1% of internal memory.
Multi-Channel Deep Feature Learning for Intrusion Detection

Networks had an increasing impact on modern life since network cybersecurity has become an important research field. Several machine learning techniques have been developed to build network intrusion detection systems for correctly detecting unforeseen cyber-attacks at the network-level. For example, deep artificial neural network architectures have recently achieved state-of-the-art results. In this paper a novel deep neural network architecture is defined, in order to learn flexible and effective intrusion detection models, by combining an unsupervised stage for multi-channel feature learning with a supervised one exploiting feature dependencies on cross channels. The aim is to investigate whether class-specific features of the network flows could be learned and added to the original ones in order to increase the model accuracy. In particular, in the unsupervised stage, two autoencoders are separately learned on normal and attack flows, respectively. As the top layer in the decoder of these autoencoders reconstructs samples in the same space as the input one, they could be used to define two new feature vectors allowing the representation of each network flow as a multi-channel sample. In the supervised stage, a multi-channel parametric convolution is adopted, in order to learn the effect of each channel on the others. In particular, as the samples belong to two different distributions (normal and attack flows), the samples labelled as normal should be more similar to the representation reconstructed with the normal autoencoder than that of the attack one, and viceversa. This expected dependency will be exploited to better disentangle the differences between normal and attack flows. The proposed neural network architecture leads to better predictive accuracy when compared to competitive intrusion detection architectures on three benchmark datasets.
AIR: A Fast but Lazy Timing-Driven FPGA Router

Routing is a key step in the FPGA design process, which significantly impacts design implementation quality. Routing is also very time-consuming, and can scale poorly to very large designs. This paper describes the Adaptive Incremental Router (AIR), a high-performance timing-driven FPGA router. AIR dynamically adapts to the routing problem, which it solves ‘lazily’ to minimize work. Compared to the widely used VPR 7 router, AIR significantly reduces route-time ($7.1 \times$ faster), while also improving quality (15% wirelength, and 18% critical path delay reductions). We also show how these techniques enable efficient incremental improvement of existing routing.
Towards Read-Intensive Key-Value Stores with Tidal Structure Based on LSM-Tree

Key-value store has played a critical role in many large-scale data storage applications. The log-structured merge-tree (LSM-tree) based key-value store achieves excellent performance on write-intensive workloads which is mainly benefited from the mechanism of converting a batch of random writes into sequential writes. However, LSM-tree doesn’t improve a lot in read-intensive workloads which takes a higher latency. The main reason lies in the hierarchical search mechanism in LSM-tree structure. The key challenge is how to propose new strategies based on the existing LSM-tree structure to improve read efficiency and reduce read amplifications.This paper proposes Tidal-tree, a novel data structure where data flows inside LSM-tree like Tidal waves. Tidal-tree targets at improving read efficiency in read-intensive workloads. Tidal-tree allows frequently accessed files at the bottom of LSM-tree to move to higher positions, thereby reducing read latency. Tidal-tree also makes LSM-tree into a variable shape to cater for different characteristic workloads. To evaluate the performance of Tidal-tree, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree can significantly improve read efficiency and reduce read amplifications compared with representative schemes.
Double-Gate Junctionless 1T DRAM With Physical Barriers for Retention Improvement

