Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase Noise of Optical Pulse Trains Generated by Talbot Effect in Frequency Shifting Loops

High repetition rate optical pulse trains are essential for a wide range of applications, including photonic-assisted sampling. Among the different solutions proposed so far, Talbot lasers, based on the generation of an optical frequency comb in a frequency shifting loop, are a simple yet efficient source of short transform-limited pulses. By controlling the comb spectral phase, the repetition rate is reconfigurable. It can be set to a (possibly large) multiple of the comb spacing and reach the GHz range, while only requiring a low bandwidth synthesizer (typ. 100 MHz). The noise characteristics of this pulse trains is a key factor for its application. Therefore, we investigate the phase noise properties of Talbot lasers. In particular, we prove theoretically and demonstrate experimentally that the phase noise at high offset frequency from the carrier, does not depend on the multiplication factor <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>. We also demonstrate that the phase noise at low offset frequencies, which increases as <inline-formula><tex-math notation="LaTeX">$20 \log q$</tex-math></inline-formula>, can be strongly reduced by a simple locking technique. Finally, we show that in addition to their reconfigurability, Talbot lasers can provide timing jitters at 8.2 GHz as low as 350 fs (integrated from <inline-formula><tex-math notation="LaTeX">$10^{5}$</tex-math></inline-formula> Hz to the Nyquist frequency), making them suitable candidates for time metrology applications.
Ultra-Low Noise Schottky Junction Tri-Gate Silicon Nanowire FET on Bonded Silicon-on-Insulator Substrate

Random trapping and detrapping of charged carriers in the vicinity of gate oxide/Si interface has for long been considered as the dominant noise source in Si nanowire (SiNW) FET-based biochemical sensors. Here we extend our previous work presenting a Schottky junction tri-gate SiNWFETs (SJGFET) fabricated on a bonded silicon-on-insulator (SOI) substrate, aiming for ultra-low device noise generation. The SJGFET exhibits near-ideal gate coupling efficiency with a subthreshold swing of ~66 mV/dec. Its gate-referred voltage noise, <inline-formula> <tex-math notation="LaTeX">$S_{\text {vg}}$ </tex-math></inline-formula>, are <inline-formula> <tex-math notation="LaTeX">${1.2}\times {10}^{-{10}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${1.1}\times {10}^{-{11}}\,\,\text {V}^{{2}}\,\,\mu \text{m}^{{2}}$ </tex-math></inline-formula>/Hz at 1 and 10 Hz, respectively. These <inline-formula> <tex-math notation="LaTeX">$S_{\text {vg}}$ </tex-math></inline-formula> values are significantly lower than that of previously reported FET-based sensors. More importantly, <inline-formula> <tex-math notation="LaTeX">$S_{\text {vg}}$ </tex-math></inline-formula> of the SJGFET are below the reported voltage noise generated by the oxide/electrolyte sensing interface. Using our SJGFET as the signal transducer can greatly relieve the concern of the adverse effect from the intrinsic device noise in biochemical sensing applications.
Piezoresistive Characteristics of MOSFET Channels Determined With Indentation

The stress-related change in the characteristics of transistors manufactured in the 22 nm fully depleted silicon on insulator (FDSOI) CMOS technology node is studied with advanced experimental indentation setups. Precisely, NAND and NOR ring oscillator circuits are used to monitor the strain-caused mobility deviations in the silicon transistor channels. Piezoresistive coefficients for strained silicon are calculated from the experimental indentations data using spherical and cylindrical tip geometries. In contrast to spherical tips, the cylindrical indentation tips enable to induce the stress more selectively into a desired direction. To set up the experimental details appropriately, finite element (FE) simulations have been used. Additionally, FE method (FEM) studies are conducted to compute the quantitative strain values in the silicon transistor channels as a function of contact load as well as chip and tip geometries. Using the signal deviations of the RO circuits subjected to strain from spherical and cylindrical indentation, a set of equations using the linearized piezoresistive model are created to determine the directional piezoresistive coefficients.
ONet – A Temporal Meta Embedding Network for MOOC Dropout Prediction

While massive open online courses (MOOCs) represent a promising approach to support education for all, their effectiveness is limited by high dropout rates. Based on the analysis that dropout is deeply correlated with the interaction patterns of a user, this paper proposes an attention meta embedding based deep temporal network to predict user dropout in MOOCs. The attention meta embedding gives an attention based weighted representation of different candidate embeddings and temporal modeling is done by Bi-GRU. An experimental evaluation of our solution on the KDDCUP 2015 and XuetangX datasets shows better results (AUC and F1 score) compared to both handcrafted feature techniques and all but one of the auto feature generated techniques. We obtain comparable results to the best previous method, but without having to use different architectures for different datasets.
Emergence of Motor Synergy in Multi-directional Reaching with Deep Reinforcement Learning

In this study, we apply Deep Reinforcement Learning for handling full-dimensional 7 degrees of freedom arm reaching, and demonstrate the relations among motion error, energy, and synergy emergence during the learning process, to reveal the mechanism of employing motor synergy. Although synergy information has never been encoded into the reward function, the synergy effect naturally emerges, leading to a similar situation as human motion learning. To the best of our knowledge, this is a pioneer study verifying a concurrent relation between the error-energy index and synergy development in DRL for multi-directional reaching tasks. In addition, our proposed feedback-augmented DRL controller shows better capability over DRL only in terms of error-energy index.
Growth, survival, and heavy metal (Cd and Ni) uptake of spinach (Spinacia oleracea) and fenugreek (Trigonella corniculata) in a biochar‐amended sewage‐irrigated contaminated soil

Irrigation of arable land with contaminated sewage waters leads to the accumulation of trace metals in soils with subsequent phyto-/zootoxic consequences. In this study, biochar derived from cotton sticks was used to amend an agricultural silt-loam soil that had been previously irrigated with trace metal contaminated sewage waters. Metal accumulation and toxicity to spinach (Spinacia oleracea) and fenugreek (Trigonella corniculata) was investigated by measuring concentrations of Cd and Ni in plant tissues and various photosynthetic and biochemical activities of plants. Positive impacts of biochar on both spinach and fenugreek were observed in terms of biomass production that increased from 29% to 36% in case of spinach, while for fenugreek this increase was 32% to 36%. In the control treatment there was an increase in malondialdihyde, soluble sugar, and ascorbic acid contents, indicating heavy metal stress. Biochar applications increased soluble proteins and amino acids in plants and reduced the uptake of Cd from 5.42 mg kg−1 at control to 3.45 mg kg−1 at 5% biochar amended soil and Ni (13.8 mg kg−1 to 7.3 mg kg−1 at 5% biochar) by the spinach plants. In fenugreek, the Cd was reduced from 7.72 mg kg−1 to 3.88 mg kg−1 and reduction in Ni was from 15.45 mg kg−1 to 9.46 mg kg−1 at 5% biochar treated soil, reducing the possibility of transfer up the food chain. This study demonstrates that the use of biochar made from cotton-sticks, as an amendment to arable soils that have received contaminated irrigation water, could improve plant growth and decrease Cd and Ni uptake to crops, alleviating some of the negative impacts of using sewage waters on arable land.
One-Volt, Solution-Processed InZnO Thin-Film Transistors

In this letter, we report solution-processed, high-performance Indium-Zinc-Oxide (IZO) thin-film transistors (TFTs). The annealing temperature of IZO films are studied and found that devices annealed at 350 °C exhibit the best performance. With the use of a thin AlxOy layer as the gate dielectric, one-volt IZO TFTs are demonstrated, showing a high current on/off ratio of > 105, a high mobility over 10 cm2/Vs, and a low subthreshold swing (SS) of 83 mV/dec, which is fairly close to the theoretical limit of SS at 300 K. Such a high device performance is also found comparable to those deposited using vacuum-based methods. As a result, the presented devices might possess a great potential in low-cost, low-power electronics.
Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

This paper presents an overview of device identification techniques and the Manufacturer Usage Description (MUD) standard used for the Internet of things to reduce the IoT attack surface. The ongoing diversity and the sheer increase in the number of connected IoT devices have crumpled security efforts. There is a need to reconsider and redesign the underlying concept of developing security systems to resolve IoT security challenges. In this backdrop, device profiling and identification have emerged as an exciting technique that helps to reduce IoT device attack surface. One of the known approaches for device identification is to fingerprint a device. There are many ways to fingerprint the device, mostly using device network flows or device local attributes. The device identification ensures the authenticity of the device attached to the network, like user authentication. Since IoT devices mostly work using machine-to-machine (M2M) communication, this requires identifying each device properly. But there is no unified approach for device identification for the ever-growing world of IoT devices and applications. One of the major steps forward in this direction is the development of the Manufacturer Usage Description (MUD) standard that defines the role of a device within the network. It limits the device to execute the primary task only, which will help to reduce the attack surface. Since the inception of MUD, many security frameworks use this standard for IoT security. However, there is a need to scrutinize the security frameworks based on the MUD, to find out the claimed effectiveness of the standard in IoT security. This paper initially identifies and classifies the potential vulnerabilities in IoT devices. Then, the study provides an overview of the research that focuses on device identification techniques and analyzes their role in IoT security. Finally, the research presents an overview of MUD technology, its implementation scenarios, the limitation of the latest MUD standard, and its applications in the industry. The prime aim of this work is to examine the MUD benefits in IoT security along with the weaknesses and challenges while implementing this standard along with future directions.
A Weighted Method for Fast Resolution of Strictly Hierarchical Robot Task Specifications Using Exact Penalty Functions