In this article, a double-gate (DG) junctionless (JL) transistor with physical barriers is proposed for one-transistor dynamic random-access memory (1T DRAM) application. In this topology, the holes are stored in the region blocked by physical barriers constructed by oxides underneath the source and drain regions rather than a potential well formed by n<sup>+</sup>-p-n<sup>+</sup> as in the conventional structures. The proposed topology achieves an elongated retention time (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {ret}}$ </tex-math></inline-formula>) with larger physical barrier thickness (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {oxPB}}$ </tex-math></inline-formula>) and wider barrier offset length (<inline-formula> <tex-math notation="LaTeX">${L}_{\text {BO}}$ </tex-math></inline-formula>) due to a reduction in band-to-band tunneling (BTBT) (during hold “0”) and recombination (during hold “1”). Maximum retention times of ~2.5 s and ~33 ms have been achieved for channel doping of 10<sup>19</sup> cm<sup>−3</sup> at 27 °C and 85 °C, respectively, with gate length (<inline-formula> <tex-math notation="LaTeX">${L}_{g}$ </tex-math></inline-formula>) of 100 nm at small drain bias (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}$ </tex-math></inline-formula>) of 1 V during write “1. ” Results demonstrate a better gate length scalability and a retention time of ~4 ms at <inline-formula> <tex-math notation="LaTeX">${L}_{g}$ </tex-math></inline-formula> of 15 nm with thinner Si channel thickness under the gate (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {Si}}$ </tex-math></inline-formula>) and thicker <inline-formula> <tex-math notation="LaTeX">${T}_{\text {oxPB}}$ </tex-math></inline-formula>. In addition, the effect of temperature on retention time has been analyzed. With optimized <inline-formula> <tex-math notation="LaTeX">${T}_{\text {oxPB}}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">${L}_{g} = {100}$ </tex-math></inline-formula> nm, the retention time decreases due to thermal generation and recombination from ~2.5 s at 27 °C to ~3 ms at 125 °C.
IMAC: In-Memory Multi-Bit Multiplication and ACcumulation in 6T SRAM Array

‘In-memory computing’ is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck. This emerging paradigm aims at embedding some aspects of computations inside the memory array, thereby avoiding frequent and expensive movement of data between the compute unit and the storage memory. In-memory computing with respect to Silicon memories has been widely explored on various memory bit-cells. Embedding computation inside the 6 transistor (6T) SRAM array is of special interest since it is the most widely used on-chip memory. In this paper, we present a novel in-memory multiplication followed by accumulation operation capable of performing parallel dot products within 6T SRAM without any changes to the standard bitcell. We, further, study the effect of circuit non-idealities and process variations on the accuracy of the LeNet-5 and VGG neural network architectures against the MNIST and CIFAR-10 datasets, respectively. The proposed in-memory dot-product mechanism achieves 88.8% and 99% accuracy for the CIFAR-10 and MNIST, respectively. Compared to the standard von Neumann system, the proposed system is <inline-formula> <tex-math notation="LaTeX">$6.24\times $ </tex-math></inline-formula> better in energy consumption and <inline-formula> <tex-math notation="LaTeX">$9.42\times $ </tex-math></inline-formula> better in delay.
Seemingly unrelated and fixed-effect panel regressions: collinearity and singular dispersion

The paper discusses identification conditions, representations and relations of generalized least squares estimators of regression parameters in multivariate linear regression models such as seemingly unrelated and fixed effect panel models. Results are presented on identification for unrestricted dispersion structure and general heteroskedasticity and cross-equation dependence, considering explicit and implicit restrictions, singularity of the dispersion and multicollinearity in the design matrix.
Symmetrized poly-Bernoulli numbers and combinatorics

Poly-Bernoulli numbers are one of generalizations of the classical Bernoulli numbers. Since a negative index poly-Bernoulli number is an integer, it is an interesting problem to study this number from combinatorial viewpoint. In this short article, we give a new combinatorial relation between symmetrized poly-Bernoulli numbers and the Dumont-Foata polynomials.
Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat

Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis – neural word embeddings – can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight “fill in the blanks” about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.
The Network Dynamics of Social and Technological Conventions

When innovations compete for adoption, chance historical events can allow an inferior strategy to spread at the expense of superior alternatives. However, advantage is not always due to chance, and networks have emerged as an important determinant of organizational behavior. To understand what factors can impact the likelihood that the best alternative will be adopted, this paper asks: how does network structure shape the emergence of social and technological conventions? Prior research has found that highly influential people, or "central" nodes, can be beneficial from the perspective of a single innovation because promotion by central nodes can increase the speed of adoption. In contrast, when considering the competition of multiple strategies, the presence of central nodes may pose a risk, and the resulting "centralized" networks are not guaranteed to favor the optimal strategy. This paper uses agent-based simulation to investigate the effect of network structure on a standard model of convention formation, finding that network centralization increases the speed of convention formation but also decreases the likelihood that the best strategy will become widely adopted. Surprisingly, this finding does not indicate a speed/optimality trade-off: dense networks are both fast and optimal.