Extensive work has been done on efficiently resolving hierarchical robot task specifications that minimize the <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula>-2 norm of linear constraint violations, but not for <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula>-1 norm, in which there has recently been growing interest for sparse control. Both approaches require solving a cascade of quadratic programs (QP) or linear programs (LP). In this letter, we introduce alternate and more efficient approaches to hierarchical <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula>-1 norm minimization by formulating it as a <italic>single</italic> LP that can be solved by any off-the-shelf solver. The first approach is a recursive method that transforms the lexicographic LP (LLP) into a single objective problem using Lagrangian duality. The second approach, which forms the main focus of this letter, is a weighted method based on the exact penalty method, that is equivalent to the original LLP for a well chosen set of weights. We propose methods to compute and adapt these weights. The algorithms were applied on an interesting dual arm robot task. We discuss and benchmark the computational efficiency of these methods. Simplicity of the weighted method makes it a promising approach for tackling challenging prioritized robot control problems involving a control horizon or nonlinear constraints. Within this letter, we take the first step towards that goal by demonstrating the efficacy of the weighted method on a simpler instantaneous robot control problem with linear constraints.
The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.
State-feedback control of grid-connected RSDCHBMLI with $LCL$ filter

This paper presents the grid-connected operation of a single phase reduced switch single DC-source cascaded H-bridge multilevel inverter (RSDCHBMLI). The RSDCHBMLI topology with voltage boosting capacity generates 7-level waveform with minimum number of components. Unlike other multilevel inverter topologies, RSDCHBMLI doesn't require isolated DC-sources and also provides inherent isolation between the grid and inverter-side due to presence of transformers. In grid connected operation, the current injected by the RSDCHBMLI into the grid is regulated using state-feedback plus integral (SFBI) control. The SFBI control is used with a single carrier multilevel-sinusoidal pulse width modulation (ML-SPWM) method to generate switching signals with constant switching frequency. The SFBI control gives good dynamic performance with constant switching frequency operation. The harmonic distortion of grid injected current is reduced to low values. The constant switching frequency permits easier design of the $LCL$ filter values which is used for interfacing the inverter with the grid. The selection of the filter parameter values is also presented here. The synchronization with the grid is obtained using adaptive time-delay phase locked loop (ATD-PLL) which tracks the frequency and phase changes in the PCC voltage. The inverter operation is verified for step changes in reference power to be injected into the grid, and DC voltage.
Channel Geometry Scaling Effect in Printed Inorganic Electrolyte-Gated Transistors

Unlike nanoscale silicon transistors, printed thin-film transistors usually rely on micrometer size channel lengths. The device dimensions, the morphology of the channel material, and the interface properties strongly influence important device parameters such as the threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {th}}$ </tex-math></inline-formula>) and the transconductance parameter (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>). In this article, we analyze and model the dependence of critical electrical device properties of printed, electrolyte-gated transistors, based on crystalline indium oxide channel, for various device geometries. It is shown that the threshold voltage scales with the charge density at the electrolyte channel interface and is hence linearly dependent on the channel length (<inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>). Furthermore, nonlinear scaling effects in the transconductance parameter and in the ON-current are studied, which turn out to be dependent on both, channel width and length. Finally, we present a scaling model capturing the width/length dependencies of the studied transistor technology which enables the correct simulation of logic gates.
Impacts of Nonlinear Energy Harvesting and Residual Self-Interference on the Performance of Full-Duplex Decode-and-Forward Relay System

In this paper, we investigate the usage of a nonlinear energy harvester at a full-duplex (FD) relay, which can harvest the energy from radio frequency (RF) signals transmitted from the source. We mathematically derive the exact closed-form expressions of outage probability (OP), throughput, and average symbol error rate (ASER) of the nonlinear energy harvester-wireless information and power transfer-FD relaying (N-WIPT-FDR) system over Rayleigh fading channels. Unlike linear energy harvester, nonlinear energy harvester results in more computational complexity. Furthermore, it makes the outage floor happen faster even when self-interference cancellation (SIC) techniques are effectively applied due to the saturation power threshold of the nonlinear energy harvester. The combination of nonlinear energy harvester characteristic and residual self-interference (RSI) has significant impacts on the OP, throughput, and ASER of the considered N-WIPT-FDR system. On the other hand, there is an optimal energy harvesting (EH) time duration, which minimizes the OP. This optimal value depends on the transmission power of the source and the saturation power threshold of the nonlinear energy harvester. Monte-Carlo simulations are conducted to validate the derived mathematical expressions.
Enhanced Thermal Stability of Elevated-Metal Metal-Oxide Thin-Film Transistors via Low-Temperature Nitrogen Post-Annealing

Effects of low-temperature annealing in nitrogen atmosphere on elevated-metal metal-oxide (EMMO) thin-film transistors (TFTs) are investigated and reported in this article. Compared with the short-circuit behavior of EMMO TFTs after annealing in nitrogen atmosphere at 300 °C, it is found that TFTs received annealing in nitrogen atmosphere at 200 °C prior to that at 300 °C not only remain the transfer characteristics, but also exhibit improved on-state current and subthreshold characteristics. Both the results of X-ray photoelectron spectroscopy (XPS) and persistent photoconductivity (PPC) confirmed the reduction of oxygen vacancies in the a-IGZO after annealing in nitrogen atmosphere at 200 °C, indicating that the passivation of oxygen vacancy with nitrogen is dominant rather than the generation of oxygen vacancy. Furthermore, the thermal stability improvement could not be realized when the annealing in nitrogen atmosphere at 200 °C was performed before the source/drain formation annealing at 400 °C, which emphasizes the importance of annealing procedures during device fabrication.
Synthetic Biological Signals Machine-Generated by GPT-2 Improve the Classification of EEG and EMG Through Data Augmentation

Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and with a scarcity of training samples. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Those can rarely be generalised to the whole population and appear to over complicate simple action recognition such as grasp and release (standard actions in robotic prosthetics and manipulators). We show for the first time that multiple GPT-2 models can machine-generate synthetic biological signals (EMG and EEG) and improve real data classification. Models trained solely on GPT-2 generated EEG data can classify a real EEG dataset at 74.71% accuracy and models trained on GPT-2 EMG data can classify real EMG data at 78.24% accuracy. Synthetic and calibration data are then introduced within each cross validation fold when benchmarking EEG and EMG models. Results show algorithms are improved when either or both additional data are used. A Random Forest achieves a mean 95.81% (1.46) classification accuracy of EEG data, which increases to 96.69% (1.12) when synthetic GPT-2 EEG signals are introduced during training. Similarly, the Random Forest classifying EMG data increases from 93.62% (0.8) to 93.9% (0.59) when training data is augmented by synthetic EMG signals. Additionally, as predicted, augmentation with synthetic biological signals also increases the classification accuracy of data from new subjects that were not observed during training. A Robotiq 2F-85 Gripper was finally used for real-time gesture-based control, with synthetic EMG data augmentation remarkably improving gesture recognition accuracy, from 68.29% to 89.5%.
CTI ANT: Hunting for Chinese Threat Intelligence

As cybercriminals continually challenge the security of enterprises with sophisticated and rapidly evolving exploits, cyber threat intelligence (CTI) has emerged as a promising solution to enhance resilience against threats by understanding and limiting your focus on enemies that target your industry, efficiently leveraging the limited resources that hinder all SOCs. With a wide array of public and commercial sources that distribute threat intelligence, extending intelligence collection and analysis has become indispensable in developing effective cybersecurity measures.Unstructured data like technical articles and reports are known to be difficult to analyze and formalize. While many previous research works attempt to semantically extract unstructured threat intelligence, none of them is applied for Chinese data sources. As China is both the largest source and victim of cyberattacks, lacking visibility of Chinese data sources is a major blind spot of CTI.In this paper, we present CTI ANT, the first automatic system for analyzing Chinese CTI, which enlarges the threat intelligence visibility to Chinese data sources. CTI ANT is constructed of three sub-systems: an automatic classification system (CSAC) for inspecting threat intelligence types, a recommendation system (CTRS) that identifies trending keywords to assist threat analysts, and a Web API to label MITRE ATT&CK® techniques in Chinese APT reports. Evaluation results confirm that the proposed CSAC and CTRS have achieved superior performance with an average accuracy exceeding 93% and 80%, respectively. Moreover, the MITRE ATT&CK Web API presents precise labeling in Chinese ATT&CK reports, stimulating new insights on Chinese CTI.